Search

Purpose: Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players. Methods: We designed a prospective cohort study over 2 seasons (2019–20 and 2021–22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload. Results: Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P <.05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10−4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index:.74). Conclusions: Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model. [ABSTRACT FROM AUTHOR]

AbstractBabesiosis, caused by protozoan parasites of the genus Babesia, is an emerging tick-borne disease of significance for both human and animal health. Babesia parasites infect erythrocytes of vertebrate hosts where they develop and multiply rapidly to cause the pathological symptoms associated with the disease. The identification of new Babesia species underscores the ongoing risk of zoonotic pathogens capable of infecting humans, a concern amplified by anthropogenic activities and environmental changes. One such pathogen, Babesia MO1, previously implicated in severe cases of human babesiosis in the United States, was initially considered a subspecies of B. divergens, the predominant agent of human babesiosis in Europe. Here we report comparative multiomics analyses of B. divergens and B. MO1 that offer insight into their biology and evolution. Our analysis shows that despite their highly similar genomic sequences, substantial genetic and genomic divergence occurred throughout their evolution resulting in major differences in gene functions, expression and regulation, replication rates and susceptibility to antiparasitic drugs. Furthermore, both pathogens have evolved distinct classes of multigene families, crucial for their pathogenicity and adaptation to specific mammalian hosts. Leveraging genomic information for B. MO1, B. divergens, and other members of the Babesiidae family within Apicomplexa provides valuable insights into the evolution, diversity, and virulence of these parasites. This knowledge serves as a critical tool in preemptively addressing the emergence and rapid transmission of more virulent strains.

Maintenance of water homeostasis is a fundamental cellular process required by all living organisms. Here, we use the single-celled green alga Chlamydomonas reinhardtii to establish a foundational understanding of osmotic-stress signaling pathways through transcriptomics, phosphoproteomics, and functional genomics approaches. Comparison of pathways identified through these analyses with yeast and Arabidopsis allows us to infer their evolutionary conservation and divergence across these lineages. 76 genes, acting across diverse cellular compartments, were found to be important for osmotic-stress tolerance in Chlamydomonas through their functions in cytoskeletal organization, potassium transport, vesicle trafficking, mitogen-activated protein kinase and chloroplast signaling. We show that homologs for five of these genes have conserved functions in stress tolerance in Arabidopsis and reveal a novel PROFILIN-dependent stage of acclimation affecting the actin cytoskeleton that ensures tissue integrity upon osmotic stress. This study highlights the conservation of the stress response in algae and land plants, and establishes Chlamydomonas as a unicellular plant model system to dissect the osmotic stress signaling pathway.

Cardiac allograft vasculopathy (CAV) causes late graft failure and mortality after heart transplantation. Donor-specific antibodies (DSAs) lead to chronic endothelial cell injury, inflammation, and arterial intimal thickening. In this study, GeoMx digital spatial profiling was used to analyze arterial areas of interest (AOIs) from CAV+DSA+ rejected cardiac allografts (N = 3; 22 AOIs total). AOIs were categorized based on CAV neointimal thickening and underwent whole transcriptome and protein profiling. By comparing our transcriptomic data with that of healthy control vessels of rapid autopsy myocardial tissue, we pinpointed specific pathways and transcripts indicative of heightened inflammatory profiles in CAV lesions. Moreover, we identified protein and transcriptomic signatures distinguishing CAV lesions exhibiting low and high neointimal lesions. AOIs with low neointima showed increased markers for activated inflammatory infiltrates, endothelial cell activation transcripts, and gene modules involved in metalloproteinase activation and TP53 regulation of caspases. Inflammatory and apoptotic proteins correlated with inflammatory modules in low neointima AOIs. High neointima AOIs exhibited elevated TGFβ-regulated transcripts and modules enriched for platelet activation/aggregation. Proteins associated with growth factors/survival correlated with modules enriched for proliferation/repair in high neointima AOIs. Our findings reveal novel insight into immunological mechanisms mediating CAV pathogenesis.

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training (ExT) and sex on its molecular landscape is not fully established. Utilizing an integrative multi-omics approach, and leveraging data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we show profound sexual dimorphism in the scWAT of sedentary rats and in the dynamic response of this tissue to ExT. Specifically, the scWAT of sedentary females displays -omic signatures related to insulin signaling and adipogenesis, whereas the scWAT of sedentary males is enriched in terms related to aerobic metabolism. These sex-specific -omic signatures are preserved or amplified with ExT. Integration of multi-omic analyses with phenotypic measures identifies molecular hubs predicted to drive sexually distinct responses to training. Overall, this study underscores the powerful impact of sex on adipose tissue biology and provides a rich resource to investigate the scWAT response to ExT.

BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).

Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single-cell omics, encompassing techniques such as single-cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.

PURPOSE: Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need. EXPERIMENTAL DESIGN: Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non-small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink. RESULTS: Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression. CONCLUSIONS: The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m-2·s-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Trastuzumab therapy in HER2+ breast cancer patients has mixed success owing to acquired resistance to therapy. A detailed understanding of downstream molecular cascades resulting from trastuzumab resistance is yet to emerge. In this study, we investigate the cellular mechanisms underlying acquired resistance using trastuzumab-sensitive and -resistant cancer cells (BT474 and BT474R) treated with endogenous ligands EGF and HRG across time. We probe early receptor organization through microscopy and signaling events through multiomics measurements and assess the bioenergetic state through mitochondrial measurements. Integrative analyses of our measurements reveal significant alterations in EGF-treated BT474 HER2 membrane dynamics and robust downstream activation of PI3K/AKT/mTORC1 signaling. EGF-treated BT474R shows a sustained interferon-independent activation of the IRF1/STAT1 cascade, potentially contributing to trastuzumab resistance. Both cell lines exhibit temporally divergent metabolic demands and HIF1A-mediated stress responses. BT474R demonstrates inherently increased mitochondrial activity. HRG treatment in BT474R leads to a pronounced reduction in AR expression, affecting downstream lipid metabolism with implications for treatment response. Our results provide novel insights into mechanistic changes underlying ligand treatment in BT474 and BT474R and emphasize the pivotal role of endogenous ligands. These results can serve as a framework for furthering the understanding of trastuzumab resistance, with therapeutic implications for women with acquired resistance.

Ulcerative colitis (UC) is driven by immune and stromal subsets, culminating in epithelial injury. Vedolizumab (VDZ) is an anti-integrin antibody that is effective for treating UC. VDZ is known to inhibit lymphocyte trafficking to the intestine, but its broader effects on other cell subsets are less defined. To identify the inflammatory cells that contribute to colitis and are affected by VDZ, we perform single-cell transcriptomic and proteomic analyses of peripheral blood and colonic biopsies in healthy controls and patients with UC on VDZ or other therapies. Here we show that VDZ treatment is associated with alterations in circulating and tissue mononuclear phagocyte (MNP) subsets, along with modest shifts in lymphocytes. Spatial multi-omics of formalin-fixed biopsies demonstrates trends towards increased abundance and proximity of MNP and fibroblast subsets in active colitis. Spatial transcriptomics of archived specimens pre-treatment identifies epithelial-, MNP-, and fibroblast-enriched genes related to VDZ responsiveness, highlighting important roles for these subsets in UC.

Vitellogenesis is the most important process in animal reproduction, in which yolk proteins play a vital role. Among multiple yolk protein precursors, vitellogenin (Vtg) is a well-known major yolk protein (MYP) in most oviparous animals. However, the nature of MYP in the freshwater gastropod snail Biomphalaria glabrata remains elusive. In the current study, we applied bioinformatics, tissue-specific transcriptomics, ovotestis-targeted proteomics, and phylogenetics to investigate the large lipid transfer protein (LLTP) superfamily and ferritin-like family in B. glabrata. Four members of LLTP superfamily (BgVtg1, BgVtg2, BgApo1, and BgApo2), one yolk ferritin (Bg yolk ferritin), and four soma ferritins (Bg ferritin 1, 2, 3, and 4) were identified in B. glabrata genome. The proteomic analysis demonstrated that, among the putative yolk proteins, BgVtg1 was the yolk protein appearing in the highest amount in the ovotestis, followed by Bg yolk ferritin. RNAseq profile showed that the leading synthesis sites of BgVtg1 and Bg yolk ferritin are in the ovotestis (presumably follicle cells) and digestive gland, respectively. Phylogenetic analysis indicated that BgVtg1 is well clustered with Vtgs of other vertebrates and invertebrates. We conclude that, vitellogenin (BgVtg1), not yolk ferritin (Bg yolk ferritin), is the major yolk protein precursor in the schistosomiasis vector snail B. glabrata.

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

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. [ABSTRACT FROM AUTHOR]

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to kidney failure. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single-nucleus multimodal atlas of an orthologous mouse PKD model at early, mid, and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalog differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single-nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution. [ABSTRACT FROM AUTHOR]

Background: The ability of yaks to adapt to the extreme environment of low temperatures and hypoxia at cold seasons on the Qinghai-Tibet Plateau (QTP) is related to the host genome; however, the convergent evolution of rumen microbiomes in host adaption is unknown. Methods: Here, we conducted a multi-omics study on the rumen fluid of grazing yaks from warm (July) and cold (December) seasons on the QTP to evaluate the convergent evolution of rumen microbiomes in the adaptation of grazing yaks to cold-seasons environments. Results: The results showed that grazing yaks at cold seasons had higher fibrolytic enzyme activities and volatile fatty acids (VFAs) concentrations, and the relative abundance of Firmicutes and the ratio Firmicutes to Bacteroidetes was significantly higher than that of yaks at warm seasons. Macrogenomic analyses showed that genes involved in forming VFAs and arginine were significantly enriched in cold-season yaks. Transcriptome analyses of the rumen epithelium showed that 72 genes associated with VFAs absorption and transport were significantly upregulated in cold-season yaks. Metabolomic analyses showed that the levels of ornithine, related to efficient nitrogen utilization, were significantly upregulated in cold-season yaks. Conclusion: The synergistic role of rumen microbiomes in the adaptation of grazing yaks to extreme environments at cold seasons was revealed by multiomics study. [ABSTRACT FROM AUTHOR]

Introduction: Adoptive cell therapy using TCR-engineered T-cells is one of the most effective strategies against tumor cells. The TCR T-cell approach has been well tested against a variety of blood neoplasms but is yet to be deeply tested against solid tumors. Among solid tumors, cancer-testis antigens are the most prominent targets for tumor-specific therapy, as they are usually found on cells that lie behind blood-tissue barriers. Methods: We have employed a novel efficient protocol for MAGE-A3-specific Tcell clonal expansion, performed single-cell multi-omic analysis of the expanded T-cells via BD Rhapsody, engineered a selected T-cell receptor into a lentiviral construct, and tested it in an in vitro LDH-cytotoxicity test. Results and discussion: We have observed a 191-fold increase in the MAGE-A3-specific T-cell abundance, obtained a dominant T-cell receptor via single-cell multi-omic BD Rhapsody data analysis in the TCRscape bioinformatics tool, and observed potent cytotoxicity of the dominant-clonotype transduced TCR T-cells against a MAGE-A3-positive tumor. We have demonstrated the efficiency of our Tcell enrichment protocol in obtaining potent anti-tumor T-cells and their T-cell receptors, especially when paired with the modern single-cell analysis methods. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory condition. Given patients with COPD continue to experience exacerbations despite the availability of effective therapies, anti-inflammatory treatments targeting novel pathways are needed. Kinases, notably the phosphoinositide 3-kinases (PI3K), are thought to be involved in chronic airway inflammation, with this pathway proposed as a critical regulator of inflammation and oxidative stress response in COPD. CHF6523 is an inhaled PI3Kδ inhibitor that has shown positive preclinical results. This manuscript reports the results of a study of CHF6523 in patients with stable COPD (chronic bronchitis phenotype), and who had evidence of type-2 inflammation. Methods: This randomised, double-blind, placebo-controlled, two-way crossover study comprised two 28-day treatment periods separated by a 28-day washout. Patients (N = 44) inhaled CHF6523 in one period, and placebo in the other, both twice daily. The primary objective was to assess the safety and tolerability of CHF6523; the secondary objective was to assess CHF6523 pharmacokinetics. Exploratory endpoints included target engagement (the relative reduction in phosphatidylinositol (3,4,5)-trisphosphate [PIP3]), pharmacodynamic evaluations such as airflow obstruction, and hyperinflation, and to identify biomarker(s) of drug response using proteomics and transcriptomics. Results: CHF6523 plasma pharmacokinetics were characterised by an early maximum concentration (Cmax), reached 15 and 10 min after dosing on Days 1 and 28, respectively, followed by a rapid decline. Systemic exposure on Day 28 showed limited accumulation, with ratios < 1.6 for Cmax and area under the curve from 0 to 12 h post-dose, and with steady state achieved on Day 20. Target engagement was confirmed by a significant 29.7% reduction from baseline in induced sputum PIP3 (29.5% reduction vs. placebo; adjusted ratio 0.705 [0.580, 0.856]; p = 0.001), but this did not translate into an anti-inflammatory pharmacodynamic effect, as assessed through measures including biomarkers and multi-omics. Additionally, although CHF6523 was generally well-tolerated, 95.2% of patients reported cough as an adverse event, most mild to moderate and resolving within one-hour post-dose. Conclusions: These data, together with those from other PI3K inhibitors, suggest that PI3Kδ is not a suitable pathway for the management of COPD, as the achieved target engagement did not translate into any pharmacodynamic anti-inflammatory effect. Trial registration: ClinicalTrials.gov (NCT04032535); posted 23rd July 2019. [ABSTRACT FROM AUTHOR]

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64–0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752–0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients. Fluid management in septic shock lacks personalized approaches, which are critical for improving patient outcomes. Here, the authors show that a proteomic signature can help identify patients who may benefit from a restrictive fluid strategy, potentially reducing hospital mortality rates. [ABSTRACT FROM AUTHOR]

Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with unclear etiology. Growing evidence suggests the microbiome plays a role in SLE pathogenesis. However, findings are inconsistent across studies due to factors like small sample sizes and geographical variations. A comprehensive meta-analysis is needed to elucidate microbiome alterations in SLE. Objective: This study aimed to provide a systematic overview of microbiota dysbiosis across body sites in SLE through a meta-analysis of alpha diversity indices, beta diversity indices, and abundance taxa of microbiome. Methods: A literature search was conducted across four databases to identify relevant studies comparing SLE patients and healthy controls. Extracted data encompassed alpha and beta diversity metrics, as well as bacterial, fungal, and viral abundance across gut, oral, skin, and other microbiota. Study quality was assessed using the Newcastle-Ottawa Scale. Standardized mean differences and pooled effect sizes were calculated through meta-analytical methods. Results: The analysis showed reduced alpha diversity and distinct beta diversity in SLE, particularly in the gut microbiota. Taxonomic analysis revealed compositional variations in bacteria from the gut and oral cavity. However, results for fungi, viruses, and bacteria from other sites were inconsistent due to limited studies. Conclusions: This meta-analysis offers a comprehensive perspective on microbiome dysbiosis in SLE patients across diverse body sites and taxa. The observed variations underscore the microbiome's potential role in SLE pathogenesis. Future research should address geographical variations, employ longitudinal designs, and integrate multi-omics approaches. [ABSTRACT FROM AUTHOR]

A healthy heart comprises various cell types, including cardiomyocytes, endothelial cells, fibroblasts, immune cells, and among others, which work together to maintain optimal cardiac function. These cells engage in complex communication networks, known as cell-cell interactions (CCIs), which are essential for homeostasis, cardiac structure, and efficient function. However, in the context of cardiac diseases, the heart undergoes damage, leading to alterations in the cellular composition. Such pathological conditions trigger significant changes in CCIs, causing cell rearrangement and the transition between cell types. Studying these interactions can provide valuable insights into cardiac biology and disease mechanisms, enabling the development of new therapeutic strategies. While the development of cardiac organoids and advanced 3D co-culture technologies has revolutionized in vitro studies of CCIs, recent advancements in single-cell and spatial multi-omics technologies provide researchers with powerful and convenient tools to investigate CCIs at unprecedented resolution. This article provides a concise overview of CCIs observed in both normal and injured heart, with an emphasis on the cutting-edge methods used to study these interactions. It highlights recent advancements such as 3D co-culture systems, single-cell and spatial omics technologies, that have enhanced the understanding of CCIs. Additionally, it summarizes the practical applications of CCI research in advancing cardiovascular therapies, offering potential solutions for treating heart disease by targeting intercellular communication. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common malignant tumours worldwide and its high mortality rate makes it a leading cause of cancer-related deaths. To address this daunting challenge, we need a comprehensive understanding of the pathogenesis and progression of lung cancer in order to adopt more effective therapeutic strategies. In this regard, integrating multi-omics data of the lung provides a highly promising avenue. Multi-omics approaches such as genomics, transcriptomics, proteomics, and metabolomics have become key tools in the study of lung cancer. The application of these methods not only helps to resolve the immunotherapeutic mechanisms of lung cancer, but also provides a theoretical basis for the development of personalised treatment plans. By integrating multiomics, we have gained amore comprehensive understanding of the process of lung cancer development and progression, and discovered potential immunotherapy targets. This review summarises the studies on multi-omics and immunology in lung cancer, and explores the application of these studies in early diagnosis, treatment selection and prognostic assessment of lung cancer, with the aim of providing more personalised and effective treatment options for lung cancer patients. [ABSTRACT FROM AUTHOR]

Background: Connecting the composition and function of industrial microbiomes is a major aspiration in microbial biotechnology. Here, we address this question in wine fermentation, a model system where the diversity and functioning of fermenting yeast species are determinant of the flavor and quality of the resulting wines. Results: First, we surveyed yeast communities associated with grape musts collected across wine appellations, revealing the importance of environmental (i.e., biogeography) and anthropic factors (i.e., farming system) in shaping community composition and structure. Then, we assayed the fermenting yeast communities in synthetic grape must under common winemaking conditions. The dominating yeast species defines the fermentation performance and metabolite profile of the resulting wines, and it is determined by the initial fungal community composition rather than the imposed fermentation conditions. Yeast dominance also had a more pronounced impact on wine meta-transcriptome than fermentation conditions. We unveiled yeast-specific transcriptomic profiles, leveraging different molecular functioning strategies in wine fermentation environments. We further studied the orthologs responsible for metabolite production, revealing modules associated with the dominance of specific yeast species. This emphasizes the unique contributions of yeast species to wine flavor, here summarized in an array of orthologs that defines the individual contribution of yeast species to wine ecosystem functioning. Conclusions: Our study bridges the gap between yeast community composition and wine metabolite production, providing insights to harness diverse yeast functionalities with the final aim to producing tailored high-quality wines. 74dL8hDE5Rym1TqBfqxSSD Video Abstract [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma with cirrhosis promotes the advancement of malignancy and the development of fibrosis in normal liver tissues. Understanding the pathological mechanisms underlying the development of HCC with cirrhosis is important for developing effective therapeutic strategies. Herein, the RNA-sequencing (RNA-seq) data and corresponding clinical features of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) database using the University of California Santa Cruz (UCSC) Xena platform. The enrichment degree of hallmarkers for each TCGA-LIHC cohort was quantified by ssGSEA algorithm. Weighted gene co-expression network analysis (WGCNA) revealed two gene module eigengenes (MEs) associated with cirrhosis, namely, MEbrown and MEgreen. Analysis of these modules using AUCell showed that MEbrown had higher enrichment scores in all immune cells, whereas MEgreen had higher enrichment scores in malignant cells. The CellChat package revealed that both immune and malignant cells contributed to the fibrotic activity of myofibroblasts through diverse signaling pathways. Additionally, spatial transcriptomic data showed that hepatocytes, proliferating hepatocytes, macrophages, and myofibroblasts were located in closer proximity in HCC tissues. These cells may potentially participate in the process of stimulating myofibroblast fibrotic activity, which may be related to the development of liver fibrosis. In summary, we made full use of multi-omics data to explore gene networks and cell types that may be involved in the development and progression of cirrhosis in HCC. [ABSTRACT FROM AUTHOR]

Myofibers are large multinucleated cells that have long thought to have a rather simple organization. Single-nucleus transcriptomics, spatial transcriptomics and spatial metabolomics analysis have revealed distinct transcription profiles in myonuclei related to myofiber type. However, the use of local tissue collection or dissociation methods have obscured the spatial organization. To elucidate the full tissue architecture, we combine two spatial omics, RNA tomography and mass spectrometry imaging. This enables us to map the spatial transcriptomic, metabolomic and lipidomic organization of the whole murine tibialis anterior muscle. Our findings on heterogeneity in fiber type proportions are validated with multiplexed immunofluorescence staining in tibialis anterior, extensor digitorum longus and soleus. Our results demonstrate unexpectedly strong regionalization of gene expression, metabolic differences and variable myofiber type proportion along the proximal-distal axis. These new insights in whole-tissue level organization reconcile sometimes conflicting results coming from previous studies relying on local sampling methods. Multi-dimensional analysis of gene expression, myofiber types and metabolic activity in whole skeletal muscle reveals specific regionalization of gene expression and metabolites related to the glycolytic and oxidative potential of myofibers [ABSTRACT FROM AUTHOR]

Neurons have a unique polarized nature that must adapt to environmental changes throughout their lifespan. During embryonic development, axon elongation is led by the growth cone, 1 culminating in the formation of a presynaptic terminal. After synapses are formed, axons elongate in a growth cone-independent manner to accompany body growth while maintaining their ultrastructure and function. 2,3,4,5,6 To further understand mechanical strains on the axon shaft, we developed a computer-controlled stretchable microfluidic platform compatible with multi-omics and live imaging. Our data show that sensory embryonic dorsal root ganglia (DRGs) neurons have high plasticity, with axon shaft microtubules decreasing polymerization rates, aligning with the direction of tension, and undergoing stabilization. Moreover, in embryonic DRGs, stretch triggers yes-associated protein (YAP) nuclear translocation, supporting its participation in the regulatory network that enables tension-driven axon growth. Other than cytoskeleton remodeling, stretch prompted MARCKS-dependent formation of plasmalemmal precursor vesicles (PPVs), resulting in new membrane incorporation throughout the axon shaft. In contrast, adolescent DRGs showed a less robust adaptation, with axonal microtubules being less responsive to stretch. Also, while adolescent DRGs were still amenable to strain-induced PPV formation at higher stretch rates, new membrane incorporation in the axon shaft failed to occur. In summary, we developed a new resource to study the biology of axon stretch growth. By unraveling cytoskeleton adaptation and membrane remodeling in the axon shaft of stretched neurons, we are moving forward in understanding axon growth. [Display omitted] • Computer-controlled, live-imaging-compatible platform enables study of axon stretch • Stretch elicits microtubule repair and stabilization in embryonic DRGs • PPV and new membrane formation are triggered by tension in embryonic DRGs • Cytoskeleton and membrane of adult DRGs show less robust adaptation to stretch Axon growth elicits tension of the axon shaft. Sousa et al. develop a computer-controlled, live-imaging-compatible platform to study axon stretch. In embryonic DRG neurons, microtubules decrease polymerization and undergo repair and stabilization, while PPV secretion and membrane formation occur. In contrast, adult DRGs show less robust adaptation. [ABSTRACT FROM AUTHOR]

Benign prostatic hyperplasia (BPH) as a common geriatric disease in urology, the incidence and prevalence are rapidly increasing with the aging society, prompting an urgent need for effective prevention and treatment of BPH. However, limited therapeutic efficacy and higher risk of complications result in the treatment of BPH remaining challenging. The unclear pathogenic mechanism also hampers further exploration of therapeutic approaches for BPH. In this study, we used multi-omics methods to integrate genomics, transcriptomics, immunomics, and metabolomics data and identify biomolecules associated with BPH. We performed transcriptomic imputation, summary data-based Mendelian randomization (SMR), joint/conditional analysis, colocalization analysis, and FOCUS to explore high-confidence genes associated with BPH in blood and prostate tissue. Subsequently, three-step SMR was used to identify the DNA methylation sites regulating high-confidence genes to improve the pathogenic pathways of BPH. We also used cis-instruments of druggable genes to conduct SMR analysis to find potential drug targets for BPH. Finally, we used MR analysis to explore the immune pathways and metabolomics related to BPH. Multiple analytical methods identified BTN3A2 (Blood: TWAS Z score = 5.02912, TWAS P = 4.93 × 10–7; Prostate: TWAS Z score = 4.89, TWAS P = 1.01 × 10–6) and C4A (Blood: TWAS Z score = 4.90754, TWAS P = 9.22 × 10–7; Prostate: TWAS Z score = 5.084, TWAS P = 3.70 × 10–7) as high-confidence genes for BPH and identified the cg14345882-BTN3A2-BPH pathogenic pathway. We also used druggable gene data to identify 30 promising therapeutic target genes, including BTN3A2 and C4A. For MR analysis of immune pathways, we identified immune cell surface molecules as well as the inflammatory factor IL-17 (OR = 1.25, 95% CI = 1.09–1.43, PFDR = 0.12, Maximum likelihood) as risk factors for BPH. In addition, we found that disulfide levels of cysteinylglycine (OR = 1.11, 95% CI = 1.05–1.18, P = 5.18 × 10–4, Weighted median), oxidation levels of cysteinylglycine (OR = 1.09, 95% CI = 1.04–1.14, P = 3.87 × 10–4, Weighted median), and sebacate levels (OR = 1.05, 95% CI = 1.02–1.08, P = 3.0 × 10–4, Maximum likelihood) increase the risk of BPH. This multi-omics study explored biomolecules associated with BPH, improved the pathogenic pathways of BPH, and identified promising therapeutic targets. Our results provide evidence for future studies aimed at developing appropriate therapeutic interventions. [ABSTRACT FROM AUTHOR]

In this editorial, we discuss the need for a new, long-term strategy for managing human excrement (feces and urine) to facilitate health equity and promote environmental sustainability. Human excrement composting (HEC), a human-directed process driven by highly variable and diverse microbiomes, provides a means to advance this need and we discuss how microbiome science can help to advance HEC research. We argue that the technological advancements that have driven the growth of microbiome science, including microbiome and untargeted metabolome profiling, can be leveraged to enhance our understanding of safe and efficient HEC. We conclude by presenting our perspective on how we can begin applying these technologies to develop accessible procedures for safe HEC. [ABSTRACT FROM AUTHOR]

The genetic analysis of potato is hampered by the complexity of tetrasomic inheritance. An ongoing effort aims to transform the clonally propagated tetraploid potato into a seed-propagated diploid crop, which would make genetic analyses much easier owing to disomic inheritance. Here, we construct and report the large-scale genetic and heterotic characteristics of a diploid F2 potato population derived from the cross of two highly homozygous inbred lines. We investigate 20,382 traits generated from multi-omics dataset and identify 25,770 quantitative trait loci (QTLs). Coupled with gene expression data, we construct a systems-genetics network for gene discovery in potatoes. Importantly, we explore the genetic basis of heterosis in this population, especially for yield and male fertility heterosis. We find that positive heterotic effects of yield-related QTLs and negative heterotic effects of metabolite QTLs (mQTLs) contribute to yield heterosis. Additionally, we identify a PME gene with a dominance heterotic effect that plays an important role in male fertility heterosis. This study provides genetic resources for the potato community and will facilitate the application of heterosis in diploid potato breeding. An ongoing effort aims to transform the clonally propagated tetraploid potato to a seed-propagated diploid crop, but our understanding of it disomic inheritance is limited. Here, the authors report genetic basis of heterosis in the elite hybrid potato and identify a male fertility-related PME gene with dominance heterotic effect. [ABSTRACT FROM AUTHOR]

Space exploration's advancement toward long-duration missions prompts intensified research on physiological effects. Despite adaptive physiological stability in some variables, persistent changes affect genome integrity, immune response, and cognitive function. Our study, utilizing multi-omics data from GeneLab, provides crucial insights investigating muscle atrophy during space mission. Leveraging NASA GeneLab's data resources, we apply systems biology-based analyses, facilitating comprehensive understanding and enabling meta-analysis. Through transcriptomics, we establish a reference profile of biological processes underlying muscle atrophy, crucial for intervention development. We emphasize the often-overlooked role of glycosylation in muscle atrophy. Our research sheds light on fundamental molecular mechanisms, bridging gaps between space research and terrestrial conditions. This study underscores the importance of interdisciplinary collaboration and data-sharing initiatives like GeneLab in advancing space medicine research. [ABSTRACT FROM AUTHOR]

Background: Focal cortical dysplasia (FCD) is a heterogeneous group of cortical developmental malformations that constitute a common cause of medically intractable epilepsy. FCD type IIIa (FCD IIIa) refers to temporal neocortex alterations in architectural organisation or cytoarchitectural composition in the immediate vicinity of hippocampal sclerosis. Slight alterations in the temporal neocortex of FCD IIIa patients pose a challenge for the preoperative diagnosis and definition of the resection range. Methods: We have performed multimodal integration of single‐nucleus RNA sequencing and single‐nucleus assay for transposase‐accessible chromatin sequencing in the epileptogenic cortex of four patients with FCD IIIa, and three relatively normal temporal neocortex were chosen as controls. Results: Our study revealed that the most significant dysregulation occurred in excitatory neurons (ENs) and oligodendrocyte precursor cells (OPCs) in the epileptogenic cortex of FCD IIIa patients. In ENs, we constructed a transcription factor (TF)‐hub gene regulatory network and found DAB1high ENs subpopulation mediates neuronal immunity characteristically in FCD IIIa. Western blotting and immunofluorescence were used to validate the changes in protein expression levels caused by some of the key genes. The OPCs were activated and exhibited aberrant phenotypes in FCD IIIa, and TFs regulating reconstructed pseudotime trajectory were identified. Finally, our results revealed aberrant intercellular communication between ENs and OPCs in FCD IIIa patients. Conclusions: Our study revealed significant and intricate alterations in the transcriptomes and epigenomes in ENs and OPCs of FCD IIIa patients, shedding light on their cell type‐specific regulation and potential pathogenic involvement in this disorder. This work will help evaluate the pathogenesis of cortical dysplasia and epilepsy and explore potential therapeutic targets. Key points: Paired snRNA‐seq and snATAC‐seq data were intergrated and analysed to identify crucial subpopulations of ENs and OPCs in the epileptogenic cortex of FCD IIIa patients and explore their possible pathogenic role in the disease.A TF‐hub gene regulatory network was constructed in ENs, and the DAB1high Ex‐1 mediated neuronal immunity was characterstically in FCD IIIa patients.The OPCs were activated and exhibited aberrant phenotypes in FCD IIIa patients, and TFs regulating reconstructed pseudotime traectory were identified.Aberrant intercelluar communications between ENs and OPCs in FCD IIIa patients were identified. [ABSTRACT FROM AUTHOR]

Aim: To investigate the relationship and potential causality between biological ageing and periodontitis. Materials and Methods: We obtained the National Health and Nutrition Examination Survey (NHANES) and genome‐wide association study (GWAS) summary statistics as well as single‐cell sequencing data. Multivariate regression analysis based on cross‐sectional data, Mendelian randomization (MR) and multi‐omics integration analysis were employed to explore the causal association and potential molecular mechanisms between biological ageing and periodontitis. Additionally, two‐step MR mediation analysis explored the risk factors in biological ageing‐mediated periodontitis. Results: We analysed data from 3189 participants in the NHANES data and found that higher biological age was associated with increased risk of periodontitis. MR analyses revealed causal associations between biological age measures and periodontitis risk. Frailty (odds ratio [OR] = 2.08, 95% confidence interval [CI]: 1.04–4.18, p =.039) and GrimAge acceleration (OR = 1.16, 95% CI: 1.01–1.32, p =.033) were causally associated with periodontitis risk, and these results were validated in a large‐scale meta‐periodontitis GWAS dataset. Additionally, the risk effects of body mass index, waist circumference and lifetime smoking on periodontitis were partially mediated by frailty and GrimAge acceleration. Conclusions: Evidence from cross‐sectional survey and MR analysis suggests that biological ageing increases the risk of periodontitis. Additionally, improving the associated risk factors can help prevent both ageing and periodontitis. [ABSTRACT FROM AUTHOR]

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI. [ABSTRACT FROM AUTHOR]

Background: Cancer survival prediction is vital in improving patients' prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein–Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein–Barr virus infection, which are linked to lung cancer progression. [ABSTRACT FROM AUTHOR]

The Special Issue "Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine" in the International Journal of Molecular Sciences showcases cutting-edge research in bioinformatics and multi-omics integration for precision medicine. The issue includes 12 papers demonstrating the application of computational approaches in various medical research areas, such as COVID-19-induced pulmonary fibrosis, cancer research, periodontitis, microbiome interactions, Alzheimer's disease, reproductive health, and kidney renal clear cell carcinoma. While multi-omics offers promise for personalized healthcare, challenges include data integration, volume management, and ethical considerations, requiring validation and real-world applications for successful integration into healthcare infrastructures. The continued development of computational approaches is crucial for shaping the future of precision medicine. [Extracted from the article]

As the most abundant class of transcription factors in eukaryotes, C2H2-type zinc finger proteins (C2H2-ZFPs) play critical roles in various biological processes. Despite being extensively studied in mammals, C2H2-ZFPs remain poorly characterized in birds. Recent accumulation of multi-omics data for chicken enables the genome-wide investigation of C2H2-ZFPs in birds. The purpose of this study is to reveal the genomic occurrence and evolutionary signature of chicken C2H2-ZFPs, and further depict their expression profiles across diverse chicken tissues. Here, we annotated 301 C2H2-ZFPs in chicken genome, which are associated with different effector domains, including KRAB, BTB, HOMEO, PHD, SCAN, and SET. Among them, most KRAB-ZFPs lack orthologues in mammals and tend to form clusters by duplication, supporting their fast evolution in chicken. We also annotated a unique and previously unidentified SCAN-ZFP, which is lineage-specific and highly expressed in ovary and testis. By integrating 101 RNA-seq datasets for 32 tissues, we found that most C2H2-ZFPs have tissue-specific expression. Particularly, 74 C2H2-ZFPs—including 27 KRAB-ZFPs—show blastoderm-enriched expression, indicating their association with early embryo development. Overall, this study performs comprehensive annotation and expression profiling of C2H2 ZFPs in diverse chicken tissues, which gives new insights into the evolution and potential function of C2H2-ZFPs in avian species. [ABSTRACT FROM AUTHOR]

The incorporation of multi-omics data methodologies facilitates the concurrent examination of proteins, metabolites, and genes associated with inflammation, thereby leveraging multi-dimensional biological data to achieve a comprehensive understanding of the complexities involved in the progression of inflammation. Inspired by ensemble learning principles, we implemented ID normalization preprocessing, categorical sampling homogenization, and pathway enrichment across each sample matrix derived from multi-omics datasets available in the literature, directing our focus on inflammation-related targets within lipopolysaccharide (LPS)-stimulated RAW264.7 cells towards β-alanine metabolism. Additionally, through the use of LPS-treated RAW264.7 cells, we tentatively validated the anti-inflammatory properties of the metabolite Ureidopropionic acid, originating from β-alanine metabolism, by evaluating cell viability, nitric oxide production levels, and mRNA expression of inflammatory biomarkers. In conclusion, our research represents the first instance of an integrated analysis of multi-omics datasets pertaining to LPS-stimulated RAW264.7 cells as documented in the literature, underscoring the pivotal role of β-alanine metabolism in cellular inflammation and successfully identifying Ureidopropionic acid as a novel anti-inflammatory compound. Moreover, the findings from database predictions and molecular docking studies indicated that the inflammatory-related pathways and proteins may serve as potential mechanistic targets for Ureidopropionic acid. [ABSTRACT FROM AUTHOR]

Simple Summary: Each patient responds uniquely to treatment, which makes personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) a promising strategy that delivers high-dose radiation at extended intervals for tailored adaptation. Currently, treatment modifications mainly rely on physicians' assessments of tumor size changes. Our study aims to develop a more objective multiomics-based approach for predicting treatment outcomes in PULSAR, including radiomics, dosiomics, and delta features. By leveraging multiomics analysis and machine learning, we intend to transition the adaptation and decision-making process from empirical judgments to a more data-informed strategy, allowing clinicians to swiftly respond to changes in tumor behavior and provide more personalized treatment for each patient. Objectives: This retrospective study aims to develop a multiomics approach that integrates radiomics, dosiomics, and delta features to predict treatment responses in brain metastasis (BM) patients undergoing PULSAR. Methods: A retrospective study encompassing 39 BM patients with 69 lesions treated with PULSAR was undertaken. Radiomics, dosiomics, and delta features were extracted from both pre-treatment and intra-treatment MRI scans alongside dose distributions. Six individual models, alongside an ensemble feature selection (EFS) model, were evaluated. The classification task focused on distinguishing between two lesion groups based on whether they exhibited a volume reduction of more than 20% at follow-up. Performance metrics, including sensitivity, specificity, accuracy, precision, F1 score, and the area under the receiver operating characteristic (ROC) curve (AUC), were assessed. Results: The EFS model integrated the features from pre-treatment radiomics, pre-treatment dosiomics, intra-treatment radiomics, and delta radiomics. It outperformed six individual models, achieving an AUC of 0.979, accuracy of 0.917, and F1 score of 0.821. Among the top nine features of the EFS model, six features came from post-wavelet transformation and three from original images. Conclusions: The study demonstrated the feasibility of employing a data-driven multiomics approach to predict treatment outcomes in BM patients receiving PULSAR treatment. Integrating multiomics with intra-treatment decision support in PULSAR shows promise for optimizing patient management and reducing the risks of under- or over-treatment. [ABSTRACT FROM AUTHOR]

Women's health and sex differences research remain understudied. In 2022, to address the topic of sex differences, the Ludeman Family Center for Women's Health Research (LFCWHR) at the University of Colorado (LudemanCenter.org) held its third National Conference, "Sex Differences Across the Lifespan: A Focus on Metabolism." The research presentations and discussions from the 2022 conference addressed cardiometabolic sex differences across the lifespan and included sessions focusing on scientific methods with which to study sex differences, effects of estrogen on metabolism, and sex differences in cardiovascular disease—implications for women and policy among others. Over 100 participants, including basic scientists, clinical scientists, policymakers, advocacy group leaders, and federal agency leadership participated. The meeting proceedings reveal that although exciting advances in the area of sex differences have taken place, significant questions and gaps remain about women's health and sex differences in critical areas of health. Identifying these gaps and the subsequent research that will result may lead to important breakthroughs. [ABSTRACT FROM AUTHOR]

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients. [ABSTRACT FROM AUTHOR]

The linear mixed model (LMM) has become a standard in genetic association studies to account for population stratification and relatedness in the samples to reduce false positives. Much recent progresses in LMM focused on approximate computations. Exact methods remained computationally demanding and without theoretical assurance. The computation is particularly challenging for multiomics studies where tens of thousands of phenotypes are tested for association with millions of genetic markers. We present IDUL and IDUL † that use iterative dispersion updates to fit LMMs, where IDUL † is a modified version of IDUL that guarantees likelihood increase between updates. Practically, IDUL and IDUL † produced identical results, both are markedly more efficient than the state-of-the-art Newton–Raphson method, and in particular, both are highly efficient for additional phenotypes, making them ideal to study genetic determinants of multiomics phenotypes. Theoretically, the LMM likelihood is asymptotically unimodal, and therefore the gradient ascent algorithm IDUL † is asymptotically exact. A software package implementing IDUL and IDUL † for genetic association studies is freely available at https://github.com/haplotype/IDUL. [ABSTRACT FROM AUTHOR]

The barramundi (Lates calcarifer), a significant aquaculture species, typically displays silver to bronze coloration. However, attention is now drawn to rare variants like the "panda" phenotype, characterized by blotch‐like patterns of black (PB) and golden (PG) patches. This phenotype presents an opportunity to explore the molecular mechanisms underlying color variations in teleosts. Unlike stable color patterns in many fish, the "panda" variant demonstrates phenotypic plasticity, responding dynamically to unknown cues. We propose a complex interplay of genetic factors and epigenetic modifications, focusing on DNA methylation. Through a multiomics approach, we analyze transcriptomic and methylation patterns between PB and PG patches. Our study reveals differential gene expression related to melanosome trafficking and chromatophore differentiation. Although the specific gene responsible for the PB–PG difference remains elusive, candidate genes like asip1, asip2, mlph, and mreg have been identified. Methylation emerges as a potential contributor to the "panda" phenotype, with changes in gene promoters like hand2 and dynamin possibly influencing coloration. This research lays the groundwork for further exploration into rare barramundi color patterns, enhancing our understanding of color diversity in teleosts. Additionally, it underscores the "panda" phenotype's potential as a model for studying adult skin coloration. [ABSTRACT FROM AUTHOR]

To understand the effects and related potential mechanism of H2O2 on pigment metabolism in postharvest broccoli, an integrated analysis of transcriptome and metabolome was performed. Results suggested that 65 differentially expressed genes and 26 differentially accumulated metabolites involved in chlorophyll, carotenoid, and flavonoid metabolism were identified. H2O2 treatment delayed the decrease of chlorophyll content by upregulating the expressions of chlorophyll synthetic genes, thylakoid synthetic genes, and 15 light‐harvesting complex genes compared with the control and diphenylene iodonium treatments. H2O2 treatment decreased the accumulation of 11 flavonoids and 5 flavonols by downregulating the flavonoid synthetic genes. In addition, H2O2 treatment promoted carotenoid biosynthesis to eliminate reactive oxygen species in thylakoids, thereby protecting chlorophyll molecules from degradation. The inhibition of flavonoids and flavonols accumulation and chlorophyll decrease was the crucial reason for the delayed yellowing in H2O2 treatment. This study provides a new method and theoretical support for delaying the yellowing process in postharvest broccoli. [ABSTRACT FROM AUTHOR]

Akebia trifoliata is a medicinal plant with high oil content and broad pharmacological effects. To investigate the regulatory mechanisms of key metabolic pathways during seed development, we conducted an integrated multi-omics analysis, including transcriptomics, proteomics, and metabolomics, exploring the dynamic changes in carbon and lipid metabolism. Metabolomics analysis revealded that glucose and sucrose levels decreased, while glycolytic intermediate phosphoenolpyruvate and fatty acids increased with seed development, indicating a shift in carbon flux towards fatty acid synthesis. Integrated transcriptomic and proteomic analyses showed that 70 days after flowering, the expression levels of genes and proteins associated with carbon and fatty acid metabolism were upregulated, suggesting an increased energy demand. Additionally, LEC2, LEC1, WRI1, FUS3, and ABI3 were identified as vital regulators of lipid synthesis. By constructing a multi-omics co-expression network, we identified hub genes such as aroE, GAPDH, KCS, TPS, and hub proteins like PGM, PDH, ENO, PFK, PK, ACCase, SAD, PLC, and OGDH that play critical regulatory roles in seed lipid synthesis. This study provides new ideas for the molecular basis of lipid synthesis in Akebia trifoliata seeds and can facilitate future research on the genetic improvement through molecular-assisted breeding. [ABSTRACT FROM AUTHOR]

Background: The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. Method: In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Results: Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. Conclusion: We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample. [ABSTRACT FROM AUTHOR]