miRNA analysis reveals novel dysregulated pathways in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Its complex pathogenesis and phenotypic heterogeneity hinder therapeutic development and early diagnosis. Altered RNA metabolism is a recurrent pathophysiologic theme, including distinct microRNA (miRNA) profiles in ALS tissues. We profiled miRNAs in accessible biosamples, including skin fibroblasts and whole blood and compared them in age- and sex-matched healthy controls versus ALS participants with and without repeat expansions to chromosome 9 open reading frame 72 (C9orf72; C9-ALS and nonC9-ALS), the most frequent ALS mutation. We identified unique and shared profiles of differential miRNA (DmiRNA) levels in each C9-ALS and nonC9-ALS tissues versus controls. Fibroblast DmiRNAs were validated by quantitative real-time PCR and their target mRNAs by 5-bromouridine and 5-bromouridine-chase sequencing. We also performed pathway analysis to infer biological meaning, revealing anticipated, tissue-specific pathways and pathways previously linked to ALS, as well as novel pathways that could inform future research directions. Overall, we report a comprehensive study of a miRNA profile dataset from C9-ALS and nonC9-ALS participants across two accessible biosamples, providing evidence of dysregulated miRNAs in ALS and possible targets of interest. Distinct miRNA patterns in accessible tissues may also be leveraged to distinguish ALS participants from healthy controls for earlier diagnosis. Future directions may look at potential correlations of miRNA profiles with clinical parameters.

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis.

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Perspectives in systems nephrology.

Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world's population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.

Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer.

Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised k-means and partition around medoids (pam) to cluster metabolomics data from two data sets. The first comprised 271 BC tumors (data set 1) that were estrogen receptor (ER) positive (ER+, n = 204) or negative (ER-, n = 67) with 162 identified and validated metabolites. The second data set contained 67 BC samples (data set 2; ER+, n = 33; ER-, n = 34) and 352 known metabolites. Significance Analysis of Microarrays (SAM) was used to identify the most significant metabolites among these clusters, which were then reassigned into new clusters using prediction analysis of microarrays (PAM). Generally, metabolome-defined BC subtypes identified from either data set 1 or data set 2 were different from the well-known receptor- or transcriptome-defined subtypes. Metabolomics-directed clustering of data set 2 identified distinctive BC tumors characterized by metabolome profiles that associated with DNA methylation (p-value = 0.000 048, χ2 test). Pathway analysis of cluster metabolites revealed that nitrogen metabolism and aminoacyl-tRNA biosynthesis were highly related to BC subtyping. The pipeline may be run from GitHub: https://github.com/FADHLyemen/Metabolomics_signature. Our proposed bioinformatics pipeline analyzed metabolomics data from BC tumors, revealing clusters characterized by unique metabolic signatures that may potentially stratify BC patients and tailor precision treatment.

Untargeted metabolomics yields insight into ALS disease mechanisms.

OBJECTIVE: To identify dysregulated metabolic pathways in amyotrophic lateral sclerosis (ALS) versus control participants through untargeted metabolomics. METHODS: Untargeted metabolomics was performed on plasma from ALS participants (n=125) around 6.8 months after diagnosis and healthy controls (n=71). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explored sub-pathway-level differences. Adjustment parameters included sex, age and body mass index (BMI). Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Finally, machine learning classification algorithms applied to group lasso-selected metabolites were evaluated for classifying case status. RESULTS: There were no group differences in sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. 'Benzoate metabolism', 'ceramides', 'creatine metabolism', 'fatty acid metabolism (acyl carnitine, polyunsaturated)' and 'hexosylceramides' sub-pathways were enriched by all methods, and 'sphingomyelins' by all but Wilcoxon, indicating these pathways significantly associate with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%. CONCLUSION: In our analysis, ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Interactive Effects of Empagliflozin and Hyperglycemia on Urinary Amino Acids in Individuals With Type 1 Diabetes.

Optimizing energy use in the kidney is critical for normal kidney function. Here, we investigate the effect of hyperglycemia and sodium-glucose cotransporter 2 (SGLT2) inhibition on urinary amino acid excretion in individuals with type 1 diabetes (T1D). The open-label ATIRMA trial assessed the impact of 8 weeks of 25 mg empagliflozin orally once per day in 40 normotensive normoalbuminuric young adults with T1D. A consecutive 2-day assessment of clamped euglycemia and hyperglycemia was evaluated at baseline and posttreatment visits. Principal component analysis was performed on urinary amino acids grouped into representative metabolic pathways using MetaboAnalyst. At baseline, acute hyperglycemia was associated with changes in 25 of the 33 urinary amino acids or their metabolites. The most significant amino acid metabolites affected by acute hyperglycemia were 3-hydroxykynurenine, serotonin, glycyl-histidine, and nicotinic acid. The changes in amino acid metabolites were reflected by the induction of four biosynthetic pathways: aminoacyl-tRNA; valine, leucine, and isoleucine; arginine; and phenylalanine, tyrosine, and tryptophan. In acute hyperglycemia, empagliflozin significantly attenuated the increases in aminoacyl-tRNA biosynthesis and valine, leucine, and isoleucine biosynthesis. Our findings using amino acid metabolomics indicate that hyperglycemia stimulates biosynthetic pathways in T1D. SGLT2 inhibition may attenuate the increase in biosynthetic pathways to optimize kidney energy metabolism.

Association of mental health-related patient reported outcomes with blood pressure in adults and children with primary proteinuric glomerulopathies.

INTRODUCTION: The prevalence of mental health disorders including anxiety and depression is increasing and is linked to hypertension in healthy individuals. However, the relationship of psychosocial patient-reported outcomes on blood pressure (BP) in primary proteinuric glomerulopathies is not well characterized. This study explored longitudinal relationships between psychosocial patient-reported outcomes and BP status among individuals with proteinuric glomerulopathies. METHODS: An observational cohort study was performed using data from 745 adults and children enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). General Estimating Equations for linear regression and binary logistic analysis for odds ratios were performed to analyze relationships between the exposures, longitudinal Patient-Reported Outcome Measurement Information System (PROMIS) measures and BP and hypertension status as outcomes. RESULTS: In adults, more anxiety was longitudinally associated with higher systolic and hypertensive BP. In children, fatigue was longitudinally associated with increased odds of hypertensive BP regardless of the PROMIS report method. More stress, anxiety, and depression were longitudinally associated with higher systolic BP index, higher diastolic BP index, and increased odds of hypertensive BP index in children with parent-proxy patient-reported outcomes. DISCUSSION/CONCLUSION: Chronically poor psychosocial patient-reported outcomes may be significantly associated with higher BP and hypertension in adults and children with primary proteinuric glomerulopathies. This interaction appears strong in children but should be interpreted with caution, as multiple confounders related to glomerular disease may influence both mental health and BP independently. That said, access to mental health resources may help control BP, and proper disease and BP management may improve overall mental health.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease statuses. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

The chromatin landscape of healthy and injured cell types in the human kidney.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Severe preeclampsia is not associated with significant DNA methylation changes but cell proportion changes in the cord blood - caution on the importance of confounding adjustment.

Epigenome-wide DNA methylation analysis (EWAS) is an important approach to identify biomarkers for early disease detection and prognosis prediction, yet its results could be confounded by other factors such as cell-type heterogeneity and patient characteristics. In this study, we address the importance of confounding adjustment by examining DNA methylation patterns in cord blood exposed to severe preeclampsia (PE), a prevalent and potentially fatal pregnancy complication. Without such adjustment, a misleading global hypomethylation pattern is obtained. However, after adjusting cell type proportions and patient clinical characteristics, most of the so-called significant CpG methylation changes associated with severe PE disappear. Rather, the major effect of PE on cord blood is through the proportion changes in different cell types. These results are validated using a previously published cord blood DNA methylation dataset, where global hypomethylation pattern was also wrongfully obtained without confounding adjustment. Additionally, several cell types significantly change as gestation progress (eg. granulocyte, nRBC, CD4T, and B cells), further confirming the importance of cell type adjustment in EWAS study of cord blood tissues. Our study urges the community to perform confounding adjustments in EWAS studies, based on cell type heterogeneity and other patient characteristics.

Integrative Metatranscriptomic Analysis Reveals Disease-specific Microbiome-host Interactions in Oral Squamous Cell Carcinoma.

Studies on the microbiome of oral squamous cell carcinoma (OSCC) have been limited to 16S rRNA gene sequencing. Here, laser microdissection coupled with brute-force, deep metatranscriptome sequencing was employed to simultaneously characterize the microbiome and host transcriptomes and predict their interaction in OSCC. The analysis involved 20 HPV16/18-negative OSCC tumor/adjacent normal tissue pairs (TT and ANT) along with deep tongue scrapings from 20 matched healthy controls (HC). Standard bioinformatic tools coupled with in-house algorithms were used to map, analyze, and integrate microbial and host data. Host transcriptome analysis identified enrichment of known cancer-related gene sets, not only in TT versus ANT and HC, but also in the ANT versus HC contrast, consistent with field cancerization. Microbial analysis identified a low abundance yet transcriptionally active, unique multi-kingdom microbiome in OSCC tissues predominated by bacteria and bacteriophages. HC showed a different taxonomic profile yet shared major microbial enzyme classes and pathways with TT/ANT, consistent with functional redundancy. Key taxa enriched in TT/ANT compared with HC were Cutibacterium acnes, Malassezia restricta, Human Herpes Virus 6B, and bacteriophage Yuavirus. Functionally, hyaluronate lyase was overexpressed by C. acnes in TT/ANT. Microbiome-host data integration revealed that OSCC-enriched taxa were associated with upregulation of proliferation-related pathways. In a preliminary in vitro validation experiment, infection of SCC25 oral cancer cells with C. acnes resulted in upregulation of MYC expression. The study provides a new insight into potential mechanisms by which the microbiome can contribute to oral carcinogenesis, which can be validated in future experimental studies. Significance: Studies have shown that a distinct microbiome is associated with OSCC, but how the microbiome functions within the tumor interacts with the host cells remains unclear. By simultaneously characterizing the microbial and host transcriptomes in OSCC and control tissues, the study provides novel insights into microbiome-host interactions in OSCC which can be validated in future mechanistic studies.

SGLT2 inhibitors mitigate kidney tubular metabolic and mTORC1 perturbations in youth-onset type 2 diabetes.

The molecular mechanisms of sodium-glucose cotransporter-2 (SGLT2) inhibitors (SGLT2i) remain incompletely understood. Single-cell RNA sequencing and morphometric data were collected from research kidney biopsies donated by young persons with type 2 diabetes (T2D), aged 12 to 21 years, and healthy controls (HCs). Participants with T2D were obese and had higher estimated glomerular filtration rates and mesangial and glomerular volumes than HCs. Ten T2D participants had been prescribed SGLT2i (T2Di[+]) and 6 not (T2Di[-]). Transcriptional profiles showed SGLT2 expression exclusively in the proximal tubular (PT) cluster with highest expression in T2Di(-) patients. However, transcriptional alterations with SGLT2i treatment were seen across nephron segments, particularly in the distal nephron. SGLT2i treatment was associated with suppression of transcripts in the glycolysis, gluconeogenesis, and tricarboxylic acid cycle pathways in PT, but had the opposite effect in thick ascending limb. Transcripts in the energy-sensitive mTORC1-signaling pathway returned toward HC levels in all tubular segments in T2Di(+), consistent with a diabetes mouse model treated with SGLT2i. Decreased levels of phosphorylated S6 protein in proximal and distal tubules in T2Di(+) patients confirmed changes in mTORC1 pathway activity. We propose that SGLT2i treatment benefits the kidneys by mitigating diabetes-induced metabolic perturbations via suppression of mTORC1 signaling in kidney tubules.

Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative.

BACKGROUND: AKI is associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19); however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. METHODS: Electronic health record data were obtained from 53 health systems in the United States in the National COVID Cohort Collaborative. We selected hospitalized adults diagnosed with COVID-19 between March 6, 2020, and January 6, 2022. AKI was determined with serum creatinine and diagnosis codes. Time was divided into 16-week periods (P1-6) and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. RESULTS: Of a total cohort of 336,473, 129,176 (38%) patients had AKI. Fifty-six thousand three hundred and twenty-two (17%) lacked a diagnosis code but had AKI based on the change in serum creatinine. Similar to patients coded for AKI, these patients had higher mortality compared with those without AKI. The incidence of AKI was highest in P1 (47%; 23,097/48,947), lower in P2 (37%; 12,102/32,513), and relatively stable thereafter. Compared with the Midwest, the Northeast, South, and West had higher adjusted odds of AKI in P1. Subsequently, the South and West regions continued to have the highest relative AKI odds. In multivariable models, AKI defined by either serum creatinine or diagnostic code and the severity of AKI was associated with mortality. CONCLUSIONS: The incidence and distribution of COVID-19-associated AKI changed since the first wave of the pandemic in the United States. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_08_08_CJN0000000000000192.mp3.

An integrated organoid omics map extends modeling potential of kidney disease.

Kidney organoids are a promising model to study kidney disease, but their use is constrained by limited knowledge of their functional protein expression profile. Here, we define the organoid proteome and transcriptome trajectories over culture duration and upon exposure to TNFα, a cytokine stressor. Older organoids increase deposition of extracellular matrix but decrease expression of glomerular proteins. Single cell transcriptome integration reveals that most proteome changes localize to podocytes, tubular and stromal cells. TNFα treatment of organoids results in 322 differentially expressed proteins, including cytokines and complement components. Transcript expression of these 322 proteins is significantly higher in individuals with poorer clinical outcomes in proteinuric kidney disease. Key TNFα-associated protein (C3 and VCAM1) expression is increased in both human tubular and organoid kidney cell populations, highlighting the potential for organoids to advance biomarker development. By integrating kidney organoid omic layers, incorporating a disease-relevant cytokine stressor and comparing with human data, we provide crucial evidence for the functional relevance of the kidney organoid model to human kidney disease.

The chromatin landscape of healthy and injured cell types in the human kidney.

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. However, comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measured dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We established a comprehensive and spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we noted distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3 , KLF6 , and KLF10 regulated the transition between health and injury, while in thick ascending limb cells this transition was regulated by NR2F1 . Further, combined perturbation of ELF3 , KLF6 , and KLF10 distinguished two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Plasma Metabolomics and Lipidomics Differentiate Obese Individuals by Peripheral Neuropathy Status.

CONTEXT: Peripheral neuropathy (PN) is a frequent prediabetes and type 2 diabetes (T2D) complication. Multiple clinical studies reveal that obesity and dyslipidemia can also drive PN progression, independent of glycemia, suggesting a complex interplay of specific metabolite and/or lipid species may underlie PN. OBJECTIVE: This work aimed to identify the plasma metabolomics and lipidomics signature that underlies PN in an observational study of a sample of individuals with average class 3 obesity. METHODS: We performed plasma global metabolomics and targeted lipidomics on obese participants with (n = 44) and without PN (n = 44), matched for glycemic status, vs lean nonneuropathic controls (n = 43). We analyzed data by Wilcoxon, logistic regression, partial least squares-discriminant analysis, and group-lasso to identify differential metabolites and lipids by obesity and PN status. We also conducted subanalysis by prediabetes and T2D status. RESULTS: Lean vs obese comparisons, regardless of PN status, identified the most significant differences in gamma-glutamyl and branched-chain amino acid metabolism from metabolomics analysis and triacylglycerols from lipidomics. Stratification by PN status within obese individuals identified differences in polyamine, purine biosynthesis, and benzoate metabolism. Lipidomics found diacylglycerols as the most significant subpathway distinguishing obese individuals by PN status, with additional contributions from phosphatidylcholines, sphingomyelins, ceramides, and dihydroceramides. Stratifying the obese group by glycemic status did not affect discrimination by PN status. CONCLUSION: Obesity may be as strong a PN driver as prediabetes or T2D in a sample of individuals with average class 3 obesity, at least by plasma metabolomics and lipidomics profile. Metabolic and complex lipid pathways can differentiate obese individuals with and without PN, independent of glycemic status.

COVID-19-associated AKI in hospitalized US patients: incidence, temporal trends, geographical distribution, risk factors and mortality.

Background: Acute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. Methods: Electronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. Results: Out of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. Conclusions: Uncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.

Construction of gene regulatory networks using biclustering and Bayesian networks.

BACKGROUND: Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling. RESULTS: In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method. CONCLUSIONS: Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.