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Wang H, Xiao F, Gao Z, Guo L, Yang L, Li G, Kong Q. Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging. Aging Cell 2024; 23:e14163. [PMID: 38566438 PMCID: PMC11258444 DOI: 10.1111/acel.14163] [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: 12/15/2023] [Revised: 03/12/2024] [Accepted: 03/15/2024] [Indexed: 04/04/2024] Open
Abstract
The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.
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Affiliation(s)
- Hao‐Tian Wang
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Fu‐Hui Xiao
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Zong‐Liang Gao
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Li‐Yun Guo
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Li‐Qin Yang
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Gong‐Hua Li
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Qing‐Peng Kong
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- CAS Center for Excellence in Animal Evolution and GeneticsChinese Academy of SciencesKunmingChina
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Zhao BJ, Song SY, Zhao WM, Xu HB, Peng K, Shan XS, Chen QC, Liu H, Liu HY, Ji FH. The effect of sevoflurane exposure on cell-type-specific changes in the prefrontal cortex in young mice. J Neurochem 2024; 168:1080-1096. [PMID: 38317263 DOI: 10.1111/jnc.16068] [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: 09/25/2023] [Revised: 12/29/2023] [Accepted: 01/18/2024] [Indexed: 02/07/2024]
Abstract
Sevoflurane, the predominant pediatric anesthetic, has been linked to neurotoxicity in young mice, although the underlying mechanisms remain unclear. This study focuses on investigating the impact of neonatal sevoflurane exposure on cell-type-specific alterations in the prefrontal cortex (PFC) of young mice. Neonatal mice were subjected to either control treatment (60% oxygen balanced with nitrogen) or sevoflurane anesthesia (3% sevoflurane in 60% oxygen balanced with nitrogen) for 2 hours on postnatal days (PNDs) 6, 8, and 10. Behavioral tests and single-nucleus RNA sequencing (snRNA-seq) of the PFC were conducted from PNDs 31 to 37. Mechanistic exploration included clustering analysis, identification of differentially expressed genes (DEGs), enrichment analyses, single-cell trajectory analysis, and genome-wide association studies (GWAS). Sevoflurane anesthesia resulted in sociability and cognition impairments in mice. Novel specific marker genes identified 8 distinct cell types in the PFC. Most DEGs between the control and sevoflurane groups were unique to specific cell types. Re-defining 15 glutamatergic neuron subclusters based on layer identity revealed their altered expression profiles. Notably, sevoflurane disrupted the trajectory from oligodendrocyte precursor cells (OPCs) to oligodendrocytes (OLs). Validation of disease-relevant candidate genes across the main cell types demonstrated their association with social dysfunction and working memory impairment. Behavioral results and snRNA-seq collectively elucidated the cellular atlas in the PFC of young male mice, providing a foundation for further mechanistic studies on developmental neurotoxicity induced by anesthesia.
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Affiliation(s)
- Bao-Jian Zhao
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
- Department of Anesthesiology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Shao-Yong Song
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
- Department of Pain Medicine, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, Jiangsu, China
| | - Wei-Ming Zhao
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Han-Bing Xu
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Ke Peng
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Xi-Sheng Shan
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Qing-Cai Chen
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
| | - Hong Liu
- Department of Anesthesiology and Pain Medicine, University of California Davis Health, Sacramento, California, USA
| | - Hua-Yue Liu
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
- Ambulatory Surgery Center, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Fu-Hai Ji
- Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, China
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3
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Giudice L, Mohamed A, Malm T. StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling. PLoS Comput Biol 2024; 20:e1012022. [PMID: 38607982 PMCID: PMC11042724 DOI: 10.1371/journal.pcbi.1012022] [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: 11/08/2023] [Revised: 04/24/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.
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Affiliation(s)
- Luca Giudice
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ahmed Mohamed
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tarja Malm
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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Jones EF, Haldar A, Oza VH, Lasseigne BN. Quantifying transcriptome diversity: a review. Brief Funct Genomics 2024; 23:83-94. [PMID: 37225889 DOI: 10.1093/bfgp/elad019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/14/2023] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.
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Affiliation(s)
- Emma F Jones
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vishal H Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Brittany N Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
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Kim K, Kim S, Ahn T, Kim H, Shin SJ, Choi CH, Park S, Kim YB, No JH, Suh DH. A differential diagnosis between uterine leiomyoma and leiomyosarcoma using transcriptome analysis. BMC Cancer 2023; 23:1215. [PMID: 38066476 PMCID: PMC10709939 DOI: 10.1186/s12885-023-11394-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 09/11/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, 17 genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), random forest (RF), and gradient boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using GeneMANIA. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). RESULTS We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed sensitivity 0.88, 0.77 (8/9, 7/9) while the specificity 1.0 (8/8, 8/8) in two test data set supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. CONCLUSION The transcriptome-based classifier accurately distinguished uterine leiomyosarcoma from leiomyoma. Our method can be helpful in clinical practice through the biopsy of sample in advance of surgery. Identification of leiomyosarcoma let the doctor avoid of laparoscopic surgery, thus it minimizes un-wanted tumor spread.
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Affiliation(s)
- Kidong Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sarah Kim
- Department of Life Science, Handong Global University, Pohang, Republic of Korea
| | - TaeJin Ahn
- Department of Life Science, Handong Global University, Pohang, Republic of Korea.
| | - Hyojin Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - So-Jin Shin
- Department of Gynecology and Obstetrics, School of Medicine, Keimyung University, Daegu, Republic of Korea
| | - Chel Hun Choi
- Department of Obstetrics and Gynecology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sungmin Park
- Department of Life Science, Handong Global University, Pohang, Republic of Korea
| | - Yong-Beom Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jae Hong No
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Dong Hoon Suh
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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Li H, Khang TF. clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution. PeerJ 2023; 11:e16126. [PMID: 37790621 PMCID: PMC10544356 DOI: 10.7717/peerj.16126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/27/2023] [Indexed: 10/05/2023] Open
Abstract
Background Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean. Methods Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data. Results Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.
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Affiliation(s)
- Hongxiang Li
- Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Tsung Fei Khang
- Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Malaysia
- Universiti Malaya Centre for Data Analytics, Universiti Malaya, Kuala Lumpur, Malaysia
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Wolf S, Melo D, Garske KM, Pallares LF, Lea AJ, Ayroles JF. Characterizing the landscape of gene expression variance in humans. PLoS Genet 2023; 19:e1010833. [PMID: 37410774 DOI: 10.1371/journal.pgen.1010833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/15/2023] [Indexed: 07/08/2023] Open
Abstract
Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.
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Affiliation(s)
- Scott Wolf
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Diogo Melo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kristina M Garske
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Luisa F Pallares
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Amanda J Lea
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Child and Brain Development, Canadian Institute for Advanced Research, Toronto, Canada
| | - Julien F Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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Grandt CL, Brackmann LK, Foraita R, Schwarz H, Hummel-Bartenschlager W, Hankeln T, Kraemer C, Zahnreich S, Drees P, Mirsch J, Spix C, Blettner M, Schmidberger H, Binder H, Hess M, Galetzka D, Marini F, Poplawski A, Marron M. Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation. Mol Med 2023; 29:41. [PMID: 36997855 PMCID: PMC10061869 DOI: 10.1186/s10020-023-00629-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/20/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation. METHODS Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures. RESULTS We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data. CONCLUSION N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.
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Affiliation(s)
- Caine Lucas Grandt
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Achterstr. 30, 28359, Bremen, Germany.
- Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany.
| | - Lara Kim Brackmann
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Achterstr. 30, 28359, Bremen, Germany
| | - Ronja Foraita
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Achterstr. 30, 28359, Bremen, Germany
| | - Heike Schwarz
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Achterstr. 30, 28359, Bremen, Germany
| | | | - Thomas Hankeln
- Institute of Organismic and Molecular Evolution, Molecular Genetics and Genome Analysis, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Christiane Kraemer
- Institute of Organismic and Molecular Evolution, Molecular Genetics and Genome Analysis, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sebastian Zahnreich
- Department of Radiation Oncology and Radiation Therapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Philipp Drees
- Department of Orthopaedics and Traumatology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Johanna Mirsch
- Radiation Biology and DNA Repair, Technical University of Darmstadt, Darmstadt, Germany
| | - Claudia Spix
- Division of Childhood Cancer Epidemiology, German Childhood Cancer Registry, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Maria Blettner
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Center of the Johannes, University Medical, Gutenberg University, Mainz, Germany
| | - Heinz Schmidberger
- Department of Radiation Oncology and Radiation Therapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics, University Medical Center, Freiburg, Germany
| | - Moritz Hess
- Institute of Medical Biometry and Statistics, University Medical Center, Freiburg, Germany
| | - Danuta Galetzka
- Department of Radiation Oncology and Radiation Therapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Center of the Johannes, University Medical, Gutenberg University, Mainz, Germany
| | - Alicia Poplawski
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Center of the Johannes, University Medical, Gutenberg University, Mainz, Germany
| | - Manuela Marron
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Achterstr. 30, 28359, Bremen, Germany
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Le Priol C, Azencott CA, Gidrol X. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression. PLoS Comput Biol 2023; 19:e1010342. [PMID: 36893104 PMCID: PMC9997931 DOI: 10.1371/journal.pcbi.1010342] [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: 07/01/2022] [Accepted: 02/09/2023] [Indexed: 03/10/2023] Open
Abstract
The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.
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Affiliation(s)
- Christophe Le Priol
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, Biomics, Grenoble, France
- * E-mail: (CLP); (XG)
| | - Chloé-Agathe Azencott
- Center for Computational Biology, Mines ParisTech, PSL Research University, Paris, France
- Institut Curie, Paris, France
- INSERM U900, Paris, France
| | - Xavier Gidrol
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, Biomics, Grenoble, France
- * E-mail: (CLP); (XG)
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Wang Y, Du T, Li A, Qiao L, Zhang Z, Sun W. Establishment and application of a silkworm CRISPR/Cas9 tool for conditionally manipulating gene disruption in the epidermis. INSECT BIOCHEMISTRY AND MOLECULAR BIOLOGY 2022; 151:103861. [PMID: 36332793 DOI: 10.1016/j.ibmb.2022.103861] [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: 04/08/2022] [Revised: 09/29/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Spatial or temporal specific gene knockout system is a valuable tool for studying the molecular mechanisms underlying developmental processes. The integument is essential for insect fitness and survival, but tools for dissecting function of genes in this tissue are lacking. In this study, we firstly identified an epidermis specifically expressed gene of the domesticated silkworm, BmCPG25, by comparative transcriptomic analysis. Furthermore, a transgenic silkworm expressing the RNA dependent CRISPR-Cas9 protein driven by the regulatory region of the BmCPG25 was established. Immunochemistry analysis showed the endonuclease was specifically expressed in the nuclear of epidermal cells. We also validated the efficiency of this system by disrupting the function of an epidermis specifically expressed cuticular protein gene (Cpr21) and a ubiquitously expressed cuticular gene (Cph18), respectively. In summary, we successfully constructed a conditional knockout toolkit to manipulate the gene editing in epidermal cells, which provides a valuable approach to study the molecular mechanism of integument development.
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Affiliation(s)
- Yun Wang
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, 401331, China
| | - Tianyi Du
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, 401331, China
| | - Ainan Li
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, 401331, China
| | - Liang Qiao
- Chongqing Key Laboratory of Vector Insects, Institute of Entomology and Molecular Biology, College of Life Sciences, Chongqing Normal University, Chongqing, 401331, China
| | - Ze Zhang
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, 401331, China.
| | - Wei Sun
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, 401331, China.
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11
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Komodromos M, Aboagye EO, Evangelou M, Filippi S, Ray K. Variational Bayes for high-dimensional proportional hazards models with applications within gene expression. Bioinformatics 2022; 38:3918-3926. [PMID: 35751586 PMCID: PMC9364383 DOI: 10.1093/bioinformatics/btac416] [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: 12/15/2021] [Revised: 04/27/2022] [Accepted: 06/23/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertainty at high (unscalable) computational expense. RESULTS We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as sparse variational Bayes. Our method, based on a mean-field variational approximation, overcomes the high computational cost of Markov chain Monte Carlo, whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, and how the uncertainty quantification offered by our method can be used to provide an interpretable assessment of patient risk. AVAILABILITY AND IMPLEMENTATION our method has been implemented as a freely available R package survival.svb (https://github.com/mkomod/survival.svb). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College London, London W12 0NN, UK
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12
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Wang Y, Zou H, Lai J, Zhang Z, Sun W. The miR-282-5p regulates larval moulting process by targeting chitinase 5 in Bombyx mori. INSECT MOLECULAR BIOLOGY 2022; 31:190-201. [PMID: 34862684 DOI: 10.1111/imb.12750] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/16/2021] [Accepted: 11/29/2021] [Indexed: 06/13/2023]
Abstract
Moulting is critical for growth, development and survival in insects. As the main components of cuticle, dynamic change of chitin is consistent with the moulting process. Chitinase is the main enzyme to mediate chitin metabolism in the old cuticle. To avoid over-degrading chitin from the new cuticle, the expression of chitinase must be precisely regulated. In this study, we performed microRNA-sequencing to investigate expression change of microRNAs in silkworm epidermis during the moulting process. A comparative microRNA transcriptomic analysis from different moulting stages and 20-hydroxyecdysone (20E) treatment identified bmo-miR-282-5p as a candidate. By the bioinformatic analysis, chitinase 5 (BmCht5) was predicted to be a target of bmo-miR-282-5p. Meanwhile, a temporal expression analysis revealed that BmCht5 only expressed at moulting D3 stage, whereas bmo-miR-282-5p showed a converse pattern, in which its transcript signal disappeared at this time point. Furthermore, a luciferase assay and agomir treatment demonstrated that bmo-miR-282-5p suppressed transcript of BmCht5 in vivo. As a result, injection of 282-5p agomir triggered 40% death due to moulting failure. In addition, RNA interference (RNAi)-mediated silencing of BmCht5 caused 30% developmental defect. Taken together, our data demonstrate the coordinated regulation of chitinase 5 by conserved miR-282-5p, and the 20E signalling pathway is essential for the normal moulting process in the domesticated silkworm.
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Affiliation(s)
- Yun Wang
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, China
| | - Hongbin Zou
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, China
| | - Juan Lai
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, China
| | - Ze Zhang
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, China
| | - Wei Sun
- Laboratory of Evolutionary and Functional Genomics, School of Life Sciences, Chongqing University, Chongqing, China
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13
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Roberts AGK, Catchpoole DR, Kennedy PJ. Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability. NAR Genom Bioinform 2022; 4:lqab124. [PMID: 35047816 PMCID: PMC8759562 DOI: 10.1093/nargab/lqab124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 11/19/2021] [Accepted: 12/16/2021] [Indexed: 12/13/2022] Open
Abstract
There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
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14
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Hitzemann R, Lockwood DR, Ozburn AR, Phillips TJ. On the Use of Heterogeneous Stock Mice to Map Transcriptomes Associated With Excessive Ethanol Consumption. Front Psychiatry 2021; 12:725819. [PMID: 34712155 PMCID: PMC8545898 DOI: 10.3389/fpsyt.2021.725819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/30/2021] [Indexed: 01/11/2023] Open
Abstract
We and many others have noted the advantages of using heterogeneous (HS) animals to map genes and gene networks associated with both behavioral and non-behavioral phenotypes. Importantly, genetically complex Mus musculus crosses provide substantially increased resolution to examine old and new relationships between gene expression and behavior. Here we report on data obtained from two HS populations: the HS/NPT derived from eight inbred laboratory mouse strains and the HS-CC derived from the eight collaborative cross inbred mouse strains that includes three wild-derived strains. Our work has focused on the genes and gene networks associated with risk for excessive ethanol consumption, individual variation in ethanol consumption and the consequences, including escalation, of long-term ethanol consumption. Background data on the development of HS mice is provided, including advantages for the detection of expression quantitative trait loci. Examples are also provided of using HS animals to probe the genes associated with ethanol preference and binge ethanol consumption.
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Affiliation(s)
- Robert Hitzemann
- Department of Behavioral Neuroscience and Portland Alcohol Research Center, Oregon Health & Science University, Portland, OR, United States
| | - Denesa R. Lockwood
- Department of Behavioral Neuroscience and Portland Alcohol Research Center, Oregon Health & Science University, Portland, OR, United States
| | - Angela R. Ozburn
- Department of Behavioral Neuroscience and Portland Alcohol Research Center, Oregon Health & Science University, Portland, OR, United States
- Veterans Affairs Portland Health Care System, Portland, OR, United States
| | - Tamara J. Phillips
- Department of Behavioral Neuroscience and Portland Alcohol Research Center, Oregon Health & Science University, Portland, OR, United States
- Veterans Affairs Portland Health Care System, Portland, OR, United States
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15
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He L, Pratt H, Gao M, Wei F, Weng Z, Struhl K. YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation. eLife 2021; 10:e67312. [PMID: 34463254 PMCID: PMC8463077 DOI: 10.7554/elife.67312] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 08/26/2021] [Indexed: 12/12/2022] Open
Abstract
The YAP and TAZ paralogs are transcriptional co-activators recruited to target sites by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB (a member of the AP-1 family) and STAT3, key transcription factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, and they stimulate transcriptional activation by AP-1 proteins. JUNB, STAT3, and TEAD co-localize at virtually all YAP/TAZ target sites, yet many target sites only contain individual AP-1, TEAD, or STAT3 motifs. This observation and differences in relative crosslinking efficiencies of JUNB, TEAD, and STAT3 at YAP/TAZ target sites suggest that YAP/TAZ is recruited by different forms of an AP-1/STAT3/TEAD complex depending on the recruiting motif. The different classes of YAP/TAZ target sites are associated with largely non-overlapping genes with distinct functions. A small minority of target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes from shared YAP/TAZ target sites. Genes containing either the AP-1 or TEAD class of YAP/TAZ sites are associated with poor survival of breast cancer patients with the triple-negative form of the disease.
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Affiliation(s)
- Lizhi He
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical SchoolBostonUnited States
| | - Henry Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical SchoolWorcesterUnited States
| | - Mingshi Gao
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical SchoolWorcesterUnited States
| | - Fengxiang Wei
- Genetics Laboratory, Shenzhen Longgang District Maternity and Child Healthcare HospitalShenzhenChina
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical SchoolWorcesterUnited States
| | - Kevin Struhl
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical SchoolBostonUnited States
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16
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Venit T, El Said NH, Mahmood SR, Percipalle P. A dynamic actin-dependent nucleoskeleton and cell identity. J Biochem 2021; 169:243-257. [PMID: 33351909 DOI: 10.1093/jb/mvaa133] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Actin is an essential regulator of cellular functions. In the eukaryotic cell nucleus, actin regulates chromatin as a bona fide component of chromatin remodelling complexes, it associates with nuclear RNA polymerases to regulate transcription and is involved in co-transcriptional assembly of nascent RNAs into ribonucleoprotein complexes. Actin dynamics are, therefore, emerging as a major regulatory factor affecting diverse cellular processes. Importantly, the involvement of actin dynamics in nuclear functions is redefining the concept of nucleoskeleton from a rigid scaffold to a dynamic entity that is likely linked to the three-dimensional organization of the nuclear genome. In this review, we discuss how nuclear actin, by regulating chromatin structure through phase separation may contribute to the architecture of the nuclear genome during cell differentiation and facilitate the expression of specific gene programs. We focus specifically on mitochondrial genes and how their dysregulation in the absence of actin raises important questions about the role of cytoskeletal proteins in regulating chromatin structure. The discovery of a novel pool of mitochondrial actin that serves as 'mitoskeleton' to facilitate organization of mtDNA supports a general role for actin in genome architecture and a possible function of distinct actin pools in the communication between nucleus and mitochondria.
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Affiliation(s)
- Tomas Venit
- Science Division, Biology Program, New York University Abu Dhabi (NYUAD), PO Box 129188, Abu Dhabi United Arab Emirates
| | - Nadine Hosny El Said
- Science Division, Biology Program, New York University Abu Dhabi (NYUAD), PO Box 129188, Abu Dhabi United Arab Emirates
| | - Syed Raza Mahmood
- Science Division, Biology Program, New York University Abu Dhabi (NYUAD), PO Box 129188, Abu Dhabi United Arab Emirates.,Department of Biology, New York University, 100 Washington Square East, 1009 Silver Center, New York, NY 10003, USA
| | - Piergiorgio Percipalle
- Science Division, Biology Program, New York University Abu Dhabi (NYUAD), PO Box 129188, Abu Dhabi United Arab Emirates.,Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Svante Arrhenius väg 20C, 114 18 Stockholm, Sweden
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17
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Alexandre PA, Hudson NJ, Lehnert SA, Fortes MRS, Naval-Sánchez M, Nguyen LT, Porto-Neto LR, Reverter A. Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance. Genes (Basel) 2020; 11:E1231. [PMID: 33092259 PMCID: PMC7593939 DOI: 10.3390/genes11101231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 10/15/2020] [Indexed: 12/17/2022] Open
Abstract
Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.
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Affiliation(s)
- Pâmela A. Alexandre
- CSIRO Agriculture & Food, St Lucia, QLD 4067, Australia; (S.A.L.); (L.R.P.-N.); (A.R.)
| | - Nicholas J. Hudson
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;
| | - Sigrid A. Lehnert
- CSIRO Agriculture & Food, St Lucia, QLD 4067, Australia; (S.A.L.); (L.R.P.-N.); (A.R.)
| | - Marina R. S. Fortes
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia;
| | - Marina Naval-Sánchez
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia;
| | - Loan T. Nguyen
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia;
| | - Laercio R. Porto-Neto
- CSIRO Agriculture & Food, St Lucia, QLD 4067, Australia; (S.A.L.); (L.R.P.-N.); (A.R.)
| | - Antonio Reverter
- CSIRO Agriculture & Food, St Lucia, QLD 4067, Australia; (S.A.L.); (L.R.P.-N.); (A.R.)
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18
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Hu H, Lin S, Wang S, Chen X. The Role of Transcription Factor 21 in Epicardial Cell Differentiation and the Development of Coronary Heart Disease. Front Cell Dev Biol 2020; 8:457. [PMID: 32582717 PMCID: PMC7290112 DOI: 10.3389/fcell.2020.00457] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/18/2020] [Indexed: 02/02/2023] Open
Abstract
Transcription factor 21 (TCF21) is specific for mesoderm and is expressed in the embryos' mesenchymal derived tissues, such as the epicardium. It plays a vital role in regulating cell differentiation and cell fate specificity through epithelial-mesenchymal transformation during cardiac development. For instance, TCF21 could promote cardiac fibroblast development and inhibit vascular smooth muscle cells (VSMCs) differentiation of epicardial cells. Recent large-scale genome-wide association studies have identified a mass of loci associated with coronary heart disease (CHD). There is mounting evidence that TCF21 polymorphism might confer genetic susceptibility to CHD. However, the molecular mechanisms of TCF21 in heart development and CHD remain fundamentally problematic. In this review, we are committed to providing a detailed introduction of the biological roles of TCF21 in epicardial fate determination and the development of CHD.
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Affiliation(s)
- Haochang Hu
- School of Medicine, Ningbo University, Ningbo, China.,Department of Cardiology, Ningbo City First Hospital, Ningbo, China
| | - Shaoyi Lin
- School of Medicine, Ningbo University, Ningbo, China.,Department of Cardiology, Ningbo City First Hospital, Ningbo, China
| | | | - Xiaomin Chen
- School of Medicine, Ningbo University, Ningbo, China.,Department of Cardiology, Ningbo City First Hospital, Ningbo, China
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19
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Joilin G, Gray E, Thompson AG, Bobeva Y, Talbot K, Weishaupt J, Ludolph A, Malaspina A, Leigh PN, Newbury SF, Turner MR, Hafezparast M. Identification of a potential non-coding RNA biomarker signature for amyotrophic lateral sclerosis. Brain Commun 2020; 2:fcaa053. [PMID: 32613197 PMCID: PMC7329382 DOI: 10.1093/braincomms/fcaa053] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Objective biomarkers for the clinically heterogeneous adult-onset neurodegenerative disorder amyotrophic lateral sclerosis are crucial to facilitate assessing emerging therapeutics and improve the diagnostic pathway in what is a clinically heterogeneous syndrome. With non-coding RNA transcripts including microRNA, piwi-RNA and transfer RNA present in human biofluids, we sought to identify whether non-coding RNA in serum could be biomarkers for amyotrophic lateral sclerosis. Serum samples from our Oxford Study for Biomarkers in motor neurone disease/amyotrophic lateral sclerosis discovery cohort of amyotrophic lateral sclerosis patients (n = 48), disease mimics (n = 16) and age- and sex-matched healthy controls (n = 24) were profiled for non-coding RNA expression using RNA-sequencing, which showed a wide range of non-coding RNA to be dysregulated. We confirmed significant alterations with reverse transcription-quantitative PCR in the expression of hsa-miR-16-5p, hsa-miR-21-5p, hsa-miR-92a-3p, hsa-piR-33151, TRV-AAC4-1.1 and TRA-AGC6-1.1. Furthermore, hsa-miR-206, a previously identified amyotrophic lateral sclerosis biomarker, showed a binary-like pattern of expression in our samples. Using the expression of these non-coding RNA, we were able to discriminate amyotrophic lateral sclerosis samples from healthy controls in our discovery cohort using a random forest analysis with 93.7% accuracy with promise in predicting progression rate of patients. Importantly, cross-validation of this novel signature using a new geographically distinct cohort of samples from the United Kingdom and Germany with both amyotrophic lateral sclerosis and control samples (n = 156) yielded an accuracy of 73.9%. The high prediction accuracy of this non-coding RNA-based biomarker signature, even across heterogeneous cohorts, demonstrates the strength of our approach as a novel platform to identify and stratify amyotrophic lateral sclerosis patients.
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Affiliation(s)
- Greig Joilin
- School of Life Sciences, University of Sussex, Falmer, Brighton, UK
| | - Elizabeth Gray
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Yoana Bobeva
- Centre for Neuroscience and Trauma, Blizard Institute, Queen Mary University of London, London, UK
| | - Kevin Talbot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Albert Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Andrea Malaspina
- Centre for Neuroscience and Trauma, Blizard Institute, Queen Mary University of London, London, UK
| | - P Nigel Leigh
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Falmer, Brighton, UK
| | - Sarah F Newbury
- Department of Clinical and Experimental Medicine, Brighton and Sussex Medical School, University of Sussex, Falmer, Brighton, UK
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Majid Hafezparast
- School of Life Sciences, University of Sussex, Falmer, Brighton, UK
- Correspondence to: Majid Hafezparast, School of Life Sciences, University of Sussex, Falmer Brighton, BN1 9QG, UK E-mail:
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20
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Fernandez GJ, Ferreira JH, Vechetti IJ, de Moraes LN, Cury SS, Freire PP, Gutiérrez J, Ferretti R, Dal-Pai-Silva M, Rogatto SR, Carvalho RF. MicroRNA-mRNA Co-sequencing Identifies Transcriptional and Post-transcriptional Regulatory Networks Underlying Muscle Wasting in Cancer Cachexia. Front Genet 2020; 11:541. [PMID: 32547603 PMCID: PMC7272700 DOI: 10.3389/fgene.2020.00541] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/05/2020] [Indexed: 12/23/2022] Open
Abstract
Cancer cachexia is a metabolic syndrome with alterations in gene regulatory networks that consequently lead to skeletal muscle wasting. Integrating microRNAs-mRNAs omics profiles offers an opportunity to understand transcriptional and post-transcriptional regulatory networks underlying muscle wasting. Here, we used RNA sequencing to simultaneously integrate and explore microRNAs and mRNAs expression profiles in the tibialis anterior (TA) muscles of the Lewis Lung Carcinoma (LLC) model of cancer cachexia. We found 1,008 mRNAs and 18 microRNAs differentially expressed in cachectic mice compared with controls. Although our transcriptomic analysis demonstrated a high heterogeneity in mRNA profiles of cachectic mice, we identified a reduced number of differentially expressed genes that were uniformly regulated within cachectic muscles. This set of uniformly regulated genes is associated with the extracellular matrix (ECM), proteolysis, and inflammatory response. We also used transcriptomic data to perform enrichment analysis of transcriptional factor binding sites in promoter sequences, which revealed activation of the atrophy-related transcription factors NF-κB, Stat3, AP-1, and FoxO. Furthermore, the integration of mRNA and microRNA expression profiles identified post-transcriptional regulation by microRNAs of genes involved in ECM organization, cell migration, transcription factors binding, ion transport, and the FoxO signaling pathway. Our integrative analysis of microRNA-mRNA co-profiles comprehensively characterized regulatory relationships of molecular pathways and revealed microRNAs targeting ECM-associated genes in cancer cachexia.
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Affiliation(s)
- Geysson Javier Fernandez
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil.,Faculty of Medicine, University of Antioquia, Medellín, Colombia
| | - Juarez Henrique Ferreira
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Ivan José Vechetti
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Leonardo Nazario de Moraes
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Sarah Santiloni Cury
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Paula Paccielli Freire
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Jayson Gutiérrez
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Renato Ferretti
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Maeli Dal-Pai-Silva
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
| | - Silvia Regina Rogatto
- Department of Clinical Genetics, University Hospital of Southern Denmark, Vejle, Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Robson Francisco Carvalho
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, Brazil
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21
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Kosti I, Lyalina S, Pollard KS, Butte AJ, Sirota M. Meta-Analysis of Vaginal Microbiome Data Provides New Insights Into Preterm Birth. Front Microbiol 2020; 11:476. [PMID: 32322240 PMCID: PMC7156768 DOI: 10.3389/fmicb.2020.00476] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 03/04/2020] [Indexed: 11/13/2022] Open
Abstract
Preterm birth (PTB) is defined as the birth of an infant before 37 weeks of gestational age. It is the leading cause of perinatal morbidity and mortality worldwide. In this study, we present a comprehensive meta-analysis of vaginal microbiome in PTB. We integrated raw longitudinal 16S rRNA vaginal microbiome data from five independent studies across 3,201 samples and were able to gain new insights into the vaginal microbiome state in women who deliver preterm in comparison to those who deliver at term. We found that women who deliver prematurely show higher within-sample variance in vaginal microbiome abundance, with the most significant difference observed during the first trimester. Modeling the data longitudinally revealed a number of microbial genera as associated with PTB, including several that were previously known and two newly identified by this meta-analysis: Olsenella and Clostridium sensu stricto. New hypotheses emerging from this integrative analysis can lead to novel diagnostics to identify women who are at higher risk for PTB and potentially inform new therapeutic interventions.
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Affiliation(s)
- Idit Kosti
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Svetlana Lyalina
- Integrative Program in Quantitative Biology, Gladstone Institutes, University of California, San Francisco, San Francisco, CA, United States
| | - Katherine S. Pollard
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Epidemiology & Biostatistics, Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, United States
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Chan Zuckerberg Biohub, San Francisco, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
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22
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Malamon JS, Kriete A. Erosion of Gene Co-expression Networks Reveal Deregulation of Immune System Processes in Late-Onset Alzheimer's Disease. Front Neurosci 2020; 14:228. [PMID: 32265636 PMCID: PMC7099620 DOI: 10.3389/fnins.2020.00228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 03/02/2020] [Indexed: 12/16/2022] Open
Abstract
We have applied a novel and integrative analysis framework for next-generation sequencing (NGS) data to 503 human subjects provided by the Religious Orders Study and Memory and Aging Project (ROSMAP) to examine changes in transcriptomic organization and common variants in association with late-onset Alzheimer's disease (LOAD). Our framework identified seven reproducible, co-regulated modules after quality control (QC), clinical segregation, preservation filtering, and functional ontology analysis. These modules were specifically enriched in several innate and adaptive immune system processes, the synaptic vesicle cycle, and Hippo signaling. Topological and functional erosion of these modules due to shedding of genes and loss of in-module connectivity was diagnostic of disease progression. Perturbation analysis revealed that only 1% of eQTLs overlapped genes participating in these co-regulated modules. Common variants nevertheless identified components of the immune systems like human leukocyte antigen (HLA) complex and microtubule-associated protein tau (MAPT) regions in association with LOAD. Our results implicate microglial function, adaptive immune response, and the structural degeneration of neurons as contributors to the transcriptional deregulation observed along with common genetic variants in the progression of LOAD.
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Affiliation(s)
- John Stephen Malamon
- Bossone Research Center, School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Andres Kriete
- Bossone Research Center, School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
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23
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Green DJ, Sallah SR, Ellingford JM, Lovell SC, Sergouniotis PI. Variability in Gene Expression is Associated with Incomplete Penetrance in Inherited Eye Disorders. Genes (Basel) 2020; 11:genes11020179. [PMID: 32050448 PMCID: PMC7074066 DOI: 10.3390/genes11020179] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 01/29/2020] [Accepted: 02/06/2020] [Indexed: 12/21/2022] Open
Abstract
Inherited eye disorders (IED) are a heterogeneous group of Mendelian conditions that are associated with visual impairment. Although these disorders often exhibit incomplete penetrance and variable expressivity, the scale and mechanisms of these phenomena remain largely unknown. Here, we utilize publicly-available genomic and transcriptomic datasets to gain insights into variable penetrance in IED. Variants in a curated set of 340 IED-implicated genes were extracted from the Human Gene Mutation Database (HGMD) 2019.1 and cross-checked with the Genome Aggregation Database (gnomAD) 2.1 control-only dataset. Genes for which >1 variants were encountered in both HGMD and gnomAD were considered to be associated with variable penetrance (n = 56). Variability in gene expression levels was then estimated for the subset of these genes that was found to be adequately expressed in two relevant resources: the Genotype-Tissue Expression (GTEx) and Eye Genotype Expression (EyeGEx) datasets. We found that genes suspected to be associated with variable penetrance tended to have significantly more variability in gene expression levels in the general population (p = 0.0000015); this finding was consistent across tissue types. The results of this study point to the possible influence of cis and/or trans-acting elements on the expressivity of variants causing Mendelian disorders. They also highlight the potential utility of quantifying gene expression as part of the investigation of families showing evidence of variable penetrance.
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Affiliation(s)
- David J. Green
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester M13 9PT, UK; (D.J.G.); (S.R.S.); (J.M.E.); (S.C.L.)
| | - Shalaw R. Sallah
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester M13 9PT, UK; (D.J.G.); (S.R.S.); (J.M.E.); (S.C.L.)
| | - Jamie M. Ellingford
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester M13 9PT, UK; (D.J.G.); (S.R.S.); (J.M.E.); (S.C.L.)
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Simon C. Lovell
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester M13 9PT, UK; (D.J.G.); (S.R.S.); (J.M.E.); (S.C.L.)
| | - Panagiotis I. Sergouniotis
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester M13 9PT, UK; (D.J.G.); (S.R.S.); (J.M.E.); (S.C.L.)
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
- Correspondence: ; Tel.: +44-(0)161-27-55748
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24
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Network Medicine Approach for Analysis of Alzheimer's Disease Gene Expression Data. Int J Mol Sci 2020; 21:ijms21010332. [PMID: 31947790 PMCID: PMC6981840 DOI: 10.3390/ijms21010332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 12/21/2019] [Accepted: 12/30/2019] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD) is the most widespread diagnosed cause of dementia in the elderly. It is a progressive neurodegenerative disease that causes memory loss as well as other detrimental symptoms that are ultimately fatal. Due to the urgent nature of this disease, and the current lack of success in treatment and prevention, it is vital that different methods and approaches are applied to its study in order to better understand its underlying mechanisms. To this end, we have conducted network-based gene co-expression analysis on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. By processing and filtering gene expression data taken from the blood samples of subjects with varying disease states and constructing networks based on that data to evaluate gene relationships, we have been able to learn about gene expression correlated with the disease, and we have identified several areas of potential research interest.
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25
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Single-Cell Expression Variability Implies Cell Function. Cells 2019; 9:cells9010014. [PMID: 31861624 PMCID: PMC7017299 DOI: 10.3390/cells9010014] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 11/26/2019] [Accepted: 11/27/2019] [Indexed: 12/11/2022] Open
Abstract
As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.
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26
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Bashkeel N, Perkins TJ, Kærn M, Lee JM. Human gene expression variability and its dependence on methylation and aging. BMC Genomics 2019; 20:941. [PMID: 31810449 PMCID: PMC6898959 DOI: 10.1186/s12864-019-6308-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 11/18/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood. RESULTS Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated. CONCLUSIONS We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.
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Affiliation(s)
- Nasser Bashkeel
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario K1H 8M5 Canada
| | - Theodore J. Perkins
- Ottawa Hospital Research Institute, 501 Smyth Rd, Ottawa, Ontario K1H 8L6 Canada
| | - Mads Kærn
- Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario K1H 8M5 Canada
| | - Jonathan M. Lee
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario K1H 8M5 Canada
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27
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Mossman JA, Biancani LM, Rand DM. Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks. BMC Genomics 2019; 20:691. [PMID: 31477008 PMCID: PMC6719383 DOI: 10.1186/s12864-019-6061-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/26/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Mitochondria perform many key roles in their eukaryotic hosts, from integrating signaling pathways through to modulating whole organism phenotypes. The > 1 billion years of nuclear and mitochondrial gene co-evolution has necessitated coordinated expression of gene products from both genomes that maintain mitochondrial, and more generally, eukaryotic cellular function. How mitochondrial DNA (mtDNA) variation modifies host fitness has proved a challenging question but has profound implications for evolutionary and medical genetics. In Drosophila, we have previously shown that recently diverged mtDNA haplotypes within-species can have more impact on organismal phenotypes than older, deeply diverged haplotypes from different species. Here, we tested the effects of mtDNA haplotype variation on gene expression in Drosophila under standardized conditions. Using the Drosophila Genetic Reference Panel (DGRP), we constructed a panel of mitonuclear genotypes that consists of factorial variation in nuclear and mtDNA genomes, with mtDNAs originating in D. melanogaster (2x haplotypes) and D. simulans (2x haplotypes). RESULTS We show that mtDNA haplotype variation unequivocally alters nuclear gene expression in both females and males, and mitonuclear interactions are pervasive modifying factors for gene expression. There was appreciable overlap between the sexes for mtDNA-sensitive genes, and considerable transcriptional variation attributed to particular mtDNA contrasts. These genes are generally found in low-connectivity gene co-expression networks, occur in gene clusters along chromosomes, are often flanked by non-coding RNA, and are under-represented among housekeeping genes. Finally, we identify the giant (gt) transcription factor motif as a putative regulatory sequence associated with mtDNA-sensitive genes. CONCLUSIONS There are predictive conditions for nuclear genes that are influenced by mtDNA variation.
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Affiliation(s)
- Jim A Mossman
- Department of Ecology and Evolutionary Biology, Box G, Brown University, Providence, RI, 02912, USA.
| | - Leann M Biancani
- Department of Ecology and Evolutionary Biology, Box G, Brown University, Providence, RI, 02912, USA
- Present Address: Department of Biology, University of Maryland, College Park, MD, 20742, USA
| | - David M Rand
- Department of Ecology and Evolutionary Biology, Box G, Brown University, Providence, RI, 02912, USA.
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28
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Bar H, Schifano ED. Differential variation and expression analysis. Stat (Int Stat Inst) 2019. [DOI: 10.1002/sta4.237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Haim Bar
- Department of Statistics University of Connecticut Storrs CT 06269
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29
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Coulibaly A, Velásquez SY, Sticht C, Figueiredo AS, Himmelhan BS, Schulte J, Sturm T, Centner FS, Schöttler JJ, Thiel M, Lindner HA. AKIRIN1: A Potential New Reference Gene in Human Natural Killer Cells and Granulocytes in Sepsis. Int J Mol Sci 2019; 20:ijms20092290. [PMID: 31075840 PMCID: PMC6539838 DOI: 10.3390/ijms20092290] [Citation(s) in RCA: 5] [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: 03/07/2019] [Revised: 04/27/2019] [Accepted: 05/07/2019] [Indexed: 12/11/2022] Open
Abstract
Timely and reliable distinction of sepsis from non-infectious systemic inflammatory response syndrome (SIRS) supports adequate antimicrobial therapy and saves lives but is clinically challenging. Blood transcriptional profiling promises to deliver insights into the pathomechanisms of SIRS and sepsis and to accelerate the discovery of urgently sought sepsis biomarkers. However, suitable reference genes for normalizing gene expression in these disease conditions are lacking. In addition, variability in blood leukocyte subtype composition complicates gene profile interpretation. Here, we aimed to identify potential reference genes in natural killer (NK) cells and granulocytes from patients with SIRS and sepsis on intensive care unit (ICU) admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells including AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. Initially, no candidate genes could be validated in patient granulocytes. However, we determined highly similar AKIRIN1 expression also in SIRS and sepsis granulocytes and no change by in vitro LPS challenge in granulocytes from healthy donors. Inspection of external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. As a potential new reference gene in NK cells and granulocytes in infectious and inflammatory diseases, AKIRIN1 may improve our pathomechanistic understanding of SIRS and sepsis and help identifying new sepsis biomarkers.
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Affiliation(s)
- Anna Coulibaly
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Sonia Y Velásquez
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Carsten Sticht
- Medical Research Center, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Ana Sofia Figueiredo
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Bianca S Himmelhan
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Jutta Schulte
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Timo Sturm
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Franz-Simon Centner
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Jochen J Schöttler
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Manfred Thiel
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
| | - Holger A Lindner
- Department of Anesthesiology and Surgical Intensive Care Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.
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30
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Mar JC. The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond. Biophys Rev 2019; 11:89-94. [PMID: 30617454 PMCID: PMC6381358 DOI: 10.1007/s12551-018-0494-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.
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Affiliation(s)
- Jessica C Mar
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, QLD, Brisbane, 4072, Australia.
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31
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Cesari G, Algaba E, Moretti S, Nepomuceno JA. An application of the Shapley value to the analysis of co-expression networks. APPLIED NETWORK SCIENCE 2018; 3:35. [PMID: 30839839 PMCID: PMC6214322 DOI: 10.1007/s41109-018-0095-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 08/14/2018] [Indexed: 06/09/2023]
Abstract
We study the problem of identifying relevant genes in a co-expression network using a (cooperative) game theoretic approach. The Shapley value of a cooperative game is used to asses the relevance of each gene in interaction with the others, and to stress the role of nodes in the periphery of a co-expression network for the regulation of complex biological pathways of interest. An application of the method to the analysis of gene expression data from microarrays is presented, as well as a comparison with classical centrality indices. Finally, making further assumptions about the a priori importance of genes, we combine the game theoretic model with other techniques from cluster analysis.
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Affiliation(s)
- Giulia Cesari
- Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Encarnación Algaba
- Department of Applied Mathematics and IMUS, University of Seville, Seville, Spain
| | - Stefano Moretti
- Université Paris-Dauphine, PSL Research University, CNRS, LAMSADE, Paris, 75016 France
| | - Juan A. Nepomuceno
- Department of Computer Languages and Systems, University of Seville, Seville, Spain
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32
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Schlachetzki JCM, Prots I, Tao J, Chun HB, Saijo K, Gosselin D, Winner B, Glass CK, Winkler J. A monocyte gene expression signature in the early clinical course of Parkinson's disease. Sci Rep 2018; 8:10757. [PMID: 30018301 PMCID: PMC6050266 DOI: 10.1038/s41598-018-28986-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 06/26/2018] [Indexed: 11/21/2022] Open
Abstract
Microglia are the main immune cells of the brain and express a large genetic pattern of genes linked to Parkinson's disease risk alleles. Monocytes like microglia are myeloid-lineage cells, raising the questions of the extent to which they share gene expression with microglia and whether they are already altered early in the clinical course of the disease. To decipher a monocytic gene expression signature in Parkinson's disease, we performed RNA-seq and applied the two-sample Kolmogorov-Smirnov test to identify differentially expressed genes between controls and patients with Parkinson's disease and changes in gene expression variability and dysregulation. The gene expression profiles of normal human monocytes and microglia showed a plethora of differentially expressed genes. Additionally, we identified a distinct gene expression pattern of monocytes isolated from Parkinson's disease patients at an early disease stage compared to controls using the Kolmogorov-Smirnov test. Differentially expressed genes included genes involved in immune activation such as HLA-DQB1, MYD88, REL, and TNF-α. Our data suggest that future studies of distinct leukocyte subsets are warranted to identify possible surrogate biomarkers and may lead to the identification of novel interventions early in the disease course.
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Affiliation(s)
- Johannes C M Schlachetzki
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander Universität (FAU) Erlangen-Nürnberg, 91054, Erlangen, Germany.
- Department of Cellular and Molecular Medicine, University of California, San Diego at La Jolla, CA, 92093-0651, USA.
| | - Iryna Prots
- Department of Stem Cell Biology, FAU Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Jenhan Tao
- Department of Cellular and Molecular Medicine, University of California, San Diego at La Jolla, CA, 92093-0651, USA
| | - Hyun B Chun
- Department of Cellular and Molecular Medicine, University of California, San Diego at La Jolla, CA, 92093-0651, USA
| | - Kaoru Saijo
- Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720-3200, USA
| | - David Gosselin
- Department of Cellular and Molecular Medicine, University of California, San Diego at La Jolla, CA, 92093-0651, USA
- Department of Molecular Medicine, Centre de Recherche du CHU de Québec - Université Laval, Québec, G1V 4G2, Canada
| | - Beate Winner
- Department of Stem Cell Biology, FAU Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Christopher K Glass
- Department of Cellular and Molecular Medicine, University of California, San Diego at La Jolla, CA, 92093-0651, USA
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander Universität (FAU) Erlangen-Nürnberg, 91054, Erlangen, Germany.
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33
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Iancu OD, Colville AM, Wilmot B, Searles R, Darakjian P, Zheng C, McWeeney S, Kawane S, Crabbe JC, Metten P, Oberbeck D, Hitzemann R. Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs. Alcohol Clin Exp Res 2018; 42:1454-1465. [PMID: 29786871 DOI: 10.1111/acer.13777] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 05/10/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Transcriptional differences between heterogeneous stock mice and high drinking-in-the-dark selected mouse lines have previously been described based on microarray technology coupled with network-based analysis. The network changes were reproducible in 2 independent selections and largely confined to 2 distinct network modules; in contrast, differential expression appeared more specific to each selected line. This study extends these results by utilizing RNA-Seq technology, allowing evaluation of the relationship between genetic risk and transcription of noncoding RNA (ncRNA); we additionally evaluate sex-specific transcriptional effects of selection. METHODS Naïve mice (N = 24/group and sex) were utilized for gene expression analysis in the ventral striatum; the transcriptome was sequenced with the Illumina HiSeq platform. Differential gene expression and the weighted gene co-expression network analysis were implemented largely as described elsewhere, resulting in the identification of genes that change expression level or (co)variance structure. RESULTS Across both sexes, we detect selection effects on the extracellular matrix and synaptic signaling, although the identity of individual genes varies. A majority of nc RNAs cluster in a single module of relatively low density in both the male and female network. The most strongly differentially expressed transcript in both sexes was Gm22513, a small nuclear RNA with unknown function. Associated with selection, we also found a number of network hubs that change edge strength and connectivity. At the individual gene level, there are many sex-specific effects; however, at the annotation level, results are more concordant. CONCLUSIONS In addition to demonstrating sex-specific effects of selection on the transcriptome, the data point to the involvement of extracellular matrix genes as being associated with the binge drinking phenotype.
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Affiliation(s)
- Ovidiu Dan Iancu
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Alex M Colville
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Beth Wilmot
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Robert Searles
- Integrated Genomics Laboratory, Oregon Health & Science University, Portland, Oregon
| | - Priscila Darakjian
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Christina Zheng
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.,Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Shannon McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Sunita Kawane
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - John C Crabbe
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.,VA Portland Health Care System , Portland, Oregon
| | - Pamela Metten
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.,VA Portland Health Care System , Portland, Oregon
| | - Denesa Oberbeck
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Robert Hitzemann
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
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34
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Igolkina AA, Armoskus C, Newman JRB, Evgrafov OV, McIntyre LM, Nuzhdin SV, Samsonova MG. Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling. Front Mol Neurosci 2018; 11:192. [PMID: 29942251 PMCID: PMC6004421 DOI: 10.3389/fnmol.2018.00192] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/15/2018] [Indexed: 01/02/2023] Open
Abstract
Schizophrenia (SCZ) is a psychiatric disorder of unknown etiology. There is evidence suggesting that aberrations in neurodevelopment are a significant attribute of schizophrenia pathogenesis and progression. To identify biologically relevant molecular abnormalities affecting neurodevelopment in SCZ we used cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells). Here, we tested the hypothesis that variance in gene expression differs between individuals from SCZ and control groups. In CNON cells, variance in gene expression was significantly higher in SCZ samples in comparison with control samples. Variance in gene expression was enriched in five molecular pathways: serine biosynthesis, PI3K-Akt, MAPK, neurotrophin and focal adhesion. More than 14% of variance in disease status was explained within the logistic regression model (C-value = 0.70) by predictors accounting for gene expression in 69 genes from these five pathways. Structural equation modeling (SEM) was applied to explore how the structure of these five pathways was altered between SCZ patients and controls. Four out of five pathways showed differences in the estimated relationships among genes: between KRAS and NF1, and KRAS and SOS1 in the MAPK pathway; between PSPH and SHMT2 in serine biosynthesis; between AKT3 and TSC2 in the PI3K-Akt signaling pathway; and between CRK and RAPGEF1 in the focal adhesion pathway. Our analysis provides evidence that variance in gene expression is an important characteristic of SCZ, and SEM is a promising method for uncovering altered relationships between specific genes thus suggesting affected gene regulation associated with the disease. We identified altered gene-gene interactions in pathways enriched for genes with increased variance in expression in SCZ. These pathways and loci were previously implicated in SCZ, providing further support for the hypothesis that gene expression variance plays important role in the etiology of SCZ.
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Affiliation(s)
- Anna A Igolkina
- Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
| | - Chris Armoskus
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jeremy R B Newman
- Department of Molecular Genetics & Microbiology, Genetics Institute, University of Florida, Gainesville, FL, United States
| | - Oleg V Evgrafov
- Department of Cell Biology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Lauren M McIntyre
- Department of Molecular Genetics & Microbiology, Genetics Institute, University of Florida, Gainesville, FL, United States
| | - Sergey V Nuzhdin
- Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.,Molecular and Computation Biology, University of Southern California, Los Angeles, CA, United States
| | - Maria G Samsonova
- Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
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35
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Shao T, Wang G, Chen H, Xie Y, Jin X, Bai J, Xu J, Li X, Huang J, Jin Y, Li Y. Survey of miRNA-miRNA cooperative regulation principles across cancer types. Brief Bioinform 2018; 20:1621-1638. [DOI: 10.1093/bib/bby038] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 04/05/2018] [Indexed: 02/06/2023] Open
Abstract
AbstractCooperative regulation among multiple microRNAs (miRNAs) is a complex type of posttranscriptional regulation in human; however, the global view of the system-level regulatory principles across cancers is still unclear. Here, we investigated miRNA-miRNA cooperative regulatory landscape across 18 cancer types and summarized the regulatory principles of miRNAs. The miRNA-miRNA cooperative pan-cancer network exhibited a scale-free and modular architecture. Cancer types with similar tissue origins had high similarity in cooperative network structure and expression of cooperative miRNA pairs. In addition, cooperative miRNAs showed divergent properties, including higher expression, greater expression variation and a stronger regulatory strength towards targets and were likely to regulate cancer hallmark-related functions. We found a marked rewiring of miRNA-miRNA cooperation between various cancers and revealed conserved and rewired network miRNA hubs. We further identified the common hubs, cancer-specific hubs and other hubs, which tend to target known anticancer drug targets. Finally, miRNA cooperative modules were found to be associated with patient survival in several cancer types. Our study highlights the potential of pan-cancer miRNA-miRNA cooperative regulation as a novel paradigm that may aid in the discovery of tumorigenesis mechanisms and development of anticancer drugs.
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Affiliation(s)
- Tingting Shao
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Guangjuan Wang
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Hong Chen
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Yunjin Xie
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Xiyun Jin
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Jing Bai
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Jin
- Department of Medical Genetics, Harbin Medical University, Harbin 150081, China
| | - Yongsheng Li
- College of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China
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36
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Aunins TR, Erickson KE, Prasad N, Levy SE, Jones A, Shrestha S, Mastracchio R, Stodieck L, Klaus D, Zea L, Chatterjee A. Spaceflight Modifies Escherichia coli Gene Expression in Response to Antibiotic Exposure and Reveals Role of Oxidative Stress Response. Front Microbiol 2018; 9:310. [PMID: 29615983 PMCID: PMC5865062 DOI: 10.3389/fmicb.2018.00310] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 02/09/2018] [Indexed: 11/13/2022] Open
Abstract
Bacteria grown in space experiments under microgravity conditions have been found to undergo unique physiological responses, ranging from modified cell morphology and growth dynamics to a putative increased tolerance to antibiotics. A common theory for this behavior is the loss of gravity-driven convection processes in the orbital environment, resulting in both reduction of extracellular nutrient availability and the accumulation of bacterial byproducts near the cell. To further characterize the responses, this study investigated the transcriptomic response of Escherichia coli to both microgravity and antibiotic concentration. E. coli was grown aboard International Space Station in the presence of increasing concentrations of the antibiotic gentamicin with identical ground controls conducted on Earth. Here we show that within 49 h of being cultured, E. coli adapted to grow at higher antibiotic concentrations in space compared to Earth, and demonstrated consistent changes in expression of 63 genes in response to an increase in drug concentration in both environments, including specific responses related to oxidative stress and starvation response. Additionally, we find 50 stress-response genes upregulated in response to the microgravity when compared directly to the equivalent concentration in the ground control. We conclude that the increased antibiotic tolerance in microgravity may be attributed not only to diminished transport processes, but also to a resultant antibiotic cross-resistance response conferred by an overlapping effect of stress response genes. Our data suggest that direct stresses of nutrient starvation and acid-shock conveyed by the microgravity environment can incidentally upregulate stress response pathways related to antibiotic stress and in doing so contribute to the increased antibiotic stress tolerance observed for bacteria in space experiments. These results provide insights into the ability of bacteria to adapt under extreme stress conditions and potential strategies to prevent antimicrobial-resistance in space and on Earth.
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Affiliation(s)
- Thomas R Aunins
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States
| | - Keesha E Erickson
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States
| | - Nripesh Prasad
- Genomic Services Laboratory, HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
| | - Shawn E Levy
- Genomic Services Laboratory, HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
| | - Angela Jones
- Genomic Services Laboratory, HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States
| | - Shristi Shrestha
- Genomic Services Laboratory, HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States.,Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, AL, United States
| | - Rick Mastracchio
- Astronaut Office, Johnson Space Center, National Aeronautics and Space Administration, Washington, DC, United States
| | - Louis Stodieck
- BioServe Space Technologies, Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, United States
| | - David Klaus
- Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, United States
| | - Luis Zea
- BioServe Space Technologies, Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, United States
| | - Anushree Chatterjee
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States.,BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States
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A Patient-Specific Stem Cell Model to Investigate the Neurological Phenotype Observed in Ataxia-Telangiectasia. Methods Mol Biol 2018; 1599:391-400. [PMID: 28477134 DOI: 10.1007/978-1-4939-6955-5_28] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The molecular pathogenesis of ataxia-telangiectasia (A-T) is not yet fully understood, and a versatile cellular model is required for in vitro studies. The occurrence of continuous neurogenesis and easy access make the multipotent adult stem cells from the olfactory mucosa within the nasal cavity a potential cellular model. We describe an efficient method to establish neuron-like cells from olfactory mucosa biopsies derived from A-T patients for the purpose of studying the cellular and molecular aspects of this debilitating disease.
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Yang E, Wang G, Yang J, Zhou B, Tian Y, Cai JJ. Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action. Hum Mol Genet 2018; 25:4911-4919. [PMID: 28171656 DOI: 10.1093/hmg/ddw314] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 08/19/2016] [Accepted: 09/12/2016] [Indexed: 11/14/2022] Open
Abstract
Increasing evidence shows that phenotypic variance is genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e. genomic loci associated with gene expression variance, in humans. We discovered that common genetic variants may contribute to increasing gene expression variance via two distinct modes of action—epistasis and destabilization. Specifically, epistasis explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of ‘third-party’ eQTLs that influence the gene expression mean in a fraction, rather than the entire set, of sampled individuals. On the other hand, the destabilization model explains the other three-quarters of evQTLs, caused by mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and estimated the stability of gene expression in single samples using repetitive qRT-PCR assays. The mutations that cause destabilizing evQTLs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common genetic variants work either interactively or independently to shape the variability of gene expression in humans. Our findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variance-centred analytic methods for quantitative trait mapping.
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Affiliation(s)
- Ence Yang
- Department of Veterinary Integrative Biosciences.,Institute for Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Gang Wang
- Department of Veterinary Integrative Biosciences
| | - Jizhou Yang
- Department of Veterinary Integrative Biosciences
| | - Beiyan Zhou
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA.,Department of Immunology, University of Connecticut Health Center, Farmington, CT, USA
| | - Yanan Tian
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - James J Cai
- Department of Veterinary Integrative Biosciences.,Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX, USA
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González-Casacuberta I, Morén C, Juárez-Flores DL, Esteve-Codina A, Sierra C, Catalán-García M, Guitart-Mampel M, Tobías E, Milisenda JC, Pont-Sunyer C, Martí MJ, Cardellach F, Tolosa E, Artuch R, Ezquerra M, Fernández-Santiago R, Garrabou G. Transcriptional alterations in skin fibroblasts from Parkinson's disease patients with parkin mutations. Neurobiol Aging 2018; 65:206-216. [PMID: 29501959 DOI: 10.1016/j.neurobiolaging.2018.01.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 01/23/2018] [Accepted: 01/26/2018] [Indexed: 11/29/2022]
Abstract
Mutations in the parkin gene (PRKN) are the most common cause of autosomal-recessive juvenile Parkinson's disease (PD). PRKN encodes an E3 ubiquitin ligase that is involved in multiple regulatory functions including proteasomal-mediated protein turnover, mitochondrial function, mitophagy, and cell survival. However, the precise molecular events mediated by PRKN mutations in PRKN-associated PD (PRKN-PD) remain unknown. To elucidate the cellular impact of parkin mutations, we performed an RNA sequencing study in skin fibroblasts from PRKN-PD patients carrying different PRKN mutations (n = 4) and genetically unrelated healthy subjects (n = 4). We identified 343 differentially expressed genes in PRKN-PD fibroblasts. Gene ontology and canonical pathway analysis revealed enrichment of differentially expressed genes in processes such as cell adhesion, cell growth, and amino acid and folate metabolism among others. Our findings indicate that PRKN mutations are associated with large global gene expression changes as observed in fibroblasts from PRKN-PD patients and support the view of PD as a systemic disease affecting also non-neural peripheral tissues such as the skin.
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Affiliation(s)
- Ingrid González-Casacuberta
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Constanza Morén
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Diana-Luz Juárez-Flores
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Anna Esteve-Codina
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Cristina Sierra
- Department of Clinical Biochemistry, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Marc Catalán-García
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Mariona Guitart-Mampel
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Ester Tobías
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - José César Milisenda
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Claustre Pont-Sunyer
- Laboratory of Parkison Disease and Other Neurodegenerative Movement Disorders: Clinical and Experimental Research-CELLEX, IDIBAPS, Faculty of Medicine and Health Sciences, UB, Department of Neurology-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - María José Martí
- Laboratory of Parkison Disease and Other Neurodegenerative Movement Disorders: Clinical and Experimental Research-CELLEX, IDIBAPS, Faculty of Medicine and Health Sciences, UB, Department of Neurology-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Francesc Cardellach
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Eduard Tolosa
- Laboratory of Parkison Disease and Other Neurodegenerative Movement Disorders: Clinical and Experimental Research-CELLEX, IDIBAPS, Faculty of Medicine and Health Sciences, UB, Department of Neurology-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Artuch
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; Department of Clinical Biochemistry, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Mario Ezquerra
- Laboratory of Parkison Disease and Other Neurodegenerative Movement Disorders: Clinical and Experimental Research-CELLEX, IDIBAPS, Faculty of Medicine and Health Sciences, UB, Department of Neurology-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain.
| | - Rubén Fernández-Santiago
- Laboratory of Parkison Disease and Other Neurodegenerative Movement Disorders: Clinical and Experimental Research-CELLEX, IDIBAPS, Faculty of Medicine and Health Sciences, UB, Department of Neurology-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain.
| | - Glòria Garrabou
- Laboratory of Muscle Research and Mitochondrial Function-CELLEX, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Faculty of Medicine and Health Sciences, University of Barcelona (UB), Department of Internal Medicine-Hospital Clínic of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain.
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Qi J, Ma L, Wang X, Li Y, Wang K. Observation of significant biomarkers in osteosarcoma via integrating module- identification method with attract. Cancer Biomark 2017; 20:87-93. [PMID: 28759958 DOI: 10.3233/cbm-170144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Osteosarcoma (OS) is the most frequent type of bone malignancy, and this disease has a poor prognosis. We aimed to identify the significant genes related with OS by integrating module-identification method and attract approach. METHODS OS-related microarray data E-GEOD-36001 were obtained from ArrayExpress database, and then protein-protein interaction (PPI) networks of normal and OS were re-weighted by means of spearman correlation coefficient (SCC). Next, maximal cliques were detected from the re-weighted PPI networks using clusteringbased on maximal cliques approach. Afterwards, highly overlapped cliques were merged according to the interconnectivity, following by candidate modules and seed modules identification. Attract proposed by Mar et al. who have suggested that this approach can extract and annotate the gene-sets which can distinguish between disease and control samples, and obtained differences of these gene-sets among the expression profile of samples were defined as attractors. Thus, we applied attract method to extract differential modules from the seed modules, and these obtained differential modules were defined as attractors. The genes in attractors were determined as attractor genes. RESULTS After eliminating the maximal cliques with nodes less than 4, there were 1,884 and 528 maximal cliques in normal and OS PPI networks, which were used to conduct module analysis. A total of 60 and 19 candidate modules were obtained in control and OS PPI networks, respectively. By comparing with normal group, 2 seed module pairs with similar gene composition were found. Significantly, based on attract method, we found that these 2 modules were differential. These 2 modules had the same gene size with 4 genes. Of note, genes CCNB1 and KIF11 simultaneously appeared in these two attractors. CONCLUSIONS We successfully identified two attractors via integrating module-identification method and attract approach, and attractor genes, for example, CCNB1 and KIF11 might play pathophysiological roles in OS development and progression.
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Affiliation(s)
- Jie Qi
- Department of Orthopaedics, Shaanxi Provicial People's Hospital, Xi'an 710068, Shaanxi, China
| | - Liang Ma
- Department of Orthopaedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, China
| | - Xiaogang Wang
- Out-patient Department, Affiliated Tumor Hospital of Xinjiang Medical University, Wuluumuqi 830011, Xinjiang, China
| | - Ying Li
- Beijing Spirallink Medical Research Institute, Beijing 100054, China
| | - Kejun Wang
- Department of Orthopaedics, Jingzhou Central Hospital, Jingzhou 434020, Hubei, China
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de Torrente L, Zimmerman S, Taylor D, Hasegawa Y, Wells CA, Mar JC. pathVar: a new method for pathway-based interpretation of gene expression variability. PeerJ 2017; 5:e3334. [PMID: 28560097 PMCID: PMC5444375 DOI: 10.7717/peerj.3334] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 04/18/2017] [Indexed: 12/31/2022] Open
Abstract
Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show how pathVar can be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.
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Affiliation(s)
- Laurence de Torrente
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States of America
| | - Samuel Zimmerman
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States of America
| | - Deanne Taylor
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.,Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Yu Hasegawa
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States of America
| | - Christine A Wells
- Department of Anatomy and Neuroscience, University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica C Mar
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States of America.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States of America.,University of Queensland, Australian Institute for Bioengineering and Nanotechnology, Brisbane, Queensland, Australia
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42
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SHH Protein Variance in the Limb Bud Is Constrained by Feedback Regulation and Correlates with Altered Digit Patterning. G3-GENES GENOMES GENETICS 2017; 7:851-858. [PMID: 28131983 PMCID: PMC5345715 DOI: 10.1534/g3.116.033019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
mRNA variance has been proposed to play key roles in normal development, population fitness, adaptability, and disease. While variance in gene expression levels may be beneficial for certain cellular processes, for example in a cell’s ability to respond to external stimuli, variance may be detrimental for the development of some organs. In the bilaterally symmetric vertebrate limb buds, the amount of Sonic Hedgehog (SHH) protein present at specific stages of development is essential to ensure proper patterning of this structure. To our surprise, we found that SHH protein variance is present during the first 10 hr of limb development. The variance is virtually eliminated after the first 10 hr of limb development. By examining mutant animals, we determined that the ability of the limb bud apical ectodermal ridge (AER) to respond to SHH protein was required for reducing SHH variance during limb formation. One consequence of the failure to eliminate variance in SHH protein was the presence of polydactyly and an increase in digit length. These data suggest a potential novel mechanism in which alterations in SHH variance during evolution may have driven changes in limb patterning and digit length.
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Tee JY, Sutharsan R, Fan Y, Mackay-Sim A. Cell migration in schizophrenia: Patient-derived cells do not regulate motility in response to extracellular matrix. Mol Cell Neurosci 2017; 80:111-122. [PMID: 28286248 DOI: 10.1016/j.mcn.2017.03.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 01/30/2017] [Accepted: 03/06/2017] [Indexed: 01/22/2023] Open
Abstract
Schizophrenia is a highly heritable psychiatric disorder linked to a large number of risk genes. The function of these genes in disease etiology is not fully understood but pathway analyses of genomic data suggest developmental dysregulation of cellular processes such as neuronal migration and axon guidance. Previous studies of patient-derived olfactory cells show them to be more motile than control-derived cells when grown on a fibronectin substrate, motility that is dependent on focal adhesion kinase signaling. The aim of this study was to investigate whether schizophrenia patient-derived cells are responsive to other extracellular matrix (ECM) proteins that bind integrin receptors. Olfactory neurosphere-derived cells from nine patients and nine matched controls were grown on ECM protein substrates at increasing concentrations and their movement was tracked for 24h using automated high-throughput imaging. Control-derived cells increased their motility as the ECM substrate concentration increased, whereas patient-derived cell motility was little affected by ECM proteins. Patient and control cells had appropriate integrin receptors for these ECM substrates and detected them as shown by increases in focal adhesion number and size in response to ECM proteins, which also induced changes in cell morphology and cytoskeleton. These observations indicate that patient cells failed to translate the detection of ECM proteins into appropriate changes in cell motility. In a sense, patient cells act like a moving car whose accelerator is jammed, moving at the same speed without regard to the external environment. This focuses attention on cell motility regulation rather than speed as key to impairment of neuronal migration in the developing brain in schizophrenia.
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Affiliation(s)
- Jing Yang Tee
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Ratneswary Sutharsan
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Yongjun Fan
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia
| | - Alan Mackay-Sim
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland, Australia.
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Bottje W, Kong BW, Reverter A, Waardenberg AJ, Lassiter K, Hudson NJ. Progesterone signalling in broiler skeletal muscle is associated with divergent feed efficiency. BMC SYSTEMS BIOLOGY 2017; 11:29. [PMID: 28235404 PMCID: PMC5324283 DOI: 10.1186/s12918-017-0396-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 01/16/2017] [Indexed: 01/08/2023]
Abstract
Background We contrast the pectoralis muscle transcriptomes of broilers selected from within a single genetic line expressing divergent feed efficiency (FE) in an effort to improve our understanding of the mechanistic basis of FE. Results Application of a virtual muscle model to gene expression data pointed to a coordinated reduction in slow twitch muscle isoforms of the contractile apparatus (MYH15, TPM3, MYOZ2, TNNI1, MYL2, MYOM3, CSRP3, TNNT2), consistent with diminishment in associated slow machinery (myoglobin and phospholamban) in the high FE animals. These data are in line with the repeated transition from red slow to white fast muscle fibres observed in agricultural species selected on mass and FE. Surprisingly, we found that the expression of 699 genes encoding the broiler mitoproteome is modestly–but significantly–biased towards the high FE group, suggesting a slightly elevated mitochondrial content. This is contrary to expectation based on the slow muscle isoform data and theoretical physiological capacity arguments. Reassuringly, the extreme 40 most DE genes can successfully cluster the 12 individuals into the appropriate FE treatment group. Functional groups contained in this DE gene list include metabolic proteins (including opposing patterns of CA3 and CA4), mitochondrial proteins (CKMT1A), oxidative status (SEPP1, HIG2A) and cholesterol homeostasis (APOA1, INSIG1). We applied a differential network method (Regulatory Impact Factors) whose aim is to use patterns of differential co-expression to detect regulatory molecules transcriptionally rewired between the groups. This analysis clearly points to alterations in progesterone signalling (via the receptor PGR) as the major driver. We show the progesterone receptor localises to the mitochondria in a quail muscle cell line. Conclusions Progesterone is sometimes used in the cattle industry in exogenous hormone mixes that lead to a ~20% increase in FE. Because the progesterone receptor can localise to avian mitochondria, our data continue to point to muscle mitochondrial metabolism as an important component of the phenotypic expression of variation in broiler FE. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0396-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Walter Bottje
- Department of Poultry Science, University of Arkansas, Fayetteville, AR, USA
| | - Byung-Whi Kong
- Department of Poultry Science, University of Arkansas, Fayetteville, AR, USA
| | - Antonio Reverter
- Agriculture, Commonwealth Science and Industrial Research Organisation, 306 Carmody Road, Brisbane, QLD, 4072, Australia
| | - Ashley J Waardenberg
- Agriculture, Commonwealth Science and Industrial Research Organisation, 306 Carmody Road, Brisbane, QLD, 4072, Australia.,Children's Medical Research Institute, University of Sydney, 214 Hawkesbury Road, Westmead, NSW, 2145, Australia
| | - Kentu Lassiter
- Department of Poultry Science, University of Arkansas, Fayetteville, AR, USA
| | - Nicholas J Hudson
- School of Agriculture and Food Science, University of Queensland, Gatton, QLD, 4343, Australia.
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Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution. mSphere 2017; 2:mSphere00009-17. [PMID: 28217741 PMCID: PMC5311112 DOI: 10.1128/msphere.00009-17] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 01/23/2017] [Indexed: 01/22/2023] Open
Abstract
Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.
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Colville AM, Iancu OD, Oberbeck DL, Darakjian P, Zheng CL, Walter NAR, Harrington CA, Searles RP, McWeeney S, Hitzemann RJ. Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice. GENES BRAIN AND BEHAVIOR 2017; 16:462-471. [PMID: 28058793 DOI: 10.1111/gbb.12367] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 12/16/2016] [Accepted: 01/03/2017] [Indexed: 12/15/2022]
Abstract
Previous studies on changes in murine brain gene expression associated with the selection for ethanol preference have used F2 intercross or heterogeneous stock (HS) founders, derived from standard laboratory strains. However, these populations represent only a small proportion of the genetic variance available in Mus musculus. To investigate a wider range of genetic diversity, we selected mice for ethanol preference using an HS derived from the eight strains of the collaborative cross. These HS mice were selectively bred (four generations) for high and low ethanol preference. The nucleus accumbens shell of naive S4 mice was interrogated using RNA sequencing (RNA-Seq). Gene networks were constructed using the weighted gene coexpression network analysis assessing both coexpression and cosplicing. Selection targeted one of the network coexpression modules (greenyellow) that was significantly enriched in genes associated with receptor signaling activity including Chrna7, Grin2a, Htr2a and Oprd1. Connectivity in the module as measured by changes in the hub nodes was significantly reduced in the low preference line. Of particular interest was the observation that selection had marked effects on a large number of cell adhesion molecules, including cadherins and protocadherins. In addition, the coexpression data showed that selection had marked effects on long non-coding RNA hub nodes. Analysis of the cosplicing network data showed a significant effect of selection on a large cluster of Ras GTPase-binding genes including Cdkl5, Cyfip1, Ndrg1, Sod1 and Stxbp5. These data in part support the earlier observation that preference is linked to Ras/Mapk pathways.
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Affiliation(s)
- A M Colville
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - O D Iancu
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - D L Oberbeck
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - P Darakjian
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - C L Zheng
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - N A R Walter
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - C A Harrington
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - R P Searles
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - S McWeeney
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - R J Hitzemann
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA.,Research Service, Portland Veterans Affairs Medical Center, Portland, OR, USA
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Investigation of coordination and order in transcription regulation of innate and adaptive immunity genes in type 1 diabetes. BMC Med Genomics 2017; 10:7. [PMID: 28143555 PMCID: PMC5282641 DOI: 10.1186/s12920-017-0243-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 01/25/2017] [Indexed: 01/19/2023] Open
Abstract
Background Type 1 diabetes (T1D) is an autoimmune disease and extensive evidence has indicated a critical role of both the innate and the adaptive arms of immune system in disease development. To date most clinical trials of immunomodulation therapies failed to show efficacy. A number of gene expression studies of T1D have been carried out. However, a systems analysis of the expression variations of the innate and adaptive immunity gene sets, or their co-expression network structures in cohorts at different disease states or of different disease risks, is not available till now. Methods We utilized data from a large gene expression study that included transcription profiles of control peripheral blood mononuclear cells (PBMC) exposed to plasma of 148 human subjects from four cohorts that included unrelated healthy controls (uHC), recent onset T1D patients (RO-T1D), and healthy siblings of probands that possess high (HRS, High Risk Sibling) or low (LRS, Low Risk Sibling) risk HLA haplotypes. Both weighted and non-weighted co-expression networks were constructed in each cohort separately, and edge weight distribution and the activation of known protein complexes were examined. The co-expression networks of the innate and adaptive immunity genes were further examined in more detail through a number of network measures that included network density, Shannon entropy, h-index, and the scaling exponent γ of degree distribution. Pathway analysis was carried out using CoGA, a tool for detecting significant network structural changes of a gene set. Results Weighted network edge distribution revealed a globally weakened co-expression network induced by the RO-T1D cohort as compared to that by the uHC, suggesting a broad spectrum loss of transcriptional coordination. The two healthy T1D family cohorts (HRS and LRS) induced more active but heterogeneous transcription coordination globally, and among both the innate and the adaptive immunity genes, than the uHC. This finding is consistent with our previous report of these cohorts sharing a heightened innate inflammatory state. The spike-in of IL-1RA to RO-T1D sera improved co-expression network strength of both the innate and the adaptive immunity genes, and enabled a global order recovery in transcription regulation that resulted in significantly increased number of activated protein complexes. Many of the top pathways that showed significant difference in co-expression network structures and order between RO-T1D and uHC have strong links to T1D. Conclusions Network level analysis of the innate and adaptive immunity genes, and the whole genome, revealed striking cohort-dependent differences in co-expression network structural measures, suggesting their potential in cohort classification and disease-relevant pathway identification. The results demonstrated the advantages of systems analysis in defining molecular signatures as well as in predicting targets in future research. Electronic supplementary material The online version of this article (doi:10.1186/s12920-017-0243-8) contains supplementary material, which is available to authorized users.
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Wang K, Vijay V, Fuscoe JC. Stably Expressed Genes Involved in Basic Cellular Functions. PLoS One 2017; 12:e0170813. [PMID: 28125669 PMCID: PMC5268456 DOI: 10.1371/journal.pone.0170813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 01/11/2017] [Indexed: 11/19/2022] Open
Abstract
Stably Expressed Genes (SEGs) whose expression varies within a narrow range may be involved in core cellular processes necessary for basic functions. To identify such genes, we re-analyzed existing RNA-Seq gene expression profiles across 11 organs at 4 developmental stages (from immature to old age) in both sexes of F344 rats (n = 4/group; 320 samples). Expression changes (calculated as the maximum expression / minimum expression for each gene) of >19000 genes across organs, ages, and sexes ranged from 2.35 to >109-fold, with a median of 165-fold. The expression of 278 SEGs was found to vary ≤4-fold and these genes were significantly involved in protein catabolism (proteasome and ubiquitination), RNA transport, protein processing, and the spliceosome. Such stability of expression was further validated in human samples where the expression variability of the homologous human SEGs was significantly lower than that of other genes in the human genome. It was also found that the homologous human SEGs were generally less subject to non-synonymous mutation than other genes, as would be expected of stably expressed genes. We also found that knockout of SEG homologs in mouse models was more likely to cause complete preweaning lethality than non-SEG homologs, corroborating the fundamental roles played by SEGs in biological development. Such stably expressed genes and pathways across life-stages suggest that tight control of these processes is important in basic cellular functions and that perturbation by endogenous (e.g., genetics) or exogenous agents (e.g., drugs, environmental factors) may cause serious adverse effects.
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Affiliation(s)
- Kejian Wang
- Division of Systems Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - Vikrant Vijay
- Division of Systems Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - James C. Fuscoe
- Division of Systems Biology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, United States of America
- * E-mail:
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49
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Ecker S, Chen L, Pancaldi V, Bagger FO, Fernández JM, Carrillo de Santa Pau E, Juan D, Mann AL, Watt S, Casale FP, Sidiropoulos N, Rapin N, Merkel A, Stunnenberg HG, Stegle O, Frontini M, Downes K, Pastinen T, Kuijpers TW, Rico D, Valencia A, Beck S, Soranzo N, Paul DS. Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types. Genome Biol 2017; 18:18. [PMID: 28126036 PMCID: PMC5270224 DOI: 10.1186/s13059-017-1156-8] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 01/17/2017] [Indexed: 12/11/2022] Open
Abstract
Background A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. Results We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. Conclusions Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1156-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Simone Ecker
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain. .,UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
| | - Lu Chen
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK.,Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, Hinxton, UK
| | - Vera Pancaldi
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - Frederik O Bagger
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, Hinxton, UK.,National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - José María Fernández
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - Enrique Carrillo de Santa Pau
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - David Juan
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - Alice L Mann
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
| | - Stephen Watt
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
| | - Francesco Paolo Casale
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Nikos Sidiropoulos
- The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.,Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.,The Bioinformatics Centre, Department of Biology, Faculty of Natural Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Nicolas Rapin
- The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.,Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.,The Bioinformatics Centre, Department of Biology, Faculty of Natural Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Angelika Merkel
- National Center for Genomic Analysis (CNAG), Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Carrer Baldiri i Reixac 4, 08028, Barcelona, Spain
| | | | - Hendrik G Stunnenberg
- Department of Molecular Biology, Faculty of Science, Radboud University, Nijmegen, 6525GA, The Netherlands
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, Hinxton, UK.,National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,British Heart Foundation Centre of Excellence, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, Hinxton, UK.,National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Tomi Pastinen
- Department of Human Genetics, McGill University, 740 Dr. Penfield, Montreal, H3A 0G1, Canada
| | - Taco W Kuijpers
- Blood Cell Research, Sanquin Research and Landsteiner Laboratory, Plesmanlaan 125, Amsterdam, 1066CX, The Netherlands.,Emma Children's Hospital, Academic Medical Center (AMC), University of Amsterdam, Location H7-230, Meibergdreef 9, Amsterdam, 1105AX, The Netherlands
| | - Daniel Rico
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain.,Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Alfonso Valencia
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro 3, 28029, Madrid, Spain
| | - Stephan Beck
- UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK
| | - Nicole Soranzo
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK. .,Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, Hinxton, UK.
| | - Dirk S Paul
- UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK. .,Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK.
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50
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Rahmatallah Y, Zybailov B, Emmert-Streib F, Glazko G. GSAR: Bioconductor package for Gene Set analysis in R. BMC Bioinformatics 2017; 18:61. [PMID: 28118818 PMCID: PMC5259853 DOI: 10.1186/s12859-017-1482-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 01/10/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical methods that either summarize gene-level statistics for a gene set or apply a multivariate statistic that accounts for intergene correlations. Most available methods detect complex departures from the null hypothesis but lack the ability to identify the specific alternative hypothesis that rejects the null. RESULTS GSAR (Gene Set Analysis in R) is an open-source R/Bioconductor software package for gene set analysis (GSA). It implements self-contained multivariate non-parametric statistical methods testing a complex null hypothesis against specific alternatives, such as differences in mean (shift), variance (scale), or net correlation structure. The package also provides a graphical visualization tool, based on the union of two minimum spanning trees, for correlation networks to examine the change in the correlation structures of a gene set between two conditions and highlight influential genes (hubs). CONCLUSIONS Package GSAR provides a set of multivariate non-parametric statistical methods that test a complex null hypothesis against specific alternatives. The methods in package GSAR are applicable to any type of omics data that can be represented in a matrix format. The package, with detailed instructions and examples, is freely available under the GPL (> = 2) license from the Bioconductor web site.
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Affiliation(s)
- Yasir Rahmatallah
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
| | - Boris Zybailov
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Frank Emmert-Streib
- Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, FI-33720, Finland
| | - Galina Glazko
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
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