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Aberrant enhancer activation is a key mechanism driving oncogene expression in many cancers. While much is known about the regulation of larger chromosome domains in eukaryotes, the details of enhancer-promoter interactions remain poorly understood. Recent work suggests co-activators like BRD4 and Mediator have little impact on enhancer-promoter interactions. In leukemias controlled by the MLL-AF4 fusion protein, we use the ultra-high resolution technique Micro-Capture-C (MCC) to show that MLL-AF4 binding promotes broad, high-density regions of enhancer-promoter interactions at a subset of key targets. These enhancers are enriched for transcription elongation factors like PAF1C and FACT, and the loss of these factors abolishes enhancer-promoter contact. This work not only provides an additional model for how MLL-AF4 is able to drive high levels of transcription at key genes in leukemia but also suggests a more general model linking enhancer-promoter crosstalk and transcription elongation. Previous studies have reported MLL-AF4 binding at intragenic and intergenic enhancers, however, the role of MLL-AF4 in enhancer function remains to be investigated. Here, the authors show that MLL-AF4 cooperates with PAF1 and FACT at enhancers to promote high-density interactions with oncogene promoters in leukemia. [ABSTRACT FROM AUTHOR]

Kidney organoids are a promising model to study kidney disease, but their use is constrained by limited knowledge of their functional protein expression profile. Here, we define the organoid proteome and transcriptome trajectories over culture duration and upon exposure to TNFα, a cytokine stressor. Older organoids increase deposition of extracellular matrix but decrease expression of glomerular proteins. Single cell transcriptome integration reveals that most proteome changes localize to podocytes, tubular and stromal cells. TNFα treatment of organoids results in 322 differentially expressed proteins, including cytokines and complement components. Transcript expression of these 322 proteins is significantly higher in individuals with poorer clinical outcomes in proteinuric kidney disease. Key TNFα-associated protein (C3 and VCAM1) expression is increased in both human tubular and organoid kidney cell populations, highlighting the potential for organoids to advance biomarker development. By integrating kidney organoid omic layers, incorporating a disease-relevant cytokine stressor and comparing with human data, we provide crucial evidence for the functional relevance of the kidney organoid model to human kidney disease. Lassé et al. show that genes involved in kidney organoid proteomic response to TNFα segregate a subset of individuals with poor outcomes in proteinuric kidney disease, demonstrating the relevance of kidney organoid modeling to human kidney disease. [ABSTRACT FROM AUTHOR]

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes. Approximately 40 percent of people with type 1 diabetes develop kidney disease, but the risk factors are not well understood. Here, the authors identify DNA methylation signatures associated with diabetic kidney disease, of which 21 biomarkers predict the development of kidney failure. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. Methods: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. Results: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10−9; although not withstanding correction for multiple testing, p>9.3×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10−6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10−6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10−11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10−8] and negatively with tubulointerstitial fibrosis [p=2.0×10−9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10−16], and SNX30 expression correlated positively with eGFR [p=5.8×10−14] and negatively with fibrosis [p<2.0×10−16]). Conclusions/interpretation: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). [ABSTRACT FROM AUTHOR]

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/. [ABSTRACT FROM AUTHOR]

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification-mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data. [ABSTRACT FROM AUTHOR]

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences. [ABSTRACT FROM AUTHOR]

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a growing health burden currently affecting 10–15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. We analyze eGFR data from the Nord-Trøndelag Health Study and Michigan Genomics Initiative and perform a GWAS meta-analysis with public summary statistics, more than doubling the sample size of previous meta-analyses. We identify 147 loci (53 novel) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Additionally, sex-stratified analysis identifies one locus with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD with only modest improvements in detection compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD. Estimated glomerular filtration rate (eGFR) is a measure of kidney function and used to characterize chronic kidney disease. Here, Graham et al. identify 53 novel loci for eGFR in a GWAS meta-analysis, a subset of which are associated with other common diseases, such as diabetes and hypertension, based on PheWAS. [ABSTRACT FROM AUTHOR]