Addressing the heterogeneity in liver diseases using biological networks

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis.

Measurement(s)	gene expression, metabolic relationships
Technology Type(s)	Genome Scale Metabolic Models; Computational network biology
Sample Characteristic - Organism	Homo sapiens

Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases

Neurodegenerative diseases, including Alzheimer's (AD) and Parkinson's diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analysed transcriptomic data from AD and PD patients, and stratified these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identified retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrated that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments.

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Three consistent molecular ccRCC subtypes were found to guide patients' prognoses

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REOs-based biomarker was developed to robustly classify patients at individual level

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SOAT1 is identified as a common drug target for all ccRCC subtypes

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Mitotane was repositioned treatment of ccRCC via inhibiting SOAT1

Bioinformatic and biochemical findings disclosed anti-hepatic steatosis mechanism of calycosin

As revealed in previous reports, calycosin is a functional flavonoid characterized with identified pharmacological activities. Most of evidences are used to demonstrate the anti-cancer benefits of calycosin, however, the existing study of anti-fatty liver medicated by calycosin is limitedly reported. Recently, an emerging avenue based on network pharmacology may contribute to excavate the biological targets and molecular mechanisms of calycosin for anti-fatty liver. In confirmatory experiments, the human and animal studies were subjected to verify some of bioinformatic results. Accordingly, bioinformatic data based on network pharmacology suggested that discoverable biotargets of calycosin for anti-fatty liver were aldehyde dehydrogenase (ALDH2), Niemann pick C1 (NPC1), high mobility group protein 1 (HMGB1), bilirubin UDP glucuronosyltransferase 1 (UGT1A1), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), hydroxytryptamine receptor 2 (HTR2), migration inhibitory factor (MIF), cytochrome P450, family 19A1 (CYP19A1). Furthermore, all significant biological characteristics and mechanisms of to treat fatty liver were revealed in several. In human findings, the blood tests showed changed glucose and lipid contents, elevated insulin resistance and inflammatory stress. And fatty liver sections from patients resulted in negative expressions of ALDH2, NPC1, and positive HMGB1 expression. In a study in vivo, calycosin-treated high fat diet (HFD)-fed mice exhibited reduced liver weights, decreased fasting serum glucose and insulin, liver functional transaminases, blood lipids, metabolic enzymes, and inflammatory cytokines. And the data in gene tests displayed up-regulations of ALDH2, NPC1 mRNAs, and down-regulation of HMGB1 mRNA in calycosin-treated liver samples. Together, the current bioinformatic data demonstrate biological targets, functions and mechanisms of calycosin for anti-fatty liver. Interestingly, these bioinformatic findings can be partially verified with clinical and animal samples.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.