MuscleAtlasExplorer: a web service for studying gene expression in human skeletal muscle.

MuscleAtlasExplorer is a freely available web application that allows for the exploration of gene expression data from human skeletal muscle. It draws from an extensive publicly available dataset of 1654 skeletal muscle expression microarray samples. Detailed, manually curated, patient phenotype data, with information such as age, sex, BMI and disease status, are combined with skeletal muscle gene expression to provide insights into gene function in skeletal muscle. It aims to facilitate easy exploration of the data using powerful data visualization functions, while allowing for sample selection, in-depth inspection and further analysis using external tools. Availability: MuscleAtlasExplorer is available at https://mae.crc.med.lu.se/mae2 (username 'muscle' and password 'explorer' pre-publication).

Publisher Correction: Building an international consortium for tracking coronavirus health status.

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Blood transcriptome profile induced by an efficacious vaccine formulated with salivary antigens from cattle ticks.

Ticks cause massive damage to livestock and vaccines are one sustainable alternative for the acaricide poisons currently heavily used to control infestations. An experimental vaccine adjuvanted with alum and composed by four recombinant salivary antigens mined with reverse vaccinology from a transcriptome of salivary glands from Rhipicephalus microplus ticks was previously shown to present an overall efficacy of 73.2% and cause a significant decrease of tick loads in artificially tick-infested, immunized heifers; this decrease was accompanied by increased levels of antigen-specific IgG1 and IgG2 antibodies, which were boosted during a challenge infestation. In order to gain insights into the systemic effects induced by the vaccine and by the tick challenge we now report the gene expression profile of these hosts' whole-blood leukocytes with RNA-seq followed by functional analyses. These analyses show that vaccination induced unique responses to infestations; genes upregulated in the comparisons were enriched for processes associated with chemotaxis, cell adhesion, T-cell responses and wound repair. Blood transcriptional modules were enriched for activation of dendritic cells, cell cycle, phosphatidylinositol signaling, and platelets. Together, the results indicate that by neutralizing the tick's salivary mediators of parasitism with vaccine-induced antibodies, the bovine host is able to mount normal homeostatic responses that hinder tick attachment and haematophagy and that the tick otherwise suppresses with its saliva.

Aberration hubs in protein interaction networks highlight actionable targets in cancer.

Despite efforts for extensive molecular characterization of cancer patients, such as the international cancer genome consortium (ICGC) and the cancer genome atlas (TCGA), the heterogeneous nature of cancer and our limited knowledge of the contextual function of proteins have complicated the identification of targetable genes. Here, we present Aberration Hub Analysis for Cancer (AbHAC) as a novel integrative approach to pinpoint aberration hubs, i.e. individual proteins that interact extensively with genes that show aberrant mutation or expression. Our analysis of the breast cancer data of the TCGA and the renal cancer data from the ICGC shows that aberration hubs are involved in relevant cancer pathways, including factors promoting cell cycle and DNA replication in basal-like breast tumors, and Src kinase and VEGF signaling in renal carcinoma. Moreover, our analysis uncovers novel functionally relevant and actionable targets, among which we have experimentally validated abnormal splicing of spleen tyrosine kinase as a key factor for cell proliferation in renal cancer. Thus, AbHAC provides an effective strategy to uncover novel disease factors that are only identifiable by examining mutational and expression data in the context of biological networks.

Adaptive Mistranslation Accelerates the Evolution of Fluconazole Resistance and Induces Major Genomic and Gene Expression Alterations in Candida albicans.

Regulated erroneous protein translation (adaptive mistranslation) increases proteome diversity and produces advantageous phenotypic variability in the human pathogen Candida albicans. It also increases fitness in the presence of fluconazole, but the underlying molecular mechanism is not understood. To address this question, we evolved hypermistranslating and wild-type strains in the absence and presence of fluconazole and compared their fluconazole tolerance and resistance trajectories during evolution. The data show that mistranslation increases tolerance and accelerates the acquisition of resistance to fluconazole. Genome sequencing, array-based comparative genome analysis, and gene expression profiling revealed that during the course of evolution in fluconazole, the range of mutational and gene deregulation differences was distinctively different and broader in the hypermistranslating strain, including multiple chromosome duplications, partial chromosome deletions, and polyploidy. Especially, the increased accumulation of loss-of-heterozygosity events, aneuploidy, translational and cell surface modifications, and differences in drug efflux seem to mediate more rapid drug resistance acquisition under mistranslation. Our observations support a pivotal role for adaptive mistranslation in the evolution of drug resistance in C. albicans. IMPORTANCE Infectious diseases caused by drug-resistant fungi are an increasing threat to public health because of the high mortality rates and high costs associated with treatment. Thus, understanding of the molecular mechanisms of drug resistance is of crucial interest for the medical community. Here we investigated the role of regulated protein mistranslation, a characteristic mechanism used by C. albicans to diversify its proteome, in the evolution of fluconazole resistance. Such codon ambiguity is usually considered highly deleterious, yet recent studies found that mistranslation can boost adaptation in stressful environments. Our data reveal that CUG ambiguity diversifies the genome in multiple ways and that the full spectrum of drug resistance mechanisms in C. albicans goes beyond the traditional pathways that either regulate drug efflux or alter the interactions of drugs with their targets. The present work opens new avenues to understand the molecular and genetic basis of microbial drug resistance.

Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression.

Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of ∼20,000 genes and ∼28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.

HMGB1 binds to the rs7903146 locus in TCF7L2 in human pancreatic islets.

The intronic SNP rs7903146 in the T-cell factor 7-like 2 gene (TCF7L2) is the common genetic variant most highly associated with Type 2 diabetes known to date. The risk T-allele is located in an open chromatin region specific to human pancreatic islets of Langerhans, thereby accessible for binding of regulatory proteins. The risk T-allele locus exhibits stronger enhancer activity compared to the non-risk C-allele. The aim of this study was to identify transcriptional regulators that bind the open chromatin region in the rs7903146 locus and thereby potentially regulate TCF7L2 expression and activity. Using affinity chromatography followed by Edman sequencing, we identified one candidate regulatory protein, i.e. high-mobility group protein B1 (HMGB1). The binding of HMGB1 to the rs7903146 locus was confirmed in pancreatic islets from human deceased donors, in HCT116 and in HEK293 cell lines using: (i) protein purification on affinity columns followed by Western blot, (ii) chromatin immunoprecipitation followed by qPCR and (iii) electrophoretic mobility shift assay. The results also suggested that HMGB1 might have higher binding affinity to the C-allele of rs7903146 compared to the T-allele, which was supported in vitro using Dynamic Light Scattering, possibly in a tissue-specific manner. The functional consequence of HMGB1 depletion in HCT116 and INS1 cells was reduced insulin and TCF7L2 mRNA expression, TCF7L2 transcriptional activity and glucose stimulated insulin secretion. These findings suggest that the rs7903146 locus might exert its enhancer function by interacting with HMGB1 in an allele dependent manner.

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research.

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase.

A novel atlas of gene expression in human skeletal muscle reveals molecular changes associated with aging.

BACKGROUND: Although high-throughput studies of gene expression have generated large amounts of data, most of which is freely available in public archives, the use of this valuable resource is limited by computational complications and non-homogenous annotation. To address these issues, we have performed a complete re-annotation of public microarray data from human skeletal muscle biopsies and constructed a muscle expression compendium consisting of nearly 3000 samples. The created muscle compendium is a publicly available resource including all curated annotation. Using this data set, we aimed to elucidate the molecular mechanism of muscle aging and to describe how physical exercise may alleviate negative physiological effects. RESULTS: We find 957 genes to be significantly associated with aging (p < 0.05, FDR = 5 %, n = 361). Aging was associated with perturbation of many central metabolic pathways like mitochondrial function including reduced expression of genes in the ATP synthase, NADH dehydrogenase, cytochrome C reductase and oxidase complexes, as well as in glucose and pyruvate processing. Among the genes with the strongest association with aging were H3 histone, family 3B (H3F3B, p = 3.4 × 10(-13)), AHNAK nucleoprotein, desmoyokin (AHNAK, p = 6.9 × 10(-12)), and histone deacetylase 4 (HDAC4, p = 4.0 × 10(-9)). We also discover genes previously not linked to muscle aging and metabolism, such as fasciculation and elongation protein zeta 2 (FEZ2, p = 2.8 × 10(-8)). Out of the 957 genes associated with aging, 21 (p < 0.001, false discovery rate = 5 %, n = 116) were also associated with maximal oxygen consumption (VO2MAX). Strikingly, 20 out of those 21 genes are regulated in opposite direction when comparing increasing age with increasing VO2MAX. CONCLUSIONS: These results support that mitochondrial dysfunction is a major age-related factor and also highlight the beneficial effects of maintaining a high physical capacity for prevention of age-related sarcopenia.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

The impact of low-frequency and rare variants on lipid levels.

Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing.

Functional loss of IκBε leads to NF-κB deregulation in aggressive chronic lymphocytic leukemia.

NF-κB is constitutively activated in chronic lymphocytic leukemia (CLL); however, the implicated molecular mechanisms remain largely unknown. Thus, we performed targeted deep sequencing of 18 core complex genes within the NF-κB pathway in a discovery and validation CLL cohort totaling 315 cases. The most frequently mutated gene was NFKBIE (21/315 cases; 7%), which encodes IκBε, a negative regulator of NF-κB in normal B cells. Strikingly, 13 of these cases carried an identical 4-bp frameshift deletion, resulting in a truncated protein. Screening of an additional 377 CLL cases revealed that NFKBIE aberrations predominated in poor-prognostic patients and were associated with inferior outcome. Minor subclones and/or clonal evolution were also observed, thus potentially linking this recurrent event to disease progression. Compared with wild-type patients, NFKBIE-deleted cases showed reduced IκBε protein levels and decreased p65 inhibition, along with increased phosphorylation and nuclear translocation of p65. Considering the central role of B cell receptor (BcR) signaling in CLL pathobiology, it is notable that IκBε loss was enriched in aggressive cases with distinctive stereotyped BcR, likely contributing to their poor prognosis, and leading to an altered response to BcR inhibitors. Because NFKBIE deletions were observed in several other B cell lymphomas, our findings suggest a novel common mechanism of NF-κB deregulation during lymphomagenesis.

Toward computational cumulative biology by combining models of biological datasets.

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Tandem RNA chimeras contribute to transcriptome diversity in human population and are associated with intronic genetic variants.

Chimeric RNAs originating from two or more different genes are known to exist not only in cancer, but also in normal tissues, where they can play a role in human evolution. However, the exact mechanism of their formation is unknown. Here, we use RNA sequencing data from 462 healthy individuals representing 5 human populations to systematically identify and in depth characterize 81 RNA tandem chimeric transcripts, 13 of which are novel. We observe that 6 out of these 81 chimeras have been regarded as cancer-specific. Moreover, we show that a prevalence of long introns at the fusion breakpoint is associated with the chimeric transcripts formation. We also find that tandem RNA chimeras have lower abundances as compared to their partner genes. Finally, by combining our results with genomic data from the same individuals we uncover intronic genetic variants associated with the chimeric RNA formation. Taken together our findings provide an important insight into the chimeric transcripts formation and open new avenues of research into the role of intronic genetic variants in post-transcriptional processing events.

TCF7L2 is a master regulator of insulin production and processing.

Genome-wide association studies have revealed >60 loci associated with type 2 diabetes (T2D), but the underlying causal variants and functional mechanisms remain largely elusive. Although variants in TCF7L2 confer the strongest risk of T2D among common variants by presumed effects on islet function, the molecular mechanisms are not yet well understood. Using RNA-sequencing, we have identified a TCF7L2-regulated transcriptional network responsible for its effect on insulin secretion in rodent and human pancreatic islets. ISL1 is a primary target of TCF7L2 and regulates proinsulin production and processing via MAFA, PDX1, NKX6.1, PCSK1, PCSK2 and SLC30A8, thereby providing evidence for a coordinated regulation of insulin production and processing. The risk T-allele of rs7903146 was associated with increased TCF7L2 expression, and decreased insulin content and secretion. Using gene expression profiles of 66 human pancreatic islets donors', we also show that the identified TCF7L2-ISL1 transcriptional network is regulated in a genotype-dependent manner. Taken together, these results demonstrate that not only synthesis of proinsulin is regulated by TCF7L2 but also processing and possibly clearance of proinsulin and insulin. These multiple targets in key pathways may explain why TCF7L2 has emerged as the gene showing one of the strongest associations with T2D.

Expression of phosphofructokinase in skeletal muscle is influenced by genetic variation and associated with insulin sensitivity.

Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci [eQTLs]) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate [FDR] <5%; P < 1.96 × 10(-5)) and 49 expression-insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment-insulin resistance, and BMI) (FDR <5%; P = 1.34 × 10(-4)). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 10(-6)) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016-0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r(2) = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7 × 10(-3)). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity.

Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene.

BACKGROUND: RNA sequencing has opened new avenues for the study of transcriptome composition. Significant evidence has accumulated showing that the human transcriptome contains in excess of a hundred thousand different transcripts. However, it is still not clear to what extent this diversity prevails when considering the relative abundances of different transcripts from the same gene. RESULTS: Here we show that, in a given condition, most protein coding genes have one major transcript expressed at significantly higher level than others, that in human tissues the major transcripts contribute almost 85 percent to the total mRNA from protein coding loci, and that often the same major transcript is expressed in many tissues. We detect a high degree of overlap between the set of major transcripts and a recently published set of alternatively spliced transcripts that are predicted to be translated utilizing proteomic data. Thus, we hypothesize that although some minor transcripts may play a functional role, the major ones are likely to be the main contributors to the proteome. However, we still detect a non-negligible fraction of protein coding genes for which the major transcript does not code a protein. CONCLUSIONS: Overall, our findings suggest that the transcriptome from protein coding loci is dominated by one transcript per gene and that not all the transcripts that contribute to transcriptome diversity are equally likely to contribute to protein diversity. This observation can help to prioritize candidate targets in proteomics research and to predict the functional impact of the detected changes in variation studies.

Reversion of a fungal genetic code alteration links proteome instability with genomic and phenotypic diversification.

Many fungi restructured their proteomes through incorporation of serine (Ser) at thousands of protein sites coded by the leucine (Leu) CUG codon. How these fungi survived this potentially lethal genetic code alteration and its relevance for their biology are not understood. Interestingly, the human pathogen Candida albicans maintains variable Ser and Leu incorporation levels at CUG sites, suggesting that this atypical codon assignment flexibility provided an effective mechanism to alter the genetic code. To test this hypothesis, we have engineered C. albicans strains to misincorporate increasing levels of Leu at protein CUG sites. Tolerance to the misincorporations was very high, and one strain accommodated the complete reversion of CUG identity from Ser back to Leu. Increasing levels of Leu misincorporation decreased growth rate, but production of phenotypic diversity on a phenotypic array probing various metabolic networks, drug resistance, and host immune cell responses was impressive. Genome resequencing revealed an increasing number of genotype changes at polymorphic sites compared with the control strain, and 80% of Leu misincorporation resulted in complete loss of heterozygosity in a large region of chromosome V. The data unveil unanticipated links between gene translational fidelity, proteome instability and variability, genome diversification, and adaptive phenotypic diversity. They also explain the high heterozygosity of the C. albicans genome and open the door to produce microorganisms with genetic code alterations for basic and applied research.