Functional genetic variants can mediate their regulatory effects through alteration of transcription factor binding

Importance of Transcript Variants in Transcriptome Analyses

RNA sequencing (RNA-Seq) has become a widely adopted technique for studying gene expression. However, conventional RNA-Seq analyses rely on gene expression (GE) values that aggregate all the transcripts produced under a single gene identifier, overlooking the complexity of transcript variants arising from different transcription start sites or alternative splicing. Transcript variants may encode proteins with diverse functional domains, or noncoding RNAs. This study explored the implications of neglecting transcript variants in RNA-Seq analyses. Among the 1334 transcription factor (TF) genes expressed in mouse embryonic stem (ES) or trophoblast stem (TS) cells, 652 were differentially expressed in TS cells based on GE values (365 upregulated and 287 downregulated, ≥absolute 2-fold changes, false discovery rate (FDR) p-value ≤ 0.05). The 365 upregulated genes expressed 883 transcript variants. Further transcript expression (TE) based analyses identified only 174 (<20%) of the 883 transcripts to be upregulated. The remaining 709 transcripts were either downregulated or showed no significant changes. Meanwhile, the 287 downregulated genes expressed 856 transcript variants and only 153 (<20%) of the 856 transcripts were downregulated. The other 703 transcripts were either upregulated or showed no significant change. Additionally, the 682 insignificant TF genes (GE values < absolute 2-fold changes and/or FDR p-values > 0.05) between ES and TS cells expressed 2215 transcript variants. These included 477 (>21%) differentially expressed transcripts (276 upregulated and 201 downregulated, ≥absolute 2-fold changes, FDR p-value ≤ 0.05). Hence, GE based RNA-Seq analyses do not represent accurate expression levels due to divergent transcripts expression from the same gene. Our findings show that by including transcript variants in RNA-Seq analyses, we can generate a precise understanding of a gene's functional and regulatory landscape; ignoring the variants may result in an erroneous interpretation.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

Background

Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories.

Results

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

Conclusions

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants

BACKGROUND

Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.

RESULTS

We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA.

CONCLUSIONS

We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

Methods for Functional Characterization of Genetic Polymorphisms of Non-Coding Regulatory Regions of the Human Genome

Currently, numerous associations between genetic polymorphisms and various diseases have been characterized through the Genome-Wide Association Studies. Majority of the clinically significant polymorphisms are localized in non-coding regions of the genome. While modern bioinformatic resources make it possible to predict molecular mechanisms that explain influence of the non-coding polymorphisms on gene expression, such hypotheses require experimental verification. This review discusses the methods for elucidating molecular mechanisms underlying dependence of the disease pathogenesis on specific genetic variants within the non-coding sequences. A particular focus is on the methods for identification of transcription factors with binding efficiency dependent on polymorphic variations. Despite remarkable progress in bioinformatic resources enabling prediction of the impact of polymorphisms on the disease pathogenesis, there is still the need for experimental approaches to investigate this issue.

Complex regulatory networks influence pluripotent cell state transitions in human iPSCs

Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.

Parkinson's disease risk enhancers in microglia

Genome-wide association studies have identified thousands of single nucleotide polymorphisms that associate with increased risk for Parkinson's disease (PD), but the functions of most of them are unknown. Using assay for transposase-accessible chromatin (ATAC) and H3K27ac chromatin immunoprecipitation (ChIP) sequencing data, we identified 73 regulatory elements in microglia that overlap PD risk SNPs. To determine the target genes of a "risk enhancer" within intron two of SNCA, we used CRISPR-Cas9 to delete the open chromatin region where two PD risk SNPs reside. The loss of the enhancer led to reduced expression of multiple genes including SNCA and the adjacent gene MMRN1. It also led to expression changes of genes involved in glucose metabolism, a process that is known to be altered in PD patients. Our work expands the role of SNCA in PD and provides a connection between PD-associated genetic variants and underlying biology that points to a risk mechanism in microglia.

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10-8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10-7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10-6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10-5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Characterizing genetic variation in the regulation of the ER stress response through computational and cis-eQTL analyses

Misfolded proteins in the endoplasmic reticulum (ER) elicit the ER stress response, a large transcriptional response driven by 3 well-characterized transcription factors (TFs). This transcriptional response is variable across different genetic backgrounds. One mechanism in which genetic variation can lead to transcriptional variability in the ER stress response is through altered binding and activity of the 3 main TFs: XBP1, ATF6, and ATF4. This work attempts to better understand this mechanism by first creating a computational pipeline to identify potential binding sites throughout the human genome. We utilized GTEx data sets to identify cis-eQTLs that fall within predicted TF binding sites (TFBSs). We also utilized the ClinVar database to compare the number of pathogenic vs benign variants at different positions of the binding motifs. Finally, we performed a cis-eQTL analysis on human cell lines experiencing ER stress to identify cis-eQTLs that regulate the variable ER stress response. The majority of these cis-eQTLs are unique to a given condition: control or ER stress. Some of these stress-specific cis-eQTLs fall within putative binding sites of the 3 main ER stress response TFs, providing a potential mechanism by which these cis-eQTLs might be impacting gene expression under ER stress conditions through altered TF binding. This study represents the first cis-eQTL analysis on human samples experiencing ER stress and is a vital step toward identifying the genetic components responsible for the variable ER stress response.

Gene as a dynamical notion: An extensive and integrative vision. Redefining the gene concept, from traditional to genic-interaction, as a new dynamical version

The current concept of gene has been very useful during the 20th and 21st centuries. However, recent advances in molecular biology and bioinformatics, which have further diversified the functional and adaptive profile of genetic information and its integration with cell physiology and environmental response, have contributed to focusing on additional new gene properties besides the traditional definition. Considering the inherent complexity of gene expression, whose adaptive objective must be referred to the Tortoise-Hare model, in which two tendencies converge, one focused on rapid adaptation to achieve survival, and the other that prevents an over-adaptation effect. In this context, a revision of the gene concept must be made, which must include these new mechanisms and approaches. In this paper, we propose a new conception of the idea of a gene that moves from a static and defined version of hereditary information to a dynamic idea that preponderates gene interaction (circumscribed to that established between protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid) and the selection it exerts, as the irreducible element that works in a coordinated way in a genomic regulatory network (GRN).

Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): An expression quantitative trait loci analysis

Meat tenderness is considered the most important trait contributing to beef quality, level of consumer satisfaction, willingness to pay premium prices and industry profit. Genomic selection method would be helpful for genetic improvement of traits with low heritability and that are difficult to measure. The identification of core genes can aid genomic selection for complex traits with low heritability that are difficult to measure. We performed statistical analysis of associations between longissimus dorsi muscle tenderness and gene expression in 20 Hanwoo cattle, using Warner-Bratzler shear force and RNAseq data, respectively. We found a total of 166 core genes, from which six (ASAP1, CAPN5, ELN, SUMF2, TTC8 and MGAT4A) were regulated by 16 cis-expression quantitative trait loci (eQTL) SNPs. Notably, we found that a cis-eQTL SNP of the ELN gene contained an MFZ-1 binding site in its putative promoter region. These findings provide useful information for genomic prediction of beef tenderness in Hanwoo cattle.

Analysis of regulatory network modules in hundreds of human stem cell lines reveals complex epigenetic and genetic factors contribute to pluripotency state differences between subpopulations

Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discovered 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which were highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlied the coordinated expression of genes in the GNMs. Epigenetic analyses revealed that regulatory networks underlying self-renewal and pluripotency have a surprising level of complexity. Genetic analyses identified thousands of regulatory variants that overlapped predicted transcription factor binding sites and were associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network were significantly enriched for regulatory variants with large effects, suggesting that they may play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work captures the coordinated activity of tens of thousands of regulatory elements in hiPSCs and bins these elements into discrete functionally characterized regulatory networks, shows that regulatory elements in pluripotency networks harbor variants with large effects, and provides a rich resource for future pluripotent stem cell research.

Decoding the genetic and epigenetic basis of asthma

Asthma is a complex and heterogeneous chronic inflammatory disease of the airways. Alongside environmental factors, asthma susceptibility is strongly influenced by genetics. Given its high prevalence and our incomplete understanding of the mechanisms underlying disease susceptibility, asthma is frequently studied in genome-wide association studies (GWAS), which have identified thousands of genetic variants associated with asthma development. Virtually all these genetic variants reside in non-coding genomic regions, which has obscured the functional impact of asthma-associated variants and their translation into disease-relevant mechanisms. Recent advances in genomics technology and epigenetics now offer methods to link genetic variants to gene regulatory elements embedded within non-coding regions, which have started to unravel the molecular mechanisms underlying the complex (epi)genetics of asthma. Here, we provide an integrated overview of (epi)genetic variants associated with asthma, focusing on efforts to link these disease associations to biological insight into asthma pathophysiology using state-of-the-art genomics methodology. Finally, we provide a perspective as to how decoding the genetic and epigenetic basis of asthma has the potential to transform clinical management of asthma and to predict the risk of asthma development.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

CRISPR base editing of cis-regulatory elements enables the perturbation of neurodegeneration-linked genes

CRISPR technology has demonstrated broad utility for controlling target gene expression; however, there remains a need for strategies capable of modulating expression via the precise editing of non-coding regulatory elements. Here, we demonstrate that CRISPR base editors, a class of gene-modifying proteins capable of creating single-base substitutions in DNA, can be used to perturb gene expression via their targeted mutagenesis of cis-acting sequences. Using the promoter region of the human huntingtin (HTT) gene as an initial target, we show that editing of the binding site for the transcription factor NF-κB led to a marked reduction in HTT gene expression in base-edited cell populations. We found that these gene perturbations were persistent and specific, as a transcriptome-wide RNA analysis revealed minimal off-target effects resulting from the action of the base editor protein. We further demonstrate that this base-editing platform could influence gene expression in vivo as its delivery to a mouse model of Huntington's disease led to a potent decrease in HTT mRNA in striatal neurons. Finally, to illustrate the applicability of this concept, we target the amyloid precursor protein, showing that multiplex editing of its promoter region significantly perturbed its expression. These findings demonstrate the potential for base editors to regulate target gene expression.

An integrated transcriptome mapping the regulatory network of coding and long non-coding RNAs provides a genomics resource in chickpea

Large-scale transcriptome analysis can provide a systems-level understanding of biological processes. To accelerate functional genomic studies in chickpea, we perform a comprehensive transcriptome analysis to generate full-length transcriptome and expression atlas of protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) from 32 different tissues/organs via deep sequencing. The high-depth RNA-seq dataset reveal expression dynamics and tissue-specificity along with associated biological functions of PCGs and lncRNAs during development. The coexpression network analysis reveal modules associated with a particular tissue or a set of related tissues. The components of transcriptional regulatory networks (TRNs), including transcription factors, their cognate cis-regulatory motifs, and target PCGs/lncRNAs that determine developmental programs of different tissues/organs, are identified. Several candidate tissue-specific and abiotic stress-responsive transcripts associated with quantitative trait loci that determine important agronomic traits are also identified. These results provide an important resource to advance functional/translational genomic and genetic studies during chickpea development and environmental conditions.

A full-length transcriptome and expression atlas of protein-coding genes and long non-coding RNAs is generated in chickpea. Components of transcriptional regulatory networks and candidate tissue-specific transcripts associated with quantitative trait loci are identified.

Thymocyte regulatory variant alters transcription factor binding and protects from type 1 diabetes in infants

We recently mapped a genetic susceptibility locus on chromosome 6q22.33 for type 1 diabetes (T1D) diagnosed below the age of 7 years between the PTPRK and thymocyte-selection-associated (THEMIS) genes. As the thymus plays a central role in shaping the T cell repertoire, we aimed to identify the most likely causal genetic factors behind this association using thymocyte genomic data. In four thymocyte populations, we identified 253 DNA sequence motifs underlying histone modifications. The G insertion allele of rs138300818, associated with protection from diabetes, created thymocyte motifs for multiple histone modifications and thymocyte types. In a parallel approach to identifying variants that alter transcription factor binding motifs, the same variant disrupted a predicted motif for Rfx7, which is abundantly expressed in the thymus. Chromatin state and RNA sequencing data suggested strong transcription overlapping rs138300818 in fetal thymus, while expression quantitative trait locus and chromatin conformation data associate the insertion with lower THEMIS expression. Extending the analysis to other T1D loci further highlighted rs66733041 affecting the GATA3 transcription factor binding in the AFF3 locus. Taken together, our results support a role for thymic THEMIS gene expression and the rs138300818 variant in promoting the development of early-onset T1D.

The role of neurotrophin genes involved in the vulnerability to gambling disorder

Evidence about the involvement of genetic factors in the development of gambling disorder (GD) has been assessed. Among studies assessing heritability and biological vulnerability for GD, neurotrophin (NTF) genes have emerged as promising targets, since a growing literature showed a possible link between NTF and addiction-related disorders. Thus, we aimed to explore the role of NTF genes and GD with the hypothesis that some NTF gene polymorphisms could constitute biological risk factors. The sample included 166 patients with GD and 191 healthy controls. 36 single nucleotide polymorphisms (SNPs) from NTFs (NGF, NGFR, NTRK1, BDNF, NTRK2, NTF3, NTRK3, NTF4, CNTF and CNTFR) were selected and genotyped. Linkage disequilibrium (LD) and haplotype constructions were analyzed, in relationship with the presence of GD. Finally, regulatory elements overlapping the identified SNPs variants associated with GD were searched. The between groups comparisons of allele frequencies indicated that 6 SNPs were potentially associated with GD. Single and multiple-marker analyses showed a strong association between both NTF3 and NTRK2 genes, and GD. The present study supports the involvement of the NTF family in the aetiopathogenesis of GD. An altered cross-regulation of different NTF members signalling pathways might be considered as a biological vulnerability factor for GD.

Genetic variants associated mRNA stability in lung

Background

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).

Results

Here, we presented a computational framework that takes advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3942 genes and 186,132 eQTLs for 4751 genes from 15,122,700 genetic variants for 13,476 genes on the autosomes, respectively. Interestingly, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08405-y.

Genome-wide discovery of DNA polymorphisms via resequencing of chickpea cultivars with contrasting response to drought stress

Drought stress limits plant growth, resulting in a significant yield loss in chickpea. The diversification in genome sequence and selective sweep of allele(s) in different genotypes of a crop plant may play an important role in the determination of agronomic traits, including drought stress response. We investigated, via whole genome resequencing, the DNA polymorphisms between two sets of chickpea genotypes with contrasting drought stress responses (3 drought-sensitive vs. 6 drought-tolerant). In total, 36,406 single nucleotide polymorphisms (SNPs) and 3407 insertions or deletions (InDels) differentiating drought-sensitive and drought-tolerant chickpea genotypes were identified. Interestingly, most (91%) of these DNA polymorphisms were located in chromosomes 1 and 4. The genes harboring DNA polymorphisms in their promoter and/or coding regions and exhibiting differential expression under control and/or drought stress conditions between/within the drought-sensitive and tolerant genotypes were found implicated in the stress response. Furthermore, we identified DNA polymorphisms within the cis-regulatory motifs in the promoter region of abiotic stress-related and QTL-associated genes, which might contribute to the differential expression of the candidate drought-responsive genes. In addition, we revealed the effect of nonsynonymous SNPs on mutational sensitivity and stability of the encoded proteins. Taken together, we identified DNA polymorphisms having relevance in drought stress response and revealed candidate genes to engineer drought tolerance in chickpea.

Discovery of DNA polymorphisms via whole genome resequencing and their functional relevance in salinity stress response in chickpea

Salinity stress is one of the major constraints for plant growth and yield. The salinity stress response of different genotypes of crop plants may largely be governed by DNA polymorphisms. To determine the molecular genetic factors involved in salinity stress tolerance in chickpea, we performed a whole genome resequencing data analysis of three each of salinity-sensitive and salinity-tolerant genotypes. A total of 6173 single nucleotide polymorphisms and 920 insertions and deletions differentiating the chickpea genotypes with contrasting salinity stress responses were identified. Gene ontology analysis revealed the enrichment of functional terms related to stress response and development among the genes harboring DNA polymorphisms in their promoter and/or coding regions. DNA polymorphisms located within the cis-regulatory motifs of the quantitative trait loci (QTL)-associated and abiotic stress related genes were identified, which may influence salinity stress response via modulating binding affinity of the transcription factors. Several genes including QTL-associated and abiotic stress response related genes harboring DNA polymorphisms exhibited differential expression in response to salinity stress especially at the reproductive stage of development in the salinity-tolerant genotype. Furthermore, effects of non-synonymous DNA polymorphisms on mutational sensitivity and structural integrity of the encoded proteins by the candidate QTL-associated and abiotic stress response related genes were revealed. The results suggest that DNA polymorphisms may determine salinity stress response via influencing differential gene expression in genotype and/or stage-dependent manner. Altogether, we provide a high-quality set of DNA polymorphisms and candidate genes that may govern salinity stress tolerance in chickpea.

A single genetic locus controls both expression of DPEP1/CHMP1A and kidney disease development via ferroptosis

Genome-wide association studies (GWAS) have identified loci for kidney disease, but the causal variants, genes, and pathways remain unknown. Here we identify two kidney disease genes Dipeptidase 1 (DPEP1) and Charged Multivesicular Body Protein 1 A (CHMP1A) via the triangulation of kidney function GWAS, human kidney expression, and methylation quantitative trait loci. Using single-cell chromatin accessibility and genome editing, we fine map the region that controls the expression of both genes. Mouse genetic models demonstrate the causal roles of both genes in kidney disease. Cellular studies indicate that both Dpep1 and Chmp1a are important regulators of a single pathway, ferroptosis and lead to kidney disease development via altering cellular iron trafficking.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases

Variants of transcription factor binding sites (TFBSs) constitute an important part of the human genome. Current evidence demonstrates close links between nucleotides within TFBSs and gene expression. There are multiple pathways through which genomic sequences located in TFBSs regulate gene expression, and recent genome-wide association studies have shown the biological significance of TFBS variation in human phenotypes. However, numerous challenges remain in the study of TFBS polymorphisms. This article aims to cover the current state of understanding as regards the genomic features of TFBSs and TFBS variants; the mechanisms through which TFBS variants regulate gene expression; the approaches to studying the effects of nucleotide changes that create or disrupt TFBSs; the challenges faced in studies of TFBS sequence variations; the effects of natural selection on collections of TFBSs; in addition to the insights gained from the study of TFBS alleles related to gout, its associated comorbidities (increased body mass index, chronic kidney disease, diabetes, dyslipidemia, coronary artery disease, ischemic heart disease, hypertension, hyperuricemia, osteoporosis, and prostate cancer), and the treatment responses of patients.

Dual DNA and protein tagging of open chromatin unveils dynamics of epigenomic landscapes in leukemia

The architecture of chromatin regulates eukaryotic cell states by controlling transcription factor access to sites of gene regulation. Here we describe a dual transposase-peroxidase approach, integrative DNA and protein tagging (iDAPT), which detects both DNA (iDAPT-seq) and protein (iDAPT-MS) associated with accessible regions of chromatin. In addition to direct identification of bound transcription factors, iDAPT enables the inference of their gene regulatory networks, protein interactors and regulation of chromatin accessibility. We applied iDAPT to profile the epigenomic consequences of granulocytic differentiation of acute promyelocytic leukemia, yielding previously undescribed mechanistic insights. Our findings demonstrate the power of iDAPT as a platform for studying the dynamic epigenomic landscapes and their transcription factor components associated with biological phenomena and disease.

Significant association of LXRβ (NR1H2) polymorphisms (rs28514894, rs2303044) with type 2 diabetes mellitus and laboratory characteristics

Purpose

To investigate if single-nucleotide polymorphisms (SNPs) in the NR1H2 gene encoding LXRβ contribute to the development of type-2 diabetes mellitus (T2DM) and whether genotypes of two NR1H2 polymorphisms, rs28514894 and rs2303044, are associated with laboratory characteristics of T2DM patients.

Method

A total of 900 subjects (450 T2DM cases and 450 healthy subjects) of Iranian ancestry were genotyped for NR1H2 polymorphisms via ARMS-PCR and PCR-RFLP techniques.

Result

Our findings showed a significant correlation between both polymorphisms and increased risk of T2DM. The haplotype analysis showed an association between the C A haplotype with enhanced risk of T2DM. In T2DM patients, the mean level of HbA1C and BUN significantly differed among carriers of CC and TT genotypes of the rs28514894 polymorphism (P = 0.05 and P < 0.0001, respectively); while in the control group, no significant difference was noticed between subjects with these genotypes. The mean BUN levels also significantly differed among carriers of TC and TT genotypes of this variant in T2DM patients (P = 0.01) and controls (P = 0.04). As for rs2303044 polymorphism, only the mean BUN level significantly differed between GA and GG carriers in T2DM patients (P = 0.006). Compared with CT and TT genotypes, the CC genotype of rs28514894 polymorphism was more frequent in overweight T2DM patients ( 25 < body mass index < 30).

Conclusions

The present research provided the first documents of the correlation of NR1H2 rs28514894 and rs2303044 polymorphisms with susceptibility to T2DM. Replicated case-control studies on larger populations are needed to validate these findings.

Genetic Rescue and the Plight of Ponui Hybrids

Long-term sustainable and resilient populations is a key goal of conservation. How to best achieve this is controversial. There are, for instance, polarized views concerning the fitness and conservation value of hybrid populations founded through multi-origin translocations. A classic example concerns Apteryx (kiwi) in New Zealand. The A. mantelli of Ponui Island constitute a hybrid population where the birds are highly successful in their island habitat. A key dilemma for managers is understanding the reason for this success. Are the hybrid birds of Ponui Island of “no future conservation value” as recently asserted, or do they represent an outstanding example of genetic rescue and an important resource for future translocations? There has been a paradigm shift in scientific thinking concerning hybrids, but the ecological significance of admixed genomes remains difficult to assess. This limits what we can currently predict in conservation science. New understanding from genome science challenges the sufficiency of population genetic models to inform decision making and suggests instead that the contrasting outcomes of hybridization, “outbreeding depression” and “heterosis,” require understanding additional factors that modulate gene and protein expression and how these factors are influenced by the environment. We discuss these findings and the investigations that might help us to better understand the birds of Ponui, inform conservation management of kiwi and provide insight relevant for the future survival of Apteryx.

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

A Cellular Stress Response Induced by the CRISPR-dCas9 Activation System Is Not Heritable Through Cell Divisions

The CRISPR-Cas9 system can be modified to perform "epigenetic editing" by utilizing the catalytically inactive (dead) Cas9 (dCas9) to recruit regulatory proteins to specific genomic locations. In prior studies, epigenetic editing with multimers of the transactivator VP16 and guide RNAs (gRNAs) was found to cause adverse cellular responses. These side effects may confound studies inducing new cellular properties, especially if the cellular responses are maintained through cell divisions-an epigenetic regulatory property. Here, we show how distinct components of this CRISPR-dCas9 activation system, particularly dCas9 with untargeted gRNAs, upregulate genes associated with transcriptional stress, defense response, and regulation of cell death. Our results highlight a previously undetected acute stress response to CRISPR-dCas9 components in human cells, which is transient and not maintained through cell divisions.

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

The role of genomic variation in differentiation is currently not well understood. Here, the genomic variations were determined with the whole-genome sequencing for three pairs of pluripotent stem cell lines and their corresponding BMP4-induced trophoblast cell lines. We identified ∼3,500 single nucleotide variations and ∼4,500 indels by comparing the genome sequenced data between the stem cell lines and the matched BMP4-induced trophoblast cell lines and annotated them by integrating the epigenomic and transcriptomic datasets. Relatively, introns enrich more variations. We found ∼45% (42 genes) of the differentially expressed genes in trophoblasts that associate genomic variations. Six variations, located at transcription factor binding sites where H3K4me3 and H3K27ac are enriched in both H1 and H1_BMP4, were identified. The epigenetic status around the genomic variations in H1 was similar to that in H1_BMP4. This means that the variation-associated gene’s expression change can not be attributed to epigenetic alteration. The genes associated with the six variations were upregulated in differentiation. We inferred that during the differentiation, an increased in the expression level of the MEF2C gene is due to a genomic variation in chromosomes 5: 88179358 A > G, which is at a binding site of TFs KLF16, NR2C2, and ZNF740 to MEF2C. Allele G shows a higher affinity to the TFs in the induced cells. The increased expression of MEF2C leads to an increased expression of TF MEF2C’s target genes, subsequently affecting the differentiation. Although genomic variation should not be a dominant factor in differentiation, we believe that genomic variation could indeed play a role in the differentiation from stem cells into trophoblast.

Genomic organization of the molt-inhibiting hormone gene in the red swamp crayfish Procambarus clarkii and characterization of single-nucleotide polymorphisms associated with growth

Molt-inhibiting hormone (MIH), a neuropeptide synthesized in the eyestalk in crustaceans, is mainly responsible for the molting by negatively controlling the ecdysteroids secretion. Although there are several reports of the isolation and protein sequencing of MIH in the red swamp crayfish, little is known about the nucleotide sequence and gene organization of this neuropeptide, even less about the association of MIH polymorphisms and growth traits. Here, a 1237 bp full-length MIH cDNA was obtained from the crayfish eyestalk, which encodes a putative protein of 106 amino acids, with a 191 bp 5'-UTR and a 728 bp 3'-UTR. The MIH genomic DNA sequence is 4205 bp in length, which includes three exons interrupted by two introns, and a 929 bp 5'-flanking region. Potential transcription initiation site and transcription factor binding sites were identified in the 5'-flanking region, implying a potential role in transcriptional regulation. Seventeen SNPs in the 5'-flanking region and 3'-UTR were identified, and the associations between these SNPs and growth traits were evaluated with a two-stage design. A SNPs g. -12C > G that showed a significant association with body weight was identified. Individuals with GG genotype had a significantly higher body weight than those with CC genotype (43.98 ± 9.82 g vs. 34.27 ± 6.87 g; P ﹤ 0.001), indicating a beneficial effect of the G allele on the growth of red swamp crayfish. The obtained MIH gene, as well as the identified SNPs, may serve as targets for molecular marker-aided selection in growth improvement of the red swamp crayfish in future studies.

High-efficiency genomic editing in Epstein-Barr virus-transformed lymphoblastoid B cells using a single-stranded donor oligonucleotide strategy

While human lymphoblastoid cell lines represent a valuable resource for population genetic studies, they have usually been regarded as difficult for CRISPR-mediated genomic editing because of very inefficient DNA transfection and retroviral or lentiviral transduction in these cells, which becomes a substantial problem when multiple constructs need to be co-expressed. Here we describe a protocol using a single-stranded donor oligonucleotide strategy for 'scarless' editing in lymphoblastoid cells, yielding 12/60 (20%) of clones with homology-directed recombination, when rates of <5-10% are frequently typical for many other cell types. The protocol does not require the use of lentiviruses or stable transfection, permitting lymphoblastoid cell lines to be used for CRISPR-mediated genomic targeting and screening in population genetic studies.

Barriers and Research Priorities for Implementing Precision Medicine

Effective management of complex pancreatic diseases demands precision medicine. While this new approach is technically feasible, many barriers to implementation impede realization of its promised benefits. Barriers exist in the acquisition and utilization of high quality, accurate, specific and quantitative information from both the clinical records and basic sciences. Barriers exist in integrating various domains of knowledge. Barriers exist in translating new insights from the bench to the bedside. Logistical barriers prevent seamless connection and interpretation of the necessary data elements. Barriers also exist with acceptance and application of precision medicine by major institutions and payers. Overcome these barriers will require high-level planning, clarity of vision into short and long term steps, and the commitment of innovative leaders, representing all stakeholders, to work together to reach a common goal.