Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

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 <i>P</i>-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

Plasma lipid metabolites associate with diabetic polyneuropathy in a cohort with type 2 diabetes

ObjectiveThe global rise in type 2 diabetes is associated with a concomitant increase in diabetic complications. Diabetic polyneuropathy is the most frequent type 2 diabetes complication and is associated with poor outcomes. The metabolic syndrome has emerged as a major risk factor for diabetic polyneuropathy; however, the metabolites associated with the metabolic syndrome that correlate with diabetic polyneuropathy are unknown.MethodsWe conducted a global metabolomics analysis on plasma samples from a subcohort of participants from the Danish arm of Anglo‐Danish‐Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION‐Denmark) with and without diabetic polyneuropathy versus lean control participants.ResultsCompared to lean controls, type 2 diabetes participants had significantly higher HbA1c (p = 0.0028), BMI (p = 0.0004), and waist circumference (p = 0.0001), but lower total cholesterol (p = 0.0001). Out of 991 total metabolites, we identified 15 plasma metabolites that differed in type 2 diabetes participants by diabetic polyneuropathy status, including metabolites belonging to energy, lipid, and xenobiotic pathways, among others. Additionally, these metabolites correlated with alterations in plasma lipid metabolites in type 2 diabetes participants based on neuropathy status. Further evaluating all plasma lipid metabolites identified a shift in abundance, chain length, and saturation of free fatty acids in type 2 diabetes participants. Importantly, the presence of diabetic polyneuropathy impacted the abundance of plasma complex lipids, including acylcarnitines and sphingolipids.InterpretationOur explorative study suggests that diabetic polyneuropathy in type 2 diabetes is associated with novel alterations in plasma metabolites related to lipid metabolism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167813/1/acn351367_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167813/2/acn351367.pd

Plasma lipid metabolites associate with diabetic polyneuropathy in a cohort with type 2 diabetes

ObjectiveThe global rise in type 2 diabetes is associated with a concomitant increase in diabetic complications. Diabetic polyneuropathy is the most frequent type 2 diabetes complication and is associated with poor outcomes. The metabolic syndrome has emerged as a major risk factor for diabetic polyneuropathy; however, the metabolites associated with the metabolic syndrome that correlate with diabetic polyneuropathy are unknown.MethodsWe conducted a global metabolomics analysis on plasma samples from a subcohort of participants from the Danish arm of Anglo‐Danish‐Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION‐Denmark) with and without diabetic polyneuropathy versus lean control participants.ResultsCompared to lean controls, type 2 diabetes participants had significantly higher HbA1c (p = 0.0028), BMI (p = 0.0004), and waist circumference (p = 0.0001), but lower total cholesterol (p = 0.0001). Out of 991 total metabolites, we identified 15 plasma metabolites that differed in type 2 diabetes participants by diabetic polyneuropathy status, including metabolites belonging to energy, lipid, and xenobiotic pathways, among others. Additionally, these metabolites correlated with alterations in plasma lipid metabolites in type 2 diabetes participants based on neuropathy status. Further evaluating all plasma lipid metabolites identified a shift in abundance, chain length, and saturation of free fatty acids in type 2 diabetes participants. Importantly, the presence of diabetic polyneuropathy impacted the abundance of plasma complex lipids, including acylcarnitines and sphingolipids.InterpretationOur explorative study suggests that diabetic polyneuropathy in type 2 diabetes is associated with novel alterations in plasma metabolites related to lipid metabolism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167813/1/acn351367_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167813/2/acn351367.pd

An integrated organoid omics map extends modeling potential of kidney disease

Abstract 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

Abstract 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

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\u27s 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