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Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively.

ABSTRACT: Sepsis-associated acute kidney injury (SA-AKI) is a common complication in critically ill patients with high morbidity and mortality. SA-AKI varies considerably in disease presentation, progression, and response to treatment, highlighting the heterogeneity of the underlying biological mechanisms. In this review, we briefly describe the pathophysiology of SA-AKI, biomarkers, reference databases, and available omics techniques. Advances in omics technology allow for comprehensive analysis of SA-AKI, and the integration of multiple omics provides an opportunity to understand the information flow behind the disease. These approaches will drive a shift in current paradigms for the prevention, diagnosis, and staging and provide the renal community with significant advances in precision medicine in SA-AKI analysis.

SUBMITTER: Qiao J

PROVIDER: S-EPMC9300837 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively.

Qiao Jiao J Cui Liyan L

Frontiers in immunology 20220707

Sepsis-associated acute kidney injury (SA-AKI) is a common complication in critically ill patients with high morbidity and mortality. SA-AKI varies considerably in disease presentation, progression, and response to treatment, highlighting the heterogeneity of the underlying biological mechanisms. In this review, we briefly describe the pathophysiology of SA-AKI, biomarkers, reference databases, and available omics techniques. Advances in omics technology allow for comprehensive analysis of SA-AK ...[more]

PMID: 35874763

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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.

Project description:BackgroundSepsis-associated acute kidney injury (SA-AKI) is a frequent complication in patients with sepsis and is associated with high mortality. Therefore, early recognition of SA-AKI is essential for administering supportive treatment and preventing further damage. This study aimed to identify and validate metabolite biomarkers of SA-AKI to assist in early clinical diagnosis.MethodsUntargeted renal proteomic and metabolomic analyses were performed on the renal tissues of LPS-induced SA-AKI and sepsis mice. Glomerular filtration rate (GFR) monitoring technology was used to evaluate real-time renal function in mice. To elucidate the distinctive characteristics of SA-AKI, a multi-omics Spearman correlation network was constructed integrating core metabolites, proteins, and renal function. Subsequently, metabolomics analysis was used to explore the dynamic changes of core metabolites in the serum of SA-AKI mice at 0, 8, and 24 h. Finally, a clinical cohort (28 patients with SA-AKI vs. 28 patients with sepsis) serum quantitative metabolomic analysis was carried out to build a diagnostic model for SA-AKI via logistic regression (LR).ResultsThirteen differential renal metabolites and 112 differential renal proteins were identified through a multi-omics study of SA-AKI mice. Subsequently, a multi-omics correlation network was constructed to highlight five core metabolites, i.e., 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, creatine, myristic acid, and inosine, the early changes of which were then observed via serum time series experiments of SA-AKI mice. The levels of 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, and creatine increased significantly at 24 h, myristic acid increased at 8 h, while inosine decreased at 8 h. Ultimately, based on the identified core metabolites, we recruited 56 patients and constructed a diagnostic model named IC3, using inosine, creatine, and 3-hydroxybutyric acid, to early identify SA-AKI (AUC = 0.90).ConclusionsWe proposed a blood metabolite model consisting of inosine, creatine, and 3-hydroxybutyric acid for the early screening of SA-AKI. Future studies will observe the performance of these metabolites in other clinical populations to evaluate their diagnostic role.

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 .

Project description:Periodontitis is closely related to renal health, but the specific influence of Porphyromonas gingivalis (P. gingivalis), a key pathogen in periodontitis, on the development of acute kidney injury (AKI) in mice has not been fully elucidated. In our study, AKI was induced in mice through ischemia-reperfusion injury while administering oral infection with P. gingivalis. Comprehensive analyses were conducted, including 16S rRNA sequencing, liquid chromatography-mass spectrometry (LC-MS) metabolomics, and transcriptome sequencing. In vitro, the identified metabolite was used to stimulate mouse neutrophils. Subsequently, these modified neutrophils were co-cultured with mouse renal tubular epithelial cells. The results showed that oral infection with P. gingivalis significantly exacerbated AKI in mice. 16S rRNA sequencing revealed notable shifts in gut microbiota composition. LC-MS metabolomics analysis identified significant metabolic alterations, particularly the upregulation of 3-indoleacrylic acid in the serum. Transcriptome sequencing showed an increased expression of neutrophilic granule protein (Ngp), which was closely associated with 3-indoleacrylic acid, and the presence of Porphyromonas. Cellular experiments demonstrated that 3-indoleacrylic acid could activate neutrophils, leading to an elevation in NGP protein levels, a response that was associated with renal epithelial cell injury. Oral infection with P. gingivalis exacerbated AKI through the gut-kidney axis, involving gut microbiota dysbiosis, metabolic disturbances, and increased renal expression of Ngp.ImportanceThis study provides novel insights into the relationship between periodontal health and renal function. Porphyromonas gingivalis oral infection disrupted the balance of gut microbiota and was an important modifier determining the severity of acute kidney injury. Under the "gut-kidney axis," P. gingivalis might cause an increase in the level of the gut microbial metabolite 3-indoleacrylic acid, interfering with kidney immunity and disrupting the maintenance of kidney epithelium.

Dataset Information

Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively.

Publications

Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively.

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