Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from ...23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the ...potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.
Type 2 inflammation occurs in a large subgroup of asthmatics, and novel cytokine-directed therapies are being developed to treat this population. In mouse models, interleukin-33 (IL-33) activates ...lung resident innate lymphoid type 2 cells (ILC2s) to initiate airway type 2 inflammation. In human asthma, which is chronic and difficult to model, the role of IL-33 and the target cells responsible for persistent type 2 inflammation remain undefined. Full-length IL-33 is a nuclear protein and may function as an “alarmin” during cell death, a process that is uncommon in chronic stable asthma. We demonstrate a previously unidentified mechanism of IL-33 activity that involves alternative transcript splicing, which may operate in stable asthma. In human airway epithelial cells, alternative splicing of the IL-33 transcript is consistently present, and the deletion of exons 3 and 4 (Δ exon 3,4) confers cytoplasmic localization and facilitates extracellular secretion, while retaining signaling capacity. In nonexacerbating asthmatics, the expression of Δ exon 3,4 is strongly associated with airway type 2 inflammation, whereas full-length IL-33 is not. To further define the extracellular role of IL-33 in stable asthma, we sought to determine the cellular targets of its activity. Comprehensive flow cytometry and RNA sequencing of sputum cells suggest basophils and mast cells, not ILC2s, are the cellular sources of type 2 cytokines in chronic asthma. We conclude that IL-33 isoforms activate basophils and mast cells to drive type 2 inflammation in chronic stable asthma, and novel IL-33 inhibitors will need to block all biologically active isoforms.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six ...types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks.
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•Mutations perturbing signaling networks are systematically classified and interpreted•Several such functional mutations are identified in cancer and experimentally validated•The results suggest that a single point mutant can have profound signaling effects•Systematic interpretation of genomic data may assist future precision-medicine efforts
A systematic classification of genomic variants in cancer reveals the many ways in which signaling networks can be perturbed, including rewiring and the creation or destruction of phosphorylation sites.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In recent years, studies on the human intestinal microbiota have attracted tremendous attention. Application of next generation sequencing for mapping of bacterial phylogeny and function has opened ...new doors to this field of research. However, little attention has been given to the effects of choice of methodology on the output resulting from such studies.
IN THIS STUDY WE CONDUCTED A SYSTEMATIC COMPARISON OF THE DNA EXTRACTION METHODS USED BY THE TWO MAJOR COLLABORATIVE EFFORTS: The European MetaHIT and the American Human Microbiome Project (HMP). Additionally, effects of homogenizing the samples before extraction were addressed. We observed significant differences in distribution of bacterial taxa depending on the method. While eukaryotic DNA was most efficiently extracted by the MetaHIT protocol, DNA from bacteria within the Bacteroidetes phylum was most efficiently extracted by the HMP protocol.
Whereas it is comforting that the inter-individual variation clearly exceeded the variation resulting from choice of extraction method, our data highlight the challenge of comparing data across studies applying different methodologies.
Respiratory illness caused by viral infection is associated with the development and exacerbation of childhood asthma. Little is known about the effects of respiratory viral infections in the absence ...of illness. Using quantitative PCR (qPCR) for common respiratory viruses and for two genes known to be highly upregulated in viral infections (CCL8/CXCL11), we screened 92 asthmatic and 69 healthy children without illness for respiratory virus infections.
We found 21 viral qPCR-positive and 2 suspected virus-infected subjects with high expression of CCL8/CXCL11. We applied a dual RNA-seq workflow to these subjects, together with 25 viral qPCR-negative subjects, to compare qPCR with sequencing-based virus detection and to generate the airway transcriptome for analysis. RNA-seq virus detection achieved 86% sensitivity when compared to qPCR-based screening. We detected additional respiratory viruses in the two CCL8/CXCL11-high subjects and in two of the qPCR-negative subjects. Viral read counts varied widely and were used to stratify subjects into Virus-High and Virus-Low groups. Examination of the host airway transcriptome found that the Virus-High group was characterized by immune cell airway infiltration, downregulation of cilia genes, and dampening of type 2 inflammation. Even the Virus-Low group was differentiated from the No-Virus group by 100 genes, some involved in eIF2 signaling.
Respiratory virus infection without illness is not innocuous but may determine the airway function of these subjects by driving immune cell airway infiltration, cellular remodeling, and alteration of asthmogenic gene expression.
Mechanisms governing regional human adipose tissue (AT) development remain undefined. Here, we show that the long non-coding RNA HOTAIR (HOX transcript antisense RNA) is exclusively expressed in ...gluteofemoral AT, where it is essential for adipocyte development. We find that HOTAIR interacts with polycomb repressive complex 2 (PRC2) and we identify core HOTAIR-PRC2 target genes involved in adipocyte lineage determination. Repression of target genes coincides with PRC2 promoter occupancy and H3K27 trimethylation. HOTAIR is also involved in modifying the gluteal adipocyte transcriptome through alternative splicing. Gluteal-specific expression of HOTAIR is maintained by defined regions of open chromatin across the HOTAIR promoter. HOTAIR expression levels can be modified by hormonal (estrogen, glucocorticoids) and genetic variation (rs1443512 is a HOTAIR eQTL associated with reduced gynoid fat mass). These data identify HOTAIR as a dynamic regulator of the gluteal adipocyte transcriptome and epigenome with functional importance for human regional AT development.
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•HOTAIR is expressed specifically in the gluteal adipose tissue depot•In vitro impairment of HOTAIR prevents gluteal adipocyte development•Minor allele carriers of a HOTAIR-lowering eQTL have reduced lower-body fat mass
Kuo et al. identify HOTAIR as a lower-body-specific regulator of human fat tissue development. HOTAIR deficiency results in impaired gluteal preadipocyte proliferation and differentiation. Genetic associations in a population of 25,200 healthy individuals confirm that carriers of a HOTAIR-lowering genetic variant have reduced lower-body fat mass.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard ...RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki .
Several distinct differentiation protocols for deriving pancreatic progenitors (PPs) from human pluripotent stem cells have been described, but it remains to be shown how similar the PPs are across ...protocols and how well they resemble their in vivo counterparts. Here, we evaluated three differentiation protocols, performed RNA and assay for transposase-accessible chromatin using sequencing on isolated PPs derived with these, and compared them with fetal human pancreas populations. This enabled us to define a shared transcriptional and epigenomic signature of the PPs, including several genes not previously implicated in pancreas development. Furthermore, we identified a significant and previously unappreciated cross-protocol variation of the PPs through multi-omics analysis and demonstrate how such information can be applied to refine differentiation protocols for derivation of insulin-producing beta-like cells. Together, our study highlights the importance of a detailed characterization of defined cell populations derived from distinct differentiation protocols and provides a valuable resource for exploring human pancreatic development.
•Systematic comparison of three protocols for hPSC-pancreatic progenitor differentiation•Individual hPSC lines differentiate preferentially with different protocols•Shared transcriptomic and epigenomic signatures of hPSC-pancreatic progenitors•Molecular characterization of hPSC-pancreatic progenitors guides protocol refinement
Wesolowska-Andersen and colleagues present a comprehensive molecular and functional analysis of human PSC-derived pancreatic progenitors derived using three differentiation protocols. They define their shared transcriptomic and epigenomic signatures but also highlight significant differences between protocols, which are then used to guide protocol modifications to enhance further differentiation toward endocrine lineage.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Results The relative proportion of sphingolipids with long-chain fatty acids (C22-C32) was decreased in lesional AD skin (Mean±SD 73.9±4.1%, 80.4±5.5% of total ceramides for AD lesional skin and ...normal skin, respectively, p<0.05) while short-chain fatty acid sphingolipids (C14-C20) were increased in AD.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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