Integrative cross-species transcriptome analysis reveals earlier occurrence of myelopoiesis in pre-circulation primates compared to mice.

Hematopoiesis originates in the yolk sac, which forms prior to the establishment of blood circulation and exhibits distinct developmental processes between primates and mice. Despite increasing appreciation of yolk sac hematopoiesis for its lifelong contribution to the adult hematopoietic system and its regulatory roles in organogenesis, cross-species differences, particularly before the onset of blood circulation, remain incompletely understood. In this study, we constructed an integrative cross-species transcriptome atlas of pre-circulation hematopoiesis in humans, monkeys ( Macaca fascicularis), and mice. This analysis identified conserved populations between primates and mice, while also revealing more differentiated myeloid, erythroid, and megakaryocytic lineages in pre-circulation primates compared to mice. Specifically, SPP1-expressing macrophages were detected in primates before the onset of blood circulation but were absent in mice. Cell-cell communication analysis identified CSF1 + extraembryonic mesoderm cells as a potential supportive niche for macrophage generation, with ligand-receptor interactions between macrophages and other cell populations in the human yolk sac. Interestingly, pre-circulation SPP1 + macrophages exhibited hallmark signatures reminiscent of a macrophage subset that positively regulates hematopoietic stem cell generation. Our findings provide a valuable cross-species resource, advancing our understanding of human pre-circulation yolk sac hematopoiesis and offering a theoretical basis for the regeneration of functional blood cells.

Corrigendum: Comprehensive analysis of the prognostic and immunological role of PAFAH1B in pan-cancer.

[This corrects the article DOI: 10.3389/fmolb.2021.799497.].

Heterogeneity-aware integrative regression for ancestry-specific association studies.

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.

Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes.

Breast cancer is a heterogeneous disease composed of various biologically distinct subtypes, each characterized by unique molecular features. Its formation and progression involve a complex, multistep process that includes the accumulation of numerous genetic and epigenetic alterations. Although integrating RNA-seq transcriptome data with ATAC-seq epigenetic information provides a more comprehensive understanding of gene regulation and its impact across different conditions, no classification model has yet been developed for breast cancer intrinsic subtypes based on such integrative analyses. In this study, we employed machine learning algorithms to predict intrinsic subtypes through the integrative analysis of ATAC-seq and RNA-seq data. We identified 10 signature genes (CDH3, ERBB2, TYMS, GREB1, OSR1, MYBL2, FAM83D, ESR1, FOXC1, and NAT1) using recursive feature elimination with cross-validation (RFECV) and a support vector machine (SVM) based on SHAP (SHapley Additive exPlanations) feature importance. Furthermore, we found that these genes were primarily associated with immune responses, hormone signaling, cancer progression, and cellular proliferation.

Identification of susceptibility loci and relevant cell type for IgA nephropathy in Han Chinese by integrative genome-wide analysis.

Although many susceptibility loci for IgA nephropathy (IgAN) have been identified, they only account for 11.0% of the overall IgAN variance. We performed a large genome-wide meta-analysis of IgAN in Han Chinese with 3616 cases and 10 417 controls to identify additional genetic loci of IgAN. Considering that inflammatory bowel disease (IBD) and asthma might share an etiology of dysregulated mucosal immunity with IgAN, we performed cross-trait integrative analysis by leveraging functional annotations of relevant cell type and the pleiotropic information from IBD and asthma. Among 8 669 456 imputed variants, we identified a novel locus at 4p14 containing the long noncoding RNA LOC101060498. Cell type enrichment analysis based on annotations suggested that PMA-I-stimulated CD4+CD25-IL17+ Th17 cell was the most relevant cell type for IgAN, which highlights the essential role of Th17 pathway in the pathogenesis of IgAN. Furthermore, we identified six more novel loci associated with IgAN, which included three loci showing pleiotropic effects with IBD or asthma (2q35/PNKD, 6q25.2/SCAF8, and 22q11.21/UBE2L3) and three loci specific to IgAN (14q32.32/TRAF3, 16q22.2/TXNL4B, and 21q21.3/LINC00113) in the pleiotropic analysis. Our findings support the involvement of mucosal immunity, especially T cell immune response and IL-17 signal pathway, in the development of IgAN and shed light on further investigation of IgAN.

Genomic and transcriptomic landscape to decipher the genetic basis of hyperpigmentation in Lanping black-boned sheep (Ovis aries).

BACKGROUND: Lanping black-boned sheep (LPB) represent a distinctive mammalian species characterized by hyperpigmentation, resulting in black bone and muscle features, in contrast to their conventional counterparts exhibiting red muscle and white bone. The genetic basis underlying LPB hyperpigmentation has remained enigmatic. METHODS: In this study, we conducted whole-genome sequencing of 100 LPB and 50 Lanping normal sheep (LPN), and integrated this data with 421 sequenced datasets from wild and domestic sheep, shedding light on the genetic backdrop and genomic variations associated with LPB. Furthermore, we performed comparative RNA-Seq analysis using liver sample to pinpoint genes implicated in the pigmentation process. We generated a comprehensive dataset comprising 97,944,357 SNPs from 571 sheep, facilitating an in-depth exploration of genetic factors. RESULTS: Population genetic structure analysis revealed that the LPB breed traces its origin back to LPN, having evolved into a distinct breed. The integration of positively selected genes with differentially expressed genes identified two candidates, ERBB4 and ROR1, potentially linked to LPB hyperpigmentation. Comparative analysis of ERBB4 and ROR1 mRNA relative expression levels in liver, spleen, and kidney tissues of LPB, in comparison to Diqing sheep, revealed significant upregulation, except for ERBB4 in the liver. Gene expression heatmaps further underscored marked allelic frequency disparities in different populations. CONCLUSION: Our findings establish the evolutionary lineage of the LPB breed from LPN and underscore the involvement of ERBB4 and ROR1 genes in melanin synthesis. These results enhance our comprehension of the molecular basis of hyperpigmentation and contribute to a more comprehensive depiction of sheep diversity.

Subchronic Chloroform Exposure Causes Intestinal Damage and Induces Gut Microbiota Disruption and Metabolic Dysregulation in Mice.

Chloroform is a prevalent toxic environmental pollutant in urban settings, posing risks to human health through exposure via various mediums such as air and tap water. The gut microbiota plays a pivotal role in maintaining host health. However, there is a paucity of research elucidating the impact of chloroform exposure on the gut microbiota. In this investigation, 18 SPF Kunming female mice were stratified into three groups (n = 6) and subjected to oral gavage with chloroform doses equivalent to 0, 50, and 150 mg/kg of body weight over 30 days. Our findings demonstrate that subchronic chloroform exposure significantly perturbs hematological parameters in mice and induces histopathological alterations in cecal tissues, consequently engendering marked disparities in the functional composition of cecal microbiota and metabolic equilibrium of cecal contents. Ultimately, our investigation revealed a statistically robust correlation, exhibiting a high degree of significance, between the intestinal microbiome composition and the metabolites that were differentially expressed consequent to chloroform exposure.

CHIKV infection drives shifts in the gastrointestinal microbiome and metabolites in rhesus monkeys.

BACKGROUND: Many studies have demonstrated the association between intestinal microbiota and joint diseases. The "gut-joint axis" also has potential roles in chikungunya virus (CHIKV) infection. Pro-inflammatory arthritis after CHIKV infection might disrupt host homeostasis and lead to dysbacteriosis. This study investigated the characteristics of fecal and gut microbiota, intestinal metabolites, and the changes in gene regulation of intestinal tissues after CHIKV infection using multi-omics analysis to explore the involvement of gut microbiota in the pathogenesis of CHIKV infection. RESULTS: CHIKV infection increases the systemic burden of inflammation in the GI system of infected animals. Moreover, infection-induced alterations in GI microbiota and metabolites may be indirectly involved in the modulation of GI and bone inflammation after CHIKV infection, including the modulation of inflammasomes and interleukin-17 inflammatory cytokine levels. CONCLUSION: Our results suggest that the GI tract and its microbes are involved in the modulation of CHIKV infection, which could serve as an indicator for the adjuvant treatment of CHIKV infection. Video Abstract.

Integrative microbiome and metabolome profiles reveal the impacts of periodontitis via oral-gut axis in first-trimester pregnant women.

BACKGROUND: Periodontitis results from host-microbe dysbiosis and the resultant dysregulated immunoinflammatory response. Importantly, it closely links to numerous systemic comorbidities, and perplexingly contributes to adverse pregnancy outcomes (APOs). Currently, there are limited studies on the distal consequences of periodontitis via oral-gut axis in pregnant women. This study investigated the integrative microbiome-metabolome profiles through multi-omics approaches in first-trimester pregnant women and explored the translational potentials. METHODS: We collected samples of subgingival plaques, saliva, sera and stool from 54 Chinese pregnant women at the first trimester, including 31 maternal periodontitis (Perio) subjects and 23 Non-Perio controls. By integrating 16S rRNA sequencing, untargeted metabolomics and clinical traits, we explored the oral-gut microbial and metabolic connection resulting from periodontitis among early pregnant women. RESULTS: We demonstrated a novel bacterial distinguisher Coprococcus from feces of periodontitis subjects in association with subgingival periodontopathogens, being different from other fecal genera in Lachnospiraceae family. The ratio of fecal Coprococcus to Lachnoclostridium could discriminate between Perio and Non-Perio groups as the ratio of subgingival Porphyromonas to Rothia did. Furthermore, there were differentially abundant fecal metabolic features pivotally enriched in periodontitis subjects like L-urobilin and kynurenic acid. We revealed a periodontitis-oriented integrative network cluster, which was centered with fecal Coprococcus and L-urobilin as well as serum triglyceride. CONCLUSIONS: The current findings about the notable influence of periodontitis on fecal microbiota and metabolites in first-trimester pregnant women via oral-gut axis signify the importance and translational implications of preconceptional oral/periodontal healthcare for enhancing maternal wellbeing.

Identification of novel myelodysplastic syndromes prognostic subgroups by integration of inflammation, cell-type composition, and immune signatures in the bone marrow.

Mutational profiles of myelodysplastic syndromes (MDS) have established that a relatively small number of genetic aberrations, including SF3B1 and SRSF2 spliceosome mutations, lead to specific phenotypes and prognostic subgrouping. We performed a multi-omics factor analysis (MOFA) on two published MDS cohorts of bone marrow mononuclear cells (BMMNCs) and CD34 + cells with three data modalities (clinical, genotype, and transcriptomics). Seven different views, including immune profile, inflammation/aging, retrotransposon (RTE) expression, and cell-type composition, were derived from these modalities to identify the latent factors with significant impact on MDS prognosis. SF3B1 was the only mutation among 13 mutations in the BMMNC cohort, indicating a significant association with high inflammation. This trend was also observed to a lesser extent in the CD34 + cohort. Interestingly, the MOFA factor representing the inflammation shows a good prognosis for MDS patients with high inflammation. In contrast, SRSF2 mutant cases show a granulocyte-monocyte progenitor (GMP) pattern and high levels of senescence, immunosenescence, and malignant myeloid cells, consistent with their poor prognosis. Furthermore, MOFA identified RTE expression as a risk factor for MDS. This work elucidates the efficacy of our integrative approach to assess the MDS risk that goes beyond all the scoring systems described thus far for MDS.

Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities.

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future.

HERC5: a comprehensive in silico analysis of its diagnostic, prognostic, and therapeutic potential in cancer.

HERC5, a vital protein in the HERC family, plays crucial roles in immune response, cancer progression, and antiviral defense. This bioinformatic study comprehensively assessed HERC5's significance across various malignancies by analyzing its gene expression, immune and molecular subtype expressions, target proteins, biological functions, and prognostic and diagnostic values in pan-cancer. We further examined its correlation with clinical features, co-expressed and differentially expressed genes, and prognosis in clinical subgroups, focusing on endometrial cancer (UCEC). Our findings showed that HERC5 RNA is expressed at low levels in most cancers and significantly differs across immune and molecular subtypes. HERC5 accurately predicts cancer and correlates with most cancer prognoses. In UCEC, HERC5 was significantly associated with age, hormonal status, clinical stage, treatment status, and metastasis. Elevated HERC5 expression was linked to worse progression-free interval, disease-specific survival, and overall survival in UCEC, particularly in diverse clinical subgroups. Significant differences in HERC5 expression were also observed in various human cancer cell line validations. In summary, HERC5 may be a critical biomarker for pan-cancer prognosis, progression, and diagnosis, as well as a promising new target for cancer therapy.

Integration of proteomics and metabolomics analysis investigate mechanism of As-induced immune injury in rat spleen.

Arsenic (As) is a widespread metalloid and human carcinogen found in the natural environment, and multiple toxic effects have been shown to be associated with As exposure. As can be accumulated in the spleen, the largest peripheral lymphatic organ, and long-term exposure to As can lead to splenic injury. In this study, a Sprague-Dawley (SD) rat model of As-poisoned was established, aiming to explore the molecular mechanism of As-induced immune injury through the combined analysis of proteomics and metabolomics of rats' spleen. After feeding the rats with As diet (50 mg/kg) for 90 days, the spleen tissue of the rats in the As-poisoned group was damaged, the level of As was significantly higher than that of the control group (P < 0.001), and the level of inflammatory cytokine interleukin-6 (IL-6) was decreased (P < 0.01). Proteomics and metabolomics results showed that a total of 134 differentially expressed proteins (DEPs) (P < 0.05 and fold change > 1.2) and 182 differentially expressed metabolites (DEMs) (VIP >1 and P < 0.05) were identified in the spleens of the As poisoned group compared to the control group (As/Ctrl). The proteomic results highlight the role of hypoxia-inducible factors (HIF), natural killer cell mediated cytotoxicity, and ribosomes. The major pathways of metabolic disruption included arachidonic acid (AA) metabolism, glycerophospholipid metabolism and folate single-carbon pool. The integrated analysis of these two omics suggested that Hmox1, Stat3, arachidonic acid, phosphatidylcholine and leukotriene B4 may play key roles in the mechanism of immune injury to the spleen by As exposure. The results indicate that As exposure can cause spleen damage in rats. Through proteomic and metabolomic analysis, the key proteins and metabolites and their associated mechanisms were obtained, which provided a basis for further understanding of the molecular mechanism of spleen immune damage caused by As exposure.

Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers.

Changes in plasma and fecal metabolomes in colorectal cancer (CRC) progression (normal-adenoma-CRC) remain unclear. Here, plasma and fecal samples were collected from four independent cohorts of 1,251 individuals (422 CRC, 399 colorectal adenoma [CRA], and 430 normal controls [NC]). By metabolomic profiling, signature plasma and fecal metabolites with consistent shift across NC, CRA, and CRC are identified, including CRC-enriched oleic acid and CRC-depleted allocholic acid. Oleic acid exhibits pro-tumorigenic effects in CRC cells, patient-derived organoids, and two murine CRC models, whereas allocholic acid has opposing effects. By integrative analysis, we found that oleic acid or allocholic acid directly binds to α-enolase or farnesoid X receptor-1 in CRC cells, respectively, to modulate cancer-associated pathways. Clinically, we establish a panel of 17 plasma metabolites that accurately diagnoses CRC in a discovery and three validation cohorts (AUC = 0.848-0.987). Overall, we characterize metabolite signatures, mechanistic significance, and diagnostic potential of plasma and fecal metabolomes in CRC.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

Unveiling Commonalities and Differences in Genetic Regulations via Two-Way Fusion.

Understanding the genetic regulation, for example, gene expressions (GEs) by copy number variations and methylations, is crucial to uncover the development and progression of complex diseases. Advancing from early studies that are mostly focused on homogeneous groups of patients, some recent studies have shifted their focus toward different patient groups, explored their commonalities and differences, and led to insightful findings. However, the analysis can be very challenging with one GE possibly regulated by multiple regulators and one regulator potentially regulating the expressions of multiple genes, leading to two distinct types of commonalities/differences in the patterns of genetic regulation. In addition, the high dimensionality of both sides of regulation poses challenges to computation. In this study, we develop a two-way fusion integrative analysis approach, which innovatively applies two fusion penalties to simultaneously identify commonalities/differences in the regulated pattern of GEs and regulating pattern of regulators, and adopt a Huber loss function to accommodate the possible data contamination. Moreover, a simple yet efficient iterative optimization algorithm is developed, which does not need to introduce any auxiliary variables and extra tuning parameters and is guaranteed to converge to a globally optimal solution. The advantages of the proposed approach are demonstrated in extensive simulations. The analysis of The Cancer Genome Atlas data on melanoma and lung cancer leads to interesting findings and satisfactory prediction performance.

Pan-cancer analysis of the prognostic and immunological role of RPL4.

Ribosomal proteins (RPs) play an important role in the overall stability, function, and integrity of ribosomes. Ribosomal protein L4 (RPL4), which is encoded by RPL4, is assumed to play different roles in different cancers due to the strong correlation between them. However, research based on the underlying mechanisms of this correlations is limited. Therefore, this study investigated the biological role of RPL4 in various cancers. The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to compare the differential expression of RPL4 in tumor and normal tissues. The Sangerbox database and Kaplan-Meier method were employed to assess RPL4's impact on the prognosis of pan-cancer. Analyses using the cBioPortal tool, Shiny Methylation Analysis Resource Tool (SMART), and MethSurv provided insights into the methylation and epigenetic alterations of RPL4. Gene enrichment analysis revealed that RPL4 is involved in ribosome biogenesis through multiple pathways, and its enrichment in signaling pathways directly or indirectly influence tumor development. Tumor Immune Single-cell Hub (TISCH) was used to analyze RPL4 expression levels and cellular functions in the tumor microenvironment. Tumor Immune Estimation Resource Database 2.0 (TIMER2.0) and Tumor-Immune System Interactions Database (TISIDB) tools revealed that RPL4 affected the immune infiltration potential of tumors. Furthermore, the application of the ROC mapper and CellMiner databases indicated an association between RPL4 and sensitivity to multiple antitumor drugs. Additionally, RPL4 was found to remodel the tumor immune microenvironment, leading to the development of chemoresistance. In conclusion, the findings suggest that RPL4 can be used as a potential tumor biomarker and may serve as a target for immunotherapy in various cancers. Genetic testing of RPL4 provides a foundation for the diagnosis, prognosis, and treatment of clinical tumors.

A critical review of the impact of candidate copy number variants on autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder (NDD) influenced by genetic, epigenetic, and environmental factors. Recent advancements in genomic analysis have shed light on numerous genes associated with ASD, highlighting the significant role of both common and rare genetic mutations, as well as copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and unique de novo variants. These genetic variations disrupt neurodevelopmental pathways, contributing to the disorder's complexity. Notably, CNVs are present in 10 %-20 % of individuals with autism, with 3 %-7 % detectable through cytogenetic methods. While the role of submicroscopic CNVs in ASD has been recently studied, their association with genomic loci and genes has not been thoroughly explored. In this review, we focus on 47 CNV regions linked to ASD, encompassing 1632 genes, including protein-coding genes and long non-coding RNAs (lncRNAs), of which 659 show significant brain expression. Using a list of ASD-associated genes from SFARI, we detect 17 regions harboring at least one known ASD-related protein-coding gene. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding gene with brain-enriched expression and a nervous system phenotype in mouse mutants, and one lncRNA with both brain-enriched expression and upregulation in iPSC to neuron differentiation. This review not only expands our understanding of the genetic diversity associated with ASD but also underscores the potential of lncRNAs in contributing to its etiology. Additionally, the discovered CNVs will be a valuable resource for future diagnostic, therapeutic, and research endeavors aimed at prioritizing genetic variations in ASD.