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Islam SMR, Ahmed R, Sharmen F, Hossain MM, Chakma K, Tanni AA, Akash MAA, Hossain ME, Chowdhury MSN, Siddiki AMAMZ, Hossain A, Mandal SC, Crandall KA, Rahnavard A, Sharifuzzaman SM, Mannan A. Genome sequence of white spot syndrome virus (WSSV) infecting cultured black tiger shrimp ( Penaeus monodon) in Bangladesh. Microbiol Resour Announc 2024; 13:e0121123. [PMID: 38501780 PMCID: PMC11008216 DOI: 10.1128/mra.01211-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/08/2024] [Indexed: 03/20/2024] Open
Abstract
The white spot syndrome virus (WSSV) is a causative agent of white spot disease (WSD) in crustaceans, especially in cultivated black tiger shrimp (Penaeus monodon), leading to significant economic losses in the aquaculture sector. The present study describes four whole genome sequences of WSSV obtained from coastal regions of Bangladesh.
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Affiliation(s)
- S. M. Rafiqul Islam
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Robel Ahmed
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Farjana Sharmen
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Md. Mobarok Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh
| | - Kallyan Chakma
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Afroza Akter Tanni
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Md. Ashikur Alim Akash
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
| | - Mohammad Enayet Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh
| | | | - AMAM Zonaed Siddiki
- Department of Pathology and Parasitology, Genomics Research Group, Chattogram Veterinary and Animal Sciences University, Chattogram, Bangladesh
| | - Anwar Hossain
- Department of Fisheries, Aquaculture Genomics Laboratory, University of Dhaka, Dhaka, Bangladesh
| | - Shankar C. Mandal
- Department of Fisheries, Aquaculture Genomics Laboratory, University of Dhaka, Dhaka, Bangladesh
| | - Keith A. Crandall
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA
| | - Ali Rahnavard
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA
| | - SM Sharifuzzaman
- Institute of Marine Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Adnan Mannan
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chattogram, Bangladesh
- Next-generation Sequencing, Research and Innovation Laboratory Chittagong (NRICh), Biotechnology Research and Innovation Centre (BRIC), University of Chittagong, Chattogram, Bangladesh
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Buendia P, Fernandez K, Raley C, Rahnavard A, Crandall KA, Castro JG. Hospital antimicrobial stewardship: profiling the oral microbiome after exposure to COVID-19 and antibiotics. Front Microbiol 2024; 15:1346762. [PMID: 38476940 PMCID: PMC10927822 DOI: 10.3389/fmicb.2024.1346762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction During the COVID-19 Delta variant surge, the CLAIRE cross-sectional study sampled saliva from 120 hospitalized patients, 116 of whom had a positive COVID-19 PCR test. Patients received antibiotics upon admission due to possible secondary bacterial infections, with patients at risk of sepsis receiving broad-spectrum antibiotics (BSA). Methods The saliva samples were analyzed with shotgun DNA metagenomics and respiratory RNA virome sequencing. Medical records for the period of hospitalization were obtained for all patients. Once hospitalization outcomes were known, patients were classified based on their COVID-19 disease severity and the antibiotics they received. Results Our study reveals that BSA regimens differentially impacted the human salivary microbiome and disease progression. 12 patients died and all of them received BSA. Significant associations were found between the composition of the COVID-19 saliva microbiome and BSA use, between SARS-CoV-2 genome coverage and severity of disease. We also found significant associations between the non-bacterial microbiome and severity of disease, with Candida albicans detected most frequently in critical patients. For patients who did not receive BSA before saliva sampling, our study suggests Staphylococcus aureus as a potential risk factor for sepsis. Discussion Our results indicate that the course of the infection may be explained by both monitoring antibiotic treatment and profiling a patient's salivary microbiome, establishing a compelling link between microbiome and the specific antibiotic type and timing of treatment. This approach can aid with emergency room triage and inpatient management but also requires a better understanding of and access to narrow-spectrum agents that target pathogenic bacteria.
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Affiliation(s)
| | | | - Castle Raley
- The George Washington University Genomics Core, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Ali Rahnavard
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Keith A. Crandall
- The George Washington University Genomics Core, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Jose Guillermo Castro
- Division of Infectious Diseases, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States
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Sylvetsky AC, Clement RA, Stearrett N, Issa NT, Dore FJ, Mazumder R, King CH, Hubal MJ, Walter PJ, Cai H, Sen S, Rother KI, Crandall KA. Consumption of sucralose- and acesulfame-potassium-containing diet soda alters the relative abundance of microbial taxa at the species level: findings of two pilot studies. Appl Physiol Nutr Metab 2024; 49:125-134. [PMID: 37902107 DOI: 10.1139/apnm-2022-0471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Sucralose and acesulfame-potassium consumption alters gut microbiota in rodents, with unclear effects in humans. We examined effects of three-times daily sucralose- and acesulfame-potassium-containing diet soda consumption for 1 (n = 17) or 8 (n = 8) weeks on gut microbiota composition in young adults. After 8 weeks of diet soda consumption, the relative abundance of Proteobacteria, specifically Enterobacteriaceae, increased; and, increased abundance of two Proteobacteria taxa was also observed after 1 week of diet soda consumption compared with sparkling water. In addition, three taxa in the Bacteroides genus increased following 1 week of diet soda consumption compared with sparkling water. The clinical relevance of these findings and effects of sucralose and acesulfame-potassium consumption on human gut microbiota warrant further investigation in larger studies. Clinical trial registration: NCT02877186 and NCT03125356.
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Affiliation(s)
- Allison C Sylvetsky
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, 950 New Hampshire Ave NW, Washington, DC 20052, USA
| | - Rebecca A Clement
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW, Science & Engineering Hall, Washington, DC 20052, USA
| | - Nathaniel Stearrett
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW, Science & Engineering Hall, Washington, DC 20052, USA
| | - Najy T Issa
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, 950 New Hampshire Ave NW, Washington, DC 20052, USA
| | - Fiona J Dore
- Department of Medicine, George Washington University School of Medicine, 2300 Eye Street NW, Washington, DC 20037, USA
| | - Raja Mazumder
- Department of Biochemistry, George Washington University School of Medicine, 2300 Eye Street NW, Washington, DC 20037, USA
| | - Charles Hadley King
- Department of Biochemistry, George Washington University School of Medicine, 2300 Eye Street NW, Washington, DC 20037, USA
| | - Monica J Hubal
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, 950 New Hampshire Ave NW, Washington, DC 20052, USA
| | - Peter J Walter
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 8C432A, Bethesda, MD 20892, USA
| | - Hongyi Cai
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 8C432A, Bethesda, MD 20892, USA
| | - Sabyasachi Sen
- Department of Medicine, George Washington University School of Medicine, 2300 Eye Street NW, Washington, DC 20037, USA
| | - Kristina I Rother
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 8C432A, Bethesda, MD 20892, USA
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW, Science & Engineering Hall, Washington, DC 20052, USA
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, 800 22nd Street NW, Science & Engineering Hall, Washington, DC 20052, USA
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Wolfe JM, Ballou L, Luque J, Watson-Zink VM, Ahyong ST, Barido-Sottani J, Chan TY, Chu KH, Crandall KA, Daniels SR, Felder DL, Mancke H, Martin JW, Ng PKL, Ortega-Hernández J, Palacios Theil E, Pentcheff ND, Robles R, Thoma BP, Tsang LM, Wetzer R, Windsor AM, Bracken-Grissom HD. Convergent adaptation of true crabs (Decapoda: Brachyura) to a gradient of terrestrial environments. Syst Biol 2023:syad066. [PMID: 37941464 DOI: 10.1093/sysbio/syad066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Indexed: 11/10/2023] Open
Abstract
For much of terrestrial biodiversity, the evolutionary pathways of adaptation from marine ancestors are poorly understood, and have usually been viewed as a binary trait. True crabs, the decapod crustacean infraorder Brachyura, comprise over 7,600 species representing a striking diversity of morphology and ecology, including repeated adaptation to non-marine habitats. Here, we reconstruct the evolutionary history of Brachyura using new and published sequences of 10 genes for 344 tips spanning 88 of 109 brachyuran families. Using 36 newly vetted fossil calibrations, we infer that brachyurans most likely diverged in the Triassic, with family-level splits in the late Cretaceous and early Paleogene. By contrast, the root age is underestimated with automated sampling of 328 fossil occurrences explicitly incorporated into the tree prior, suggesting such models are a poor fit under heterogeneous fossil preservation. We apply recently defined trait-by-environment associations to classify a gradient of transitions from marine to terrestrial lifestyles. We estimate that crabs left the marine environment at least seven and up to 17 times convergently, and returned to the sea from non-marine environments at least twice. Although the most highly terrestrial- and many freshwater-adapted crabs are concentrated in Thoracotremata, Bayesian threshold models of ancestral state reconstruction fail to identify shifts to higher terrestrial grades due to the degree of underlying change required. Lineages throughout our tree inhabit intertidal and marginal marine environments, corroborating the inference that the early stages of terrestrial adaptation have a lower threshold to evolve. Our framework and extensive new fossil and natural history datasets will enable future comparisons of non-marine adaptation at the morphological and molecular level. Crabs provide an important window into the early processes of adaptation to novel environments, and different degrees of evolutionary constraint that might help predict these pathways.
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Affiliation(s)
- Joanna M Wolfe
- Museum of Comparative Zoology and Department of Organismic & Evolutionary Biology, Harvard University, 26 Oxford St, Cambridge, MA 02138, USA
| | - Lauren Ballou
- Institute of Environment and Department of Biological Sciences, Florida International University, Biscayne Bay Campus, North Miami, FL 33181, USA
| | - Javier Luque
- Museum of Comparative Zoology and Department of Organismic & Evolutionary Biology, Harvard University, 26 Oxford St, Cambridge, MA 02138, USA
- Institute of Environment and Department of Biological Sciences, Florida International University, Biscayne Bay Campus, North Miami, FL 33181, USA
| | | | - Shane T Ahyong
- Australian Museum, 1 William St, Sydney, NSW 2010, Australia
- School of Biological, Earth & Environmental Sciences, University of New South Wales, Kensington, NSW 2052, Australia
| | - Joëlle Barido-Sottani
- Institut de Biologie de l'École Normale Supérieure (IBENS), ENS, CNRS, INSERM, Université PSL (Paris Sciences & Lettres), Paris, France
| | - Tin-Yam Chan
- Institute of Marine Biology and Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung 202301, Taiwan, ROC
| | - Ka Hou Chu
- Simon F. S. Li Marine Science Laboratory, School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA
| | - Savel R Daniels
- Department of Botany and Zoology, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa
| | - Darryl L Felder
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA
- Department of Biology and Laboratory for Crustacean Research, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Harrison Mancke
- Institute of Environment and Department of Biological Sciences, Florida International University, Biscayne Bay Campus, North Miami, FL 33181, USA
| | - Joel W Martin
- Research and Collections, Natural History Museum of Los Angeles County, 900 Exposition Boulevard, Los Angeles, California 90007, USA
| | - Peter K L Ng
- Lee Kong Chian Natural History Museum, Faculty of Science, National University of Singapore, 2 Conservatory Drive, 117377 Singapore, Singapore
| | - Javier Ortega-Hernández
- Museum of Comparative Zoology and Department of Organismic & Evolutionary Biology, Harvard University, 26 Oxford St, Cambridge, MA 02138, USA
| | - Emma Palacios Theil
- Department of Invertebrate Zoology and Hydrobiology, University of Łódź, ul. Banacha 12/16, 90237 Łódź, Poland
| | - N Dean Pentcheff
- Research and Collections, Natural History Museum of Los Angeles County, 900 Exposition Boulevard, Los Angeles, California 90007, USA
| | - Rafael Robles
- Department of Biology and Laboratory for Crustacean Research, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
- Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Campeche, San Francisco de Campeche, Campeche, México
| | - Brent P Thoma
- Department of Biology, Jackson State University, P.O. Box 18540, Jackson, MS 39217, USA
| | - Ling Ming Tsang
- Simon F. S. Li Marine Science Laboratory, School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Regina Wetzer
- Research and Collections, Natural History Museum of Los Angeles County, 900 Exposition Boulevard, Los Angeles, California 90007, USA
| | - Amanda M Windsor
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA
- United States Food and Drug Administration, Office of Regulatory Science, 5001 Campus Dr. College Park, MD 20740, USA
| | - Heather D Bracken-Grissom
- Institute of Environment and Department of Biological Sciences, Florida International University, Biscayne Bay Campus, North Miami, FL 33181, USA
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA
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Pérez-Losada M, Crandall KA. Spatial diversity of the skin bacteriome. Front Microbiol 2023; 14:1257276. [PMID: 37795302 PMCID: PMC10546022 DOI: 10.3389/fmicb.2023.1257276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/04/2023] [Indexed: 10/06/2023] Open
Abstract
The bacterial communities of the human skin impact its physiology and homeostasis, hence elucidating the composition and structure of the healthy skin bacteriome is paramount to understand how bacterial imbalance (i.e., dysbiosis) may lead to disease. To obtain an integrated view of the spatial diversity of the skin bacteriome, we surveyed from 2019 to 2023 five skin regions (belly button, behind ears, between toes, calves and forearms) with different physiological characteristics (dry, moist and sebaceous) in 129 healthy adults (579 samples - after data cleaning). Estimating bacterial diversity through 16S rRNA metataxonomics, we identified significant (p < 0.0001) differences in the bacterial relative abundance of the four most abundant phyla and 11 genera, alpha- and beta-diversity indices and predicted functional profiles (36 to 400 metabolic pathways) across skin regions and microenvironments. No significant differences, however, were observed across genders, ages, and ethnicities. As previously suggested, dry skin regions (forearms and calves) were more even, richer, and functionally distinct than sebaceous (behind ears) and moist (belly button and between toes) regions. Within skin regions, bacterial alpha- and beta-diversity also varied significantly for some of the years compared, suggesting that skin bacterial stability may be region and subject dependent. Our results, hence, confirm that the skin bacteriome varies systematically across skin regions and microenvironments and provides new insights into the internal and external factors driving bacterial diversity.
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Affiliation(s)
- Marcos Pérez-Losada
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
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Odom AR, Faits T, Castro-Nallar E, Crandall KA, Johnson WE. Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data. Sci Rep 2023; 13:13957. [PMID: 37633998 PMCID: PMC10460424 DOI: 10.1038/s41598-023-40799-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 08/16/2023] [Indexed: 08/28/2023] Open
Abstract
Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools.
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Affiliation(s)
- Aubrey R Odom
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Tyler Faits
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Eduardo Castro-Nallar
- Departamento de Microbiología, Facultad de Ciencias de la Salud, Universidad de Talca, Campus Talca, Avda. Lircay S/N, Talca, Chile
- Centro de Ecología Integrativa, Universidad de Talca, Campus Talca, Avda. Lircay S/N, Talca, Chile
| | - Keith A Crandall
- Department of Biostatistics & Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - W Evan Johnson
- Division of Infectious Disease, Center for Data Science, Rutgers University - New Jersey Medical School, Newark, NJ, USA.
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Bernot JP, Owen CL, Wolfe JM, Meland K, Olesen J, Crandall KA. Major Revisions in Pancrustacean Phylogeny and Evidence of Sensitivity to Taxon Sampling. Mol Biol Evol 2023; 40:msad175. [PMID: 37552897 PMCID: PMC10414812 DOI: 10.1093/molbev/msad175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 08/10/2023] Open
Abstract
The clade Pancrustacea, comprising crustaceans and hexapods, is the most diverse group of animals on earth, containing over 80% of animal species and half of animal biomass. It has been the subject of several recent phylogenomic analyses, yet relationships within Pancrustacea show a notable lack of stability. Here, the phylogeny is estimated with expanded taxon sampling, particularly of malacostracans. We show small changes in taxon sampling have large impacts on phylogenetic estimation. By analyzing identical orthologs between two slightly different taxon sets, we show that the differences in the resulting topologies are due primarily to the effects of taxon sampling on the phylogenetic reconstruction method. We compare trees resulting from our phylogenomic analyses with those from the literature to explore the large tree space of pancrustacean phylogenetic hypotheses and find that statistical topology tests reject the previously published trees in favor of the maximum likelihood trees produced here. Our results reject several clades including Caridoida, Eucarida, Multicrustacea, Vericrustacea, and Syncarida. Notably, we find Copepoda nested within Allotriocarida with high support and recover a novel relationship between decapods, euphausiids, and syncarids that we refer to as the Syneucarida. With denser taxon sampling, we find Stomatopoda sister to this latter clade, which we collectively name Stomatocarida, dividing Malacostraca into three clades: Leptostraca, Peracarida, and Stomatocarida. A new Bayesian divergence time estimation is conducted using 13 vetted fossils. We review our results in the context of other pancrustacean phylogenetic hypotheses and highlight 15 key taxa to sample in future studies.
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Affiliation(s)
- James P Bernot
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
| | - Christopher L Owen
- Systematic Entomology Laboratory, USDA-ARS, ℅ National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Joanna M Wolfe
- Museum of Comparative Zoology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Kenneth Meland
- Department of Biology, University of Bergen, Bergen, Norway
| | - Jørgen Olesen
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - Keith A Crandall
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
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8
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Kanholm T, Rentia U, Hadley M, Karlow JA, Cox OL, Diab N, Bendall ML, Dawson T, McDonald JI, Xie W, Crandall KA, Burns KH, Baylin SB, Easwaran H, Chiappinelli KB. Oncogenic Transformation Drives DNA Methylation Loss and Transcriptional Activation at Transposable Element Loci. Cancer Res 2023; 83:2584-2599. [PMID: 37249603 PMCID: PMC10527578 DOI: 10.1158/0008-5472.can-22-3485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/30/2023] [Accepted: 05/25/2023] [Indexed: 05/31/2023]
Abstract
Transposable elements (TE) are typically silenced by DNA methylation and repressive histone modifications in differentiated healthy human tissues. However, TE expression increases in a wide range of cancers and is correlated with global hypomethylation of cancer genomes. We assessed expression and DNA methylation of TEs in fibroblast cells that were serially transduced with hTERT, SV40, and HRASR24C to immortalize and then transform them, modeling the different steps of the tumorigenesis process. RNA sequencing and whole-genome bisulfite sequencing were performed at each stage of transformation. TE expression significantly increased as cells progressed through transformation, with the largest increase in expression after the final stage of transformation, consistent with data from human tumors. The upregulated TEs were dominated by endogenous retroviruses [long terminal repeats (LTR)]. Most differentially methylated regions (DMR) in all stages were hypomethylated, with the greatest hypomethylation in the final stage of transformation. A majority of the DMRs overlapped TEs from the RepeatMasker database, indicating that TEs are preferentially demethylated. Many hypomethylated TEs displayed a concordant increase in expression. Demethylation began during immortalization and continued into transformation, while upregulation of TE transcription occurred in transformation. Numerous LTR elements upregulated in the model were also identified in The Cancer Genome Atlas datasets of breast, colon, and prostate cancer. Overall, these findings indicate that TEs, specifically endogenous retroviruses, are demethylated and transcribed during transformation. SIGNIFICANCE Analysis of epigenetic and transcriptional changes in a transformation model reveals that transposable element expression and methylation are dysregulated during oncogenic transformation.
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Affiliation(s)
- Tomas Kanholm
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
- The Institute for Biomedical Sciences at the George Washington University
| | - Uzma Rentia
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
| | - Melissa Hadley
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
| | - Jennifer A. Karlow
- Department of Pathology, Dana-Farber Cancer Institute / Harvard Medical School, Boston, MA, USA
| | - Olivia L. Cox
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
| | - Noor Diab
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
- George Washington University School of Medicine and Health Sciences
| | - Matthew L. Bendall
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Tyson Dawson
- The Institute for Biomedical Sciences at the George Washington University
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - James I. McDonald
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
| | - Wenbing Xie
- Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Keith A. Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Kathleen H. Burns
- Department of Pathology, Dana-Farber Cancer Institute / Harvard Medical School, Boston, MA, USA
| | - Stephen B. Baylin
- Department of Oncology, The Johns Hopkins School of Medicine, The Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA
| | - Hari Easwaran
- Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Katherine B. Chiappinelli
- The George Washington University Cancer Center (GWCC), Washington, DC, USA
- Department of Microbiology, Immunology & Tropical Medicine, The George Washington University, Washington, DC, USA
- The Institute for Biomedical Sciences at the George Washington University
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9
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Dawson T, Rentia U, Sanford J, Cruchaga C, Kauwe JSK, Crandall KA. Locus specific endogenous retroviral expression associated with Alzheimer's disease. Front Aging Neurosci 2023; 15:1186470. [PMID: 37484691 PMCID: PMC10359044 DOI: 10.3389/fnagi.2023.1186470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/13/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Human endogenous retroviruses (HERVs) are transcriptionally-active remnants of ancient retroviral infections that may play a role in Alzheimer's disease. Methods We combined two, publicly available RNA-Seq datasets with a third, novel dataset for a total cohort of 103 patients with Alzheimer's disease and 45 healthy controls. We use telescope to perform HERV quantification for these samples and simultaneously perform gene expression analysis. Results We identify differentially expressed genes and differentially expressed HERVs in Alzheimer's disease patients. Differentially expressed HERVs are scattered throughout the genome; many of them are members of the HERV-K superfamily. A number of HERVs are correlated with the expression of dysregulated genes in Alzheimer's and are physically proximal to genes which drive disease pathways. Discussion Dysregulated expression of ancient retroviral insertions in the human genome are present in Alzheimer's disease and show localization patterns that may explain how these elements drive pathogenic gene expression.
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Affiliation(s)
- Tyson Dawson
- Computational Biology Institute, The George Washington University, Washington, DC, United States
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Uzma Rentia
- Computational Biology Institute, The George Washington University, Washington, DC, United States
| | - Jessie Sanford
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - John S. K. Kauwe
- Department of Biology, Brigham Young University, Provo, UT, United States
| | - Keith A. Crandall
- Computational Biology Institute, The George Washington University, Washington, DC, United States
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
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10
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Yaparla A, Stern DB, Hossainey MRH, Crandall KA, Grayfer L. Amphibian myelopoiesis. Dev Comp Immunol 2023; 146:104701. [PMID: 37196852 DOI: 10.1016/j.dci.2023.104701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 05/19/2023]
Abstract
Macrophage-lineage cells are indispensable to immunity and physiology of all vertebrates. Amongst these, amphibians represent a key stage in vertebrate evolution and are facing decimating population declines and extinctions, in large part due to emerging infectious agents. While recent studies indicate that macrophages and related innate immune cells are critically involved during these infections, much remains unknown regarding the ontogeny and functional differentiation of these cell types in amphibians. Accordingly, in this review we coalesce what has been established to date about amphibian blood cell development (hematopoiesis), the development of key amphibian innate immune cells (myelopoiesis) and the differentiation of amphibian macrophage subsets (monopoiesis). We explore the current understanding of designated sites of larval and adult hematopoiesis across distinct amphibian species and consider what mechanisms may lend to these species-specific adaptations. We discern the identified molecular mechanisms governing the functional differentiation of disparate amphibian (chiefly Xenopus laevis) macrophage subsets and describe what is known about the roles of these subsets during amphibian infections with intracellular pathogens. Macrophage lineage cells are at the heart of so many vertebrate physiological processes. Thus, garnering greater understanding of the mechanisms responsible for the ontogeny and functionality of these cells in amphibians will lend to a more comprehensive view of vertebrate evolution.
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Affiliation(s)
- Amulya Yaparla
- Department of Biological Sciences, George Washington University, Washington, DC, 20052, USA
| | - David B Stern
- Milken Institute School of Public Health, Computational Biology Institute, George Washington University, Washington, DC, 20052, USA
| | | | - Keith A Crandall
- Milken Institute School of Public Health, Computational Biology Institute, George Washington University, Washington, DC, 20052, USA
| | - Leon Grayfer
- Department of Biological Sciences, George Washington University, Washington, DC, 20052, USA.
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11
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Sun X, Liu Z, Li Z, Zeng Z, Peng W, Zhu J, Zhao J, Zhu C, Zeng C, Stearrett N, Crandall KA, Bachali P, Grammer AC, Lipsky PE. Abnormalities in intron retention characterize patients with systemic lupus erythematosus. Sci Rep 2023; 13:5141. [PMID: 36991079 PMCID: PMC10060252 DOI: 10.1038/s41598-023-31890-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
Regulation of intron retention (IR), a form of alternative splicing, is a newly recognized checkpoint in gene expression. Since there are numerous abnormalities in gene expression in the prototypic autoimmune disease systemic lupus erythematosus (SLE), we sought to determine whether IR was intact in patients with this disease. We, therefore, studied global gene expression and IR patterns of lymphocytes in SLE patients. We analyzed RNA-seq data from peripheral blood T cell samples from 14 patients suffering from systemic lupus erythematosus (SLE) and 4 healthy controls and a second, independent data set of RNA-seq data from B cells from16 SLE patients and 4 healthy controls. We identified intron retention levels from 26,372 well annotated genes as well as differential gene expression and tested for differences between cases and controls using unbiased hierarchical clustering and principal component analysis. We followed with gene-disease enrichment analysis and gene-ontology enrichment analysis. Finally, we then tested for significant differences in intron retention between cases and controls both globally and with respect to specific genes. Overall decreased IR was found in T cells from one cohort and B cells from another cohort of patients with SLE and was associated with increased expression of numerous genes, including those encoding spliceosome components. Different introns within the same gene displayed both up- and down-regulated retention profiles indicating a complex regulatory mechanism. These results indicate that decreased IR in immune cells is characteristic of patients with active SLE and may contribute to the abnormal expression of specific genes in this autoimmune disease.
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Affiliation(s)
- Xiaoqian Sun
- Computer Science Department, George Washington University, Washington, DC, 20052, USA
| | - Zhichao Liu
- Physics Department, George Washington University, Washington, DC, 20052, USA
| | - Zongzhu Li
- Physics Department, George Washington University, Washington, DC, 20052, USA
| | - Zhouhao Zeng
- Physics Department, George Washington University, Washington, DC, 20052, USA
| | - Weiqun Peng
- Physics Department, George Washington University, Washington, DC, 20052, USA
| | - Jun Zhu
- Mokobio Biotechnology R&D Center, 1445 Research Blvd, Suite 150, Rockville, MD, 20850, USA
| | - Joel Zhao
- Walt Whitman High School, Bethesda, MD, 20817, USA
| | | | - Chen Zeng
- Physics Department, George Washington University, Washington, DC, 20052, USA.
| | - Nathaniel Stearrett
- Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC, 20052, USA.
| | - Prathyusha Bachali
- RILITE Research Institute and AMPEL BioSolutions, 250 W Main St, Ste 300, Charlottesville, VA, 22902, USA
| | - Amrie C Grammer
- RILITE Research Institute and AMPEL BioSolutions, 250 W Main St, Ste 300, Charlottesville, VA, 22902, USA
| | - Peter E Lipsky
- RILITE Research Institute and AMPEL BioSolutions, 250 W Main St, Ste 300, Charlottesville, VA, 22902, USA.
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12
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Hahn A, Burrell A, Chaney H, Sami I, Koumbourlis AC, Freishtat RJ, Crandall KA, Zemanick ET. Therapeutic beta-lactam dosages and broad-spectrum antibiotics are associated with reductions in microbial richness and diversity in persons with cystic fibrosis. Sci Rep 2023; 13:1217. [PMID: 36681756 PMCID: PMC9867719 DOI: 10.1038/s41598-023-27628-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 01/04/2023] [Indexed: 01/22/2023] Open
Abstract
Persons with cystic fibrosis (PwCF) suffer from pulmonary exacerbations (PEx) related in part to lung infection. While higher microbial diversity is associated with higher lung function, the data on the impact of short-term antibiotics on changes in microbial diversity is conflicting. Further, Prevotella secretes beta-lactamases, which may influence recovery of lung function. We hypothesize that sub-therapeutic and broad spectrum antibiotic exposure leads to decreasing microbial diversity. Our secondary aim was to evaluate the concerted association of beta-lactam pharmacokinetics (PK), antibiotic spectrum, microbial diversity, and antibiotic resistance on lung function recovery using a pathway analysis. This was a retrospective observational study of persons with CF treated with IV antibiotics for PEx between 2016 and 2020 at Children's National Hospital; respiratory samples and clinical information were collected at hospital admission for PEx (E), end of antibiotic treatment (T), and follow-up (F). Metagenomic sequencing was performed; PathoScope 2.0 and AmrPlusPlus were used for taxonomic assignment of sequences to bacteria and antibiotic resistance genes (ARGs). M/W Pharm was used for PK modeling. Comparison of categorical and continuous variables and pathway analysis were performed in STATA. Twenty-two PwCF experienced 43 PEx. The study cohort had a mean age of 14.6 years. Only 12/43 beta-lactam courses had therapeutic PK, and 18/43 were broad spectrum. A larger decrease in richness between E and T was seen in the therapeutic PK group (sufficient - 20.1 vs. insufficient - 1.59, p = 0.025) and those receiving broad spectrum antibiotics (broad - 14.5 vs. narrow - 2.8, p = 0.030). We did not detect differences in the increase in percent predicted forced expiratory volume in one second (ppFEV1) at end of treatment compared to PEx based on beta-lactam PK (sufficient 13.6% vs. insufficient 15.1%) or antibiotic spectrum (broad 11.5% vs. narrow 16.6%). While both therapeutic beta-lactam PK and broad-spectrum antibiotics decreased richness between PEx and the end of treatment, we did not detect longstanding changes in alpha diversity or an association with superior recovery of lung function compared with subtherapeutic PK and narrow spectrum antimicrobials.
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Affiliation(s)
- Andrea Hahn
- Division of Infectious Diseases, Children's National Hospital (CNH), Washington, DC, USA.
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA.
- Department of Pediatrics, George Washington University (GWU), Washington, DC, USA.
| | - Aszia Burrell
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
| | - Hollis Chaney
- Department of Pediatrics, George Washington University (GWU), Washington, DC, USA
- Division of Pulmonary Medicine, CNH, Washington, DC, USA
| | - Iman Sami
- Department of Pediatrics, George Washington University (GWU), Washington, DC, USA
- Division of Pulmonary Medicine, CNH, Washington, DC, USA
| | - Anastassios C Koumbourlis
- Department of Pediatrics, George Washington University (GWU), Washington, DC, USA
- Division of Pulmonary Medicine, CNH, Washington, DC, USA
| | - Robert J Freishtat
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
- Department of Pediatrics, George Washington University (GWU), Washington, DC, USA
- Division of Emergency Medicine, CNH, Washington, DC, USA
| | - Keith A Crandall
- Deptartment of Biostatistics and Bioinformatics, Milken Institute School of Public Health, GWU, Washington, DC, USA
| | - Edith T Zemanick
- Deptartment of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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13
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Shumyatsky G, Burrell A, Chaney H, Sami I, Koumbourlis AC, Freishtat RJ, Crandall KA, Zemanick ET, Hahn A. Using metabolic potential within the airway microbiome as predictors of clinical state in persons with cystic fibrosis. Front Med (Lausanne) 2023; 9:1082125. [PMID: 36698799 PMCID: PMC9868313 DOI: 10.3389/fmed.2022.1082125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Pulmonary exacerbations (PEx) in persons with cystic fibrosis (CF) are primarily related to acute or chronic inflammation associated with bacterial lung infections, which may be caused by several bacteria that activate similar bacterial genes and produce similar by-products. The goal of our study was to perform a stratified functional analysis of bacterial genes at three distinct time points in the treatment of a PEx in order to determine the role that specific airway microbiome community members may play within each clinical state (i.e., PEx, end of antibiotic treatment, and follow-up). Our secondary goal was to compare the change between clinical states with the metabolic activity of specific airway microbiome community members. Methods This was a prospective observational study of persons with CF treated with intravenous antibiotics for PEx between 2016 and 2020 at Children's National Hospital. Demographic and clinical information as well as respiratory samples were collected at hospital admission for PEx, end of antibiotic treatment, and follow-up. Metagenomic sequencing was performed; MetaPhlAn3 and HUMANn3 were used to assign sequences to bacterial species and bacterial metabolic genes, respectively. Results Twenty-two persons with CF, with a mean age of 14.5 (range 7-23) years, experienced 45 PEx during the study period. Two-hundred twenty-one bacterial species were identified in the respiratory samples from the study cohort. Ten bacterial species had differential gene abundance across changes in the clinical state including Staphylococcus aureus, Streptococcus salivarius, and Veillonella atypica (all padj < 0.01 and log2FoldChange > |2|). These corresponded to a differential abundance of bacterial genes, with S. aureus accounting for 81% of the genes more abundant in PEx and S. salivarius accounting for 83% of the genes more abundant in follow-up, all compared to the end of treatment. Lastly, 8,653 metabolic pathways were identified across samples, with again S. aureus and S. salivarius contributing to the differential abundance of pathways (106 in PEx vs. 66 in follow-up, respectively). V. atypica was associated with a single metabolic pathway (UDP-N-acetyl-D-glucosamine biosynthesis) increased in follow-up compared to PEx. Discussion Taken together, these data suggest that the metabolic potential of bacterial species can provide more insight into changes across clinical states than the relative abundance of the bacteria alone.
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Affiliation(s)
- Gabriella Shumyatsky
- Jefferson Biotechnology Program, Thomas Jefferson University, Philadelphia, PA, United States
| | - Aszia Burrell
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, United States
| | - Hollis Chaney
- Department of Pediatrics, George Washington University (GWU), Washington, DC, United States.,Division of Pulmonary Medicine, Children's National Hospital (CNH), Washington, DC, United States
| | - Iman Sami
- Department of Pediatrics, George Washington University (GWU), Washington, DC, United States.,Division of Pulmonary Medicine, Children's National Hospital (CNH), Washington, DC, United States
| | - Anastassios C Koumbourlis
- Department of Pediatrics, George Washington University (GWU), Washington, DC, United States.,Division of Pulmonary Medicine, Children's National Hospital (CNH), Washington, DC, United States
| | - Robert J Freishtat
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, United States.,Department of Pediatrics, George Washington University (GWU), Washington, DC, United States.,Division of Emergency Medicine, CNH, Washington, DC, United States
| | - Keith A Crandall
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, GWU, Washington, DC, United States
| | - Edith T Zemanick
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Andrea Hahn
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, United States.,Department of Pediatrics, George Washington University (GWU), Washington, DC, United States.,Division of Infectious Diseases, CNH, Washington, DC, United States
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14
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Kassaye SG, Grossman Z, Vengurlekar P, Chai W, Wallace M, Rhee SY, Meyer WA, Kaufman HW, Castel A, Jordan J, Crandall KA, Kang A, Kumar P, Katzenstein DA, Shafer RW, Maldarelli F. Insights into HIV-1 Transmission Dynamics Using Routinely Collected Data in the Mid-Atlantic United States. Viruses 2022; 15:68. [PMID: 36680108 PMCID: PMC9863702 DOI: 10.3390/v15010068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022] Open
Abstract
Background: Molecular epidemiological approaches provide opportunities to characterize HIV transmission dynamics. We analyzed HIV sequences and virus load (VL) results obtained during routine clinical care, and individual’s zip-code location to determine utility of this approach. Methods: HIV-1 pol sequences aligned using ClustalW were subtyped using REGA. A maximum likelihood (ML) tree was generated using IQTree. Transmission clusters with ≤3% genetic distance (GD) and ≥90% bootstrap support were identified using ClusterPicker. We conducted Bayesian analysis using BEAST to confirm transmission clusters. The proportion of nucleotides with ambiguity ≤0.5% was considered indicative of early infection. Descriptive statistics were applied to characterize clusters and group comparisons were performed using chi-square or t-test. Results: Among 2775 adults with data from 2014−2015, 2589 (93%) had subtype B HIV-1, mean age was 44 years (SD 12.7), 66.4% were male, and 25% had nucleotide ambiguity ≤0.5. There were 456 individuals in 193 clusters: 149 dyads, 32 triads, and 12 groups with ≥ four individuals per cluster. More commonly in clusters were males than females, 349 (76.5%) vs. 107 (23.5%), p < 0.0001; younger individuals, 35.3 years (SD 12.1) vs. 44.7 (SD 12.3), p < 0.0001; and those with early HIV-1 infection by nucleotide ambiguity, 202/456 (44.3%) vs. 442/2133 (20.7%), p < 0.0001. Members of 43/193 (22.3%) of clusters included individuals in different jurisdictions. Clusters ≥ four individuals were similarly found using BEAST. HIV-1 viral load (VL) ≥3.0 log10 c/mL was most common among individuals in clusters ≥ four, 18/21, (85.7%) compared to 137/208 (65.8%) in clusters sized 2−3, and 927/1169 (79.3%) who were not in a cluster (p < 0.0001). Discussion: HIV sequence data obtained for HIV clinical management provide insights into regional transmission dynamics. Our findings demonstrate the additional utility of HIV-1 VL data in combination with phylogenetic inferences as an enhanced contact tracing tool to direct HIV treatment and prevention services. Trans-jurisdictional approaches are needed to optimize efforts to end the HIV epidemic.
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Affiliation(s)
- Seble G. Kassaye
- Department of Medicine, Georgetown University, Washington, DC 20057, USA
| | - Zehava Grossman
- HIV Dynamics and Replication Program, National Cancer Institute, Frederick, MD 21702, USA
- School of Public Health, Tel Aviv University, Tel Aviv 69978, Israel
| | | | - William Chai
- Warren Alpert Medical School, Brown University, Providence, RI 02912, USA
| | - Megan Wallace
- Department of Medicine, Georgetown University, Washington, DC 20057, USA
| | - Soo-Yon Rhee
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | | | - Amanda Castel
- Department of Epidemiology, George Washington University, Washington, DC 20052, USA
| | - Jeanne Jordan
- Department of Epidemiology, George Washington University, Washington, DC 20052, USA
| | - Keith A. Crandall
- Computational Biology Institute, George Washington University, Ashburn, VA 20147, USA
| | - Alisa Kang
- Department of Medicine, Georgetown University, Washington, DC 20057, USA
| | - Princy Kumar
- Department of Medicine, Georgetown University, Washington, DC 20057, USA
| | | | - Robert W. Shafer
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Frank Maldarelli
- HIV Dynamics and Replication Program, National Cancer Institute, Frederick, MD 21702, USA
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15
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Bustamante JM, Dawson T, Loeffler C, Marfori Z, Marchesi JR, Mullish BH, Thompson CC, Crandall KA, Rahnavard A, Allegretti JR, Cummings BP. Impact of Fecal Microbiota Transplantation on Gut Bacterial Bile Acid Metabolism in Humans. Nutrients 2022; 14:5200. [PMID: 36558359 PMCID: PMC9785599 DOI: 10.3390/nu14245200] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/29/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Fecal microbiota transplantation (FMT) is a promising therapeutic modality for the treatment and prevention of metabolic disease. We previously conducted a double-blind, randomized, placebo-controlled pilot trial of FMT in obese metabolically healthy patients in which we found that FMT enhanced gut bacterial bile acid metabolism and delayed the development of impaired glucose tolerance relative to the placebo control group. Therefore, we conducted a secondary analysis of fecal samples collected from these patients to assess the potential gut microbial species contributing to the effect of FMT to improve metabolic health and increase gut bacterial bile acid metabolism. Fecal samples collected at baseline and after 4 weeks of FMT or placebo treatment underwent shotgun metagenomic analysis. Ultra-high-performance liquid chromatography-mass spectrometry was used to profile fecal bile acids. FMT-enriched bacteria that have been implicated in gut bile acid metabolism included Desulfovibrio fairfieldensis and Clostridium hylemonae. To identify candidate bacteria involved in gut microbial bile acid metabolism, we assessed correlations between bacterial species abundance and bile acid profile, with a focus on bile acid products of gut bacterial metabolism. Bacteroides ovatus and Phocaeicola dorei were positively correlated with unconjugated bile acids. Bifidobacterium adolescentis, Collinsella aerofaciens, and Faecalibacterium prausnitzii were positively correlated with secondary bile acids. Together, these data identify several candidate bacteria that may contribute to the metabolic benefits of FMT and gut bacterial bile acid metabolism that requires further functional validation.
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Affiliation(s)
- Jessica-Miranda Bustamante
- Department of Surgery, School of Medicine, Center for Alimentary and Metabolic Science, University of California, Sacramento, CA 95817, USA
| | - Tyson Dawson
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Caitlin Loeffler
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Zara Marfori
- Department of Surgery, School of Medicine, Center for Alimentary and Metabolic Science, University of California, Sacramento, CA 95817, USA
| | - Julian R. Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St. Mary’s Hospital Campus, Imperial College London, London W2 1NY, UK
| | - Benjamin H. Mullish
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, St. Mary’s Hospital Campus, Imperial College London, London W2 1NY, UK
| | - Christopher C. Thompson
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Keith A. Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Jessica R. Allegretti
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Bethany P. Cummings
- Department of Surgery, School of Medicine, Center for Alimentary and Metabolic Science, University of California, Sacramento, CA 95817, USA
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16
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Inam Z, Felton E, Burrell A, Chaney H, Sami I, Koumbourlis AC, Freishtat RJ, Zemanick ET, Crandall KA, Hahn A. Impact of Antibiotics on the Lung Microbiome and Lung Function in Children with Cystic Fibrosis One Year after Hospitalization for an Initial Pulmonary Exacerbation. Open Forum Infect Dis 2022; 9:ofac466. [PMID: 36168550 PMCID: PMC9511275 DOI: 10.1093/ofid/ofac466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/09/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Cystic fibrosis (CF) is characterized by recurrent pulmonary exacerbations (PEx) and lung function decline. PEx are frequently treated with antibiotics. However, little is known about the cumulative effects of antibiotics on the airway microbiome of persons with CF over time. The purpose of this study was to evaluate changes in the microbiome and lung function in persons with CF over one-year following an initial study pulmonary exacerbation (iPEx).
Methods
Twenty children with CF ≤18 years of age were enrolled in the study which occurred prior to the routine administration of highly effective modulator therapy. Respiratory samples and spirometry were obtained at a minimum of quarterly visits and up to 1-year after an iPEx. Metagenomic sequencing was performed, and bacterial taxa were assigned using MetaPhlAn 2.0. Paired t test, ANOVA, and GLS regression were used to compare outcome variables.
Results
The mean (±SD) age of study participants at the time of the iPEx was 10.6 years. There was 3 ± 1.6 PEx treated with antibiotics per person with CF during the study period. Bacterial richness was similar at 1 year compared to iPEx (40.3 vs 39.3, p = 0.852), whereas the mean Shannon diversity index was significantly higher at one year (2.84 vs 1.62, p < 0.001). The number of PEx treated with IV or oral antibiotics over the year was not associated with changes in microbial diversity but was associated with changes in ppFVC (p < 0.001).
Conclusions
In our one-year prospective evaluation of children with CF hospitalized for IV antibiotic treatment of an initial PEx we found microbial diversity increased despite decreases in lung function associated with repeated PEx events requiring antibiotic therapy.
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Affiliation(s)
- Zaina Inam
- Pediatric Residency Program, Children’s National Hospital (CNH) , Washington, DC , USA
| | - Erin Felton
- George Washington University School of Medicine and Health Sciences (GWU SMHS) , Washington, DC , USA
| | - Aszia Burrell
- Center for Genetic Medicine, Children’s National Research Institute , Washington, DC , USA
| | - Hollis Chaney
- Division of Pulmonary and Sleep Medicine, CNH , Washington, DC , USA
- Department of Pediatrics, GWU SMHS , Washington, DC , USA
| | - Iman Sami
- Division of Pulmonary and Sleep Medicine, CNH , Washington, DC , USA
- Department of Pediatrics, GWU SMHS , Washington, DC , USA
| | - Anastassios C Koumbourlis
- Division of Pulmonary and Sleep Medicine, CNH , Washington, DC , USA
- Department of Pediatrics, GWU SMHS , Washington, DC , USA
| | - Robert J Freishtat
- George Washington University School of Medicine and Health Sciences (GWU SMHS) , Washington, DC , USA
- Department of Pediatrics, GWU SMHS , Washington, DC , USA
- Division of Emergency Medicine, CNH , Washington, DC , USA
| | - Edith T Zemanick
- Department of Pediatrics, University of Colorado Anschutz Medical Campus , Aurora, CO , USA
| | - Keith A Crandall
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, GWU , Washington, DC , USA
| | - Andrea Hahn
- George Washington University School of Medicine and Health Sciences (GWU SMHS) , Washington, DC , USA
- Department of Pediatrics, GWU SMHS , Washington, DC , USA
- Division of Infectious Diseases, CNH , Washington, DC , USA
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17
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Guajardo-Leiva S, Alarcón J, Gutzwiller F, Gallardo-Cerda J, Acuña-Rodríguez IS, Molina-Montenegro M, Crandall KA, Pérez-Losada M, Castro-Nallar E. Source and acquisition of rhizosphere microbes in Antarctic vascular plants. Front Microbiol 2022; 13:916210. [PMID: 36160194 PMCID: PMC9493328 DOI: 10.3389/fmicb.2022.916210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/12/2022] [Indexed: 11/27/2022] Open
Abstract
Rhizosphere microbial communities exert critical roles in plant health, nutrient cycling, and soil fertility. Despite the essential functions conferred by microbes, the source and acquisition of the rhizosphere are not entirely clear. Therefore, we investigated microbial community diversity and potential source using the only two native Antarctic plants, Deschampsia antarctica (Da) and Colobanthus quitensis (Cq), as models. We interrogated rhizosphere and bulk soil microbiomes at six locations in the Byers Peninsula, Livingston Island, Antarctica, both individual plant species and their association (Da.Cq). Our results show that host plant species influenced the richness and diversity of bacterial communities in the rhizosphere. Here, the Da rhizosphere showed the lowest richness and diversity of bacteria compared to Cq and Da.Cq rhizospheres. In contrast, for rhizosphere fungal communities, plant species only influenced diversity, whereas the rhizosphere of Da exhibited higher fungal diversity than the Cq rhizosphere. Also, we found that environmental geographic pressures (i.e., sampling site, latitude, and altitude) and, to a lesser extent, biotic factors (i.e., plant species) determined the species turnover between microbial communities. Moreover, our analysis shows that the sources of the bacterial communities in the rhizosphere were local soils that contributed to homogenizing the community composition of the different plant species growing in the same sampling site. In contrast, the sources of rhizosphere fungi were local (for Da and Da.Cq) and distant soils (for Cq). Here, the host plant species have a specific effect in acquiring fungal communities to the rhizosphere. However, the contribution of unknown sources to the fungal rhizosphere (especially in Da and Da.Cq) indicates the existence of relevant stochastic processes in acquiring these microbes. Our study shows that rhizosphere microbial communities differ in their composition and diversity. These differences are explained mainly by the microbial composition of the soils that harbor them, acting together with plant species-specific effects. Both plant species acquire bacteria from local soils to form part of their rhizosphere. Seemingly, the acquisition process is more complex for fungi. We identified a significant contribution from unknown fungal sources due to stochastic processes and known sources from soils across the Byers Peninsula.
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Affiliation(s)
- Sergio Guajardo-Leiva
- Departamento de Microbiología, Facultad de Ciencias de la Salud, Universidad de Talca, Talca, Chile
- Centro de Ecología Integrativa, Universidad de Talca, Talca, Chile
| | - Jaime Alarcón
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Florence Gutzwiller
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Jorge Gallardo-Cerda
- Laboratorio de Ecología Integrativa, Instituto de Ciencias Biológicas, Universidad de Talca, Talca, Chile
| | | | - Marco Molina-Montenegro
- Centro de Ecología Integrativa, Universidad de Talca, Talca, Chile
- Laboratorio de Ecología Integrativa, Instituto de Ciencias Biológicas, Universidad de Talca, Talca, Chile
- Centro de Estudios Avanzados en Zonas Áridas, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile
- Centro de Investigación en Estudios Avanzados del Maule, Universidad Católica del Maule, Talca, Chile
| | - Keith A. Crandall
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, George Washington University, Washington, DC, United States
| | - Marcos Pérez-Losada
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, George Washington University, Washington, DC, United States
- Division of Emergency Medicine, Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Children’s National Hospital, Washington, DC, United States
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Eduardo Castro-Nallar
- Departamento de Microbiología, Facultad de Ciencias de la Salud, Universidad de Talca, Talca, Chile
- Centro de Ecología Integrativa, Universidad de Talca, Talca, Chile
- *Correspondence: Eduardo Castro-Nallar,
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18
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Macedo AB, Levinger C, Nguyen BN, Richard J, Gupta M, Cruz CRY, Finzi A, Chiappinelli KB, Crandall KA, Bosque A. The HIV Latency Reversal Agent HODHBt Enhances NK Cell Effector and Memory-Like Functions by Increasing Interleukin-15-Mediated STAT Activation. J Virol 2022; 96:e0037222. [PMID: 35867565 PMCID: PMC9364794 DOI: 10.1128/jvi.00372-22] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/29/2022] [Indexed: 11/20/2022] Open
Abstract
Elimination of human immunodeficiency virus (HIV) reservoirs is a critical endpoint to eradicate HIV. One therapeutic intervention against latent HIV is "shock and kill." This strategy is based on the transcriptional activation of latent HIV with a latency-reversing agent (LRA) with the consequent killing of the reactivated cell by either the cytopathic effect of HIV or the immune system. We have previously found that the small molecule 3-hydroxy-1,2,3-benzotriazin-4(3H)-one (HODHBt) acts as an LRA by increasing signal transducer and activator of transcription (STAT) factor activation mediated by interleukin-15 (IL-15) in cells isolated from aviremic participants. The IL-15 superagonist N-803 is currently under clinical investigation to eliminate latent reservoirs. IL-15 and N-803 share similar mechanisms of action by promoting the activation of STATs and have shown some promise in preclinical models directed toward HIV eradication. In this work, we evaluated the ability of HODHBt to enhance IL-15 signaling in natural killer (NK) cells and the biological consequences associated with increased STAT activation in NK cell effector and memory-like functions. We showed that HODHBt increased IL-15-mediated STAT phosphorylation in NK cells, resulting in increases in the secretion of CXCL-10 and interferon gamma (IFN-γ) and the expression of cytotoxic proteins, including granzyme B, granzyme A, perforin, granulysin, FASL, and TRAIL. This increased cytotoxic profile results in increased cytotoxicity against HIV-infected cells and different tumor cell lines. HODHBt also improved the generation of cytokine-induced memory-like NK cells. Overall, our data demonstrate that enhancing the magnitude of IL-15 signaling with HODHBt favors NK cell cytotoxicity and memory-like generation, and thus, targeting this pathway could be further explored for HIV cure interventions. IMPORTANCE Several clinical trials targeting the HIV latent reservoir with LRAs have been completed. In spite of a lack of clinical benefit, they have been crucial to elucidate hurdles that "shock and kill" strategies have to overcome to promote an effective reduction of the latent reservoir to lead to a cure. These hurdles include low reactivation potential mediated by LRAs, the negative influence of some LRAs on the activity of natural killer and effector CD8 T cells, an increased resistance to apoptosis of latently infected cells, and an exhausted immune system due to chronic inflammation. To that end, finding therapeutic strategies that can overcome some of these challenges could improve the outcome of shock and kill strategies aimed at HIV eradication. Here, we show that the LRA HODHBt also improves IL-15-mediated NK cell effector and memory-like functions. As such, pharmacological enhancement of IL-15-mediated STAT activation can open new therapeutic avenues toward an HIV cure.
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Affiliation(s)
- Amanda B. Macedo
- Department of Microbiology, Immunology, & Tropical Medicine, The George Washington University, Washington, DC, USA
| | - Callie Levinger
- Department of Microbiology, Immunology, & Tropical Medicine, The George Washington University, Washington, DC, USA
| | - Bryan N. Nguyen
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Jonathan Richard
- Centre de Recherche du CHUM, Montreal, Quebec, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, Quebec, Canada
| | - Mamta Gupta
- Department of Biochemistry & Molecular Medicine, School of Medicine & Health Sciences, The George Washington University, Washington, DC, USA
- GW Cancer Center, Washington, DC, USA
| | - Conrad Russell Y. Cruz
- GW Cancer Center, Washington, DC, USA
- Children’s National Medical Center, Washington, DC, USA
| | - Andrés Finzi
- Centre de Recherche du CHUM, Montreal, Quebec, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, Quebec, Canada
| | - Katherine B. Chiappinelli
- Department of Microbiology, Immunology, & Tropical Medicine, The George Washington University, Washington, DC, USA
- GW Cancer Center, Washington, DC, USA
| | - Keith A. Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Alberto Bosque
- Department of Microbiology, Immunology, & Tropical Medicine, The George Washington University, Washington, DC, USA
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19
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Aira M, Pérez-Losada M, Crandall KA, Domínguez J. Host taxonomy determines the composition, structure, and diversity of the earthworm cast microbiome under homogenous feeding conditions. FEMS Microbiol Ecol 2022; 98:6655979. [PMID: 35927583 DOI: 10.1093/femsec/fiac093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 11/12/2022] Open
Abstract
Host evolutionary history is a key factor shaping the earthworm cast microbiome, although its effect can be shadowed by the earthworm's diet. To untangle dietary from taxon effects, we raised nine earthworm species on a uniform diet of cow manure and compared cast microbiome across species while controlling for diet. Our results showed that, under controlled laboratory conditions, earthworm microbiomes are species-specific, more diverse than that of the controlled diet, and mainly comprised of native bacteria (i.e., not acquired from the diet). Furthermore, diet has a medium to large convergence effect on microbiome composition since earthworms shared 16 to 74% of their bacterial amplicon sequence variants (ASV). The inter-species core microbiome included 10 ASVs, while their intra-species core microbiomes were larger and varied in ASV richness (24-48%) and sequence abundance across earthworm species. This specificity in core microbiomes and variable degree of similarity in bacterial composition suggest that phylosymbiosis could determine earthworm microbiome assembly. However, lack of congruence between the earthworm phylogeny and the microbiome dendrogram suggests that a consistent diet fed over several generations may have weakened potential phylosymbiotic effects. Thus, cast microbiome assembly in earthworms seem to be the result of an interplay among host phylogeny and diet.
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Affiliation(s)
- Manuel Aira
- Grupo de Ecoloxía Animal (GEA), Universidad de Vigo, Ourense E-36310, España
| | - Marcos Pérez-Losada
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661 Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Jorge Domínguez
- Grupo de Ecoloxía Animal (GEA), Universidad de Vigo, Ourense E-36310, España
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20
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Hoque MN, Rahman MS, Islam T, Sultana M, Crandall KA, Hossain MA. Induction of mastitis by cow-to-mouse fecal and milk microbiota transplantation causes microbiome dysbiosis and genomic functional perturbation in mice. Anim Microbiome 2022; 4:43. [PMID: 35794639 PMCID: PMC9258091 DOI: 10.1186/s42523-022-00193-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 06/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mastitis pathogenesis involves a wide range of opportunistic and apparently resident microorganims including bacteria, viruses and archaea. In dairy animals, microbes reside in the host, interact with environment and evade the host immune system, providing a potential for host-tropism to favor mastitis pathogenesis. To understand the host-tropism phenomena of bovine-tropic mastitis microbiomes, we developed a cow-to-mouse mastitis model. METHODS A cow-to-mouse mastitis model was established by fecal microbiota transplantation (FMT) and milk microbiota transplantation (MMT) to pregnant mice to assess microbiome dysbiosis and genomic functional perturbations through shotgun whole metagenome sequencing (WMS) along with histopathological changes in mice mammary gland and colon tissues. RESULTS The cow-to-mouse FMT and MMT from clinical mastitis (CM) cows induced mastitis syndromes in mice as evidenced by histopathological changes in mammary gland and colon tissues. The WMS of 24 samples including six milk (CM = 3, healthy; H = 3), six fecal (CM = 4, H = 2) samples from cows, and six fecal (CM = 4, H = 2) and six mammary tissue (CM = 3, H = 3) samples from mice generating 517.14 million reads (average: 21.55 million reads/sample) mapped to 2191 bacterial, 94 viral and 54 archaeal genomes. The Kruskal-Wallis test revealed significant differences (p = 0.009) in diversity, composition, and relative abundances in microbiomes between CM- and H-metagenomes. These differences in microbiome composition were mostly represented by Pseudomonas aeruginosa, Lactobacillus crispatus, Klebsiella oxytoca, Enterococcus faecalis, Pantoea dispersa in CM-cows (feces and milk), and Muribaculum spp., Duncaniella spp., Muribaculum intestinale, Bifidobacterium animalis, Escherichia coli, Staphylococcus aureus, Massilia oculi, Ralstonia pickettii in CM-mice (feces and mammary tissues). Different species of Clostridia, Bacteroida, Actinobacteria, Flavobacteriia and Betaproteobacteria had a strong co-occurrence and positive correlation as the indicator species of murine mastitis. However, both CM cows and mice shared few mastitis-associated microbial taxa (1.14%) and functional pathways regardless of conservation of mastitis syndromes, indicating the higher discrepancy in mastitis-associated microbiomes among lactating mammals. CONCLUSIONS We successfully induced mastitis by FMT and MMT that resulted in microbiome dysbiosis and genomic functional perturbations in mice. This study induced mastitis in a mouse model through FMT and MMT, which might be useful for further studies- focused on pathogen(s) involved in mastitis, their cross-talk among themselves and the host.
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Affiliation(s)
- M Nazmul Hoque
- Department of Gynecology, Obstetrics and Reproductive Health, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
| | - M Shaminur Rahman
- Department of Microbiology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering (IBGE), BSMRAU, Gazipur, 1706, Bangladesh
| | - Munawar Sultana
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Keith A Crandall
- Computational Biology Institute and Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - M Anwar Hossain
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh.
- Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
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21
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Hill G, Pérez-Losada M, Delgado E, Benito S, Montero V, Gil H, Sánchez M, Cañada-García JE, García-Bodas E, Crandall KA, Thomson MM. The Origin, Epidemiology, and Phylodynamics of Human Immunodeficiency Virus Type 1 CRF47_BF. Front Microbiol 2022; 13:863123. [PMID: 35685934 PMCID: PMC9172993 DOI: 10.3389/fmicb.2022.863123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
CRF47_BF is a circulating recombinant form (CRF) of the human immunodeficiency virus type 1 (HIV-1), the etiological agent of AIDS. CRF47_BF represents one of 19 CRFx_BFs and has a geographic focus in Spain, where it was first identified in 2010. Since its discovery, CRF47_BF has expanded considerably in Spain, predominantly through heterosexual contact (∼56% of the infections). Little is known, however, about the origin and diversity of this CRF or its epidemiological correlates, as very few samples have been available so far. This study conducts a phylogenetic analysis with representatives of all CRFx_BF sequence types along with HIV-1 M Group subtypes to validate that the CRF47_BF sequences share a unique evolutionary history. The CRFx_BF sequences cluster into a single, not well supported, clade that includes their dominant parent subtypes (B and F). This clade also includes subtype D and excludes sub-subtype F2. However, the CRF47_BF sequences all share a most recent common ancestor. Further analysis of this clade couples CRF47_BF protease-reverse transcriptase sequences and epidemiological data from an additional 87 samples collected throughout Spain, as well as additional CRF47_BF database sequences from Brazil and Spain to investigate the origin and phylodynamics of CRF47_BF. The Spanish region with the highest proportion of CRF47_BF samples in the data set was the Basque Country (43.7%) with Navarre next highest at 19.5%. We include in our analysis epidemiological data on host sex, mode of transmission, time of collection, and geographic region. The phylodynamic analysis indicates that CRF47_BF originated in Brazil around 1999–2000 and spread to Spain from Brazil in 2002–2003. The virus spread rapidly throughout Spain with an increase in population size from 2011 to 2015 and leveling off more recently. Three strongly supported clusters associated with Spanish regions (Basque Country, Navarre, and Aragon), together comprising 60.8% of the Spanish samples, were identified, one of which was also associated with transmission among men who have sex with men. The expansion in Spain of CRF47_BF, together with that of other CRFs and subtype variants of South American origin, previously reported, reflects the increasing relationship between the South American and European HIV-1 epidemics.
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Affiliation(s)
- Gracelyn Hill
- Computational Biology Institute, George Washington University, Washington, DC, United States
| | - Marcos Pérez-Losada
- Computational Biology Institute, George Washington University, Washington, DC, United States.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Porto, Portugal
| | - Elena Delgado
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Sonia Benito
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Vanessa Montero
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Horacio Gil
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Mónica Sánchez
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Javier E Cañada-García
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Elena García-Bodas
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
| | - Keith A Crandall
- Computational Biology Institute, George Washington University, Washington, DC, United States.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
| | - Michael M Thomson
- HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
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22
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Pérez-Losada M, Narayanan DB, Kolbe AR, Ramos-Tapia I, Castro-Nallar E, Crandall KA, Domínguez J. Comparative Analysis of Metagenomics and Metataxonomics for the Characterization of Vermicompost Microbiomes. Front Microbiol 2022; 13:854423. [PMID: 35620097 PMCID: PMC9127802 DOI: 10.3389/fmicb.2022.854423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/21/2022] [Indexed: 11/21/2022] Open
Abstract
The study of microbial communities or microbiotas in animals and environments is important because of their impact in a broad range of industrial applications, diseases and ecological roles. High throughput sequencing (HTS) is the best strategy to characterize microbial composition and function. Microbial profiles can be obtained either by shotgun sequencing of genomes, or through amplicon sequencing of target genes (e.g., 16S rRNA for bacteria and ITS for fungi). Here, we compared both HTS approaches at assessing taxonomic and functional diversity of bacterial and fungal communities during vermicomposting of white grape marc. We applied specific HTS workflows to the same 12 microcosms, with and without earthworms, sampled at two distinct phases of the vermicomposting process occurring at 21 and 63 days. Metataxonomic profiles were inferred in DADA2, with bacterial metabolic pathways predicted via PICRUSt2. Metagenomic taxonomic profiles were inferred in PathoScope, while bacterial functional profiles were inferred in Humann2. Microbial profiles inferred by metagenomics and metataxonomics showed similarities and differences in composition, structure, and metabolic function at different taxonomic levels. Microbial composition and abundance estimated by both HTS approaches agreed reasonably well at the phylum level, but larger discrepancies were observed at lower taxonomic ranks. Shotgun HTS identified ~1.8 times more bacterial genera than 16S rRNA HTS, while ITS HTS identified two times more fungal genera than shotgun HTS. This is mainly a consequence of the difference in resolution and reference richness between amplicon and genome sequencing approaches and databases, respectively. Our study also revealed great differences and even opposite trends in alpha- and beta-diversity between amplicon and shotgun HTS. Interestingly, amplicon PICRUSt2-imputed functional repertoires overlapped ~50% with shotgun Humann2 profiles. Finally, both approaches indicated that although bacteria and fungi are the main drivers of biochemical decomposition, earthworms also play a key role in plant vermicomposting. In summary, our study highlights the strengths and weaknesses of metagenomics and metataxonomics and provides new insights on the vermicomposting of white grape marc. Since both approaches may target different biological aspects of the communities, combining them will provide a better understanding of the microbiotas under study.
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Affiliation(s)
- Marcos Pérez-Losada
- Computational Biology Institute, The George Washington University, Washington, DC, United States.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Dhatri Badri Narayanan
- Computational Biology Institute, The George Washington University, Washington, DC, United States.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Allison R Kolbe
- Computational Biology Institute, The George Washington University, Washington, DC, United States.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Ignacio Ramos-Tapia
- Instituto de Investigación Interdisciplinaria (I3), Universidad de Talca, Talca, Chile
| | - Eduardo Castro-Nallar
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal.,Instituto de Investigación Interdisciplinaria (I3), Universidad de Talca, Talca, Chile.,Departamento de Microbiología, Facultad de Ciencias de la Salud, Universidad de Talca, Talca, Chile
| | - Keith A Crandall
- Computational Biology Institute, The George Washington University, Washington, DC, United States.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States
| | - Jorge Domínguez
- Grupo de Ecoloxía Animal (GEA), Universidade de Vigo, Vigo, Spain
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23
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Aira M, Pérez-Losada M, Crandall KA, Domínguez J. Composition, Structure and Diversity of Soil Bacterial Communities before, during and after Transit through the Gut of the Earthworm Aporrectodea caliginosa. Microorganisms 2022; 10:microorganisms10051025. [PMID: 35630467 PMCID: PMC9144582 DOI: 10.3390/microorganisms10051025] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 01/04/2023] Open
Abstract
Earthworms heavily modify the soil microbiome as it passes throughout their guts. However, there are no detailed studies describing changes in the composition, structure and diversity of soil microbiomes during gut transit and once they are released back to the soil as casts. To address this knowledge gap, we used 16S rRNA next-generation sequencing to characterize the microbiomes of soil, gut and casts from the earthworm Aporrectodea caliginosa. We also studied whether these three microbiomes are clearly distinct in composition or can be merged into metacommunities. A large proportion of bacteria was unique to each microbiome—soil (82%), gut (89%) and casts (75%), which indicates that the soil microbiome is greatly modified during gut transit. The three microbiomes also differed in alpha diversity, which peaked during gut transit and decreased in casts. Furthermore, gut transit also modified the structure of the soil microbiome, which clustered away from those of the earthworm gut and cast samples. However, this clustering pattern was not supported by metacommunity analysis, which indicated that soil and gut samples make up one metacommunity and cast samples another. These results have important implications for understanding the dynamics of soil microbial communities and nutrient cycles.
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Affiliation(s)
- Manuel Aira
- Grupo de Ecoloxía Animal (GEA), Universidade de Vigo, 36310 Vigo, Spain;
- Correspondence:
| | - Marcos Pérez-Losada
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA; (M.P.-L.); (K.A.C.)
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661 Vila do Conde, Portugal
| | - Keith A. Crandall
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA; (M.P.-L.); (K.A.C.)
| | - Jorge Domínguez
- Grupo de Ecoloxía Animal (GEA), Universidade de Vigo, 36310 Vigo, Spain;
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Knyazev S, Chhugani K, Sarwal V, Ayyala R, Singh H, Karthikeyan S, Deshpande D, Baykal PI, Comarova Z, Lu A, Porozov Y, Vasylyeva TI, Wertheim JO, Tierney BT, Chiu CY, Sun R, Wu A, Abedalthagafi MS, Pak VM, Nagaraj SH, Smith AL, Skums P, Pasaniuc B, Komissarov A, Mason CE, Bortz E, Lemey P, Kondrashov F, Beerenwinkel N, Lam TTY, Wu NC, Zelikovsky A, Knight R, Crandall KA, Mangul S. Unlocking capacities of genomics for the COVID-19 response and future pandemics. Nat Methods 2022; 19:374-380. [PMID: 35396471 PMCID: PMC9467803 DOI: 10.1038/s41592-022-01444-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated development of testing methods, and allowed timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific, and organizational challenges. Here, we discuss the application of genomic and computational methods for the efficient data driven COVID-19 response, advantages of democratization of viral sequencing around the world, and challenges associated with viral genome data collection and processing.
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Affiliation(s)
- Sergey Knyazev
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Karishma Chhugani
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA
| | - Varuni Sarwal
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Ram Ayyala
- Department of Translational Biomedical Informatics, University of Southern California, Los Angeles, CA, USA
| | - Harman Singh
- Department of Electrical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, India
| | - Smruthi Karthikeyan
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Dhrithi Deshpande
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA
| | - Pelin Icer Baykal
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Zoia Comarova
- Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA
| | - Angela Lu
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA
| | - Yuri Porozov
- World-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
| | - Tetyana I Vasylyeva
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Braden T Tierney
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Charles Y Chiu
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, Division of Infectious Diseases, University of California, San Francisco, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, University of California, San Francisco, San Francisco, CA, USA
| | - Ren Sun
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, P.R. China
| | - Aiping Wu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Suzhou Institute of Systems Medicine, Suzhou, China
| | - Malak S Abedalthagafi
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
| | - Victoria M Pak
- Emory University, School of Nursing, Atlanta, GA, CA, USA
- Emory University, Rollins School of Public Health, Department of Epidemiology, Atlanta, GA, CA, USA
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | - Adam L Smith
- Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA
| | - Pavel Skums
- Department of Computer Science, College of Art and Science, Georgia State University, Atlanta, GA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrey Komissarov
- Smorodintsev Research Institute of Influenza, Saint Petersburg, Russia
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Eric Bortz
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven-University of Leuven, Leuven, Belgium
| | - Fyodor Kondrashov
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Tommy Tsan-Yuk Lam
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P.R. China
- Laboratory of Data Discovery for Health Limited, Hong Kong SAR, P.R. China
- Centre for Immunology & Infection Limited, Hong Kong SAR, P.R. China
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alex Zelikovsky
- Department of Computer Science, College of Art and Science, Georgia State University, Atlanta, GA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Keith A Crandall
- Computational Biology Institute and Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Serghei Mangul
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.
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Pfaff ER, Girvin AT, Gabriel DL, Kostka K, Morris M, Palchuk MB, Lehmann HP, Amor B, Bissell M, Bradwell KR, Gold S, Hong SS, Loomba J, Manna A, McMurry JA, Niehaus E, Qureshi N, Walden A, Zhang XT, Zhu RL, Moffitt RA, Haendel MA, Chute CG, Adams WG, Al-Shukri S, Anzalone A, Baghal A, Bennett TD, Bernstam EV, Bernstam EV, Bissell MM, Bush B, Campion TR, Castro V, Chang J, Chaudhari DD, Chen W, Chu S, Cimino JJ, Crandall KA, Crooks M, Davies SJD, DiPalazzo J, Dorr D, Eckrich D, Eltinge SE, Fort DG, Golovko G, Gupta S, Haendel MA, Hajagos JG, Hanauer DA, Harnett BM, Horswell R, Huang N, Johnson SG, Kahn M, Khanipov K, Kieler C, Luzuriaga KRD, Maidlow S, Martinez A, Mathew J, McClay JC, McMahan G, Melancon B, Meystre S, Miele L, Morizono H, Pablo R, Patel L, Phuong J, Popham DJ, Pulgarin C, Santos C, Sarkar IN, Sazo N, Setoguchi S, Soby S, Surampalli S, Suver C, Vangala UMR, Visweswaran S, von Oehsen J, Walters KM, Wiley L, Williams DA, Zai A. Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative. J Am Med Inform Assoc 2022; 29:609-618. [PMID: 34590684 PMCID: PMC8500110 DOI: 10.1093/jamia/ocab217] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/19/2021] [Accepted: 09/23/2021] [Indexed: 02/01/2023] Open
Abstract
OBJECTIVE In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.
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Affiliation(s)
- Emily R Pfaff
- Department of Medicine, UNC Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | | | - Davera L Gabriel
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristin Kostka
- The OHDSI Center at the Roux Institute, Northeastern University, Portland, Maine, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Harold P Lehmann
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | | | | | | | - Sigfried Gold
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Stephanie S Hong
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | - Anita Walden
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Richard L Zhu
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
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Bernot JP, Avdeyev P, Zamyatin A, Dreyer N, Alexeev N, Pérez-Losada M, Crandall KA. Chromosome-level genome assembly, annotation, and phylogenomics of the gooseneck barnacle Pollicipes pollicipes. Gigascience 2022; 11:giac021. [PMID: 35277961 PMCID: PMC8917513 DOI: 10.1093/gigascience/giac021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/09/2022] [Accepted: 02/11/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The barnacles are a group of >2,000 species that have fascinated biologists, including Darwin, for centuries. Their lifestyles are extremely diverse, from free-swimming larvae to sessile adults, and even root-like endoparasites. Barnacles also cause hundreds of millions of dollars of losses annually due to biofouling. However, genomic resources for crustaceans, and barnacles in particular, are lacking. RESULTS Using 62× Pacific Biosciences coverage, 189× Illumina whole-genome sequencing coverage, 203× HiC coverage, and 69× CHi-C coverage, we produced a chromosome-level genome assembly of the gooseneck barnacle Pollicipes pollicipes. The P. pollicipes genome is 770 Mb long and its assembly is one of the most contiguous and complete crustacean genomes available, with a scaffold N50 of 47 Mb and 90.5% of the BUSCO Arthropoda gene set. Using the genome annotation produced here along with transcriptomes of 13 other barnacle species, we completed phylogenomic analyses on a nearly 2 million amino acid alignment. Contrary to previous studies, our phylogenies suggest that the Pollicipedomorpha is monophyletic and sister to the Balanomorpha, which alters our understanding of barnacle larval evolution and suggests homoplasy in a number of naupliar characters. We also compared transcriptomes of P. pollicipes nauplius larvae and adults and found that nearly one-half of the genes in the genome are differentially expressed, highlighting the vastly different transcriptomes of larvae and adult gooseneck barnacles. Annotation of the genes with KEGG and GO terms reveals that these stages exhibit many differences including cuticle binding, chitin binding, microtubule motor activity, and membrane adhesion. CONCLUSION This study provides high-quality genomic resources for a key group of crustaceans. This is especially valuable given the roles P. pollicipes plays in European fisheries, as a sentinel species for coastal ecosystems, and as a model for studying barnacle adhesion as well as its key position in the barnacle tree of life. A combination of genomic, phylogenetic, and transcriptomic analyses here provides valuable insights into the evolution and development of barnacles.
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Affiliation(s)
- James P Bernot
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20012, USA
| | - Pavel Avdeyev
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Anton Zamyatin
- Computer Technologies Laboratory, ITMO University, Saint-Petersburg 197101, Russia
| | - Niklas Dreyer
- Department of Life Science, National Taiwan Normal University, Taipei 106, Taiwan
- Biodiversity Program, International Graduate Program, Academia Sinica, Taipei, Taiwan
- Biodiversity Research Center, Academia Sinica, Taipei 115, Taiwan
- Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, DK-2100, Copenhagen, Denmark
| | - Nikita Alexeev
- Computer Technologies Laboratory, ITMO University, Saint-Petersburg 197101, Russia
| | - Marcos Pérez-Losada
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão 4485-661, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
- Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20012, USA
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
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Chow JH, Rahnavard A, Gomberg-Maitland M, Chatterjee R, Patodi P, Yamane DP, Levine AR, Davison D, Hawkins K, Jackson AM, Quintana MT, Lankford AS, Keneally RJ, Al-Mashat M, Fisher D, Williams J, Berger JS, Mazzeffi MA, Crandall KA. Association of Early Aspirin Use With In-Hospital Mortality in Patients With Moderate COVID-19. JAMA Netw Open 2022; 5:e223890. [PMID: 35323950 PMCID: PMC8948531 DOI: 10.1001/jamanetworkopen.2022.3890] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 02/02/2022] [Indexed: 01/08/2023] Open
Abstract
Importance Prior observational studies suggest that aspirin use may be associated with reduced mortality in high-risk hospitalized patients with COVID-19, but aspirin's efficacy in patients with moderate COVID-19 is not well studied. Objective To assess whether early aspirin use is associated with lower odds of in-hospital mortality in patients with moderate COVID-19. Design, Setting, and Participants Observational cohort study of 112 269 hospitalized patients with moderate COVID-19, enrolled from January 1, 2020, through September 10, 2021, at 64 health systems in the United States participating in the National Institute of Health's National COVID Cohort Collaborative (N3C). Exposure Aspirin use within the first day of hospitalization. Main Outcome and Measures The primary outcome was 28-day in-hospital mortality, and secondary outcomes were pulmonary embolism and deep vein thrombosis. Odds of in-hospital mortality were calculated using marginal structural Cox and logistic regression models. Inverse probability of treatment weighting was used to reduce bias from confounding and balance characteristics between groups. Results Among the 2 446 650 COVID-19-positive patients who were screened, 189 287 were hospitalized and 112 269 met study inclusion. For the full cohort, Median age was 63 years (IQR, 47-74 years); 16.1% of patients were African American, 3.8% were Asian, 52.7% were White, 5.0% were of other races and ethnicities, 22.4% were of unknown race and ethnicity. In-hospital mortality occurred in 10.9% of patients. After inverse probability treatment weighting, 28-day in-hospital mortality was significantly lower in those who received aspirin (10.2% vs 11.8%; odds ratio [OR], 0.85; 95% CI, 0.79-0.92; P < .001). The rate of pulmonary embolism, but not deep vein thrombosis, was also significantly lower in patients who received aspirin (1.0% vs 1.4%; OR, 0.71; 95% CI, 0.56-0.90; P = .004). Patients who received early aspirin did not have higher rates of gastrointestinal hemorrhage (0.8% aspirin vs 0.7% no aspirin; OR, 1.04; 95% CI, 0.82-1.33; P = .72), cerebral hemorrhage (0.6% aspirin vs 0.4% no aspirin; OR, 1.32; 95% CI, 0.92-1.88; P = .13), or blood transfusion (2.7% aspirin vs 2.3% no aspirin; OR, 1.14; 95% CI, 0.99-1.32; P = .06). The composite of hemorrhagic complications did not occur more often in those receiving aspirin (3.7% aspirin vs 3.2% no aspirin; OR, 1.13; 95% CI, 1.00-1.28; P = .054). Subgroups who appeared to benefit the most included patients older than 60 years (61-80 years: OR, 0.79; 95% CI, 0.72-0.87; P < .001; >80 years: OR, 0.79; 95% CI, 0.69-0.91; P < .001) and patients with comorbidities (1 comorbidity: 6.4% vs 9.2%; OR, 0.68; 95% CI, 0.55-0.83; P < .001; 2 comorbidities: 10.5% vs 12.8%; OR, 0.80; 95% CI, 0.69-0.93; P = .003; 3 comorbidities: 13.8% vs 17.0%, OR, 0.78; 95% CI, 0.68-0.89; P < .001; >3 comorbidities: 17.0% vs 21.6%; OR, 0.74; 95% CI, 0.66-0.84; P < .001). Conclusions and Relevance In this cohort study of US adults hospitalized with moderate COVID-19, early aspirin use was associated with lower odds of 28-day in-hospital mortality. A randomized clinical trial that includes diverse patients with moderate COVID-19 is warranted to adequately evaluate aspirin's efficacy in patients with high-risk conditions.
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Affiliation(s)
- Jonathan H. Chow
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Ali Rahnavard
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
| | - Mardi Gomberg-Maitland
- Division of Cardiology, Department of Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Ranojoy Chatterjee
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
| | - Pranay Patodi
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
| | - David P. Yamane
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
- Department of Emergency Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Andrea R. Levine
- Division of Pulmonary and Critical Care, Department of Medicine, University of Maryland School of Medicine, Baltimore
| | - Danielle Davison
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Katrina Hawkins
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Amanda M. Jackson
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Megan T. Quintana
- Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Allison S. Lankford
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Maryland School of Medicine, Baltimore
- Division of Critical Care Medicine, Department of Surgery, R. Adams Cowley Shock Trauma Center, Baltimore, Maryland
| | - Ryan J. Keneally
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Mustafa Al-Mashat
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Daniel Fisher
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Jeffrey Williams
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Jeffrey S. Berger
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Michael A. Mazzeffi
- Department of Anesthesiology and Critical Care Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Keith A. Crandall
- George Washington University Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, Washington, DC
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Alam ASMRU, Islam OK, Hasan MS, Islam MR, Mahmud S, Al‐Emran HM, Jahid IK, Crandall KA, Hossain MA. Dominant clade-featured SARS-CoV-2 co-occurring mutations reveal plausible epistasis: An in silico based hypothetical model. J Med Virol 2022; 94:1035-1049. [PMID: 34676891 PMCID: PMC8661685 DOI: 10.1002/jmv.27416] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 01/18/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into eight fundamental clades with four of these clades (G, GH, GR, and GV) globally prevalent in 2020. To explain plausible epistatic effects of the signature co-occurring mutations of these circulating clades on viral replication and transmission fitness, we proposed a hypothetical model using in silico approach. Molecular docking and dynamics analyses showed the higher infectiousness of a spike mutant through more favorable binding of G614 with the elastase-2. RdRp mutation p.P323L significantly increased genome-wide mutations (p < 0.0001), allowing for more flexible RdRp (mutated)-NSP8 interaction that may accelerate replication. Superior RNA stability and structural variation at NSP3:C241T might impact protein, RNA interactions, or both. Another silent 5'-UTR:C241T mutation might affect translational efficiency and viral packaging. These four G-clade-featured co-occurring mutations might increase viral replication. Sentinel GH-clade ORF3a:p.Q57H variants constricted the ion-channel through intertransmembrane-domain interaction of cysteine(C81)-histidine(H57). The GR-clade N:p.RG203-204KR would stabilize RNA interaction by a more flexible and hypo-phosphorylated SR-rich region. GV-clade viruses seemingly gained the evolutionary advantage of the confounding factors; nevertheless, N:p.A220V might modulate RNA binding with no phenotypic effect. Our hypothetical model needs further retrospective and prospective studies to understand detailed molecular events and their relationship to the fitness of SARS-CoV-2.
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Affiliation(s)
| | - Ovinu Kibria Islam
- Department of MicrobiologyJashore University of Science and TechnologyJashoreBangladesh
| | - Md. Shazid Hasan
- Department of MicrobiologyJashore University of Science and TechnologyJashoreBangladesh
| | - Mir Raihanul Islam
- Division of Poverty, Health, and NutritionInternational Food Policy Research InstituteBangladesh
| | - Shafi Mahmud
- Department Genetic Engineering and BiotechnologyUniversity of RajshahiRajshahiBangladesh
| | - Hassan M. Al‐Emran
- Department of Biomedical EngineeringJashore University of Science and TechnologyJashoreBangladesh
| | - Iqbal Kabir Jahid
- Department of MicrobiologyJashore University of Science and TechnologyJashoreBangladesh
| | - Keith A. Crandall
- Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public HealthThe George Washington UniversityWashington DCUSA
| | - M. Anwar Hossain
- Office of the Vice ChancellorJashore University of Science and TechnologyJashoreBangladesh
- Department of MicrobiologyUniversity of DhakaDhakaBangladesh
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Lawniczak MKN, Durbin R, Flicek P, Lindblad-Toh K, Wei X, Archibald JM, Baker WJ, Belov K, Blaxter ML, Marques Bonet T, Childers AK, Coddington JA, Crandall KA, Crawford AJ, Davey RP, Di Palma F, Fang Q, Haerty W, Hall N, Hoff KJ, Howe K, Jarvis ED, Johnson WE, Johnson RN, Kersey PJ, Liu X, Lopez JV, Myers EW, Pettersson OV, Phillippy AM, Poelchau MF, Pruitt KD, Rhie A, Castilla-Rubio JC, Sahu SK, Salmon NA, Soltis PS, Swarbreck D, Thibaud-Nissen F, Wang S, Wegrzyn JL, Zhang G, Zhang H, Lewin HA, Richards S. Standards recommendations for the Earth BioGenome Project. Proc Natl Acad Sci U S A 2022; 119:e2115639118. [PMID: 35042802 PMCID: PMC8795494 DOI: 10.1073/pnas.2115639118] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
A global international initiative, such as the Earth BioGenome Project (EBP), requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress toward its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies, and challenges may improve or change in the future, requiring flexibility for the EBP to meet its goals. Here, we describe some highlights from the proposed standards, and areas where additional challenges will need to be met.
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Affiliation(s)
- Mara K N Lawniczak
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Richard Durbin
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
- Department of Genetics, University of Cambridge, Cambridge CB3 0DH, United Kingdom
| | - Paul Flicek
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, United Kingdom
| | - Kerstin Lindblad-Toh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University 751 23 Uppsala, Sweden
| | | | - John M Archibald
- Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - William J Baker
- Department of Accelerated Taxonomy, Royal Botanic Gardens, Kew, Surrey TW9 3AE, United Kingdom
| | - Katherine Belov
- School of Life and Environmental Sciences, Faculty of Science, University of Sydney, Sydney, NSW 2006, Australia
| | - Mark L Blaxter
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Tomas Marques Bonet
- Institute of Evolutionary Biology, Consejo Superior de Investigaciones Científicas-Universitat Pompeau Fabra, Parc de Rechercha Biomédica Barcelona 08003 Barcelona, Spain
- Catalan Institution of Research and Advanced Studies 08010 Barcelona, Spain
- Centre Nacional d'Anàlisi Geonòmica - Centre for Genomic Regulation, Barcelona Institute of Science and Technology 08028 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona 08193 Barcelona, Spain
| | - Anna K Childers
- Bee Research Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705
| | - Jonathan A Coddington
- Smithsonian Institution, National Museum of Natural History, Washington, DC 20560-0105
| | - Keith A Crandall
- Computational Biology Institute and Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052
| | - Andrew J Crawford
- Department of Biological Sciences, Universidad de los Andes 111711 Bogotá, Colombia
| | - Robert P Davey
- Engineering Biology, Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
| | | | - Qi Fang
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen 518083, China
| | - Wilfried Haerty
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
| | - Neil Hall
- Genome British Columbia, Vancouver, BC V5Z 0C4, Canada
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
| | - Katharina J Hoff
- Institute of Mathematics and Computer Science, Center for Functional Genomics of Microbes, University of Greifswald 17489 Greifswald, Germany
| | - Kerstin Howe
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Erich D Jarvis
- Vertebrate Genomes Lab, The Rockefeller University, New York, NY 10065
- HHMI, Chevy Chase, MD 20815
| | - Warren E Johnson
- Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA 22630
- The Walter Reed Biosystematics Unit, Museum Support Center MRC-534, Smithsonian Institution, Suitland, MD 20746-2863
| | - Rebecca N Johnson
- Smithsonian Institution, National Museum of Natural History, Washington, DC 20560-0105
| | - Paul J Kersey
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, United Kingdom
| | - Xin Liu
- China National GeneBank, Shenzhen 518120, China
| | - Jose Victor Lopez
- Halmos College of Arts and Sciences, Guy Harvey Oceanographic Center, Nova Southeastern University, Dania Beach, FL 33004
| | - Eugene W Myers
- Department of Systems Biology, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden 01307, Germany
| | | | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20894
| | - Monica F Poelchau
- National Agricultural Library, USDA Agricultural Research Service, Beltsville, MD 20705
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20894
| | | | - Sunil Kumar Sahu
- China National GeneBank, Shenzhen 518120, China
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen 518083, China
| | - Nicholas A Salmon
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Pamela S Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
| | - David Swarbreck
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894
| | - Sibo Wang
- China National GeneBank, Shenzhen 518120, China
| | - Jill L Wegrzyn
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269
- Institute for Systems Genomics, Computational Biology Core, University of Connecticut, Storrs, CT 06269
| | - Guojie Zhang
- Villum Center for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen 1165 Copenhagen, Denmark
- China National Genebank, BGI-Shenzhen 518083 Shenzhen, China
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences 650223 Kunming, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences 650223 Kunming, China
| | - He Zhang
- BGI-Qingdao, BGI-Shenzhen 266555 Qingdao, China
| | - Harris A Lewin
- University of California Davis Genome Center, University of California, Davis, CA 95616
- Department of Evolution and Ecology, University of California, Davis, CA 95616
| | - Stephen Richards
- University of California Davis Genome Center, University of California, Davis, CA 95616;
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Blaxter M, Archibald JM, Childers AK, Coddington JA, Crandall KA, Di Palma F, Durbin R, Edwards SV, Graves JAM, Hackett KJ, Hall N, Jarvis ED, Johnson RN, Karlsson EK, Kress WJ, Kuraku S, Lawniczak MKN, Lindblad-Toh K, Lopez JV, Moran NA, Robinson GE, Ryder OA, Shapiro B, Soltis PS, Warnow T, Zhang G, Lewin HA. Why sequence all eukaryotes? Proc Natl Acad Sci U S A 2022; 119:e2115636118. [PMID: 35042801 PMCID: PMC8795522 DOI: 10.1073/pnas.2115636118] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Life on Earth has evolved from initial simplicity to the astounding complexity we experience today. Bacteria and archaea have largely excelled in metabolic diversification, but eukaryotes additionally display abundant morphological innovation. How have these innovations come about and what constraints are there on the origins of novelty and the continuing maintenance of biodiversity on Earth? The history of life and the code for the working parts of cells and systems are written in the genome. The Earth BioGenome Project has proposed that the genomes of all extant, named eukaryotes-about 2 million species-should be sequenced to high quality to produce a digital library of life on Earth, beginning with strategic phylogenetic, ecological, and high-impact priorities. Here we discuss why we should sequence all eukaryotic species, not just a representative few scattered across the many branches of the tree of life. We suggest that many questions of evolutionary and ecological significance will only be addressable when whole-genome data representing divergences at all of the branchings in the tree of life or all species in natural ecosystems are available. We envisage that a genomic tree of life will foster understanding of the ongoing processes of speciation, adaptation, and organismal dependencies within entire ecosystems. These explorations will resolve long-standing problems in phylogenetics, evolution, ecology, conservation, agriculture, bioindustry, and medicine.
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Affiliation(s)
- Mark Blaxter
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom;
| | - John M Archibald
- Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS B3H 4H7, Canada
| | - Anna K Childers
- Bee Research Laboratory, Agricultural Research Service, US Department of Agriculture (USDA), Beltsville, MD 20705
| | - Jonathan A Coddington
- Global Genome Initiative, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, George Washington University, Washington, DC 20052
- Department of Invertebrate Zoology, Smithsonian Institution, Washington, DC 20013
| | - Federica Di Palma
- School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Richard Durbin
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Scott V Edwards
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
- Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138
| | - Jennifer A M Graves
- School of Life Sciences, La Trobe University, Bundoora, VIC 751 23, Australia
- University of Canberra, Bruce, ACT 2617, Australia
| | - Kevin J Hackett
- Crop Production and Protection, Office of National Programs, Agricultural Research Service, USDA, Beltsville, MD 20705
| | - Neil Hall
- Earlham Institute, Norwich, Norfolk NR4 7UZ, United Kingdom
| | - Erich D Jarvis
- Laboratory of the Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- Howard Hughes Medical Institute, Chevy Chase, MD 20815
| | - Rebecca N Johnson
- National Museum of Natural History, Smithsonian Institution, Washington, DC 20560
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - W John Kress
- Botany, National Museum of Natural History, Smithsonian Institution, Washington, DC 20013-7012
| | - Shigehiro Kuraku
- Department of Genomics and Evolutionary Biology, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
- Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
| | | | - Kerstin Lindblad-Toh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala 751 23, Sweden
| | - Jose V Lopez
- Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Dania Beach, FL 33004
- Guy Harvey Oceanographic Center, Dania Beach, FL 33004
| | - Nancy A Moran
- Integrative Biology, University of Texas at Austin, Austin, TX 78712
| | - Gene E Robinson
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Department of Entomology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Oliver A Ryder
- Conservation Genetics, Division of Biology, San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Department of Evolution, Behavior and Ecology, University of California, San Diego, La Jolla, CA 92039
| | - Beth Shapiro
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95064
| | - Pamela S Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
- Biodiversity Institute, University of Florida, Gainesville, FL 32611
| | - Tandy Warnow
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61301
| | - Guojie Zhang
- Villum Center for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen 2100, Denmark
- China National Genebank, Beijing Genomics Institute-Shenzhen, Shenzhen 518083, China
| | - Harris A Lewin
- Department of Evolution and Ecology, College of Biological Sciences, University of California, Davis, CA 95616
- Department of Population Health and Reproduction, University of California, Davis, CA 95616
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Lewin HA, Richards S, Lieberman Aiden E, Allende ML, Archibald JM, Bálint M, Barker KB, Baumgartner B, Belov K, Bertorelle G, Blaxter ML, Cai J, Caperello ND, Carlson K, Castilla-Rubio JC, Chaw SM, Chen L, Childers AK, Coddington JA, Conde DA, Corominas M, Crandall KA, Crawford AJ, DiPalma F, Durbin R, Ebenezer TE, Edwards SV, Fedrigo O, Flicek P, Formenti G, Gibbs RA, Gilbert MTP, Goldstein MM, Graves JM, Greely HT, Grigoriev IV, Hackett KJ, Hall N, Haussler D, Helgen KM, Hogg CJ, Isobe S, Jakobsen KS, Janke A, Jarvis ED, Johnson WE, Jones SJM, Karlsson EK, Kersey PJ, Kim JH, Kress WJ, Kuraku S, Lawniczak MKN, Leebens-Mack JH, Li X, Lindblad-Toh K, Liu X, Lopez JV, Marques-Bonet T, Mazard S, Mazet JAK, Mazzoni CJ, Myers EW, O'Neill RJ, Paez S, Park H, Robinson GE, Roquet C, Ryder OA, Sabir JSM, Shaffer HB, Shank TM, Sherkow JS, Soltis PS, Tang B, Tedersoo L, Uliano-Silva M, Wang K, Wei X, Wetzer R, Wilson JL, Xu X, Yang H, Yoder AD, Zhang G. The Earth BioGenome Project 2020: Starting the clock. Proc Natl Acad Sci U S A 2022; 119:e2115635118. [PMID: 35042800 PMCID: PMC8795548 DOI: 10.1073/pnas.2115635118] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Harris A Lewin
- Department of Evolution and Ecology, College of Biological Sciences, University of California, Davis, CA 95616;
- Department of Population Health and Reproduction, University of California, Davis, CA 95616
| | - Stephen Richards
- University of California Davis Genome Center, University of California, Davis, CA 95616
| | - Erez Lieberman Aiden
- DNA Zoo and The Center for Genome Architecture, Baylor College of Medicine, Houston, TX 77030
| | - Miguel L Allende
- Center for Genome Regulation, Universidad de Chile 3425 Santiago, Chile
- Facultad de Ciencias, Universidad de Chile 3425 Santiago, Chile
| | - John M Archibald
- Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, NS B3H 4H7, Canada
| | - Miklós Bálint
- LOEWE Centre of Translational Biodiversity Genomics, Senckenberg Leibniz Institution for Biodiversity and Earth System Research 60325 Frankfurt am Main, Germany
- Institute for Insect Biotechnology, Justus-Liebig University 35392 Giessen, Germany
| | - Katharine B Barker
- Global Genome Biodiversity Network Secretariat, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560
| | | | - Katherine Belov
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia
| | - Giorgio Bertorelle
- Department of Life Sciences and Biotechnology, University of Ferrara 44121 Ferrara, Italy
| | - Mark L Blaxter
- Tree of Life, Wellcome Sanger Institute, Cambridge CB10 1SA, United Kingdom
| | - Jing Cai
- School of Ecology and Environment, Northwestern Polytechnical University 710072 Xi'an, China
| | - Nicolette D Caperello
- University of California Davis Genome Center, University of California, Davis, CA 95616
| | - Keith Carlson
- The Novim Group, University of California, Santa Barbara, CA 93106
| | | | - Shu-Miaw Chaw
- Biodiversity Research Center, Academia Sinica 11529 Taipei, Taiwan
| | - Lei Chen
- School of Ecology and Environment, Northwestern Polytechnical University 710072 Xi'an, China
| | - Anna K Childers
- Bee Research Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Agriculture Research Service, Beltsville, MD 20705
| | - Jonathan A Coddington
- Global Genome Initiative, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560
| | - Dalia A Conde
- Conservation Science, Species360 Conservation Science Alliance, Bloomington, MN 55425
- Department of Biology, University of Southern Denmark 5230 Odense M, Denmark
| | - Montserrat Corominas
- Department of Genetics, Microbiology, and Statistics, Universitat de Barcelona 08028 Barcelona, Spain
- Catalan Society for Biology, Institute for Catalan Studies 08001 Barcelona, Spain
| | - Keith A Crandall
- Department of Biostatistics & Bioinformatics, Computational Biology Institute, George Washington University, Washington, DC 20052
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
| | - Andrew J Crawford
- Department of Biological Sciences, Universidad de los Andes 111711 Bogotá, Colombia
| | | | - Richard Durbin
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
- Wellcome Sanger Institute, Cambridge CB10 1SA, United Kingdom
| | - ThankGod E Ebenezer
- UniProt, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, United Kingdom
| | - Scott V Edwards
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
- Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138
| | - Olivier Fedrigo
- Laboratory of the Neurogenetics of Language, The Rockefeller University, New York, NY 10065
| | - Paul Flicek
- Wellcome Sanger Institute, Cambridge CB10 1SA, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, United Kingdom
| | - Giulio Formenti
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY 10065
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030
| | - M Thomas P Gilbert
- GLOBE Institute, University of Copenhagen 1350 Copenhagen, Denmark
- University Museum, Norwegian University of Science and Technology 7491 Trondheim, Norway
| | - Melissa M Goldstein
- Department of Health Policy and Management, George Washington University, Washington, DC 20052
| | - Jennifer Marshall Graves
- School of Life Sciences, La Trobe University, Bundoora, VIC 3086, Australia
- Institute for Applied Ecology, University of Canberra, Bruce, ACT 2617, Australia
| | - Henry T Greely
- Stanford Law School, Stanford University, Stanford, CA 94305
| | - Igor V Grigoriev
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720
| | - Kevin J Hackett
- Office of National Programs, US Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705
| | - Neil Hall
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
| | - David Haussler
- Genome Institute, University of California, Santa Cruz, CA 95060
- HHMI, Chevy Chase, MD 20815
| | - Kristofer M Helgen
- Australian Museum Research Institute, Australian Museum, Sydney, NSW 2000, Australia
| | - Carolyn J Hogg
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia
| | - Sachiko Isobe
- Department of Frontier Research and Development, Kazusa DNA Research Institute, Chiba 292-0818, Japan
| | | | - Axel Janke
- LOEWE Centre of Translational Biodiversity Genomics, Senckenberg Leibniz Institution for Biodiversity and Earth System Research 60325 Frankfurt am Main, Germany
| | - Erich D Jarvis
- Laboratory of the Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- HHMI, Chevy Chase, MD 20815
| | - Warren E Johnson
- Walter Reed Biosystematics Unit, Smithsonian Institution, Suitland, MD 20746
- Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA 22630
| | - Steven J M Jones
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Paul J Kersey
- Royal Botanic Gardens, Kew, Richmond TW9 3AE, United Kingdom
| | - Jin-Hyoung Kim
- Division of Life Sciences, Korea Polar Research Institute 21990 Incheon, South Korea
| | - W John Kress
- Museum of Natural History, Smithsonian Institution, Washington, DC 20013-7012
| | - Shigehiro Kuraku
- Department of Genomics and Evolutionary Biology, National Institute of Genetics 411-8540 Shizuoka, Japan
- Laboratory for Phyloinformatics, RIKEN Center for Biosystems Dynamics Research 650-0047 Hyogo, Japan
| | - Mara K N Lawniczak
- Tree of Life, Wellcome Sanger Institute, Cambridge CB10 1SA, United Kingdom
| | | | - Xueyan Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences 650223 Yunnan, China
| | - Kerstin Lindblad-Toh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University 752 36 Uppsala, Sweden
| | - Xin Liu
- BGI-Research, Beijing Genomics Institute-Shenzhen 518083 Shenzhen, China
| | - Jose V Lopez
- Department of Biological Sciences, Halmos College of Arts and Sciences, Nova Southeastern University, Dania Beach, FL 33004
- Guy Harvey Oceanographic Center, Dania Beach, FL 33004
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology, Pompeu Fabra University, Consejo Superior de Investigaciones Cientificas, Parc de Recerca Biomedica de Barcelona 08003 Barcelona, Spain
- Catalan Institute of Research and Advanced Studies 08010 Barcelona, Spain
- Centre Nacional d'Anàlisi Genòmica, Centre for Genomic Regulation, Barcelona Institute of Science and Technology 08028 Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona 08193 Barcelona, Spain
| | - Sophie Mazard
- Bioplatforms Australia, Macquarie University, Sydney, NSW 2109, Australia
| | - Jonna A K Mazet
- One Health Institute, University of California Davis, CA 95616
| | - Camila J Mazzoni
- Berlin Center for Genomics in Biodiversity Research 14195 Berlin, Germany
- Evolutionary Genetics Department, Leibniz Institute for Zoo and Wildlife Research 10315 Berlin, Germany
| | - Eugene W Myers
- Max Planck Institute for Molecular Cell Biology and Genetics 01307 Dresden, Germany
| | - Rachel J O'Neill
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT 06269
| | - Sadye Paez
- Laboratory of the Neurogenetics of Language, The Rockefeller University, New York, NY 10065
| | - Hyun Park
- Division of Biotechnology, Korea University 02841 Seoul, Korea
| | - Gene E Robinson
- Department of Entomology, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Cristina Roquet
- Systematics and Evolution of Vascular Plants Associated Unit to Consejo Superior de Investigaciones Cientificas, Departament de Biologia Animal, Biologia Vegetal i Ecologia, Universitat Autònoma de Barcelona 08193 Bellaterra, Spain
- Laboratoire d'Ecologie Alpine, University Grenoble Alpes, University Savoie Mont Blanc, CNRS 38000 Grenoble, France
| | - Oliver A Ryder
- Conservation Genetics, San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Division of Biology, Department of Evolution, Behavior, and Ecology, University of California, San Diego, La Jolla, CA 92039
| | - Jamal S M Sabir
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University 21589 Jeddah, Saudi Arabia
- Centre of Excellence in Bionanoscience Research, King Abdulaziz University 21589 Jeddah, Saudi Arabia
| | - H Bradley Shaffer
- La Kretz Center for California Conservation Science, Institute of Environment and Sustainability, University of California, Los Angeles, CA 90024
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095
| | - Timothy M Shank
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543
| | - Jacob S Sherkow
- Department of Entomology, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- College of Law, University of Illinois at Urbana-Champaign, Champaign, IL 61820
| | - Pamela S Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
- Biodiversity Institute, University of Florida, Gainesville, FL 32611
| | - Boping Tang
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Provincial Key Laboratory of Coastal Wetland Bioresources and Environmental Protection, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetlands, Yancheng Teachers University 224002 Yancheng, China
| | - Leho Tedersoo
- Center of Mycology and Microbiology, University of Tartu 50411 Tartu, Estonia
- College of Science, King Saud University 11451 Riyadh, Saudi Arabia
| | | | - Kun Wang
- School of Ecology and Environment, Northwestern Polytechnical University 710072 Xi'an, China
| | - Xiaofeng Wei
- BGI-Research, Beijing Genomics Institute-Shenzhen 518083 Shenzhen, China
| | - Regina Wetzer
- Research and Collections, Natural History Museum of Los Angeles County, Los Angeles, CA 90007
- Biological Sciences, University of Southern California, Los Angeles, CA 90089
| | - Julia L Wilson
- Wellcome Sanger Institute, Cambridge CB10 1SA, United Kingdom
| | - Xun Xu
- BGI-Research, Beijing Genomics Institute-Shenzhen 518083 Shenzhen, China
| | - Huanming Yang
- BGI-Research, Beijing Genomics Institute-Shenzhen 518083 Shenzhen, China
| | - Anne D Yoder
- Department of Biology, Duke University, Durham, NC 27708
- Duke Center for Genomic and Computational Biology, Duke University, Durham, NC 27708
| | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences 650223 Yunnan, China
- BGI-Research, Beijing Genomics Institute-Shenzhen 518083 Shenzhen, China
- Villum Center for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen 2100 Copenhagen, Denmark
- China National Genebank, Beijing Genomics Institute 51803 Shenzhen, China
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Hoque MN, Sarkar MMH, Rahman MS, Akter S, Banu TA, Goswami B, Jahan I, Hossain MS, Shamsuzzaman AKM, Nafisa T, Molla MMA, Yeasmin M, Ghosh AK, Osman E, Alam SKS, Uzzaman MS, Habib MA, Mahmud ASM, Crandall KA, Islam T, Khan MS. SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome with inclusion of pathobionts. Sci Rep 2021; 11:24042. [PMID: 34911967 PMCID: PMC8674272 DOI: 10.1038/s41598-021-03245-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 11/08/2021] [Indexed: 01/02/2023] Open
Abstract
The microbiota of the nasopharyngeal tract (NT) play a role in host immunity against respiratory infectious diseases. However, scant information is available on interactions of SARS-CoV-2 with the nasopharyngeal microbiome. This study characterizes the effects of SARS-CoV-2 infection on human nasopharyngeal microbiomes and their relevant metabolic functions. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 patients = 8, recovered humans = 7, and healthy people = 7) were collected, and underwent to RNAseq-based metagenomic investigation. Our RNAseq data mapped to 2281 bacterial species (including 1477, 919 and 676 in healthy, COVID-19 and recovered metagenomes, respectively) indicating a distinct microbiome dysbiosis. The COVID-19 and recovered samples included 67% and 77% opportunistic bacterial species, respectively compared to healthy controls. Notably, 79% commensal bacterial species found in healthy controls were not detected in COVID-19 and recovered people. Similar dysbiosis was also found in viral and archaeal fraction of the nasopharyngeal microbiomes. We also detected several altered metabolic pathways and functional genes in the progression and pathophysiology of COVID-19. The nasopharyngeal microbiome dysbiosis and their genomic features determined by our RNAseq analyses shed light on early interactions of SARS-CoV-2 with the nasopharyngeal resident microbiota that might be helpful for developing microbiome-based diagnostics and therapeutics for this novel pandemic disease.
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Affiliation(s)
- M Nazmul Hoque
- Department of Gynecology, Obstetrics and Reproductive Health, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | - Md Murshed Hasan Sarkar
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | - M Shaminur Rahman
- Department of Microbiology, Jashore University of Science Technology, Jashore, 7408, Bangladesh
| | - Shahina Akter
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | - Tanjina Akhtar Banu
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | - Barna Goswami
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | - Iffat Jahan
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | - M Saddam Hossain
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | | | - Tasnim Nafisa
- National Institute of Laboratory Medicine and Referral Center, Dhaka, 1207, Bangladesh
| | - M Maruf Ahmed Molla
- National Institute of Laboratory Medicine and Referral Center, Dhaka, 1207, Bangladesh
| | - Mahmuda Yeasmin
- National Institute of Laboratory Medicine and Referral Center, Dhaka, 1207, Bangladesh
| | - Asish Kumar Ghosh
- National Institute of Laboratory Medicine and Referral Center, Dhaka, 1207, Bangladesh
| | - Eshrar Osman
- SciTech Consulting and Solutions, Dhaka, 1213, Bangladesh
| | - S K Saiful Alam
- Shaheed Tajuddin Ahmad Medical College, Gazipur, 1700, Bangladesh
| | | | - Md Ahashan Habib
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh
| | | | - Keith A Crandall
- Computational Biology Institute and Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering (IBGE), BSMRAU, Gazipur, 1706, Bangladesh.
| | - Md Salim Khan
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh.
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Rahnavard A, Dawson T, Clement R, Stearrett N, Pérez-Losada M, Crandall KA. Epidemiological associations with genomic variation in SARS-CoV-2. Sci Rep 2021; 11:23023. [PMID: 34837008 PMCID: PMC8626494 DOI: 10.1038/s41598-021-02548-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/16/2021] [Indexed: 11/24/2022] Open
Abstract
SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3′-to-5′ exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic—coherence—and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
| | - Tyson Dawson
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Rebecca Clement
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Nathaniel Stearrett
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
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34
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Islam MR, Hoque MN, Rahman MS, Alam ASMRU, Akther M, Puspo JA, Akter S, Sultana M, Crandall KA, Hossain MA. Author Correction: Genome-wide analysis of SARS-CoV-2 virus strains circulating worldwide implicates heterogeneity. Sci Rep 2021; 11:20568. [PMID: 34642347 PMCID: PMC8506079 DOI: 10.1038/s41598-021-00133-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- M Rafiul Islam
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - M Nazmul Hoque
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh.,Department of Gynecology, Obstetrics and Reproductive Health, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh
| | - M Shaminur Rahman
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - A S M Rubayet Ul Alam
- Department of Microbiology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
| | - Masuda Akther
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - J Akter Puspo
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Salma Akter
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh.,Department of Microbiology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
| | - Munawar Sultana
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, USA
| | - M Anwar Hossain
- Department of Microbiology, University of Dhaka, Dhaka, 1000, Bangladesh. .,Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
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35
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Bernot JP, Boxshall GA, Crandall KA. A synthesis tree of the Copepoda: integrating phylogenetic and taxonomic data reveals multiple origins of parasitism. PeerJ 2021; 9:e12034. [PMID: 34466296 PMCID: PMC8380027 DOI: 10.7717/peerj.12034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/01/2021] [Indexed: 11/20/2022] Open
Abstract
The Copepoda is a clade of pancrustaceans containing 14,485 species that are extremely varied in their morphology and lifestyle. Not only do copepods dominate marine plankton and sediment communities and make up a sizeable component of the freshwater plankton, but over 6,000 species are symbiotically associated with every major phylum of marine metazoans, mostly as parasites. Unfortunately, our understanding of copepod evolutionary relationships is relatively limited in part because of their extremely divergent morphology, sparse taxon sampling in molecular phylogenetic analyses, a reliance on only a handful of molecular markers, and little taxonomic overlap between phylogenetic studies. Here, a synthesis tree method is used to integrate published phylogenies into a more comprehensive tree of copepods by leveraging phylogenetic and taxonomic data. A literature review in this study finds fewer than 500 species of copepods have been sampled in molecular phylogenetic studies. Using the Open Tree of Life platform, those taxa that have been sampled in previous phylogenetic studies are grafted together and combined with the underlying copepod taxonomic hierarchy from the Open Tree of Life Taxonomy to make a synthesis phylogeny of all copepod species. Taxon sampling with respect to molecular phylogenetic analyses is reviewed for all orders of copepods and shows only 3% of copepod species have been sampled in phylogenetic studies. The resulting synthesis phylogeny reveals copepods have transitioned to a parasitic lifestyle on at least 14 occasions. We examine the underlying phylogenetic, taxonomic, and natural history data supporting these transitions to parasitism; review the species diversity of each parasitic clade; and identify key areas for further phylogenetic investigation.
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Affiliation(s)
- James P Bernot
- Department of Invertebrate Zoology, Smithsonian National Museum of Natural History, Washington, DC, United States of America.,Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC, United States of America
| | | | - Keith A Crandall
- Department of Invertebrate Zoology, Smithsonian National Museum of Natural History, Washington, DC, United States of America.,Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC, United States of America
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Abstract
Presented is an account of the crayfish genus Creaserinus Hobbs, 1973 for Texas, based on materials gathered during a 13-year survey of the state. Home to Texas are six members of the genus, including C. hedgpethi (Hobbs, 1948) stat. rev., n. comb., which is resurrected from the synonymy of C. fodiens; and five species new to science described herein, including C. brevistylus n. sp., C. clausus n. sp., C. crenastylus n. sp., C. limulus n. sp., and C. trinensis n. sp. Collections of these species except for C. trinensis n. sp. were previously known and studied but ascribed to C. fodiens (Cottle, 1863), which is removed from the fauna of the state. Support for the taxonomic acts comes from genetics, morphology, distribution, life history, habitat, and syntopy. Accounts are provided for each species and include illustrations and information on distribution, color pattern, relationships, life history, ecology, size, variations, and crayfish associates. A key to the species in the state based on form I males is provided. Creaserinus limulus n. sp. is extraordinary in that a majority of its populations sampled have been composed mostly or entirely of females. Additions to the faunas of Texass neighboring states include C. clausus n. sp. (Louisiana), C. crenastylus n. sp. (Louisiana), and C. limulus n. sp. (Arkansas and Oklahoma).
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Affiliation(s)
| | - David B Stern
- University of Wisconsin-Madison, Department of Integrative Biology, 430 Lincoln Dr., Birge Hall, Madison, WI 53706, USA. .
| | - Keith A Crandall
- Computational Biology Institute, George Washington University, SEH 7000D, 800 22nd St. NW, Washington DC, 20052, USA and Department of Invertebrate Zoology, Smithsonian Institution, Washington, DC, USA. .
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Suvorov A, Scornavacca C, Fujimoto MS, Bodily P, Clement M, Crandall KA, Whiting MF, Schrider DR, Bybee SM. Deep ancestral introgression shapes evolutionary history of dragonflies and damselflies. Syst Biol 2021; 71:526-546. [PMID: 34324671 PMCID: PMC9017697 DOI: 10.1093/sysbio/syab063] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 07/20/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Introgression is an important biological process affecting at least 10% of the extant species in the animal kingdom. Introgression significantly impacts inference of phylogenetic species relationships where a strictly binary tree model cannot adequately explain reticulate net-like species relationships. Here we use phylogenomic approaches to understand patterns of introgression along the evolutionary history of a unique, non-model insect system: dragonflies and damselflies (Odonata). We demonstrate that introgression is a pervasive evolutionary force across various taxonomic levels within Odonata. In particular, we show that the morphologically "intermediate" species of Anisozygoptera (one of the three primary suborders within Odonata besides Zygoptera and Anisoptera), which retain phenotypic characteristics of the other two suborders, experienced high levels of introgression likely coming from zygopteran genomes. Additionally, we find evidence for multiple cases of deep inter-superfamilial ancestral introgression.
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Affiliation(s)
- Anton Suvorov
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Celine Scornavacca
- Institut des Sciences de l'Evolution Université de Montpellier, CNRS, IRD, EPHE CC 064, Place Eugène Bataillon, 34095 Montpellier Cedex 05, France
| | - M Stanley Fujimoto
- Department of Computer Science, Brigham Young University, Provo, UT, United States
| | - Paul Bodily
- Department of Computer Science, Idaho State University, Pocatello, ID, United States
| | - Mark Clement
- Department of Computer Science, Brigham Young University, Provo, UT, United States
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
| | - Michael F Whiting
- Department of Biology, Brigham Young University, Provo, UT, United States.,M.L. Bean Museum, Brigham Young University, Provo, UT, United States
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Seth M Bybee
- Department of Biology, Brigham Young University, Provo, UT, United States.,M.L. Bean Museum, Brigham Young University, Provo, UT, United States
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38
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Sun J, Patel RC, Zheng Q, Madhira V, Olex AL, Islam JY, French E, Chiang TPY, Akselrod H, Moffitt R, Alexander GC, Andersen KM, Vinson AJ, Brown TT, Chute CG, Crandall KA, Franceschini N, Mannon RB, Kirk GD. COVID-19 Disease Severity among People with HIV Infection or Solid Organ Transplant in the United States: A Nationally-representative, Multicenter, Observational Cohort Study. medRxiv 2021:2021.07.26.21261028. [PMID: 34341798 PMCID: PMC8328066 DOI: 10.1101/2021.07.26.21261028] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Individuals with immune dysfunction, including people with HIV (PWH) or solid organ transplant recipients (SOT), might have worse outcomes from COVID-19. We compared odds of COVID-19 outcomes between patients with and without immune dysfunction. Methods We evaluated data from the National COVID-19 Cohort Collaborative (N3C), a multicenter retrospective cohort of electronic medical record (EMR) data from across the United States, on. 1,446,913 adult patients with laboratory-confirmed SARS-CoV-2 infection. HIV, SOT, comorbidity, and HIV markers were identified from EMR data prior to SARS-CoV-2 infection. COVID-19 disease severity within 45 days of SARS-CoV-2 infection was classified into 5 categories: asymptomatic/mild disease with outpatient care; mild disease with emergency department (ED) visit; moderate disease requiring hospitalization; severe disease requiring ventilation or extracorporeal membrane oxygenation (ECMO); and death. We used multivariable, multinomial logistic regression models to compare odds of COVID-19 outcomes between patients with and without immune dysfunction. Findings Compared to patients without immune dysfunction, PWH and SOT had a greater likelihood of having ED visits (adjusted odds ratio [aOR]: 1.28, 95% confidence interval [CI] 1.27-1.29; aOR: 2.61, CI: 2.58-2.65, respectively), requiring ventilation or ECMO (aOR: 1.43, CI: 1.43-1.43; aOR: 4.82, CI: 4.78-4.86, respectively), and death (aOR: 1.20, CI: 1.19-1.20; aOR: 3.38, CI: 3.35-3.41, respectively). Associations were independent of sociodemographic and comorbidity burden. Compared to PWH with CD4>500 cells/mm3, PWH with CD4<350 cells/mm3 were independently at 4.4-, 5.4-, and 7.6-times higher odds for hospitalization, requiring ventilation, and death, respectively. Increased COVID-19 severity was associated with higher levels of HIV viremia. Interpretation Individuals with immune dysfunction have greater risk for severe COVID-19 outcomes. More advanced HIV disease (greater immunosuppression and HIV viremia) was associated with higher odds of severe COVID-19 outcomes. Appropriate prevention and treatment strategies should be investigated to reduce the higher morbidity and mortality associated with COVID-19 among PWH and SOT.
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Affiliation(s)
- Jing Sun
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rena C. Patel
- Departments of Medicine and Global Health, University of Washington, Seattle WA, USA
| | - Qulu Zheng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Amy L. Olex
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA
| | - Jessica Y. Islam
- Center for Immunization and Infection in Cancer, Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Evan French
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA
| | - Teresa Po-Yu Chiang
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hana Akselrod
- Division of Infectious Diseases, George Washington University School of Medicine and Health Sciences, Washington DC, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook Cancer Center, New York, NY, USA
| | - G. Caleb Alexander
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kathleen M. Andersen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Amanda J. Vinson
- Department of Medicine, Division of Nephrology, Dalhousie University, Halifax, NS, Canada
| | - Todd T. Brown
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Keith A. Crandall
- Computational Biology Institute, Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington DC, USA
| | - Nora Franceschini
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Roslyn B. Mannon
- Department of Medicine, Division of Nephrology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Bendall ML, Gibson KM, Steiner MC, Rentia U, Pérez-Losada M, Crandall KA. HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intrahost Viral Populations. Mol Biol Evol 2021; 38:1677-1690. [PMID: 33367849 PMCID: PMC8042772 DOI: 10.1093/molbev/msaa315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Deep sequencing of viral populations using next-generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intrahost viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here, we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.
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Affiliation(s)
- Matthew L Bendall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Keylie M Gibson
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Margaret C Steiner
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Uzma Rentia
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
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40
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Steiner MC, Marston JL, Iñiguez LP, Bendall ML, Chiappinelli KB, Nixon DF, Crandall KA. Locus-Specific Characterization of Human Endogenous Retrovirus Expression in Prostate, Breast, and Colon Cancers. Cancer Res 2021; 81:3449-3460. [PMID: 33941616 PMCID: PMC8260468 DOI: 10.1158/0008-5472.can-20-3975] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/31/2021] [Accepted: 04/27/2021] [Indexed: 11/16/2022]
Abstract
Human endogenous retroviruses (HERV) have been implicated in a variety of diseases including cancers. Recent research implicates HERVs in epigenetic gene regulation. Here we utilize a recently developed bioinformatics tool for identifying HERV expression at the locus-specific level to identify differential expression of HERVs in matched tumor-normal RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas. Data from 52 prostate cancer, 111 breast cancer, and 24 colon cancer cases were analyzed. Locus-specific analysis identified active HERV elements and differentially expressed HERVs in prostate cancer, breast cancer, and colon cancer. In addition, differentially expressed host genes were identified across prostate, breast, and colon cancer datasets, respectively, including several involved in demethylation and antiviral response pathways, supporting previous findings regarding the pathogenic mechanisms of HERVs. A majority of differentially expressed HERVs intersected protein coding genes or lncRNAs in each dataset, and a subset of differentially expressed HERVs intersected differentially expressed genes in prostate, breast, and colon cancers, providing evidence towards regulatory function. Finally, patterns in HERV expression were identified in multiple cancer types, with 155 HERVs differentially expressed in all three cancer types. This analysis extends previous results identifying HERV transcription in cancer RNA-seq datasets to a locus-specific level, and in doing so provides a foundation for future studies investigating the functional role of HERV in cancers and identifies a number of novel targets for cancer biomarkers and immunotherapy. SIGNIFICANCE: Expressed human endogenous retroviruses are mapped at locus-specific resolution and linked to specific pathways to identify potential biomarkers and therapeutic targets in prostate, breast, and colon cancers.
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Affiliation(s)
- Margaret C Steiner
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, D.C
| | - Jez L Marston
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Luis P Iñiguez
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Matthew L Bendall
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Katherine B Chiappinelli
- Department of Microbiology, Immunology, and Tropical Medicine, The George Washington University, Washington, D.C
- The GW Cancer Center, The George Washington University, Washington, D.C
| | - Douglas F Nixon
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, D.C.
- The GW Cancer Center, The George Washington University, Washington, D.C
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, D.C
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41
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Rao S, Yang X, Wang Z, Ohshiro K, Zaidi S, Jogunoori W, Nguyen B, Crandall KA, Latham PS, Shetty K, Mishra L. Abstract 2910: A TGF-β-ALDH2 axis controls liver- brain-gut microbiome driven obesity, metabolic syndrome and cancer. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-2910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background/Aims: ALDH2 (Aldehyde dehydrogenase 2) is associated with multiple human diseases including cancers, Asian flush syndrome (deficiencies affect 35%-40% of East Asians), and alcoholic liver disease. Yet, oncogenic mechanisms and pathways that ALDH2 interacts with remain unclear. Previously we have demonstrated that TGF-β-deficient mutants derived from the loss of Smad3 and its adaptor Sptbn1 are exquisitely sensitive to alcohol, with impaired DNA damage repair. ALDH2 levels are altered in the liver tissues of the mouse mutants, and the Sptbn1-/- phenotype is similar to ALDH2-FancD2 mutants. We, therefore, hypothesized that disruption of TGF-β signaling combined with ALDH2 deficiency would increase the susceptibility of liver diseases and cancer.
Methods: Aldh2-/- mice were intercrossed with Sptbn1+/-, Smad3+/- mice. Control mice and intercrosses were fed with high-fat diet (HFD) or chow diet or alcohol diet, or hepatic vagotomy followed by phenotypic and mechanistic analyses through RNA-seq, lipidomics, metabolomics, western blot analyses, RTPCR, structure modeling, cell fractionation, and immunohistochemistry. Fecal samples from these mice underwent shotgun metagenomic sequencing.
Results: Strikingly, compared to WT, Aldh2-/-Sptbn1+/- (ASKO) mice on a normal diet develop metabolic syndromes with truncal obesity, insulin resistance, with increased blood glucose (272.3±28.6mg/dl vs 189.9 ±7.0mg/dl, p<0.05), serum triglyceride (185.2±40.0 mg/dl vs 83.7 ±7.8 mg/dl, p<0.05). Nonalcoholic steatohepatitis (NASH), and cancer, with raised ALT and AST levels, also develop in the mutant mice. HFD exacerbated obesity and NASH in Aldh2-/-Sptbn1+/- on HFD with substantial additional visceral fat accumulation and hyperglycemia with Zone 3 hepatic macro-steatosis and inflammation, which correlated with increased fatty acid metabolism and gluconeogenesis. ASKO mice had significantly altered neurotransmitter receptors in the liver including cholinergic receptors (e.g., Chrnb1, and Chrna2) and altered gut microbiome composition with increased abundance of S. pseudoporcinus (Aldh2-/-Smad3+/- vs WT: 85.6±29 vs 2.71 ±1.44, p<0.05) and decreased A. propionicum (ASKO vs WT: 65±21 vs 168±31, p<0.05).
Conclusions: Aldh2-/-Sptbn1+/- mice develop metabolic syndrome with alterations in the cholinergic pathway and microbiome species, suggesting a disruption in afferent vagal activity. ALDH2/SPTBN1 is therefore potentially a major liver-brain-gut vagal regulator of obesity. Aldh2 and TGF-β signaling are important in maintaining normal gut microbiome composition. These studies highlight the potential role of the gut-liver axis in regulating obesity and liver disease. With > 35% Asian population harboring ALDH2 alterations, our studies potentially have a high impact on these patient populations with a high risk of metabolic syndrome.
Citation Format: Shuyun Rao, Xiaochun Yang, Zhanhuai Wang, Kazufumi Ohshiro, Sobia Zaidi, Wilma Jogunoori, Bryan Nguyen, Keith A. Crandall, Patricia S. Latham, Kirti Shetty, Lopa Mishra. A TGF-β-ALDH2 axis controls liver- brain-gut microbiome driven obesity, metabolic syndrome and cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2910.
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Affiliation(s)
- Shuyun Rao
- 1George Washington University, Washington, DC
| | | | - Zhanhuai Wang
- 2Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | | | - Sobia Zaidi
- 1George Washington University, Washington, DC
| | | | | | | | | | | | - Lopa Mishra
- 1George Washington University, Washington, DC
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Alekseyenko AV, Hamidi B, Faith TD, Crandall KA, Powers JG, Metts CL, Madory JE, Carroll SL, Obeid JS, Lenert LA. Each patient is a research biorepository: informatics-enabled research on surplus clinical specimens via the living BioBank. J Am Med Inform Assoc 2021; 28:138-143. [PMID: 33166379 PMCID: PMC7810447 DOI: 10.1093/jamia/ocaa236] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 09/09/2020] [Indexed: 02/02/2023] Open
Abstract
The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu.
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Affiliation(s)
- Alexander V Alekseyenko
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Oral Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Bashir Hamidi
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Trevor D Faith
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Keith A Crandall
- Department of Biostatistics & Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington DC, USA
| | | | - Christopher L Metts
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.,Division of Pathology Informatics, Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - James E Madory
- Division of Pathology Informatics, Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Steven L Carroll
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jihad S Obeid
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Leslie A Lenert
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.,Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
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Hahn A, Burrell A, Chaney H, Sami I, Koumbourlis AC, Freishtat RJ, Zemanick ET, Louie S, Crandall KA. Importance of beta-lactam pharmacokinetics and pharmacodynamics on the recovery of microbial diversity in the airway of persons with cystic fibrosis. J Investig Med 2021; 69:1350-1359. [PMID: 34021052 PMCID: PMC8485129 DOI: 10.1136/jim-2021-001824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2021] [Indexed: 11/04/2022]
Abstract
Cystic fibrosis (CF) is a chronic lung disease characterized by acute pulmonary exacerbations (PExs) that are frequently treated with antibiotics. The impact of antibiotics on airway microbial diversity remains a critical knowledge gap. We sought to define the association between beta-lactam pharmacokinetic (PK) and pharmacodynamic target attainment on richness and alpha diversity. Twenty-seven children <18 years of age with CF participated in the prospective study. Airway samples were collected at hospital admission for PEx, end of antibiotic treatment (Tr), and >1 month in follow-up (FU). Metagenomic sequencing was performed to determine richness, alpha diversity, and the presence of antibiotic resistance genes. Free plasma beta-lactam levels were measured, and PK modeling was performed to determine time above the minimum inhibitory concentration (fT>MIC). 52% of study subjects had sufficient fT>MIC for optimal bacterial killing. There were no significant differences in demographics or PEx characteristics, except for F508del homozygosity. No significant differences were noted in richness or alpha diversity at individual time points, and both groups experienced a decrease in richness and alpha diversity at Tr compared with PEx. However, alpha diversity remained decreased at FU compared with PEx in those with sufficient fT>MIC but increased in those with insufficient fT>MIC (Shannon -0.222 vs +0.452, p=0.031, and inverse Simpson -1.376 vs +1.388, p=0.032). Fluoroquinolone resistance was also more frequently detected in those with insufficient fT>MIC (log2 fold change (log2FC) 2.29, p=0.025). These findings suggest sufficient beta-lactam fT>MIC is associated with suppressed recovery of alpha diversity following the antibiotic exposure period.
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Affiliation(s)
- Andrea Hahn
- Division of Infectious Diseases, Children's National Hospital, Washington, DC, USA .,Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
| | - Aszia Burrell
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
| | - Hollis Chaney
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Division of Pulmonary and Sleep Medicine, Children's National Hospital, Washington, DC, USA
| | - Iman Sami
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Division of Pulmonary and Sleep Medicine, Children's National Hospital, Washington, DC, USA
| | - Anastassios C Koumbourlis
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Division of Pulmonary and Sleep Medicine, Children's National Hospital, Washington, DC, USA
| | - Robert J Freishtat
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA.,Division of Emergency Medicine, Children's National Hospital, Washington, DC, USA
| | - Edith T Zemanick
- Department of Pediatrics, University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA
| | - Stan Louie
- Department of Clinical Pharmacy, University of Southern California School of Pharmacy, Los Angeles, California, USA
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, The George Washington University Milken Institute of Public Health, Washington, DC, USA
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Gogate N, Lyman D, Bell A, Cauley E, Crandall KA, Joseph A, Kahsay R, Natale DA, Schriml LM, Sen S, Mazumder R. COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model. Brief Bioinform 2021; 22:6278606. [PMID: 34015823 PMCID: PMC8195003 DOI: 10.1093/bib/bbab191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/29/2021] [Accepted: 04/26/2021] [Indexed: 12/23/2022] Open
Abstract
In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors—compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.
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Affiliation(s)
- Nikhita Gogate
- George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA
| | - Daniel Lyman
- George Washington University School of Medicine and Health Sciences, Department of Biochemistry and Molecular Medicine, Washington, DC 20037, USA
| | - Amanda Bell
- George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA
| | - Edmund Cauley
- George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA
| | - Keith A Crandall
- Computational Biology Institute at The George Washington University, Washington, DC 20037, USA
| | - Ashia Joseph
- George Washington University, Washington, DC 20037, USA
| | - Robel Kahsay
- George Washington University School of Medicine and Health Sciences, Department of Biochemistry and Molecular Medicine, Washington, DC 20037, USA
| | - Darren A Natale
- Georgetown University Medical Center, Washington, DC 20037, USA
| | - Lynn M Schriml
- University of Maryland, School of Medicine in Baltimore, MD, USA
| | - Sabyasach Sen
- George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine, The George Washington University Medical Center, Washington, DC 20037, USA
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Rahnavard A, Chatterjee S, Sayoldin B, Crandall KA, Tekola-Ayele F, Mallick H. Omics community detection using multi-resolution clustering. Bioinformatics 2021; 37:3588-3594. [PMID: 33974004 PMCID: PMC8545346 DOI: 10.1093/bioinformatics/btab317] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/23/2021] [Accepted: 04/26/2021] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step towards deriving mechanistic insights into complex biological phenomena. Here we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. RESULTS We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns, and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. AVAILABILITY omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Bahar Sayoldin
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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Knyazev S, Chhugani K, Sarwal V, Ayyala R, Singh H, Karthikeyan S, Deshpande D, Comarova Z, Lu A, Porozov Y, Wu A, Abedalthagafi MS, Nagaraj SH, Smith AL, Skums P, Ladner J, Tsan-Yuk Lam T, Wu NC, Zelikovsky A, Knight R, Crandall KA, Mangul S. Unlocking capacities of viral genomics for the COVID-19 pandemic response. ARXIV 2021. [PMCID: PMC8109901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.
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Affiliation(s)
- Sergey Knyazev
- Department of Computer Science, College of Art and Science, Georgia State University, 1 Park Place, Room 618, Atlanta, GA 30303, USA,Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, 30333 GA, USA,Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Karishma Chhugani
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Room 713. Los Angeles, CA 90089, USA
| | - Varuni Sarwal
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA 90095, USA
| | - Ram Ayyala
- Department of Neuroscience, College of Life Sciences, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA 90095, USA
| | - Harman Singh
- Department of Electrical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Smruthi Karthikeyan
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Dhrithi Deshpande
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Room 713. Los Angeles, CA 90089, USA
| | - Zoia Comarova
- Paradigm Environmental, 3911 Old Lee Highway, Fairfax, VA 22030
| | - Angela Lu
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Room 713. Los Angeles, CA 90089-9121, USA
| | - Yuri Porozov
- World-Class Research Center “Digital biodesign and personalized healthcare”, I.M. Sechenov First Moscow State Medical University, Moscow, Russia,Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
| | - Aiping Wu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China,Suzhou Institute of Systems Medicine, Suzhou, 215123, China
| | - Malak S. Abedalthagafi
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Shivashankar H. Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia,Translational Research Institute, Brisbane, Australia
| | - Adam L. Smith
- Astani Department of Civil and Environmental Engineering, University of Southern California, 3620 South Vermont Avenue, Los Angeles, CA 90089
| | - Pavel Skums
- Department of Computer Science, College of Art and Science, Georgia State University, 1 Park Place, Floor 6, Atlanta, GA 30303, USA
| | - Jason Ladner
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ 86011
| | - Tommy Tsan-Yuk Lam
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA,Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Alex Zelikovsky
- Department of Computer Science, College of Art and Science, Georgia State University, 1 Park Place, Floor 6, Atlanta, GA 30303, USA,The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA,Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA,Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Keith A. Crandall
- Computational Biology Institute and Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
| | - Serghei Mangul
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, 1540 Alcazar Street, Los Angeles, CA 90033, USA
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Knyazev S, Chhugani K, Sarwal V, Ayyala R, Singh H, Karthikeyan S, Deshpande D, Comarova Z, Lu A, Porozov Y, Wu A, Abedalthagafi MS, Nagaraj SH, Smith AL, Skums P, Ladner J, Lam TTY, Wu NC, Zelikovsky A, Knight R, Crandall KA, Mangul S. Unlocking capacities of viral genomics for the COVID-19 pandemic response. ArXiv 2021:arXiv:2104.14005v3. [PMID: 33948451 PMCID: PMC8095210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 06/04/2021] [Indexed: 12/25/2022]
Abstract
More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.
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Affiliation(s)
- Sergey Knyazev
- Department of Computer Science, College of Art and Science, Georgia State University, 1 Park Place, Room 618, Atlanta, GA 30303, USA
| | - Karishma Chhugani
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Room 713. Los Angeles, CA 90089, USA
| | - Varuni Sarwal
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA 90095, USA
| | - Ram Ayyala
- Department of Neuroscience, College of Life Sciences, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA 90095, USA
| | - Harman Singh
- Department of Electrical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Smruthi Karthikeyan
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Dhrithi Deshpande
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Room 713. Los Angeles, CA 90089, USA
| | - Zoia Comarova
- Paradigm Environmental, 3911 Old Lee Highway, Fairfax, VA 22030
| | - Angela Lu
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Room 713. Los Angeles, CA 90089-9121, USA
| | - Yuri Porozov
- World-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
| | - Aiping Wu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
- Suzhou Institute of Systems Medicine, Suzhou, 215123, China
| | - Malak S Abedalthagafi
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
- Translational Research Institute, Brisbane, Australia
| | - Adam L Smith
- Astani Department of Civil and Environmental Engineering, University of Southern California, 3620 South Vermont Avenue, Los Angeles, CA 90089
| | - Pavel Skums
- Department of Computer Science, College of Art and Science, Georgia State University, 1 Park Place, Floor 6, Atlanta, GA 30303, USA
| | - Jason Ladner
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ 86011
| | - Tommy Tsan-Yuk Lam
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Alex Zelikovsky
- Department of Computer Science, College of Art and Science, Georgia State University, 1 Park Place, Floor 6, Atlanta, GA 30303, USA
- The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Keith A Crandall
- Computational Biology Institute and Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052
| | - Serghei Mangul
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, 1540 Alcazar Street, Los Angeles, CA 90033, USA
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Stearrett N, Dawson T, Rahnavard A, Bachali P, Bendall ML, Zeng C, Caricchio R, Pérez-Losada M, Grammer AC, Lipsky PE, Crandall KA. Expression of Human Endogenous Retroviruses in Systemic Lupus Erythematosus: Multiomic Integration With Gene Expression. Front Immunol 2021; 12:661437. [PMID: 33986751 PMCID: PMC8112243 DOI: 10.3389/fimmu.2021.661437] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/12/2021] [Indexed: 11/20/2022] Open
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies predominantly to nuclear material. Many aspects of disease pathology are mediated by the deposition of nucleic acid containing immune complexes, which also induce the type 1interferon response, a characteristic feature of SLE. Notably, SLE is remarkably heterogeneous, with a variety of organs involved in different individuals, who also show variation in disease severity related to their ancestries. Here, we probed one potential contribution to disease heterogeneity as well as a possible source of immunoreactive nucleic acids by exploring the expression of human endogenous retroviruses (HERVs). We investigated the expression of HERVs in SLE and their potential relationship to SLE features and the expression of biochemical pathways, including the interferon gene signature (IGS). Towards this goal, we analyzed available and new RNA-Seq data from two independent whole blood studies using Telescope. We identified 481 locus specific HERV encoding regions that are differentially expressed between case and control individuals with only 14% overlap of differentially expressed HERVs between these two datasets. We identified significant differences between differentially expressed HERVs and non-differentially expressed HERVs between the two datasets. We also characterized the host differentially expressed genes and tested their association with the differentially expressed HERVs. We found that differentially expressed HERVs were significantly more physically proximal to host differentially expressed genes than non-differentially expressed HERVs. Finally, we capitalized on locus specific resolution of HERV mapping to identify key molecular pathways impacted by differential HERV expression in people with SLE.
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Affiliation(s)
- Nathaniel Stearrett
- Computational Biology Institute, George Washington University, Washington, DC, United States
| | - Tyson Dawson
- Computational Biology Institute, George Washington University, Washington, DC, United States
| | - Ali Rahnavard
- Computational Biology Institute, George Washington University, Washington, DC, United States
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
| | - Prathyusha Bachali
- RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, United States
| | - Matthew L. Bendall
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC, United States
| | - Roberto Caricchio
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States
| | - Marcos Pérez-Losada
- Computational Biology Institute, George Washington University, Washington, DC, United States
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Amrie C. Grammer
- RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, United States
| | - Peter E. Lipsky
- RILITE Research Institute and AMPEL BioSolutions, Charlottesville, VA, United States
| | - Keith A. Crandall
- Computational Biology Institute, George Washington University, Washington, DC, United States
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, United States
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Zimmermann BL, Buzatto I, Santos S, Giri F, Teixeira de Mello F, Crandall KA, Pérez‐Losada M, Bartholomei‐Santos ML. Entangled Aeglidae (Decapoda, Anomura): Additional evidence for cryptic species. ZOOL SCR 2021. [DOI: 10.1111/zsc.12483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Bianca L. Zimmermann
- Programa de Pós‐graduação em Biodiversidade Animal Universidade Federal de Santa Maria Santa Maria Brazil
- Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Sul Ibirubá Brazil
| | - Ivanice Buzatto
- Programa de Pós‐graduação em Biodiversidade Animal Universidade Federal de Santa Maria Santa Maria Brazil
| | - Sandro Santos
- Programa de Pós‐graduação em Biodiversidade Animal Universidade Federal de Santa Maria Santa Maria Brazil
| | - Federico Giri
- Laboratorio de Macrocrustáceos Instituto Nacional de Limnología Consejo Nacional de Investigaciones Científicas y Técnicas Universidad Nacional del Litoral Santa Fe Argentina
| | - Franco Teixeira de Mello
- Departamento de Ecología y Gestión Ambiental Centro Universitario Regional Este (CURE) Universidad de la República Maldonado Uruguay
| | - Keith A. Crandall
- Computational Biology Institute George Washington University Washington DC USA
- Department of Invertebrate Zoology US National Museum of Natural History Smithsonian Institution Washington DC USA
| | - Marcos Pérez‐Losada
- Computational Biology Institute George Washington University Washington DC USA
- Department of Invertebrate Zoology US National Museum of Natural History Smithsonian Institution Washington DC USA
- CIBIO‐InBIO Centro de Investigação em Biodiversidade e Recursos Genéticos Vairão Portugal
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Chan BKK, Dreyer N, Gale AS, Glenner H, Ewers-Saucedo C, Pérez-Losada M, Kolbasov GA, Crandall KA, Høeg JT. The evolutionary diversity of barnacles, with an updated classification of fossil and living forms. Zool J Linn Soc 2021. [DOI: 10.1093/zoolinnean/zlaa160] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Abstract
We present a comprehensive revision and synthesis of the higher-level classification of the barnacles (Crustacea: Thecostraca) to the genus level and including both extant and fossils forms. We provide estimates of the number of species in each group. Our classification scheme has been updated based on insights from recent phylogenetic studies and attempts to adjust the higher-level classifications to represent evolutionary lineages better, while documenting the evolutionary diversity of the barnacles. Except where specifically noted, recognized taxa down to family are argued to be monophyletic from molecular analysis and/or morphological data. Our resulting classification divides the Thecostraca into the subclasses Facetotecta, Ascothoracida and Cirripedia. The whole class now contains 14 orders, 65 families and 367 genera. We estimate that barnacles consist of 2116 species. The taxonomy is accompanied by a discussion of major morphological events in barnacle evolution and justifications for the various rearrangements we propose.
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Affiliation(s)
- Benny K K Chan
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Niklas Dreyer
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
- Department of Life Science, National Taiwan Normal University, Taipei, Taiwan
- Biodiversity Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
- Natural History Museum of Denmark, Invertebrate Zoology, University of Copenhagen, Universitetsparken, Copenhagen, Denmark
| | - Andy S Gale
- School of Earth and Environmental Sciences, University of Portsmouth, Portsmouth, UK
- Department of Earth Sciences, The Natural History Museum, London, UK
| | - Henrik Glenner
- Marine Biodiversity Group, Department of Biology, University of Bergen, Bergen, Norway
- Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | | | - Marcos Pérez-Losada
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, George Washington University, Washington, DC, USA
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Gregory A Kolbasov
- White Sea Biological Station, Biological Faculty of Moscow State University, Moscow, Russia
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, George Washington University, Washington, DC, USA
- Department of Invertebrate Zoology, US National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Jens T Høeg
- Marine Biology Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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