Human genetics: GTEx pilot quantifies eQTL variation across tissues and individuals

Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging.Genotype, clinical and demographic data of subjects (aged 60-73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

Enhancer/gene relationships: Need for more reliable genome-wide reference sets

Differences in cells' functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.

Exploring common genetic contributors to neuroprotection from amyloid pathology

Preclinical Alzheimer's disease describes some individuals who harbour Alzheimer's pathologies but are asymptomatic. For this study, we hypothesized that genetic variation may help protect some individuals from Alzheimer's-related neurodegeneration. We therefore conducted a genome-wide association study using 5 891 064 common variants to assess whether genetic variation modifies the association between baseline beta-amyloid, as measured by both cerebrospinal fluid and positron emission tomography, and neurodegeneration defined using MRI measures of hippocampal volume. We combined and jointly analysed genotype, biomarker and neuroimaging data from non-Hispanic white individuals who were enrolled in four longitudinal ageing studies (n = 1065). Using regression models, we examined the interaction between common genetic variants (Minor Allele Frequency >0.01), including APOE-ɛ4 and APOE-ɛ2, and baseline cerebrospinal levels of amyloid (CSF Aβ42) on baseline hippocampal volume and the longitudinal rate of hippocampal atrophy. For targeted replication of top findings, we analysed an independent dataset (n = 808) where amyloid burden was assessed by Pittsburgh Compound B ([11C]-PiB) positron emission tomography. In this study, we found that APOE-ɛ4 modified the association between baseline CSF Aβ42 and hippocampal volume such that APOE-ɛ4 carriers showed more rapid atrophy, particularly in the presence of enhanced amyloidosis. We also identified a novel locus on chromosome 3 that interacted with baseline CSF Aβ42. Minor allele carriers of rs62263260, an expression quantitative trait locus for the SEMA5B gene (P = 1.46 × 10-8; 3:122675327) had more rapid neurodegeneration when amyloid burden was high and slower neurodegeneration when amyloid was low. The rs62263260 × amyloid interaction on longitudinal change in hippocampal volume was replicated in an independent dataset (P = 0.0112) where amyloid burden was assessed by positron emission tomography. In addition to supporting the established interaction between APOE and amyloid on neurodegeneration, our study identifies a novel locus that modifies the association between beta-amyloid and hippocampal atrophy. Annotation results may implicate SEMA5B, a gene involved in synaptic pruning and axonal guidance, as a high-quality candidate for functional confirmation and future mechanistic analysis.

ZEB2 Shapes the Epigenetic Landscape of Atherosclerosis

BACKGROUND

Smooth muscle cells (SMCs) transition into a number of different phenotypes during atherosclerosis, including those that resemble fibroblasts and chondrocytes, and make up the majority of cells in the atherosclerotic plaque. To better understand the epigenetic and transcriptional mechanisms that mediate these cell state changes, and how they relate to risk for coronary artery disease (CAD), we have investigated the causality and function of transcription factors at genome-wide associated loci.

METHODS

We used CRISPR-Cas 9 genome and epigenome editing to identify the causal gene and cells for a complex CAD genome-wide association study signal at 2q22.3. Single-cell epigenetic and transcriptomic profiling in murine models and human coronary artery smooth muscle cells were used to understand the cellular and molecular mechanism by which this CAD risk gene exerts its function.

RESULTS

CRISPR-Cas 9 genome and epigenome editing showed that the complex CAD genetic signals within a genomic region at 2q22.3 lie within smooth muscle long-distance enhancers for ZEB2, a transcription factor extensively studied in the context of epithelial mesenchymal transition in development of cancer. Zeb2 regulates SMC phenotypic transition through chromatin remodeling that obviates accessibility and disrupts both Notch and transforming growth factor β signaling, thus altering the epigenetic trajectory of SMC transitions. SMC-specific loss of Zeb2 resulted in an inability of transitioning SMCs to turn off contractile programing and take on a fibroblast-like phenotype, but accelerated the formation of chondromyocytes, mirroring features of high-risk atherosclerotic plaques in human coronary arteries.

CONCLUSIONS

These studies identify ZEB2 as a new CAD genome-wide association study gene that affects features of plaque vulnerability through direct effects on the epigenome, providing a new therapeutic approach to target vascular disease.

INTS8 is a therapeutic target for intrahepatic cholangiocarcinoma via the integration of bioinformatics analysis and experimental validation

Intrahepatic cholangiocarcinoma (CHOL) remains a rare malignancy, ranking as the leading lethal primary liver cancer worldwide. However, the biological functions of integrator complex subunit 8 (INTS8) in CHOL remain unknown. Thus, this research aimed to explore the potential role of INTS8 as a novel diagnostic or therapeutic target in CHOL. Differentially expressed genes (DEGs) in two Gene Expression Omnibus (GEO) datasets were obtained by the “RRA” package in R software. The “maftools” package was used to visualize the CHOL mutation data from The Cancer Genome Atlas (TCGA) database. The expression of INTS8 was detected by performing quantitative reverse transcription-PCR (qRT-PCR) and immunohistochemistry in cell lines and human samples. The association between subtypes of tumour-infiltrating immune cells (TIICs) and INTS8 expression in CHOL was determined by using CIBERSORT tools. We evaluated the correlations between INTS8 expression and mismatch repair (MMR) genes and DNA methyltransferases (DNMTs) in pan-cancer analysis. Finally, the pan-cancer prognostic signature of INTS8 was identified by univariate analysis. We obtained the mutation landscapes of an RRA gene set in CHOL. The expression of INTS8 was upregulated in CHOL cell lines and human CHOL samples. Furthermore, INTS8 expression was closely associated with a distinct landscape of TIICs, MMR genes, and DNMTs in CHOL. In addition, the high INTS8 expression group presented significantly poor outcomes, including overall survival (OS), disease-specific survival (DSS) and disease-free interval (DFI) (p < 0.05) in pan-cancer. INTS8 contributes to the tumorigenesis and progression of CHOL. Our study highlights the significant role of INTS8 in CHOL and pan-cancers, providing a valuable molecular target for cancer research.

Gene Set Knowledge Discovery with Enrichr

Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2: Searching Enrichr by a single gene or key search term Basic Protocol 3: Preparing raw or processed RNA-seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4: Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5: Using Enrichr in Geneshot Basic Protocol 6: Using Enrichr in ARCHS4 Basic Protocol 7: Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8: Using the Enrichr API Basic Protocol 9: Adding an Enrichr button to a website.

A PDCD1 Role in the Genetic Predisposition to NAFLD-HCC?

Obesity and non-alcoholic fatty liver disease (NAFLD) are contributing to the global rise in deaths from hepatocellular carcinoma (HCC). The pathogenesis of NAFLD-HCC is not well understood. The severity of hepatic steatosis, steatohepatitis and fibrosis are key pathogenic mechanisms, but animal studies suggest altered immune responses are also involved. Genetic studies have so far highlighted a major role of gene variants promoting fat deposition in the liver (PNPLA3 rs738409; TM6SF2 rs58542926). Here, we have considered single-nucleotide polymorphisms (SNPs) in candidate immunoregulatory genes (MICA rs2596542; CD44 rs187115; PDCD1 rs7421861 and rs10204525), in 594 patients with NAFLD and 391 with NAFLD-HCC, from three European centres. Associations between age, body mass index, diabetes, cirrhosis and SNPs with HCC development were explored. PNPLA3 and TM6SF2 SNPs were associated with both progression to cirrhosis and NAFLD-HCC development, while PDCD1 SNPs were specifically associated with NAFLD-HCC risk, regardless of cirrhosis. PDCD1 rs7421861 was independently associated with NAFLD-HCC development, while PDCD1 rs10204525 acquired significance after adjusting for other risks, being most notable in the smaller numbers of women with NAFLD-HCC. The study highlights the potential impact of inter individual variation in immune tolerance induction in patients with NAFLD, both in the presence and absence of cirrhosis.

Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics

Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.

Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits

Background

Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles.

Results

Whereas a relatively small number of molecular phenotypes were significantly heritable (h² > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles.

Conclusions

We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.

Genetic variants in m6A modification genes are associated with colorectal cancer risk

The N6-methyladenosine (m6A) modification plays important regulatory roles in gene expression, cancer occurrence and metastasis. Herein, we aimed to explore the association between genetic variants in m6A modification genes and susceptibility to colorectal cancer. We used logistic regression models to investigate the associations between candidate single-nucleotide polymorphisms (SNPs) in 20 m6A modification genes and colorectal cancer risk. The false discovery rate (FDR) method was used for multiple comparisons. Dual luciferase assays and RNA m6A quantifications were applied to assess transcriptional activity and measure m6A levels, respectively. We found that SND1 rs118049207 was significantly associated with colorectal cancer risk in a Nanjing population (odds ratio (OR) = 1.69, 95% confidence interval (95% CI) = 1.31-2.18, P = 6.51 × 10-6). This finding was further replicated in an independent Beijing population (OR = 1.36, 95% CI = 1.04-1.79, P = 2.41 × 10-2) and in a combined analysis (OR = 1.52, 95% CI = 1.27-1.84, P = 8.75 × 10-6). Stratification and interaction analyses showed that SND1 rs118049207 multiplicatively interacted with the sex and drinking status of the patients to enhance their colorectal cancer risk (P = 1.56 × 10-3 and 1.41 × 10-2, respectively). Furthermore, rs118049207 served as an intronic enhancer on SND1 driven by DMRT3. SND1 mRNA expression was markedly increased in colorectal tumour tissues compared with adjacent normal tissues. The colorimetric m6A quantification strategy revealed that SND1 could alter m6A levels in colorectal cancer cell lines. Our findings indicated that genetic variants in m6A modification genes might be promising predictors of colorectal cancer risk.

Disease-associated KIF3A variants alter gene methylation and expression impacting skin barrier and atopic dermatitis risk

Single nucleotide polymorphisms (SNPs) in the gene encoding kinesin family member 3A, KIF3A, have been associated with atopic dermatitis (AD), a chronic inflammatory skin disorder. We find that KIF3A SNP rs11740584 and rs2299007 risk alleles create cytosine-phosphate-guanine sites, which are highly methylated and result in lower KIF3A expression, and this methylation is associated with increased transepidermal water loss (TEWL) in risk allele carriers. Kif3a^K14∆/∆ mice have increased TEWL, disrupted junctional proteins, and increased susceptibility to develop AD. Thus, KIF3A is required for skin barrier homeostasis whereby decreased KIF3A skin expression causes disrupted skin barrier function and promotes development of AD.

Genetic variants in KIF3A are associated with atopic dermatitis (AD). Here, the authors identify two AD-risk alleles that show high methylation resulting in lower KIF3A expression. Mice with epidermis-specific loss of Kif3a show disrupted skin barrier homeostasis and increased AD susceptibility.

Mechanisms of immunogenicity in colorectal cancer

Background

The immune response in cancer is increasingly understood to be important in determining clinical outcomes, including responses to cancer therapies. New insights into the mechanisms underpinning the immune microenvironment in colorectal cancer are helping to develop the role of immunotherapy and suggest targeted approaches to the management of colorectal cancer at all disease stages.

Method

A literature search was performed in PubMed, MEDLINE and Cochrane Library databases to identify relevant articles. This narrative review discusses the current understanding of the contributors to immunogenicity in colorectal cancer and potential applications for targeted therapies.

Results

Responsiveness to immunotherapy in colorectal cancer is non-uniform. Several factors, both germline and tumour-related, are potential determinants of immunogenicity in colorectal cancer. Current approaches target tumours with high immunogenicity driven by mutations in DNA mismatch repair genes. Recent work suggests a role for therapies that boost the immune response in tumours with low immunogenicity.

Conclusion

With the development of promising therapies to boost the innate immune response, there is significant potential for the expansion of the role of immunotherapy as an adjuvant to surgical treatment in colorectal cancer.

THREE-WAY CLUSTERING OF MULTI-TISSUE MULTI-INDIVIDUAL GENE EXPRESSION DATA USING SEMI-NONNEGATIVE TENSOR DECOMPOSITION

The advent of high-throughput sequencing technologies has led to an increasing availability of large multi-tissue data sets which contain gene expression measurements across different tissues and individuals. In this setting, variation in expression levels arises due to contributions specific to genes, tissues, individuals, and interactions thereof. Classical clustering methods are ill-suited to explore these three-way interactions and struggle to fully extract the insights into transcriptome complexity contained in the data. We propose a new statistical method, called MultiCluster, based on semi-nonnegative tensor decomposition which permits the investigation of transcriptome variation across individuals and tissues simultaneously. We further develop a tensor projection procedure which detects covariate-related genes with high power, demonstrating the advantage of tensor-based methods in incorporating information across similar tissues. Through simulation and application to the GTEx RNA-seq data from 53 human tissues, we show that MultiCluster identifies three-way interactions with high accuracy and robustness.

Molecular windows into the human brain for psychiatric disorders

Delineating the pathophysiology of psychiatric disorders has been extremely challenging but technological advances in recent decades have facilitated a deeper interrogation of molecular processes in the human brain. Initial candidate gene expression studies of the postmortem brain have evolved into genome wide profiling of the transcriptome and the epigenome, a critical regulator of gene expression. Here, we review the potential and challenges of direct molecular characterization of the postmortem human brain, and provide a brief overview of recent transcriptional and epigenetic studies with respect to neuropsychiatric disorders. Such information can now be leveraged and integrated with the growing number of genome-wide association databases to provide a functional context of trait-associated genetic variants linked to psychiatric illnesses and related phenotypes. While it is clear that the field is still developing and challenges remain to be surmounted, these recent advances nevertheless hold tremendous promise for delineating the neurobiological underpinnings of mental diseases and accelerating the development of novel medication strategies.

Expression quantitative trait loci (eQTLs) in human placentas suggest developmental origins of complex diseases

Epidemiologic studies support that at least part of the risk of chronic diseases in childhood and even adulthood may have an in utero origin, and the placenta is a key organ that plays a pivotal role in fetal growth and development. The transcriptomes of 159 human placenta tissues were profiled by genome-wide RNA sequencing (Illumina High-Seq 2500), and linked to fetal genotypes assessed by a high density single nucleotide polymorphism (SNP) genotyping array (Illumina MegaEx). Expression quantitative trait loci (eQTLs) across all annotated transcripts were mapped and examined for enrichment for disease susceptibility loci annotated in the genome-wide association studies (GWAS) catalog. We discovered 3218 cis- and 35 trans-eQTLs at ≤10% false discovery rate in human placentas. Among the 16 439 known disease loci of genome-wide significance, 835 were placental eSNPs (enrichment fold = 1.68, P = 7.41e-42). Stronger effect sizes were observed between GWAS SNPs and gene expression in placentas than what has been reported in other tissues, such as the correlation between asthma risk allele, rs7216389-T and Gasdermin-B (GSDMB) in placenta (r2=27%) versus lung (r2=6%). Finally, our results suggest the placental eQTLs may mediate the function of GWAS loci on postnatal disease susceptibility. Results suggest that transcripts in placenta are under tight genetic control, and that placental gene networks may influence postnatal risk of multiple human diseases lending support for the Developmental Origins of Health and Disease.

Human protein secretory pathway genes are expressed in a tissue-specific pattern to match processing demands of the secretome

Protein secretory pathway in eukaryal cells is responsible for delivering functional secretory proteins. The dysfunction of this pathway causes a range of important human diseases from congenital disorders to cancer. Despite the piled-up knowledge on the molecular biology and biochemistry level, the tissue-specific expression of the secretory pathway genes has not been analyzed on the transcriptome level. Based on the recent RNA-sequencing studies, the largest fraction of tissue-specific transcriptome encodes for the secretome (secretory proteins). Here, the question arises that if the expression levels of the secretory pathway genes have a tissue-specific tuning. In this study, we tackled this question by performing a meta-analysis of the recently published transcriptome data on human tissues. As a result, we detected 68 as called “extreme genes” which show an unusual expression pattern in specific gene families of the secretory pathway. We also inspected the potential functional link between detected extreme genes and the corresponding tissues enriched secretome. As a result, the detected extreme genes showed correlation with the enrichment of the nature and number of specific post-translational modifications in each tissue’s secretome. Our findings conciliate both the housekeeping and tissue-specific nature of the protein secretory pathway, which we attribute to a fine-tuned regulation of defined gene families to support the diversity of secreted proteins and their modifications.

The secretory pathway, an ubiquitous cellular machinery in human cells, is here shown to have tissue-specific characteristics. A research team led by Prof. Jens Nielsen at the Chalmers University of Technology performed a meta-analysis of the gene expressions of the secretory pathway’ component. They detected that even though most of these components are expressed in all tissues, there exist distinct components with fine-tuned expression. They further evaluated the functional link between the detected tuning and the processes that are demanded to make a set of tissue-specific proteins such as endocrine system hormones or their receptors. These findings open up a new avenue to understand the function of the secretion pathway in human tissues with possible applications for improving production of pharmaceutical proteins and getting more insight into the mechanisms underlying diseases such as diabetes that are connected with the endocrine system.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

Transcriptomics resources of human tissues and organs

Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large‐scale transcriptomics studies have analyzed the expression of protein‐coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue‐restricted manner. Here, we review publicly available human transcriptome resources and discuss body‐wide data from independent genome‐wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome‐wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue‐restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome‐scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts.