Flavor characterization of pork cuts in Chalu black pigs using multi-omics analysis.

The study investigated the flavor variations in four different fresh pork cuts (longissimus thoracis, LT; trapezius muscle, TM; hamstring muscle, HM; Pork Belly, PB) from Chalu black pigs (ten castrated boars) using multi-omics techniques. The research also explored the influence of muscle fiber type on the flavor profiles of these cuts. Results from quantitative real-time PCR (qRT-PCR) indicated significant differences in muscle fiber type across the four pork cuts in various anatomical locations. Each cut exhibited distinctive volatile organic compounds (VOCs) profiles, with HM displaying a sweet and fruity green flavor, LT showcasing a fatty and nutty taste, PB presenting a fresh, citrusy, and green flavor, and TM offering a floral and bitter note. Variations in fatty acid carbon number and saturation were observed among the cuts, with HM, LT, and PB being rich in fatty acids with C16-18, C19-21, and 3 double bonds, respectively. The metabolites specific to each cut were found to play key roles in different metabolic pathways, such as protein-related pathways for HM, arginine biosynthesis for LT, lysine biosynthesis for PB, and D-arginine and D-ornithine metabolism for TM. Differentially expressed genes (DEGs) were associated with amino acid metabolism for HM, glycolysis/gluconeogenesis for LT, and cellular aromatic compound organization for PB. Notably, HM and PB displayed unique flavor characteristics, while TM exhibited relatively neutral features. The study also identified correlations among VOCs, muscle fiber type, lipids, metabolites, and gene patterns specific to each cut, highlighting the complex interplay of factors influencing pork flavor.

Discovering Novel Proteoforms Using Proteogenomic Workflows Within the Galaxy Bioinformatics Platform.

Proteogenomics is a growing "multi-omics" research area that combines mass spectrometry-based proteomics and high-throughput nucleotide sequencing technologies. Proteogenomics has helped in genomic annotation for organisms whose complete genome sequences became available by using high-throughput DNA sequencing technologies. Apart from genome annotation, this multi-omics approach has also helped researchers confirm expression of variant proteins belonging to unique proteoforms that could have resulted from single-nucleotide polymorphism (SNP), insertion and deletions (Indels), splice isoforms, or other genome or transcriptome variations.A proteogenomic study depends on a multistep informatics workflow, requiring different software at each step. These integrated steps include creating an appropriate protein sequence database, matching spectral data against these sequences, and finally identifying peptide sequences corresponding to novel proteoforms followed by variant classification and functional analysis. The disparate software required for a proteogenomic study is difficult for most researchers to access and use, especially those lacking computational expertise. Furthermore, using them disjointedly can be error-prone as it requires setting up individual parameters for each software. Consequently, reproducibility suffers. Managing output files from each software is an additional challenge. One solution for these challenges in proteogenomics is the open-source Web-based computational platform Galaxy. Its capability to create and manage workflows comprised of disparate software while recording and saving all important parameters promotes both usability and reproducibility. Here, we describe a workflow that can perform proteogenomic analysis on a Galaxy-based platform. This Galaxy workflow facilitates matching of spectral data with a customized protein sequence database, identifying novel protein variants, assessing quality of results, and classifying variants along with visualization against the genome.

Extracting Chromosome Structural Information as One-Dimensional Metrics and Integrating Them with Epigenomics.

Hi-C is a powerful method for obtaining genome-wide chromosomal structural information. The typical Hi-C analysis utilizes a two-dimensional (2D) contact matrix, which poses challenges for quantitative comparisons, visualizations, and integrations across multiple datasets. Here, we present a protocol for extracting one-dimensional (1D) features from chromosome structure data by HiC1Dmetrics. Leveraging these 1D features enables integrated analysis of Hi-C and epigenomic data.

Exploring Cohesin-Related Multiomics Information in the Web Browser.

Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.

Deciphering the acidophilia and acid resistance in Acetilactobacillus jinshanensis dominating baijiu fermentation through multi-omics analysis.

Lactic acid bacteria (LAB) are pivotal in constructing the intricate bio-catalytic networks underlying traditional fermented foods such as Baijiu. However, LAB and their metabolic mechanisms are partially understood in Moutai flavor Baijiu fermentation. Here, we found that Acetilactobacillus jinshanensis became the· dominant species with relative abundance reaching 92%, where the acid accumulated rapidly and peaked at almost 30 g/kg in Moutai flavor Baijiu. After separation, purification, and cultivation, A. jinshanensis exhibited pronounced acidophilia and higher acid resistance compared to other LAB. Further integrated multi-omics analysis revealed that fatty acid synthesis, cell membrane integrity, pHi and redox homeostasis maintenance, protein and amide syntheses were possibly crucial acid-resistant mechanisms in A. jinshanensis. Structural proteomics indicated that the surfaces of A. jinshanensis proteases contained more positively charged amino acid residues to maintain protein stability in acidic environments. The genes HSP20 and acpP were identified as acid-resistant genes for A. jinshanensis by heterologous expression analysis. These findings not only enhance our understanding of LAB in Baijiu, providing a scientific basis for acid regulation for production process, but also offer valuable insights for studying core species in other fermentation systems.

Genome-wide RNA-seq, DNA methylation and small RNA-seq analysis unraveled complex gene regulatory networks in psoriasis pathogenesis.

Psoriasis is a complex inflammatory skin disease characterized by reversible albeit relapsing red scaly plaques in the skin of a patient. In addition to the genetic predisposition, involvement of epigenetic and non-coding RNAs have also been liked with the disease. Nevertheless, any comprehensive study involving transcriptomic, small-RNA and DNA methylation at the genomic level from same patients is lacking. To investigate the complex regulation of molecular pathways in psoriasis, we carried out multi-omics integrative analysis of RNA-sequencing, small RNA-sequencing and DNA methylation profiling from the psoriatic and adjacent normal skin tissues. Our multi-omics analysis identified the genes and biological processes regulated either independently or in combination by DNA methylation and microRNAs. We identified miRNAs that specifically regulated keratinocyte hyper-proliferation, and cell cycle progression and checkpoint signaling in psoriasis. On contrary, DNA methylation was found to be more predominant in regulating immune and inflammatory responses, another causative factor in psoriasis pathogenesis. Many characteristic pathways in psoriasis e.g., Th17 cell differentiation and JAK-STAT signaling, were found to be regulated by both miRNAs and DNA methylation. We carried out functional characterization of a downregulated miRNA hsa-let-7c-5p, predicted to target upregulated genes in psoriasis involved in cell cycle processes, Th17 cell differentiation and JAK-STAT signaling pathways. Overexpression of hsa-let-7c-5p in keratinocytes caused the downregulation of its target genes, resulting in reduced cell proliferation and migration rates, demonstrating potential of miRNAs in regulating psoriasis pathogenesis. In conclusion, our findings identified distinct and shared gene-networks regulated by DNA methylation and miRNAs of a complex disease with reversible phenotype.

Multi-omics and network pharmacology approaches reveal Gui-Ling-Ji alleviates oligoasthenoteratozoospermia by regulating arachidonic acid pathway.

BACKGROUND: Gui-Ling-Ji (GLJ) described in the ancient medical book 'Yunji Qijian' is a traditional Chinese medicine formula used to improve male fertility. It is now available for the treatment of oligoasthenoteratozoospermia (OAT). However, the active ingredients and mechanism of GLJ are not clear. PURPOSE: The aim of this study was to clarify the active ingredients and mechanism of GLJ in OAT. METHODS: Firstly, the cyclophosphamide-induced OAT rat model was established to evaluate the efficacy of GLJ. Secondly, serum/urine-based metabolomics and lipidomics and tissue-based transcriptomics were performed to discover the differential metabolites and genes in rats. Furthermore, network pharmacology was constructed to explore the associated mechanisms based on the results of multi-omics analysis. Finally, cellular experiment on testicular mesenchymal stromal cells (TM3) was used to validate the active ingredients and the key metabolic pathway. RESULTS: Rats were administered GLJ by gavage every day for 3 weeks. Testicular damage and weight loss caused by cyclophosphamide were restored in rats, the sperm count and motility were improved, and levels of luteinizing hormone (LH), follicle-stimulating hormone (FSH) and testosterone (T) secretion were also elevated. Compared to the metabolites of OAT rats, 51 and 37 differential metabolites regulated by GLJ were identified from serum and urine respectively, 54 lipid differential metabolites regulated by GLJ were identified by lipidomics. At the same time, 23 of the 258 differential genes were found to be regulated by OAT rats and then reverse-regulated by GLJ. Network pharmacology has identified 13 pathways (Steroid hormone biosynthesis, Taurine and hypotaurine metabolism, Primary bile acid biosynthesis, Linoleic acid metabolism, Retinol metabolism, Glycerophospholipid metabolism, Ether lipid metabolism, Sphingolipid metabolism, Arachidonic acid metabolism, Glutathione metabolism, Arginine biosynthesis, Arginine and proline metabolism, D-Arginine and D-ornithine metabolism), four metabolites (arachidonic acid, oestrone sulphate, phosphatidylglycerol choline and sphingomyelin) and 15 targets (ABCB11, ALDH18A1, CCL3, CD244, CIITA, CYP2C8, DLL1, ITGA4, ESR1, AR, ABCB1, ABCC1, ALB, PLA2G1B and NOS2). GLJ, psoralen, isopsoralen, liquiritin, isoliquiritin, liquiritigenin, and ginsenoside Ro could significantly promote T secretion from TM3 cells. Additionally, arachidonic acid metabolism particularly the cyclooxygenase pathway, is closely related to the promotion of testosterone secretion by GLJ in TM3. CONCLUSION: GLJ has a therapeutic efficacy in cyclophosphamide-induced OAT rats, which can modulate the disorders of lipid metabolism and amino acid metabolism. Arachidonic acid metabolism may be a key pathway, and six prototype compounds are potential key active ingredients for GLJ.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Integrated proteomics and transcriptomics analysis reveals insights into differences in premature mortality associated with disparate pathogenic RBM20 variants.

Variants in RNA binding motif protein 20 (RBM20) are causative in a severe form of dilated cardiomyopathy referred to as RBM20 cardiomyopathy, yet the mechanisms are unclear. Moreover, the reason(s) for phenotypic heterogeneity in carriers with different pathogenic variants are similarly opaque. To gain insight, we carried out multi-omics analysis, including the first analysis of gene expression changes at the protein level, of mice carrying two different pathogenic variants in the RBM20 nuclear localization signal (NLS). Direct comparison of the phenotypes confirmed greater premature morality in S639G variant carrying mice compared to mice with the S637A variant despite similar cardiac remodeling and dysfunction. Analysis of differentially spliced genes uncovered alterations in the splicing of both RBM20 target genes and non-target genes, including several genes previously implicated in arrhythmia. Global proteomics analysis found that a greater number of proteins were differentially expressed in the hearts of Rbm20S639G mice relative to WT than in Rbm20S637A versus WT. Gene ontology analysis suggested greater mitochondrial dysfunction in Rbm20S639G mice, although direct comparison of protein expression in the hearts of Rbm20S639G versus Rbm20S637A mice failed to identify any significant differences. Similarly, few differences were found by direct comparison of gene expression at the transcript level in Rbm20S639G and Rbm20S637A despite greater coverage. Our data provide a comprehensive overview of gene splicing and expression differences associated with pathogenic variants in RBM20, as well as insights into the molecular underpinnings of phenotypic heterogeneity associated with different dilated cardiomyopathy-associated variants.

Integrated multi-omics revealed that dysregulated lipid metabolism played an important role in RA patients with metabolic diseases.

OBJECTIVES: Patients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach. METHODS: Fecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman's correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis. RESULTS: Our results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD. CONCLUSIONS: The integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Decoding mutational hotspots in human disease through the gene modules governing thymic regulatory T cells.

Computational strategies to extract meaningful biological information from multiomics data are in great demand for effective clinical use, particularly in complex immune-mediated disorders. Regulatory T cells (Tregs) are essential for immune homeostasis and self-tolerance, controlling inflammatory and autoimmune processes in many diseases with a multigenic basis. Here, we quantify the Transcription Factor (TF) differential occupancy landscape to uncover the Gene Regulatory Modules governing lineage-committed Tregs in the human thymus, and show that it can be used as a tool to prioritise variants in complex diseases. We combined RNA-seq and ATAC-seq and generated a matrix of differential TF binding to genes differentially expressed in Tregs, in contrast to their counterpart conventional CD4 single-positive thymocytes. The gene loci of both established and novel genetic interactions uncovered by the Gene Regulatory Modules were significantly enriched in rare variants carried by patients with common variable immunodeficiency, here used as a model of polygenic-based disease with severe inflammatory and autoimmune manifestations. The Gene Regulatory Modules controlling the Treg signature can, therefore, be a valuable resource for variant classification, and to uncover new therapeutic targets. Overall, our strategy can also be applied in other biological processes of interest to decipher mutational hotspots in individual genomes.

Development and applications of metabolic models in plant multi-omics research.

Plant growth and development are characterized by systematic and continuous processes, each involving intricate metabolic coordination mechanisms. Mathematical models are essential tools for investigating plant growth and development, metabolic regulation networks, and growth patterns across different stages. These models offer insights into secondary metabolism patterns in plants and the roles of metabolites. The proliferation of data related to plant genomics, transcriptomics, proteomics, and metabolomics in the last decade has underscored the growing importance of mathematical modeling in this field. This review aims to elucidate the principles and types of metabolic models employed in studying plant secondary metabolism, their strengths, and limitations. Furthermore, the application of mathematical models in various plant systems biology subfields will be discussed. Lastly, the review will outline how mathematical models can be harnessed to address research questions in this context.

The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis.

The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.

Multi-omics revealed that ELAVL3 regulates MYCN in neuroblastoma via immunogenic cell death: Risk stratification and experimental research.

Neuroblastoma (NB), a common and highly lethal malignant disease in pediatrics, still lacks an effective therapeutic approach that addresses all conditions. Immunogenic Cell Death (ICD) plays a crucial role in tumor cell death and triggers a potent anti-tumor immune response. In this study, we report an ICD-related index (ICDR-Index) in NB through various machine learning methodologies, utilizing bulk transcriptome data from 1244 NB samples and 16 scRNA-seq datasets. Our results showed that the ICDR-Index could accurately identify different risk subtypes of patients with NB and provide predictive value for prognosis. Importantly, we found that high-risk patients with NB exhibited significantly poor overall survival (OS) rates, adverse clinical phenotypes, poor immune cell infiltration, and low sensitivity to immunotherapy. Furthermore, we identified ELAVL3 as a key gene within the ICDR-Index, where high expression levels were associated with malignancy and poor OS in NB. Additionally, targeted silencing of ELAVL3 down-regulated MYCN gene expression and reduced the malignancy of NB cells. Notably, the si-ELAVL3-transfected NB cells enhanced the anti-tumor activity of NK cells. Collectively, this study offers avenues for predicting the risk stratification of patients with NB and reveals a potential mechanism by which ELAVL3 regulates NB cell death.

Therapeutic potential of omaveloxolone in counteracting muscle atrophy post-denervation: a multi-omics approach.

BACKGROUND: Muscle atrophy caused by denervation is common in neuromuscular diseases, leading to loss of muscle mass and function. However, a comprehensive understanding of the overall molecular network changes during muscle denervation atrophy is still deficient, hindering the development of effective treatments. METHOD: In this study, a sciatic nerve transection model was employed in male C57BL/6 J mice to induce muscle denervation atrophy. Gastrocnemius muscles were harvested at 3 days, 2 weeks, and 4 weeks post-denervation for transcriptomic and proteomic analysis. An integrative multi-omics approach was utilized to identify key genes essential for disease progression. Targeted proteomics using PRM was then employed to validate the differential expression of central genes. Combine single-nucleus sequencing results to observe the expression levels of PRM-validated genes in different cell types within muscle tissue.Through upstream regulatory analysis, NRF2 was identified as a potential therapeutic target. The therapeutic potential of the NRF2-targeting drug Omaveloxolone was evaluated in the mouse model. RESULT: This research examined the temporal alterations in transcripts and proteins during muscle atrophy subsequent to denervation. A comprehensive analysis identified 54,534 transcripts and 3,218 proteins, of which 23,282 transcripts and 1,852 proteins exhibited statistically significant changes at 3 days, 2 weeks, and 4 weeks post-denervation. Utilizing multi-omics approaches, 30 hubgenes were selected, and PRM validation confirmed significant expression variances in 23 genes. The findings highlighted the involvement of mitochondrial dysfunction, oxidative stress, and metabolic disturbances in the pathogenesis of muscle atrophy, with a pronounced impact on type II muscle fibers, particularly type IIb fibers. The potential therapeutic benefits of Omaveloxolone in mitigating oxidative stress and preserving mitochondrial morphology were confirmed, thereby presenting novel strategies for addressing muscle atrophy induced by denervation. GSEA analysis results show that Autophagy, glutathione metabolism, and PPAR signaling pathways are significantly upregulated, while inflammation-related and neurodegenerative disease-related pathways are significantly inhibited in the Omaveloxolone group.GSR expression and the GSH/GSSG ratio were significantly higher in the Omaveloxolone group compared to the control group, while MuSK expression was significantly lower than in the control group. CONCLUSION: In our study, we revealed the crucial role of oxidative stress, glucose metabolism, and mitochondrial dysfunction in denervation-induced muscle atrophy, identifying NRF2 as a potential therapeutic target. Omaveloxolone was shown to stabilize mitochondrial function, enhance antioxidant capacity, and protect neuromuscular junctions, thereby offering promising therapeutic potential for treating denervation-induced muscle atrophy.

Comparison of chromatin accessibility remodeling of granulosa cells in patients with endometrioma or pelvic/tubal infertility.

PURPOSE: To elucidatethe epigenetic alteration associated with impaired oogenesis in endometrioma using multi-omic approaches. METHODS: ATAC-seq was performed on the granulosa cells (GCs) of 6 patients (3 with endometrioma and 3 without). Follicular samples from another 20 patients (10 with endometrioma and 10 without) were collected for mRNA-seq analysis of GCs and extracellular vesicles (EVs) of follicular fluid. qRT-PCR validated candidate genes in GCs from 44 newly enrolled patients (19 with endometrioma and 25 without). mRNA abundance was compared with the Mann-Whitney test. Pearson's correlation analyzed relationships between candidate genes and oocyte parameters. RESULTS: Chromatin accessibility and gene expression profiles of GCs from endometrioma patients differed significantly from the pelvic/tubal infertility group. RNA-seq revealed most differentially expressed genes were downregulated (6216/7325) and enriched in the cellular localization pathway. Multi-omics analyses identified 22 significantly downregulated genes in the GCs of endometrioma patients, including PPIF (P < 0.0001) and VEGFA (P = 0.0148). Both genes were further confirmed by qRT-PCR. PPIF (r = 0.46, p = 0.043) and VEGFA (r = 0.45, p = 0.048) correlated with the total number of retrieved oocytes. CONCLUSIONS: GC chromatin remodeling may disrupt GC and EV transcriptomes, interfering with somatic cell-oocyte communication and leading to compromised oogenesis in endometrioma patients.

Animal Wellness: The Power of Multiomics and Integrative Strategies: Multiomics in Improving Animal Health.

The livestock industry faces significant challenges, with disease outbreaks being a particularly devastating issue. These diseases can disrupt the food supply chain and the livelihoods of those involved in the sector. To address this, there is a growing need to enhance the health and well-being of livestock animals, ultimately improving their performance while minimizing their environmental impact. To tackle the considerable challenge posed by disease epidemics, multiomics approaches offer an excellent opportunity for scientists, breeders, and policymakers to gain a comprehensive understanding of animal biology, pathogens, and their genetic makeup. This understanding is crucial for enhancing the health of livestock animals. Multiomic approaches, including phenomics, genomics, epigenomics, metabolomics, proteomics, transcriptomics, microbiomics, and metaproteomics, are widely employed to assess and enhance animal health. High-throughput phenotypic data collection allows for the measurement of various fitness traits, both discrete and continuous, which, when mathematically combined, define the overall health and resilience of animals, including their ability to withstand diseases. Omics methods are routinely used to identify genes involved in host-pathogen interactions, assess fitness traits, and pinpoint animals with disease resistance. Genome-wide association studies (GWAS) help identify the genetic factors associated with health status, heat stress tolerance, disease resistance, and other health-related characteristics, including the estimation of breeding value. Furthermore, the interaction between hosts and pathogens, as observed through the assessment of host gut microbiota, plays a crucial role in shaping animal health and, consequently, their performance. Integrating and analyzing various heterogeneous datasets to gain deeper insights into biological systems is a challenging task that necessitates the use of innovative tools. Initiatives like MiBiOmics, which facilitate the visualization, analysis, integration, and exploration of multiomics data, are expected to improve prediction accuracy and identify robust biomarkers linked to animal health. In this review, we discuss the details of multiomics concerning the health and well-being of livestock animals.

Causal Effects of Gut Microbiota on Gout and Hyperuricemia: Insights from Genome-Wide Mendelian Randomization, RNA-Sequencing, 16S rRNA Sequencing, and Metabolomes.

BACKGROUND: This study investigated the causal relationship between gut microbiota (GM), serum metabolome, and host transcriptome in the development of gout and hyperuricemia (HUA) using genome-wide association studies (GWAS) data and HUA mouse model experiments.  Methods: Mendelian randomization (MR) analysis of GWAS summary statistics was performed using an inverse variance weighted (IVW) approach to determine predict the causal role of the gut microbiota on gout. The HUA mouse model was used to characterize changes in the gut microbiome, host metabolome, and host kidney transcriptome by integrating cecal 16S rRNA sequencing, untargeted serum metabolomics, and host mRNA sequencing.

Single-cell transcriptomics reveals IRF7 regulation of the tumor microenvironment in isocitrate dehydrogenase wild-type glioma.

Mutations in isocitrate dehydrogenase (IDH) are important markers of glioma prognosis. However, few studies have examined the gene expression regulatory network (GRN) in IDH-mutant and wild-type gliomas. In this study, single-cell RNA sequencing and spatial transcriptome sequencing were used to analyze the GRN of cell subsets in patients with IDH-mutant and wild-type gliomas. Through gene transcriptional regulation analysis, we identified the M4 module, whose transcription factor activity is highly expressed in IDH wild-type gliomas compared to IDH-mutants. Enrichment analysis revealed that these genes were predominantly expressed in microglia and macrophages, with significant enrichment in interferon-related signaling pathways. Interferon regulatory factor 7 (IRF7), a transcription factor within this pathway, showed the highest percentage of enrichment and was primarily localized in the core region of wild-type IDH tumors. A machine-learning prognostic model identified novel subgroups within the wild-type IDH population. Additionally, IRF7 was shown to promote the proliferation and migration of T98G and U251 cells in vitro, and its knockdown affected glioma cell proliferation in vivo. This study systematically established the regulatory mechanism of IDH transcriptional activity in gliomas at the single-cell level and drew a corresponding cell map. The study presents a transcriptional regulatory activity map for IDH wild-type gliomas, involving single-cell RNA sequencing and spatial transcriptomics to identify gene regulatory networks, machine learning models for IDH subtyping, and experimental validation, highlighting the role of IRF7 in glioma progression.

Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders.

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders.