Runs of homozygosity (ROHs) are widely observed across the genomes of various species and have been reported to be associated with many traits and common diseases, as well as rare recessive diseases, in human populations. Although single nucleotide polymorphism (SNP) array data have been used in previous studies on ROHs, recent advances in whole-genome sequencing (WGS) technologies and the development of nationwide cohorts/biobanks are making high-density genomic data increasingly available, and it is consequently becoming more feasible to detect ROHs at higher resolution. In the study, we searched for ROHs in two high-coverage WGS datasets from 3552 Japanese individuals and 192 three-generation families (consisting of 1120 family members) in prospective genomic cohorts. The results showed that a considerable number of ROHs, especially short ones that may have remained undetected in conventionally used SNP-array data, can be detected in the WGS data. By filtering out sequencing errors and leveraging pedigree information, longer ROHs are more likely to be detected in WGS data than in SNP-array data. Additionally, we identified gene families within ROH islands that are associated with enriched pathways related to sensory perception of taste and odors, suggesting potential signatures of selection in these key genomic regions.

The mechanisms underlying the evolution of lifespan across organisms remain mysterious. This study computes multiple large datasets and reveals that noncoding RNAs (ncRNAs), rather than proteins, drive animal lifespan evolution. Species in the animal kingdom evolutionarily increase their ncRNA length in their genomes, coinciding with trimming of the mitochondrial genome length. This leads to a low energy consumption and longevity. Notably, as species evolve and extend their lifespans, they tend to acquire long-lived ncRNA motifs while simultaneously losing short-lived ones, in contrast to the conservative patterns observed in protein evolution. These longevity-associated ncRNA motifs, such as GGTGCG, are particularly active in crucial tissues including the endometrium, ovaries, testes, and cerebral cortex. The ovary and endometrium carry more activating ncRNAs than the testis, offering insight into why women generally outlive men. Taken together, ncRNAs drive the evolution of the two most important traits of organisms: longevity and reproduction, and they execute many more fundamental functions than those conventionally thought. This discovery provides the foundation for combating longevity and aging.

RNA sequencing of whole blood has been increasingly employed to find transcriptomic signatures of disease states. These studies traditionally utilize short-read sequencing of cDNA, missing important aspects of RNA expression such as differential isoform abundance and poly(A) tail length variation.

We used Oxford Nanopore Technologies sequencing to sequence native mRNA extracted from whole blood from 12 patients with definite bacterial and viral sepsis and compared with results from matching Illumina short-read cDNA sequencing data. Additionally, we explored poly(A) tail length variation, novel transcript identification, and differential transcript usage.

The correlation of gene count data between Illumina cDNA- and Nanopore RNA-sequencing strongly depended on the choice of analysis pipeline; NanoCount for Nanopore and Kallisto for Illumina data yielded the highest mean Pearson's correlation of 0.927 at the gene level and 0.736 at the transcript isoform level. We identified 2 genes with differential polyadenylation, 9 genes with differential expression and 4 genes with differential transcript usage between bacterial and viral infection. Gene ontology gene set enrichment analysis of poly(A) tail length revealed enrichment of long tails in mRNA of genes involved in signaling and short tails in oxidoreductase molecular functions. Additionally, we detected 240 non-artifactual novel transcript isoforms.

Nanopore RNA- and Illumina cDNA-gene counts are strongly correlated, indicating that both platforms are suitable for discovery and validation of gene count biomarkers. Nanopore direct RNA-seq provides additional advantages by uncovering additional post- and co-transcriptional biomarkers, such as poly(A) tail length variation and transcript isoform usage.

CTCF, a highly studied transcription factor, is essential for chromatin interaction maintenance. Several independent studies report that CTCF interacts with RNAs in vitro and in cells. Yet continuous debates about the authenticity of the RNA-binding affinity of CTCF and its biological role remain in large part due to limited research techniques available, such as CLIP-seq.

Here, we investigate RNA's role in CTCF's transcription factor function through its chromatin occupancy. To systematically explore whether RNAs affect CTCF's ability to bind DNA, we perturb CTCF-RNA interactions by three independent approaches and examine CTCF genome occupancy by ChIP-seq. Although RNase A and triptolide treatment each affect a certain number of CTCF-binding peaks, few peaks overlap between treatment groups indicating the effect of RNA in regulating CTCF's DNA binding affinity is variable between loci. In addition, limited transcriptional or chromatin accessibility changes occur between cells expressing wild-type CTCF or CTCF lacking the RNA binding region.

Our data provide a complementary approach and in silico evidence to consider the significance of RNA affecting CTCF's DNA binding affinity globally.

Brain metastases (BrM) arising from breast cancer (BC) are an increasing consequence of advanced disease, with up to half of patients with metastatic HER2 + or triple negative BC experiencing central nervous system (CNS) recurrence. The genomic alterations driving CNS recurrence, along with contributions of the immune microenvironment, particularly by intrinsic subtype, remain unclear.

We characterized the genomic and immune landscape of BCBrM from a cohort of 42 patients by sequencing whole-exome DNA (WES) and total RNA libraries from frozen and FFPE BrM and FFPE extracranial tumors (ECT). Analyses included PAM50 intrinsic subtypes, somatic mutations, copy number variations (CNV), pathway alterations, immune cell type deconvolution, and associations with clinical outcomes RESULTS: Intrinsic subtype calls were concordant for the majority of BrM-ECT pairs (60%). Across all BrM and ECT samples, the most common somatic gene mutation was TP53 (64%, 30/47). For patients with matched FFPE BrM-FFPE ECT, alterations tended to be conserved across tissue type, although differential somatic mutations and CNV in specific genes were observed. Several genomic pathways were differentially expressed between patient-matched BrM-ECT; MYC targets, DNA damage repair, cholesterol homeostasis, and oxidative phosphorylation were higher in BrM, while immune-related pathways were lower in BrM. Deconvolution of immune populations between BrM-ECT demonstrated activated dendritic cell populations were higher in BrM compared to ECT. Increased expression of several oncogenic preselected pathways in BrM were associated with inferior survival, including DNA damage repair, inflammatory response, and oxidative phosphorylation CONCLUSIONS: Collectively, this study illustrates that while some genomic alterations are shared between BrM and ECT, there are also unique aspects of BrM including somatic mutations, CNV, pathway alterations, and immune landscape. A deeper understanding of differences inherent to BrM will contribute to the development of BrM-tailored therapeutic strategies. Additional analyses are warranted in larger cohorts, particularly with additional matched BrM-ECT.

Mitochondria are essential organelles involved in cell metabolism and are closely linked to various metabolic disorders. In this study, we aimed to develop a prognostic model for endometrial cancer (EC) patients based on mitochondria-related genes (MRGs), and to investigate the role of MACC1 in EC. As shown in the graphic summary, we retrieved gene expression and clinical data from open-access databases. To construct a predictive signature, we applied the Lasso Cox regression algorithm to MRGs. The predictive performance, immune features, and anti-tumor response of the mitochondrial signature were evaluated through multiple algorithms. Additionally, expression levels of key genes were validated using quantitative Real-Time PCR and Western Blot. A total of 2030 MRGs were retrieved, and 267 were found to be prognostically relevant. Eight MRGs-MACC1, CMPK2, NDUFAF6, DUSP18, TOMM40L, MT-TP, SAMM50, and MAIP1-were identified to construct a prognostic signature for EC. The MRG signature demonstrated significant associations with drug sensitivity, immune therapy, and immune cell infiltration. Based on comprehensive bioinformatic analysis, MACC1 was identified as the most promising MRG candidate in EC. Systematic experimental validation, including both in vitro and in vivo approaches, demonstrated that MACC1 down-regulation significantly suppressed EC progression, highlighting its potential as a therapeutic target.

In metastatic clear-cell renal cell carcinoma (mccRCC), choosing between immuno-oncology (IO) combinations and IO plus anti-VEGF therapies is uncertain. The BIONIKK trial revealed that ipilimumab plus nivolumab (Ipi/Nivo) achieved a 70% objective response rate in angiogenic cluster1/2 versus 41% in cluster4/5, which featured T-effector/cell-cycle signatures (p = 0.048). Complete responses were exclusively observed in cluster1/2 (p = 0.012), with longer progression-free survival (p = 0.014). Ipi/Nivo may particularly benefit angiogenic mccRCC, supporting molecular subtype-based treatment strategies.

Persistent HIV reservoirs in CD4+ T cells pose a barrier to curing HIV infection. We identify overexpression of enhancer of zeste homolog 2 (EZH2) in HIV-infected CD4+ T cells that survive cytotoxic T lymphocyte (CTL) exposure, suggesting a mechanism of CTL resistance. Inhibition of EZH2 with the US Food and Drug Administration-approved drug tazemetostat increases surface expression of major histocompatibility complex (MHC) class I on CD4+ T cells, counterbalancing HIV Nef-mediated MHC class I downregulation. This improves CTL-mediated elimination of HIV-infected cells and suppresses viral replication in vitro. In a participant-derived xenograft mouse model, tazemetostat elevates MHC class I and the pro-apoptotic protein BIM in CD4+ T cells, facilitating CD8+ T cell-mediated reductions of HIV reservoir seeding. Additionally, tazemetostat promotes sustained skewing of CD8+ T cells toward less-differentiated and exhausted phenotypes. Our findings reveal EZH2 overexpression as a mechanism of CTL resistance and support the clinical evaluation of tazemetostat as a method of enhancing clearance of HIV reservoirs and improving CD8+ T cell function.

The expressions of long noncoding RNAs (lncRNAs) and their roles in epidermal differentiation have been previously defined using bulk RNA sequencing. Despite their tissue-specific expression profiles, most lncRNAs are not well-annotated at the single-cell level. In this study, we evaluated the use of single-cell RNA sequencing to profile and characterize lncRNAs using data from 6 patients with psoriasis with paired uninvolved and lesional psoriatic skin. Despite their overall lower expression, we were able to detect >7000 skin-expressing lncRNAs and their cellular sources. Differential gene expression analysis revealed 137 differentially expressed lncRNAs in lesional psoriasis skin and identified 169 cell-type-specific lncRNAs. Keratinocytes had the highest number of differentially expressed lncRNA in psoriatic skin, which we validated using spatial transcriptomic data. We further showed that expression of the keratinocyte-specific lncRNA, AC020916.1, upregulated in lesional skin, is significantly correlated with expressions of genes participating in cell proliferation/epidermal differentiation, including SPRR2E and transcription factor ZFP36, particularly in the psoriatic skin. Our study highlights the potential for using single-cell RNA sequencing to profile skin-expressing lncRNA transcripts and to infer their cellular origins, providing a crucial approach that can be applied to the study of other inflammatory skin conditions.

A subgroup of patients with acute depression show an impaired regulation of the hypothalamic-pituitary-adrenocortical axis, which can be sensitively diagnosed with the combined dexamethasone (dex)/corticotropin releasing hormone (CRH)-test. This neuropathological alteration is assumed to be a result of hyperactive AVP/V1b signalling. Given the complicated procedure of the dex/CRH-test, this study aimed to develop a genetic variants-based alternative approach to predict the outcome of the dex/CRH-test in acute depression.Using data of a representative cohort of 352 patients with severe depression participating in the dex/CRH-test, a genome-wide interaction analysis was performed starting with an anchor single nucleotide polymorphism located in the upstream transcriptional region of the human V1b-receptor gene to predict the adrenocorticotropic hormone (ACTH) response to this test. A probabilistic neural-network-algorithm was used to develop the optimal prediction model.Overall prediction accuracy for correctly identifying high ACTH responders in the dex/CRH-test was 93.5% (sensitivity 90%; specificity 95%). Analysis of pituitary RNAseq expression data confirmed that the identified genetic interactions of the gene test translate into an interactive network of corresponding transcripts in the pituitary gland, which is the biologically relevant target tissue, with the aggregated strength of the transcript interactions significantly stronger than expected from chance.The findings suggest the suitability of the presented gene test as a proxy for hyperactive AVP/V1b signalling during an acute depressive episode, highlighting its potential as companion test for identifying patients with acute depression whose pathology can be optimally treated by specific drugs targeting the AVP/V1b-signaling cascade.

Endogenous retroviruses (ERVs) shape human genome functionality and influence disease pathogenesis, including cancer. ERVK-7, a significant ERV, acts as an immune modulator and prognostic marker in lung adenocarcinoma (LUAD). Although ERVK-7 overexpression has been linked to the amplification of the 1q22 locus in approximately 10% of LUAD cases, it predominantly arises from alternative regulatory mechanisms. Our findings indicate that the canonical 5' long terminal repeat (LTR) of ERVK-7 is methylated and inactive, necessitating the use of alternative upstream promoters. We identified two novel transcripts, ERVK-7.long and ERVK-7.short, arising from distinct promoters located 2.8 kb and 13.8 kb upstream of the 5'LTR of ERVK-7, respectively. ERVK-7.long is predominantly overexpressed in LUAD. Through comprehensive epigenetic mapping and single-cell transcriptomics, we demonstrate that ERVK-7.long activation is predetermined by cell lineage, specifically in small airway epithelial cells (SAECs), where its promoter displays tumor-specific H3K4me3 modifications. Single-cell RNA sequencing further reveals a distinct enrichment of ERVK-7.long in LUAD tumor cells and alveolar type 2 epithelial cells, underscoring a cell-type-specific origin. Additionally, inflammatory signaling significantly influences ERVK-7 expression; TNF-α enhances ERVK-7.long, while interferon signaling preferentially augments ERVK-7.short by differential recruitment of NF-κB/RELA and IRF to their respective promoters. This differential regulation clarifies the elevated ERVK-7 expression in LUAD compared to lung squamous cell carcinoma (LUSC). Our study elucidates the complex regulatory mechanisms governing ERVK-7 in LUAD and proposes these transcripts as potential biomarkers and therapeutic targets, offering new avenues to improve patient outcomes.

Expansion of the CAG trinucleotide repeat tract in exon 1 of the Huntingtin (HTT) gene causes Huntington's disease (HD) through the expression of a polyglutamine-expanded form of the HTT protein. This mutation triggers cellular and biochemical pathologies, leading to cognitive, motor, and psychiatric symptoms in HD patients. Targeting HTT splicing with small molecule drugs is a compelling approach to lowering HTT protein levels to treat HD, and splice modulators are currently being tested in the clinic. Here, we identify PRMT5 as a novel regulator of HTT messenger RNA (mRNA) splicing and alternative polyadenylation. PRMT5 inhibition disrupts the splicing of HTT introns 9 and 10, leading to the activation of multiple proximal intronic polyadenylation sites within these introns and promoting premature termination, cleavage, and polyadenylation of the HTT mRNA. This suggests that HTT protein levels may be lowered due to this mechanism. We also detected increasing levels of these truncated HTT transcripts across a series of neuronal differentiation samples, which correlated with lower PRMT5 expression. Notably, PRMT5 inhibition in glioblastoma stem cells potently induced neuronal differentiation. We posit that PRMT5-mediated regulation of intronic polyadenylation, premature termination, and cleavage of the HTT mRNA modulates HTT expression and plays an important role during neuronal differentiation.

Obesity is a significant public health concern that is associated with numerous health risks. Infections are a major complication of obesity, but the mechanisms responsible for increased infection risk are poorly defined. Here, we use a diet induced obesity mouse model and investigate how obesity impacts urinary tract infection (UTI) susceptibility and bladder immune defenses. Our results show that high-fat diet fed female and male mice exhibit increased susceptibility to uropathogenic E. coli (UPEC) following experimental UTI. Transcriptomic analysis of bladder urothelial cells shows that obesity alters gene expression in a sex-specific manner, with distinct differentially expressed genes in male and female mice, but shared activation of focal adhesion and extracellular matrix signaling. Western blot and immunostaining confirm activation of focal adhesion kinase, a central component of the focal adhesion pathway, in the bladders of obese female and male mice. Mechanistically, experiments using primary human urothelial cells demonstrate that focal adhesion kinase overexpression promotes UPEC invasion. These findings demonstrate that obesity enhances UTI susceptibility by activating focal adhesion kinase and promoting bacterial invasion of the urothelium. Together, they explain how obesity promotes UTI vulnerability and identify modifiable targets for managing obesity-associated UTI.

Obesity is associated with an increased risk of urinary tract infections (UTIs), but the underlying mechanisms promoting infection susceptibility remain poorly understood. Here, we show that diet-induced obesity drives sex-specific changes in bladder urothelial gene expression, including distinct immune responses in male and female mice. Despite these differences, both sexes exhibit activation of focal adhesion kinase (FAK). FAK overexpression promotes bacterial invasion into human bladder cells. These findings provide a mechanistic explanation for obesity-associated UTI susceptibility and suggest that targeting FAK signaling could offer a therapeutic strategy to prevent UTIs, with implications for personalized interventions in obesity.

Clear cell renal cell carcinoma (ccRCC) accounts for 70% of renal cell carcinoma (RCC) cases. Although surgery remains the mainstay treatment, renal injury and high metastasis rates after nephrectomy dramatically reduce patient quality of life. Drugs that stimulate the immune system by targeting checkpoint pathways improve overall survival in patients with RCC. Here, we investigated the applicability of lysophosphatidylcholine acyltransferase 3 (LPCAT3) as a target for immunotherapy.

In the present study, high LPCAT3 expression in ccRCC was identified using The Cancer Genome Atlas (TCGA) data and validated in two external cohorts from the Gene Expression Omnibus (GEO) database. qRT-PCR was performed to identify the mRNA level of LPCAT3 in tumors and adjacent normal tissues. And immunohistochemistry was used to evaluate the protein level of LPCAT3 between two groups of samples. Furthermore, gene set enrichment analysis was performed to explore the biological processes and pathways related to LPCAT3 expression. Key gene expression and correlation analyses were performed to determine the crosstalk among LPCAT3 expression, ferroptosis, and endoplasmic reticulum stress (ERS). Subsequently, CIBERSORT was used to analyze the immune infiltration status of patients with high and low LPCAT3 expression.

TCGA and GEO data revealed that LPCAT3 expression in ccRCC tumor tissues was higher than that in adjacent normal tissues; moreover, patients with high LPCAT3 expression had better survival outcomes. qRT-PCR and immunohistochemistry verified the high LPCAT3 expression in tumor tissue. Pathways related to ferroptosis and ERS were upregulated in patients with high LPCAT3 expression. Univariate and multivariate regression analyses revealed that low LPCAT3 levels represent an independent risk factor for ccRCC. LPCAT3 expression was positively correlated with M2 macrophage infiltration levels but negatively correlated with the memory B cell, CD8+ T cell, follicular helper T cell, regulatory T cell, activated natural killer cell, and activated memory CD4+ T cell infiltration levels.

LPCAT3was identified as a ccRCC biomarker and may regulate immune infiltration and prognosis in ccRCC by mediating ferroptosis and ERS. Thus, it has potential for exploitation as a prognostic and immune therapeutic target for patients with ccRCC.

Patient-derived xenograft (PDX) models are widely used in cancer research. Genomic and transcriptomic profiling of PDXs are inevitably contaminated by sequencing reads originated from mouse cells. Here, we examine the impact of mouse read contamination on RNA sequencing (RNAseq), Whole Exome Sequencing (WES), and Whole Genome Sequencing (WGS) data of 21 PDXs. We also systematically benchmark the performance of 12 computational protocols for removing mouse reads from PDXs. We find that mouse read contamination increases expression of immune and stromal related genes, and inflates the number of somatic mutations. However, detection of gene fusions and copy number alterations is minimally affected by mouse read contamination. Using gold standard datasets, we find that pseudo-alignment protocols often demonstrate better prediction performance and computing efficiency. The best performing tool is a relatively new tool Xengsort. Our results emphasize the importance of removing mouse reads from PDXs and the need to adopt new tools in PDX genomic studies.

In mammals, spermatogonial cells (SPGs) are undifferentiated male germ cells in testis that are quiescent until birth and then self-renew and differentiate to produce spermatogenic cells and functional sperm from early postnatal life throughout adulthood. The transcriptome of SPGs is highly dynamic and timely regulated during postnatal development. We examined if such dynamics involves changes in chromatin organization by profiling the transcriptome and chromatin accessibility of SPGs from early postnatal stages to adulthood in mice using deep RNA-seq, ATAC-seq and computational deconvolution analyses. By integrating transcriptomic and epigenomic features, we show that SPGs undergo massive chromatin remodeling during postnatal development that partially correlates with distinct gene expression profiles and transcription factors (TF) motif enrichment. We identify genomic regions with significantly different chromatin accessibility in adult SPGs that are marked by histone modifications associated with enhancers and promoters. Some of the regions with increased accessibility correspond to transposable element subtypes enriched in multiple TFs motifs and close to differentially expressed genes. Our results underscore the dynamics of chromatin organization in developing germ cells and complement existing datasets on SPGs by providing maps of the regulatory genome at high resolution from the same cell populations at early postnatal, late postnatal and adult stages collected from single individuals.

Human papillomavirus (HPV) integration has been implicated in transforming HPV infection into cancer. To resolve genome dysregulation associated with HPV integration, we performed Oxford Nanopore Technologies long-read sequencing on 72 cervical cancer genomes from a Ugandan data set that was previously characterized using short-read sequencing. We find recurrent structural rearrangement patterns at HPV integration events, which we categorize as del(etion)-like, dup(lication)-like, translocation, multi-breakpoint, or repeat region integrations. Integrations involving amplified HPV-human concatemers, particularly multi-breakpoint events, frequently harbor heterogeneous forms and copy numbers of the viral genome. Transcriptionally active integrants are characterized by unmethylated regions in both the viral and human genomes downstream from the viral transcription start site, resulting in HPV-human fusion transcripts. In contrast, integrants without evidence of expression lack consistent methylation patterns. Furthermore, whereas transcriptional dysregulation is limited to genes within 200 kb of an HPV integrant, dysregulation of the human epigenome in the form of allelic differentially methylated regions affects megabase expanses of the genome, irrespective of the integrant's transcriptional status. By elucidating the structural, epigenetic, and allele-specific impacts of HPV integration, we provide insight into the role of integrated HPV in cervical cancer.

Long-read RNA-seq has shed light on transcriptomic complexity, but questions remain about the functionality of downstream protein products. We introduce Biosurfer, a computational approach for comparing protein isoforms, while systematically tracking the transcriptional, splicing, and translational variations that underlie differences in the sequences of the protein products. Using Biosurfer, we analyzed the differences in 35,082 pairs of GENCODE annotated protein isoforms, finding a majority (70%) of variable N-termini are due to the alternative transcription start sites, while only 9% arise from 5' UTR alternative splicing (AS). Biosurfer's detailed tracking of nucleotide-to-residue relationships helps reveal an uncommonly tracked source of single amino acid residue changes arising from the codon splits at junctions. For 17% of internal sequence changes, such split codon patterns lead to single residue differences, termed "ragged codons." Of variable C-termini, 72% involve splice- or intron retention-induced reading frameshifts. We systematically characterize an unusual pattern of reading frame changes, in which the first frameshift is closely followed by a distinct second frameshift that restores the original frame, which we term a "snapback" frameshift. We analyze the long-read RNA-seq-predicted proteome of a human cell line and find similar trends as compared to our GENCODE analysis, with the exception of a higher proportion of transcripts predicted to undergo nonsense-mediated decay. Biosurfer's comprehensive characterization of long-read RNA-seq data sets should accelerate insights of the functional role of protein isoforms, providing mechanistic explanation of the origins of the proteomic diversity driven by the AS. Biosurfer is available as a Python package.

Despite progress in improving outcomes for oligoarticular and polyarticular juvenile idiopathic arthritis (JIA), the field still faces considerable challenges. More than half of adults who have had JIA continue to have active disease and have developed functional limitations. Medication side effects are common and intrusive. Thus, the field continues to search for therapeutic agents that target specific aspects of disease pathobiology and will be accompanied by fewer and less intrusive side effects. We identified 28 candidate target genes that were associated with JIA according to Open Targets Genetics and were also differentially expressed in the CD4+ T cells of children with active JIA (when compared to healthy control subjects). Of the 28 candidates, the strongest new target to emerge was homeodomain-interacting protein kinase 1 (HIPK1), which suppresses T cell activation and is within the PTPN22 locus tagged by rs6679677. This locus includes an enhancer element that contacts the HIPK1 promoter, and HIPK1 shows decreased expression in JIA CD4+ T cells when compared to controls. Gene Ontology terms associated with HIPK1 were overrepresented among the differentially expressed genes between JIA and controls, and PML, a known coregulator of HIPK1, showed a similar suppressed gene expression profile. Two downstream transcription factors of HIPK1, TP53 and GATA4, showed enriched binding patterns near the promoters of JIA up-regulated genes. Taken together, these data suggest a pathogenic role for HIPK1 in JIA and make it a prime candidate for therapeutic modulation.

Tetralogy of Fallot (TOF) is the most common cyanotic heart defect in neonates. While there is compelling evidence of genetic contribution to the etiology of TOF, the contribution of noncoding variants to the development of the defect remains unexplored. Potentially damaging noncoding de novo variants (NC DNVs) were detected from 141 Chinese nonsyndromic TOF trios (CHN-TOF) and compared with those detected in the Pediatric Cardiac Genomics Consortium (PCGC). Bioinformatic analyses on noncoding and previously detected coding DNVs were performed to identify developmental pathways affected in TOF. Chinese but not PCGC-TOF patients showed a notably increased burden of putative damaging NC DNVs (n = 249). In Chinese, NC and coding DNVs were predominantly associated with cardiomyocyte differentiation and with chamber/valve/aorta development, respectively, producing a combined enrichment in NOTCH signaling (p = 1.1 × 10-6) and outflow tract morphogenesis (p = 2.2 × 10-5). Genes with NC DNVs (e.g., EFNB2, HEY2, and PITX2) interacted with NOTCH1 and FLT4 in a tight STRING protein-protein interaction (PPI) network. During the in vitro cardiac differentiation process, these noncoding candidate genes, which harbored potentially damaging regulatory NC DNVs, exhibited co-expression with NOTCH signaling genes and demonstrated dysregulated gene expression at various differentiation stages following NOTCH1 downregulation. In summary, our findings highlight a significant contribution of NC DNVs to TOF and suggest the presence of population genetic heterogeneity. Integrative analyses implicate dysregulation of NOTCH signaling, with converging influences from both coding and noncoding variants, in TOF within the Chinese population.

The biological functions of extragenic enhancer RNAs and their impact on disease risk remain relatively underexplored. In this work, we develop in silico models of genetically regulated expression of enhancer RNAs across 49 cell and tissue types, characterizing their degree of genetic control. Leveraging the estimated genetically regulated expression for enhancer RNAs and canonical genes in a large-scale DNA biobank (N > 70,000) and high-resolution Hi-C contact data, we train a deep learning-based model of pairwise three-dimensional chromatin contact frequency for enhancer-enhancer and enhancer-gene pairs in cerebellum and whole blood. Notably, the use of genetically regulated expression of enhancer RNAs provides substantial tissue-specific predictive power, supporting a role for these transcripts in modulating spatial chromatin organization. We identify schizophrenia-associated enhancer RNAs independent of GWAS loci using enhancer RNA-based TWAS and determine the causal effects of these enhancer RNAs using Mendelian randomization. Using enhancer RNA-based TWAS, we generate a comprehensive resource of tissue-specific enhancer associations with complex traits in the UK Biobank. Finally, we show that a substantially greater proportion (63%) of GWAS associations colocalize with causal regulatory variation when enhancer RNAs are included.

Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Real-world networks, such as biological networks, often exhibit complex structures and have attributes associated with nodes, which leads to significant challenges for analysis and modeling. Community detection algorithms can help identify groups of nodes of particular importance. However, traditional methods focus primarily on topological information, overlooking the importance of attribute-based similarities. This limitation hinders their ability to identify functionally coherent subnetworks. To address this, we propose a new scoring method for graph partitioning on the basis of a novel similarity function between node attributes. We then adapt the Louvain algorithm to optimize this scoring function, enabling the identification of communities that are both densely connected and functionally coherent. Extensive experiments on diverse biological networks, including artificial and real-world datasets, demonstrate the superiority of our approach over state-of-the-art methods. By leveraging both topological and attribute-based information, our approach provides a powerful tool for uncovering biologically meaningful modules and gaining deeper insights into complex biological processes.

Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression profiles because of modeling challenges. Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. Using statistics derived from Borzoi's predicted coverage, we isolate and accurately score DNA variant effects across multiple layers of regulation, including transcription, splicing and polyadenylation. Evaluated on quantitative trait loci, Borzoi is competitive with and often outperforms state-of-the-art models trained on individual regulatory functions. By applying attribution methods to the derived statistics, we extract cis-regulatory motifs driving RNA expression and post-transcriptional regulation in normal tissues. The wide availability of RNA-seq data across species, conditions and assays profiling specific aspects of regulation emphasizes the potential of this approach to decipher the mapping from DNA sequence to regulatory function.

Small open-reading frame-encoded micropeptides within long noncoding RNAs (lncRNAs) are often overlooked due to their small size and low abundance. However, emerging evidence links these micropeptides to various biological pathways, though their roles in neural development and neurodegeneration remain unclear. Here, we investigate the function of murine micropeptide Sertm2, encoded by the lncRNA A730046J19Rik, during spinal motor neuron (MN) development. Sertm2 is predicted to be a conserved transmembrane protein found in both mouse and human, with subcellular analysis revealing that it is enriched in the cytoplasm and neurites. By generating C terminally Flag-tagged Sertm2 and expressing it from the A730046J19Rik locus, we demonstrate that the Sertm2 micropeptide localizes in spinal MNs in mice. The GDNF signaling-induced Etv4+ motor pool is impaired in Sertm2 knockout mice, which display motor nerve arborization defects that culminate in impaired motor coordination and muscle weakness. Similarly, human SERTM2 knockout iPSC-derived MNs also display reduced ETV4+ motor pools, highlighting that Sertm2 is a novel, evolutionarily conserved micropeptide essential for maintaining GDNF-induced MN subtype identity.

GATA6 expression is recognized as a favorable prognostic marker of pancreatic cancer, whereas TP53 is a poor prognostic marker. We evaluated treatment outcomes by genetic alterations in TP53 and GATA6 to determine the prognostic and predictive impact of co-alterations.

A single institution retrospective analysis was performed on patients diagnosed with pancreatic ductal adenocarcinoma between 2014 and 2023. TP53 genotype and GATA6 amplification status were included in an analysis of overall survival (OS) and progression-free survival (PFS). Previously published patient-derived organoids were used to investigate correlation between genetic status and drug sensitivity.

Patients with TP53 mutations had worse OS compared with the wild-type TP53 population. Patients with GATA6 amplification had better OS and a trend toward better PFS than the nonamplified population. Among patients with a TP53 mutation, patients with GATA6 co-alteration had longer OS compared with those who were not GATA6 amplified. In contrast, among patients who were TP53 wild-type, the presence or absence of a GATA6 amplification did not impact OS or PFS. GATA6 genotype was not associated chemotherapy drug response in an organoid pharmacotyping model.

We found that GATA6 amplification appeared to attenuate poor prognosis observed in TP53-mutant patients regardless of the type of standard chemotherapy received, suggesting the GATA6 amplification is a prognostic biomarker but not a predictive biomarker of standard-of-care chemotherapy.

N6-methyladenosine (m6A), the most abundant internal RNA modification in humans, regulates most aspects of RNA processing. Prostate cancer is characterized by widespread transcriptomic dysregulation; therefore, we characterized the m6A landscape of 162 localized prostate tumors with matched DNA, RNA and protein profiling. m6A abundance varied dramatically across tumors, with global patterns emerging via complex germline-somatic cooperative regulation. Individual germline polymorphisms regulated m6A abundance, cooperating with somatic mutation of cancer driver genes and m6A regulators. The resulting complex patterns were associated with prognostic clinical features and established the biomarker potential of global and locus-specific m6A patterns. Tumor hypoxia dysregulates m6A profiles, bridging prior genomic and proteomic observations. Specific m6A sites, such as those in VCAN, drive disease aggression, associating with poor outcomes, tumor growth and metastasis. m6A dysregulation is thus associated with key events in the natural history of prostate cancer: germline risk, microenvironmental dysregulation, somatic mutation and metastasis.

Atopic dermatitis is a highly heritable and common inflammatory skin condition affecting children and adults worldwide. Multi-ancestry approaches to atopic dermatitis genetic association studies are poised to boost power to detect genetic signal and identify loci contributing to atopic dermatitis risk. Here, we present a multi-ancestry GWAS meta-analysis of twelve atopic dermatitis cohorts from five ancestral populations totaling 56,146 cases and 602,280 controls. We report 101 genomic loci associated with atopic dermatitis, including 16 loci that have not been previously associated with atopic dermatitis or eczema. Fine-mapping, QTL colocalization, and cell-type enrichment analyses identified genes and cell types implicated in atopic dermatitis pathophysiology. Functional analyses in keratinocytes provide evidence for genes that could play a role in atopic dermatitis through epidermal barrier function. Our study provides insights into the etiology of atopic dermatitis by harnessing multiple genetic and functional approaches to unveil the mechanisms by which atopic dermatitis-associated variants impact genes and cell types.

Checkpoint blockade combined with chemotherapy has become an important treatment option for lung cancer patients in clinical settings. However, biomarkers that effectively identify true responders remain lacking. We assessed the potential of plasma small extracellular vesicle (sEV)-derived microRNAs (miRNAs) as biomarkers for predicting and identifying responders to combined immunochemotherapy. A total of 29 patients with lung adenocarcinoma who received pembrolizumab combined with pemetrexed and carboplatin were enrolled. The efficacy evaluation revealed that 24 patients obtained durable clinical benefits from combined immunochemotherapy, and the rest experienced disease progression. Using unsupervised hierarchical clustering, 56 differentially expressed miRNAs (DEMs) were identified between responders and nonresponders. Efficacy prediction models incorporating a combination of sEV miRNAs were established and showed good performance (area under the curve (AUC) > 0.9). In addition, we found that miR-96-5p and miR-6815-5p were notably downregulated in the nonresponder group, while miR-99b-3p, miR-100-5p, miR-193a-5p, and miR-320d were upregulated. These findings were further confirmed by clinical imaging. sEV miRNAs derived from patients with lung cancer showed promise for identifying true responders to combined immunochemotherapy.