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Scarpa JR, Elemento O. Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review. Br J Anaesth 2023:S0007-0912(23)00125-3. [PMID: 37055274 DOI: 10.1016/j.bja.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/21/2023] [Accepted: 03/04/2023] [Indexed: 04/15/2023] Open
Abstract
Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.
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Affiliation(s)
- Joseph R Scarpa
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA
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2
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Long KLP, Muroy SE, Sorooshyari SK, Ko MJ, Jaques Y, Sudmant P, Kaufer D. Transcriptomic profiles of stress susceptibility and resilience in the amygdala and hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527777. [PMID: 36798395 PMCID: PMC9934702 DOI: 10.1101/2023.02.08.527777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Abstract
A single, severe episode of stress can bring about myriad responses amongst individuals, ranging from cognitive enhancement to debilitating and persistent anxiety; however, the biological mechanisms that contribute to resilience versus susceptibility to stress are poorly understood. The dentate gyrus (DG) of the hippocampus and the basolateral nucleus of the amygdala (BLA) are key limbic regions that are susceptible to the neural and hormonal effects of stress. Previous work has also shown that these regions contribute to individual variability in stress responses; however, the molecular mechanisms underlying the role of these regions in susceptibility and resilience are unknown. In this study, we profiled the transcriptomic signatures of the DG and BLA of rats with divergent behavioral outcomes after a single, severe stressor. We subjected rats to three hours of immobilization with exposure to fox urine and conducted a behavioral battery one week after stress to identify animals that showed persistent, high anxiety-like behavior. We then conducted bulk RNA sequencing of the DG and BLA from susceptible, resilient, and unexposed control rats. Differential gene expression analyses revealed that the molecular signatures separating each of the three groups were distinct and non-overlapping between the DG and BLA. In the amygdala, key genes associated with insulin and hormonal signaling corresponded with vulnerability. Specifically, Inhbb, Rab31 , and Ncoa3 were upregulated in the amygdala of stress-susceptible animals compared to resilient animals. In the hippocampus, increased expression of Cartpt - which encodes a key neuropeptide involved in reward, reinforcement, and stress responses - was strongly correlated with vulnerability to anxiety-like behavior. However, few other genes distinguished stress-susceptible animals from control animals, while a larger number of genes separated stress-resilient animals from control and stress-susceptible animals. Of these, Rnf112, Tbx19 , and UBALD1 distinguished resilient animals from both control and susceptible animals and were downregulated in resilience, suggesting that an active molecular response in the hippocampus facilitates protection from the long-term consequences of severe stress. These results provide novel insight into the mechanisms that bring about individual variability in the behavioral responses to stress and provide new targets for the advancement of therapies for stress-induced neuropsychiatric disorders.
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Toh H, Bagheri A, Dewey C, Stewart R, Yan L, Clegg D, Thomson JA, Jiang P. A Nile rat transcriptomic landscape across 22 organs by ultra-deep sequencing and comparative RNA-seq pipeline (CRSP). Comput Biol Chem 2023; 102:107795. [PMID: 36436489 DOI: 10.1016/j.compbiolchem.2022.107795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
RNA sequencing (RNA-seq) has been a widely used high-throughput method to characterize transcriptomic dynamics spatiotemporally. However, RNA-seq data analysis pipelines typically depend on either a sequenced genome and/or corresponding reference transcripts. This limitation is a challenge for species lacking sequenced genomes and corresponding reference transcripts. The Nile rat (Arvicanthis niloticus) has two key features - it is daytime active, and it is prone to diet-induced diabetes, which makes it more similar to humans than regular laboratory rodents. However, at the time of this study, neither a Nile rat genome nor a reference transcript set were available, making it technically challenging to perform large-scale RNA-seq based transcriptomic studies. This genome-independent work progressed concurrently with our generation of a Nile rat genome. A well-annotated genome requires several iterations of manually reviewing curated transcripts and takes years to achieve. Here, we developed a Comparative RNA-Seq Pipeline (CRSP), integrating a comparative species strategy independent of a specific sequenced genome or species-matched reference transcripts. We performed benchmarking to validate that our CRSP tool can accurately quantify gene expression levels. In this study, we generated the first ultra-deep (2.3 billion × 2 paired-end) Nile rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial gene expression reference for Nile rat researchers. Importantly, CRSP is not limited to the Nile rat species and can be applied to any species without prior genomic knowledge. To facilitate a general use of CRSP, we also characterized the number of RNA-seq reads required for accurate estimation via simulation studies. CRSP and documents are available at: https://github.com/pjiang1105/CRSP.
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Affiliation(s)
- Huishi Toh
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Atefeh Bagheri
- Department of Biological, Geological and Environmental Sciences, Cleveland State University, Cleveland, OH 44115, USA; Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH 44115, USA
| | - Colin Dewey
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Computer Science, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ron Stewart
- Morgridge Institute for Research, Madison, WI 53706, USA
| | - Lili Yan
- Department of Psychology and Neuroscience Program, Michigan State University, East Lansing, MI, USA
| | - Dennis Clegg
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - James A Thomson
- Morgridge Institute for Research, Madison, WI 53706, USA; Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Peng Jiang
- Department of Biological, Geological and Environmental Sciences, Cleveland State University, Cleveland, OH 44115, USA; Center for Gene Regulation in Health and Disease, Cleveland State University, Cleveland, OH 44115, USA; Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA.
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4
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Christensen KA, Rondeau EB, Sakhrani D, Biagi CA, Johnson H, Joshi J, Flores AM, Leelakumari S, Moore R, Pandoh PK, Withler RE, Beacham TD, Leggatt RA, Tarpey CM, Seeb LW, Seeb JE, Jones SJM, Devlin RH, Koop BF. The pink salmon genome: Uncovering the genomic consequences of a two-year life cycle. PLoS One 2021; 16:e0255752. [PMID: 34919547 PMCID: PMC8682878 DOI: 10.1371/journal.pone.0255752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/02/2021] [Indexed: 12/30/2022] Open
Abstract
Pink salmon (Oncorhynchus gorbuscha) adults are the smallest of the five Pacific salmon native to the western Pacific Ocean. Pink salmon are also the most abundant of these species and account for a large proportion of the commercial value of the salmon fishery worldwide. A two-year life history of pink salmon generates temporally isolated populations that spawn either in even-years or odd-years. To uncover the influence of this genetic isolation, reference genome assemblies were generated for each year-class and whole genome re-sequencing data was collected from salmon of both year-classes. The salmon were sampled from six Canadian rivers and one Japanese river. At multiple centromeres we identified peaks of Fst between year-classes that were millions of base-pairs long. The largest Fst peak was also associated with a million base-pair chromosomal polymorphism found in the odd-year genome near a centromere. These Fst peaks may be the result of a centromere drive or a combination of reduced recombination and genetic drift, and they could influence speciation. Other regions of the genome influenced by odd-year and even-year temporal isolation and tentatively under selection were mostly associated with genes related to immune function, organ development/maintenance, and behaviour.
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Affiliation(s)
- Kris A. Christensen
- West Vancouver, Fisheries and Oceans Canada, British Columbia, Canada
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada
- * E-mail: (KAC); (BFK)
| | - Eric B. Rondeau
- West Vancouver, Fisheries and Oceans Canada, British Columbia, Canada
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada
- Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia, Canada
| | - Dionne Sakhrani
- West Vancouver, Fisheries and Oceans Canada, British Columbia, Canada
| | - Carlo A. Biagi
- West Vancouver, Fisheries and Oceans Canada, British Columbia, Canada
| | - Hollie Johnson
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada
| | - Jay Joshi
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada
| | - Anne-Marie Flores
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada
| | - Sreeja Leelakumari
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Richard Moore
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Pawan K. Pandoh
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Ruth E. Withler
- Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia, Canada
| | - Terry D. Beacham
- Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia, Canada
| | | | - Carolyn M. Tarpey
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America
| | - Lisa W. Seeb
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America
| | - James E. Seeb
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America
| | - Steven J. M. Jones
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Robert H. Devlin
- West Vancouver, Fisheries and Oceans Canada, British Columbia, Canada
| | - Ben F. Koop
- Department of Biology, University of Victoria, Victoria, British Columbia, Canada
- * E-mail: (KAC); (BFK)
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5
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Gui S, Liu Y, Pu J, Song X, Chen X, Chen W, Zhong X, Wang H, Liu L, Xie P. Comparative analysis of hippocampal transcriptional features between major depressive disorder patients and animal models. J Affect Disord 2021; 293:19-28. [PMID: 34161882 DOI: 10.1016/j.jad.2021.06.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/03/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a psychiatric disorder caused by various etiologies. Chronic stress models are used to simulate the heterogeneous pathogenic processes of depression. However, few studies have compared transcriptional features between stress models and MDD patients. METHODS We generated hippocampal transcriptional profiles of the chronic social defeat model by RNA sequencing and downloaded raw data of the same brain region from public databases of the chronic unpredictable mild stress model, the learned helplessness model, and MDD patients. Differential expression and gene co-expression analyses were integrated to compare transcriptional features between stress models and MDD patients. RESULTS Each stress model shared 11.4% to 16.3% of differentially expressed genes with MDD patients. Functional analysis at the gene expression level identified altered ensheathment of neurons in both stress models and MDD patients. At the gene network level, each stress model shared 20.9% to 41.6% of co-expressed genes with MDD patients. Functional analysis based on these genes found that axon guidance signaling is the most significantly enriched pathway that was shared by all stress models and MDD patients. LIMITATIONS This study was limited by considering only a single brain region and a single sex of stress model animals. CONCLUSIONS Our results show that hippocampal transcriptional features of stress models partially overlap with those of MDD patients. The canonical pathways of MDD patients, including ensheathment of neurons, PTEN signaling, and axonal guidance signaling, were shared with all stress models. Our findings provide further clues to understand the molecular mechanisms of depression.
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Affiliation(s)
- Siwen Gui
- College of Biomedical Engineering, Chongqing Medical University, Chongqing 40016, China; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing 40016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yiyun Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xuemian Song
- College of Biomedical Engineering, Chongqing Medical University, Chongqing 40016, China; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing 40016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiaopeng Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Weiyi Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiaogang Zhong
- College of Stomatology and Affiliated Stomatological Hospital of Chongqing Medical University, Chongqing 401147, China
| | - Haiyang Wang
- College of Stomatology and Affiliated Stomatological Hospital of Chongqing Medical University, Chongqing 401147, China
| | - Lanxiang Liu
- Department of Neurology, Yongchuan Hospital, Chongqing Medical University, Chongqing 402160, China
| | - Peng Xie
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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6
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Scarpa JR, Jiang P, Gao VD, Vitaterna MH, Turek FW, Kasarskis A. NREM delta power and AD-relevant tauopathy are associated with shared cortical gene networks. Sci Rep 2021; 11:7797. [PMID: 33833255 PMCID: PMC8032807 DOI: 10.1038/s41598-021-86255-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/27/2020] [Indexed: 02/01/2023] Open
Abstract
Reduced NREM sleep in humans is associated with AD neuropathology. Recent work has demonstrated a reduction in NREM sleep in preclinical AD, pointing to its potential utility as an early marker of dementia. We test the hypothesis that reduced NREM delta power and increased tauopathy are associated with shared underlying cortical molecular networks in preclinical AD. We integrate multi-omics data from two extensive public resources, a human Alzheimer's disease cohort from the Mount Sinai Brain Bank (N = 125) reflecting AD progression and a (C57BL/6J × 129S1/SvImJ) F2 mouse population in which NREM delta power was measured (N = 98). Two cortical gene networks, including a CLOCK-dependent circadian network, are associated with NREM delta power and AD tauopathy progression. These networks were validated in independent mouse and human cohorts. Identifying gene networks related to preclinical AD elucidate possible mechanisms associated with the early disease phase and potential targets to alter the disease course.
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Affiliation(s)
- Joseph R Scarpa
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Peng Jiang
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL, 60208, USA
| | - Vance D Gao
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL, 60208, USA
| | - Martha H Vitaterna
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL, 60208, USA
| | - Fred W Turek
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL, 60208, USA
| | - Andrew Kasarskis
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Winiger EA, Ellingson JM, Morrison CL, Corley RP, Pasman JA, Wall TL, Hopfer CJ, Hewitt JK. Sleep deficits and cannabis use behaviors: an analysis of shared genetics using linkage disequilibrium score regression and polygenic risk prediction. Sleep 2021; 44:zsaa188. [PMID: 32935850 PMCID: PMC7953210 DOI: 10.1093/sleep/zsaa188] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 08/27/2020] [Indexed: 12/17/2022] Open
Abstract
STUDY OBJECTIVES Estimate the genetic relationship of cannabis use with sleep deficits and an eveningness chronotype. METHODS We used linkage disequilibrium score regression (LDSC) to analyze genetic correlations between sleep deficits and cannabis use behaviors. Secondly, we generated sleep deficit polygenic risk score (PRS) and estimated their ability to predict cannabis use behaviors using linear and logistic regression. Summary statistics came from existing genome-wide association studies of European ancestry that were focused on sleep duration, insomnia, chronotype, lifetime cannabis use, and cannabis use disorder (CUD). A target sample for PRS prediction consisted of high-risk participants and participants from twin/family community-based studies (European ancestry; n = 760, male = 64%; mean age = 26.78 years). Target data consisted of self-reported sleep (sleep duration, feeling tired, and taking naps) and cannabis use behaviors (lifetime ever use, number of lifetime uses, past 180-day use, age of first use, and lifetime CUD symptoms). RESULTS Significant genetic correlation between lifetime cannabis use and an eveningness chronotype (rG = 0.24, p < 0.001), as well as between CUD and both short sleep duration (<7 h; rG = 0.23, p = 0.017) and insomnia (rG = 0.20, p = 0.020). Insomnia PRS predicted earlier age of first cannabis use (OR = 0.92, p = 0.036) and increased lifetime CUD symptom count (OR = 1.09, p = 0.012). CONCLUSION Cannabis use is genetically associated with both sleep deficits and an eveningness chronotype, suggesting that there are genes that predispose individuals to both cannabis use and sleep deficits.
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Affiliation(s)
- Evan A Winiger
- Institute for Behavioral Genetics, University of Colorado Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, CO
| | - Jarrod M Ellingson
- Institute for Behavioral Genetics, University of Colorado Boulder, CO
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO
| | - Claire L Morrison
- Institute for Behavioral Genetics, University of Colorado Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, CO
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado Boulder, CO
| | - Joëlle A Pasman
- Behavioural Science Institute, Radboud University Nijmegen, Amsterdam, The Netherlands
| | - Tamara L Wall
- Department of Psychiatry, University of California, San Diego, CA
| | - Christian J Hopfer
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado Boulder, CO
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Yu H, Villanueva N, Bittar T, Arsenault E, Labonté B, Huan T. Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns. Anal Chim Acta 2020; 1136:168-177. [PMID: 33081941 DOI: 10.1016/j.aca.2020.09.051] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/17/2020] [Accepted: 09/25/2020] [Indexed: 12/24/2022]
Abstract
Global profiling of the metabolome and lipidome of specific brain regions is essential to understanding the cellular and molecular mechanisms regulating brain activity. Given the limited amount of starting material, conventional mouse studies comparing brain regions have mainly targeted a set of known metabolites in large brain regions (e.g., cerebrum, cortex). In this work, we developed a multimodal analytical pipeline enabling parallel analyses of metabolomic and lipidomic profiles from anatomically distinct mouse brain regions starting with less than 0.2 mg of protein content. This analytical pipeline is composed of (1) sonication-based tissue homogenization, (2) parallel metabolite and lipid extraction, (3) BCA-based sample normalization, (4) ultrahigh performance liquid chromatography-mass spectrometry-based multimodal metabolome and lipidome profiling, (5) streamlined data processing, and (6) chord plot-based data visualization. We applied this pipeline to the study of four brain regions in males including the amygdala, dorsal hippocampus, nucleus accumbens and ventral tegmental area. With this novel approach, we detected over 5000 metabolic and 6000 lipid features, among which 134 metabolites and 479 lipids were directly confirmed via automated MS2 spectral matching. Interestingly, our analysis identified unique metabolic and lipid profiles in each brain regions. Furthermore, we identified functional relationships amongst metabolic and lipid subclasses, potentially underlying cellular and functional differences across all four brain regions. Overall, our novel workflow generates comprehensive region-specific metabolomic and lipidomic profiles using very low amount of brain sub-regional tissue sample, which could be readily integrated with region-specific genomic, transcriptomic, and proteomic data to reveal novel insights into the molecular mechanisms underlying the activity of distinct brain regions.
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Affiliation(s)
- Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, BC, Canada
| | - Nathaniel Villanueva
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, BC, Canada
| | - Thibault Bittar
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, CERVO Brain Research Center, Québec, G1J 2G3, QC, Canada
| | - Eric Arsenault
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, CERVO Brain Research Center, Québec, G1J 2G3, QC, Canada
| | - Benoit Labonté
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, CERVO Brain Research Center, Québec, G1J 2G3, QC, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, BC, Canada.
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9
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Scarpa JR, Fatma M, Loh YHE, Traore SR, Stefan T, Chen TH, Nestler EJ, Labonté B. Shared Transcriptional Signatures in Major Depressive Disorder and Mouse Chronic Stress Models. Biol Psychiatry 2020; 88:159-168. [PMID: 32169281 PMCID: PMC7740570 DOI: 10.1016/j.biopsych.2019.12.029] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 12/03/2019] [Accepted: 12/24/2019] [Indexed: 01/06/2023]
Abstract
BACKGROUND Most of our knowledge of the biological basis of major depressive disorder (MDD) is derived from studies of chronic stress models in rodents. While these models capture certain aspects of the behavioral and neuroendocrine features of MDD, the extent to which they reproduce the molecular pathology of the human syndrome remains unknown. METHODS We systematically compared transcriptional signatures in two brain regions implicated in depression-medial prefrontal cortex and nucleus accumbens-of humans with MDD and of 3 chronic stress models in mice: chronic variable stress, adult social isolation, and chronic social defeat stress. We used differential expression analysis combined with weighted gene coexpression network analysis to create interspecies gene networks and assess the capacity of each stress paradigm to recapitulate the transcriptional organization of gene networks in human MDD. RESULTS Our results show significant overlap between transcriptional alterations in medial prefrontal cortex and nucleus accumbens in human MDD and the 3 mouse chronic stress models, with each of the chronic stress paradigms capturing distinct aspects of MDD abnormalities. Chronic variable stress and adult social isolation better reproduce differentially expressed genes, while chronic social defeat stress and adult social isolation better reproduce gene networks characteristic of human MDD. We also identified several gene networks and their constituent genes that are most significantly associated with human MDD and mouse stress models. CONCLUSIONS This study demonstrates the ability of 3 chronic stress models in mice to recapitulate distinct aspects of the broad molecular pathology of human MDD, with no one mouse model apparently better than another.
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Affiliation(s)
- Joseph R Scarpa
- Department of Anesthesiology, Weill Cornell Medicine, New York, New York
| | - Mena Fatma
- Department of Psychiatry and Neurosciences, Laval University, Québec, Québec, Canada
| | - Yong-Hwee E Loh
- Norris Medical Library, University of Southern California, Los Angeles, California
| | - Said Romaric Traore
- Department of Psychiatry and Neurosciences, Laval University, Québec, Québec, Canada
| | - Theo Stefan
- Department of Psychiatry and Neurosciences, Laval University, Québec, Québec, Canada
| | - Ting Huei Chen
- Department of Psychiatry and Neurosciences, Laval University, Québec, Québec, Canada
| | - Eric J Nestler
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benoit Labonté
- Department of Psychiatry and Neurosciences, Laval University, Québec, Québec, Canada.
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10
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Finkle JD, Bagheri N. Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights. Bioinformatics 2020; 35:4671-4678. [PMID: 30994899 PMCID: PMC6853664 DOI: 10.1093/bioinformatics/btz256] [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: 11/02/2018] [Revised: 03/07/2019] [Accepted: 04/11/2019] [Indexed: 12/02/2022] Open
Abstract
Motivation To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data. Results To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data. Availability and implementation DiffExPy is available on GitHub (https://github.com/bagherilab/diffexpy). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justin D Finkle
- Interdisciplinary Biological Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Neda Bagheri
- Interdisciplinary Biological Sciences, Northwestern University, Evanston, IL 60208, USA.,Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.,Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA.,Chemistry of Life Processes, Northwestern University, Evanston, IL 60208, USA
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11
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Winiger EA, Huggett SB, Hatoum AS, Friedman NP, Drake CL, Wright KP, Hewitt JK. Onset of regular cannabis use and young adult insomnia: an analysis of shared genetic liability. Sleep 2020; 43:zsz293. [PMID: 31855253 PMCID: PMC7368342 DOI: 10.1093/sleep/zsz293] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/22/2019] [Indexed: 01/13/2023] Open
Abstract
STUDY OBJECTIVES Estimate the genetic and environmental influences on the relationship between onset of regular cannabis use and young adult insomnia. METHODS In a population-based twin cohort of 1882 twins (56% female, mean age = 22.99, SD = 2.97) we explored the genetic/environmental etiology of the relationship between onset of regular cannabis use and insomnia-related outcomes via multivariate twin models. RESULTS Controlling for sex, current depression symptoms, and prior diagnosis of an anxiety or depression disorder, adult twins who reported early onset for regular cannabis use (age 17 or younger) were more likely to have insomnia (β = 0.07, p = 0.024) and insomnia with short sleep on weekdays (β = 0.08, p = 0.003) as young adults. We found significant genetic contributions for the onset of regular cannabis use (a2 = 76%, p < 0.001), insomnia (a2 = 44%, p < 0.001), and insomnia with short sleep on weekdays (a2 = 37%, p < 0.001). We found significant genetic correlations between onset of regular use and both insomnia (rA = 0.20, p = 0.047) and insomnia with short sleep on weekdays (rA = 0.25, p = 0.008) but no significant environmental associations between these traits. CONCLUSIONS We found common genetic liabilities for early onset of regular cannabis use and insomnia, implying pleiotropic influences of genes on both traits.
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Affiliation(s)
- Evan A Winiger
- Institute for Behavioral Genetics, University of Colorado - Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado - Boulder, Boulder, CO
| | - Spencer B Huggett
- Institute for Behavioral Genetics, University of Colorado - Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado - Boulder, Boulder, CO
| | - Alexander S Hatoum
- Institute for Behavioral Genetics, University of Colorado - Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado - Boulder, Boulder, CO
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado - Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado - Boulder, Boulder, CO
| | | | - Kenneth P Wright
- Department of Integrative Physiology, University of Colorado - Boulder, Boulder, CO
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado - Boulder, Boulder, CO
- Department of Psychology and Neuroscience, University of Colorado - Boulder, Boulder, CO
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12
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Abstract
Sleep is a ubiquitous and complex behavior in both its manifestation and regulation. Despite its essential role in maintaining optimal performance, health, and well-being, the genetic mechanisms underlying sleep remain poorly understood. Here, we review the forward genetic approaches undertaken in the last four years to elucidate the genes and gene pathways affecting sleep and its regulation. Despite an increasing number of studies and mining large databases, a coherent picture on “sleep” genes has yet to emerge. We highlight the results achieved by using unbiased genetic screens mainly in humans, mice, and fruit flies with an emphasis on normal sleep and make reference to lessons learned from the circadian field.
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Affiliation(s)
- Maxime Jan
- Centre for Integrative Genomics, University of Lausanne, Lausanne, 1015, Switzerland
| | - Bruce F O'Hara
- Department of Biology, University of Kentucky, Lexington, 40515, USA
| | - Paul Franken
- Centre for Integrative Genomics, University of Lausanne, Lausanne, 1015, Switzerland
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13
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Hu Z, Jiao R, Wang P, Zhu Y, Zhao J, De Jager P, Bennett DA, Jin L, Xiong M. Shared Causal Paths underlying Alzheimer's dementia and Type 2 Diabetes. Sci Rep 2020; 10:4107. [PMID: 32139775 PMCID: PMC7058072 DOI: 10.1038/s41598-020-60682-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 02/03/2020] [Indexed: 12/19/2022] Open
Abstract
Although Alzheimer's disease (AD) is a central nervous system disease and type 2 diabetes MELLITUS (T2DM) is a metabolic disorder, an increasing number of genetic epidemiological studies show clear link between AD and T2DM. The current approach to uncovering the shared pathways between AD and T2DM involves association analysis; however such analyses lack power to discover the mechanisms of the diseases. As an alternative, we developed novel causal inference methods for genetic studies of AD and T2DM and pipelines for systematic multi-omic casual analysis to infer multilevel omics causal networks for the discovery of common paths from genetic variants to AD and T2DM. The proposed pipelines were applied to 448 individuals from the ROSMAP Project. We identified 13 shared causal genes, 16 shared causal pathways between AD and T2DM, and 754 gene expression and 101 gene methylation nodes that were connected to both AD and T2DM in multi-omics causal networks.
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Affiliation(s)
- Zixin Hu
- State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Rong Jiao
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Panpan Wang
- State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Yun Zhu
- Department of Epidemiology, University of Florida, Florida, USA
| | - Jinying Zhao
- Department of Epidemiology, University of Florida, Florida, USA
| | - Phil De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, 10033, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Momiao Xiong
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
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14
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Winiger EA, Huggett SB, Hatoum AS, Stallings MC, Hewitt JK. Onset of regular cannabis use and adult sleep duration: Genetic variation and the implications of a predictive relationship. Drug Alcohol Depend 2019; 204:107517. [PMID: 31698253 PMCID: PMC7053256 DOI: 10.1016/j.drugalcdep.2019.06.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/14/2019] [Accepted: 06/19/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Limited evidence suggests that early cannabis use is associated with sleep problems. Research is needed to understand the developmental impact of early regular cannabis use on later adult sleep duration. METHODS In a sample of 1656 adult twins (56% female, Mean age = 25.79yrs), linear mixed effects models were used to analyze the influence of retrospectively assessed age of onset of regular cannabis use on adult sleep duration controlling for sex, depression, and current substance use. Twin analyses provided genetic and environmental variance estimates as well as insights into the association and potential casual relationships between these traits. RESULTS Earlier age of onset for regular cannabis use was significantly associated with shorter adult sleep duration on both weekdays (β = -0.13, 95% CI = [-0.23, -0.04]) and weekends (β = -0.18, 95% CI = [-0.27, -0.08]). Additive genetics significantly contributed to the onset of regular cannabis use (a2 = 76%, 95% CI = [68, 85]) and adult weekend sleep duration (a2 = 20%, 95% CI = [11, 32]). We found evidence of a significant genetic correlation (rA = -0.31, 95% CI = [-0.41, -0.15]) between these two traits and our best fitting model was consistent with early onset of regular cannabis use causing shorter adult weekend sleep duration (β = -0.11, 95% CI = [-0.18, -0.03]). CONCLUSIONS Our results are consistent with the hypothesis that early onset of regular cannabis use may have a negative impact on adult sleep duration.
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Affiliation(s)
- Evan A. Winiger
- Institute for Behavioral Genetics, University of Colorado, Boulder, East Campus, 1480 30th Street, Boulder, CO 80309, United States,Department of Psychology and Neuroscience, University of Colorado, Boulder, Muenzinger Psychology Building, 1905 Colorado Ave, Boulder, CO 80309, United States,Corresponding author at: Institute for Behavioral Genetics, University of Colorado, Boulder, Boulder, CO 80309, United States. (E.A. Winiger)
| | - Spencer B. Huggett
- Institute for Behavioral Genetics, University of Colorado, Boulder, East Campus, 1480 30th Street, Boulder, CO 80309, United States,Department of Psychology and Neuroscience, University of Colorado, Boulder, Muenzinger Psychology Building, 1905 Colorado Ave, Boulder, CO 80309, United States
| | - Alexander S. Hatoum
- Institute for Behavioral Genetics, University of Colorado, Boulder, East Campus, 1480 30th Street, Boulder, CO 80309, United States,Department of Psychology and Neuroscience, University of Colorado, Boulder, Muenzinger Psychology Building, 1905 Colorado Ave, Boulder, CO 80309, United States
| | - Michael C. Stallings
- Institute for Behavioral Genetics, University of Colorado, Boulder, East Campus, 1480 30th Street, Boulder, CO 80309, United States,Department of Psychology and Neuroscience, University of Colorado, Boulder, Muenzinger Psychology Building, 1905 Colorado Ave, Boulder, CO 80309, United States
| | - John K. Hewitt
- Institute for Behavioral Genetics, University of Colorado, Boulder, East Campus, 1480 30th Street, Boulder, CO 80309, United States,Department of Psychology and Neuroscience, University of Colorado, Boulder, Muenzinger Psychology Building, 1905 Colorado Ave, Boulder, CO 80309, United States
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15
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Molecular programs underlying differences in the expression of mood disorders in males and females. Brain Res 2019; 1719:89-103. [DOI: 10.1016/j.brainres.2019.05.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 04/20/2019] [Accepted: 05/13/2019] [Indexed: 01/13/2023]
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16
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Walter A, Herrold AA, Gallagher VT, Lee R, Scaramuzzo M, Bream T, Seidenberg PH, Vandenbergh D, O'Connor K, Talavage TM, Nauman EA, Slobounov SM, Breiter HC. KIAA0319 Genotype Predicts the Number of Past Concussions in a Division I Football Team: A Pilot Study. J Neurotrauma 2019; 36:1115-1124. [DOI: 10.1089/neu.2017.5622] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Affiliation(s)
- Alexa Walter
- Department of Kinesiology, The Pennsylvania State University, University Park, Pennsylvania
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Amy A. Herrold
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Edward Hines Jr., VA Hospital, Hines, Illinois
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Virginia T. Gallagher
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Rosa Lee
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Madeleine Scaramuzzo
- Athletic Department, The Pennsylvania State University, University Park, Pennsylvania
| | - Tim Bream
- Athletic Department, The Pennsylvania State University, University Park, Pennsylvania
| | - Peter H. Seidenberg
- Athletic Department, The Pennsylvania State University, University Park, Pennsylvania
| | - David Vandenbergh
- Department of Biobehavioral Health, Molecular and Cellular Biosciences Program and Institute for the Neurosciences, The Pennsylvania State University, University Park, Pennsylvania
| | - Kailyn O'Connor
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Thomas M. Talavage
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Eric A. Nauman
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Semyon M. Slobounov
- Department of Kinesiology, The Pennsylvania State University, University Park, Pennsylvania
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
| | - Hans C. Breiter
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
- Concussion Neuroimaging Consortium, Florida State University, Florida; Harvard University, Massachusetts; Michigan State University, Michigan; Northwestern University, Illinois; Ohio State University, Ohio; Purdue University, Indiana; The Pennsylvania State University, Pennsylvania; University of Central Florida, Florida; University of Nebraska, Nebraska
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17
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Parkinson's Disease is Associated with Dysregulations of a Dopamine-Modulated Gene Network Relevant to Sleep and Affective Neurobehaviors in the Striatum. Sci Rep 2019; 9:4808. [PMID: 30886221 PMCID: PMC6423036 DOI: 10.1038/s41598-019-41248-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 02/07/2019] [Indexed: 12/11/2022] Open
Abstract
In addition to the characteristic motor symptoms, Parkinson’s disease (PD) often involves a constellation of sleep and mood symptoms. However, the mechanisms underlying these comorbidities are largely unknown. We have previously reconstructed gene networks in the striatum of a population of (C57BL/6J x A/J) F2 mice and associated the networks to sleep and affective phenotypes, providing a resource for integrated analyses to investigate perturbed sleep and affective functions at the gene network level. Combining this resource with PD-relevant transcriptomic datasets from humans and mice, we identified four networks that showed elevated gene expression in PD patients, including a circadian clock and mitotic network that was altered similarly in mouse models of PD. We then utilized multiple types of omics data from public databases and linked this gene network to postsynaptic dopamine signaling in the striatum, CDK1-modulated transcriptional regulation, and the genetic susceptibility of PD. These findings suggest that dopamine deficiency, a key aspect of PD pathology, perturbs a circadian/mitotic gene network in striatal neurons. Since the normal functions of this network were relevant to sleep and affective behaviors, these findings implicate that dysregulation of functional gene networks may be involved in the emergence of non-motor symptoms in PD. Our analyses present a framework for integrating multi-omics data from diverse sources in mice and humans to reveal insights into comorbid symptoms of complex diseases.
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18
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REM sleep's unique associations with corticosterone regulation, apoptotic pathways, and behavior in chronic stress in mice. Proc Natl Acad Sci U S A 2019; 116:2733-2742. [PMID: 30683720 PMCID: PMC6377491 DOI: 10.1073/pnas.1816456116] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Sleep disturbances are common in stress-related disorders but the nature of these sleep disturbances and how they relate to changes in the stress hormone corticosterone and changes in gene expression remained unknown. Here we demonstrate that in response to chronic mild stress, rapid–eye-movement sleep (REMS), a sleep state involved in emotion regulation and fear conditioning, changed first and more so than any other measured sleep characteristic. Transcriptomic profiles related to REMS continuity and theta oscillations overlapped with those for corticosterone, as well as with predictors for anhedonia, and were enriched for apoptotic pathways. These data highlight the central role of REMS in response to stress and warrant further investigation into REMS’s involvement in stress-related mental health disorders. One of sleep’s putative functions is mediation of adaptation to waking experiences. Chronic stress is a common waking experience; however, which specific aspect of sleep is most responsive, and how sleep changes relate to behavioral disturbances and molecular correlates remain unknown. We quantified sleep, physical, endocrine, and behavioral variables, as well as the brain and blood transcriptome in mice exposed to 9 weeks of unpredictable chronic mild stress (UCMS). Comparing 46 phenotypic variables revealed that rapid–eye-movement sleep (REMS), corticosterone regulation, and coat state were most responsive to UCMS. REMS theta oscillations were enhanced, whereas delta oscillations in non-REMS were unaffected. Transcripts affected by UCMS in the prefrontal cortex, hippocampus, hypothalamus, and blood were associated with inflammatory and immune responses. A machine-learning approach controlling for unspecific UCMS effects identified transcriptomic predictor sets for REMS parameters that were enriched in 193 pathways, including some involved in stem cells, immune response, and apoptosis and survival. Only three pathways were enriched in predictor sets for non-REMS. Transcriptomic predictor sets for variation in REMS continuity and theta activity shared many pathways with corticosterone regulation, in particular pathways implicated in apoptosis and survival, including mitochondrial apoptotic machinery. Predictor sets for REMS and anhedonia shared pathways involved in oxidative stress, cell proliferation, and apoptosis. These data identify REMS as a core and early element of the response to chronic stress, and identify apoptosis and survival pathways as a putative mechanism by which REMS may mediate the response to stressful waking experiences.
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19
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Kelley KW, Nakao-Inoue H, Molofsky AV, Oldham MC. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat Neurosci 2018; 21:1171-1184. [PMID: 30154505 PMCID: PMC6192711 DOI: 10.1038/s41593-018-0216-z] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 07/10/2018] [Indexed: 02/08/2023]
Abstract
It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer's disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource ( http://oldhamlab.ctec.ucsf.edu/ ) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.
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Affiliation(s)
- Kevin W Kelley
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California at San Francisco, San Francisco, CA, USA
| | - Hiromi Nakao-Inoue
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Anna V Molofsky
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA
- Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Michael C Oldham
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA.
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA.
- Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA.
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20
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Diessler S, Jan M, Emmenegger Y, Guex N, Middleton B, Skene DJ, Ibberson M, Burdet F, Götz L, Pagni M, Sankar M, Liechti R, Hor CN, Xenarios I, Franken P. A systems genetics resource and analysis of sleep regulation in the mouse. PLoS Biol 2018; 16:e2005750. [PMID: 30091978 PMCID: PMC6085075 DOI: 10.1371/journal.pbio.2005750] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 07/06/2018] [Indexed: 12/30/2022] Open
Abstract
Sleep is essential for optimal brain functioning and health, but the biological substrates through which sleep delivers these beneficial effects remain largely unknown. We used a systems genetics approach in the BXD genetic reference population (GRP) of mice and assembled a comprehensive experimental knowledge base comprising a deep "sleep-wake" phenome, central and peripheral transcriptomes, and plasma metabolome data, collected under undisturbed baseline conditions and after sleep deprivation (SD). We present analytical tools to interactively interrogate the database, visualize the molecular networks altered by sleep loss, and prioritize candidate genes. We found that a one-time, short disruption of sleep already extensively reshaped the systems genetics landscape by altering 60%-78% of the transcriptomes and the metabolome, with numerous genetic loci affecting the magnitude and direction of change. Systems genetics integrative analyses drawing on all levels of organization imply α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor trafficking and fatty acid turnover as substrates of the negative effects of insufficient sleep. Our analyses demonstrate that genetic heterogeneity and the effects of insufficient sleep itself on the transcriptome and metabolome are far more widespread than previously reported.
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Affiliation(s)
- Shanaz Diessler
- Center for Integrative Genomics, University of Lausanne, Switzerland
| | - Maxime Jan
- Center for Integrative Genomics, University of Lausanne, Switzerland
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Yann Emmenegger
- Center for Integrative Genomics, University of Lausanne, Switzerland
| | - Nicolas Guex
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Benita Middleton
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Debra J. Skene
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Mark Ibberson
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Frederic Burdet
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lou Götz
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Pagni
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Martial Sankar
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Robin Liechti
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Charlotte N. Hor
- Center for Integrative Genomics, University of Lausanne, Switzerland
| | - Ioannis Xenarios
- Center for Integrative Genomics, University of Lausanne, Switzerland
- Vital-IT Systems Biology Division, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Paul Franken
- Center for Integrative Genomics, University of Lausanne, Switzerland
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21
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Scarpa JR, Jiang P, Gao VD, Fitzpatrick K, Millstein J, Olker C, Gotter A, Winrow CJ, Renger JJ, Kasarskis A, Turek FW, Vitaterna MH. Cross-species systems analysis identifies gene networks differentially altered by sleep loss and depression. SCIENCE ADVANCES 2018; 4:eaat1294. [PMID: 30050989 PMCID: PMC6059761 DOI: 10.1126/sciadv.aat1294] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 06/18/2018] [Indexed: 06/08/2023]
Abstract
To understand the transcriptomic organization underlying sleep and affective function, we studied a population of (C57BL/6J × 129S1/SvImJ) F2 mice by measuring 283 affective and sleep phenotypes and profiling gene expression across four brain regions. We identified converging molecular bases for sleep and affective phenotypes at both the single-gene and gene-network levels. Using publicly available transcriptomic datasets collected from sleep-deprived mice and patients with major depressive disorder (MDD), we identified three cortical gene networks altered by the sleep/wake state and depression. The network-level actions of sleep loss and depression were opposite to each other, providing a mechanistic basis for the sleep disruptions commonly observed in depression, as well as the reported acute antidepressant effects of sleep deprivation. We highlight one particular network composed of circadian rhythm regulators and neuronal activity-dependent immediate-early genes. The key upstream driver of this network, Arc, may act as a nexus linking sleep and depression. Our data provide mechanistic insights into the role of sleep in affective function and MDD.
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Affiliation(s)
- Joseph R. Scarpa
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Peng Jiang
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Vance D. Gao
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Karrie Fitzpatrick
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | | | - Christopher Olker
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Anthony Gotter
- Department of Neuroscience, Merck Research Laboratories, West Point, PA 19486, USA
| | | | - John J. Renger
- Department of Neuroscience, Merck Research Laboratories, West Point, PA 19486, USA
| | - Andrew Kasarskis
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Fred W. Turek
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Martha H. Vitaterna
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
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22
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Quantitative phosphoproteomic analysis of the molecular substrates of sleep need. Nature 2018; 558:435-439. [PMID: 29899451 DOI: 10.1038/s41586-018-0218-8] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 05/01/2018] [Indexed: 12/25/2022]
Abstract
Sleep and wake have global effects on brain physiology, from molecular changes1-4 and neuronal activities to synaptic plasticity3-7. Sleep-wake homeostasis is maintained by the generation of a sleep need that accumulates during waking and dissipates during sleep8-11. Here we investigate the molecular basis of sleep need using quantitative phosphoproteomic analysis of the sleep-deprived and Sleepy mouse models of increased sleep need. Sleep deprivation induces cumulative phosphorylation of the brain proteome, which dissipates during sleep. Sleepy mice, owing to a gain-of-function mutation in the Sik3 gene 12 , have a constitutively high sleep need despite increased sleep amount. The brain proteome of these mice exhibits hyperphosphorylation, similar to that seen in the brain of sleep-deprived mice. Comparison of the two models identifies 80 mostly synaptic sleep-need-index phosphoproteins (SNIPPs), in which phosphorylation states closely parallel changes of sleep need. SLEEPY, the mutant SIK3 protein, preferentially associates with and phosphorylates SNIPPs. Inhibition of SIK3 activity reduces phosphorylation of SNIPPs and slow wave activity during non-rapid-eye-movement sleep, the best known measurable index of sleep need, in both Sleepy mice and sleep-deprived wild-type mice. Our results suggest that phosphorylation of SNIPPs accumulates and dissipates in relation to sleep need, and therefore SNIPP phosphorylation is a molecular signature of sleep need. Whereas waking encodes memories by potentiating synapses, sleep consolidates memories and restores synaptic homeostasis by globally downscaling excitatory synapses4-6. Thus, the phosphorylation-dephosphorylation cycle of SNIPPs may represent a major regulatory mechanism that underlies both synaptic homeostasis and sleep-wake homeostasis.
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23
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Wu H, Yesilyurt HG, Yoon J, Terman JR. The MICALs are a Family of F-actin Dismantling Oxidoreductases Conserved from Drosophila to Humans. Sci Rep 2018; 8:937. [PMID: 29343822 PMCID: PMC5772675 DOI: 10.1038/s41598-017-17943-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 11/30/2017] [Indexed: 12/27/2022] Open
Abstract
Cellular form and function – and thus normal development and physiology – are specified via proteins that control the organization and dynamic properties of the actin cytoskeleton. Using the Drosophila model, we have recently identified an unusual actin regulatory enzyme, Mical, which is directly activated by F-actin to selectively post-translationally oxidize and destabilize filaments – regulating numerous cellular behaviors. Mical proteins are also present in mammals, but their actin regulatory properties, including comparisons among different family members, remain poorly defined. We now find that each human MICAL family member, MICAL-1, MICAL-2, and MICAL-3, directly induces F-actin dismantling and controls F-actin-mediated cellular remodeling. Specifically, each human MICAL selectively associates with F-actin, which directly induces MICALs catalytic activity. We also find that each human MICAL uses an NADPH-dependent Redox activity to post-translationally oxidize actin’s methionine (M) M44/M47 residues, directly dismantling filaments and limiting new polymerization. Genetic experiments also demonstrate that each human MICAL drives F-actin disassembly in vivo, reshaping cells and their membranous extensions. Our results go on to reveal that MsrB/SelR reductase enzymes counteract each MICAL’s effect on F-actin in vitro and in vivo. Collectively, our results therefore define the MICALs as an important phylogenetically-conserved family of catalytically-acting F-actin disassembly factors.
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Affiliation(s)
- Heng Wu
- Departments of Neuroscience and Pharmacology, Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Hunkar Gizem Yesilyurt
- Departments of Neuroscience and Pharmacology, Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jimok Yoon
- Departments of Neuroscience and Pharmacology, Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.,Drug Development Center, SK biopharmaceuticals Co. Ltd., Seongnam, 13494, Korea
| | - Jonathan R Terman
- Departments of Neuroscience and Pharmacology, Harold C Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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24
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Labonté B, Engmann O, Purushothaman I, Menard C, Wang J, Tan C, Scarpa JR, Moy G, Loh YHE, Cahill M, Lorsch ZS, Hamilton PJ, Calipari ES, Hodes GE, Issler O, Kronman H, Pfau M, Obradovic ALJ, Dong Y, Neve RL, Russo S, Kazarskis A, Tamminga C, Mechawar N, Turecki G, Zhang B, Shen L, Nestler EJ. Sex-specific transcriptional signatures in human depression. Nat Med 2017; 23:1102-1111. [PMID: 28825715 DOI: 10.1038/nm.4386] [Citation(s) in RCA: 469] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 07/17/2017] [Indexed: 02/08/2023]
Abstract
Major depressive disorder (MDD) is a leading cause of disease burden worldwide. While the incidence, symptoms and treatment of MDD all point toward major sex differences, the molecular mechanisms underlying this sexual dimorphism remain largely unknown. Here, combining differential expression and gene coexpression network analyses, we provide a comprehensive characterization of male and female transcriptional profiles associated with MDD across six brain regions. We overlap our human profiles with those from a mouse model, chronic variable stress, and capitalize on converging pathways to define molecular and physiological mechanisms underlying the expression of stress susceptibility in males and females. Our results show a major rearrangement of transcriptional patterns in MDD, with limited overlap between males and females, an effect seen in both depressed humans and stressed mice. We identify key regulators of sex-specific gene networks underlying MDD and confirm their sex-specific impact as mediators of stress susceptibility. For example, downregulation of the female-specific hub gene Dusp6 in mouse prefrontal cortex mimicked stress susceptibility in females, but not males, by increasing ERK signaling and pyramidal neuron excitability. Such Dusp6 downregulation also recapitulated the transcriptional remodeling that occurs in prefrontal cortex of depressed females. Together our findings reveal marked sexual dimorphism at the transcriptional level in MDD and highlight the importance of studying sex-specific treatments for this disorder.
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Affiliation(s)
- Benoit Labonté
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Olivia Engmann
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Immanuel Purushothaman
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Caroline Menard
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Junshi Wang
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Chunfeng Tan
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Joseph R Scarpa
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gregory Moy
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yong-Hwee E Loh
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Michael Cahill
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zachary S Lorsch
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Peter J Hamilton
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erin S Calipari
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Georgia E Hodes
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Orna Issler
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hope Kronman
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Madeline Pfau
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Aleksandar L J Obradovic
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yan Dong
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rachael L Neve
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Scott Russo
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Andrew Kazarskis
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carol Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Naguib Mechawar
- Department of Psychiatry, McGill University, Montreal, Québec, Canada.,McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, Québec, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, Québec, Canada.,McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, Québec, Canada
| | - Bin Zhang
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Li Shen
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric J Nestler
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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25
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Santiago JA, Bottero V, Potashkin JA. Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology. Front Aging Neurosci 2017; 9:166. [PMID: 28611656 PMCID: PMC5446999 DOI: 10.3389/fnagi.2017.00166] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 05/12/2017] [Indexed: 12/27/2022] Open
Abstract
Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer’s (AD), Parkinson’s (PD) and Huntington’s diseases (HD). We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.
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Affiliation(s)
- Jose A Santiago
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States
| | - Virginie Bottero
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States
| | - Judith A Potashkin
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States
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26
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Gutierrez Najera NA, Resendis-Antonio O, Nicolini H. "Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases. Front Physiol 2017; 8:286. [PMID: 28536537 PMCID: PMC5422874 DOI: 10.3389/fphys.2017.00286] [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: 09/20/2016] [Accepted: 04/19/2017] [Indexed: 01/28/2023] Open
Abstract
The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.
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Affiliation(s)
| | - Osbaldo Resendis-Antonio
- Instituto Nacional de Medicina GenómicaMexico City, Mexico.,Human Systems Biology Laboratory, Coordinación de la Investigación Científica - Red de Apoyo a la Investigación, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, National Autonomous University of Mexico (UNAM)Mexico City, Mexico
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27
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Scarpa JR, Jiang P, Losic B, Readhead B, Gao VD, Dudley JT, Vitaterna MH, Turek FW, Kasarskis A. Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease. PLoS Genet 2016; 12:e1006137. [PMID: 27390852 PMCID: PMC4938493 DOI: 10.1371/journal.pgen.1006137] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 05/31/2016] [Indexed: 12/22/2022] Open
Abstract
Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.
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Affiliation(s)
- Joseph R. Scarpa
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Peng Jiang
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Bojan Losic
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ben Readhead
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Vance D. Gao
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Joel T. Dudley
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Martha H. Vitaterna
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Fred W. Turek
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Andrew Kasarskis
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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28
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Mader EC, Mader ACL. Sleep as spatiotemporal integration of biological processes that evolved to periodically reinforce neurodynamic and metabolic homeostasis: The 2m3d paradigm of sleep. J Neurol Sci 2016; 367:63-80. [PMID: 27423566 DOI: 10.1016/j.jns.2016.05.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 05/12/2016] [Accepted: 05/13/2016] [Indexed: 11/19/2022]
Abstract
Sleep continues to perplex scientists and researchers. Despite decades of sleep research, we still lack a clear understanding of the biological functions and evolution of sleep. In this review, we will examine sleep from a functional and phylogenetic perspective and describe some important conceptual gaps in understanding sleep. Classical theories of the biology and evolution of sleep emphasize sensory activation, energy balance, and metabolic homeostasis. Advances in electrophysiology, functional neuroimaging, and neuroplasticity allow us to view sleep within the framework of neural dynamics. With this paradigm shift, we have come to realize the importance of neurodynamic homeostasis in shaping the biology of sleep. Evidently, animals sleep to achieve neurodynamic and metabolic homeostasis. We are not aware of any framework for understanding sleep where neurodynamic, metabolic, homeostatic, chronophasic, and afferent variables are all taken into account. This motivated us to propose the two-mode three-drive (2m3d) paradigm of sleep. In the 2m3d paradigm, local neurodynamic/metabolic (N/M) processes switch between two modes-m0 and m1-in response to three drives-afferent, chronophasic, and homeostatic. The spatiotemporal integration of local m0/m1 operations gives rise to the global states of sleep and wakefulness. As a framework of evolution, the 2m3d paradigm allows us to view sleep as a robust adaptive strategy that evolved so animals can periodically reinforce neurodynamic and metabolic homeostasis while remaining sensitive to their internal and external environment.
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Affiliation(s)
- Edward Claro Mader
- Louisiana State University Health Sciences Center, Department of Neurology, New Orleans, LA 70112, USA.
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29
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Calderone A, Formenti M, Aprea F, Papa M, Alberghina L, Colangelo AM, Bertolazzi P. Comparing Alzheimer's and Parkinson's diseases networks using graph communities structure. BMC SYSTEMS BIOLOGY 2016; 10:25. [PMID: 26935435 PMCID: PMC4776441 DOI: 10.1186/s12918-016-0270-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 02/16/2016] [Indexed: 01/01/2023]
Abstract
BACKGROUND Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. RESULTS In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). CONCLUSIONS This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.
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Affiliation(s)
- Alberto Calderone
- Institute of Systems Analysis and Computer Science, National Research Council of Italy, Via dei Taurini, 19, Roma, 00185, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Matteo Formenti
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Federica Aprea
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Michele Papa
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, Second University of Naples, Naples, Italy, Via L. Armanni, 5, Napoli, 80138, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Lilia Alberghina
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. .,NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, Piazza della Scienza, 4, Milano, 20126, Italy.
| | - Anna Maria Colangelo
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. .,NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, Piazza della Scienza, 4, Milano, 20126, Italy.
| | - Paola Bertolazzi
- Institute of Systems Analysis and Computer Science, National Research Council of Italy, Via dei Taurini, 19, Roma, 00185, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
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