scholarly journals The non-coding genome in genetic brain disorders: new targets for therapy?

2021 ◽  
Author(s):  
Eva Medico-Salsench ◽  
Faidra Karkala ◽  
Kristina Lanko ◽  
Tahsin Stefan Barakat
Keyword(s):  
Gene Regulation ◽  
Large Scale ◽  
Human Genetics ◽  
Genetic Disorders ◽  
Regulatory Elements ◽  
Genetic Alterations ◽  
Brain Diseases ◽  
Brain Disorders ◽  
Intrinsic Complexity ◽  
Spatio Temporal

Abstract The non-coding genome, consisting of more than 98% of all genetic information in humans and once judged as ‘Junk DNA’, is increasingly moving into the spotlight in the field of human genetics. Non-coding regulatory elements (NCREs) are crucial to ensure correct spatio-temporal gene expression. Technological advancements have allowed to identify NCREs on a large scale, and mechanistic studies have helped to understand the biological mechanisms underlying their function. It is increasingly becoming clear that genetic alterations of NCREs can cause genetic disorders, including brain diseases. In this review, we concisely discuss mechanisms of gene regulation and how to investigate them, and give examples of non-coding alterations of NCREs that give rise to human brain disorders. The cross-talk between basic and clinical studies enhances the understanding of normal and pathological function of NCREs, allowing better interpretation of already existing and novel data. Improved functional annotation of NCREs will not only benefit diagnostics for patients, but might also lead to novel areas of investigations for targeted therapies, applicable to a wide panel of genetic disorders. The intrinsic complexity and precision of the gene regulation process can be turned to the advantage of highly specific treatments. We further discuss this exciting new field of ‘enhancer therapy’ based on recent examples.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

Common genetic variation influencing human white matter microstructure

Science ◽  
2021 ◽  
Vol 372 (6548) ◽  
pp. eabf3736
Author(s):  
Bingxin Zhao ◽  
Tengfei Li ◽  
Yue Yang ◽  
Xifeng Wang ◽  
Tianyou Luo ◽  
...  
Keyword(s):  
White Matter ◽  
Complex Traits ◽  
Large Scale ◽  
Wide Spectrum ◽  
Genetic Correlations ◽  
Regulatory Elements ◽  
Brain Regions ◽  
Brain Diseases ◽  
White Matter Microstructure ◽  
Common Genetic Variants

Brain regions communicate with each other through tracts of myelinated axons, commonly referred to as white matter. We identified common genetic variants influencing white matter microstructure using diffusion magnetic resonance imaging of 43,802 individuals. Genome-wide association analysis identified 109 associated loci, 30 of which were detected by tract-specific functional principal components analysis. A number of loci colocalized with brain diseases, such as glioma and stroke. Genetic correlations were observed between white matter microstructure and 57 complex traits and diseases. Common variants associated with white matter microstructure altered the function of regulatory elements in glial cells, particularly oligodendrocytes. This large-scale tract-specific study advances the understanding of the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

scholarly journals Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance

2021 ◽  
Author(s):  
Soheil Yousefi ◽  
Ruizhi Deng ◽  
Kristina Lanko ◽  
Eva Medico Salsench ◽  
Anita Nikoncuk ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Human Brain ◽  
Brain Development ◽  
Fetal Brain ◽  
Regulatory Elements ◽  
Developmental Genetics ◽  
Brain Disorders ◽  
Genetic Changes ◽  
Human Brain Development ◽  
Depth Analysis

Background: Non-coding regulatory elements (NCREs), such as enhancers, play a crucial role in gene regulation and genetic aberrations in NCREs can lead to human disease, including brain disorders. The human brain is complex and can be affected by numerous disorders; many of these are caused by genetic changes, but a multitude remain currently unexplained. Understanding NCREs acting during brain development has the potential to shed light on previously unrecognised genetic causes of human brain disease. Despite immense community-wide efforts to understand the role of the non-coding genome and NCREs, annotating functional NCREs remains challenging. Results: Here we performed an integrative computational analysis of virtually all currently available epigenome data sets related to human fetal brain. Our in-depth analysis unravels 39,709 differentially active enhancers (DAEs) that show dynamic epigenomic rearrangement during early stages of human brain development, indicating likely biological function. Many of these DAEs are linked to clinically relevant genes, and functional validation of selected DAEs in cell models and zebrafish confirms their role in gene regulation. Compared to enhancers without dynamic epigenomic rearrangement, these regions are subjected to higher sequence constraints in humans, have distinct sequence characteristics and are bound by a distinct transcription factor landscape. DAEs are enriched for GWAS loci for brain related traits and for genetic variation found in individuals with neurodevelopmental disorders, including autism. Conclusion: Our compendium of high-confidence enhancers will assist in deciphering the mechanism behind developmental genetics of the human brain and will be relevant to uncover missing heritability in human genetic brain disorders.

scholarly journals Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Soheil Yousefi ◽  
Ruizhi Deng ◽  
Kristina Lanko ◽  
Eva Medico Salsench ◽  
Anita Nikoncuk ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Human Brain ◽  
Brain Development ◽  
Fetal Brain ◽  
Regulatory Elements ◽  
Developmental Genetics ◽  
Brain Disorders ◽  
Genetic Changes ◽  
Human Brain Development ◽  
Depth Analysis

Abstract Background Non-coding regulatory elements (NCREs), such as enhancers, play a crucial role in gene regulation, and genetic aberrations in NCREs can lead to human disease, including brain disorders. The human brain is a complex organ that is susceptible to numerous disorders; many of these are caused by genetic changes, but a multitude remain currently unexplained. Understanding NCREs acting during brain development has the potential to shed light on previously unrecognized genetic causes of human brain disease. Despite immense community-wide efforts to understand the role of the non-coding genome and NCREs, annotating functional NCREs remains challenging. Methods Here we performed an integrative computational analysis of virtually all currently available epigenome data sets related to human fetal brain. Results Our in-depth analysis unravels 39,709 differentially active enhancers (DAEs) that show dynamic epigenomic rearrangement during early stages of human brain development, indicating likely biological function. Many of these DAEs are linked to clinically relevant genes, and functional validation of selected DAEs in cell models and zebrafish confirms their role in gene regulation. Compared to enhancers without dynamic epigenomic rearrangement, DAEs are subjected to higher sequence constraints in humans, have distinct sequence characteristics and are bound by a distinct transcription factor landscape. DAEs are enriched for GWAS loci for brain-related traits and for genetic variation found in individuals with neurodevelopmental disorders, including autism. Conclusion This compendium of high-confidence enhancers will assist in deciphering the mechanism behind developmental genetics of human brain and will be relevant to uncover missing heritability in human genetic brain disorders.

scholarly journals A sequence-based global map of regulatory activity for deciphering human genetics

2021 ◽  
Author(s):  
Kathleen M Chen ◽  
Aaron K Wong ◽  
Olga G Troyanskaya ◽  
Jian Zhou
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Human Genetics ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Sequence Information ◽  
Regulatory Activity ◽  
Tissue Specific ◽  
Regulatory Architecture ◽  
Sequence Class

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals Epigenomic Analysis of RAD51 ChIP-seq Data Reveals cis-regulatory Elements Associated with Autophagy in Cancer Cell Lines

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2547
Author(s):  
Keunsoo Kang ◽  
Yoonjung Choi ◽  
Hyeonjin Moon ◽  
Chaelin You ◽  
Minjin Seo ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Homologous Recombination ◽  
Cell Lines ◽  
Regulatory Elements ◽  
Binding Motif ◽  
Genome Wide ◽  
E Box ◽  
Autophagy Pathway ◽  
Mcf 7

RAD51 is a recombinase that plays a pivotal role in homologous recombination. Although the role of RAD51 in homologous recombination has been extensively studied, it is unclear whether RAD51 can be involved in gene regulation as a co-factor. In this study, we found evidence that RAD51 may contribute to the regulation of genes involved in the autophagy pathway with E-box proteins such as USF1, USF2, and/or MITF in GM12878, HepG2, K562, and MCF-7 cell lines. The canonical USF binding motif (CACGTG) was significantly identified at RAD51-bound cis-regulatory elements in all four cell lines. In addition, genome-wide USF1, USF2, and/or MITF-binding regions significantly coincided with the RAD51-associated cis-regulatory elements in the same cell line. Interestingly, the promoters of genes associated with the autophagy pathway, such as ATG3 and ATG5, were significantly occupied by RAD51 and regulated by RAD51 in HepG2 and MCF-7 cell lines. Taken together, these results unveiled a novel role of RAD51 and provided evidence that RAD51-associated cis-regulatory elements could possibly be involved in regulating autophagy-related genes with E-box binding proteins.

scholarly journals Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK

Science ◽  
2021 ◽  
pp. eabf2946
Author(s):  
Louis du Plessis ◽  
John T. McCrone ◽  
Alexander E. Zarebski ◽  
Verity Hill ◽  
Christopher Ruis ◽  
...  
Keyword(s):  
Large Scale ◽  
Phylogenetic Analyses ◽  
Transmission Dynamics ◽  
Genetic Lineage ◽  
Size Dependent ◽  
Spatio Temporal ◽  
The Uk ◽  
Lineage Structure ◽  
And Control ◽  
Lineage Diversity

The UK’s COVID-19 epidemic during early 2020 was one of world’s largest and unusually well represented by virus genomic sampling. Here we reveal the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 SARS-CoV-2 genomes, including 26,181 from the UK sampled throughout the country’s first wave of infection. Using large-scale phylogenetic analyses, combined with epidemiological and travel data, we quantify the size, spatio-temporal origins and persistence of genetically-distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown tended to be larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, while lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.

scholarly journals MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford
Keyword(s):  
Genomic Prediction ◽  
Large Scale ◽  
Mixed Model ◽  
Human Genetics ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Statistical Framework ◽  
Effect Model ◽  
Plant Data ◽  
Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

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