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]

Exposure to polychlorinated biphenyls (PCBs) is linked to neurotoxic effects. This study aims to close knowledge gaps regarding the specific modes of action of PCBs in female C57BL/6J mice (>6 weeks) orally exposed for 7 weeks to a human-relevant PCB mixture (MARBLES mix) at 0, 0.1, 1, and 6 mg/kg body weight/day. PCB and hydroxylated PCB (OH-PCBs) levels were quantified in the brain, liver, and serum; RNA sequencing was performed in the striatum, prefrontal cortex, and liver, and metabolomic analyses were performed in the striatum. Profiles of PCBs but not their hydroxylated metabolites were similar in all tissues. In the prefrontal cortex, PCB exposure activated the oxidative phosphorylation respiration pathways, while suppressing the axon guidance pathway. PCB exposure significantly changed the expression of genes associated with neurodevelopmental and neurodegenerative diseases in the striatum, impacting pathways like growth hormone synthesis and dendrite development. PCBs did not affect the striatal metabolome. In contrast to the liver, which showed activation of metabolic processes following PCB exposure and the induction of cytochrome P450 enzymes, the expression of xenobiotic processing genes was not altered by PCB exposure in either brain region. Network analysis revealed complex interactions between individual PCBs (e.g., PCB28 [2,4,4'-trichlorobiphenyl]) and their hydroxylated metabolites and specific differentially expressed genes (DEGs), underscoring the need to characterize the association between specific PCBs and DEGs. These findings enhance the understanding of PCB neurotoxic mechanisms and their potential implications for human health.

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.

SUMMARY Toxicodendron species are economically and medicinally important trees because of their rich sources of natural products. We present three chromosome‐level genome assemblies of Toxicodendron vernicifluum ‘Dali’, Toxicodendron succedaneum ‘Vietnam’, and T. succedaneum ‘Japan’, which display diverse production capacities of specialized metabolites. Genome synteny and structural variation analyses revealed large genomic differences between the two species (T. vernicifluum and T. succedaneum) but fewer differences between the two cultivars within the species. Despite no occurrence of recent whole‐genome duplications, Toxicodendron showed evidence of local duplications. The genomic modules with high expression of genes encoding metabolic flux regulators and chalcone synthase‐like enzymes were identified via multiomics analyses, which may be responsible for the greater urushiol accumulation in T. vernicifluum ‘Dali’ than in other Toxicodendron species. In addition, our analyses revealed the regulatory patterns of lipid metabolism in T. succedaneum ‘Japan’, which differ from those of other Toxicodendron species and may contribute to its high lipid accumulation. Furthermore, we identified the key regulatory elements of lipid metabolism at each developmental stage, which could aid in molecular breeding to improve the production of urushiol and lipids in Toxicodendron species. In summary, this study provides new insights into the genomic underpinnings of the evolution and diversity of specialized metabolic pathways in three Toxicodendron cultivars through multiomics studies. [ABSTRACT FROM AUTHOR]

Early and accurate diagnosis is crucial to prevent disease development and define therapeutic strategies. Due to predominantly unspecific symptoms, diagnosis of autoimmune diseases (AID) is notoriously challenging. Clinical decision support systems (CDSS) are a promising method with the potential to enhance and expedite precise diagnostics by physicians. However, due to the difficulties of integrating and encoding multi-omics data with clinical values, as well as a lack of standardization, such systems are often limited to certain data types. Accordingly, even sophisticated data models fall short when making accurate disease diagnoses and presenting data analyses in a user-friendly form. Therefore, the integration of various data types is not only an opportunity but also a competitive advantage for research and industry. We have developed an integration pipeline to enable the use of machine learning for patient classification based on multi-omics data in combination with clinical values and laboratory results. The application of our framework resulted in up to 96% prediction accuracy of autoimmune diseases with machine learning models. Our results deliver insights into autoimmune disease research and have the potential to be adapted for applications across disease conditions. [ABSTRACT FROM AUTHOR]

Background: The relationship between epigenomics and plant specialised metabolism remains largely unexplored despite the fundamental importance of epigenomics in gene regulation and, potentially, yield of products of plant specialised metabolic pathways. The glandular trichomes of Cannabis sativa are an emerging model system that produce large quantities of cannabinoid and terpenoid specialised metabolites with known medicinal and commercial value. To address this lack of epigenomic data, we mapped H3K4 trimethylation, H3K56 acetylation, H3K27 trimethylation post-translational modifications and the histone variant H2A.Z, using chromatin immunoprecipitation, in C. sativa glandular trichomes, leaf, and stem tissues. Corresponding transcriptomic (RNA-seq) datasets were integrated, and tissue-specific analyses conducted to relate chromatin states to glandular trichome specific gene expression. Results: The promoters of cannabinoid and terpenoid biosynthetic genes, specialised metabolite transporter genes, defence related genes, and starch and sucrose metabolism were enriched specifically in trichomes for histone marks H3K4me3 and H3K56ac, consistent with active transcription. We identified putative trichome-specific enhancer elements by identifying intergenic regions of H3K56ac enrichment, a histone mark that maintains enhancer accessibility, then associated these to putative target genes using the tissue specific gene transcriptomic data. Bi-valent chromatin loci specific to glandular trichomes, marked with H3K4 trimethylation and H3K27 trimethylation, were associated with genes of MAPK signalling pathways and plant specialised metabolism pathways, supporting recent hypotheses that implicate bi-valent chromatin in plant defence. The histone variant H2A.Z was largely found in intergenic regions and enriched in chromatin that contained genes involved in DNA homeostasis. Conclusion: We report the first genome-wide histone post-translational modification maps for C. sativa glandular trichomes, and more broadly for glandular trichomes in plants. Our findings have implications in plant adaptation and stress responses and provide a basis for enhancer-mediated, targeted, gene transformation studies in plant glandular trichomes. [ABSTRACT FROM AUTHOR]

Background and Purpose Experimental Approach Key Results Conclusion and Implications Pneumoconiosis, especially silicosis, is a prevalent occupational disease with substantial global economic implications and lacks a definitive cure. Both pneumoconiosis and idiopathic pulmonary fibrosis (IPF) are interstitial lung diseases, which share many common physiological characteristics. Because pirfenidone and nintedanib are approved to treat IPF, their potential efficacy as antifibrotic agents in advanced silicosis deserves further exploration. Thus, we aimed to evaluate the individual and combined effects of pirfenidone and nintedanib in treating advanced silicosis mice and elucidate the underlying mechanisms of their therapeutic actions via multiomics.We administered monotherapy or combined therapy of pirfenidone and nintedanib, with low and high doses, in silicosis established after 6 weeks and evaluated lung function, inflammatory responses and fibrotic status. Additionally, we employed transcriptomic and metabolomic analyses to uncover the mechanisms underlying different therapeutic strategies.Both pirfenidone and nintedanib were effective in treating advanced silicosis, with superior outcomes observed in combination therapy. Transcriptomic and metabolomic analyses revealed that pirfenidone and nintedanib primarily exerted their therapeutic effects by modulating immune responses, signalling cascades and metabolic processes involving lipids, nucleotides and carbohydrates. Furthermore, we experimentally validated both monotherapy and combined therapy yielded therapeutic benefits through two common signalling pathways: steroid biosynthesis and purine metabolism.In conclusion, pirfenidone and nintedanib, either individually or in combination, demonstrate substantial potential in advanced silicosis. Furthermore, combined therapy outperformed monotherapy, even at low doses. These therapeutic benefits are attributed to their influence on diverse signalling pathways and metabolic processes. [ABSTRACT FROM AUTHOR]

Oligosaccharides, a low polymerization degree of carbohydrate, possess various physiological activities, such as anti-diabetes, anti-obesity, anti-aging, anti-viral, and gut microbiota regulation, having a widely used in food and medical fields. However, due to the limited natural oligosaccharides, many un-natural oligosaccharides from complex polysaccharides are being studied for amplifying the available pool of oligosaccharides. More recently, various oligosaccharides were developed by using several artificial strategies, such as chemical degradation, enzyme catalysis, and biosynthesis, then they can be applied in various sectors. Moreover, it has gradually become a trend to use biosynthesis to realize the synthesis of oligosaccharides with clear structure. Emerging research has found that un-natural oligosaccharides exert more comprehensive effects against various human diseases through multiple mechanisms. However, these oligosaccharides from various routes have not been critical reviewed and summarized. Therefore, the purpose of this review is to present the various routes of oligosaccharides preparations and healthy effects, with a focus on diabetes, obesity, aging, virus, and gut microbiota. Additionally, the application of multi-omics for these natural and un-natural oligosaccharides has also been discussed. Especially, the multi-omics are needed to apply in various disease models to find out various biomarkers to respond to the dynamic change process of oligosaccharides. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify DNA methylation biomarkers associated with myopia using summary-data-based Mendelian randomization (SMR). Methods: A systematic search of the PubMed, Web of Science, Cochrane Library, and Embase databases was conducted up to March 27, 2024. SMR analyses were performed to integrate genome-wide association study (GWAS) with methylation quantitative trait loci (mQTL) and expression quantitative trait loci (eQTL) studies. The heterogeneity in the dependent instrument (HEIDI) test was utilized to distinguish pleiotropic associations from linkage disequilibrium. Results: The systematic review identified 26 DNA methylation biomarkers in five studies, with no overlap observed among those identified by different studies. After integrating GWAS with multi-omics data of mQTL and eQTL, six genes were significantly associated with myopia: PRMT6 (cg00944433 and cg15468180), SH3YL1 (cg03299269, cg11361895, and cg13354988), ZKSCAN4 (cg01192291), GATS (cg17830204), NPAT (cg04826772), and UBE2I (cg03545757 and cg08025960). Conclusions: We identified six methylation biomarkers associated with the risk of myopia that may be helpful to elucidate the etiology mechanisms of myopia. Further experimental validation studies are required to corroborate these findings. [ABSTRACT FROM AUTHOR]

dCTP pyrophosphatase 1 (DCTPP1) is widely expressed in tumors and the immune system and plays a critical role in human cancer progression. However, multi-omics characterization of DCTPP1 and its role in prognosis and immune microenvironment of tumor patients has not been explored. We collected data from 33 cancers and comprehensively analyzed the aberrant expression, prognostic role, pathway enrichment, immune microenvironment, and association with drug sensitivity of DCTPP1 in cancers, as well as the prediction of patients' immunotherapeutic response to ICIs and targeted small molecule drugs. Finally, we experimentally confirmed the role of DCTPP1 in renal clear cell carcinoma. We discovered that DCTPP1 has a differentiable expression and a diagnostic biomarker significance in cancer. Additionally, we discovered that DCTPP1 is important for the tumor microenvironment and pan-cancer. TMB and MSI are frequent immunological checkpoints that are significantly correlated with DCTPP1 expression. Patients who express high levels of DCTPP1 have greater rates of survival and therapeutic response following immunotherapy. Ultimately, it was discovered that renal clear cell carcinoma cells' invasion and proliferation were suppressed by DCTPP1 knockdown. Our findings demonstrate the significant potential of DCTPP1 as a biomarker for prognosis and immunotherapeutic response, which may pave the way for more investigation into the mechanism of tumor infiltration and DCTPP1's potential for cancer therapy. [ABSTRACT FROM AUTHOR]

Polymicrobial co- and superinfections involving bacterial and fungal pathogens pose serious challenges for diagnosis and therapy, and are associated with elevated morbidity and mortality. However, the metabolic dynamics of bacterial–fungal interactions (BFI) and the resulting impact on disease outcome remain largely unknown. The fungus Aspergillus fumigatus and the bacterium Klebsiella pneumoniae are clinically important pathogens sharing common niches in the human body, especially in the lower respiratory tract. We have exploited an integrated multi-omics approach to unravel the complex and multifaceted processes implicated in the interspecies communication involving these pathogens in mixed biofilms. In this setting, A. fumigatus responds to the bacterial challenge by rewiring its metabolism, attenuating the translational machineries, and by connecting secondary with primary metabolism, while K. pneumoniae maintains its central metabolism and translation activity. The flexibility in the metabolism of A. fumigatus and the ability to quickly adapt to the changing microenvironment mediated by the bacteria highlight new possibilities for studying the impact of cross-communication between competing interaction partners. The data underscore the complexity governing the dynamics underlying BFI, such as pronounced metabolic changes mounted in A. fumigatus interacting with K. pneumoniae. Our findings identify candidate biomarkers potentially exploitable for improved clinical management of BFI. A multi-omics study highlights the flexibility in the metabolism of Aspergillus fumigatus in microenvironments co-shared with Klebsiella pneumoniae, uncovering potential biomarkers to enhance clinical care for polymicrobial infections. [ABSTRACT FROM AUTHOR]

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition. Objectives: We hypothesized that the unbiased integration of different COPD lung omics using a novel multilayer approach might unravel mechanisms associated with clinical characteristics. Methods: We profiled mRNA, microRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer), we built a patient network on the basis of molecular similarity. The three networks were used to build a multilayer network, and optimization of multiplex modularity was used to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. Measurements and Main Results: We identified five patient communities in the multilayer network that were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema but were molecularly different: C#3, but not C#4, presented B- and T-cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort and to validate them in an independent cohort. Finally, using spatial transcriptomics, we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T-/B-cell homing chemokines and bacterial response genes in C#3. Conclusions: A novel multilayer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation. [ABSTRACT FROM AUTHOR]

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers. Patient-derived xenografts (PDX) could contribute to understanding how colorectal cancer (CRC) responds to targeted therapies like cetuximab. Here, the authors characterise the response to cetuximab in 231 CRC PDXs using multiomics and develop an integrative ensemble classifier - CeSta - to predict sensitivity to cetuximab. [ABSTRACT FROM AUTHOR]

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein--protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets. [ABSTRACT FROM AUTHOR]

The formation of human collagen requires the presence of Prolyl 3-hydroxylase 1 (P3H1), but the regulatory mechanism of P3H1 remained insufficiently understood. Our study aimed to identify the role of P3H1 in clear cell renal cell carcinoma (ccRCC). P3H1 expression in ccRCC was validated using multiple databases and in vitro experiments. We performed a correlation analysis of P3H1 with drug sensitivity, immune checkpoints, and immune cell infiltration using transcriptome and single-cell sequencing. Drawing upon the Encyclopedia of RNA Interactomes database, we selected P3H1 as the focal point of our investigation, meticulously uncovering the intricate network of microRNAs and lncRNAs that potentially orchestrate ceRNA mechanisms. This study employs a multidimensional approach integrating vitro assays and multi-omics bioinformatics analyses to investigate P3H1's impact on ccRCC prognosis, immune modulation, immune checkpoints, ceRNA regulatory network, drug sensitivity, and therapeutic responses, aiming to uncover new insights into its therapeutic potential and inform future clinical strategies. [ABSTRACT FROM AUTHOR]

The dysfunction of gut microbiome and bile acid metabolism might cause the incidence and relapse of ulcerative colitis (UC). Thus, natural products have been considered effective for UC through the regulation of gut microbiome and bile acid. In this study, we evaluated the regulatory effect of berberine on gut microbiome and bile acid metabolism in UC. Results showed that the relative abundances of beneficial bacteria showed a decreasing trend in the UC model, and the taurine conjugated bile acids increased from the liver tissue to the colon tissue. Berberine inhibited the colonization of harmful bacteria and promoted the primary bile acid metabolism. Moreover, we used multi-omics technology (metagenomics, metabolomics, and transcriptomics technology) to reveal that berberine restored the intestinal barrier function through bile acid/S1PR2/RhoA/ROCK pathway. The result of transmission electron microscopy directly showed that the damaged intestinal mucosal barrier was repaired through the berberine treatment. This study revealed the treatment influence on UC through multi-omics technology in vitro and in vivo models, which provides references for explaining the mechanism of berberine on UC. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. Methods: Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed. Results: A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58–0.85] and a specificity of 0.91 [0.87–0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21–14.50], the negative likelihood ratio (NLR) was 0.28 [0.16–0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96–78.97]. The area under the curve (AUC) was 0.93 [0.90–0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis. Conclusion: In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. Trial registration: PROSPERO (CRD42024497599). [ABSTRACT FROM AUTHOR]

Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people's health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity. [ABSTRACT FROM AUTHOR]

A new case of dementia is diagnosed every 3 s. Beyond age, risk prediction of dementia is challenging. There is growing evidence of underlying processes that connect aging across organ systems and may provide insight for early detection, and there is a need to identify early biomarkers at an age when action can be taken to mitigate cognitive decline. We hypothesized that timing of menopause, a marker of ovarian aging, predicts brain age decades later. We used 2086 subjects with multiple “omics” measurements from post‐mortem brain samples. Age at menopause (AAM) is positively correlated with cognitive function and negatively correlated with pre‐frontal cortex aging acceleration (calculated as estimated biological age from DNA methylation minus chronological age). Genetic correlations showed that at least part of these associations is derived from shared heritability. To dissect the mechanism linking AAM to cognitive decline, we turned to transcriptomic data which confirmed that later AAM was associated with gene expression in pre‐frontal cortex consistent with better cognition, and among those who reached menopause naturally, decreased gene expression of pathways implicated in aging. Those with surgical menopause displayed different molecular changes, including perturbed nicotinamide adenine dinucleotide (NAD+) activity, validated by metabolomics. Bile acid metabolism was perturbed in both groups, although different bile acid ratios were associated with AAM in each. Together, our data suggest that AAM is predictive of brain aging and cognition, with potential mediation by the gut, although through different mechanisms depending on the type of menopause. [ABSTRACT FROM AUTHOR]

Endoplasmic reticulum (ER) and inflammatory stress responses contribute to islet dysfunction in type 2 diabetes (T2D). Comprehensive genomic understanding of these human islet stress responses and whether T2D-associated genetic variants modulate them is lacking. Here, comparative transcriptome and epigenome analyses of human islets exposed ex vivo to these stressors revealed 30% of expressed genes and 14% of islet cis -regulatory elements (CREs) as stress responsive, modulated largely in an ER- or cytokine-specific fashion. T2D variants overlapped 86 stress-responsive CREs, including 21 induced by ER stress. We linked the rs6917676-T T2D risk allele to increased islet ER-stress-responsive CRE accessibility and allele-specific β cell nuclear factor binding. MAP3K5 , the ER-stress-responsive putative rs6917676 T2D effector gene, promoted stress-induced β cell apoptosis. Supporting its pro-diabetogenic role, MAP3K5 expression correlated inversely with human islet β cell abundance and was elevated in T2D β cells. This study provides genome-wide insights into human islet stress responses and context-specific T2D variant effects. [Display omitted] • Genome-wide comparative human islet ER/cytokine stress response maps • T2D variants overlap 86 stress-responsive cis -regulatory elements (CREs) • Variant-to-function links between T2D variant rs6917676, MAP3K5 , and islet apoptosis • Risk allele increased ER-stress-induced CRE accessibility and MAP3K5 -mediated death This multi-omic study dissects the genome-wide effects of ER stress and pro-inflammatory cytokines on human islet transcriptional regulatory networks and motivates variant-to-function studies linking the rs6917676 T2D risk allele to enhanced ER-stress-responsive MAP3K5- mediated β cell apoptosis. The findings suggest that MAP3K5 inhibitors (e.g., selonsertib) could therapeutically reduce stress-induced β cell apoptosis. [ABSTRACT FROM AUTHOR]

Bacteriocin is a kind of natural substance that can effectively inhibit bacteria, but its production usually limited by environment. Co-culture is a strategy to stimulate bacteriocin production. Bifidocin A produced by Bifidobacterium animalis BB04, is a novel bacteriocin with a broad-spectrum antimicrobial active of foodborne bacteria. In order to enhance bifidocin A production, bacteriocin-inducing strains were screened firstly in co-cultivation. Then, the molecular mechanism of co-cultural induction was investigated by transcriptomic and proteomic analysis. Finally, the key inducing metabolites were identified by using targeted metabolomic technology. The results showed that Wickerhamomyces anomalus Y-5 in co-cultivation could significantly enhance bifidocin A production, with a 3.00-fold increase compared to mono-culture. The induction may not depend on direct contact with cells and may instead be attributed to be continuous exposure to inducing substances at specific concentration. In co-cultivation, W. anomalus Y-5 up-regulated Hxk2 and Tap42 to activate Glucose-cAMP and Tor and HOG-MAPK pathway, stimulated the expression of the retrograde gene, produced glutamine and glycerol to maintain activity. During this process, glutamine, inosine, guanosine, adenine, uracil, fumaric acid and pyruvic acid produced by W. anomalus Y-5 could induce the synthesis of bifidocin A. In conclusion, W. anomalus Y-5 in co-cultivation induced the synthesis of bifidocin A by regulating various signaling pathways to produce inducing substances. These findings establish a foundation for high-efficient synthesis of bifidocin A and provide a new perspective into the industrial production of bacteriocin. [ABSTRACT FROM AUTHOR]

Single cell methods have proliferated in recent years, made feasible by droplet microfluidic technologies, including commercialized technologies. Although the concept of single cell barcoding using these methodologies is straightforward, these assays require several assumptions about the underlying molecular biology for data interpretation. Following the initial droplet-based single cell demonstrations, new experimental and bioinformatics methods have evolved the baseline expectations of these assays, producing more complex and higher quality data, including single cell multi-omics. By generalizing themes from prior bioinformatics and experimental innovation, new users and experienced technology developers can identify opportunities to maximize data generation, improve analytical interpretation, and avoid failure modes in establishing new assays. Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies. [ABSTRACT FROM AUTHOR]

For research on HIV/AIDS, it is important to elucidate the complex viral–host interaction, host dependency factors (HDFs), and restriction factors. However, the regulatory network of HIV-resistance-related factors remains not well understood. Therefore, we integrated four publicly available HIV-related transcriptome datasets, along with three datasets on HIV-infection-related DNA methylation, miRNA, and ChIP-seq, to predict the factors influencing HIV resistance and infection. Our approach involved differential analysis, functional annotation, and protein–protein interaction network analysis. Through comprehensive analyses, we identified 25 potential HIV-resistance-related genes (including shared EGF) and 24 HIV-infection-related hub genes (including shared JUN). Additionally, we pinpointed five key differentially methylated genes, five crucial differentially expressed microRNAs, and five significant pathways associated with HIV resistance. We mapped the potential regulatory pathways involving these HIV-resistance-related factors. Among the predicted factors, RHOA, RAD51, GATA1, IRF4, and CXCL8 have been validated as HDFs or restriction factors. The identified factors, such as JUN, EGF, and PLEK, are potential HDFs or restriction factors. This study uncovers the gene signatures and regulatory networks associated with HIV-1 resistance, suggesting potential targets for the development of new therapies against HIV/AIDS. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer affects millions of women worldwide, and many experience fatigue across the various disease and treatment stages. Currently, there are no approved treatments for this fatigue. In this study, we tested pioglitazone, an FDA-approved drug, in a mouse model of breast cancer. Over four weeks, we examined how the drug impacted the body's metabolism, gene activity, levels of metabolites and lipids, and muscle function. Our results showed molecular improvements in muscle tissue, although the drug did not improve muscle fatigue. These findings show that pioglitazone may offer some restorative benefits at the molecular level and supports future research directions into treating breast cancer-related fatigue. Background/Objectives: Breast cancer (BC) is the second most commonly diagnosed cancer worldwide and is accompanied by fatigue during both active disease and remission in the majority of cases. Our lab has measured fatigue in isolated muscles from treatment-naive BC patient-derived orthotopic xenograft (BC-PDOX) mice. Here, we conducted a preclinical trial of pioglitazone in BC-PDOX mice to determine its efficacy in ameliorating BC-induced muscle fatigue, as well as its effects on transcriptomic, metabolomic, and lipidomic profiles in skeletal muscle. Methods: The pioglitazone and vehicle groups were treated orally for 4 weeks upon reaching a tumor volume of 600 mm3. Whole-animal indirect calorimetry was used to evaluate systemic metabolic states. The transcriptome was profiled using short-read bulk RNA sequencing (RNA-seq). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to profile the metabolome and lipidome. Fast and slow skeletal muscle function were evaluated using isolated ex vivo testing. Results: Pioglitazone was associated with a 16.634% lower average O2 consumption (mL∙h−1, p = 0.035), 16.309% lower average CO2 production (mL∙h−1, p = 0.022), and 16.4% lower cumulative energy expenditure (EE) (kcal∙h−1, p = 0.035), with no changes in substrate utilization. RNA-seq supported the downstream effects of pioglitazone on target genes and displayed considerable upregulation of mitochondrial bioenergetic pathways. K-means cluster 5 showed enrichment of the PPAR signaling pathway (adj. p < 0.05, Log2FC = 2.58). Skeletal muscle metabolomic and lipidomic profiles exhibited dysregulation in response to BC, which was partially restored in pioglitazone-treated mice compared to vehicle-treated BC-PDOX mice. In particular, the overall abundance of total ceramide levels was significantly lower in the PioTx group (−46.327%, p = 0.048). Despite molecular support for pioglitazone's efficacy, isolated muscle function was not affected by pioglitazone treatment. No significant difference in the area under the fatigue curve (AUC) was found between the pioglitazone and vehicle groups (p = 0.596). Conclusions: BC induces multi-omic dysregulation in skeletal muscle, which pioglitazone partially ameliorates. Future research should focus on profiling systemic metabolic dysfunction, identifying molecular biomarkers of fatigue, and testing alternative pioglitazone treatment regimens. [ABSTRACT FROM AUTHOR]

Background: Non-tuberculous mycobacterial (NTM) and Mycobacterium tuberculosis (MTB) infections are difficult to diagnose and treat, significantly burdening global health. The host immune status is generally believed to be associated with the onset and progression of NTM and MTB infections, but its specific impact remains unclear. Methods: In the present study, proteomics and lipidomics analysis of serum from normal controls (n = 26) and patients with MTB (n = 26), rapidly growing NTM (RGM, n = 15), and slowly growing NTM (SGM, n = 21) were conducted using the Olink technique based on a highly sensitive and specific neighborhood extension assay and the lipidomics technique. Results: IFN-γ, CXCL9, CXCL10, CXCL11, and CXCL13, etc. were simultaneously upregulated in MTB, RGM, and SGM, while lipids FAHFA 22:3, FAHFA 26:4, FAHFA 24:4, FAHFA 20:5, FAHFA 18:2 simultaneously downregulated. IL8, CCL3, CXCL5, and MCP-2, etc. were simultaneously upregulated in RGM and SGM compared to MTB, as well as PCs, LPCs, PEs, and LPEs. Compared with RGM, IL7, CD27, CCL17, CXCL12, and LPC 28:7-SN2 were downregulated in SGM. Pathway analyses revealed that tuberculosis, sphingolipid signaling pathway, and adipocytokine signaling pathway were regulated at the protein level and metabolite level. Diagnostic panels comprising immune-associated proteins and lipids greatly enhance diagnostic specificity and sensitivity. Conclusion: This integrated multi-omics analysis provides a more comprehensive understanding of the molecular landscape of NTM and MTB, which may provide molecular targets for specialized therapies. [ABSTRACT FROM AUTHOR]

The world's increasing need for protein faces challenges in aquaculture production. New applications and tools will need to be added at every stage of the manufacturing line to attain this expansion sustainably, safely, and effectively. Utilizing experimental methods to increase aquatic animal production has become more common as aquatic biotechnology has advanced. High‐throughput omics technologies have been introduced to address these issues, including transcriptomic, metabolomic, proteomic, and genomes. But it also faces many difficulties, like other food manufacturing industries. One of the best and most durable approaches to address these issues is probably to understand nutritional requirements and modify diet based on need. Molecular approaches are a subset of multiomics technology. Previously, most of the published work was devoted to the biochemical aspects of protein–lipid interactions in biological systems. In this review, we explore this idea and highlight various works that fall under the umbrella of nutrigenomics, with a particular emphasis on protein utilization and its interactions with carbohydrates and lipids. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) is a common genetic heart disease. Women with HCM tend to have a later onset but more severe disease course. However, the underlying pathobiological mechanisms for these differences remain unknown. Myectomy samples from 97 patients (53 males/44 females) with symptomatic obstructive HCM and 23 control cardiac tissues were included in this study. RNA-sequencing was performed on all samples. Mass spectrometry-based proteomics and phosphoproteomics was performed on a representative subset of samples. The transcriptome, proteome, and phosphoproteome was similar between sexes and did not separate on PCA plotting. Overall, there were 482 differentially expressed genes (DEGs) between control females and control males while there were only 53 DEGs between HCM females and HCM males. There were 1983 DEGs between HCM females and control females compared to 1064 DEGs between HCM males and control males. Additionally, there was increased transcriptional downregulation of hypertrophy pathways in HCM females and in HCM males. HCM females had 119 differentially expressed proteins compared to control females while HCM males only had 27 compared to control males. Finally, the phosphoproteome showed females had 341 differentially phosphorylated proteins (DPPs) compared to controls while males only had 184. Interestingly, there was hypophosphorylation and inactivation of hypertrophy pathways in females but hyperphosphorylation and activation in males. There are subtle, but biologically relevant differences in the multi-omics profile of HCM. This study provides the most comprehensive atlas of sex-specific differences in the transcriptome, proteome, and phosphoproteome present at the time of surgical myectomy for obstructive HCM. At baseline females have transcriptional upregulation of hypertrophy pathways. HCM abolishes sex specific differences. Females with HCM have increased dysregulation. Made with Biorender.com. [Display omitted] • At baseline females have transcriptional activation of hypertrophy pathways. • HCM abolishes normal sex-specific differences in cardiac tissue. • Females with HCM have increased dysregulation as compared to males with HCM. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-omics clustering method for cancer subtyping by utilizing clinic information based on SHAP values to extract interpretable features from multi-omics datasets. • We apply singular value decomposition to integrate the extracted features of different omics by constructing a latent subspace, followed by shared nearest neighbor-based spectral clustering on the latent representation. • Experimental results on several cancer datasets across three omics levels demonstrate the superior performance of our proposed method in multi-omics data analysis for cancer subtyping, compared to other state-of-the-art multi-omics clustering methods. In recent years, multi-omics clustering has become a powerful tool in cancer research, offering a comprehensive perspective on the diverse molecular characteristics inherent to various cancer subtypes. However, most existing multi-omics clustering methods directly integrate heterogeneous features from different omics, which may struggle to deal with the noise or redundancy of multi-omics data and lead to poor clustering results. Therefore, we propose a novel multi-omics clustering method to extract interpretable and discriminative features from various omics before data integration. The clinical information is used to supervise the process of feature extraction based on SHAP (SHapley Additive exPlanation) values. Singular value decomposition (SVD) is then applied to integrate the extracted features of different omics by constructing a latent subspace. Finally, we utilize shared nearest neighbor-based spectral clustering on the latent representation to obtain the clustering result. The proposed method is evaluated on several cancer datasets across three levels of omics, in comparison to several state-of-the-art multi-omics clustering methods. The comparison results demonstrate the superior performance of the proposed method in multi-omics data analysis for cancer subtyping. Additionally, experiments reveal the efficacy of utilizing clinical information based on SHAP values for feature extraction, enhancing the performance of clustering analyses. Moreover, enrichment analysis of the identified gene signatures in different subtypes is also performed to further demonstrate the effectiveness of the proposed method. Availability: The proposed method can be freely accessible at https://github.com/Tianyi-Shi-Tsukuba/Multi-omics-clustering-based-on-SHAP. Data will be made available on request. [ABSTRACT FROM AUTHOR]

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE. [ABSTRACT FROM AUTHOR]

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies — including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) — can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies. Technological advances continue to enhance the clinical value of kidney biopsies. Here, the authors consider the potential of using spatial-omics in transplantation, including the use of mass spectrometry imaging, as graft monitoring and diagnostic tools, to improve patient management and outcomes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Do Plants have intelligence system to sense different sources of sound vibrations? • SV treatment triggers short-term molecular reprogramming, long-term changes in plant architecture, and developmental and adaptive responses. • Plants use SVs for their mode of communication and alarming signal. • Role of different molecular players such as cell wall and plasma membrane and their signaling molecules involved in SV perception and signal transduction are highlighted. • High-throughput tools to decipher the molecular complexity of plant SV signaling. • How plants balance trade-offs mechanism between SV and other stimuli are addressed. How plants emit, perceive, and respond to sound vibrations (SVs) is a long-standing question in the field of plant sensory biology. In recent years, there have been numerous studies on how SVs affect plant morphological, physiological, and biochemical traits related to growth and adaptive responses. For instance, under drought SVs navigate plant roots towards water, activate their defence responses against stressors, and increase nectar sugar in response to pollinator SVs. Also, plants emit SVs during stresses which are informative in terms of ecological and adaptive perspective. However, the molecular mechanisms underlying the SV perception and emission in plants remain largely unknown. Therefore, deciphering the complexity of plant-SV interactions and identifying bonafide receptors and signaling players will be game changers overcoming the roadblocks in phytoacoustics. The aim of this review is to provide an overview of recent developments in phytoacoustics. We primarily focuss on SV signal perception and transduction with current challenges and future perspectives. Timeline breakthroughs in phytoacoustics have constantly shaped our understanding and belief that plants may emit and respond to SVs like other species. However, unlike other plant mechanostimuli, little is known about SV perception and signal transduction. Here, we provide an update on phytoacoustics and its ecological importance. Next, we discuss the role of cell wall receptor-like kinases, mechanosensitive channels, intracellular organelle signaling, and other key players involved in plant-SV receptive pathways that connect them. We also highlight the role of calcium (Ca2+), reactive oxygen species (ROS), hormones, and other emerging signaling molecules in SV signal transduction. Further, we discuss the importance of molecular, biophysical, computational, and live cell imaging tools for decoding the molecular complexity of acoustic signaling in plants. Finally, we summarised the role of SV priming in plants and discuss how SVs could modulate plant defense and growth trade-offs during other stresses. [ABSTRACT FROM AUTHOR]

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented. [ABSTRACT FROM AUTHOR]

Recent technological advancements in mass spectrometry (MS)‐based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS‐based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re‐examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi‐omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets. [ABSTRACT FROM AUTHOR]

The transformative role of artificial intelligence (AI) and multiomics could enhance the diagnostic and prognostic capabilities of liquid biopsy (LB) for lung cancer (LC). Despite advances, the transition from tissue biopsies to more sophisticated, non‐invasive methods like LB has been impeded by challenges such as the heterogeneity of biomarkers and the low concentration of tumour‐related analytes. The advent of multiomics – enabled by deep learning algorithms – offers a solution by allowing the simultaneous analysis of various analytes across multiple biological fluids, presenting a paradigm shift in cancer diagnostics. Through multi‐marker, multi‐analyte and multi‐source approaches, this review showcases how AI and multiomics are identifying clinically valuable biomarker combinations that correlate with patients' health statuses. However, the path towards clinical implementation is fraught with challenges, including study reproducibility and lack of methodological standardization, thus necessitating urgent solutions to solve these common issues. [ABSTRACT FROM AUTHOR]