RESUMEN
Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.
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Proteínas de Ciclo Celular , Proteínas Cromosómicas no Histona , Cohesinas , Navegador Web , Proteínas Cromosómicas no Histona/metabolismo , Proteínas Cromosómicas no Histona/genética , Proteínas de Ciclo Celular/metabolismo , Proteínas de Ciclo Celular/genética , Humanos , Programas Informáticos , Biología Computacional/métodos , Genómica/métodos , Bases de Datos Genéticas , Cromatina/metabolismo , Cromatina/genética , Internet , MultiómicaRESUMEN
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a chronic, progressive liver disease that encompasses a spectrum of steatosis, steatohepatitis (or MASH), and fibrosis. Evidence suggests that dietary restriction (DR) and sleeve gastrectomy (SG) can lead to remission of hepatic steatosis and inflammation through weight loss, but it is unclear whether these procedures induce distinct metabolic or immunological changes in MASLD livers. This study aims to elucidate the intricate hepatic changes following DR, SG or sham surgery in rats fed a high-fat diet as a model of obesity-related MASLD, in comparison to a clinical cohort of patients undergoing SG. Single-cell and single-nuclei transcriptome analysis, spatial metabolomics, and immunohistochemistry revealed the liver landscape, while circulating biomarkers were measured in serum samples. Artificial intelligence (AI)-assisted image analysis characterized the spatial distribution of hepatocytes, myeloid cells and lymphocytes. In patients and experimental MASLD rats, SG improved body mass index, circulating liver injury biomarkers and triglyceride levels. Both DR and SG attenuated liver steatosis and fibrosis in rats. Metabolism-related genes (Ppara, Cyp2e1 and Cyp7a1) were upregulated in hepatocytes upon DR and SG, while SG broadly upregulated lipid metabolism on cholangiocytes, monocytes, macrophages, and neutrophils. Furthermore, SG promoted restorative myeloid cell accumulation in the liver not only ameliorating inflammation but activating liver repair processes. Regions with potent myeloid infiltration were marked with enhanced metabolic capacities upon SG. Additionally, a disruption of periportal hepatocyte functions was observed upon DR. In conclusion, this study indicates a dynamic cellular crosstalk in steatotic livers of patients undergoing SG. Notably, PPARα- and gut-liver axis-related processes, and metabolically active myeloid cell infiltration indicate intervention-related mechanisms supporting the indication of SG for the treatment of MASLD.
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Hígado Graso , Gastrectomía , Animales , Ratas , Masculino , Hígado Graso/metabolismo , Humanos , Hígado/metabolismo , Dieta Alta en Grasa/efectos adversos , Ratas Sprague-Dawley , Metabolómica , Restricción Calórica , MultiómicaRESUMEN
Purpose: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment. Materials and Methods: A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent 68Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics features from whole exome sequencing, and pathomics features derived from immunohistochemical staining of 11 biomarkers. Based on the multiomics dataset, five ML models were established and validated using 100-fold Monte Carlo cross-validation. Results: Among five ML models, the random forest (RF) model performed best in terms of the area under the curve (AUC). Compared to bxGG assessment alone, the RF model was superior in terms of AUC (0.87 vs 0.75), specificity (0.72 vs 0.61), positive predictive value (0.79 vs 0.75), and accuracy (0.78 vs 0.77) and showed slightly decreased sensitivity (0.83 vs 0.89) and negative predictive value (0.80 vs 0.81). Among the feature categories, bxGG was identified as the most important feature, followed by pathomics, clinical, radiomics and genomics features. The three important individual features were bxGG, PSA staining and one intensity-related radiomics feature. Conclusion: The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP.
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Aprendizaje Automático , Clasificación del Tumor , Prostatectomía , Neoplasias de la Próstata , Humanos , Masculino , Neoplasias de la Próstata/cirugía , Neoplasias de la Próstata/patología , Neoplasias de la Próstata/genética , Neoplasias de la Próstata/diagnóstico por imagen , Prostatectomía/métodos , Anciano , Persona de Mediana Edad , Estudios Retrospectivos , Estudios Prospectivos , Proyectos Piloto , Tomografía de Emisión de Positrones/métodos , Imagen por Resonancia Magnética/métodos , Genómica/métodos , MultiómicaRESUMEN
BACKGROUND: Microbial anaerobic metabolism is a key driver of biogeochemical cycles, influencing ecosystem function and health of both natural and engineered environments. However, the temporal dynamics of the intricate interactions between microorganisms and the organic metabolites are still poorly understood. Leveraging metagenomic and metabolomic approaches, we unveiled the principles governing microbial metabolism during a 96-day anaerobic bioreactor experiment. RESULTS: During the turnover and assembly of metabolites, homogeneous selection was predominant, peaking at 84.05% on day 12. Consistent dynamic coordination between microbes and metabolites was observed regarding their composition and assembly processes. Our findings suggested that microbes drove deterministic metabolite turnover, leading to consistent molecular conversions across parallel reactors. Moreover, due to the more favorable thermodynamics of N-containing organic biotransformations, microbes preferentially carried out sequential degradations from N-containing to S-containing compounds. Similarly, the metabolic strategy of C18 lipid-like molecules could switch from synthesis to degradation due to nutrient exhaustion and thermodynamical disadvantage. This indicated that community biotransformation thermodynamics emerged as a key regulator of both catabolic and synthetic metabolisms, shaping metabolic strategy shifts at the community level. Furthermore, the co-occurrence network of microbes-metabolites was structured around microbial metabolic functions centered on methanogenesis, with CH4 as a network hub, connecting with 62.15% of total nodes as 1st and 2nd neighbors. Microbes aggregate molecules with different molecular traits and are modularized depending on their metabolic abilities. They established increasingly positive relationships with high-molecular-weight molecules, facilitating resource acquisition and energy utilization. This metabolic complementarity and substance exchange further underscored the cooperative nature of microbial interactions. CONCLUSIONS: All results revealed three key rules governing microbial anaerobic degradation. These rules indicate that microbes adapt to environmental conditions according to their community-level metabolic trade-offs and synergistic metabolic functions, further driving the deterministic dynamics of molecular composition. This research offers valuable insights for enhancing the prediction and regulation of microbial activities and carbon flow in anaerobic environments. Video Abstract.
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Biodegradación Ambiental , Reactores Biológicos , Metabolómica , Microbiota , Anaerobiosis , Reactores Biológicos/microbiología , Bacterias/metabolismo , Bacterias/genética , Bacterias/clasificación , Metagenómica , Metano/metabolismo , Termodinámica , MultiómicaRESUMEN
17α-Ethinylestradiol (EE2) is known for its endocrine-disrupting effects on embryonic and adult fish. However, its impact on juvenile zebrafish has not been well established. In this study, juvenile zebrafish were exposed to EE2 at concentrations of 5 ng/L (low dose, L), 10 ng/L (medium dose, M), and 50 ng/L (high dose, H) from 21 days post-fertilization (dpf) to 49 dpf. We assessed their growth, development, behavior, transcriptome, and metabolome. The findings showed that the survival rate in the EE2-H group was 66.8 %, with all surviving fish displaying stunted growth and swollen, transparent abdomens by 49 dpf. Moreover, severe organ deformities were observed in the gills, kidneys, intestines, and heart of fish in both the EE2-H and EE2-M groups. Co-expression analysis of mRNA and lncRNA revealed that EE2 downregulated the transcription of key genes involved in the cell cycle, DNA replication, and Fanconi anemia signaling pathways. Additionally, metabolomic analysis indicated that EE2 influenced metabolism and development-related signaling pathways. These pathways were also significantly identified based on the genes regulated by lncRNA. Consequently, EE2 induced organ deformities and mortality in juvenile zebrafish by disrupting signaling pathways associated with development and metabolism. The results of this study offer new mechanistic insights into the adverse effects of EE2 on juvenile zebrafish based on multiomics analysis. The juvenile zebrafish are highly sensitive to EE2 exposure, which is not limited to adult and embryonic stages. It is a potential model for studying developmental toxicity.
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Etinilestradiol , Contaminantes Químicos del Agua , Pez Cebra , Animales , Etinilestradiol/toxicidad , Contaminantes Químicos del Agua/toxicidad , Disruptores Endocrinos/toxicidad , Transcriptoma/efectos de los fármacos , MultiómicaRESUMEN
The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4+ T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αß T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses.
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Análisis de la Célula Individual , Timocitos , Timo , Transcriptoma , Humanos , Timocitos/metabolismo , Timocitos/citología , Análisis de la Célula Individual/métodos , Timo/citología , Timo/metabolismo , Timo/inmunología , Perfilación de la Expresión Génica/métodos , Linfocitos T CD4-Positivos/metabolismo , Diferenciación Celular , Proteínas de la Membrana/metabolismo , Proteínas de la Membrana/genética , MultiómicaRESUMEN
Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood chronic kidney failure and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. With this goal, the miRNome, peptidome, and proteome of over 30 amniotic fluid samples of patients with non-severe CAKUT was compared to patients with severe CAKUT. These omics data sets were made findable, accessible, interoperable, and reusable (FAIR) to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies: integrative analysis with mixOmics, joint dimensionality reduction and pathway analysis. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development and the PI3K-AKT signalling pathway. Additionally, several key genes (CSF1, IGF2, ITGB1, and RAC1) and microRNAs were identified. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.
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Colágeno , Fosfatidilinositol 3-Quinasas , Proteínas Proto-Oncogénicas c-akt , Transducción de Señal , Humanos , Proteínas Proto-Oncogénicas c-akt/metabolismo , Proteínas Proto-Oncogénicas c-akt/genética , Fosfatidilinositol 3-Quinasas/metabolismo , Fosfatidilinositol 3-Quinasas/genética , Colágeno/metabolismo , Colágeno/genética , Biología Computacional/métodos , MicroARNs/genética , MicroARNs/metabolismo , Reflujo Vesicoureteral/genética , Reflujo Vesicoureteral/metabolismo , Femenino , Proteoma/metabolismo , Líquido Amniótico/metabolismo , Sistema Urinario/metabolismo , Multiómica , Anomalías UrogenitalesRESUMEN
Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.
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Enfermedad de Alzheimer , Diabetes Mellitus Tipo 2 , Humanos , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/metabolismo , Fenotipo , Algoritmos , Biología Computacional/métodos , Análisis por Conglomerados , MultiómicaRESUMEN
BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.
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Adenocarcinoma del Pulmón , Metilación de ADN , Regulación Neoplásica de la Expresión Génica , Neoplasias Pulmonares , Humanos , Adenocarcinoma del Pulmón/genética , Adenocarcinoma del Pulmón/patología , Adenocarcinoma del Pulmón/mortalidad , Adenocarcinoma del Pulmón/tratamiento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/tratamiento farmacológico , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/metabolismo , Análisis por Conglomerados , Genómica/métodos , Mutación , Biomarcadores de Tumor/genética , Femenino , Masculino , Secuenciación Completa del Genoma , Pronóstico , Terapia Molecular Dirigida , Perfilación de la Expresión Génica , Anciano , Persona de Mediana Edad , MultiómicaRESUMEN
Clinical biomarkers such as fasting glucose, HbA1c, and fasting insulin, which gauge glycemic status in the body, are highly influenced by diet. Indians are genetically predisposed to type 2 diabetes and their carbohydrate-centric diet further elevates the disease risk. Despite the combined influence of genetic and environmental risk factors, Indians have been inadequately explored in the studies of glycemic traits. Addressing this gap, we investigate the genetic architecture of glycemic traits at genome-wide level in 4927 Indians (without diabetes). Our analysis revealed numerous variants of sub-genome-wide significance, and their credibility was thoroughly assessed by integrating data from various levels. This identified key effector genes, ZNF470, DPP6, GXYLT2, PITPNM3, BEND7, and LORICRIN-PGLYRP3. While these genes were weakly linked with carbohydrate intake or glycemia earlier in other populations, our findings demonstrated a much stronger association in the Indian population. Associated genetic variants within these genes served as expression quantitative trait loci (eQTLs) in various gut tissues essential for digestion. Additionally, majority of these gut eQTLs functioned as methylation quantitative trait loci (meth-QTLs) observed in peripheral blood samples from 223 Indians, elucidating the underlying mechanism of their regulation of target gene expression. Specific co-localized eQTLs-meth-QTLs altered the binding affinity of transcription factors targeting crucial genes involved in glucose metabolism. Our study identifies previously unreported genetic variants that strongly influence the diet-glycemia relationship. These findings set the stage for future research into personalized lifestyle interventions integrating genetic insights with tailored dietary strategies to mitigate disease risk based on individual genetic profiles.
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Glucemia , Metabolismo de los Hidratos de Carbono , Estudio de Asociación del Genoma Completo , Polimorfismo de Nucleótido Simple , Sitios de Carácter Cuantitativo , Humanos , India/epidemiología , Glucemia/metabolismo , Masculino , Metabolismo de los Hidratos de Carbono/genética , Femenino , Diabetes Mellitus Tipo 2/genética , Adulto , Predisposición Genética a la Enfermedad , Persona de Mediana Edad , Metilación de ADN/genética , MultiómicaRESUMEN
The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, MILTON) utilizing a range of biomarkers to predict 3,213 diseases in the UK Biobank. Leveraging the UK Biobank's longitudinal health record data, MILTON predicts incident disease cases undiagnosed at time of recruitment, largely outperforming available polygenic risk scores. We further demonstrate the utility of MILTON in augmenting genetic association analyses in a phenome-wide association study of 484,230 genome-sequenced samples, along with 46,327 samples with matched plasma proteomics data. This resulted in improved signals for 88 known (P < 1 × 10-8) gene-disease relationships alongside 182 gene-disease relationships that did not achieve genome-wide significance in the nonaugmented baseline cohorts. We validated these discoveries in the FinnGen biobank alongside two orthogonal machine-learning methods built for gene-disease prioritization. All extracted gene-disease associations and incident disease predictive biomarkers are publicly available ( http://milton.public.cgr.astrazeneca.com ).
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Bancos de Muestras Biológicas , Biomarcadores , Predisposición Genética a la Enfermedad , Estudio de Asociación del Genoma Completo , Aprendizaje Automático , Humanos , Reino Unido , Estudio de Asociación del Genoma Completo/métodos , Estudios de Casos y Controles , Herencia Multifactorial/genética , Proteómica/métodos , Fenotipo , Polimorfismo de Nucleótido Simple , Algoritmos , Multiómica , Biobanco del Reino UnidoRESUMEN
BACKGROUND: Pseudomonas putida KT2440 has emerged as a promising host for industrial bioproduction. However, its strictly aerobic nature limits the scope of applications. Remarkably, this microbe exhibits high bioconversion efficiency when cultured in an anoxic bio-electrochemical system (BES), where the anode serves as the terminal electron acceptor instead of oxygen. This environment facilitates the synthesis of commercially attractive chemicals, including 2-ketogluconate (2KG). To better understand this interesting electrogenic phenotype, we studied the BES-cultured strain on a systems level through multi-omics analysis. Inspired by our findings, we constructed novel mutants aimed at improving 2KG production. RESULTS: When incubated on glucose, P. putida KT2440 did not grow but produced significant amounts of 2KG, along with minor amounts of gluconate, acetate, pyruvate, succinate, and lactate. 13C tracer studies demonstrated that these products are partially derived from biomass carbon, involving proteins and lipids. Over time, the cells exhibited global changes on both the transcriptomic and proteomic levels, including the shutdown of translation and cell motility, likely to conserve energy. These adaptations enabled the cells to maintain significant metabolic activity for several weeks. Acetate formation was shown to contribute to energy supply. Mutants deficient in acetate production demonstrated superior 2KG production in terms of titer, yield, and productivity. The ∆aldBI ∆aldBII double deletion mutant performed best, accumulating 2KG at twice the rate of the wild type and with an increased yield (0.96 mol/mol). CONCLUSIONS: By integrating transcriptomic, proteomic, and metabolomic analyses, this work provides the first systems biology insight into the electrogenic phenotype of P. putida KT2440. Adaptation to anoxic-electrogenic conditions involved coordinated changes in energy metabolism, enabling cells to sustain metabolic activity for extended periods. The metabolically engineered mutants are promising for enhanced 2KG production under these conditions. The attenuation of acetate synthesis represents the first systems biology-informed metabolic engineering strategy for enhanced 2KG production in P. putida. This non-growth anoxic-electrogenic mode expands our understanding of the interplay between growth, glucose phosphorylation, and glucose oxidation into gluconate and 2KG in P. putida.
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Gluconatos , Ingeniería Metabólica , Pseudomonas putida , Biología de Sistemas , Pseudomonas putida/metabolismo , Pseudomonas putida/genética , Gluconatos/metabolismo , Ingeniería Metabólica/métodos , Biología de Sistemas/métodos , Glucosa/metabolismo , Proteómica , MultiómicaRESUMEN
The role of Erythroid cells in immune regulation and immunosuppression is one of the emerging topics in modern immunology that still requires further clarification as Erythroid cells from different tissues and different species express different immunoregulatory molecules. In this study, we performed a thorough investigation of human bone marrow Erythroid cells from adult healthy donors and adult acute lymphoblastic leukemia patients using the state-of-the-art single-cell targeted proteomics and transcriptomics via BD Rhapsody and cancer-related gene copy number variation analysis via NanoString Sprint Profiler. We found that human bone marrow Erythroid cells express the ARG1, LGALS1, LGALS3, LGALS9, and C10orf54 (VISTA) immunosuppressive genes, CXCL5, CXCL8, and VEGFA cytokine genes, as well as the genes involved in antimicrobial immunity and MHC Class II antigen presentation. We also found that ARG1 gene expression was restricted to the single erythroid cell cluster that we termed ARG1-positive Orthochromatic erythroblasts and that late Erythroid cells lose S100A9 and gain MZB1 gene expression in case of acute lymphoblastic leukemia. These findings show that steady-state erythropoiesis bone marrow Erythroid cells express myeloid signature genes even without any transdifferentiating stimulus like cancer.
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Células Eritroides , Leucemia-Linfoma Linfoblástico de Células Precursoras , Análisis de la Célula Individual , Humanos , Células Eritroides/metabolismo , Células Eritroides/inmunología , Leucemia-Linfoma Linfoblástico de Células Precursoras/inmunología , Leucemia-Linfoma Linfoblástico de Células Precursoras/genética , Diferenciación Celular/inmunología , Proteómica/métodos , Transcriptoma , Perfilación de la Expresión Génica , Adulto , MultiómicaRESUMEN
Traumatic spinal cord injury (tSCI) is a severe injury to the central nervous system that is categorized into primary and secondary injuries. Among them, the local microenvironmental imbalance in the spinal cord caused by secondary spinal cord injury includes accumulation of cytokines and chemokines, reduced angiogenesis, dysregulation of cellular energy metabolism, and dysfunction of immune cells at the site of injury, which severely impedes neurological recovery from spinal cord injury (SCI). In recent years, single-cell techniques have revealed the heterogeneity of multiple immune cells at the genomic, transcriptomic, proteomic, and metabolomic levels after tSCI, further deepening our understanding of the mechanisms underlying tSCI. However, spatial information about the tSCI microenvironment, such as cell location and cell-cell interactions, is lost in these approaches. The application of spatial multi-omics technology can solve this problem by combining the data obtained from immunohistochemistry and multiparametric analysis to reveal the changes in the microenvironment at different times of secondary injury after SCI. In this review, we systematically review the progress of spatial multi-omics techniques in the study of the microenvironment after SCI, including changes in the immune microenvironment and discuss potential future therapeutic strategies.
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Microambiente Celular , Proteómica , Traumatismos de la Médula Espinal , Traumatismos de la Médula Espinal/inmunología , Traumatismos de la Médula Espinal/metabolismo , Humanos , Microambiente Celular/inmunología , Proteómica/métodos , Animales , Metabolómica/métodos , Genómica/métodos , Transcriptoma , Análisis de la Célula Individual , MultiómicaRESUMEN
BACKGROUND: Intracranial aneurysm (IA) is a severe cerebrovascular disease, and effective gene therapy and drug interventions for its treatment are still lacking. Oxidative stress (OS) is closely associated with the IA, but the key regulatory genes involved are still unclear. Through multiomics analysis and experimental validation, we identified two diagnostic markers for IA associated with OS. METHODS: In this study, we first analyzed the IA dataset GSE75436 and conducted a joint analysis of oxidative stress-related genes (ORGs). Differential analysis, functional enrichment analysis, immune infiltration, WGCNA, PPI, LASSO, and other methods were used to identify IA diagnostic markers related to OS. Next, the functions of TLR4 and ALOX5 expression in IA and their potential targeted therapeutic drugs were analyzed. We also performed single-cell sequencing of patient IA and control (superficial temporal artery, STA) tissues. 23,342 cells were captured from 2 IA and 3 STA samples obtained from our center. Cell clustering and annotation were conducted using R software to observe the distribution of TLR4 and ALOX5 expression in IAs. Finally, the expression of TLR4 and ALOX5 were validated in IA patients and in an elastase-induced mouse IA model using experiments such as WB and immunofluorescence. RESULTS: Through bioinformatics analysis, we identified 16 key ORGs associated with IA pathogenesis. Further screening revealed that ALOX5 and TLR4 were highly expressed to activate a series of inflammatory responses and reduce the production of myocytes. Methotrexate (MTX) may be a potential targeted drug. Single-cell analysis revealed a notable increase in immune cells in the IA group, with ALOX5 and TLR4 primarily localized to monocytes/macrophages. Validation through patient samples and mouse models confirmed high expression of ALOX5 and TLR4 in IAs. CONCLUSIONS: Bioinformatics analysis indicated that ALOX5 and TLR4 are the most significant ORGs associated with the pathogenesis of IA. Single-cell sequencing and experiments revealed that the high expression of ALOX5 and TLR4 are closely related to IA. These two genes are promising new targets for IA therapy.
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Araquidonato 5-Lipooxigenasa , Biomarcadores , Aneurisma Intracraneal , Estrés Oxidativo , Receptor Toll-Like 4 , Receptor Toll-Like 4/metabolismo , Receptor Toll-Like 4/genética , Aneurisma Intracraneal/metabolismo , Aneurisma Intracraneal/genética , Animales , Ratones , Humanos , Estrés Oxidativo/fisiología , Araquidonato 5-Lipooxigenasa/metabolismo , Araquidonato 5-Lipooxigenasa/genética , Araquidonato 5-Lipooxigenasa/biosíntesis , Biomarcadores/metabolismo , Masculino , Ratones Endogámicos C57BL , Femenino , MultiómicaRESUMEN
Extracellular adenosine is extensively involved in regulating the tumor microenvironment. Given the disappointing results of adenosine-targeted therapy trials, personalized treatment might be necessary, tailored to the microenvironment status of individual patients. Here, we introduce the adenosine signaling score (ADO-score) model using non-negative matrix fraction identified patient subtypes using publicly available melanoma dataset, which aimed to profile adenosine signaling-related genes and construct a model to predict prognosis. We analyzed 580 malignant melanoma samples and demonstrated its robust value for prognosis. Further investigation in immune checkpoint inhibitor dataset suggests its potential as a stratified factor of immune checkpoint inhibitor efficacy. We validated the power of the ADO-score at the protein level immunofluorescence in a melanoma cohort from Xiangya Hospital. More importantly, single-cell and spatial transcriptomic data highlighted the cell-specific expression patterns of adenosine signaling-related genes and the existence of adenosine signaling-mediated crosstalk between tumor cells and immune cells in melanoma. Our study reveals a robust connection between adenosine signaling and clinical benefits in melanoma patients and proposes a universally applicable adenosine signaling model, the ADO-score, in gene expression profiles and histological sections. This model enables us to more precisely and conveniently select patients who are likely to benefit from immunotherapy.
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Adenosina , Inmunoterapia , Melanoma , Transducción de Señal , Microambiente Tumoral , Humanos , Melanoma/genética , Melanoma/patología , Melanoma/inmunología , Melanoma/tratamiento farmacológico , Adenosina/metabolismo , Adenosina/genética , Microambiente Tumoral/genética , Microambiente Tumoral/inmunología , Transducción de Señal/genética , Pronóstico , Regulación Neoplásica de la Expresión Génica/genética , Transcriptoma/genética , Perfilación de la Expresión Génica , Inhibidores de Puntos de Control Inmunológico/uso terapéutico , Femenino , Masculino , MultiómicaRESUMEN
Background: Mitochondrial dysfunction plays a crucial role in Type 2 Diabetes Mellitus (T2DM) and its complications. However, the genetic pathophysiology remains under investigation. Through multi-omics Mendelian Randomization (MR) and colocalization analyses, we identified mitochondrial-related genes causally linked with T2DM and its complications. Methods: Summary-level quantitative trait loci data at methylation, RNA, and protein levels were retrieved from European cohort studies. GWAS summary statistics for T2DM and its complications were collected from the DIAGRAM and FinnGen consortiums, respectively. Summary-data-based MR was utilized to estimate the causal effects. The heterogeneity in dependent instrument test assessed horizontal pleiotropy, while colocalization analysis determined whether genes and diseases share the same causal variant. Enrichment analysis, drug target analysis, and phenome-wide MR were conducted to further explore the biological functions, potential drugs, and causal associations with other diseases. Results: Integrating evidence from multi-omics, we identified 18 causal mitochondrial-related genes. Enrichment analysis revealed they were not only related to nutrient metabolisms but also to the processes like mitophagy, autophagy, and apoptosis. Among these genes, Tu translation elongation factor mitochondrial (TUFM), 3-hydroxyisobutyryl-CoA hydrolase (HIBCH), and iron-sulfur cluster assembly 2 (ISCA2) were identified as Tier 1 genes, showing causal links with T2DM and strong colocalization evidence. TUFM and ISCA2 were causally associated with an increased risk of T2DM, while HIBCH showed an inverse causal relationship. The causal associations and colocalization effects for TUFM and HIBCH were validated in specific tissues. TUFM was also found to be a risk factor for microvascular complications in T2DM patients including retinopathy, nephropathy, and neuropathy. Furthermore, drug target analysis and phenome-wide MR underscored their significance as potential therapeutic targets. Conclusions: This study identified 18 mitochondrial-related genes causally associated with T2DM at multi-omics levels, enhancing the understanding of mitochondrial dysfunction in T2DM and its complications. TUFM, HIBCH, and ISCA2 emerge as potential therapeutic targets for T2DM and its complications.
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Diabetes Mellitus Tipo 2 , Análisis de la Aleatorización Mendeliana , Mitocondrias , Humanos , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/complicaciones , Mitocondrias/metabolismo , Mitocondrias/genética , Estudio de Asociación del Genoma Completo , Sitios de Carácter Cuantitativo , Predisposición Genética a la Enfermedad , Complicaciones de la Diabetes/genética , MultiómicaRESUMEN
In this editorial, we discuss the need for a new, long-term strategy for managing human excrement (feces and urine) to facilitate health equity and promote environmental sustainability. Human excrement composting (HEC), a human-directed process driven by highly variable and diverse microbiomes, provides a means to advance this need and we discuss how microbiome science can help to advance HEC research. We argue that the technological advancements that have driven the growth of microbiome science, including microbiome and untargeted metabolome profiling, can be leveraged to enhance our understanding of safe and efficient HEC. We conclude by presenting our perspective on how we can begin applying these technologies to develop accessible procedures for safe HEC. Video Abstract.
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Compostaje , Heces , Microbiota , Humanos , Heces/microbiología , Metabolómica/métodos , Metaboloma , Orina/microbiología , MultiómicaRESUMEN
With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.
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Aprendizaje Profundo , Genómica , Humanos , Genómica/métodos , Biología Computacional/métodos , Redes Neurales de la Computación , Algoritmos , MultiómicaRESUMEN
The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.