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Down syndrome (DS), caused by the triplication of chromosome 21 (T21), is a prevalent genetic disorder with a higher incidence of obesity. Traditional approaches have struggled to differentiate T21-specific molecular dysregulation from general obesity-related processes. This study introduces the omni-PLIER framework, combining the Pathway-Level Information ExtractoR (PLIER) with the omnigenic model, to uncover molecular mechanisms underlying obesity in DS. The PLIER framework aligns gene expression data with biological pathways, facilitating the identification of relevant molecular patterns. Using RNA sequencing data from the Human Trisome Project, omni-PLIER identified latent variables (LVs) significantly associated with both T21 and body mass index (BMI). Elastic net regression and causal mediation analysis revealed LVs mediating the effect of karyotype on BMI. Notably, LVs involving glutathione peroxidase-1 (GPX1) and MCL1 apoptosis regulator, BCL2 family members emerged as crucial mediators. These findings provide insights into the molecular interplay between DS and obesity. The omni-PLIER model offers a robust methodological advancement for dissecting complex genetic disorders, with implications for understanding obesity-related processes in both DS and the general population.

Background: Lynch syndrome (LS), characterised by an increased risk for cancer, is mainly caused by germline pathogenic variants affecting a mismatch repair gene (MLH1, MSH2, MSH6, PMS2). Occasionally, LS may be caused by constitutional MLH1 epimutation (CME) characterised by soma-wide methylation of one allele of the MLH1 promoter. Most of these are "primary" epimutations, arising de novo without any apparent underlying cis-genetic cause, and are reversible between generations. We aimed to characterise genetic and gene regulatory changes associated with primary CME to elucidate possible underlying molecular mechanisms., Methods: Four carriers of a primary CME and three non-methylated relatives carrying the same genetic haplotype were included. Genetic alterations were sought using linked-read WGS in blood DNA. Transcriptome (RNA-seq), chromatin landscape (ATAC-seq, H3K27ac CUT&Tag) and 3D chromatin interactions (UMI-4C) were studied in lymphoblastoid cell lines. The MLH1 promoter SNP (c.-93G > A, rs1800734) was used as a reporter in heterozygotes to assess allele-specific chromatin conformation states., Results: MLH1 epimutant alleles presented a closed chromatin conformation and decreased levels of H3K27ac, as compared to the unmethylated allele. Moreover, the epimutant MLH1 promoter exhibited differential 3D chromatin contacts, including lost and gained interactions with distal regulatory elements. Of note, rare genetic alterations potentially affecting transcription factor binding sites were found in the promoter-contacting region of CME carriers., Conclusions: Primary CMEs present allele-specific differential interaction patterns with neighbouring genes and regulatory elements. The role of the identified cis-regulatory regions in the molecular mechanism underlying the origin and maintenance of CME requires further investigation., Competing Interests: Declarations. Ethics approval and consent to participate: Written informed consent was obtained from all individuals enrolled. This study was approved by the Ethics Committee of the Institut d’Investigació Biomèdica de Bellvitge (code PR225/11). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.

Insulinomas are rare neuroendocrine tumors arising from pancreatic β cells, characterized by aberrant proliferation and altered insulin secretion, leading to glucose homeostasis failure. With the aim of uncovering the role of noncoding regulatory regions and their aberrations in the development of these tumors, we coupled epigenetic and transcriptome profiling with whole-genome sequencing. As a result, we unraveled somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development, and epigenetic modifying genes, including polycomb complex components. Chromatin remodeling is apparent in insulinoma-selective domains shared across patients, containing a specific set of regulatory sequences dominated by the SOX17 binding motif. Moreover, many of these regions are H3K27me3 repressed in β cells, suggesting that tumoral transition involves derepression of polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements affecting the function and fate of β cells in their progression to insulinomas and a framework to identify coding and noncoding driver mutations., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Motivation: UMI-4C, a technique that combines chromosome conformation capture (4C) and unique molecular identifiers (UMI), is widely used to profile and quantitatively compare targeted chromosomal contact profiles. The analysis of UMI-4C experiments presents several computational challenges, including the removal of the PCR duplication bias and the identification of differential chromatin contacts., Results: We have developed UMI4Cats (UMI-4C Analysis Turned Simple), an R package that facilitates processing, analyzing and visualizing of data obtained by UMI-4C experiments., Availability and Implementation: UMI4Cats is implemented as an R package supported on Linux, MacOS and MS Windows. UMI4Cats is available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/UMI4Cats.html) and GitHub (https://github.com/Pasquali-lab/UMI4Cats)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

The early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. Here we show that exposure to proinflammatory cytokines reveals a marked plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and three-dimensional chromatin structure. Our data indicate that the β-cell response to cytokines is mediated by the induction of new regulatory regions as well as the activation of primed regulatory elements prebound by islet-specific transcription factors. We find that T1D-associated loci are enriched with newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human β cells. Our study illustrates how β cells respond to a proinflammatory environment and implicate a role for stimulus response islet enhancers in T1D.