Systems biology perspective for studying the gut microbiota in human physiology and liver diseases

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma

The discovery of potential disease-causing genes can aid medical progress. The post-genomic era has made this a more difficult task. Modern high-throughput methods have not solved the problem of identifying disease genes. Conventional methods cannot be used to investigate many rare or lethal diseases. Monitoring gene expression values in different samples using microarray technology is one of the best and most accurate ways to identify disease-causing genes. One of the most recent advances in experimental molecular biology is microarrays, which allow researchers to simultaneously monitor the expression levels of thousands of genes. Statistical analysis of microarray data might aid gene discovery by revealing pathways related to the target gene and facilitating identification of candidate genes. Systems biology, an interdisciplinary approach, has emerged as a crucial analytic tool with the potential to reveal previously unidentified causes and consequences of human illness. Genetic, environmental, immunological, or neurological factors have been implicated in the developing complex disorders like cancer. Because of this, it is important to approach the study of such disease from a novel perspective. The system biology approach allows us to rapidly identify disease-causing genes and assess their viability as therapeutic targets. This chapter demonstrates systems biology approaches to identify candidate genes using public database. Oral squamous cell carcinoma (OSCC) is used as a model disease to show how systems biology can be used successfully to identify and prioritize disease genes.

The microbiome as a major function of the gastrointestinal tract and its implication in micronutrient metabolism and chronic diseases

The composition and function of microbes harbored in the human gastrointestinal lumen have been underestimated for centuries because of the underdevelopment of nucleotide sequencing techniques and the lack of humanized gnotobiotic models. Now, we appreciate that the gut microbiome is an integral part of the human body and exerts considerable roles in host health and diseases. Dietary factors can induce changes in the microbial community composition, metabolism, and function, thereby altering the host immune response, and consequently, may influence disease risks. An imbalance of gut microbiome homeostasis (i.e., dysbiosis) has been linked to several chronic diseases, such as inflammatory bowel diseases, obesity, and diabetes. Remarkable progress has recently been made in better understanding the extent to which the influence of the diet-microbiota interaction on host health outcomes in both animal models and human participants. However, the exact causality of the gut microbiome on the development of diseases is still controversial. In this review, we will briefly describe the general structure and function of the intestine and the process of nutrient absorption in humans. This is followed by a summarization of the recent updates on interactions between gut microbiota and individual micronutrients, including carotenoids, vitamin A, vitamin D, vitamin C, folate, iron, and zinc. In the opinion of the authors, these nutrients were identified as representative of vitamins and minerals with sufficient research on their roles in the microbiome. The host responses to the gut microbiome will also be discussed. Future direction in microbiome research, for example, precision microbiome, will be proposed.

Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome

Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.

Gene co-expression network analysis of the human gut commensal bacterium Faecalibacterium prausnitzii in R-Shiny

Faecalibacterium prausnitzii is abundant in the healthy human intestinal microbiota, and the absence or scarcity of this bacterium has been linked with inflammatory diseases and metabolic disorders. F. prausnitzii thus shows promise as a next-generation probiotic for use in restoring the balance of the gut microbial flora and, due to its strong anti-inflammatory properties, for the treatment of certain pathological conditions. However, very little information is available about gene function and regulation in this species. Here, we utilized a systems biology approach—weighted gene co-expression network analysis (WGCNA)–to analyze gene expression in three publicly available RNAseq datasets from F. prausnitzii strain A2-165, all obtained in different laboratory conditions. The co-expression network was then subdivided into 24 co-expression gene modules. A subsequent enrichment analysis revealed that these modules are associated with different kinds of biological processes, such as arginine, histidine, cobalamin, or fatty acid metabolism as well as bacteriophage function, molecular chaperones, stress response, or SOS response. Some genes appeared to be associated with mechanisms of protection against oxidative stress and could be essential for F. prausnitzii’s adaptation and survival under anaerobic laboratory conditions. Hub and bottleneck genes were identified by analyses of intramodular connectivity and betweenness, respectively; this highlighted the high connectivity of genes located on mobile genetic elements, which could promote the genetic evolution of F. prausnitzii within its ecological niche. This study provides the first exploration of the complex regulatory networks in F. prausnitzii, and all of the “omics” data are available online for exploration through a graphical interface at https://shiny.migale.inrae.fr/app/faeprau.

The gut peptide Reg3g links the small intestine microbiome to the regulation of energy balance, glucose levels, and gut function

Changing composition of the gut microbiome is an important component of the gut adaptation to various environments, which have been implicated in various metabolic diseases including obesity and type 2 diabetes, but the mechanisms by which the microbiota influence host physiology remain contentious. Here we find that both diets high in the fermentable fiber inulin and vertical sleeve gastrectomy increase intestinal expression and circulating levels of the anti-microbial peptide Reg3g. Moreover, a number of beneficial effects of these manipulations on gut function, energy balance, and glucose regulation are absent in Reg3g knockout mice. Peripheral administration of various preparations of Reg3g improves glucose tolerance, and this effect is dependent on the putative receptor Extl3 in the pancreas. These data suggest Reg3g acts both within the lumen and as a gut hormone to link the intestinal microbiome to various aspects of host physiology that may be leveraged for novel treatment strategies.

Changes in antibiotic residues and the gut microbiota during ciprofloxacin administration throughout Silkie chicken development

The use of antibiotics leads to antibiotic residues in livestock and poultry products, adversely affecting human health. Ciprofloxacin (CFX) is a broad-spectrum antibiotic shared between animals and humans that is useful in treatments besides infections. However, changes in the gut microbiota caused by CFX and the possible link with the elimination of CFX residues have not been investigated. Herein, we used the Silkie chicken model to study the changes in the gut microbiota during the entire CFX-metabolic repertoire. We detected CFX residues in different tissues and showed that the elimination time of CFX from different tissues was dissimilar (liver > kidney > chest muscle > skin). Analysis of liver and kidney injury biomarkers and plasma antioxidant indices indicated slight hepatotoxicity and nephrotoxicity in the Silkie chickens. Importantly, the changes in the gut microbial community predominantly occurred early in the metabolic process. Correlation analysis revealed that the particular bacterial microbiota were associated with the pharmacokinetics of CFX in different Silkie chicken tissues (e.g., aerobic bacteria, including Escherichia and Coprococcus, and anaerobic bacteria, including Fusobacterium, Ruminococcus, Bifidobacterium, and Eubacterium). Collectively, certain microbiota may boost antibiotic metabolism and participate in restoring the microbial consortia after CFX is metabolized. Therefore, regulating the core intestinal microbiota may reduce foodborne antibiotics and accelerate the development of drug resistance.

Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host-microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.

Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.

Combating Childhood Infections in LMICs: evaluating the contribution of Big Data Big data, biomarkers and proteomics: informing childhood diarrhoeal disease management in Low- and Middle-Income Countries

Despite efforts to reduce the global burden of childhood diarrhoea, 50% of all cases globally occur in children under five years in Low–Income and Middle- Income Countries (LMICs) and knowledge gaps remain regarding the aetiological diagnosis, introduction of diarrhoeal vaccines, and the role of environmental enteric dysfunction and severe acute malnutrition. Biomarkers may assist in understanding disease processes, from diagnostics, to management of childhood diarrhoea and the sequelae to vaccine development. Proteomics has the potential to assist in the identification of new biomarkers to understand the processes in the development of childhood diarrhoea and to aid in developing new vaccines. Centralised repositories that enable mining of large data sets to better characterise risk factors, the proteome of both the patient and the different diarrhoeal pathogens, and the environment, could inform patient management and vaccine development, providing a systems biological approach to address the burden of childhood diarrhoea in LMICs.

Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis

The complex pathology of Alzheimer's disease (AD) emphasises the need for comprehensive modelling of the disease, which may lead to the development of efficient treatment strategies. To address this challenge, we analysed transcriptome data of post-mortem human brain samples of healthy elders and individuals with late-onset AD from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MayoRNAseq) studies in the AMP-AD consortium. In this context, we conducted several bioinformatics and systems medicine analyses including the construction of AD-specific co-expression networks and genome-scale metabolic modelling of the brain in AD patients to identify key genes, metabolites and pathways involved in the progression of AD. We identified AMIGO1 and GRPRASP2 as examples of commonly altered marker genes in AD patients. Moreover, we found alterations in energy metabolism, represented by reduced oxidative phosphorylation and ATPase activity, as well as the depletion of hexanoyl-CoA, pentanoyl-CoA, (2E)-hexenoyl-CoA and numerous other unsaturated fatty acids in the brain. We also observed that neuroprotective metabolites (e.g., vitamins, retinoids and unsaturated fatty acids) tend to be depleted in the AD brain, while neurotoxic metabolites (e.g., β-alanine, bilirubin) were more abundant. In summary, we systematically revealed the key genes and pathways related to the progression of AD, gained insight into the crucial mechanisms of AD and identified some possible targets that could be used in the treatment of AD.

Combined metabolic activators therapy ameliorates liver fat in nonalcoholic fatty liver disease patients

Nonalcoholic fatty liver disease (NAFLD) refers to excess fat accumulation in the liver. In animal experiments and human kinetic study, we found that administration of combined metabolic activators (CMAs) promotes the oxidation of fat, attenuates the resulting oxidative stress, activates mitochondria, and eventually removes excess fat from the liver. Here, we tested the safety and efficacy of CMA in NAFLD patients in a placebo-controlled 10-week study. We found that CMA significantly decreased hepatic steatosis and levels of aspartate aminotransferase, alanine aminotransferase, uric acid, and creatinine, whereas found no differences on these variables in the placebo group after adjustment for weight loss. By integrating clinical data with plasma metabolomics and inflammatory proteomics as well as oral and gut metagenomic data, we revealed the underlying molecular mechanisms associated with the reduced hepatic fat and inflammation in NAFLD patients and identified the key players involved in the host-microbiome interactions. In conclusion, we showed that CMA can be used to develop a pharmacological treatment strategy in NAFLD patients.

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.

Glycoursodeoxycholic acid ameliorates diet-induced metabolic disorders with inhibiting endoplasmic reticulum stress

Recent studies reveal that bile acid metabolite composition and its metabolism are changed in metabolic disorders, such as obesity, type 2 diabetes and metabolic associated fatty liver disease (MAFLD), yet its role and the mechanism remain largely unknown. In the present study, metabolomic analysis of 163 serum and stool samples of our metabolic disease cohort was performed, and we identified glycoursodeoxycholic acid (GUDCA), glycine-conjugated bile acid produced from intestinal bacteria, was decreased in both serum and stool samples from patients with hyperglycemia. RNA-sequencing and quantitative PCR results indicated that GUDCA alleviated endoplasmic reticulum (ER) stress in livers of high fat diet (HFD)-fed mice without alteration of liver metabolism. In vitro, GUDCA reduced palmitic acid induced-ER stress and -apoptosis, as well as stabilized calcium homeostasis. In vivo, GUDCA exerted effects on amelioration of HFD-induced insulin resistance and hepatic steatosis. In parallel, ER stress and apoptosis were decreased in GUDCA-treated mice as compared with vehicle-treated mice in liver. These findings demonstrate that reduced GUDCA is an indicator of hyperglycemia. Supplementation of GUDCA could be an option for the treatment of diet-induced metabolic disorders, including insulin resistance and hepatic steatosis, with inhibiting ER stress.

Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

[Analysis of intestinal flora in patients with chronic rhinosinusitis based on highthroughput sequencing]

OBJECTIVE

To investigate the changes in diversity, relative abundance and distribution of intestinal flora in patients with chronic rhinosinusitis and nasal polyps (CRSwNP) using high-throughput sequencing technology identify the intestinal flora significantly related to pathogenesis and progression of CRSwNP.

METHODS

Ten patients with CRSwNP hospitalized in the Department of Otolaryngology-Head and Neck Surgery of Guangdong Provincial People's Hospital were selected as the case group with 10 healthy volunteers recruited in the same period as the control group. Fecal genomic DNA extraction kit was used to extract the DNA in the fecal samples, and the DNA fragment length was measured and quantified. The V3 and V4 highly variable regions of the 16S rDNA gene of prokaryotes were amplified followed by library construction, Illumina MiSeq sequencing, sequence alignment and species identification analysis. The relative abundance, diversity and distribution characteristics of the intestinal flora were analyzed, and the relevant metabolic pathways were predicted.

RESULTS

Compared with the control group, the patients with CRSwNP had significant changes in the overall structure of the intestinal flora, highlighted by increased abundance of Saccharopolyspora and decreased contents of Ruminococcae, Coprococcus, Collinsella and Dialister. Among the metabolic pathways predicted to be associated with CRSwNP, 9 showed significant changes in patients with CRSwNP as compared with the control group (P < 0.05).

CONCLUSIONS

Patients with CRSwNP have significant changes in the structural characteristics of intestinal flora related with multiple metabolic pathways, and these changes may play an important role in the development of chronic rhinosinusitis.

Challenges and emerging systems biology approaches to discover how the human gut microbiome impact host physiology

Research in the human gut microbiome has bloomed with advances in next generation sequencing (NGS) and other high-throughput molecular profiling technologies. This has enabled the generation of multi-omics datasets which holds promises for big data-enabled knowledge acquisition in the form of understanding the normal physiological and pathological involvement of gut microbiomes. Ample evidence suggests that distinct microbial compositions in the human gut are associated with different diseases. However, the biological mechanisms underlying these associations are often unclear. There is a need to move beyond statistical associations to discover how changes in the gut microbiota mechanistically affect host physiology and disease development. This review summarises state-of-the-art big data and systems biology approaches for mechanism discovery.

Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death primarily due to the lack of effective targeted therapies. Despite the distinct morphological and phenotypic patterns of HCC, treatment strategies are restricted to relatively homogeneous therapies, including multitargeted tyrosine kinase inhibitors and immune checkpoint inhibitors. Therefore, more effective therapy options are needed to target dysregulated metabolic and molecular pathways in HCC. Integrative genomic profiling of HCC patients provides insight into the most frequently mutated genes and molecular targets, including telomerase reverse transcriptase, the TP53 gene, and the Wnt/β-catenin signaling pathway oncogene (CTNNB1). Moreover, emerging techniques, such as genome-scale metabolic models may elucidate the underlying cancer-specific metabolism, which allows for the discovery of potential drug targets and identification of biomarkers. De novo lipogenesis has been revealed as consistently upregulated since it is required for cell proliferation in all HCC patients. The metabolic network-driven stratification of HCC patients in terms of redox responses, utilization of metabolites, and subtype-specific pathways may have clinical implications to drive the development of personalized medicine. In this review, the current and emerging therapeutic targets in light of molecular approaches and metabolic network-based strategies are summarized, prompting effective treatment of HCC patients.

Classification of clear cell renal cell carcinoma based on PKM alternative splicing

Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.