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Most of Earths trees rely on critical soil nutrients that ectomycorrhizal fungi (EcMF) liberate and provide, and all of Earths land plants associate with bacteria that help them survive in nature. Yet, our understanding of how the presence of EcMF modifies soil bacterial communities, soil food webs, and root chemistry requires direct experimental evidence to comprehend the effects that EcMF may generate in the belowground plant microbiome. To this end, we grew Pinus muricata plants in soils that were either inoculated with EcMF and native forest bacterial communities or only native bacterial communities. We then profiled the soil bacterial communities, applied metabolomics and lipidomics, and linked omics data sets to understand how the presence of EcMF modifies belowground biogeochemistry, bacterial community structure, and their functional potential. We found that the presence of EcMF (i) enriches soil bacteria linked to enhanced plant growth in nature, (ii) alters the quantity and composition of lipid and non-lipid soil metabolites, and (iii) modifies plant root chemistry toward pathogen suppression, enzymatic conservation, and reactive oxygen species scavenging. Using this multi-omic approach, we therefore show that this widespread fungal symbiosis may be a common factor for structuring soil food webs.IMPORTANCEUnderstanding how soil microbes interact with one another and their host plant will help us combat the negative effects that climate change has on terrestrial ecosystems. Unfortunately, we lack a clear understanding of how the presence of ectomycorrhizal fungi (EcMF)-one of the most dominant soil microbial groups on Earth-shapes belowground organic resources and the composition of bacterial communities. To address this knowledge gap, we profiled lipid and non-lipid metabolites in soils and plant roots, characterized soil bacterial communities, and compared soils amended either with or without EcMF. Our results show that the presence of EcMF changes soil organic resource availability, impacts the proliferation of different bacterial communities (in terms of both type and potential function), and primes plant root chemistry for pathogen suppression and energy conservation. Our findings therefore provide much-needed insight into how two of the most dominant soil microbial groups interact with one another and with their host plant.

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.

CRISPR-Cas9 short guide RNA (sgRNA) library screening is a powerful approach to understand the molecular mechanisms of biological phenomena. However, its in vivo application is currently limited. Here, we developed our previously established in vitro revival screening method into an in vivo one to identify factors involved in spermatogenesis integrity by utilizing sperm capacitation as an indicator. By introducing an sgRNA library into testicular cells, we successfully pinpointed the retinal degeneration 3 (Rd3) gene as a significant factor in spermatogenesis. Single-cell RNA sequencing (scRNA-seq) analysis highlighted the high expression of Rd3 in round spermatids, and proteomics analysis indicated that Rd3 interacts with mitochondria. To search for cell-type-specific signaling pathways based on scRNA-seq and proteomics analyses, we developed a computational tool, Hub-Explorer. Through this, we discovered that Rd3 modulates oxidative stress by regulating mitochondrial distribution upon ciliogenesis induction. Collectively, our screening system provides a valuable in vivo approach to decipher molecular mechanisms in biological processes.

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.

• Chronic pain methylates genes in the prefrontal cortex that cause both pain and cognitive dysfunction in mouse knockout experiments. • Chronic pain-induced methylation specifically targets genes associated with cognitive fluidity and memory in humans. • Cortical methylation in response to pain affects gene networks relevant to clinical and neuropathologic features of Alzheimer's disease. • Chronic pain and Alzheimer's disease converge onto microglia signaling pathways. Chronic pain is prevalent among aging adults. Epidemiologic evidence has demonstrated that individuals with chronic pain have accelerated memory decline and increased probability of dementia. Neurophysiologic, molecular, and pharmacologic hypotheses have been proposed to explain the relationship between chronic pain and cognitive decline, but there remains currently limited evidence supporting any of these. Here, we integrate multi-omic data across human cohorts and rodent species and demonstrate that methylation in the prefrontal cortex induced by chronic pain specifically targets transcriptional networks associated with cognitive ability, memory, and Alzheimer's disease in humans. We validate this with multiple independent data sets and identify cortical microglia as a likely mechanism by which chronic pain can increase dementia risk. Our analyses support the molecular hypothesis for the role of chronic pain in cognitive decline and identifies several potential therapeutic targets. [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: Long-distance road transportation is a common practice in the beef industry, frequently resulting in bovine respiratory disease (BRD) and compromised growth performance. However, a comprehensive investigation integrating clinical performance, physiological conditions, and nasopharyngeal microflora remains lacking. Methods: This study aimed to evaluate the respiratory health and immunometabolic status of 54 beef calves subjected to a 3000-km journey. The respiratory health of calves was monitored over 60 days post-arrival using a modified clinical scoring system. Nasopharyngeal microflora and venous blood samples were collected at 3 time points: before transportation (A), 30 days post-arrival (B), and 60 days post-arrival (C), for 16S rRNA microbiomics, whole-blood transcriptomics, serum metabolomics, and laboratory assays. Result: Within the first week post-arrival, the appetite and mental scores of calves dropped to zero, while other respiratory-related scores progressively declined over the 60 days. The α-diversity of nasopharyngeal microflora in calves was similar at time points A and B, both significantly higher than at time point C. The structure of these microbial communities varied significantly across different time points, with a notably higher relative abundance of BRD-related genera, such as Pasteurella and Mannheimia, detected at time point A compared to B and C. The composition and gene expression profiles of circulating blood cells at time point A were significantly different from those at B and C. Specifically, higher expression levels of oxidative- and inflammatory-related genes, cytokines, and enzymes were observed at time point A compared to B and C. Higher levels of catabolism-related metabolites and enzymes were detected at time point A, while higher levels of anabolism-related metabolites and enzymes were observed at time points B and C. Additionally, significant correlations were found among microorganisms, genes, and metabolites with differing abundances, expression levels, and concentrations across time points. Stronger correlations were observed between calves' performance and nasopharyngeal microflora and immunometabolic status at time point A compared to B or C. Conclusions: Collectively, these results confirm that 3000 km of road transportation significantly alters the composition and gene expression profiles of circulating white blood cells in calves, affects their metabolic processes, disrupts the balance of the respiratory microbial community, and leads to pronounced respiratory symptoms that persist for at least 60 days. During this period, the influenced composition and gene expression of circulating blood cells, metabolic processes, and nasopharyngeal microbial community gradually return to equilibrium, and the respiratory symptoms gradually diminish. This observational research indicates that transportation induces BRD in calves by disrupting the homeostasis of their immune function, metabolic processes, and nasopharyngeal microbial community. However, these results and their underlying molecular mechanisms warrant further validation through well-designed in vivo and in vitro confirmatory experiments with larger sample size. 6SLnr553TSMBqvLo9hfCJx Video Abstract [ABSTRACT FROM AUTHOR]

Background: Erectile dysfunction (ED) brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have restricted efficacy. Therefore, we utilized a two-sample Mendelian randomization (MR) approach to find the drug targets that might enhance the clinical outcome of ED. Methods: Genetic instruments associated with circulating inflammatory proteins were obtained from a genome-wide association study (GWAS) involving 8,293 European participants. Summary statistics for ED were extracted from a meta-analysis of the United Kingdom Biobank cohort compromised of 6,175 cases and 217,630 controls with European descent. We utilized multi-omics method and MR study to explore potential drug targets by integrating GWAS and protein quantity trait loci (pQTL) data. Inverse-variance weighted (IVW) method was applied as the primary approach. Cochran's Q statistics was employed to investigate the presence of heterogeneity. Furthermore, we identify the potential therapeutic drug targets for the treatment of ED utilizing molecular docking technology. Results: This MR analysis of integrating GWAS and pQTL data showed that macrophage inflammatory protein-1 alpha (MIP-1α) was causally associated with the risk of ED (OR:1.19, 95%CI:1.02–1.39, p = 0.023). Meanwhile, the results of the weighted median model were consistent with the IVW estimates (OR:1.26, 95%CI:1.04–1.52, p = 0.018). Sensitivity analysis revealed no horizontal pleiotropy and heterogeneity. Furthermore, four anti-inflammatory or tonifying small molecular compounds, encompassing echinacea, pinoresinol diglucoside, hypericin, and icariin were identified through molecular docking technology. Conclusion: This study identified MIP-1α as an underlying druggable gene and promising novel therapeutic target for ED, necessitating further investigation to detect the potential mechanisms by which MIP-1α might impact the development of ED. [ABSTRACT FROM AUTHOR]

Background: Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor with a poor prognosis, predominantly CA19-9 positive. High CA19-9 levels correlate with increased aggressiveness and worse outcomes. This study employs multi-omics analysis to reveal molecular features and identify therapeutic targets of CA19-9 positive ICC, aiming to support individualized treatment. Methods: Data from seven clinical cohorts, two whole-exome sequencing cohorts, six RNA sequencing/microarray cohorts, one proteomic cohort, 20 single-cell RNA sequencing samples, and one spatial transcriptome sample were analyzed. Key findings were validated on tissue microarrays from 52 ICC samples. Results: CA19-9 positive ICC exhibited poorer OS (median 24.1 v.s. 51.5 months) and RFS (median 11.7 v.s. 28.2 months) compared to negative group (all P < 0.05). Genomic analysis revealed a higher KRAS mutation frequency in the positive group and a greater prevalence of IDH1/2 mutations in the negative group (all P < 0.05). Transcriptomic analysis indicated upregulated glycolysis pathways in CA19-9 positive ICC. Single-cell analysis identified specific glycolysis-related cell subclusters associated with poor prognosis, including Epi_SLC2A1, CAF_VEGFA, and Mph_SPP1. Higher hypoxia in the CA19-9 positive group led to metabolic reprogramming and promoted these cells' formation. These cells formed interactive communities promoting epithelial-mesenchymal transition (EMT) and angiogenesis. Drug sensitivity analysis identified six potential therapeutic drugs. Conclusions: This study systematically elucidated the clinical, genomic, transcriptomic, and immune features of CA19-9 positive ICC. It reveals glycolysis-associated cellular communities and their cancer-promoting mechanisms, enhancing our understanding of ICC and laying the groundwork for individualized therapeutic strategies. [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]

Precise regulation of gene expression is crucial to development. Enhancers, the core of gene regulation, determine the spatiotemporal pattern of gene transcription. Since many disease-associated mutations are characterized in enhancers, the research on enhancer will provide clues to precise medicine. Rapid advances in high-throughput sequencing technology facilitate the characterization of enhancers at genome wide, but understanding the functional mechanisms of enhancers remains challenging. Herein, we provide a panorama of enhancer characteristics, including epigenetic modifications, enhancer transcripts, and enhancer-promoter interaction patterns. Furthermore, we outline the applications of high-throughput sequencing technology and functional genomics methods in enhancer research. Finally, we discuss the role of enhancers in human disease and their potential as targets for disease prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Introduction: Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, a crucial process in gene expression regulation, plays a significant role in the development and progression of malignant tumors. However, the role of chromatin regulators (CRs) in lung adenocarcinoma (LUAD) remains underexplored. Methods: This study developed a chromatin regulator-related signature (CRRS) using a 429-combination machine learning approach to predict survival outcomes in LUAD patients. The CRRS model was validated across multiple independent datasets. We also investigated the impact of CRRS on the immune microenvironment, focusing on immune cell infiltration. To identify potential therapeutic targets, TFF1, a chromatin regulator, was knocked down using siRNA in LUAD cells. We assessed its impact through apoptosis analysis, proliferation assays, and in vivo tumor growth studies. Additional validation was performed using Ki67 expression and TUNEL assays. Results: The CRRS accurately predicted survival outcomes and was shown to modulate immune cell infiltration in the tumor microenvironment. High-risk patients demonstrated increased activity in cell cycle regulation and DNA repair pathways, along with distinct mutation profiles and immune responses compared to low-risk patients. TFF1 emerged as a key therapeutic target. Knockdown of TFF1 significantly inhibited LUAD cell proliferation, induced apoptosis, and suppressed in vivo tumor growth. Ki67 and TUNEL assays confirmed the role of TFF1 in regulating tumor growth and cell death. Discussion: These findings highlight the potential of chromatin regulators in prognostic modeling and immune modulation in LUAD. TFF1 was identified as a promising therapeutic target, suggesting that targeting TFF1 could provide new treatment strategies. Further research is warranted to explore its full potential and therapeutic applicability. [ABSTRACT FROM AUTHOR]

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

Synovial Sarcomas (SS) are characterized by the presence of the SS18::SSX fusion gene, which protein product induce chromatin changes through remodeling of the BAF complex. To elucidate the genomic events that drive phenotypic diversity in SS, we performed RNA and targeted DNA sequencing on 91 tumors from 55 patients. Our results were verified by proteomic analysis, public gene expression cohorts and single‐cell RNA sequencing. Transcriptome profiling identified three distinct SS subtypes resembling the known histological subtypes: SS subtype I and was characterized by hyperproliferation, evasion of immune detection and a poor prognosis. SS subtype II and was dominated by a vascular‐stromal component and had a significantly better outcome. SS Subtype III was characterized by biphasic differentiation, increased genomic complexity and immune suppression mediated by checkpoint inhibition, and poor prognosis despite good responses to neoadjuvant therapy. Chromosomal abnormalities were an independent significant risk factor for metastasis. KRT8 was identified as a key component for epithelial differentiation in biphasic tumors, potentially controlled by OVOL1 regulation. Our findings explain the histological grounds for SS classification and indicate that a significantly larger proportion of patients have high risk tumors (corresponding to SS subtype I) than previously believed. [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]

Glioblastoma is the most fatal and common malignant brain tumor, excluding metastasis and with a median survival of approximately one year. While solid tumors benefit from newly approved drugs, immunotherapy, and prevention, none of these scenarios are opening for glioblastoma. The key to unlocking the peculiar features of glioblastoma is observing its molecular and anatomical features tightly entangled with the host's central nervous system (CNS). In June 2024, we searched the PUBMED electronic database. Data collection and analysis were conducted independently by two reviewers. Results: A total of 215 articles were identified, and 192 were excluded based on inclusion and exclusion criteria. The remaining 23 were used for collecting divergent molecular pathways and anatomical features of glioblastoma. The analysis of the selected papers revealed a multifaced tumor with extreme variability and cellular reprogramming that are observable within the same patient. All the variability of glioblastoma could be clustered into three pillars to dissect the physiology of the tumor: 1. necrotic core; 2. vascular proliferation; 3. CNS infiltration. These three pillars support glioblastoma survival, with a pivotal role of the neurovascular unit, as supported by the most recent paper published by experts in the field. [ABSTRACT FROM AUTHOR]

Host‐associated microbiomes are thought to play a key role in host physiology and fitness, but this conclusion mainly derives from systems biased towards animal models and humans.While many studies on non‐model and wild animals have characterised the taxonomic diversity of their microbiomes, few have investigated the functional potential of these microbial communities.Functional 'omics' approaches, such as metagenomics, metatranscriptomics and metabolomics, represent promising techniques to probe the significance of host‐associated microbiomes in the wild.In this review, we propose to (1) briefly define the main available functional omics tools along with their strengths and limitations, (2) summarise the key advances enabled by omics tools to understand microbiome function in human and animal models, (3) showcase examples of how these methods have already brought invaluable insights into wild host microbiomes and (4) provide guidelines on how to implement these tools to address outstanding questions in the field of wild animal microbiomes.To conclude, we suggest that, building on knowledge derived from cheaper, more traditional approaches (e.g. 16S metabarcoding and qPCR), functional omics tools represent a promising approach to test hypotheses regarding the ecological and evolutionary significance of the resident microbiota in wild animals. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. An integrated transcriptomic and epigenomic analysis assesses how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Resulting analysis identifies predictive markers of CPCs that give rise to high purity CM batches, including TTN , TRIM55 , DGKI, MEF2C, MAB21L2, MYL7 , LDB3 , SLC7A11, and CALD1. Predictive models developed from these genes provide high accuracy in determining terminal CM purities at the CPC stage. Further, insights into mechanisms of batch failure and dominant non-CM cell types generated in failed batches are elucidated. Namely EMT, MAPK, and WNT signaling emerge as significant drivers of batch divergence, giving rise to off-target populations of fibroblasts/mural cells, skeletal myocytes, epicardial cells, and a non-CPC SLC7A11+ subpopulation. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness. [Display omitted] • Genes that predict cardiac progenitor cell differentiation to cardiomyocytes. • Early epigenetic fate priming to cardiomyocyte fate in cardiac progenitor cells. • Progenitor-stage, gene-based predictive models for cardiomyocyte purity outcomes. [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]

Plastic waste accumulation has lately been identified as the leading and pervasive environmental concern, harming all living beings, natural habitats, and the global market. Given this issue, developing ecologically friendly solutions, such as biodegradation instead of standard disposal, is critical. To effectively address and develop better strategies, it is critical to understand the inter-relationship between microorganisms and plastic, the role of genes and enzymes involved in this process. However, the complex nature of microbial communities and the diverse mechanisms involved in plastic biodegradation have hindered the development of efficient plastic waste degradation strategies. Omics-driven approaches, encompassing genomics, transcriptomics and proteomics have revolutionized our understanding of microbial ecology and biotechnology. Therefore, this review explores the application of omics technologies in plastic degradation studies and discusses the key findings, challenges, and future prospects of omics-based approaches in identifying novel plastic-degrading microorganisms, enzymes, and metabolic pathways. The integration of omics technologies with advanced molecular technologies such as the recombinant DNA technology and synthetic biology would guide in the optimization of microbial consortia and engineering the microbial systems for enhanced plastic biodegradation under various environmental conditions. [ABSTRACT FROM AUTHOR]

Key message: Cellulose synthase-like OsCSLD4 plays a pivotal role in regulating diverse agronomic traits, enhancing resistance against bacterial leaf blight, and modulating metabolite indices based on the multi-omics analysis in rice. To delve deeper into this complex network between agronomic traits and metabolites in rice, we have compiled a dataset encompassing genome, phenome, and metabolome, including 524 diverse accessions, 11 agronomic traits, and 841 metabolites, enabling us to pinpoint eight hotspots through GWAS. We later discovered four distinct metabolite categories, encompassing 15 metabolites that are concurrently present on the QTL qC12.1, associated with leaf angle of flag and spikelet length, and finally focused the cellulose synthase-like OsCSLD4, which was pinpointed through a rigorous process encompassing sequence variation, haplotype, ATAC, and differential expression across diverse tissues. Compared to the wild type, csld4 exhibited significant reductions in the plant height, flag leaf length, leaf width, spikelet length, 1000-grain weight, grain width, grain thickness, fertility, yield per plant, and bacterial blight resistance. However, there were significant increase in tiller numbers, degree of leaf rolling, flowering period, growth period, grain length, and empty kernel rate. Furthermore, the content of four polyphenol metabolites, excluding metabolite N-feruloyltyramine (mr1268), notably rose, whereas the levels of the other three polyphenol metabolites, smiglaside C (mr1498), 4-coumaric acid (mr1622), and smiglaside A (mr1925) decreased significantly in mutant csld4. The content of amino acid L-tyramine (mr1446) exhibited a notable increase, whereas the alkaloid trigonelline (mr1188) displayed a substantial decrease among the mutants. This study offered a comprehensive multi-omics perspective to analyze the genetic mechanism of OsCSLD4, and breeders can potentially enhance rice's yield, bacterial leaf blight resistance, and metabolite content, leading to more sustainable and profitable rice production. [ABSTRACT FROM AUTHOR]

Background: Genome instability (GI) is a hallmark of esophageal squamous cell carcinoma (ESCC) while factors affecting GI remain unclear. Methods: Here, we aimed to characterize genomic events representing specific mechanisms of GI based on 201 ESCC samples and validated our findings at the patient, single-cell and cancer cell-line levels, including a newly generated multi-omics dataset of the trial NCT04006041. Results: A two-gene (AHNAK and AHNAK2) mutation signature was identified to define the "AHNAK1/2-mutant" cancer subtype. Single-cell-assisted multi-omics analysis showed that this subtype had a higher neoantigen load, active antigen presentation, and proficient CD8 + T cell infiltrations, which were validated at pan-cancer levels. Mechanistically, AHNAK1/2-mutant ESCC was characterized by impaired response of TGF-β and the inefficient alternative end-join repair (Alt-EJ) that might promote GI. Knockdown of AHNAK in ESCC cell lines resulted in more Alt-EJ events and increased sensitivities to cisplatin. Furthermore, this two-gene signature accurately predicted better responses to DNA-damaging therapy in various clinical settings (HR ≈ 0.25). The two-gene signature predicted higher pCR rates in ESCCs receiving neoadjuvant immunotherapy-involved treatment. Finally, a molecular classification scheme was built and outperformed established molecular typing models in the prognosis stratification of ESCC patients. Conclusion: Our study extended our understanding of the AHNAK family in promoting GI and selecting treatment responders of ESCC. [ABSTRACT FROM AUTHOR]

The obscure drug response continues to be a limiting factor for accurate cures for cancer. Next generation sequencing technologies have propelled the pharmacogenomic studies with characterized large panels of cancer cell line at multi‐omics level. However, the sufficient integration of the multi‐omics data and the efficient prediction for drug response and synergy still remain a challenge. To address these problems, ECFD is designed, an ensemble cascade forest‐based framework that predicts drug response and synergy using five types of omics data. Experimental results show the significant advantages of the ECFD model over existing models. The best integration of feature extraction is determined and the superiorities of robust stability in the face of new and small samples are highlighted. In addition, the methodological framework highlights the explainability of the model, the mechanisms of drug resistance and drug combination treatment strategies based on explainable analyses and biological networks. In sum, ECFD may facilitate the evaluation of drug response and speculation of potential synergy therapies in personalized and precision treatment. [ABSTRACT FROM AUTHOR]

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

This review explores the understudied topic of nutritional inequality among individuals with schizophrenia, highlighting the complex interplay between diet, genetics, and mental health. Unhealthy dietary patterns, socioeconomic factors, and disordered eating behaviors contribute to malnutrition, increasing the risk of physical health issues and premature mortality. Socioeconomic factors exacerbate nutritional disparities, necessitating targeted interventions. Genetic influences on nutrient metabolism remain under-researched, although nutritional genomics shows potential for personalized interventions. Current research reveals methodological gaps, urging larger sample sizes and standardized approaches. The integration of nutrigenomics, encompassing various omics disciplines, emerges as a transformative tool. The holistic life-cycle approach to schizophrenia management underscores the vital role of nutrition, calling for personalized interventions to enhance mental health outcomes. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease and is characterized by primary left ventricular hypertrophy usually caused by mutations in sarcomere genes. The mechanism underlying cardiac remodeling in HCM remains incompletely understood. An investigation of HCM through integrative analysis at multi-omics levels will be helpful for treating HCM. DNA methylation and chromatin accessibility, as well as gene expression, were assessed by nucleosome occupancy and methylome sequencing (NOMe-seq) and RNA-seq, respectively, using the cardiac tissues of HCM patients. Compared with those of the controls, the transcriptome, DNA methylome, and chromatin accessibility of the HCM myocardium showed multifaceted differences. At the transcriptome level, HCM hearts returned to the fetal gene program through decreased sarcomeric and metabolic gene expression and increased extracellular matrix gene expression. In the DNA methylome, hypermethylated and hypomethylated differentially methylated regions were identified in HCM. At the chromatin accessibility level, HCM hearts showed changes in different genome elements. Several transcription factors, including SP1 and EGR1, exhibited a fetal-like pattern of binding motifs in nucleosome-depleted regions in HCM. In particular, the inhibition of SP1 or EGR1 in an HCM mouse model harboring sarcomere mutations markedly alleviated the HCM phenotype of the mutant mice and reversed fetal gene reprogramming. Overall, this study not only provides a high-precision multi-omics map of HCM heart tissue but also sheds light on the therapeutic strategy by intervening in the fetal gene reprogramming in HCM. [ABSTRACT FROM AUTHOR]

Acyl-CoA-binding protein (ACBP) genes have been implicated in lead enrichment and translocation in plants; however, the mechanisms by which these genes contribute to the response to heavy metal stress in various taxa have not been determined. In this study, the molecular mechanisms underlying the response of Robinia pseudoacacia, an economically important deciduous tree, to Pb stress were examined using transcriptomic and metabolomic analyses. RpACBP3 overexpression increased Pb enrichment, translocation, and tolerance. After Pb stress for 3 days, 1125 differentially expressed genes (DEGs) and 485 differentially accumulated metabolites (DAMs) were identified between wild-type and RpACBP3-overexpressing R. pseudoacacia strains; after Pb stress for 45 days, 1746 DEGs and 341 DAMs were identified. Joint omics analyses showed that the DEGs and DAMs were co-enriched in α-linoleic acid metabolism and flavonoid biosynthesis pathways. In particular, DEGs and DAMs involved in α-linoleic acid metabolism and flavonoid biosynthesis were up- and down-regulated, respectively. Moreover, RpACBP3 overexpression enhanced the ability to scavenge reactive oxygen species and repair cell membranes under stress by regulating LOX gene expression and increasing the phosphatidylcholine content, thereby improving the tolerance to Pb stress. These findings lay a theoretical foundation for the future application of RpACBP3 genes in plant germplasm resource creation and phytoremediation of Pb contaminated soil in which R. pseudoacacia grow. [ABSTRACT FROM AUTHOR]

Mutations of KRAS, CDKN2A, TP53, and SMAD4 are the four major driver genes for pancreatic ductal adenocarcinoma (PDAC), of which mutations of KRAS and TP53 are the most frequently recognized. However, molecular‐targeted therapies for mutations of KRAS and TP53 have not yet been developed. To identify novel molecular targets, we newly established organoids with the Kras mutation (KrasmuOR) and Trp53 loss of function using Cre transduction and CRISPR/Cas9 (Krasmu/p53muOR) from murine epithelia of the pancreatic duct in KrasLSL‐G12D mice, and then analyzed the proteomic and metabolomic profiles in both organoids by mass spectrometry. Hyperfunction of the glycolysis pathway was recognized in Krasmu/p53muOR compared with KrasmuOR. Loss of function of triosephosphate isomerase (TPI1), which is involved in glycolysis, induced a reduction of cell proliferation in human PDAC cell lines with the TP53 mutation, but not in PDAC or in human fibroblasts without TP53 mutation. The TP53 mutation is clinically recognized in 70% of patients with PDAC. In the present study, protein expression of TPI1 and nuclear accumulation of p53 were recognized in the same patients with PDAC. TPI1 is a potential candidate therapeutic target for PDAC with the TP53 mutation. [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]

The small intestine contains a two-front nutrient supply environment created by luminal dietary and microbial metabolites (enteral side) and systemic metabolites from the host (serosal side). Yet, it is unknown how each side contributes differentially to the small intestinal physiology. Here, we generated a comprehensive, high-resolution map of the small intestinal two-front nutrient supply environment. Using in vivo tracing of macronutrients and spatial metabolomics, we visualized the spatiotemporal dynamics and cell-type tropism in nutrient absorption and the region-specific metabolic heterogeneity within the villi. Specifically, glutamine from the enteral side fuels goblet cells to support mucus production, and the serosal side loosens the epithelial barrier by calibrating fungal metabolites. Disorganized feeding patterns, akin to the human lifestyle of skipping breakfast, increase the risk of metabolic diseases by inducing epithelial memory of lipid absorption. This study improves our understanding of how the small intestine is spatiotemporally regulated by its unique nutritional environment. [Display omitted] • A comprehensive map of the small intestinal two-front nutrient supply environment • Characterization of spatiotemporal dynamics and cell-type tropism in nutrient absorption • Two nutrient supplies differentially configure the functional zonation of enterocytes • Breakfast skipping exacerbates metabolic diseases by inducing epithelial lipid absorption Spatiotemporal mapping of the environment of the mouse small intestine enables the examination of how circulating metabolites and dietary and gut microbial metabolites contribute to aspects of host metabolism and intestinal physiology. [ABSTRACT FROM AUTHOR]

Uterine sarcoma (US) is a rare malignant tumor that has various pathological types and high heterogeneity in the female reproductive system. Its subtle early symptoms, frequent recurrence, and resistance to radiation and chemotherapy make the prognosis for US patients very poor. Therefore, understanding the molecular mechanisms underlying tumorigenesis and progression is essential for an accurate diagnosis and targeted therapy to improve patient outcomes. Recent advancements in high-throughput molecular sequencing have allowed for a deeper understanding of diseases through multi-omics technologies. In this review, the latest progress and future potential of multi-omics technologies in US research is examined, and their roles in biomarker discovery and their application in the precise diagnosis and treatment of US are highlighted. [ABSTRACT FROM AUTHOR]

Introduction: Fatty liver disease in dairy cows is a metabolic disorder that significantly affects their health and productivity, imposing a notable economic burden on the global dairy industry. Traditional Chinese medicine (TCM), characterized by its multi-component and multi-target features, has shown unique advantages in the prevention and treatment of various diseases. Guiqi Yimu Powder, a traditional TCM formula, enhances growth, boosts production efficiency, and strengthens immune function in livestock by regulating antioxidant along with anti-inflammatory pathways. However, its specific regulatory mechanisms on fatty liver in dairy cows remain unclear. This study aims to investigate the molecular-level effects and potential regulatory mechanisms of Guiqi Yimu Powder in a Trimethylamine N-oxide (TMAO) induced fatty liver cell model of dairy cows. Methods: We employed a comprehensive analysis integrating transcriptomics, proteomics, metabolomics, and network pharmacology. An in vitro dairy cow fatty liver cell model was established using TMAO to induce lipid accumulation. Cells were treated with the optimal TMAO concentration identified through preliminary experiments, and further divided into a lipid accumulation group and Guiqi Yimu Powder treatment groups. The treatment groups received varying concentrations of Guiqi Yimu Powder (10, 20, 30, 40, or 50 g/L). High-throughput omics sequencing technologies were utilized to perform a comprehensive analysis of the treated cells. Bioinformatics methods were applied to explore the regulatory effects, aiming to elucidate the specific impacts of Guiqi Yimu Powder on lipid metabolism, liver function, and related signaling pathways, thereby providing scientific evidence for its potential application in the prevention and treatment of fatty liver in dairy cows. Results: Guiqi Yimu Powder treatment significantly affected 1,536 genes, 152 proteins, and 259 metabolites. KEGG enrichment analysis revealed that the significantly altered molecules are involved in multiple pathways related to the pathology of fatty liver, including metabolic pathways, glutathione metabolism, hepatitis B, and AMPK signaling pathway (p < 0.05). Notably, joint analysis highlighted the regulatory mechanisms of Guiqi Yimu Powder on glutathione cycling, with L-5-Oxoproline identified as an important metabolic compound. These findings indicate its impact on oxidative stress, energy metabolism, and liver function, suggesting potential therapeutic applications for fatty liver in dairy cows. Discussion: This study elucidated the regulatory mechanisms of Guiqi Yimu Powder on fatty liver cells in dairy cows, providing new scientific evidence for its potential application in the prevention and treatment of fatty liver disease. [ABSTRACT FROM AUTHOR]

Background: The maternal gut microbiome is the direct and important source of early colonization and development of the neonatal gut microbiome. However, differences in unique and shared features between mothers with different physiological phenotypes and their newborns still lack exhaustive investigation. Here, using a cow-to-calf model, a comprehensive investigation was conducted to elucidate the pattern and characterization of microbial transfer from the maternal source to the offspring. Results: The microbiota in the rumen and feces of dairy cows were divided into two clusters via enterotype analysis. The cows from the enterotype distinguished by Prevotella in the rumen had better production performance, whereas no difference was observed in the cows classified by feces enterotype. Furthermore, through a pairwise combination of fecal and ruminal enterotypes, we screened a group of dairy cows with excellent phenotypes. The gastrointestinal microbiomes of cows with different phenotypes and their offspring differed significantly. The rumen was a more important microbial source for meconium than feces. Transmission of beneficial bacteria from mother to offspring was observed. Additionally, the meconium inherits advantageous metabolic functions of the rumen. The resistome features of the rumen, feces, and meconium were consistent, and resistome abundance from cows to calves showed an expanding trend. The interaction between antibiotic-resistance genes and mobile genetic elements from the rumen to meconium was the most remarkable. The diversity of core metabolites from cows to calves was stable and not affected by differences in phenotypes. However, the abundance of specific metabolites varied greatly. Conclusions: Our study demonstrates the microbial taxa, metabolic function, and resistome characteristics of maternal and neonatal microbiomes, and reveals the potential vertical transmission of the microbiome from a cow-to-calf model. These findings provide new insights into the transgenerational transmission pattern of the microbiome. 2cueHjjtpyRzwmAWUdZ5oA Video Abstract [ABSTRACT FROM AUTHOR]

Introduction: Elevated blood eosinophil levels in patients with chronic obstructive pulmonary disease (COPD) with or without asthma are linked to increased exacerbations and the effectiveness of inhaled corticosteroid treatment. This study aimed to delineate the inflammatory cellular properties of eosinophils in patients with asthma-COPD overlap (ACO) and eosinophilic COPD (eCOPD). Methods: Eosinophils were isolated from the peripheral blood of healthy volunteers, patients with non-eCOPD, and those with ACO/eCOPD. Multiomics analysis involving transcriptomics, proteomics, and lipidomics was performed, followed by bioinformatic data analyses. In vitro experiments using eosinophils from healthy volunteers were conducted to investigate the molecular mechanisms underlying cellular alterations in eosinophils. Results: Proteomics and transcriptomics analyses revealed cellular characteristics in overall COPD patients represented by viral infection (elevated expression of sterol regulatory element-binding protein-1) and inflammatory responses (elevated levels of IL1 receptor-like 1, Fc epsilon receptor Ig, and transmembrane protein 176B). Cholesterol metabolism enzymes were identified as ACO/eCOPD-related factors. Gene Ontology and pathway enrichment analyses demonstrated the key roles of antiviral responses, cholesterol metabolism, and inflammatory molecules-related signaling pathways in ACO/eCOPD. Lipidomics showed the impaired synthesis of cyclooxygenase-derived mediators including prostaglandin E2 (PGE2) in ACO/eCOPD. In vitro assessment confirmed that IL-33 or TNF-α stimulation combined with IL-5 and IFN-γ stimulation induced cellular signatures in eosinophils in ACO/eCOPD. Atorvastatin, dexamethasone, and PGE2 differentially modulated these inflammatory changes. Discussion: ACO/eCOPD is associated with viral infection and an inflammatory milieu. Therapeutic strategies using statins and inhaled corticosteroids are recommended to control these pathogenic changes. [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]

Background: Although numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA). Methods: Demographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors. Results: LSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05). Conclusion: These findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed. Trial registration: Chinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/. [ABSTRACT FROM AUTHOR]

Microalgae are significantly influenced by light quality and quantity, whether in their natural habitats or under laboratory and industrial culture conditions. The present study examines the adaptive responses of the marine microalga Tetraselmis chuii to different light regimes, using a cost-effective filtering method and a multi-omics approach. Microalgal growth rates were negatively affected by all filtered light regimes. After six days of cultivation, growth rate for cultures exposed to blue and green filtered light was 67%, while for red filter was 83%, compared to control cultures. Transcriptomic analysis revealed that the usage of green filters resulted in upregulation of transcripts involved in ribosome biogenesis or coding for elongation factors, exemplified by a 2.3-fold increase of TEF3. On the other hand, a 2.7-fold downregulation was observed in photosynthesis-related petJ. Exposure to blue filtered light led to the upregulation of transcripts associated with pyruvate metabolism, while photosynthesis was negatively impacted. In contrast, application of red filter induced minor transcriptomic alterations. Regarding metabolomic analysis, sugars, amino acids, and organic acids exhibited significant changes under different light regimes. For instance, under blue filtered light sucrose accumulated over 6-fold, while aspartic acid content decreased by 4.3-fold. Lipidomics analysis showed significant accumulation of heptadecanoic and linoleic acids under green and red light filters. Together, our findings indicate that filter light can be used for targeted metabolic manipulation. [ABSTRACT FROM AUTHOR]

The ovarian microenvironment plays a crucial role in ensuring the reproductive success of viviparous teleosts. However, themolecular mechanism underlying the interaction between spermatozoa and the ovarian microenvironment has remained elusive. This study aimed to contribute to a better understanding of this process in black rockfish (Sebastes schlegelii) using integrated multi-omics approaches. The results demonstrated significant upregulation of ovarian complement-related proteins and pattern recognition receptors, along with remodeling of glycans on the surface of spermatozoa at the early spermatozoa-storage stage (1 month after mating). As spermatozoa were stored over time, ovarian complement proteins were progressively repressed by tryptophan and hippurate, indicating a remarkable adaptation of spermatozoa to the ovarian microenvironment. Before fertilization, a notable upregulation of cellular junction proteins was observed. The study revealed that spermatozoa bind to ZPB2a protein through GSTM3 and that ZPB2a promotes spermatozoa survival and movement in a GSTM3-dependent manner. These findings shed light on a key mechanism that influences the dynamics of spermatozoa in the female reproductive tract, providing valuable insights into the molecular networks regulating spermatozoa adaptation and survival in species with internal fertilization. [ABSTRACT FROM AUTHOR]

Background: Cell therapy can protect cardiomyocytes from hypoxia, primarily via paracrine secretions, including extracellular vesicles (EVs). Since EVs fulfil specific biological functions based on their cellular origin, we hypothesised that EVs from human cardiac stromal cells (CMSCLCs) obtained from coronary artery bypass surgery may have cardioprotective properties. Objectives: This study characterises CMSCLC EVs (C_EVs), miRNA cargo, cardioprotective efficacy and transcriptomic modulation of hypoxic human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). C_EVs are compared to bone marrow mesenchymal stromal cell EVs (B_EVs) which are a known therapeutic EV type. Methods: Cells were characterised for surface markers, gene expression and differentiation potential. EVs were compared for yield, phenotype, and ability to protect hiPSC-CMs from hypoxia/reoxygenation injury. EV dose was normalised by both protein concentration and particle count, allowing direct comparison. C_EV and B_EV miRNA cargo was profiled and RNA-seq was performed on EV-treated hypoxic hiPSC-CMs, then data were integrated by multi-omics. Confirmatory experiments were carried out using miRNA mimics. Results: At the same dose, C_EVs were more effective than B_EVs at protecting CM integrity, reducing apoptotic markers, and cell death during hypoxia. While C_EVs and B_EVs shared 70–77% similarity in miRNA content, C_EVs contained unique miRNAs, including miR-202-5p, miR-451a and miR-142-3p. Delivering miRNA mimics confirmed that miR-1260a and miR-202/451a/142 were cardioprotective, and the latter upregulated protective pathways similar to whole C_EVs. Conclusions: This study demonstrates the potential of cardiac tissues, routinely discarded following surgery, as a valuable source of EVs for myocardial infarction therapy. We also identify miR-1260a as protective of CM hypoxia. [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]

Glioblastoma (GBM) is a highly aggressive and treatment‐resistant malignancy that poses a significant challenge in modern medicine. Despite advances in surgical resection, radiotherapy and chemotherapy, complete eradication of GBM remains elusive due to its diffuse invasion into the brain parenchyma and propensity for recurrence. The tumour microenvironment (TME), particularly macrophages, has emerged as a critical player in GBM progression, invasion and metastasis. In the immune microenvironment of glioma, MS4A6A exhibits unique expression characteristics in macrophages. This study aimed to investigate the potential role of MS4A6A, a gene associated with aging and neurodegenerative diseases, in GBM and its potential as a prognostic biomarker and therapeutic target. [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]

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi‐omic data obtained from multi‐level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi‐omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high‐ and low‐risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype‐specific machine learning model, LUSC multi‐omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward “cold” tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies. [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]

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by impairments in social interaction, communication, and repetitive behaviors. Emerging evidence suggests that the insulin-like growth factor (IGF) signaling pathway plays a critical role in ASD pathogenesis; however, the precise pathogenic mechanisms remain elusive. This study utilizes multi-omics approaches to investigate the pathogenic mechanisms of ASD susceptibility genes within the IGF pathway. Whole-exome sequencing (WES) revealed a significant enrichment of rare variants in key IGF signaling components, particularly the IGF receptor 1 (IGF1R), in a cohort of Chinese Han individuals diagnosed with ASD, as well as in ASD patients from the SFARI SPARK WES database. Subsequent single-cell RNA sequencing (scRNA-seq) of cortical tissues from children with ASD demonstrated elevated expression of IGF receptors in parvalbumin (PV) interneurons, suggesting a substantial impact on their development. Notably, IGF1R appears to mediate the effects of IGF2R on these neurons. Additionally, transcriptomic analysis of brain organoids derived from ASD patients indicated a significant association between IGF1R and ASD. Protein-protein interaction (PPI) and gene regulatory network (GRN) analyses further identified ASD susceptibility genes that interact with and regulate IGF1R expression. In conclusion, IGF1R emerges as a central node within the IGF signaling pathway, representing a potential common pathogenic mechanism and therapeutic target for ASD. These findings highlight the need for further investigation into the modulation of this pathway as a strategy for ASD intervention. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and is associated with a significantly worse prognosis compared to diabetic patients without kidney involvement, other microvascular complications, or non-diabetic chronic kidney disease, due to its higher risk of cardiovascular events, faster progression to end-stage kidney disease, and increased mortality. In clinical practice, diagnosis is based on estimated glomerular filtration rate (eGFR) and albuminuria. However, given the limitations of these diagnostic markers, novel biomarkers must be identified. Omics is a new field of study involving the comprehensive analysis of various types of biological data at the molecular level. In different fields, they have shown promising results in (early) detection of diseases, personalized medicine, therapeutic monitoring, and understanding pathogenesis. DKD is primarily utilized in scientific research and has not yet been implemented in routine clinical practice. The aim of this review is to provide an overview of currently available data on targeted omics. After an extensive literature search, 25 different (panels of) omics were withheld and analyzed. Both serum/plasma and urine proteomics and metabolomics have been described with varying degrees of evidence. For all omics, there is still a relative paucity of data from large, prospective, longitudinal cohorts, presumably because of the heterogeneity of DKD and the lack of patient selection in studies, the complexity of omics technologies, and various practical and ethical considerations (e.g., limited accessibility, cost, and privacy concerns). [ABSTRACT FROM AUTHOR]

Simple Summary: Multi-omics has become a crucial tool in cancer biomarker discovery over the last decade, including for prostate cancer. This approach integrates data from various biological layers, enhancing our understanding of complex, heterogeneous cancers. Multi-omics accelerates precision medicine by identifying new biomarkers and therapeutic targets, potentially improving treatment options for advanced prostate cancer patients. This review discusses traditional prostate cancer biomarkers and highlights recent single-omics and multi-omics advances that have improved clinical diagnostic and prognostic capabilities. Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Introduction: The study aimed to investigate to what extent acute endurance exercise, especially eccentric exercise and cardiorespiratory fitness, affects the metabolic profile of CD4+ cells. Methods: Fifteen male, healthy adults aged between 20 and 33 yr with a maximal oxygen uptake (V̇O2max) between 44 and 63 mL·kg−1 ·min−1 performed a downhill run (DR) and a level run (LR) for 45 min at 70% of their V̇O2max on a treadmill in a crossover design. Blood samples were taken before (T0), directly after (T1), 3 h after (T3), and 24 h (T24) after each exercise for analyzing leukocyte numbers and cytokine levels. Isolated CD4+ cells were incubated for 4 h in autologous resting versus 3 h after exercise serum (T3 DR and T3 LR), and subsequently, cellular respiration, transcriptomic, and metabolomics profiles were measured. Results: The systemic immune inflammation index increased significantly after DR and LR at T1 and T3 (P < 0.001). In contrast, the transcriptomic and metabolic profile of CD4+ cells showed no significant alterations after incubation in T3 exercise serum. However, cardiorespiratory fitness positively correlated with the maximal mitochondrial respiration in CD4+ cells after incubation with T3 LR serum (r = 0.617, P = 0.033) and with gene expression of oxidative phosphorylation and levels of different metabolites. Similarly, V̇O2max was associated with an anti-inflammatory profile on RNA level. Lower lactate, methylmalonic acid, and D-gluconic acid levels were found in CD4+ cells of participants with a high V̇O2max (P < 0.001). Conclusions: Acute exercise leads to a mild proinflammatory milieu with only small changes in the metabolic homeostasis of CD4+ cells. High cardiorespiratory fitness is associated with a metabolic shift to oxidative phosphorylation in CD4+ cells. Functional relevance of this metabolic shift needs to be further investigated. [ABSTRACT FROM AUTHOR]