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Lee SHT, Garske KM, Arasu UT, Kar A, Miao Z, Alvarez M, Koka A, Darci-Maher N, Benhammou JN, Pan DZ, Örd T, Kaminska D, Männistö V, Heinonen S, Wabitsch M, Laakso M, Agopian VG, Pisegna JR, Pietiläinen KH, Pihlajamäki J, Kaikkonen MU, Pajukanta P. Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease. EBioMedicine 2024; 106:105232. [PMID: 38991381 DOI: 10.1016/j.ebiom.2024.105232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization. Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants. FINDINGS We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue. INTERPRETATION Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis. FUNDING NIH grants R01HG010505, R01DK132775, and R01HL170604; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 802825), Academy of Finland (Grants Nos. 333021), the Finnish Foundation for Cardiovascular Research the Sigrid Jusélius Foundation and the Jane and Aatos Erkko Foundation; American Association for the Study of Liver Diseases (AASLD) Advanced Transplant Hepatology award and NIH/NIDDK (P30DK41301) Pilot and Feasibility award; NIH/NIEHS F32 award (F32ES034668); Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2021), the Academy of Finland grant (Contract no. 138006); Academy of Finland (Grant Nos 335443, 314383, 272376 and 266286), Sigrid Jusélius Foundation, Finnish Medical Foundation, Finnish Diabetes Research Foundation, Novo Nordisk Foundation (#NNF20OC0060547, NNF17OC0027232, NNF10OC1013354) and Government Research Funds to Helsinki University Hospital; Orion Research Foundation, Maud Kuistila Foundation, Finish Medical Foundation, and University of Helsinki.
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Affiliation(s)
- Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Uma Thanigai Arasu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Zong Miao
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Nicholas Darci-Maher
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jihane N Benhammou
- Vatche and Tamar Manoukian Division of Digestive Diseases and Gastroenterology, Hepatology and Parenteral Nutrition, David Geffen School of Medicine at UCLA and VA Greater Los Angeles HCS, Los Angeles, CA, USA
| | - David Z Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Tiit Örd
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorota Kaminska
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Division of Cardiology, Department of Medicine, UCLA, Los Angeles, CA, USA
| | - Ville Männistö
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland; Department of Internal Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics and Adolescent Medicine, University of Ulm, Ulm, Germany
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Vatche G Agopian
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Joseph R Pisegna
- Department of Medicine and Human Genetics, Division of Gastroenterology, Hepatology and Parenteral Nutrition, David Geffen School of Medicine at UCLA and VA Greater Los Angeles HCS, Los Angeles, CA, USA
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Healthy WeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA; Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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2
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Zhuravskaya A, Yap K, Hamid F, Makeyev EV. Alternative splicing coupled to nonsense-mediated decay coordinates downregulation of non-neuronal genes in developing mouse neurons. Genome Biol 2024; 25:162. [PMID: 38902825 PMCID: PMC11188260 DOI: 10.1186/s13059-024-03305-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 06/07/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND The functional coupling between alternative pre-mRNA splicing (AS) and the mRNA quality control mechanism called nonsense-mediated decay (NMD) can modulate transcript abundance. Previous studies have identified several examples of such a regulation in developing neurons. However, the systems-level effects of AS-NMD in this context are poorly understood. RESULTS We developed an R package, factR2, which offers a comprehensive suite of AS-NMD analysis functions. Using this tool, we conducted a longitudinal analysis of gene expression in pluripotent stem cells undergoing induced neuronal differentiation. Our analysis uncovers hundreds of AS-NMD events with significant potential to regulate gene expression. Notably, this regulation is significantly overrepresented in specific functional groups of developmentally downregulated genes. Particularly strong association with gene downregulation is detected for alternative cassette exons stimulating NMD upon their inclusion into mature mRNA. By combining bioinformatic analyses with CRISPR/Cas9 genome editing and other experimental approaches we show that NMD-stimulating cassette exons regulated by the RNA-binding protein PTBP1 dampen the expression of their genes in developing neurons. We also provided evidence that the inclusion of NMD-stimulating cassette exons into mature mRNAs is temporally coordinated with NMD-independent gene repression mechanisms. CONCLUSIONS Our study provides an accessible workflow for the discovery and prioritization of AS-NMD targets. It further argues that the AS-NMD pathway plays a widespread role in developing neurons by facilitating the downregulation of functionally related non-neuronal genes.
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Affiliation(s)
- Anna Zhuravskaya
- Centre for Developmental Neurobiology, King's College London, London, SE1 1UL, UK
| | - Karen Yap
- Centre for Developmental Neurobiology, King's College London, London, SE1 1UL, UK
| | - Fursham Hamid
- Centre for Developmental Neurobiology, King's College London, London, SE1 1UL, UK.
| | - Eugene V Makeyev
- Centre for Developmental Neurobiology, King's College London, London, SE1 1UL, UK.
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3
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Nussbaum DP, Martz CA, Waters AM, Barrera A, Liu A, Rutter JC, Cerda-Smith CG, Stewart AE, Wu C, Cakir M, Levandowski CB, Kantrowitz DE, McCall SJ, Pierobon M, Petricoin EF, Joshua Smith J, Reddy TE, Der CJ, Taatjes DJ, Wood KC. Mediator kinase inhibition impedes transcriptional plasticity and prevents resistance to ERK/MAPK-targeted therapy in KRAS-mutant cancers. NPJ Precis Oncol 2024; 8:124. [PMID: 38822082 PMCID: PMC11143207 DOI: 10.1038/s41698-024-00615-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 05/03/2024] [Indexed: 06/02/2024] Open
Abstract
Acquired resistance remains a major challenge for therapies targeting oncogene activated pathways. KRAS is the most frequently mutated oncogene in human cancers, yet strategies targeting its downstream signaling kinases have failed to produce durable treatment responses. Here, we developed multiple models of acquired resistance to dual-mechanism ERK/MAPK inhibitors across KRAS-mutant pancreatic, colorectal, and lung cancers, and then probed the long-term events enabling survival against this class of drugs. These studies revealed that resistance emerges secondary to large-scale transcriptional adaptations that are diverse and cell line-specific. Transcriptional reprogramming extends beyond the well-established early response, and instead represents a dynamic, evolved process that is refined to attain a stably resistant phenotype. Mechanistic and translational studies reveal that resistance to dual-mechanism ERK/MAPK inhibition is broadly susceptible to manipulation of the epigenetic machinery, and that Mediator kinase, in particular, can be co-targeted at a bottleneck point to prevent diverse, cell line-specific resistance programs.
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Affiliation(s)
- Daniel P Nussbaum
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Colin A Martz
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
| | - Andrew M Waters
- Department of Pharmacology, University of North Carolina at Chapel Hill, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Alejandro Barrera
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Annie Liu
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Justine C Rutter
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
| | - Christian G Cerda-Smith
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
| | - Amy E Stewart
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
| | - Chao Wu
- Department of Surgery, Memorial Sloan Kettering Cancer Center, Colorectal Service, New York, NY, USA
| | - Merve Cakir
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
| | | | - David E Kantrowitz
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
| | - Shannon J McCall
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Mariaelena Pierobon
- George Mason University, Center for Applied Proteomics and Molecular Medicine, Fairfax, VA, USA
| | - Emanuel F Petricoin
- George Mason University, Center for Applied Proteomics and Molecular Medicine, Fairfax, VA, USA
| | - J Joshua Smith
- Department of Surgery, Memorial Sloan Kettering Cancer Center, Colorectal Service, New York, NY, USA
| | - Timothy E Reddy
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Channing J Der
- Department of Pharmacology, University of North Carolina at Chapel Hill, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Dylan J Taatjes
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
| | - Kris C Wood
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA.
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4
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Kawaguchi A, Wang J, Knapp D, Murawala P, Nowoshilow S, Masselink W, Taniguchi-Sugiura Y, Fei JF, Tanaka EM. A chromatin code for limb segment identity in axolotl limb regeneration. Dev Cell 2024:S1534-5807(24)00300-9. [PMID: 38788714 DOI: 10.1016/j.devcel.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 07/25/2023] [Accepted: 05/03/2024] [Indexed: 05/26/2024]
Abstract
The salamander limb correctly regenerates missing limb segments because connective tissue cells have segment-specific identities, termed "positional information". How positional information is molecularly encoded at the chromatin level has been unknown. Here, we performed genome-wide chromatin profiling in mature and regenerating axolotl limb connective tissue cells. We find segment-specific levels of histone H3K27me3 as the major positional mark, especially at limb homeoprotein gene loci but not their upstream regulators, constituting an intrinsic segment information code. During regeneration, regeneration-specific regulatory elements became active prior to the re-appearance of developmental regulatory elements. In the hand, the permissive chromatin state of the homeoprotein gene HoxA13 engages with the regeneration program bypassing the upper limb program. Comparison of regeneration regulatory elements with those found in other regenerative animals identified a core shared set of transcription factors, supporting an ancient, conserved regeneration program.
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Affiliation(s)
- Akane Kawaguchi
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Jingkui Wang
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Dunja Knapp
- DFG Research Center for Regenerative Therapies, Technische Universität Dresden, 01307 Dresden, Germany
| | - Prayag Murawala
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria; DFG Research Center for Regenerative Therapies, Technische Universität Dresden, 01307 Dresden, Germany
| | - Sergej Nowoshilow
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria; DFG Research Center for Regenerative Therapies, Technische Universität Dresden, 01307 Dresden, Germany
| | - Wouter Masselink
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Yuka Taniguchi-Sugiura
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Ji-Feng Fei
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Elly M Tanaka
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), 1030 Vienna, Austria.
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5
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Aguillon R, Rinsky M, Simon-Blecher N, Doniger T, Appelbaum L, Levy O. CLOCK evolved in cnidaria to synchronize internal rhythms with diel environmental cues. eLife 2024; 12:RP89499. [PMID: 38743049 PMCID: PMC11093582 DOI: 10.7554/elife.89499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024] Open
Abstract
The circadian clock enables anticipation of the day/night cycle in animals ranging from cnidarians to mammals. Circadian rhythms are generated through a transcription-translation feedback loop (TTFL or pacemaker) with CLOCK as a conserved positive factor in animals. However, CLOCK's functional evolutionary origin and mechanism of action in basal animals are unknown. In the cnidarian Nematostella vectensis, pacemaker gene transcript levels, including NvClk (the Clock ortholog), appear arrhythmic under constant darkness, questioning the role of NvCLK. Utilizing CRISPR/Cas9, we generated a NvClk allele mutant (NvClkΔ), revealing circadian behavior loss under constant dark (DD) or light (LL), while maintaining a 24 hr rhythm under light-dark condition (LD). Transcriptomics analysis revealed distinct rhythmic genes in wild-type (WT) polypsunder LD compared to DD conditions. In LD, NvClkΔ/Δ polyps exhibited comparable numbers of rhythmic genes, but were reduced in DD. Furthermore, under LD, the NvClkΔ/Δ polyps showed alterations in temporal pacemaker gene expression, impacting their potential interactions. Additionally, differential expression of non-rhythmic genes associated with cell division and neuronal differentiation was observed. These findings revealed that a light-responsive pathway can partially compensate for circadian clock disruption, and that the Clock gene has evolved in cnidarians to synchronize rhythmic physiology and behavior with the diel rhythm of the earth's biosphere.
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Affiliation(s)
- Raphael Aguillon
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityRamat GanIsrael
- The Multidisciplinary Brain Research Center, Bar-Ilan UniversityRamat GanIsrael
| | - Mieka Rinsky
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityRamat GanIsrael
| | - Noa Simon-Blecher
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityRamat GanIsrael
| | - Tirza Doniger
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityRamat GanIsrael
| | - Lior Appelbaum
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityRamat GanIsrael
- The Multidisciplinary Brain Research Center, Bar-Ilan UniversityRamat GanIsrael
| | - Oren Levy
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan UniversityRamat GanIsrael
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6
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Todorov-Völgyi K, González-Gallego J, Müller SA, Beaufort N, Malik R, Schifferer M, Todorov MI, Crusius D, Robinson S, Schmidt A, Körbelin J, Bareyre F, Ertürk A, Haass C, Simons M, Paquet D, Lichtenthaler SF, Dichgans M. Proteomics of mouse brain endothelium uncovers dysregulation of vesicular transport pathways during aging. NATURE AGING 2024; 4:595-612. [PMID: 38519806 DOI: 10.1038/s43587-024-00598-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/20/2024] [Indexed: 03/25/2024]
Abstract
Age-related decline in brain endothelial cell (BEC) function contributes critically to neurological disease. Comprehensive atlases of the BEC transcriptome have become available, but results from proteomic profiling are lacking. To gain insights into endothelial pathways affected by aging, we developed a magnetic-activated cell sorting-based mouse BEC enrichment protocol compatible with proteomics and resolved the profiles of protein abundance changes during aging. Unsupervised cluster analysis revealed a segregation of age-related protein dynamics with biological functions, including a downregulation of vesicle-mediated transport. We found a dysregulation of key regulators of endocytosis and receptor recycling (most prominently Arf6), macropinocytosis and lysosomal degradation. In gene deletion and overexpression experiments, Arf6 affected endocytosis pathways in endothelial cells. Our approach uncovered changes not picked up by transcriptomic studies, such as accumulation of vesicle cargo and receptor ligands, including Apoe. Proteomic analysis of BECs from Apoe-deficient mice revealed a signature of accelerated aging. Our findings provide a resource for analysing BEC function during aging.
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Affiliation(s)
- Katalin Todorov-Völgyi
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
| | - Judit González-Gallego
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Graduate School of Systemic Neuroscience (GSN), University Hospital, LMU Munich, Munich, Germany
| | - Stephan A Müller
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Neuroproteomics, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nathalie Beaufort
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Rainer Malik
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Mihail Ivilinov Todorov
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany
| | - Dennis Crusius
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Sophie Robinson
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Graduate School of Systemic Neuroscience (GSN), University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
| | - Andree Schmidt
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Neuroproteomics, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jakob Körbelin
- Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florence Bareyre
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Clinical Neuroimmunology, University Hospital, LMU Munich, Munich, Germany
- Biomedical Center Munich (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
| | - Ali Ertürk
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany
| | - Christian Haass
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Division of Metabolic Biochemistry, Biomedical Center Munich (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Mikael Simons
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Stefan F Lichtenthaler
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Neuroproteomics, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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7
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Rouanet A, Johnson R, Strauss M, Richardson S, Tom BD, White SR, Kirk PDW. Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES 2024; 73:314-339. [PMID: 38577633 PMCID: PMC7615733 DOI: 10.1093/jrsssc/qlad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Abstract
The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.
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Affiliation(s)
- Anaïs Rouanet
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
| | - Rob Johnson
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
| | - Magdalena Strauss
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sylvia Richardson
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
| | - Brian D Tom
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
| | - Simon R White
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Paul D. W. Kirk
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, U.K
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, U.K
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8
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Kim M, Jang YJ, Lee M, Guo Q, Son AJ, Kakkad NA, Roland AB, Lee BK, Kim J. The transcriptional regulatory network modulating human trophoblast stem cells to extravillous trophoblast differentiation. Nat Commun 2024; 15:1285. [PMID: 38346993 PMCID: PMC10861538 DOI: 10.1038/s41467-024-45669-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/31/2024] [Indexed: 02/15/2024] Open
Abstract
During human pregnancy, extravillous trophoblasts play crucial roles in placental invasion into the maternal decidua and spiral artery remodeling. However, regulatory factors and their action mechanisms modulating human extravillous trophoblast specification have been unknown. By analyzing dynamic changes in transcriptome and enhancer profile during human trophoblast stem cell to extravillous trophoblast differentiation, we define stage-specific regulators, including an early-stage transcription factor, TFAP2C, and multiple late-stage transcription factors. Loss-of-function studies confirm the requirement of all transcription factors identified for adequate differentiation, and we reveal that the dynamic changes in the levels of TFAP2C are essential. Notably, TFAP2C pre-occupies the regulatory elements of the inactive extravillous trophoblast-active genes during the early stage of differentiation, and the late-stage transcription factors directly activate extravillous trophoblast-active genes, including themselves as differentiation further progresses, suggesting sequential actions of transcription factors assuring differentiation. Our results reveal stage-specific transcription factors and their inter-connected regulatory mechanisms modulating extravillous trophoblast differentiation, providing a framework for understanding early human placentation and placenta-related complications.
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Affiliation(s)
- Mijeong Kim
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yu Jin Jang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Muyoung Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Qingqing Guo
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Albert J Son
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Nikita A Kakkad
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Abigail B Roland
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Bum-Kyu Lee
- Department of Biomedical Sciences, Cancer Research Center, University at Albany, State University of New York, Rensselaer, NY, 12144, USA
| | - Jonghwan Kim
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA.
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9
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Kar A, Alvarez M, Garske KM, Huang H, Lee SHT, Deal M, Das SS, Koka A, Jamal Z, Mohlke KL, Laakso M, Heinonen S, Pietiläinen KH, Pajukanta P. Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions. Genome Med 2024; 16:19. [PMID: 38297378 PMCID: PMC10829214 DOI: 10.1186/s13073-024-01291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. METHODS We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). RESULTS We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. CONCLUSIONS Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.
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Affiliation(s)
- Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Huiling Huang
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Milena Deal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Sankha Subhra Das
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Zoeb Jamal
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HealthyWeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles (UCLA), Gonda Center, Room 6357B, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA.
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA.
- Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, USA.
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10
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Xiang G, Guo Y, Bumcrot D, Sigova A. JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics. Nucleic Acids Res 2024; 52:e11. [PMID: 38055833 PMCID: PMC10810286 DOI: 10.1093/nar/gkad1146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/25/2023] [Accepted: 11/14/2023] [Indexed: 12/08/2023] Open
Abstract
Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.
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Affiliation(s)
- Guanjue Xiang
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
| | - Yuchun Guo
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
| | - David Bumcrot
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
| | - Alla Sigova
- CAMP4 Therapeutics Corp., One Kendall Square, Building 1400 West, Cambridge, MA 02139, USA
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11
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Muazzen Z, Moghrabi W, Bakheet T, Mahmoud L, Al-Saif M, Khabar KSA, Hitti EG. Global analysis of the abundance of AU-rich mRNAs in response to glucocorticoid treatment. Sci Rep 2024; 14:913. [PMID: 38195703 PMCID: PMC10776588 DOI: 10.1038/s41598-024-51301-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024] Open
Abstract
Glucocorticoids (GC) like dexamethasone (Dex) are potent anti-inflammatory agents with diverse cellular functions including the potentiation of the activity of AU-rich elements (AREs). AREs are cis-acting instability sequence elements located in the 3'UTRs of many inflammatory mediator mRNAs. Here, available RNA-seq data were used to investigate the effect of GCs on the ARE-mRNA-transcriptome. At a global scale, ARE-mRNAs had a tendency to be downregulated after GC-treatment of the A549 lung cancer cell-line, but with notable cases of upregulation. mRNA stability experiments indicated that not only the downregulated, but also the upregulated ARE-mRNAs are destabilized by Dex-treatment. Several of the most upregulated ARE-mRNAs code for anti-inflammatory mediators including the established GC targets DUSP1 and ZFP36; both code for proteins that target ARE-containing mRNAs for destruction. GCs are widely used in the treatment of COVID-19 patients; we show that ARE-mRNAs are more likely to regulate in opposite directions between Dex-treatment and SARS-CoV-2 infections compared to non-ARE mRNAs. The effect of GC treatment on ARE-mRNA abundance was also investigated in blood monocytes of COVID-19 patients. The results were heterogeneous; however, in agreement with in vitro observations, ZFP36 and DUSP1 were often amongst the most differentially expressed mRNAs. The results of this study propose a universal destabilization of ARE-mRNAs by GCs, but a diverse overall outcome in vitro likely due to induced transcription or due to the heterogeneity of COVID-19 patient's responses in vivo.
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Affiliation(s)
- Zeyad Muazzen
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia
| | - Walid Moghrabi
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia
| | - Tala Bakheet
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia
| | - Linah Mahmoud
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia
| | - Maher Al-Saif
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia
| | - Khalid S A Khabar
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia
| | - Edward G Hitti
- Molecular BioMedicine Department, Research and Innovation, King Faisal Specialist Hospital and Research Centre, 11211, Riyadh, Saudi Arabia.
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12
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Kennard AS, Velle KB, Ranjan R, Schulz D, Fritz-Laylin LK. An internally controlled system to study microtubule network diversification links tubulin evolution to the use of distinct microtubule regulators. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.573270. [PMID: 38260630 PMCID: PMC10802493 DOI: 10.1101/2024.01.08.573270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Diverse eukaryotic cells assemble microtubule networks that vary in structure and composition. While we understand how cells build microtubule networks with specialized functions, we do not know how microtubule networks diversify across deep evolutionary timescales. This problem has remained unresolved because most organisms use shared pools of tubulins for multiple networks, making it impossible to trace the evolution of any single network. In contrast, the amoeboflagellate Naegleria uses distinct tubulin genes to build distinct microtubule networks: while Naegleria builds flagella from conserved tubulins during differentiation, it uses divergent tubulins to build its mitotic spindle. This genetic separation makes for an internally controlled system to study independent microtubule networks in a single organismal and genomic context. To explore the evolution of these microtubule networks, we identified conserved microtubule binding proteins and used transcriptional profiling of mitosis and differentiation to determine which are upregulated during the assembly of each network. Surprisingly, most microtubule binding proteins are upregulated during only one process, suggesting that Naegleria uses distinct component pools to specialize its microtubule networks. Furthermore, the divergent residues of mitotic tubulins tend to fall within the binding sites of differentiation-specific microtubule regulators, suggesting that interactions between microtubules and their binding proteins constrain tubulin sequence diversification. We therefore propose a model for cytoskeletal evolution in which pools of microtubule network components constrain and guide the diversification of the entire network, so that the evolution of tubulin is inextricably linked to that of its binding partners.
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Affiliation(s)
- Andrew S. Kennard
- Department of Biology, University of Massachusetts, Amherst MA, United States
| | - Katrina B. Velle
- Department of Biology, University of Massachusetts, Amherst MA, United States
| | - Ravi Ranjan
- Genomics Resource Laboratory, Institute of Applied Life Sciences, University of Massachusetts, Amherst MA, United States
| | - Danae Schulz
- Department of Biology, Harvey Mudd College, Claremont CA, United States
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13
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Castor-Macias JA, Larouche JA, Wallace EC, Spence BD, Eames A, Duran P, Yang BA, Fraczek PM, Davis CA, Brooks SV, Maddipati KR, Markworth JF, Aguilar CA. Maresin 1 repletion improves muscle regeneration after volumetric muscle loss. eLife 2023; 12:e86437. [PMID: 38131691 PMCID: PMC10807862 DOI: 10.7554/elife.86437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 12/21/2023] [Indexed: 12/23/2023] Open
Abstract
The acute traumatic or surgical loss of skeletal muscle, known as volumetric muscle loss (VML), is a devastating type of injury that results in exacerbated and persistent inflammation followed by fibrosis. The mechanisms that mediate the magnitude and duration of the inflammatory response and ensuing fibrosis after VML remain understudied, and as such, the development of regenerative therapies has been limited. To address this need, we profiled how lipid mediators, which are potent regulators of the immune response after injury, varied with VML injuries that heal or result in fibrosis. We observed that non-healing VML injuries displayed increased pro-inflammatory eicosanoids and a lack of pro-resolving lipid mediators. Treatment of VML with a pro-resolving lipid mediator synthesized from docosahexaenoic acid, called Maresin 1, ameliorated fibrosis through reduction of neutrophils and macrophages and enhanced recovery of muscle strength. These results expand our knowledge of the dysregulated immune response that develops after VML and identify a novel immuno-regenerative therapeutic modality in Maresin 1.
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Affiliation(s)
- Jesus A Castor-Macias
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Biointerfaces Institute, University of MichiganAnn ArborUnited States
| | - Jacqueline A Larouche
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Biointerfaces Institute, University of MichiganAnn ArborUnited States
| | - Emily C Wallace
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
| | - Bonnie D Spence
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
| | - Alec Eames
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
| | - Pamela Duran
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Biointerfaces Institute, University of MichiganAnn ArborUnited States
| | - Benjamin A Yang
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Biointerfaces Institute, University of MichiganAnn ArborUnited States
| | - Paula M Fraczek
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Biointerfaces Institute, University of MichiganAnn ArborUnited States
| | - Carol A Davis
- Department of Molecular & Integrative Physiology, University of MichiganAnn ArborUnited States
| | - Susan V Brooks
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Department of Molecular & Integrative Physiology, University of MichiganAnn ArborUnited States
| | - Krishna Rao Maddipati
- Department of Pathology, Lipidomics Core Facility, Wayne State UniversityDetroitUnited States
| | - James F Markworth
- Department of Animal Sciences, Purdue UniversityWest Lafayette, IndianaUnited States
| | - Carlos A Aguilar
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Biointerfaces Institute, University of MichiganAnn ArborUnited States
- Program in Cellular and Molecular Biology, University of MichiganAnn ArborUnited States
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14
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Xiong L, Liu J, Han SY, Koppitch K, Guo JJ, Rommelfanger M, Miao Z, Gao F, Hallgrimsdottir IB, Pachter L, Kim J, MacLean AL, McMahon AP. Direct androgen receptor control of sexually dimorphic gene expression in the mammalian kidney. Dev Cell 2023; 58:2338-2358.e5. [PMID: 37673062 PMCID: PMC10873092 DOI: 10.1016/j.devcel.2023.08.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/20/2023] [Accepted: 08/04/2023] [Indexed: 09/08/2023]
Abstract
Mammalian organs exhibit distinct physiology, disease susceptibility, and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA sequencing (RNA-seq) data demonstrated that sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR)-mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation, whereas analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, disease, and metabolic linkage of sexually dimorphic gene activity.
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Affiliation(s)
- Lingyun Xiong
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Jing Liu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
| | - Seung Yub Han
- Graduate Program in Genomics and Computational Biology, Biomedical Graduate Studies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
| | - Jin-Jin Guo
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
| | - Megan Rommelfanger
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Zhen Miao
- Graduate Program in Genomics and Computational Biology, Biomedical Graduate Studies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fan Gao
- Caltech Bioinformatics Resource Center at Beckman Institute, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ingileif B Hallgrimsdottir
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Junhyong Kim
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam L MacLean
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA.
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15
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Wood K, Nussbaum D, Martz C, Waters A, Barrera A, Rutter J, Cerda-Smith C, Stewart A, Wu C, Cakir M, Levandowski C, Kantrowitz D, McCall S, Pierobon M, Petricoin E, Smith J, Der C, Taatjes D. Mediator Kinase Inhibition Impedes Transcriptional Plasticity and Prevents Resistance to ERK/MAPK-Targeted Therapy in KRAS-Mutant Cancers. RESEARCH SQUARE 2023:rs.3.rs-3511242. [PMID: 37961649 PMCID: PMC10635398 DOI: 10.21203/rs.3.rs-3511242/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Acquired resistance remains a major challenge for therapies targeting oncogene activated pathways. KRAS is the most frequently mutated oncogene in human cancers, yet strategies targeting its downstream signaling kinases have failed to produce durable treatment responses. Here, we developed multiple models of acquired resistance to dual-mechanism ERK/MAPK inhibitors across KRAS-mutant pancreatic, colorectal, and lung cancers, and then probed the long-term events enabling survival against this class of drugs. These studies revealed that resistance emerges secondary to large-scale transcriptional adaptations that are diverse and cell line-specific. Transcriptional reprogramming extends beyond the well-established early response, and instead represents a dynamic, evolved process that is refined to attain a stably resistant phenotype. Mechanistic and translational studies reveal that resistance to dual-mechanism ERK/MAPK inhibition is broadly susceptible to manipulation of the epigenetic machinery, and that Mediator kinase, in particular, can be co-targeted at a bottleneck point to prevent diverse, cell line-specific resistance programs.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Chao Wu
- Memorial Sloan Kettering Cancer Center
| | | | | | | | | | - Mariaelena Pierobon
- Center for Applied Proteomics and Molecular Medicine, George Mason University
| | | | - J Smith
- Memorial Sloan Kettering Cancer Center
| | - Channing Der
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
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16
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Schmittling SR, Muhammad D, Haque S, Long TA, Williams CM. Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response. BMC Genomics 2023; 24:620. [PMID: 37853316 PMCID: PMC10583470 DOI: 10.1186/s12864-023-09714-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 10/03/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Plants respond to stress through highly tuned regulatory networks. While prior works identified master regulators of iron deficiency responses in A. thaliana from whole-root data, identifying regulators that act at the cellular level is critical to a more comprehensive understanding of iron homeostasis. Within the root epidermis complex molecular mechanisms that facilitate iron reduction and uptake from the rhizosphere are known to be regulated by bHLH transcriptional regulators. However, many questions remain about the regulatory mechanisms that control these responses, and how they may integrate with developmental processes within the epidermis. Here, we use transcriptional profiling to gain insight into root epidermis-specific regulatory processes. RESULTS Set comparisons of differentially expressed genes (DEGs) between whole root and epidermis transcript measurements identified differences in magnitude and timing of organ-level vs. epidermis-specific responses. Utilizing a unique sampling method combined with a mutual information metric across time-lagged and non-time-lagged windows, we identified relationships between clusters of functionally relevant differentially expressed genes suggesting that developmental regulatory processes may act upstream of well-known Fe-specific responses. By integrating static data (DNA motif information) with time-series transcriptomic data and employing machine learning approaches, specifically logistic regression models with LASSO, we also identified putative motifs that served as crucial features for predicting differentially expressed genes. Twenty-eight transcription factors (TFs) known to bind to these motifs were not differentially expressed, indicating that these TFs may be regulated post-transcriptionally or post-translationally. Notably, many of these TFs also play a role in root development and general stress response. CONCLUSIONS This work uncovered key differences in -Fe response identified using whole root data vs. cell-specific root epidermal data. Machine learning approaches combined with additional static data identified putative regulators of -Fe response that would not have been identified solely through transcriptomic profiles and reveal how developmental and general stress responses within the epidermis may act upstream of more specialized -Fe responses for Fe uptake.
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Affiliation(s)
- Selene R Schmittling
- Department of Electrical & Computer Engineering, North Carolina State University, Raleigh, USA
| | | | - Samiul Haque
- Life Sciences Customer Advisory, SAS Institute Inc, Cary, USA
| | - Terri A Long
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Cranos M Williams
- Department of Electrical & Computer Engineering, North Carolina State University, Raleigh, USA.
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17
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Darwish E, Ghosh R, Bentzer J, Tsardakas Renhuldt N, Proux-Wera E, Kamal N, Spannagl M, Hause B, Sirijovski N, Van Aken O. The dynamics of touch-responsive gene expression in cereals. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:282-302. [PMID: 37159480 DOI: 10.1111/tpj.16269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/24/2023] [Accepted: 04/29/2023] [Indexed: 05/11/2023]
Abstract
Wind, rain, herbivores, obstacles, neighbouring plants, etc. provide important mechanical cues to steer plant growth and survival. Mechanostimulation to stimulate yield and stress resistance of crops is of significant research interest, yet a molecular understanding of transcriptional responses to touch is largely absent in cereals. To address this, we performed whole-genome transcriptomics following mechanostimulation of wheat, barley, and the recent genome-sequenced oat. The largest transcriptome changes occurred ±25 min after touching, with most of the genes being upregulated. While most genes returned to basal expression level by 1-2 h in oat, many genes retained high expression even 4 h post-treatment in barley and wheat. Functional categories such as transcription factors, kinases, phytohormones, and Ca2+ regulation were affected. In addition, cell wall-related genes involved in (hemi)cellulose, lignin, suberin, and callose biosynthesis were touch-responsive, providing molecular insight into mechanically induced changes in cell wall composition. Furthermore, several cereal-specific transcriptomic footprints were identified that were not observed in Arabidopsis. In oat and barley, we found evidence for systemic spreading of touch-induced signalling. Finally, we provide evidence that both the jasmonic acid-dependent and the jasmonic acid-independent pathways underlie touch-signalling in cereals, providing a detailed framework and marker genes for further study of (a)biotic stress responses in cereals.
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Affiliation(s)
- Essam Darwish
- Department of Biology, Lund University, Sölvegatan 35, 223 62, Lund, Sweden
- Plant Physiology Section, Agricultural Botany Department, Faculty of Agriculture, Cairo University, Cairo, Egypt
| | - Ritesh Ghosh
- Department of Biology, Lund University, Sölvegatan 35, 223 62, Lund, Sweden
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Johan Bentzer
- ScanOats Industrial Research Centre, Department of Chemistry, Division of Pure and Applied Biochemistry, Lund University, Lund, Sweden
| | - Nikos Tsardakas Renhuldt
- ScanOats Industrial Research Centre, Department of Chemistry, Division of Pure and Applied Biochemistry, Lund University, Lund, Sweden
| | - Estelle Proux-Wera
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Box 1031, SE-17121, Solna, Sweden
| | - Nadia Kamal
- PGSB - Plant Genome and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Manuel Spannagl
- PGSB - Plant Genome and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Bettina Hause
- Leibniz Institute of Plant Biochemistry, Weinberg 3, D06120, Halle, Germany
| | - Nick Sirijovski
- ScanOats Industrial Research Centre, Department of Chemistry, Division of Pure and Applied Biochemistry, Lund University, Lund, Sweden
| | - Olivier Van Aken
- Department of Biology, Lund University, Sölvegatan 35, 223 62, Lund, Sweden
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18
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Rivier AJ, Myers KS, Garcia AK, Sobol MS, Kaçar B. Regulatory response to a hybrid ancestral nitrogenase in Azotobacter vinelandii. Microbiol Spectr 2023; 11:e0281523. [PMID: 37702481 PMCID: PMC10581106 DOI: 10.1128/spectrum.02815-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 07/20/2023] [Indexed: 09/14/2023] Open
Abstract
Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, the engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs respond across sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to the introduced nitrogenase sequence-level perturbations. Rather the sustained physiological compatibility with the ancestral nitrogenase variant is accompanied by reduced expression of genes that support trace metal and electron resource allocation to nitrogenase. Our results spotlight gene expression changes in cellular processes adjacent to nitrogen fixation as productive engineering considerations to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs. IMPORTANCE Azotobacter vinelandii is a key model bacterium for the study of biological nitrogen fixation, an important metabolic process catalyzed by nitrogenase enzymes. Here, we demonstrate that compatibilities between engineered A. vinelandii strains and nitrogenase variants can be modulated at the regulatory level. The engineered strain studied here responds by adjusting the expression of proteins involved in cellular processes adjacent to nitrogen fixation, rather than that of nitrogenase proteins themselves. These insights can inform future strategies to transfer nitrogenase variants to non-native hosts.
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Affiliation(s)
- Alex J. Rivier
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Kevin S. Myers
- Great Lakes Bioenergy Research Center and the Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Amanda K. Garcia
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Morgan S. Sobol
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Betül Kaçar
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA
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19
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Souto-Maior C, Serrano Negron YL, Harbison ST. Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration. PLoS Comput Biol 2023; 19:e1011389. [PMID: 37561813 PMCID: PMC10443883 DOI: 10.1371/journal.pcbi.1011389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/22/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
Abstract
All but the simplest phenotypes are believed to result from interactions between two or more genes forming complex networks of gene regulation. Sleep is a complex trait known to depend on the system of feedback loops of the circadian clock, and on many other genes; however, the main components regulating the phenotype and how they interact remain an unsolved puzzle. Genomic and transcriptomic data may well provide part of the answer, but a full account requires a suitable quantitative framework. Here we conducted an artificial selection experiment for sleep duration with RNA-seq data acquired each generation. The phenotypic results are robust across replicates and previous experiments, and the transcription data provides a high-resolution, time-course data set for the evolution of sleep-related gene expression. In addition to a Hierarchical Generalized Linear Model analysis of differential expression that accounts for experimental replicates we develop a flexible Gaussian Process model that estimates interactions between genes. 145 gene pairs are found to have interactions that are different from controls. Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods. Statistical predictions were compared to experimental data from public databases on gene interactions. Mutations of candidate genes implicated by our results affected night sleep, and gene expression profiles largely met predicted gene-gene interactions.
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Affiliation(s)
- Caetano Souto-Maior
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
| | - Yazmin L. Serrano Negron
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
| | - Susan T. Harbison
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
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20
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Grenko CM, Bonnycastle LL, Taylor HJ, Yan T, Swift AJ, Robertson CC, Narisu N, Erdos MR, Collins FS, Taylor DL. Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543931. [PMID: 37333221 PMCID: PMC10274787 DOI: 10.1101/2023.06.06.543931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and ultimately type 2 diabetes (T2D). In this study, we sought to explore the effects of hyperglycemia on human pancreatic islet (HPI) gene expression by exposing HPIs from two donors to low (2.8mM) and high (15.0mM) glucose concentrations over 24 hours, assaying the transcriptome at seven time points using single-cell RNA sequencing (scRNA-seq). We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Across all cell types, we identified 1,528 genes associated with time, 1,185 genes associated with glucose exposure, and 845 genes associated with interaction effects between time and glucose. We clustered differentially expressed genes across cell types and found 347 modules of genes with similar expression patterns across time and glucose conditions, including two beta cell modules enriched in genes associated with T2D. Finally, by integrating genomic features from this study and genetic summary statistics for T2D and related traits, we nominate 363 candidate effector genes that may underlie genetic associations for T2D and related traits.
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Affiliation(s)
- Caleb M. Grenko
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lori L. Bonnycastle
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Tingfen Yan
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Amy J. Swift
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Catherine C. Robertson
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael R. Erdos
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - D. Leland Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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21
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Hasnain A, Balakrishnan S, Joshy DM, Smith J, Haase SB, Yeung E. Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics. Nat Commun 2023; 14:3148. [PMID: 37253722 PMCID: PMC10229592 DOI: 10.1038/s41467-023-37897-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/21/2023] [Indexed: 06/01/2023] Open
Abstract
A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library's ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.
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Affiliation(s)
- Aqib Hasnain
- Department of Mechanical Engineering, University of California Santa Barbara, Santa Barbara, CA, USA.
| | - Shara Balakrishnan
- Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Dennis M Joshy
- Department of Mechanical Engineering, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Jen Smith
- California Nanosystems Institute, University of California Santa Barbara, Santa Barbara, CA, USA
| | | | - Enoch Yeung
- Department of Mechanical Engineering, University of California Santa Barbara, Santa Barbara, CA, USA
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22
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Xiong L, Liu J, Han SY, Koppitch K, Guo JJ, Rommelfanger M, Gao F, Hallgrimsdottir IB, Pachter L, Kim J, MacLean AL, McMahon AP. Direct androgen receptor regulation of sexually dimorphic gene expression in the mammalian kidney. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.06.539585. [PMID: 37205355 PMCID: PMC10187285 DOI: 10.1101/2023.05.06.539585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Mammalian organs exhibit distinct physiology, disease susceptibility and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA-seq data demonstrated sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR) mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation while analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, and disease and metabolic linkage, of sexually dimorphic gene activity.
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Affiliation(s)
- Lingyun Xiong
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Jing Liu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
| | - Seung Yub Han
- Graduate Program in Genomics and Computational Biology, Biomedical Graduate Studies, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
| | - Jin-Jin Guo
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
| | - Megan Rommelfanger
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Fan Gao
- Caltech Bioinformatics Resource Center at Beckman Institute, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Junhyong Kim
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam L. MacLean
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrew P. McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90089, USA
- Lead Contact
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23
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Xiang G, Giardine B, An L, Sun C, Keller CA, Heuston EF, Anderson SM, Kirby M, Bodine D, Zhang Y, Hardison RC. Snapshot: a package for clustering and visualizing epigenetic history during cell differentiation. BMC Bioinformatics 2023; 24:102. [PMID: 36941541 PMCID: PMC10026520 DOI: 10.1186/s12859-023-05223-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 03/07/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. RESULTS To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis. CONCLUSION The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot .
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Affiliation(s)
- Guanjue Xiang
- The Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA.
| | - Belinda Giardine
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | - Lin An
- The Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Chen Sun
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Cheryl A Keller
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | | | | | | | - David Bodine
- NHGRI Hematopoiesis Section, GMBB, Bethesda, MD, USA
| | - Yu Zhang
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
| | - Ross C Hardison
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA.
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24
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Li A, Xiong S, Li J, Mallik S, Liu Y, Fei R, Zhou H, Liu G. AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1574-1580. [PMID: 35853049 DOI: 10.1109/tcbb.2022.3192306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene expression. Our method (named AngClust) uses angular features to indicate the change of trend in gene expression levels at two neighboring time points. The changes of angles at multiple time points reflects the change of trend of the overall expression levels. Such changes are used to measure whether the expression trends of different genes are similar. To obtain functionally significant clusters from the clustering results, we evaluated numbers of genes in clusters, average correlation coefficient, fluctuation, and their correlation with GO term enrichment. The efficacy of AngClust outperform two other measures, Euclidean distance (ED) and dynamic time warping of correlation (DTW), on a dataset of yeast gene expression. The ratios of GO and pathway term-enriched of clusters of AngClust is higher than or equal to that of STEM and TMixClust on human, mouse, and yeast time series of gene expression.
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25
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Dijkstra S, Hinne M, Segers E, Molenaar I. Clustering Children's learning behaviour to identify self-regulated learning support needs. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2023.107754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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26
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Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2022.107540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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27
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Ling H, Zhu X, Zhu T, Nie M, Liu Z, Liu Z. A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25020259. [PMID: 36832627 PMCID: PMC9955697 DOI: 10.3390/e25020259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/20/2023] [Accepted: 01/29/2023] [Indexed: 05/28/2023]
Abstract
Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes it difficult for existing algorithms to handle large-scale data. With the development of distributed parallel computing framework, data parallelism was proposed. However, the increase in parallelism will lead to the problem of unbalanced data distribution affecting the clustering effect. In this paper, we propose a parallel multiobjective PSO weighted average clustering algorithm based on apache Spark (Spark-MOPSO-Avg). First, the entire data set is divided into multiple partitions and cached in memory using the distributed parallel and memory-based computing of Apache Spark. The local fitness value of the particle is calculated in parallel according to the data in the partition. After the calculation is completed, only particle information is transmitted, and there is no need to transmit a large number of data objects between each node, reducing the communication of data in the network and thus effectively reducing the algorithm's running time. Second, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results. Experimental results show that the Spark-MOPSO-Avg algorithm achieves lower information loss under data parallelism, losing about 1% to 9% accuracy, but can effectively reduce the algorithm time overhead. It shows good execution efficiency and parallel computing capability under the Spark distributed cluster.
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28
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Chung CH, Murphy CM, Wingate VP, Pavlicek JW, Nakashima R, Wei W, McCarty D, Rabinowitz J, Barton E. Production of rAAV by plasmid transfection induces antiviral and inflammatory responses in suspension HEK293 cells. Mol Ther Methods Clin Dev 2023; 28:272-283. [PMID: 36819978 PMCID: PMC9937832 DOI: 10.1016/j.omtm.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023]
Abstract
Recombinant adeno-associated virus (rAAV) is a clinically proven viral vector for delivery of therapeutic genes to treat rare diseases. Improving rAAV manufacturing productivity and vector quality is necessary to meet clinical and commercial demand. These goals will require an improved understanding of the cellular response to rAAV production, which is poorly defined. We interrogated the kinetic transcriptional response of HEK293 cells to rAAV production following transient plasmid transfection, under manufacturing-relevant conditions, using RNA-seq. Time-series analyses identified a robust cellular response to transfection and rAAV production, with 1,850 transcripts differentially expressed. Gene Ontology analysis determined upregulated pathways, including inflammatory and antiviral responses, with several interferon-stimulated cytokines and chemokines being upregulated at the protein level. Literature-based pathway prediction implicated multiple pathogen pattern sensors and signal transducers in up-regulation of inflammatory and antiviral responses in response to transfection and rAAV replication. Systematic analysis of the cellular transcriptional response to rAAV production indicates that host cells actively sense vector manufacture as an infectious insult. This dataset may therefore illuminate genes and pathways that influence rAAV production, thereby enabling the rational design of next-generation manufacturing platforms to support safe, effective, and affordable AAV-based gene therapies.
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Affiliation(s)
- Cheng-Han Chung
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Christopher M. Murphy
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Vincent P. Wingate
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Jeffrey W. Pavlicek
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Reiko Nakashima
- Pfizer Inc., Worldwide Research, Development and Medical, Simulation and Modeling Sciences, Cambridge, MA 02139, USA
| | - Wei Wei
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA
| | - Douglas McCarty
- Pfizer Inc., Worldwide Research, Development and Medical, Rare Disease Research Unit, Morrisville, NC 27560, USA
| | - Joseph Rabinowitz
- Pfizer Inc., Worldwide Research, Development and Medical, Rare Disease Research Unit, Morrisville, NC 27560, USA
| | - Erik Barton
- Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA,Corresponding author: Erik Barton, Pfizer Inc., Worldwide Research, Development and Medical, Bioprocess Research and Development, Morrisville, NC 27560, USA.
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29
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Anziani P, Becker J, Mignon C, Arnaud-Barbe N, Courtois V, Izac M, Pizzato R, Abi-Ghanem J, Tran VD, Sarafian M, Bunescu A, Garnier D, Abachin E, Renauld-Mongénie G, Guyard C. Deep longitudinal multi-omics analysis of Bordetella pertussis cultivated in bioreactors highlights medium starvations and transitory metabolisms, associated to vaccine antigen biosynthesis variations and global virulence regulation. Front Microbiol 2023; 14:1036386. [PMID: 36876086 PMCID: PMC9976334 DOI: 10.3389/fmicb.2023.1036386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 02/16/2023] Open
Abstract
Bordetella pertussis is the bacterial causative agent of whooping cough, a serious respiratory illness. An extensive knowledge on its virulence regulation and metabolism is a key factor to ensure pertussis vaccine manufacturing process robustness. The aim of this study was to refine our comprehension of B. pertussis physiology during in vitro cultures in bioreactors. A longitudinal multi-omics analysis was carried out over 26 h small-scale cultures of B. pertussis. Cultures were performed in batch mode and under culture conditions intending to mimic industrial processes. Putative cysteine and proline starvations were, respectively, observed at the beginning of the exponential phase (from 4 to 8 h) and during the exponential phase (18 h 45 min). As revealed by multi-omics analyses, the proline starvation induced major molecular changes, including a transient metabolism with internal stock consumption. In the meantime, growth and specific total PT, PRN, and Fim2 antigen productions were negatively affected. Interestingly, the master virulence-regulating two-component system of B. pertussis (BvgASR) was not evidenced as the sole virulence regulator in this in vitro growth condition. Indeed, novel intermediate regulators were identified as putatively involved in the expression of some virulence-activated genes (vags). Such longitudinal multi-omics analysis applied to B. pertussis culture process emerges as a powerful tool for characterization and incremental optimization of vaccine antigen production.
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Affiliation(s)
- Paul Anziani
- Sanofi, Marcy-l'Étoile, France.,BIOASTER, Lyon, France
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30
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Transcriptome Profiling of Stem-Differentiating Xylem in Response to Abiotic Stresses Based on Hybrid Sequencing in Cunninghamia lanceolata. Int J Mol Sci 2022; 23:ijms232213986. [PMID: 36430463 PMCID: PMC9695776 DOI: 10.3390/ijms232213986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/22/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Cunninghamia lanceolata (C. lanceolata) belongs to Gymnospermae, which are fast-growing and have desirable wood properties. However, C. lanceolata's stress resistance is little understood. To unravel the physiological and molecular regulation mechanisms under environmental stresses in the typical gymnosperm species of C. lanceolata, three-year-old plants were exposed to simulated drought stress (polyethylene glycol 8000), salicylic acid, and cold treatment at 4 °C for 8 h, 32 h, and 56 h, respectively. Regarding the physiological traits, we observed a decreased protein content and increased peroxidase upon salicylic acid and polyethylene glycol treatment. Superoxide dismutase activity either decreased or increased at first and then returned to normal under the stresses. Regarding the molecular regulation, we used both nanopore direct RNA sequencing and short-read sequencing to reveal a total of 5646 differentially expressed genes in response to different stresses, of which most had functions in lignin catabolism, pectin catabolism, and xylan metabolism, indicating that the development of stem-differentiating xylem was affected upon stress treatment. Finally, we identified a total of 51 AP2/ERF, 29 NAC, and 37 WRKY transcript factors in C. lanceolata. The expression of most of the NAC TFs increased under cold stress, and the expression of most of the WRKY TFs increased under cold and SA stress. These results revealed the transcriptomics responses in C. lanceolata to short-term stresses under this study's experimental conditions and provide preliminary clues about stem-differentiating xylem changes associated with different stresses.
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31
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Kaplan AD, Greene JD, Liu VX, Ray P. Unsupervised probabilistic models for sequential Electronic Health Records. J Biomed Inform 2022; 134:104163. [PMID: 36038064 PMCID: PMC10588733 DOI: 10.1016/j.jbi.2022.104163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/23/2022] [Accepted: 08/11/2022] [Indexed: 11/18/2022]
Abstract
We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.
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Affiliation(s)
- Alan D Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, United States of America.
| | - John D Greene
- Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, United States of America
| | - Vincent X Liu
- Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, United States of America
| | - Priyadip Ray
- Computational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, United States of America
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Ye M, Pu L, Li P, Lu X, Liu Y. Time-Series-Based Personalized Lane-Changing Decision-Making Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:6659. [PMID: 36081119 PMCID: PMC9460894 DOI: 10.3390/s22176659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/29/2022] [Accepted: 09/01/2022] [Indexed: 06/15/2023]
Abstract
In recent years, autonomous driving technology has been changing from "human adapting to vehicle" to "vehicle adapting to human". To improve the adaptability of autonomous driving systems to human drivers, a time-series-based personalized lane change decision (LCD) model is proposed. Firstly, according to the characteristics of the subject vehicle (SV) with respect to speed, acceleration and headway, an unsupervised clustering algorithm, namely, a Gaussian mixture model (GMM), is used to identify its three different driving styles. Secondly, considering the interaction between the SV and the surrounding vehicles, the lane change (LC) gain value is produced by developing a gain function to characterize their interaction. On the basis of the recognition of the driving style, this gain value and LC feature parameters are employed as model inputs to develop a personalized LCD model on the basis of a long short-term memory (LSTM) recurrent neural network model (RNN). The proposed method is tested using the US Open Driving Dataset NGSIM. The results show that the accuracy, F1 score, and macro-average area under the curve (macro-AUC) value of the proposed method for LC behavior prediction are 0.965, 0.951 and 0.983, respectively, and the performance is significantly better than that of other mainstream models. At the same time, the method is able to capture the LCD behavior of different human drivers, enabling personalized driving.
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Affiliation(s)
- Ming Ye
- Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
| | - Lei Pu
- Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
| | - Pan Li
- Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
| | - Xiangwei Lu
- Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
| | - Yonggang Liu
- State Key Laboratory of Mechanical Transmissions, College of Mechanical and Vehicle Engineering, Chonqing University, Chongqing 400044, China
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Belavilas-Trovas A, Gregoriou ME, Tastsoglou S, Soukia O, Giakountis A, Mathiopoulos K. A species-specific lncRNA modulates the reproductive ability of the asian tiger mosquito. Front Bioeng Biotechnol 2022; 10:885767. [PMID: 36091452 PMCID: PMC9448860 DOI: 10.3389/fbioe.2022.885767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/11/2022] [Indexed: 11/21/2022] Open
Abstract
Long non-coding RNA (lncRNA) research has emerged as an independent scientific field in recent years. Despite their association with critical cellular and metabolic processes in plenty of organisms, lncRNAs are still a largely unexplored area in mosquito research. We propose that they could serve as exceptional tools for pest management due to unique features they possess. These include low inter-species sequence conservation and high tissue specificity. In the present study, we investigated the role of ovary-specific lncRNAs in the reproductive ability of the Asian tiger mosquito, Aedes albopictus. Through the analysis of transcriptomic data, we identified several lncRNAs that were differentially expressed upon blood feeding; we called these genes Norma (NOn-coding RNA in Mosquito ovAries). We observed that silencing some of these Normas resulted in significant impact on mosquito fecundity and fertility. We further focused on Norma3 whose silencing resulted in 43% oviposition reduction, in smaller ovaries and 53% hatching reduction of the laid eggs, compared to anti-GFP controls. Moreover, a significant downregulation of 2 mucins withing a neighboring (∼100 Kb) mucin cluster was observed in smaller anti-Norma3 ovaries, indicating a potential mechanism of in-cis regulation between Norma3 and the mucins. Our work constitutes the first experimental proof-of-evidence connecting lncRNAs with mosquito reproduction and opens a novel path for pest management.
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Affiliation(s)
- Alexandros Belavilas-Trovas
- Laboratory of Molecular Biology and Genomics, Department of Biochemistry & Biotechnology, University of Thessaly, Larissa, Greece
| | - Maria-Eleni Gregoriou
- Laboratory of Molecular Biology and Genomics, Department of Biochemistry & Biotechnology, University of Thessaly, Larissa, Greece
| | - Spyros Tastsoglou
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- Hellenic Pasteur Institute, Athens, Greece
| | - Olga Soukia
- Laboratory of Molecular Biology and Genomics, Department of Biochemistry & Biotechnology, University of Thessaly, Larissa, Greece
| | - Antonis Giakountis
- Laboratory of Molecular Biology and Genomics, Department of Biochemistry & Biotechnology, University of Thessaly, Larissa, Greece
| | - Kostas Mathiopoulos
- Laboratory of Molecular Biology and Genomics, Department of Biochemistry & Biotechnology, University of Thessaly, Larissa, Greece
- *Correspondence: Kostas Mathiopoulos,
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Bar N, Nikparvar B, Jayavelu ND, Roessler FK. Constrained Fourier estimation of short-term time-series gene expression data reduces noise and improves clustering and gene regulatory network predictions. BMC Bioinformatics 2022; 23:330. [PMID: 35945515 PMCID: PMC9364503 DOI: 10.1186/s12859-022-04839-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 07/12/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Biological data suffers from noise that is inherent in the measurements. This is particularly true for time-series gene expression measurements. Nevertheless, in order to to explore cellular dynamics, scientists employ such noisy measurements in predictive and clustering tools. However, noisy data can not only obscure the genes temporal patterns, but applying predictive and clustering tools on noisy data may yield inconsistent, and potentially incorrect, results. RESULTS To reduce the noise of short-term (< 48 h) time-series expression data, we relied on the three basic temporal patterns of gene expression: waves, impulses and sustained responses. We constrained the estimation of the true signals to these patterns by estimating the parameters of first and second-order Fourier functions and using the nonlinear least-squares trust-region optimization technique. Our approach lowered the noise in at least 85% of synthetic time-series expression data, significantly more than the spline method ([Formula: see text]). When the data contained a higher signal-to-noise ratio, our method allowed downstream network component analyses to calculate consistent and accurate predictions, particularly when the noise variance was high. Conversely, these tools led to erroneous results from untreated noisy data. Our results suggest that at least 5-7 time points are required to efficiently de-noise logarithmic scaled time-series expression data. Investing in sampling additional time points provides little benefit to clustering and prediction accuracy. CONCLUSIONS Our constrained Fourier de-noising method helps to cluster noisy gene expression and interpret dynamic gene networks more accurately. The benefit of noise reduction is large and can constitute the difference between a successful application and a failing one.
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Affiliation(s)
- Nadav Bar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Bahareh Nikparvar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Naresh Doni Jayavelu
- grid.34477.330000000122986657Division of Medical Genetics, Department of Medicine, University of Washington Seattle, Seattle, WA 98195-7720 USA
| | - Fabienne Krystin Roessler
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
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Mun S, Han K, Hyun JK. The Time Sequence of Gene Expression Changes after Spinal Cord Injury. Cells 2022; 11:cells11142236. [PMID: 35883679 PMCID: PMC9324287 DOI: 10.3390/cells11142236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/15/2022] [Accepted: 07/17/2022] [Indexed: 02/01/2023] Open
Abstract
Gene expression changes following spinal cord injury (SCI) are time-dependent, and an accurate understanding of these changes can be crucial in determining time-based treatment options in a clinical setting. We performed RNA sequencing of the contused spinal cord of rats at five different time points from the very acute to chronic stages (1 hour, 1 day, 1 week, 1 month, and 3 months) following SCI. We identified differentially expressed genes (DEGs) and Gene Ontology (GO) terms at each time point, and 14,257 genes were commonly expressed at all time points. The biological process of the inflammatory response was increased at 1 hour and 1 day, and the cellular component of the integral component of the synaptic membrane was increased at 1 day. DEGs associated with cell activation and the innate immune response were highly enriched at 1 week and 1 month, respectively. A total of 2841 DEGs were differentially expressed at any of the five time points, and 18 genes (17 upregulated and 1 downregulated) showed common expression differences at all time points. We found that interleukin signaling, neutrophil degranulation, eukaryotic translation, collagen degradation, LGI–ADAM interactions, GABA receptor, and L1CAM-ankyrin interactions were prominent after SCI depending on the time post injury. We also performed gene–drug network analysis and found several potential antagonists and agonists which can be used to treat SCI. We expect to discover effective treatments in the clinical field through further studies revealing the efficacy and safety of potential drugs.
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Affiliation(s)
- Seyoung Mun
- Department of Nanobiomedical Science & BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea;
- Center for Bio Medical Engineering Core Facility, Dankook University, Cheonan 31116, Korea;
| | - Kyudong Han
- Center for Bio Medical Engineering Core Facility, Dankook University, Cheonan 31116, Korea;
- Department of Microbiology, College of Science & Technology, Dankook University, Cheonan 31116, Korea
| | - Jung Keun Hyun
- Department of Nanobiomedical Science & BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea;
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea
- Institute of Tissue Regeneration Engineering (ITREN), Dankook University, Cheonan 31116, Korea
- Correspondence: ; Tel.: +82-10-2293-3415
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Potolitsyna E, Hazell Pickering S, Germier T, Collas P, Briand N. Long non-coding RNA HOTAIR regulates cytoskeleton remodeling and lipid storage capacity during adipogenesis. Sci Rep 2022; 12:10157. [PMID: 35710716 PMCID: PMC9203762 DOI: 10.1038/s41598-022-14296-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/03/2022] [Indexed: 11/16/2022] Open
Abstract
The long non-coding RNA HOTAIR is the most differentially expressed gene between upper- and lower-body adipose tissue, yet its functional significance in adipogenesis is unclear. We report that HOTAIR expression is transiently induced during early adipogenic differentiation of gluteofemoral adipose progenitors and repressed in mature adipocytes. Upon adipogenic commitment, HOTAIR regulates protein synthesis pathways and cytoskeleton remodeling with a later impact on mature adipocyte lipid storage capacity. Our results support novel and important functions of HOTAIR in the physiological context of adipogenesis.
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Affiliation(s)
- Evdokiia Potolitsyna
- Department of Molecular Medicine, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Blindern, PO Box 1112, 0317, Oslo, Norway
| | - Sarah Hazell Pickering
- Department of Molecular Medicine, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Blindern, PO Box 1112, 0317, Oslo, Norway.,Department of Immunology and Transfusion Medicine, Oslo University Hospital, 0424, Oslo, Norway
| | - Thomas Germier
- Department of Molecular Medicine, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Blindern, PO Box 1112, 0317, Oslo, Norway
| | - Philippe Collas
- Department of Molecular Medicine, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Blindern, PO Box 1112, 0317, Oslo, Norway. .,Department of Immunology and Transfusion Medicine, Oslo University Hospital, 0424, Oslo, Norway.
| | - Nolwenn Briand
- Department of Molecular Medicine, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Blindern, PO Box 1112, 0317, Oslo, Norway. .,Department of Immunology and Transfusion Medicine, Oslo University Hospital, 0424, Oslo, Norway.
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Madsen-Østerbye J, Abdelhalim M, Baudement MO, Collas P. Local euchromatin enrichment in lamina-associated domains anticipates their repositioning in the adipogenic lineage. Genome Biol 2022; 23:91. [PMID: 35410387 PMCID: PMC8996409 DOI: 10.1186/s13059-022-02662-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 03/31/2022] [Indexed: 12/28/2022] Open
Abstract
Background Interactions of chromatin with the nuclear lamina via lamina-associated domains (LADs) confer structural stability to the genome. The dynamics of positioning of LADs during differentiation, and how LADs impinge on developmental gene expression, remains, however, elusive. Results We examined changes in the association of lamin B1 with the genome in the first 72 h of differentiation of adipose stem cells into adipocytes. We demonstrate a repositioning of entire stand-alone LADs and of LAD edges as a prominent nuclear structural feature of early adipogenesis. Whereas adipogenic genes are released from LADs, LADs sequester downregulated or repressed genes irrelevant for the adipose lineage. However, LAD repositioning only partly concurs with gene expression changes. Differentially expressed genes in LADs, including LADs conserved throughout differentiation, reside in local euchromatic and lamin-depleted sub-domains. In these sub-domains, pre-differentiation histone modification profiles correlate with the LAD versus inter-LAD outcome of these genes during adipogenic commitment. Lastly, we link differentially expressed genes in LADs to short-range enhancers which overall co-partition with these genes in LADs versus inter-LADs during differentiation. Conclusions We conclude that LADs are predictable structural features of adipose nuclear architecture that restrain non-adipogenic genes in a repressive environment. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02662-6.
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Affiliation(s)
- Julia Madsen-Østerbye
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway
| | - Mohamed Abdelhalim
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway
| | - Marie-Odile Baudement
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway.,Present Address: Centre for Integrative Genetics, Faculty of Biosciences, Norwegian University of Life Sciences, 1430, Ås, Norway
| | - Philippe Collas
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway. .,Department of Immunology and Transfusion Medicine, Oslo University Hospital, 0424, Oslo, Norway.
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Gao Y, Selee B, Schnabel EL, Poehlman WL, Chavan SA, Frugoli JA, Feltus FA. Time Series Transcriptome Analysis in Medicago truncatula Shoot and Root Tissue During Early Nodulation. FRONTIERS IN PLANT SCIENCE 2022; 13:861639. [PMID: 35463395 PMCID: PMC9021838 DOI: 10.3389/fpls.2022.861639] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
In response to colonization by rhizobia bacteria, legumes are able to form nitrogen-fixing nodules in their roots, allowing the plants to grow efficiently in nitrogen-depleted environments. Legumes utilize a complex, long-distance signaling pathway to regulate nodulation that involves signals in both roots and shoots. We measured the transcriptional response to treatment with rhizobia in both the shoots and roots of Medicago truncatula over a 72-h time course. To detect temporal shifts in gene expression, we developed GeneShift, a novel computational statistics and machine learning workflow that addresses the time series replicate the averaging issue for detecting gene expression pattern shifts under different conditions. We identified both known and novel genes that are regulated dynamically in both tissues during early nodulation including leginsulin, defensins, root transporters, nodulin-related, and circadian clock genes. We validated over 70% of the expression patterns that GeneShift discovered using an independent M. truncatula RNA-Seq study. GeneShift facilitated the discovery of condition-specific temporally differentially expressed genes in the symbiotic nodulation biological system. In principle, GeneShift should work for time-series gene expression profiling studies from other systems.
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Affiliation(s)
- Yueyao Gao
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
| | - Bradley Selee
- Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, United States
| | - Elise L. Schnabel
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
| | - William L. Poehlman
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
- Sage Bionetworks, Seattle, WA, United States
| | - Suchitra A. Chavan
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
| | - Julia A. Frugoli
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
| | - Frank Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
- Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC, United States
- Clemson Center for Human Genetics, Greenwood, SC, United States
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ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 21:100196. [PMID: 35083394 PMCID: PMC8785973 DOI: 10.1016/j.comtox.2021.100196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene's dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in 'co-regulated' gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.
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Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat Methods 2022; 19:179-186. [PMID: 35027765 PMCID: PMC8828471 DOI: 10.1038/s41592-021-01343-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 11/05/2021] [Indexed: 01/04/2023]
Abstract
Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics. MEFISTO models bulk and single-cell multi-omics data with temporal or spatial dependencies for interpretable pattern discovery and integration.
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41
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Nezamivand-Chegini M, Ebrahimie E, Tahmasebi A, Moghadam A, Eshghi S, Mohammadi-Dehchesmeh M, Kopriva S, Niazi A. New insights into the evolution of SPX gene family from algae to legumes; a focus on soybean. BMC Genomics 2021; 22:915. [PMID: 34969367 PMCID: PMC8717665 DOI: 10.1186/s12864-021-08242-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 12/09/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND SPX-containing proteins have been known as key players in phosphate signaling and homeostasis. In Arabidopsis and rice, functions of some SPXs have been characterized, but little is known about their function in other plants, especially in the legumes. RESULTS We analyzed SPX gene family evolution in legumes and in a number of key species from algae to angiosperms. We found that SPX harboring proteins showed fluctuations in domain fusions from algae to the angiosperms with, finally, four classes appearing and being retained in the land plants. Despite these fluctuations, Lysine Surface Cluster (KSC), and the third residue of Phosphate Binding Sites (PBS) showed complete conservation in almost all of SPXs except few proteins in Selaginella moellendorffii and Papaver sumniferum, suggesting they might have different ligand preferences. In addition, we found that the WGD/segmentally or dispersed duplication types were the most frequent contributors to the SPX expansion, and that there is a positive correlation between the amount of WGD contribution to the SPX expansion in individual species and its number of EXS genes. We could also reveal that except SPX class genes, other classes lost the collinearity relationships among Arabidopsis and legume genomes. The sub- or neo-functionalization of the duplicated genes in the legumes makes it difficult to find the functional orthologous genes. Therefore, we used two different methods to identify functional orthologs in soybean and Medicago. High variance in the dynamic and spatial expression pattern of GmSPXs proved the new or sub-functionalization in the paralogs. CONCLUSION This comprehensive analysis revealed how SPX gene family evolved from algae to legumes and also discovered several new domains fused to SPX domain in algae. In addition, we hypothesized that there different phosphate sensing mechanisms might occur in S. moellendorffii and P. sumniferum. Finally, we predicted putative functional orthologs of AtSPXs in the legumes, especially, orthologs of AtPHO1, involved in long-distance Pi transportation. These findings help to understand evolution of phosphate signaling and might underpin development of new legume varieties with improved phosphate use efficiency.
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Affiliation(s)
| | - Esmaeil Ebrahimie
- Institute of biotechnology, Shiraz university, Shiraz, Iran
- La Trobe Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC, 3086, Australia
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA, 5371, Australia
| | | | - Ali Moghadam
- Institute of biotechnology, Shiraz university, Shiraz, Iran
| | - Saeid Eshghi
- Department of Horticultural Science, School of Agriculture, Shiraz University, Shiraz, Iran
| | | | - Stanislav Kopriva
- Institute for Plant Sciences, Cluster of Excellence on Plant Sciences, University of Cologne, Cologne, Germany
| | - Ali Niazi
- Institute of biotechnology, Shiraz university, Shiraz, Iran.
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Aliyu H, de Maayer P, Neumann A. Not All That Glitters Is Gold: The Paradox of CO-dependent Hydrogenogenesis in Parageobacillus thermoglucosidasius. Front Microbiol 2021; 12:784652. [PMID: 34956151 PMCID: PMC8696081 DOI: 10.3389/fmicb.2021.784652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
The thermophilic bacterium Parageobacillus thermoglucosidasius has recently gained interest due to its ability to catalyze the water gas shift reaction, where the oxidation of carbon monoxide (CO) is linked to the evolution of hydrogen (H2) gas. This phenotype is largely predictable based on the presence of a genomic region coding for a carbon monoxide dehydrogenase (CODH—Coo) and hydrogen evolving hydrogenase (Phc). In this work, seven previously uncharacterized strains were cultivated under 50% CO and 50% air atmosphere. Despite the presence of the coo—phc genes in all seven strains, only one strain, Kp1013, oxidizes CO and yields H2. The genomes of the H2 producing strains contain unique genomic regions that code for proteins involved in nickel transport and the detoxification of catechol, a by-product of a siderophore-mediated iron acquisition system. Combined, the presence of these genomic regions could potentially drive biological water gas shift (WGS) reaction in P. thermoglucosidasius.
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Affiliation(s)
- Habibu Aliyu
- Institute of Process Engineering in Life Science 2 - Technical Biology, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Pieter de Maayer
- School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa
| | - Anke Neumann
- Institute of Process Engineering in Life Science 2 - Technical Biology, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Single-cell transcriptomic analysis of zebrafish cranial neural crest reveals spatiotemporal regulation of lineage decisions during development. Cell Rep 2021; 37:110140. [PMID: 34936864 PMCID: PMC8741273 DOI: 10.1016/j.celrep.2021.110140] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/28/2021] [Accepted: 11/29/2021] [Indexed: 12/13/2022] Open
Abstract
Neural crest (NC) cells migrate throughout vertebrate embryos to give rise to a huge variety of cell types, but when and where lineages emerge and their regulation remain unclear. We have performed single-cell RNA sequencing (RNA-seq) of cranial NC cells from the first pharyngeal arch in zebrafish over several stages during migration. Computational analysis combining pseudotime and real-time data reveals that these NC cells first adopt a transitional state, becoming specified mid-migration, with the first lineage decisions being skeletal and pigment, followed by neural and glial progenitors. In addition, by computationally integrating these data with RNA-seq data from a transgenic Wnt reporter line, we identify gene cohorts with similar temporal responses to Wnts during migration and show that one, Atp6ap2, is required for melanocyte differentiation. Together, our results show that cranial NC cell lineages arise progressively and uncover a series of spatially restricted cell interactions likely to regulate such cell-fate decisions. Tatarakis et al. provide a single-cell transcriptomic timeline of cranial neural crest (NC) development in zebrafish and address long-standing questions surrounding the integration of NC cell migration and lineage specification. They find that lineages are specified mid-migration. These fate decisions correspond to shifts in Wnt signaling, and lineages rapidly segregate.
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Duran-Pinedo A, Solbiati J, Teles F, Teles R, Zang Y, Frias-Lopez J. Long-term dynamics of the human oral microbiome during clinical disease progression. BMC Biol 2021; 19:240. [PMID: 34742306 PMCID: PMC8572441 DOI: 10.1186/s12915-021-01169-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/19/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition's stability that could adversely affect the host's health status. However, the precise microbiome dynamics that lead to dysbiosis and the progression of the disease are largely unknown. The objective of our study was to investigate the long-term dynamics of periodontitis progression and its connection to dysbiosis. RESULTS We studied three different teeth groups: sites that showed disease progression, sites that remained stable during the study, and sites that exhibited a cyclic deepening followed by spontaneous recovery. Time-series analysis revealed that communities followed a characteristic succession of bacteria clusters. Stable and fluctuating sites showed high asynchrony in the communities (i.e., different species responding dissimilarly through time) and a reordering of the communities where directional changes dominated (i.e., sample distance increases over time) in the stable sites but not in the fluctuating sites. Progressing sites exhibited low asynchrony and convergence (i.e., samples distance decreases over time). Moreover, new species were more likely to be recruited in stable samples if a close relative was not recruited previously. In contrast, progressing and fluctuating sites followed a neutral recruitment model, indicating that competition between closely related species is a significant component of species-species interactions in stable samples. Finally, periodontal treatment did not select similar communities but stabilized α-diversity, centered the abundance of different clusters to the mean, and increased community rearrangement. CONCLUSIONS Here, we show that ecological principles can define dysbiosis and explain the evolution and outcomes of specific microbial communities of the oral microbiome in periodontitis progression. All sites showed an ecological succession in community composition. Stable sites were characterized by high asynchrony, a reordering of the communities where directional changes dominated, and new species were more likely to be recruited if a close relative was not recruited previously. Progressing sites were characterized by low asynchrony, community convergence, and a neutral model of recruitment. Finally, fluctuating sites were characterized by high asynchrony, community convergence, and a neutral recruitment model.
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Affiliation(s)
- Ana Duran-Pinedo
- Department of Oral Biology, University of Florida, College of Dentistry, 1395 Center Drive, Gainesville, FL, 32610-0424, USA
| | - Jose Solbiati
- Department of Oral Biology, University of Florida, College of Dentistry, 1395 Center Drive, Gainesville, FL, 32610-0424, USA
| | - Flavia Teles
- Department of Basic & Translational Sciences, University of Pennsylvania, School of Dental Medicine, 240 South 40th Street, Philadelphia, PA, 19104-6030, USA
| | - Ricardo Teles
- Department of Periodontics, University of Pennsylvania, School of Dental Medicine, 240 South 40th Street, Philadelphia, PA, 19104-6030, USA
| | - Yanping Zang
- Gene Expression & Genotyping Core, Interdisciplinary Center for Biotechnology Research, University of Florida, 178 B CGRC, 2033 Mowry Road, Gainesville, FL, 32610, USA
| | - Jorge Frias-Lopez
- Department of Oral Biology, University of Florida, College of Dentistry, 1395 Center Drive, Gainesville, FL, 32610-0424, USA.
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BinTayyash N, Georgaka S, John ST, Ahmed S, Boukouvalas A, Hensman J, Rattray M. Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments. Bioinformatics 2021; 37:3788-3795. [PMID: 34213536 PMCID: PMC10186154 DOI: 10.1093/bioinformatics/btab486] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 06/25/2021] [Accepted: 06/30/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. RESULTS The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. AVAILABILITY AND IMPLEMENTATION GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nuha BinTayyash
- School of Computer Science, University of Manchester, Manchester M13 9PL, UK
| | - Sokratia Georgaka
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - S T John
- Secondmind, Cambridge CB2 1LA, UK
- Finnish Center for Artificial Intelligence, FCAI, Department of Computer Science, Aalto University, Finland
| | - Sumon Ahmed
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
- Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh
| | | | | | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
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Ozgul OF, Bardak B, Tan M. A Convolutional Deep Clustering Framework for Gene Expression Time Series. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2198-2207. [PMID: 32324563 DOI: 10.1109/tcbb.2020.2988985] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The functional or regulatory processes within the cell are explicitly governed by the expression levels of a subset of its genes. Gene expression time series captures activities of individual genes over time and aids revealing underlying cellular dynamics. An important step in high-throughput gene expression time series experiment is clustering genes based on their temporal expression patterns and is conventionally achieved by unsupervised machine learning techniques. However, most of the clustering techniques either suffer from the short length of gene expression time series or ignore temporal structure of the data. In this work, we propose DeepTrust, a novel deep learning-based framework for gene expression time series clustering which can overcome these issues. DeepTrust initially transforms time series data into images to obtain richer data representations. Afterwards, a deep convolutional clustering algorithm is applied on the constructed images. Analyses on both simulated and biological data sets exhibit the efficiency of this new framework, compared to widely used clustering techniques. We also utilize enrichment analyses to illustrate the biological plausibility of the clusters detected by DeepTrust. Our code and data are available from http://github.com/tanlab/DeepTrust.
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Block CJ, Mitchell AV, Wu L, Glassbrook J, Craig D, Chen W, Dyson G, DeGracia D, Polin L, Ratnam M, Gibson H, Wu G. RNA binding protein RBMS3 is a common EMT effector that modulates triple-negative breast cancer progression via stabilizing PRRX1 mRNA. Oncogene 2021; 40:6430-6442. [PMID: 34608266 PMCID: PMC9421946 DOI: 10.1038/s41388-021-02030-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 09/08/2021] [Accepted: 09/20/2021] [Indexed: 12/20/2022]
Abstract
The epithelial-to-mesenchymal transition (EMT) has been recognized as a driving force for tumor progression in breast cancer. Recently, our group identified the RNA Binding Motif Single Stranded Interacting Protein 3 (RBMS3) to be significantly associated with an EMT transcriptional program in breast cancer. Additional expression profiling demonstrated that RBMS3 was consistently upregulated by multiple EMT transcription factors and correlated with mesenchymal gene expression in breast cancer cell lines. Functionally, RBMS3 was sufficient to induce EMT in two immortalized mammary epithelial cell lines. In triple-negative breast cancer (TNBC) models, RBMS3 was necessary for maintaining the mesenchymal phenotype and invasion and migration in vitro. Loss of RBMS3 significantly impaired both tumor progression and spontaneous metastasis in vivo. Using a genome-wide approach to interrogate mRNA stability, we found that ectopic expression of RBMS3 upregulates many genes that are resistant to degradation following transcriptional blockade by actinomycin D (ACTD). Specifically, RBMS3 was shown to interact with the mRNA of EMT transcription factor PRRX1 and promote PRRX1 mRNA stability. PRRX1 is required for RBMS3-mediated EMT and is partially sufficient to rescue the effect of RBMS3 knockdown in TNBC cell lines. Together, this study identifies RBMS3 as a novel and common effector of EMT, which could be a promising therapeutic target for TNBC treatment.
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Affiliation(s)
- C. James Block
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Allison V. Mitchell
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Ling Wu
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA.,Department of Molecular and Cellular Biology, McNair Medical Institute Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
| | - James Glassbrook
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Douglas Craig
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Wei Chen
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Gregory Dyson
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Donald DeGracia
- Department of Physiology, Wayne State University school of Medicine, Detroit, MI 48201, USA
| | - Lisa Polin
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Manohar Ratnam
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Heather Gibson
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI 48201, USA
| | - Guojun Wu
- Barbara Ann Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, 4100 John R, Detroit, MI, 48201, USA.
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Guijo-Rubio D, Duran-Rosal AM, Gutierrez PA, Troncoso A, Hervas-Martinez C. Time-Series Clustering Based on the Characterization of Segment Typologies. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:5409-5422. [PMID: 31945011 DOI: 10.1109/tcyb.2019.2962584] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Time-series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time-series objects of the dataset. In this article, we propose a novel technique of time-series clustering consisting of two clustering stages. In a first step, a least-squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all of the segments are projected into the same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time-series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmentation. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against three state-of-the-art methods, showing that the performance of this methodology is very promising, especially on larger datasets.
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The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation. Nat Genet 2021; 53:1564-1576. [PMID: 34650237 PMCID: PMC8763320 DOI: 10.1038/s41588-021-00947-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 09/01/2021] [Indexed: 01/24/2023]
Abstract
Transcription factors bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multiomic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep-learning framework to model the cis-regulatory logic of chromatin accessibility. This analysis framework identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter assay analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach shows the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code of dynamic gene regulation.
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Exploring the Meta-regulon of the CRP/FNR Family of Global Transcriptional Regulators in a Partial-Nitritation Anammox Microbiome. mSystems 2021; 6:e0090621. [PMID: 34636676 PMCID: PMC8510549 DOI: 10.1128/msystems.00906-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Microorganisms must respond to environmental changes to survive, often by controlling transcription initiation. Intermittent aeration during wastewater treatment presents a cyclically changing environment to which microorganisms must react. We used an intermittently aerated bioreactor performing partial nitritation and anammox (PNA) to investigate how the microbiome responds to recurring change. Meta-transcriptomic analysis revealed a dramatic disconnect between the relative DNA abundance and gene expression within the metagenome-assembled genomes (MAGs) of community members, suggesting the importance of transcriptional regulation in this microbiome. To explore how community members responded to cyclic aeration via transcriptional regulation, we searched for homologs of the catabolite repressor protein/fumarate and nitrate reductase regulatory protein (CRP/FNR) family of transcription factors (TFs) within the MAGs. Using phylogenetic analyses, evaluation of sequence conservation in important amino acid residues, and prediction of genes regulated by TFs in the MAGs, we identified homologs of the oxygen-sensing FNR in Nitrosomonas and Rhodocyclaceae, nitrogen-sensing dissimilative nitrate respiration regulator that responds to nitrogen species (DNR) in Rhodocyclaceae, and nitrogen-sensing nitrite and nitric oxide reductase regulator that responds to nitrogen species (NnrR) in Nitrospira MAGs. Our data also predict that CRP/FNR homologs in Ignavibacteria, Flavobacteriales, and Saprospiraceae MAGs sense carbon availability. In addition, a CRP/FNR homolog in a Brocadia MAG was most closely related to CRP TFs known to sense carbon sources in well-studied organisms. However, we predict that in autotrophic Brocadia, this TF most likely regulates a diverse set of functions, including a response to stress during the cyclic aerobic/anoxic conditions. Overall, this analysis allowed us to define a meta-regulon of the PNA microbiome that explains functions and interactions of the most active community members. IMPORTANCE Microbiomes are important contributors to many ecosystems, including ones where nutrient cycling is stimulated by aeration control. Optimizing cyclic aeration helps reduce energy needs and maximize microbiome performance during wastewater treatment; however, little is known about how most microbial community members respond to these alternating conditions. We defined the meta-regulon of a PNA microbiome by combining existing knowledge of how the CRP/FNR family of bacterial TFs respond to stimuli, with metatranscriptomic analyses to characterize gene expression changes during aeration cycles. Our results indicated that, for some members of the community, prior knowledge is sufficient for high-confidence assignments of TF function, whereas other community members have CRP/FNR TFs for which inferences of function are limited by lack of prior knowledge. This study provides a framework to begin elucidating meta-regulons in microbiomes, where pure cultures are not available for traditional transcriptional regulation studies. Defining the meta-regulon can help in optimizing microbiome performance.
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