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Abstract
During the course of evolution the human brain has increased in size and complexity, ultimately these differences are the result of changes at the genetic level. Identifying and characterizing molecular evolution requires an understanding of both the genetic underpinning of the system as well as the comparative genetic tools to identify signatures of selection. This chapter aims to describe our current understanding of the genetics of human brain evolution. Primarily this is the story of the evolution of the human brain since our last common ape ancestor, but where relevant we will also discuss changes that are unique to the primate brain (compared to other mammals) or various other lineages in the evolution of humans more generally. It will focus on genetic changes that both directly affected the development and function of the brain as well as those that have indirectly influenced brain evolution through both prenatal and postnatal environment. This review is not meant to be exhaustive, but rather to begin to construct a general framework for understanding the full array of data being generated.
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Affiliation(s)
- Eric J Vallender
- University of Mississippi Medical Center, Jackson, MS, United States; Tulane National Primate Research Center, Covington, LA, United States.
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Vora B, Wang A, Kosti I, Huang H, Paranjpe I, Woodruff TJ, MacKenzie T, Sirota M. Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth. Front Immunol 2018; 9:993. [PMID: 29867970 PMCID: PMC5954243 DOI: 10.3389/fimmu.2018.00993] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 04/20/2018] [Indexed: 12/27/2022] Open
Abstract
Preterm birth (PTB) is the leading cause of newborn deaths around the world. Spontaneous preterm birth (sPTB) accounts for two-thirds of all PTBs; however, there remains an unmet need of detecting and preventing sPTB. Although the dysregulation of the immune system has been implicated in various studies, small sizes and irreproducibility of results have limited identification of its role. Here, we present a cross-study meta-analysis to evaluate genome-wide differential gene expression signals in sPTB. A comprehensive search of the NIH genomic database for studies related to sPTB with maternal whole blood samples resulted in data from three separate studies consisting of 339 samples. After aggregating and normalizing these transcriptomic datasets and performing a meta-analysis, we identified 210 genes that were differentially expressed in sPTB relative to term birth. These genes were enriched in immune-related pathways, showing upregulation of innate immunity and downregulation of adaptive immunity in women who delivered preterm. An additional analysis found several of these differentially expressed at mid-gestation, suggesting their potential to be clinically relevant biomarkers. Furthermore, a complementary analysis identified 473 genes differentially expressed in preterm cord blood samples. However, these genes demonstrated downregulation of the innate immune system, a stark contrast to findings using maternal blood samples. These immune-related findings were further confirmed by cell deconvolution as well as upstream transcription and cytokine regulation analyses. Overall, this study identified a strong immune signature related to sPTB as well as several potential biomarkers that could be translated to clinical use.
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Affiliation(s)
- Bianca Vora
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Aolin Wang
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States.,Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Idit Kosti
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States.,Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Hongtai Huang
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States.,Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Ishan Paranjpe
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Tracey J Woodruff
- Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Tippi MacKenzie
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States.,Center for Maternal-Fetal Precision Medicine, University of California San Francisco, San Francisco, CA, United States.,Department of Surgery, University of California San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, United States.,Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
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Eidem HR, McGary KL, Capra JA, Abbot P, Rokas A. The transformative potential of an integrative approach to pregnancy. Placenta 2017; 57:204-215. [PMID: 28864013 DOI: 10.1016/j.placenta.2017.07.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 07/08/2017] [Accepted: 07/15/2017] [Indexed: 11/17/2022]
Abstract
BACKGROUND Complex traits typically involve diverse biological pathways and are shaped by numerous genetic and environmental factors. Pregnancy-associated traits and pathologies are further complicated by extensive communication across multiple tissues in two individuals, interactions between two genomes-maternal and fetal-that obscure causal variants and lead to genetic conflict, and rapid evolution of pregnancy-associated traits across mammals and in the human lineage. Given the multi-faceted complexity of human pregnancy, integrative approaches that synthesize diverse data types and analyses harbor tremendous promise to identify the genetic architecture and environmental influences underlying pregnancy-associated traits and pathologies. METHODS We review current research that addresses the extreme complexities of traits and pathologies associated with human pregnancy. RESULTS We find that successful efforts to address the many complexities of pregnancy-associated traits and pathologies often harness the power of many and diverse types of data, including genome-wide association studies, evolutionary analyses, multi-tissue transcriptomic profiles, and environmental conditions. CONCLUSION We propose that understanding of pregnancy and its pathologies will be accelerated by computational platforms that provide easy access to integrated data and analyses. By simplifying the integration of diverse data, such platforms will provide a comprehensive synthesis that transcends many of the inherent challenges present in studies of pregnancy.
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Affiliation(s)
- Haley R Eidem
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Kriston L McGary
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - John A Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Patrick Abbot
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37235, USA.
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