Enrichr-KG: bridging enrichment analysis across multiple libraries

BDNF-TrkB signaling orchestrates the buildup process of local sleep

Sleep debt accumulates during wakefulness, leading to increased slow wave activity (SWA) during sleep, an encephalographic marker for sleep need. The use-dependent demands of prior wakefulness increase sleep SWA locally. However, the circuitry and molecular identity of this "local sleep" remain unclear. Using pharmacology and optogenetic perturbations together with transcriptomics, we find that cortical brain-derived neurotrophic factor (BDNF) regulates SWA via the activation of tyrosine kinase B (TrkB) receptor and cAMP-response element-binding protein (CREB). We map BDNF/TrkB-induced sleep SWA to layer 5 (L5) pyramidal neurons of the cortex, independent of neuronal firing per se. Using mathematical modeling, we here propose a model of how BDNF's effects on synaptic strength can increase SWA in ways not achieved through increased firing alone. Proteomic analysis further reveals that TrkB activation enriches ubiquitin and proteasome subunits. Together, our study reveals that local SWA control is mediated by BDNF-TrkB-CREB signaling in L5 excitatory cortical neurons.

Maternal α-casein deficiency extends the lifespan of offspring and programmes their body composition

Early nutrition has significant effects on physiological outcomes during adult life. We have analysed the effect of maternal α-casein (CSN1S1) deficiency on the physiological fate of dams and their offspring. α-casein deficiency reduces maternal milk protein concentration by more than 50% and attenuates the growth of pups to 27% (p < 0.001) of controls at the point of weaning. This is associated with a permanent reduction in adult body weight (- 31% at 25 weeks). Offspring nursed by α-casein deficient dams showed a significantly increased lifespan (+ 20%, χ2: 10.6; p = 0.001). Liver transcriptome analysis of offspring nursed by α-casein deficient dams at weaning revealed gene expression patterns similar to those found in dwarf mice (reduced expression of somatotropic axis signalling genes, increased expression of xenobiotic metabolism genes). In adult mice, the expression of somatotropic axis genes returned to control levels. This demonstrates that, in contrast to dwarf mice, attenuation of the GH-IGF signalling axis in offspring nursed by α-casein deficient dams is transient, while the changes in body size and lifespan are permanent. Offspring nursed by α-casein deficient dams showed permanent changes in body composition. Absolute and relative adipose tissue weights (p < 0.05), the percentage of body fat (p < 0.001) as well as adipocyte size in epididymal white adipose tissue are all reduced. Serum leptin levels were 25% of those found in control mice (p < 0.001). Liver lipid content and lipid composition were significantly altered in response to postnatal nutrition. This demonstrates the nutrition in early life programmes adult lipid metabolism, body composition and lifespan.

Effect of Short-Term Restraint Stress on the Hypothalamic Transcriptome Profiles of Rats with Inherited Stress-Induced Arterial Hypertension (ISIAH) and Normotensive Wistar Albino Glaxo (WAG) Rats

Emotional stress is one of the health risk factors in the modern human lifestyle. Stress exposure can provoke the manifestation of various pathological conditions, one of which is a sharp increase in the blood pressure level. In the present study, we analyzed changes in the transcriptome profiles of the hypothalamus of hypertensive ISIAH and normotensive WAG rats exposed to a single short-term restraint stress (the rat was placed in a tight wire-mesh cage for 2 h). This type of stress can be considered emotional stress. The functional annotation of differentially expressed genes allowed us to identify the most significantly altered biological processes in the hypothalamus of hypertensive and normotensive rats. The study made it possible to identify a group of genes that describe a general response to stress, independent of the rat genotype, as well as a hypothalamic response to stress specific to each strain. The alternatively changing expression of the Npas4 (neuronal PAS domain protein 4) gene, which is downregulated in the hypothalamus of the control WAG rats and induced in the hypothalamus of hypertensive ISIAH rats, is suggested to be the key event for understanding inter-strain differences in the hypothalamic response to stress. The stress-dependent ISIAH strain-specific induction of Fos and Jun gene transcription may play a crucial role in neuronal activation in this rat strain. The data obtained can be potentially useful in the selection of molecular targets for the development of pharmacological approaches to the correction of stress-induced pathologies related to neuronal excitability, taking into account the hypertensive status of the patients.

Transcript-Level Biomarkers of Early Lung Carcinogenesis in Bronchial Lesions

Premalignant lesions within the bronchial epithelium signify the initial phases of squamous cell lung carcinoma, posing challenges for detection via conventional methods. Instead of focusing solely on gene expression, in this study, we explore transcriptomic alterations linked to lesion progression, with an emphasis on protein-coding transcripts. We reanalyzed a publicly available RNA-Seq dataset on airway epithelial cells from 82 smokers with and without premalignant lesions. Transcript and gene abundance were quantified using kallisto, while differential expression and transcript usage analysis was performed utilizing sleuth and RATs packages. Functional characterization involved overrepresentation analysis via clusterProfiler, weighted coexpression network analysis (WGCNA), and network analysis via Enrichr-KG. We detected 5906 differentially expressed transcripts and 4626 genes, exhibiting significant enrichment within pathways associated with oxidative phosphorylation and mitochondrial function. Remarkably, transcript-level WGCNA revealed a single module correlated with dysplasia status, notably enriched in cilium-related biological processes. Notable hub transcripts included RABL2B (ENST00000395590), DNAH1 (ENST00000420323), EFHC1 (ENST00000635996), and VWA3A (ENST00000563389) along with transcription factors such as FOXJ1 and ZNF474 as potential regulators. Our findings underscore the value of transcript-level analysis in uncovering novel insights into premalignant bronchial lesion biology, including identification of potential biomarkers associated with early lung carcinogenesis.

Small RNA signatures of acute ischemic stroke in L1CAM positive extracellular vesicles

L1CAM-positive extracellular vesicles (L1EV) are an emerging biomarker that may better reflect ongoing neuronal damage than other blood-based biomarkers. The physiological roles and regulation of L1EVs and their small RNA cargoes following stroke is unknown. We sought to characterize L1EV small RNAs following stroke and assess L1EV RNA signatures for diagnosing stroke using weighted gene co-expression network analysis and random forest (RF) machine learning algorithms. Interestingly, small RNA sequencing of plasma L1EVs from patients with stroke and control patients (n = 28) identified micro(mi)RNAs known to be enriched in the brain. Weighted gene co-expression network analysis (WGCNA) revealed small RNA transcript modules correlated to diagnosis, initial NIH stroke scale, and age. L1EV RNA signatures associated with the diagnosis of AIS were derived from WGCNA and RF classification. These small RNA signatures demonstrated a high degree of accuracy in the diagnosis of AIS with an area under the curve (AUC) of the signatures ranging from 0.833 to 0.932. Further work is necessary to understand the role of small RNA L1EV cargoes in the response to brain injury, however, this study supports the utility of L1EV small RNA signatures as a biomarker of stroke.

PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach

Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.

DNA methylation and type 2 diabetes: a systematic review

OBJECTIVE

DNA methylation influences gene expression and function in the pathophysiology of type 2 diabetes mellitus (T2DM). Mapping of T2DM-associated DNA methylation could aid early detection and/or therapeutic treatment options for diabetics.

DESIGN

A systematic literature search for associations between T2DM and DNA methylation was performed. Prospero registration ID: CRD42020140436.

METHODS

PubMed and ScienceDirect databases were searched (till October 19, 2023). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and New Castle Ottawa scale were used for reporting the selection and quality of the studies, respectively.

RESULT

Thirty-two articles were selected. Four of 130 differentially methylated genes in blood, adipose, liver or pancreatic islets (TXNIP, ABCG1, PPARGC1A, PTPRN2) were reported in > 1 study. TXNIP was hypomethylated in diabetic blood across ethnicities. Gene enrichment analysis of the differentially methylated genes highlighted relevant disease pathways (T2DM, type 1 diabetes and adipocytokine signaling). Three prospective studies reported association of methylation in IGFBP2, MSI2, FTO, TXNIP, SREBF1, PHOSPHO1, SOCS3 and ABCG1 in blood at baseline with incident T2DM/hyperglycemia. Sex-specific differential methylation was reported only for HOOK2 in visceral adipose tissue (female diabetics: hypermethylated, male diabetics: hypomethylated). Gene expression was inversely associated with methylation status in 8 studies, in genes including ABCG1 (blood), S100A4 (adipose tissue), PER2 (pancreatic islets), PDGFA (liver) and PPARGC1A (skeletal muscle).

CONCLUSION

This review summarizes available evidence for using DNA methylation patterns to unravel T2DM pathophysiology. Further validation studies in diverse populations will set the stage for utilizing this knowledge for identifying early diagnostic markers and novel druggable pathways.

Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141 , IGF1R , TTN , and TNKS. Several loci where variants were deemed insignificant in univariate genome-wide association analyses are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we found strong gene co-expression correlations between these statistical epistasis contributors in healthy hearts and a significant connectivity decrease in failing hearts. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R . Our results expand the scope of genetic regulation of cardiac structure to epistasis.

Liver-innervating vagal sensory neurons play an indispensable role in the development of hepatic steatosis and anxiety-like behavior in mice fed a high-fat diet

Background and Aims

The visceral organ-brain axis, mediated by vagal sensory neurons in the vagal nerve ganglion, is essential for maintaining various physiological functions. In this study, we investigated the impact of liver-projecting vagal sensory neurons on energy balance, hepatic steatosis, and anxiety-like behavior in mice under obesogenic conditions.

Methods

We performed single-nucleus RNA sequencing of vagal sensory neurons innervating the liver. Based on our snRNA-Seq results, we used the Avil CreERT2 strain to identify vagal sensory neurons that innervate the liver.

Results

A small subset of polymodal sensory neurons innervating the liver was located in the left and right ganglia, projecting centrally to the nucleus of the tractus solitarius, area postrema, and dorsal motor nucleus of the vagus, and peripherally to the periportal areas in the liver. Male and female control mice developed diet-induced obesity (DIO) during high-fat diet feeding. Deleting liver-projecting advillin-positive vagal sensory neurons prevented DIO in male and female mice, and these outcomes are associated with increased energy expenditure. Although males and females exhibited improved glucose homeostasis following disruption of liver-projecting vagal sensory neurons, only male mice displayed increased insulin sensitivity. The loss of liver-projecting vagal sensory neurons limited the progression of hepatic steatosis in male and female mice fed a steatogenic diet. Finally, mice lacking liver-innervating vagal sensory neurons exhibited less anxiety-like behavior compared to the control mice.

Conclusions

The liver-brain axis contributes to the regulation of energy balance, glucose tolerance, hepatic steatosis, and anxiety-like behavior depending on the nutrient status in healthy and obesogenic conditions.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Immune Characteristic Genes and Neutrophil Immune Transformation Studies in Severe COVID-19

As a disease causing a global pandemic, the progression of symptoms to severe disease in patients with COVID-19 often has adverse outcomes, but research on the immunopathology of COVID-19 severe disease remains limited. In this study, we used mRNA-seq data from the peripheral blood of COVID-19 patients to identify six COVID-19 severe immune characteristic genes (FPR1, FCGR2A, TLR4, S100A12, CXCL1, and L TF), and found neutrophils to be the critical immune cells in COVID-19 severe disease. Subsequently, using scRNA-seq data from bronchoalveolar lavage fluid from COVID-19 patients, neutrophil subtypes highly expressing the S100A family were found to be located at the end of cellular differentiation and tended to release neutrophil extracellular traps. Finally, it was also found that alveolar macrophages, macrophages, and monocytes with a high expression of COVID-19 severe disease immune characteristic genes may influence neutrophils through intercellular ligand-receptor pairs to promote neutrophil extracellular trap release. This study provides immune characteristic genes, critical immune pathways, and immune cells in COVID-19 severe disease, explores intracellular immune transitions of critical immune cells and pit-induced intercellular communication of immune transitions, and provides new biomarkers and potential drug targets for the treatment of patients with COVID-19 severe disease.

Dysregulated AEBP1 and COLEC12 Genes in Late-Onset Alzheimer's Disease: Insights from Brain Cortex and Peripheral Blood Analysis

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by memory and cognitive impairment, often accompanied by alterations in mood, confusion, and, ultimately, a state of acute mental disturbance. The cerebral cortex is considered a promising area for investigating the underlying causes of AD by analyzing transcriptional patterns, which could be complemented by investigating blood samples obtained from patients. We analyzed the RNA expression profiles of three distinct areas of the brain cortex, including the frontal cortex (FC), temporal cortex (TC), and entorhinal cortex (EC) in patients with AD. Functional enrichment analysis was performed on the differentially expressed genes (DEGs) across the three regions. The two genes with the most significant expression changes in the EC region were selected for assessing mRNA expression levels in the peripheral blood of late-onset AD patients using quantitative PCR (qPCR). We identified eight shared DEGs in these regions, including AEBP1 and COLEC12, which exhibited prominent changes in expression. Functional enrichment analysis uncovered a significant association of these DEGs with the transforming growth factor-β (TGF-β) signaling pathway and processes related to angiogenesis. Importantly, we established a robust connection between the up-regulation of AEBP1 and COLEC12 in both the brain and peripheral blood. Furthermore, we have demonstrated the potential of AEBP1 and COLEC12 genes as effective diagnostic tools for distinguishing between late-onset AD patients and healthy controls. This study unveils the intricate interplay between AEBP1 and COLEC12 in AD and underscores their potential as markers for disease detection and monitoring.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

Overview of the Knowledge Management Center for Illuminating the Druggable Genome

The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.

Identifying immune signatures of sepsis to increase diagnostic accuracy in very preterm babies

Bacterial infections are a major cause of mortality in preterm babies, yet our understanding of early-life disease-associated immune dysregulation remains limited. Here, we combine multi-parameter flow cytometry, single-cell RNA sequencing and plasma analysis to longitudinally profile blood from very preterm babies (<32 weeks gestation) across episodes of invasive bacterial infection (sepsis). We identify a dynamically changing blood immune signature of sepsis, including lymphopenia, reduced dendritic cell frequencies and myeloid cell HLA-DR expression, which characterizes sepsis even when the common clinical marker of inflammation, C-reactive protein, is not elevated. Furthermore, single-cell RNA sequencing identifies upregulation of amphiregulin in leukocyte populations during sepsis, which we validate as a plasma analyte that correlates with clinical signs of disease, even when C-reactive protein is normal. This study provides insights into immune pathways associated with early-life sepsis and identifies immune analytes as potential diagnostic adjuncts to standard tests to guide targeted antibiotic prescribing.

D2H2: diabetes data and hypothesis hub

Motivation

There is a rapid growth in the production of omics datasets collected by the diabetes research community. However, such published data are underutilized for knowledge discovery. To make bioinformatics tools and published omics datasets from the diabetes field more accessible to biomedical researchers, we developed the Diabetes Data and Hypothesis Hub (D2H2).

Results

D2H2 contains hundreds of high-quality curated transcriptomics datasets relevant to diabetes, accessible via a user-friendly web-based portal. The collected and processed datasets are curated from the Gene Expression Omnibus (GEO). Each curated study has a dedicated page that provides data visualization, differential gene expression analysis, and single-gene queries. To enable the investigation of these curated datasets and to provide easy access to bioinformatics tools that serve gene and gene set-related knowledge, we developed the D2H2 chatbot. Utilizing GPT, we prompt users to enter free text about their data analysis needs. Parsing the user prompt, together with specifying information about all D2H2 available tools and workflows, we answer user queries by invoking the most relevant tools via the tools' API. D2H2 also has a hypotheses generation module where gene sets are randomly selected from the bulk RNA-seq precomputed signatures. We then find highly overlapping gene sets extracted from publications listed in PubMed Central with abstract dissimilarity. With the help of GPT, we speculate about a possible explanation of the high overlap between the gene sets. Overall, D2H2 is a platform that provides a suite of bioinformatics tools and curated transcriptomics datasets for hypothesis generation.

Availability and implementation

D2H2 is available at: https://d2h2.maayanlab.cloud/ and the source code is available from GitHub at https://github.com/MaayanLab/D2H2-site under the CC BY-NC 4.0 license.

miRGediNET: A comprehensive examination of common genes in miRNA-Target interactions and disease associations: Insights from a grouping-scoring-modeling approach

In the broad and complex field of biological data analysis, researchers frequently gather information from a single source or database. Despite being a widespread practice, this has disadvantages. Relying exclusively on a single source can limit our comprehension as it may omit various perspectives that could be obtained by combining multiple knowledge bases. Acknowledging this shortcoming, we report on miRGediNET, a novel approach combining information from three biological databases. Our investigation focuses on microRNAs (miRNAs), small non-coding RNA molecules that regulate gene expression post-transcriptionally. We delve deeply into the knowledge of these miRNA's interactions with genes and the possible effects these interactions may have on different diseases. The scientific community has long recognized a direct correlation between the progression of specific diseases and miRNAs, as well as the genes they target. By using miRGediNET, we go beyond simply acknowledging this relationship. Rather, we actively look for the critical genes that could act as links between the actions of miRNAs and the mechanisms underlying disease. Our methodology, which carefully identifies and investigates these important genes, is supported by a strategic framework that may open up new possibilities for comprehending diseases and creating treatments. We have developed a tool on the Knime platform as a concrete application of our research. This tool serves as both a validation of our study and an invitation to the larger community to interact with, investigate, and build upon our findings. miRGediNET is publicly accessible on GitHub at https://github.com/malikyousef/miRGediNET, providing a collaborative environment for additional research and innovation for enthusiasts and fellow researchers.

Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets

OBJECTIVES

Genome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. However, identifying the causal genes, pathways, and tissues/cell types responsible for these associations remains a challenge, and standardized analysis workflows are lacking. Additionally, due to limited treatment options for obesity, there is a need for the development of new pharmacological therapies. This study aimed to address these issues by performing step-wise utilization of knowledgebase for gene prioritization and assessing the potential relevance of key obesity genes as therapeutic targets.

METHODS AND RESULTS

First, we generated a list of 28,787 obesity-associated SNPs from the publicly available GWAS dataset (approximately 800,000 individuals in the GIANT meta-analysis). Then, we prioritized 1372 genes with significant in silico evidence against genomic and transcriptomic data, including transcriptionally regulated genes in the brain from transcriptome-wide association studies. In further narrowing down the gene list, we selected key genes, which we found to be useful for the discovery of potential drug seeds as demonstrated in lipid GWAS separately. We thus identified 74 key genes for obesity, which are highly interconnected and enriched in several biological processes that contribute to obesity, including energy expenditure and homeostasis. Of 74 key genes, 37 had not been reported for the pathophysiology of obesity. Finally, by drug-gene interaction analysis, we detected 23 (of 74) key genes that are potential targets for 78 approved and marketed drugs.

CONCLUSIONS

Our results provide valuable insights into new treatment options for obesity through a data-driven approach that integrates multiple up-to-date knowledgebases.

An Anterior Second Heart Field Enhancer Regulates the Gene Regulatory Network of the Cardiac Outflow Tract

BACKGROUND

Conotruncal defects due to developmental abnormalities of the outflow tract (OFT) are an important cause of cyanotic congenital heart disease. Dysregulation of transcriptional programs tuned by NKX2-5 (NK2 homeobox 5), GATA6 (GATA binding protein 6), and TBX1 (T-box transcription factor 1) have been implicated in abnormal OFT morphogenesis. However, there remains no consensus on how these transcriptional programs function in a unified gene regulatory network within the OFT.

METHODS

We generated mice harboring a 226-nucleotide deletion of a highly conserved cardiac enhancer containing 2 GATA-binding sites located ≈9.4 kb upstream of the transcription start site of Nkx2-5 (Nkx2-5∆enh) using CRISPR-Cas9 gene editing and assessed phenotypes. Cardiac defects in Nkx2-5∆enh/∆enh mice were structurally characterized using histology and scanning electron microscopy, and physiologically assessed using electrocardiography, echocardiography, and optical mapping. Transcriptome analyses were performed using RNA sequencing and single-cell RNA sequencing data sets. Endogenous GATA6 interaction with and activity on the NKX2-5 enhancer was studied using chromatin immunoprecipitation sequencing and transposase-accessible chromatin sequencing in human induced pluripotent stem cell-derived cardiomyocytes.

RESULTS

Nkx2-5∆enh/∆enh mice recapitulated cyanotic conotruncal defects seen in patients with NKX2-5, GATA6, and TBX1 mutations. Nkx2-5∆enh/∆enh mice also exhibited defects in right Purkinje fiber network formation, resulting in right bundle-branch block. Enhancer deletion reduced embryonic Nkx2-5 expression selectively in the right ventricle and OFT of mutant hearts, indicating that enhancer activity is localized to the anterior second heart field. Transcriptional profiling of the mutant OFT revealed downregulation of important genes involved in OFT rotation and septation, such as Tbx1, Pitx2, and Sema3c. Endogenous GATA6 interacted with the highly conserved enhancer in human induced pluripotent stem cell-derived cardiomyocytes and in wild-type mouse hearts. We found critical dose dependency of cardiac enhancer accessibility on GATA6 gene dosage in human induced pluripotent stem cell-derived cardiomyocytes.

CONCLUSIONS

Our results using human and mouse models reveal an essential gene regulatory network of the OFT that requires an anterior second heart field enhancer to link GATA6 with NKX2-5-dependent rotation and septation gene programs.

PerSEveML: A Web-Based Tool to Identify Persistent Biomarker Structure for Rare Events Using Integrative Machine Learning Approach

Omics datasets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these datasets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there remains a limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach1, we introduce PerSEveML, an interactive web-based that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.

Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms.

AVAILABILITY AND IMPLEMENTATION

Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.