Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively.
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ABSTRACT:
SUBMITTER: Qiao J
PROVIDER: S-EPMC9300837 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo compare coronavirus disease 2019 (COVID-19) acute kidney injury (AKI) to sepsis-AKI (S-AKI). The morphology and transcriptomic and proteomic characteristics of autopsy kidneys were analyzed.Patients and methodsIndividuals 18 years of age and older who died from COVID-19 and had an autopsy performed at Mayo Clinic between April 2020 to October 2020 were included. Morphological evaluation of the kidneys of 17 individuals with COVID-19 was performed. In a subset of seven COVID-19 cases with postmortem interval of less than or equal to 20 hours, ultrastructural and molecular characteristics (targeted transcriptome and proteomics analyses of tubulointerstitium) were evaluated. Molecular characteristics were compared with archived cases of S-AKI and nonsepsis causes of AKI.ResultsThe spectrum of COVID-19 renal pathology included macrophage-dominant microvascular inflammation (glomerulitis and peritubular capillaritis), vascular dysfunction (peritubular capillary congestion and endothelial injury), and tubular injury with ultrastructural evidence of mitochondrial damage. Investigation of the spatial architecture using a novel imaging mass cytometry revealed enrichment of CD3+CD4+ T cells in close proximity to antigen-presenting cells, and macrophage-enriched glomerular and interstitial infiltrates, suggesting an innate and adaptive immune tissue response. Coronavirus disease 2019 AKI and S-AKI, as compared to nonseptic AKI, had an enrichment of transcriptional pathways involved in inflammation (apoptosis, autophagy, major histocompatibility complex class I and II, and type 1 T helper cell differentiation). Proteomic pathway analysis showed that COVID-19 AKI and to a lesser extent S-AKI were enriched in necroptosis and sirtuin-signaling pathways, both involved in regulatory response to inflammation. Upregulation of the ceramide-signaling pathway and downregulation of oxidative phosphorylation in COVID-19 AKI were noted.ConclusionThis data highlights the similarities between S-AKI and COVID-19 AKI and suggests that mitochondrial dysfunction may play a pivotal role in COVID-19 AKI. This data may allow the development of novel diagnostic and therapeutic targets.
| S-EPMC8279954 | biostudies-literature
Project description:BackgroundRepresenting the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators.ResultsSUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels.ConclusionsWe developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC .
| S-EPMC9442970 | biostudies-literature
Project description:Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. It is characterized by rapid deterioration of renal function associated with sepsis. The pathophysiology of S-AKI remains incompletely understood, so most therapies remain reactive and nonspecific. Possible pathogenic mechanisms to explain S-AKI include microcirculatory dysfunction, a dysregulated inflammatory response, and cellular metabolic reprogramming. In addition, several biomarkers have been developed in an attempt to improve diagnostic sensitivity and specificity of S-AKI. This article discusses the current understanding of S-AKI, recent advances in pathophysiology and biomarker development, and current preventive and therapeutic approaches.
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Project description:Amino acids play essential roles in both metabolism and the proteome. Many studies have profiled free amino acids (FAAs) or proteins; however, few have connected the measurement of FAA with individual amino acids in the proteome. In this study, we developed a metabolomics method to comprehensively analyze amino acids in different domains, using two examples of different sample types and disease models. We first examined the responses of FAAs and insoluble-proteome amino acids (IPAAs) to the Myc oncogene in Tet21N human neuroblastoma cells. The metabolic and proteomic amino acid profiles were quite different, even under the same Myc condition, and their combination provided a better understanding of the biological status. In addition, amino acids were measured in 3 domains (FAAs, free and soluble-proteome amino acids (FSPAAs), and IPAAs) to study changes in serum amino acid profiles related to colon cancer. A penalized logistic regression model based on the amino acids from the three domains had better sensitivity and specificity than that from each individual domain. To the best of our knowledge, this is the first study to perform a combined analysis of amino acids in different domains, and indicates the useful biological information available from a metabolomics analysis of the protein pellet. This study lays the foundation for further quantitative tracking of the distribution of amino acids in different domains, with opportunities for better diagnosis and mechanistic studies of various diseases.
| S-EPMC4380628 | biostudies-literature
Project description:Sepsis is characterized by a severe inflammatory response to infection, and its complications, including acute kidney injury, can be fatal. Animal models that correctly mimic human disease are extremely valuable because they hasten the development of clinically useful therapeutics. Too often, however, animal models do not properly mimic human disease. In this Review, we outline a bedside-to-bench-to-bedside approach that has resulted in improved animal models for the study of sepsis - a complex disease for which preventive and therapeutic strategies are unfortunately lacking. We also highlight a few of the promising avenues for therapeutic advances and biomarkers for sepsis and sepsis-induced acute kidney injury. Finally, we review how the study of drug targets and biomarkers are affected by and in turn have influenced these evolving animal models.
| S-EPMC2752080 | biostudies-literature
Project description:State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.
| S-EPMC4945831 | biostudies-literature
Project description:The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene .
| S-EPMC7688645 | biostudies-literature
Project description:Biological mechanisms related to cancer development can leave distinct molecular fingerprints in tumours. By leveraging multi-omics and epidemiological information, we can unveil relationships between carcinogenesis processes that would otherwise remain hidden. Our integrative analysis of DNA methylome, transcriptome, and somatic mutation profiles of kidney tumours linked ageing, epithelial-mesenchymal transition (EMT), and xenobiotic metabolism to kidney carcinogenesis. Ageing process was represented by associations with cellular mitotic clocks such as epiTOC2, SBS1, telomere length, and PBRM1 and SETD2 mutations, which ticked faster as tumours progressed. We identified a relationship between BAP1 driver mutations and the epigenetic upregulation of EMT genes (IL20RB and WT1), correlating with increased tumour immune infiltration, advanced stage, and poorer patient survival. We also observed an interaction between epigenetic silencing of the xenobiotic metabolism gene GSTP1 and tobacco use, suggesting a link to genotoxic effects and impaired xenobiotic metabolism. Our pan-cancer analysis showed these relationships in other tumour types. Our study enhances the understanding of kidney carcinogenesis and its relation to risk factors and progression, with implications for other tumour types.
| S-SCDT-10_1038-S44320-024-00072-3 | biostudies-other
Project description:Immunotherapy for lung adenocarcinoma (LUAD) is considered to be a promising treatment option, but only a minority of patients benefit from it. Therefore, it is essential to clarify the regulation mechanism of the tumor immune microenvironment (TIM) of the LUAD. Receptor-type protein tyrosine phosphatase (PTPRO) has been shown to be a tumor suppressor in a variety of tumor; however, its role in LUAD has never been reported. In this study, we first found that PTPRO was lowly expressed in LUAD and positively correlated with patient prognosis. Next, we investigated the relationship between PTPRO and clinical characteristics, and the results showed that gender, age, T, and stage were closely related to the expression level of PTPRO. Moreover, we performed univariate and multivariate analyses, and the results revealed that PTPRO was a protective factor for LUAD. By constructing a nomogram based on the expression level of PTPRO and various clinical characteristics, it was proved that the nomogram has a good predictive capacity. Furthermore, we analyzed the coexpression network of PTPRO through multiple databases and performed GO and KEGG enrichment analyses. The results demonstrated that PTPRO was involved in the regulation of multiple immune pathways. In addition, we analyzed whether PTPRO expression of LUAD regulate immune cell infiltration and the results demonstrated that PTPRO was closely related to the infiltration of various immune cells. Finally, we predicted LUAD sensitivity to chemotherapeutics and response to immunotherapy by PTPRO expression levels. The results showed that PTPRO expression level affect the sensitivity of various chemotherapeutic drugs and may be involved in the efficacy of immunotherapy. These results we obtained suggested that PTPRO is closely related to the prognosis and TIM of LUAD, which may be a potential immunotherapeutic target for LUAD.
| S-EPMC9934981 | biostudies-literature
Project description:Sepsis accounts for more than 50% of all acute kidney injury (AKI) cases, and the combination of sepsis and AKI increases the risk of mortality from sepsis alone. However, to the best of our knowledge, the specific mechanism by which sepsis causes AKI has not yet been fully elucidated, and there is no targeted therapy for sepsis-associated AKI (SA-AKI). The present study investigated gene expression profiles using RNA sequencing (RNA-Seq) and bioinformatics analyses to assess the function of differentially expressed genes (DEGs) and the molecular mechanisms relevant to the prognosis of SA-AKI. From the bioinformatics analysis, 2,256 downregulated and 3,146 upregulated genes were identified (false discovery rate <0.1 and fold-change >2). Gene Ontology analysis revealed that the genes were enriched in cellular metabolic processes, cell death and apoptosis. The enriched transcription factors were v-rel reticuloendotheliosis viral oncogene homolog A and signaling transducer and activator of transcription 3. The enriched microRNAs (miRNAs or miRs) among the DEGs were miR-30e, miR-181a, miR-340, miR-466d and miR-466l. Furthermore, the enriched pathways included toll-like receptor signaling, nod-like receptor signaling and the Janus kinase/STAT signaling pathway. In conclusion, the present study identified certain prognosis-related genes, transcription factors, miRNAs and pathways by analyzing gene expression profiles of SA-AKI using RNA-Seq, which provides some basis for future experimental studies.
| S-EPMC7485311 | biostudies-literature