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Liu L, Birling Y, Zhao Y, Ma W, Tang Y, Sun Y, Wang X, Yu M, Bi H, Liu JP, Li L, Liu Z. Mechanism of Chinese botanical drug Dizhi pill for myopia: An integrated study based on bioinformatics and network analysis. Medicine (Baltimore) 2023; 102:e34753. [PMID: 37747014 PMCID: PMC10519534 DOI: 10.1097/md.0000000000034753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 09/26/2023] Open
Abstract
To identify the active constituents, core targets, immunomodulatory functions and potential mechanisms of Dizhi pill (DZP) in the treatment of myopia. The active constituents and drug targets of DZP were searched in the TCMSP, Herb databases and correlational studies. The targets of myopia were searched in the TTD, Genecards, OMIM and Drugbank databases. Gene expression profile data of GSE136701 were downloaded from the GEO database and subjected to WGCNA and DEG analysis to screen for significant modules and targets of myopia. Intersectional targets of myopia and DZP and core targets of myopia were analyzed through the String database. The GO and KEGG enrichment analyses of the interested targets were conducted. Cibersort algorithm was used for immune infiltration analysis to investigate the immunomodulatory functions of DZP on myopia. Autodock was used to dock the important targets and active constituents. Eight targets (STAT3, PIK3CA, PIK3R1, MAPK1, MAPK3, HSP90AA1, MIP, and LGSN) and 5 active constituents (Quercetin, Beta-sitosterol, Diincarvilone A, Ferulic acid methyl ester, and Naringenin) were identified from DZP. In pathways identified by the GO and KEGG enrichment analyses, "ATP metabolic process" and "AGE-RAGE diabetes complication signaling" pathways were closely related to the mechanisms of DZP in the treatment of myopia. Molecular docking showed that both the intersectional targets and core targets of myopia could bind stably and spontaneously with the active constituents of DZP. This study suggested that the mechanisms of DZP in the treatment of myopia were related to active constituents: Quercetin, Beta-sitosterol, Diincarvilone A, Ferulic acid methyl ester and Naringenin, intersectional targets: STAT3, PIK3CA, PIK3R1, MAPK1, MAPK3, and HSP90AA1, core targets of myopia: MIP and LGSN, AGE-RAGE signaling pathway, positive regulation of ATP metabolic process pathway and immunomodulatory functions.
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Affiliation(s)
- Longkun Liu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Yoann Birling
- NICM Health Research Institute, Western Sydney University, Penrith, NSW
| | - Yan Zhao
- Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Wenxin Ma
- Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Tang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Yuxin Sun
- Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Xuehui Wang
- Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Mingkun Yu
- Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Hongsheng Bi
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Jian-ping Liu
- Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Li Li
- Beijing Institute for Drug Control, NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drugs, Beijing Key Laboratory of Analysis and Evaluation on Chinese Medicine, Beijing, China
| | - Zhaolan Liu
- Center for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
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2
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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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3
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Zhang Q, Shao M. Transcript Assembly and Annotations: Bias and Adjustment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.20.537700. [PMID: 37131680 PMCID: PMC10153229 DOI: 10.1101/2023.04.20.537700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Motivation Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. Results We investigate the impact of annotations on transcript assembly. We observe that conflicting conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.
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Affiliation(s)
- Qimin Zhang
- Department of Computer Science and Engineering, School of Electrical Engineering and Computer Science, The Pennsylvania State University
| | - Mingfu Shao
- Department of Computer Science and Engineering, School of Electrical Engineering and Computer Science, The Pennsylvania State University
- Huck Institutes of the Life Sciences, The Pennsylvania State University
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4
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Baruzzo G, Serafini A, Finotello F, Sanavia T, Cioetto-Mazzabò L, Boldrin F, Lavezzo E, Barzon L, Toppo S, Provvedi R, Manganelli R, Di Camillo B. Role of the Extracytoplasmic Function Sigma Factor SigE in the Stringent Response of Mycobacterium tuberculosis. Microbiol Spectr 2023; 11:e0294422. [PMID: 36946740 PMCID: PMC10100808 DOI: 10.1128/spectrum.02944-22] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/15/2023] [Indexed: 03/23/2023] Open
Abstract
Bacteria respond to nutrient starvation implementing the stringent response, a stress signaling system resulting in metabolic remodeling leading to decreased growth rate and energy requirements. A well-characterized model of stringent response in Mycobacterium tuberculosis is the one induced by growth in low phosphate. The extracytoplasmic function (ECF) sigma factor SigE was previously suggested as having a key role in the activation of stringent response. In this study, we challenge this hypothesis by analyzing the temporal dynamics of the transcriptional response of a sigE mutant and its wild-type parental strain to low phosphate using RNA sequencing. We found that both strains responded to low phosphate with a typical stringent response trait, including the downregulation of genes encoding ribosomal proteins and RNA polymerase. We also observed transcriptional changes that support the occurring of an energetics imbalance, compensated by a reduced activity of the electron transport chain, decreased export of protons, and a remodeling of central metabolism. The most striking difference between the two strains was the induction in the sigE mutant of several stress-related genes, in particular, the genes encoding the ECF sigma factor SigH and the transcriptional regulator WhiB6. Since both proteins respond to redox unbalances, their induction suggests that the sigE mutant is not able to maintain redox homeostasis in response to the energetics imbalance induced by low phosphate. In conclusion, our data suggest that SigE is not directly involved in initiating stringent response but in protecting the cell from stress consequent to the low phosphate exposure and activation of stringent response. IMPORTANCE Mycobacterium tuberculosis can enter a dormant state enabling it to establish latent infections and to become tolerant to antibacterial drugs. Dormant bacteria's physiology and the mechanism(s) used by bacteria to enter dormancy during infection are still unknown due to the lack of reliable animal models. However, several in vitro models, mimicking conditions encountered during infection, can reproduce different aspects of dormancy (growth arrest, metabolic slowdown, drug tolerance). The stringent response, a stress response program enabling bacteria to cope with nutrient starvation, is one of them. In this study, we provide evidence suggesting that the sigma factor SigE is not directly involved in the activation of stringent response as previously hypothesized, but it is important to help the bacteria to handle the metabolic stress related to the adaptation to low phosphate and activation of stringent response, thus giving an important contribution to our understanding of the mechanism behind stringent response development.
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Affiliation(s)
- Giacomo Baruzzo
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Agnese Serafini
- Department of Molecular Medicine, University of Padova, Padua, Italy
| | | | - Tiziana Sanavia
- Department of Information Engineering, University of Padova, Padua, Italy
| | | | - Francesca Boldrin
- Department of Molecular Medicine, University of Padova, Padua, Italy
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Padua, Italy
| | - Luisa Barzon
- Department of Molecular Medicine, University of Padova, Padua, Italy
| | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padua, Italy
| | | | | | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Padua, Italy
- Department of Comparative Biomedicine and Food Science, University of Padova, Padua, Italy
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5
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Singer-Berk M, Gudmundsson S, Baxter S, Seaby EG, England E, Wood JC, Son RG, Watts NA, Karczewski KJ, Harrison SM, MacArthur DG, Rehm HL, O'Donnell-Luria A. Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.08.23286955. [PMID: 36945502 PMCID: PMC10029069 DOI: 10.1101/2023.03.08.23286955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines's PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.
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Affiliation(s)
- Moriel Singer-Berk
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Sanna Gudmundsson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eleanor G Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Genomic Informatics Group, University Hospital Southampton, Southampton, United Kingdom
| | - Eleina England
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordan C Wood
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel G Son
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Watts
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ambry Genetics, Aliso Viejo, CA, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine & Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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6
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Kalwat MA, Scheuner D, Rodrigues-dos-Santos K, Eizirik DL, Cobb MH. The Pancreatic ß-cell Response to Secretory Demands and Adaption to Stress. Endocrinology 2021; 162:bqab173. [PMID: 34407177 PMCID: PMC8459449 DOI: 10.1210/endocr/bqab173] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Pancreatic β cells dedicate much of their protein translation capacity to producing insulin to maintain glucose homeostasis. In response to increased secretory demand, β cells can compensate by increasing insulin production capability even in the face of protracted peripheral insulin resistance. The ability to amplify insulin secretion in response to hyperglycemia is a critical facet of β-cell function, and the exact mechanisms by which this occurs have been studied for decades. To adapt to the constant and fast-changing demands for insulin production, β cells use the unfolded protein response of the endoplasmic reticulum. Failure of these compensatory mechanisms contributes to both type 1 and 2 diabetes. Additionally, studies in which β cells are "rested" by reducing endogenous insulin demand have shown promise as a therapeutic strategy that could be applied more broadly. Here, we review recent findings in β cells pertaining to the metabolic amplifying pathway, the unfolded protein response, and potential advances in therapeutics based on β-cell rest.
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Affiliation(s)
- Michael A Kalwat
- Indiana Biosciences Research Institute, Indianapolis, IN 46202, USA
| | - Donalyn Scheuner
- Indiana Biosciences Research Institute, Indianapolis, IN 46202, USA
| | | | - Decio L Eizirik
- Indiana Biosciences Research Institute, Indianapolis, IN 46202, USA
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Melanie H Cobb
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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7
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Sanavia T, Huang C, Manduchi E, Xu Y, Dadi PK, Potter LA, Jacobson DA, Di Camillo B, Magnuson MA, Stoeckert CJ, Gu G. Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving β-Cell Maturation. Front Cell Dev Biol 2021; 9:648791. [PMID: 34017831 PMCID: PMC8129579 DOI: 10.3389/fcell.2021.648791] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 03/15/2021] [Indexed: 12/12/2022] Open
Abstract
Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca2+ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.
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Affiliation(s)
- Tiziana Sanavia
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Chen Huang
- Vanderbilt Program in Developmental Biology, Department of Cell and Developmental Biology, Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN, United States.,Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, United States
| | - Elisabetta Manduchi
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Yanwen Xu
- Vanderbilt Program in Developmental Biology, Department of Cell and Developmental Biology, Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Prasanna K Dadi
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Leah A Potter
- Vanderbilt Program in Developmental Biology, Department of Cell and Developmental Biology, Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - David A Jacobson
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Mark A Magnuson
- Vanderbilt Program in Developmental Biology, Department of Cell and Developmental Biology, Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN, United States.,Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Christian J Stoeckert
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Guoqiang Gu
- Vanderbilt Program in Developmental Biology, Department of Cell and Developmental Biology, Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
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8
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Oh VKS, Li RW. Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data. Genes (Basel) 2021; 12:352. [PMID: 33673721 PMCID: PMC7997275 DOI: 10.3390/genes12030352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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Affiliation(s)
- Vera-Khlara S. Oh
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
- Department of Computer Science and Statistics, College of Natural Sciences, Jeju National University, Jeju City 63243, Korea
| | - Robert W. Li
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
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9
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Khan AH, Lin A, Wang RT, Bloom JS, Lange K, Smith DJ. Pooled analysis of radiation hybrids identifies loci for growth and drug action in mammalian cells. Genome Res 2020; 30:1458-1467. [PMID: 32878976 PMCID: PMC7605260 DOI: 10.1101/gr.262204.120] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022]
Abstract
Genetic screens in mammalian cells commonly focus on loss-of-function approaches. To evaluate the phenotypic consequences of extra gene copies, we used bulk segregant analysis (BSA) of radiation hybrid (RH) cells. We constructed six pools of RH cells, each consisting of ∼2500 independent clones, and placed the pools under selection in media with or without paclitaxel. Low pass sequencing identified 859 growth loci, 38 paclitaxel loci, 62 interaction loci, and three loci for mitochondrial abundance at genome-wide significance. Resolution was measured as ∼30 kb, close to single-gene. Divergent properties were displayed by the RH-BSA growth genes compared to those from loss-of-function screens, refuting the balance hypothesis. In addition, enhanced retention of human centromeres in the RH pools suggests a new approach to functional dissection of these chromosomal elements. Pooled analysis of RH cells showed high power and resolution and should be a useful addition to the mammalian genetic toolkit.
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Affiliation(s)
- Arshad H Khan
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California 90095-1735, USA
| | - Andy Lin
- Office of Information Technology, UCLA, Los Angeles, California 90095-1557, USA
| | - Richard T Wang
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095-7088, USA
| | - Joshua S Bloom
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095-7088, USA
- Howard Hughes Medical Institute, David Geffen School of Medicine, UCLA, Los Angeles, California 90095-7088, USA
| | - Kenneth Lange
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095-7088, USA
| | - Desmond J Smith
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California 90095-1735, USA
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10
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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11
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Wang X, Zhang W, Hao Y. Improving Mycoplasma ovipneumoniae culture medium by a comparative transcriptome method. J Vet Sci 2020; 21:e30. [PMID: 32233136 PMCID: PMC7113570 DOI: 10.4142/jvs.2020.21.e30] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 11/20/2022] Open
Abstract
Mycoplasma ovipneumoniae (Mo) is difficult to culture, resulting in many difficulties in related research and application. Since nucleotide metabolism is a basic metabolism affects growth, this study conducted a “point-to-point” comparison of the corresponding growth phases between the Mo NM151 strain and the Mycoplasma mycoides subsp. capri (Mmc) PG3 strain. The results showed that the largest difference in nucleotide metabolism was found in the stationary phase. Nucleotide synthesis in PG3 was mostly de novo, while nucleotide synthesis in NM151 was primarily based on salvage synthesis. Compared with PG3, the missing reactions of NM151 referred to the synthesis of deoxythymine monophosphate. We proposed and validated a culture medium with added serine to fill this gap and prolong the stationary phase of NM151. This solved the problem of the fast death of Mo, which is significant for related research and application.
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Affiliation(s)
- Xiaohui Wang
- Key Laboratory of Microbiology & Immunology, College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, P.R. China.,School of Public Health, Baotou Medical College, Baotou 014040, P.R. China
| | - Wenguang Zhang
- Companion Animal Genetics and Genomics, Inner Mongolia Agricultural University, Hohhot 010018, P.R. China.
| | - Yongqing Hao
- Key Laboratory of Microbiology & Immunology, College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, P.R. China.
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12
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Jiang P, Chamberlain CS, Vanderby R, Thomson JA, Stewart R. TimeMeter assesses temporal gene expression similarity and identifies differentially progressing genes. Nucleic Acids Res 2020; 48:e51. [PMID: 32123905 PMCID: PMC7229845 DOI: 10.1093/nar/gkaa142] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 02/03/2020] [Accepted: 02/26/2020] [Indexed: 01/02/2023] Open
Abstract
Comparative time series transcriptome analysis is a powerful tool to study development, evolution, aging, disease progression and cancer prognosis. We develop TimeMeter, a statistical method and tool to assess temporal gene expression similarity, and identify differentially progressing genes where one pattern is more temporally advanced than the other. We apply TimeMeter to several datasets, and show that TimeMeter is capable of characterizing complicated temporal gene expression associations. Interestingly, we find: (i) the measurement of differential progression provides a novel feature in addition to pattern similarity that can characterize early developmental divergence between two species; (ii) genes exhibiting similar temporal patterns between human and mouse during neural differentiation are under strong negative (purifying) selection during evolution; (iii) analysis of genes with similar temporal patterns in mouse digit regeneration and axolotl blastema differentiation reveals common gene groups for appendage regeneration with potential implications in regenerative medicine.
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Affiliation(s)
- Peng Jiang
- Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA
| | - Connie S Chamberlain
- Department of Orthopedics and Rehabilitation, University of Wisconsin, Madison, WI 53706, USA
| | - Ray Vanderby
- Department of Orthopedics and Rehabilitation, University of Wisconsin, Madison, WI 53706, USA.,Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706, USA
| | - James A Thomson
- Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA.,Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, CA 93106, USA
| | - Ron Stewart
- Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA
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13
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Rockne RC, Branciamore S, Qi J, Frankhouser DE, O'Meally D, Hua WK, Cook G, Carnahan E, Zhang L, Marom A, Wu H, Maestrini D, Wu X, Yuan YC, Liu Z, Wang LD, Forman S, Carlesso N, Kuo YH, Marcucci G. State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia. Cancer Res 2020; 80:3157-3169. [PMID: 32414754 PMCID: PMC7416495 DOI: 10.1158/0008-5472.can-20-0354] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/06/2020] [Accepted: 05/12/2020] [Indexed: 12/13/2022]
Abstract
Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. SIGNIFICANCE: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development.See related commentary by Kuijjer, p. 3072 GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/15/3157/F1.large.jpg.
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Affiliation(s)
- Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California.
| | - Sergio Branciamore
- Department of Diabetes Complications & Metabolism, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Jing Qi
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - David E Frankhouser
- Department of Diabetes Complications & Metabolism, Beckman Research Institute, City of Hope Medical Center, Duarte, California
- Department of Population Sciences, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Denis O'Meally
- Center for Gene Therapy, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Wei-Kai Hua
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Guerry Cook
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Emily Carnahan
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Lianjun Zhang
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Ayelet Marom
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Herman Wu
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Davide Maestrini
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Xiwei Wu
- Department of Molecular Medicine; Bioinformatics Core, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Yate-Ching Yuan
- Department of Molecular Medicine; Bioinformatics Core, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Zheng Liu
- Department of Molecular and Cellular Biology; Integrative Genomics Core, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Leo D Wang
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope Medical Center, Duarte, California
- Department of Pediatrics, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Stephen Forman
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Nadia Carlesso
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
| | - Ya-Huei Kuo
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California.
| | - Guido Marcucci
- Department of Hematological Malignancies Translational Science, Hematology & Hematopoietic Cell Transplantation and the Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope Medical Center, Duarte, California
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14
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Van den Berge K, Roux de Bézieux H, Street K, Saelens W, Cannoodt R, Saeys Y, Dudoit S, Clement L. Trajectory-based differential expression analysis for single-cell sequencing data. Nat Commun 2020; 11:1201. [PMID: 32139671 PMCID: PMC7058077 DOI: 10.1038/s41467-020-14766-3] [Citation(s) in RCA: 285] [Impact Index Per Article: 71.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 01/14/2020] [Indexed: 12/31/2022] Open
Abstract
Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.
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Affiliation(s)
- Koen Van den Berge
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
- Department of Statistics, University of California, Berkeley, CA, USA
| | - Hector Roux de Bézieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Kelly Street
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wouter Saelens
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
| | - Robrecht Cannoodt
- Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Yvan Saeys
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
| | - Sandrine Dudoit
- Department of Statistics, University of California, Berkeley, CA, USA.
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
- Center for Computational Biology, University of California, Berkeley, CA, USA.
| | - Lieven Clement
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.
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15
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Spies D, Renz PF, Beyer TA, Ciaudo C. Comparative analysis of differential gene expression tools for RNA sequencing time course data. Brief Bioinform 2019; 20:288-298. [PMID: 29028903 PMCID: PMC6357553 DOI: 10.1093/bib/bbx115] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2017] [Indexed: 02/05/2023] Open
Abstract
RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.
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Affiliation(s)
- Daniel Spies
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.,Life Science Zurich Graduate School, Molecular Life Science program, University of Zürich, Switzerland
| | - Peter F Renz
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.,Life Science Zurich Graduate School, Molecular Life Science program, University of Zürich, Switzerland
| | - Tobias A Beyer
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland
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16
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Abstract
Identification of differentially expressed genes has been a high priority task of downstream analyses to further advances in biomedical research. Investigators have been faced with an array of issues in dealing with more complicated experiments and metadata, including batch effects, normalization, temporal dynamics (temporally differential expression), and isoform diversity (isoform-level quantification and differential splicing events). To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large-scale experimental designs, especially with combined metadata. In this report, we propose comprehensive analytical pipelines to precisely characterize temporal dynamics in differential expression of genes and other genomic features, i.e., the variability of transcripts, isoforms and exons, by controlling batch effects and other nuisance factors that could have significant confounding effects on the main effects of interest in comparative models and may result in misleading interpretations.
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17
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Li Q, Noel-MacDonnell JR, Koestler DC, Goode EL, Fridley BL. Subject level clustering using a negative binomial model for small transcriptomic studies. BMC Bioinformatics 2018; 19:474. [PMID: 30541426 PMCID: PMC6292049 DOI: 10.1186/s12859-018-2556-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 12/03/2018] [Indexed: 02/06/2023] Open
Abstract
Background Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies. Results We have developed a Negative Binomial Model-Based (NBMB) method to cluster samples using a stochastic version of the expectation-maximization algorithm. A simulation study involving various scenarios was completed to compare the performance of NBMB to Gaussian model-based or Gaussian mixture modeling (GMM). NBMB was also applied for the clustering of two RNA-seq studies; type 2 diabetes study (N = 96) and TCGA study of ovarian cancer (N = 295). Simulation results showed that NBMB outperforms GMM applied with different transformations in majority of scenarios with limited sample size. Additionally, we found that NBMB outperformed GMM for small clusters distance regardless of sample size. Increasing total number of genes with fixed proportion of differentially expressed genes does not change the outperformance of NBMB, but improves the overall performance of GMM. Analysis of type 2 diabetes and ovarian cancer tumor data with NBMB found good agreement with the reported disease subtypes and the gene expression patterns. This method is available in an R package on CRAN named NB.MClust. Conclusion Use of Negative Binomial model based clustering is advisable when clustering over dispersed RNA-seq count data. Electronic supplementary material The online version of this article (10.1186/s12859-018-2556-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Qian Li
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.,Health Informatics Institute, University of South Florida, Tampa, FL, USA
| | | | - Devin C Koestler
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Ellen L Goode
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.
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18
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The soil organic matter decomposition mechanisms in ectomycorrhizal fungi are tuned for liberating soil organic nitrogen. ISME JOURNAL 2018; 13:977-988. [PMID: 30538275 PMCID: PMC6461840 DOI: 10.1038/s41396-018-0331-6] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/27/2018] [Accepted: 11/28/2018] [Indexed: 02/08/2023]
Abstract
Many trees form ectomycorrhizal symbiosis with fungi. During symbiosis, the tree roots supply sugar to the fungi in exchange for nitrogen, and this process is critical for the nitrogen and carbon cycles in forest ecosystems. However, the extents to which ectomycorrhizal fungi can liberate nitrogen and modify the soil organic matter and the mechanisms by which they do so remain unclear since they have lost many enzymes for litter decomposition that were present in their free-living, saprotrophic ancestors. Using time-series spectroscopy and transcriptomics, we examined the ability of two ectomycorrhizal fungi from two independently evolved ectomycorrhizal lineages to mobilize soil organic nitrogen. Both species oxidized the organic matter and accessed the organic nitrogen. The expression of those events was controlled by the availability of glucose and inorganic nitrogen. Despite those similarities, the decomposition mechanisms, including the type of genes involved as well as the patterns of their expression, differed markedly between the two species. Our results suggest that in agreement with their diverse evolutionary origins, ectomycorrhizal fungi use different decomposition mechanisms to access organic nitrogen entrapped in soil organic matter. The timing and magnitude of the expression of the decomposition activity can be controlled by the below-ground nitrogen quality and the above-ground carbon supply.
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19
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Bacher R, Leng N, Chu LF, Ni Z, Thomson JA, Kendziorski C, Stewart R. Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 2018; 19:380. [PMID: 30326833 PMCID: PMC6192113 DOI: 10.1186/s12859-018-2405-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 10/01/2018] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND High-throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for studying detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time points. RESULTS We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene's expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-sequencing (RNA-seq) datasets. CONCLUSIONS Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available on Bioconductor with a full vignette at https://bioconductor.org/packages/release/bioc/html/Trendy.html .
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Affiliation(s)
- Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, FL, USA.
| | - Ning Leng
- Morgridge Institute for Research, Madison, WI, USA
| | - Li-Fang Chu
- Morgridge Institute for Research, Madison, WI, USA
| | - Zijian Ni
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Ron Stewart
- Morgridge Institute for Research, Madison, WI, USA.
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20
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Dupuis JR, Bremer FT, Kauwe A, San Jose M, Leblanc L, Rubinoff D, Geib SM. HiMAP: Robust phylogenomics from highly multiplexed amplicon sequencing. Mol Ecol Resour 2018. [PMID: 29633537 DOI: 10.1101/213454] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
High-throughput sequencing has fundamentally changed how molecular phylogenetic data sets are assembled, and phylogenomic data sets commonly contain 50- to 100-fold more loci than those generated using traditional Sanger sequencing-based approaches. Here, we demonstrate a new approach for building phylogenomic data sets using single-tube, highly multiplexed amplicon sequencing, which we name HiMAP (highly multiplexed amplicon-based phylogenomics) and present bioinformatic pipelines for locus selection based on genomic and transcriptomic data resources and postsequencing consensus calling and alignment. This method is inexpensive and amenable to sequencing a large number (hundreds) of taxa simultaneously and requires minimal hands-on time at the bench (<1/2 day), and data analysis can be accomplished without the need for read mapping or assembly. We demonstrate this approach by sequencing 878 amplicons in single reactions for 82 species of tephritid fruit flies across seven genera (384 individuals), including some of the most economically important agricultural insect pests. The resulting filtered data set (>150,000-bp concatenated alignment, ~20% missing character sites across all individuals and amplicons) contained >40,000 phylogenetically informative characters, and although some discordance was observed between analyses, it provided unparalleled resolution of many phylogenetic relationships in this group. Most notably, we found high support for the generic status of Zeugodacus and the sister relationship between Dacus and Zeugodacus. We discuss HiMAP, with regard to its molecular and bioinformatic strengths, and the insight the resulting data set provides into relationships of this diverse insect group.
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Affiliation(s)
- Julian R Dupuis
- U.S. Department of Agriculture-Agricultural Research Service, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, Hilo, Hawaii
- Department of Plant and Environmental Protection Services, University of Hawaii at Manoa, Honolulu, Hawaii
| | - Forest T Bremer
- U.S. Department of Agriculture-Agricultural Research Service, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, Hilo, Hawaii
- Department of Plant and Environmental Protection Services, University of Hawaii at Manoa, Honolulu, Hawaii
| | - Angela Kauwe
- U.S. Department of Agriculture-Agricultural Research Service, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, Hilo, Hawaii
| | - Michael San Jose
- Department of Plant and Environmental Protection Services, University of Hawaii at Manoa, Honolulu, Hawaii
| | - Luc Leblanc
- Department of Entomology, Plant Pathology and Nematology, University of Idaho, Moscow, Idaho
| | - Daniel Rubinoff
- Department of Plant and Environmental Protection Services, University of Hawaii at Manoa, Honolulu, Hawaii
| | - Scott M Geib
- U.S. Department of Agriculture-Agricultural Research Service, Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, Hilo, Hawaii
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21
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Insight into Genes Regulating Postharvest Aflatoxin Contamination of Tetraploid Peanut from Transcriptional Profiling. Genetics 2018; 209:143-156. [PMID: 29545468 DOI: 10.1534/genetics.118.300478] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2017] [Accepted: 03/07/2018] [Indexed: 11/18/2022] Open
Abstract
Postharvest aflatoxin contamination is a challenging issue that affects peanut quality. Aflatoxin is produced by fungi belonging to the Aspergilli group, and is known as an acutely toxic, carcinogenic, and immune-suppressing class of mycotoxins. Evidence for several host genetic factors that may impact aflatoxin contamination has been reported, e.g., genes for lipoxygenase (PnLOX1 and PnLOX2/PnLOX3 that showed either positive or negative regulation with Aspergillus infection), reactive oxygen species, and WRKY (highly associated with or differentially expressed upon infection of maize with Aspergillus flavus); however, their roles remain unclear. Therefore, we conducted an RNA-sequencing experiment to differentiate gene response to the infection by A. flavus between resistant (ICG 1471) and susceptible (Florida-07) cultivated peanut genotypes. The gene expression profiling analysis was designed to reveal differentially expressed genes in response to the infection (infected vs. mock-treated seeds). In addition, the differential expression of the fungal genes was profiled. The study revealed the complexity of the interaction between the fungus and peanut seeds as the expression of a large number of genes was altered, including some in the process of plant defense to aflatoxin accumulation. Analysis of the experimental data with "keggseq," a novel designed tool for Kyoto Encyclopedia of Genes and Genomes enrichment analysis, showed the importance of α-linolenic acid metabolism, protein processing in the endoplasmic reticulum, spliceosome, and carbon fixation and metabolism pathways in conditioning resistance to aflatoxin accumulation. In addition, coexpression network analysis was carried out to reveal the correlation of gene expression among peanut and fungal genes. The results showed the importance of WRKY, toll/Interleukin1 receptor-nucleotide binding site leucine-rich repeat (TIR-NBS-LRR), ethylene, and heat shock proteins in the resistance mechanism.
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22
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Etna MP, Sinigaglia A, Grassi A, Giacomini E, Romagnoli A, Pardini M, Severa M, Cruciani M, Rizzo F, Anastasiadou E, Di Camillo B, Barzon L, Fimia GM, Manganelli R, Coccia EM. Mycobacterium tuberculosis-induced miR-155 subverts autophagy by targeting ATG3 in human dendritic cells. PLoS Pathog 2018; 14:e1006790. [PMID: 29300789 PMCID: PMC5771628 DOI: 10.1371/journal.ppat.1006790] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 01/17/2018] [Accepted: 12/05/2017] [Indexed: 12/30/2022] Open
Abstract
Autophagy is a primordial eukaryotic pathway, which provides the immune system with multiple mechanisms for the elimination of invading pathogens including Mycobacterium tuberculosis (Mtb). As a consequence, Mtb has evolved different strategies to hijack the autophagy process. Given the crucial role of human primary dendritic cells (DC) in host immunity control, we characterized Mtb-DC interplay by studying the contribution of cellular microRNAs (miRNAs) in the post-transcriptional regulation of autophagy related genes. From the expression profile of de-regulated miRNAs obtained in Mtb-infected human DC, we identified 7 miRNAs whose expression was previously found to be altered in specimens of TB patients. Among them, gene ontology analysis showed that miR-155, miR-155* and miR-146a target mRNAs with a significant enrichment in biological processes linked to autophagy. Interestingly, miR-155 was significantly stimulated by live and virulent Mtb and enriched in polysome-associated RNA fraction, where actively translated mRNAs reside. The putative pair interaction among the E2 conjugating enzyme involved in LC3-lipidation and autophagosome formation-ATG3-and miR-155 arose by target prediction analysis, was confirmed by both luciferase reporter assay and Atg3 immunoblotting analysis of miR-155-transfected DC, which showed also a consistent Atg3 protein and LC3 lipidated form reduction. Late in infection, when miR-155 expression peaked, both the level of Atg3 and the number of LC3 puncta per cell (autophagosomes) decreased dramatically. In accordance, miR-155 silencing rescued autophagosome number in Mtb infected DC and enhanced autolysosome fusion, thereby supporting a previously unidentified role of the miR-155 as inhibitor of ATG3 expression. Taken together, our findings suggest how Mtb can manipulate cellular miRNA expression to regulate Atg3 for its own survival, and highlight the importance to develop novel therapeutic strategies against tuberculosis that would boost autophagy. Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens in human history and remains the second leading cause of death from an infectious agent worldwide. The major reason of Mtb success relies on its ability to evade host immunity. Autophagy, a cellular mechanism involved in intracellular pathogen elimination, is one of the pathways hijacked by Mtb to elude the control of dendritic cells (DC), major cellular effectors of immune response. Recently, it has become clear that Mtb infection not only alters cellular gene expression, but also controls the level of small RNA molecules, namely microRNAs (miRNAs), which function as negative regulators of mRNA translation into protein. In the present study, we observed that the infection of human DC with Mtb leads to a strong induction of host miR-155, a critical regulator of host immune response. By mean of miR-155 induction, Mtb reduces Atg3 protein content, a crucial enzyme needed for the initial phase of the autophagic process. Interestingly, miR-155 silencing during Mtb infection restores Atg3 level and rescues autophagy. These findings contribute to better elucidate Mtb-triggered escape mechanisms and highlight the importance to develop host-directed therapies to combat tuberculosis based on autophagy boosting.
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Affiliation(s)
- Marilena P. Etna
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | | | - Angela Grassi
- Department of Information Engineering, University of Padova, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padua, Italy
| | - Elena Giacomini
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | | | - Manuela Pardini
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Martina Severa
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Melania Cruciani
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Fabiana Rizzo
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Eleni Anastasiadou
- Department of Pathology, Institute for RNA Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Luisa Barzon
- Department of Molecular Medicine, University of Padova, Padua, Italy
| | - Gian Maria Fimia
- National Institute for Infectious Diseases "L. Spallanzani”, Rome, Italy
- Department of Biological and Environmental Science and Technology, University of Salento, Lecce, Italy
| | | | - Eliana M. Coccia
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
- * E-mail:
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23
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Liu Z, Wang Y, Tong X, Su Y, Yang L, Wang D, Zhao Y. De novo assembly and comparative transcriptome characterization of Poecilobdella javanica provide insight into blood feeding of medicinal leeches. Mol Omics 2018; 14:352-361. [DOI: 10.1039/c8mo00098k] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Leeches (family Hirudinidae) are classic model invertebrates used in diverse clinical treatments, such as reconstructive microsurgery, hypertension, and gangrene treatment.
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Affiliation(s)
- Zichao Liu
- Department of Life Science & Technology, Kunming University, Kunming Key Laboratory of Hydroecology Restoration of Dianchi Lake, Key Laboratory of Special Biological Resource Development & Utilization of Universities in Yunnan Province
- Kunming
- China
| | - Yanjie Wang
- Jules Stein Eye Institute, Department of Ophthalmology, University of California
- Los Angeles
- USA
| | - Xiangrong Tong
- Department of Life Science & Technology, Kunming University, Kunming Key Laboratory of Hydroecology Restoration of Dianchi Lake, Key Laboratory of Special Biological Resource Development & Utilization of Universities in Yunnan Province
- Kunming
- China
| | - Yuan Su
- Department of Life Science & Technology, Kunming University, Kunming Key Laboratory of Hydroecology Restoration of Dianchi Lake, Key Laboratory of Special Biological Resource Development & Utilization of Universities in Yunnan Province
- Kunming
- China
| | - Lijiang Yang
- Department of Life Science & Technology, Kunming University, Kunming Key Laboratory of Hydroecology Restoration of Dianchi Lake, Key Laboratory of Special Biological Resource Development & Utilization of Universities in Yunnan Province
- Kunming
- China
| | - Debin Wang
- Department of Life Science & Technology, Kunming University, Kunming Key Laboratory of Hydroecology Restoration of Dianchi Lake, Key Laboratory of Special Biological Resource Development & Utilization of Universities in Yunnan Province
- Kunming
- China
| | - Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles
- Los Angeles
- USA
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24
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Progar V, Jakše J, Štajner N, Radišek S, Javornik B, Berne S. Comparative transcriptional analysis of hop responses to infection with Verticillium nonalfalfae. PLANT CELL REPORTS 2017; 36:1599-1613. [PMID: 28698905 PMCID: PMC5602066 DOI: 10.1007/s00299-017-2177-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 07/04/2017] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE Dynamic transcriptome profiling revealed excessive, yet ineffective, immune response to V. nonalfalfae infection in susceptible hop, global gene downregulation in shoots of resistant hop and only a few infection-associated genes in roots. Hop (Humulus lupulus L.) production is hampered by Verticillium wilt, a disease predominantly caused by the soil-borne fungus Verticillium nonalfalfae. Only a few hop cultivars exhibit resistance towards it and mechanisms of this resistance have not been discovered. In this study, we compared global transcriptional responses in roots and shoots of resistant and susceptible hop plants infected by a lethal strain of V. nonalfalfae. Time-series differential gene expression profiles between infected and mock inoculated plants were determined and subjected to network-based analysis of functional enrichment. In the resistant hop cultivar, a remarkably low number of genes were differentially expressed in roots in response to V. nonalfalfae infection, while the majority of differentially expressed genes were down-regulated in shoots. The most significantly affected genes were related to cutin biosynthesis, cell wall biogenesis, lateral root development and terpenoid biosynthesis. On the other hand, susceptible hop exhibited a strong defence response in shoots and roots, including increased expression of genes associated with plant responses, such as innate immunity, wounding, jasmonic acid pathway and chitinase activity. Strong induction of defence-associated genes in susceptible hop and a low number of infection-responsive genes in the roots of resistant hop are consistent with previous findings, confirming the pattern of excessive response of the susceptible cultivar, which ultimately fails to protect the plant from V. nonalfalfae. This research offers a multifaceted overview of transcriptional responses of susceptible and resistant hop cultivars to V. nonalfalfae infection and represents a valuable resource in the study of this plant-pathogen interaction.
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Affiliation(s)
- Vasja Progar
- Department of Agronomy, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Jernej Jakše
- Department of Agronomy, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Nataša Štajner
- Department of Agronomy, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Sebastjan Radišek
- Plant Protection Department, Slovenian Institute of Hop Research and Brewing, Žalec, Slovenia
| | - Branka Javornik
- Department of Agronomy, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Sabina Berne
- Department of Agronomy, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
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25
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Facchiano A, Angelini C, Bosotti R, Guffanti A, Marabotti A, Marangoni R, Pascarella S, Romano P, Zanzoni A, Helmer-Citterich M. Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics. BMC Bioinformatics 2015; 16 Suppl 9:S1. [PMID: 26050789 PMCID: PMC4464032 DOI: 10.1186/1471-2105-16-s9-s1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014.
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