Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network

Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma

Despite high initial response rates to targeted kinase inhibitors, the majority of patients suffering from metastatic melanoma present with high relapse rates, demanding for alternative therapeutic options. We have previously developed a drug repurposing workflow to identify metabolic drug targets that, if depleted, inhibit the growth of cancer cells without harming healthy tissues. In the current study, we have applied a refined version of the workflow to specifically predict both, common essential genes across various cancer types, and melanoma-specific essential genes that could potentially be used as drug targets for melanoma treatment. The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available, and in-house, large-scale transcriptomic data metabolic models for melanoma were reconstructed enabling the prediction of 28 candidate drugs and estimating their respective efficacy. Twelve highly efficacious drugs with low half-maximal inhibitory concentration values for the treatment of other cancers, which are not yet approved for melanoma treatment, were used for in vitro validation using melanoma cell lines. Combination of the top 4 out of 6 promising candidate drugs with BRAF or MEK inhibitors, partially showed synergistic growth inhibition compared to individual BRAF/MEK inhibition. Hence, the repurposing of drugs may enable an increase in therapeutic options e.g., for non-responders or upon acquired resistance to conventional melanoma treatments.

Extracellular RNAs from immune cells under obesity-a narrative review

Background and Objective

Obesity affects hundreds of millions of people worldwide and is characterized by chronic inflammation and insulin resistance, leading to Type II diabetes and atherosclerotic cardiovascular disease. Extracellular RNAs (exRNAs) are among the components which effect immune actions under obese conditions, and technological advances in recent years have rapidly increased our understanding of their roles and functions. Here we review essential background information on exRNAs and vesicles as well as the impact of immune-derived exRNAs in obesity-related disease. We also offer perspectives on clinical applications of exRNAs and future research directions.

Methods

We searched PubMed for articles relevant to immune-derived exRNAs in obesity. Articles written in English and published prior to May 25, 2022 were included.

Key Content and Findings

We report findings on the roles of immune-derived exRNAs which are important in obesity-related disease. We also highlight several exRNAs derived from other cell lineages which act on immune cells in metabolic disease.

Conclusions

ExRNAs produced by immune cells have profound local and systemic effects under obese conditions and can impact metabolic disease phenotypes. Immune-derived exRNAs represent an important target for future research and therapy.

A Novel Glycosyltransferase-Related Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer

Background

Ovarian cancer is a highly malignant epithelial tumor. Recently, it has been reported the role of glycosyltransferases (GTs) in various cancers. However, the prognostic value of GTs-related genes in ovarian cancer remained largely unknown.

Methods

RNA-sequencing (RNA-seq) data and corresponding clinical characteristics of patients with ovarian cancer were extracted from the public database of the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). We constructed the least absolute shrinkage and selection operator (LASSO) Cox regression model to explore a multigene signature comprising GTs-related genes in the TCGA and GTEx cohort. Patients with ovarian cancer in International Cancer Genome Consortium (ICGC) database were applied for further validation. We also performed functional analysis on the differentially expressed genes (DEGs) of high-risk and low-risk groups in the TCGA cohort. Additionally, the immune status between the two risk groups was assessed, respectively.

Results

Our results showed that 64 GTs-related genes were differentially expressed between tumor tissues and normal tissues in the TCGA and GTEx cohort. A prognostic model of 15 candidate genes was constructed, which classified patients into high- and low-risk groups. Compared with low-risk patients, high-risk patients had an obvious worse overall survival (OS) (P < 0.0001 in the TCGA and GTEx cohort and P = 0.042 in the ICGC cohort). Multivariate Cox regression analysis revealed that the risk score was an independent factor for OS of ovarian cancer. Functional analysis indicated that these DEGs were also enriched in immune-related pathways, and the immune status was significantly different between the two risk groups in TCGA cohort.

Conclusion

In conclusion, a novel GTs-related gene signature may be used for the prognosis of ovarian cancer. Targeting GTs-related gene can act as a therapeutic alternative for ovarian cancer.

microRNA-27a-3p but Not -5p Is a Crucial Mediator of Human Adipogenesis

MicroRNAs (miRNAs), a class of small, non-coding RNA molecules, play an important role in the posttranscriptional regulation of gene expression, thereby influencing important cellular functions. In adipocytes, miRNAs show import regulatory features and are described to influence differentiation as well as metabolic, endocrine, and inflammatory functions. We previously identified miR-27a being upregulated under inflammatory conditions in human adipocytes and aimed to elucidate its function in adipocyte biology. Both strands of miR-27a, miR-27a-3p and -5p, were downregulated during the adipogenic differentiation of Simpson–Golabi–Behmel syndrome (SGBS) cells, human multipotent adipose-derived stem cells (hMADS), and human primary adipose-derived stromal cells (hASCs). Using miRNA-mimic transfection, we observed that miR-27a-3p is a crucial regulator of adipogenesis, while miR-27a-5p did not alter the differentiation capacity in SGBS cells. In silico screening predicted lipoprotein lipase (LPL) and peroxisome proliferator activated receptor γ (PPARγ) as potential targets of miR-27a-3p. The downregulation of both genes was verified in vitro, and the interaction of miR-27-3p with target sites in the 3′ UTRs of both genes was confirmed via a miRNA-reporter-gene assay. Here, the knockdown of LPL did not interfere with adipogenic differentiation, while PPARγ knockdown decreased adipogenesis significantly, suggesting that miR-27-3p exerts its inhibitory effect on adipogenesis by repressing PPARγ. Taken together, we identified and validated a crucial role for miR-27a-3p in human adipogenesis played by targeting the essential adipogenic transcription factor PPARγ. Though we confirmed LPL as an additional target of miR-27a-3p, it does not appear to be involved in regulating human adipogenesis. Thereby, our findings call the conclusions drawn from previous studies, which identified LPL as a crucial regulator for murine and human adipogenesis, into question.

TMBIM1 is an inhibitor of adipogenesis and its depletion promotes adipocyte hyperplasia and improves obesity-related metabolic disease

Obesity is characterized by the excessive accumulation of the white adipose tissue (WAT), but healthy expansion of WAT via adipocyte hyperplasia can offset the negative metabolic effects of obesity. Thus, identification of novel adipogenesis regulators that promote hyperplasia may lead to effective therapies for obesity-induced metabolic disorders. Using transcriptomic approaches, we identified transmembrane BAX inhibitor motif-containing 1 (TMBIM1) as an inhibitor of adipogenesis. Gain or loss of function of TMBIM1 in preadipocytes inhibited or promoted adipogenesis, respectively. In vivo, in response to caloric excess, adipocyte precursor (AP)-specific Tmbim1 knockout (KO) mice displayed WAT hyperplasia and improved systemic metabolic health, while overexpression of Tmbim1 in transgenic mice showed the opposite effects. Moreover, mature adipocyte-specific Tmbim1 KO did not affect WAT cellularity or nutrient homeostasis. Mechanistically, TMBIM1 binds to and promotes the autoubiquitination and degradation of NEDD4, which is an E3 ligase that stabilizes PPARγ. Our data show that TMBIM1 is a potent repressor of adipogenesis and a potential therapeutic target for obesity-related metabolic disease.

IDARE2-Simultaneous Visualisation of Multiomics Data in Cytoscape

Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.

High expression of CD52 in adipocytes: a potential therapeutic target for obesity with type 2 diabetes

The aim of the present study was to evaluate the involvement of CD52 in adipocytes as well as to explore its effect on type 2 diabetes mellitus (T2DM), and to improve our understanding of the potential molecular events of obesity with type 2 diabetes. Global changes in the CD52 expression patterns were detected in adipocytes and preadipocytes derived from obese and lean individuals. In particular, CD52 was identified as significantly differentially upregulated and was analyzed, both in vitro and in vivo, using various approaches. In vitro experiments, CD52 was a significantly up-regulated mRNA in mature adipocytes and preadipocytes. In addition, CD52 gradually increased with the differentiation of preadipocytes. In vivo experiments, the expression of CD52 in high-fat diet (HFD) -fed mice tended to be higher than that in regular diet (RD) -fed mice. Further analysis showed that CD52 expression was positively correlated with Smad3 and TGF-β in mice, and the downregulation of CD52 was accompanied by increased glucose tolerance and insulin sensitivity. Moreover, a comparison of CD4+CD52^high T cells and CD4+CD52^low T cells showed that many T2DM-related genes were aberrantly expressed. Overall, CD52 may functioned as an important potential target for obesity with T2DM via TGF-β/Smad3 axis.

Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes

Simple Summary

To explore some of the low-degree but topologically important nodes in the Metabolic disease (MD) network, we propose a background-corrected betweenness centrality (BC) and identify 16 novel candidates likely to play a role in MD. MD specific protein–protein interaction networks (PPINs) were constructed using two known databasesHuman Protein Reference Database (HPRD) and BioGRID. The identified candidates have been found to play a role in diverse conditions including co-morbidities of MD, neurological and immune system-related conditions.

Abstract

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

Non-coding RNA crosstalk with nuclear receptors in liver disease

The dysregulation of nuclear receptors (NRs) underlies the pathogenesis of a variety of liver disorders. Non-coding RNAs (ncRNAs) are defined as RNA molecules transcribed from DNA but not translated into proteins. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two types of ncRNAs that have been extensively studied for regulating gene expression during diverse cellular processes. NRs as therapeutic targets in liver disease have been exemplified by the successful application of their pharmacological ligands in clinics. MiRNA-based reagents or drugs are emerging as flagship products in clinical trials. Advancing our understanding of the crosstalk between NRs and ncRNAs is critical to the development of diagnostic and therapeutic strategies. This review summarizes recent findings on the reciprocal regulation between NRs and ncRNAs (mainly on miRNAs and lncRNAs) and their implication in liver pathophysiology, which might be informative to the translational medicine of targeting NRs and ncRNAs in liver disease.

ER-Negative Breast Cancer Is Highly Responsive to Cholesterol Metabolite Signalling

Interventions that alter cholesterol have differential impacts on hormone receptor positive- and negative-breast cancer risk and prognosis. This implies differential regulation or response to cholesterol within different breast cancer subtypes. We evaluated differences in side-chain hydroxycholesterol and liver X nuclear receptor signalling between Oestrogen Receptor (ER)-positive and ER-negative breast cancers and cell lines. Cell line models of ER-positive and ER-negative disease were treated with Liver X Receptor (LXR) ligands and transcriptional activity assessed using luciferase reporters, qPCR and MTT. Publicly available datasets were mined to identify differences between ER-negative and ER-positive tumours and siRNA was used to suppress candidate regulators. Compared to ER-positive breast cancer, ER-negative breast cancer cells were highly responsive to LXR agonists. In primary disease and cell lines LXRA expression was strongly correlated with its target genes in ER-negative but not ER-positive disease. Expression of LXR’s corepressors (NCOR1, NCOR2 and LCOR) was significantly higher in ER-positive disease relative to ER-negative, and their knock-down equalized sensitivity to ligand between subtypes in reporter, gene expression and viability assays. Our data support further evaluation of dietary and pharmacological targeting of cholesterol metabolism as an adjunct to existing therapies for ER-negative and ER-positive breast cancer patients.

Discovering metabolic disease gene interactions by correlated effects on cellular morphology

Objective

Impaired expansion of peripheral fat contributes to the pathogenesis of insulin resistance and Type 2 Diabetes (T2D). We aimed to identify novel disease–gene interactions during adipocyte differentiation.

Methods

Genes in disease-associated loci for T2D, adiposity and insulin resistance were ranked according to expression in human adipocytes. The top 125 genes were ablated in human pre-adipocytes via CRISPR/CAS9 and the resulting cellular phenotypes quantified during adipocyte differentiation with high-content microscopy and automated image analysis. Morphometric measurements were extracted from all images and used to construct morphologic profiles for each gene.

Results

Over 10⁷ morphometric measurements were obtained. Clustering of the morphologic profiles accross all genes revealed a group of 14 genes characterized by decreased lipid accumulation, and enriched for known lipodystrophy genes. For two lipodystrophy genes, BSCL2 and AGPAT2, sub-clusters with PLIN1 and CEBPA identifed by morphological similarity were validated by independent experiments as novel protein–protein and gene regulatory interactions.

Conclusions

A morphometric approach in adipocytes can resolve multiple cellular mechanisms for metabolic disease loci; this approach enables mechanistic interrogation of the hundreds of metabolic disease loci whose function still remains unknown.

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Loss-of-function genetic screen in human adipocytes for 125 genes selected from metabolic disease-associated loci.

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Genetic screen read out by cellular morphometry— 77,000 images taken with 400 morphological features extracted per image.

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Pairwise mechanistic interactions between genes identified by correlations of cellular morphometry—two interactions validated.

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Novel interaction between BSCL2 and PLIN1 from biophysical association of proteins at lipid droplet surface.

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Novel interaction between CEBPA and AGPAT2 from CEBPA dependent transcription of AGPAT2.

Temporal enhancer profiling of parallel lineages identifies AHR and GLIS1 as regulators of mesenchymal multipotency

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cell line (ST2) toward adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages and identifed the shared TFs involved in both differentiation processes. To take an alternative approach to prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. The combination of the two approaches identified aryl hydrocarbon receptor (AHR) and Glis family zinc finger 1 (GLIS1) as mesenchymal key TFs controlled by dynamic cell type-specific super-enhancers that become repressed in both lineages. AHR and GLIS1 control differentiation-induced genes and their overexpression can inhibit the lineage commitment of the multipotent bone marrow-derived ST2 cells.

Cell Models and Their Application for Studying Adipogenic Differentiation in Relation to Obesity: A Review

Over the last several years, the increasing prevalence of obesity has favored an intense study of adipose tissue biology and the precise mechanisms involved in adipocyte differentiation and adipogenesis. Adipocyte commitment and differentiation are complex processes, which can be investigated thanks to the development of diverse in vitro cell models and molecular biology techniques that allow for a better understanding of adipogenesis and adipocyte dysfunction associated with obesity. The aim of the present work was to update the different animal and human cell culture models available for studying the in vitro adipogenic differentiation process related to obesity and its co-morbidities. The main characteristics, new protocols, and applications of the cell models used to study the adipogenesis in the last five years have been extensively revised. Moreover, we depict co-cultures and three-dimensional cultures, given their utility to understand the connections between adipocytes and their surrounding cells in adipose tissue.

Personalized Cardiovascular Disease Prediction and Treatment-A Review of Existing Strategies and Novel Systems Medicine Tools

Cardiovascular disease (CVD) continues to constitute the leading cause of death globally. CVD risk stratification is an essential tool to sort through heterogeneous populations and identify individuals at risk of developing CVD. However, applications of current risk scores have recently been shown to result in considerable misclassification of high-risk subjects. In addition, despite long standing beneficial effects in secondary prevention, current CVD medications have in a primary prevention setting shown modest benefit in terms of increasing life expectancy. A systems biology approach to CVD risk stratification may be employed for improving risk-estimating algorithms through addition of high-throughput derived omics biomarkers. In addition, modeling of personalized benefit-of-treatment may help in guiding choice of intervention. In the area of medicine, realizing that CVD involves perturbations of large complex biological networks, future directions in drug development may involve moving away from a reductionist approach toward a system level approach. Here, we review current CVD risk scores and explore how novel algorithms could help to improve the identification of risk and maximize personalized treatment benefit. We also discuss possible future directions in the development of effective treatment strategies for CVD through the use of genome-scale metabolic models (GEMs) as well as other biological network-based approaches.

Unraveling the mechanistic complexity of Alzheimer's disease through systems biology

Alzheimer's disease (AD) is a complex, multifactorial disease that has reached global epidemic proportions. The challenge remains to fully identify its underlying molecular mechanisms that will enable development of accurate diagnostic tools and therapeutics. Conventional experimental approaches that target individual or small sets of genes or proteins may overlook important parts of the regulatory network, which limits the opportunity of identifying multitarget interventions. Our perspective is that a more complete insight into potential treatment options for AD will only be made possible through studying the disease as a system. We propose an integrative systems biology approach that we argue has been largely untapped in AD research. We present key publications to demonstrate the value of this approach and discuss the potential to intensify research efforts in AD through transdisciplinary collaboration. We highlight challenges and opportunities for significant breakthroughs that could be made if a systems biology approach is fully exploited.

CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools

Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell-cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin, gene: CDH1) is a key molecule in cell-cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database. DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL Databases, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database. This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease.

Functional Genomics Analysis of Big Data Identifies Novel Peroxisome Proliferator-Activated Receptor γ Target Single Nucleotide Polymorphisms Showing Association With Cardiometabolic Outcomes

BACKGROUND

Cardiovascular disease and type 2 diabetes mellitus represent overlapping diseases where a large portion of the variation attributable to genetics remains unexplained. An important player in their pathogenesis is peroxisome proliferator-activated receptor γ (PPARγ) that is involved in lipid and glucose metabolism and maintenance of metabolic homeostasis. We used a functional genomics methodology to interrogate human chromatin immunoprecipitation-sequencing, genome-wide association studies, and expression quantitative trait locus data to inform selection of candidate functional single nucleotide polymorphisms (SNPs) falling in PPARγ motifs.

METHODS AND RESULTS

We derived 27 328 chromatin immunoprecipitation-sequencing peaks for PPARγ in human adipocytes through meta-analysis of 3 data sets. The PPARγ consensus motif showed greatest enrichment and mapped to 8637 peaks. We identified 146 SNPs in these motifs. This number was significantly less than would be expected by chance, and Inference of Natural Selection from Interspersed Genomically coHerent elemenTs analysis indicated that these motifs are under weak negative selection. A screen of these SNPs against genome-wide association studies for cardiometabolic traits revealed significant enrichment with 16 SNPs. A screen against the MuTHER expression quantitative trait locus data revealed 8 of these were significantly associated with altered gene expression in human adipose, more than would be expected by chance. Several SNPs fall close, or are linked by expression quantitative trait locus to lipid-metabolism loci including CYP26A1.

CONCLUSIONS

We demonstrated the use of functional genomics to identify SNPs of potential function. Specifically, that SNPs within PPARγ motifs that bind PPARγ in adipocytes are significantly associated with cardiometabolic disease and with the regulation of transcription in adipose. This method may be used to uncover functional SNPs that do not reach significance thresholds in the agnostic approach of genome-wide association studies.

Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network

BACKGROUND

The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied.

METHODS

Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks.

RESULTS

To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism.

CONCLUSIONS

By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming.

Cell type-selective disease-association of genes under high regulatory load

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3' UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner.

Extensive weight loss reveals distinct gene expression changes in human subcutaneous and visceral adipose tissue

Weight loss has been shown to significantly improve Adipose tissue (AT) function, however changes in AT gene expression profiles particularly in visceral AT (VAT) have not been systematically studied. Here, we tested the hypothesis that extensive weight loss in response to bariatric surgery (BS) causes AT gene expression changes, which may affect energy and lipid metabolism, inflammation and secretory function of AT. We assessed gene expression changes by whole genome expression chips in AT samples obtained from six morbidly obese individuals, who underwent a two step BS strategy with sleeve gastrectomy as initial and a Roux-en-Y gastric bypass as second step surgery after 12 ± 2 months. Global gene expression differences in VAT and subcutaneous (S)AT were analyzed through the use of genome-scale metabolic model (GEM) for adipocytes. Significantly altered gene expressions were PCR-validated in 16 individuals, which also underwent a two-step surgery intervention. We found increased expression of cell death-inducing DFFA-like effector a (CIDEA), involved in formation of lipid droplets in both fat depots in response to significant weight loss. We observed that expression of the genes associated with metabolic reactions involved in NAD+, glutathione and branched chain amino acid metabolism are significantly increased in AT depots after surgery-induced weight loss.

Cancer cell metabolism as new targets for novel designed therapies

Metabolic processes are altered in cancer cells, which obtain advantages from this metabolic reprogramming in terms of energy production and synthesis of biomolecules that sustain their uncontrolled proliferation. Due to the conceptual progresses in the last decade, metabolic reprogramming was recently included as one of the new hallmarks of cancer. The advent of high-throughput technologies to amass an abundance of omic data, together with the development of new computational methods that allow the integration and analysis of omic data by using genome-scale reconstructions of human metabolism, have increased and accelerated the discovery and development of anticancer drugs and tumor-specific metabolic biomarkers. Here we review and discuss the latest advances in the context of metabolic reprogramming and the future in cancer research.

Systems biology of host-microbe metabolomics

The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301

For further resources related to this article, please visit the WIREs website.

Conflict of interest: The authors have declared no conflicts of interest for this article.

Expression of the myostatin gene in the adductor muscle of the Pacific lion-paw scallop Nodipecten subnodosus in association with growth and environmental conditions

The cDNA sequence of the myostatin gene in the Pacific lion-paw Nodipecten subnodosus (Ns-mstn) was characterized, and the temporal expression during grow-out was analyzed for the first time in a scallop. Ns-mstn encodes a 459-amino-acid protein in which two propeptide proteolytic sites were identified, the previously recognized (RSKR) and a second one at position 266-269 aa (RRKR). The alternative furin cleavage site could be related with post-translational processing, or it could be a tissue-specific mechanism for signaling activity. The Ns-mstn transcript was located by in situ hybridization in sarcomeres and around the nucleus of muscle fibers. The temporal expression analysis by qPCR in the adductor muscle showed that Ns-mstn expression was significantly different (P < 0.05) between months during the grow-out period, increasing largely during the summer months when both biomass and muscle weight did not increase or even decreased; muscle fiber size and number were found to decrease significantly. Exogenous and endogenous factors such as high temperature and low food availability, as well as gametogenesis and reproduction, can be associated with the growth pattern and Ns-mstn expression changes. Our results indicate that MSTN is involved in adductor muscle growth regulation in N. subnodosus as it occurs in vertebrate skeletal muscle although Ns-mstn expression in non-muscle organs/tissues suggests additional functions.

Systematic Analysis of Integrated Gene Functional Network of Four Chronic Stress-related Lifestyle Disorders

Background:

Stress is a term used to define factors involved in changes in the physiological balances resulting in disease conditions. Chronic exposure to stress conditions in modern lifestyles has resulted in a group of disorders called lifestyle disorders. Genetic background and environmental factors are interrelated to lifestyle in determining the health status of individuals. Hence, identification of disease-associated genes is the primary step toward explanations of pathogenesis of these diseases. In functional genomics, large-scale molecular and physiological data are used for the identification of causative genes associated with a disease.

Aim:

The objective of our study was to find a common set of genes involved in chronic stress-related lifestyle diseases such as cardiovascular diseases (CVDs), type 2 diabetes (T2D), hypertension (HTN), and obesity.

Materials and Methods:

In our study, we have performed a systematic analysis of the functional gene network of four chronic stress-related lifestyle diseases by retrieving genes from published databases. We have tried to systematically construct a functional protein-protein interaction (PPI) network. The goals of establishing this network were the functional enrichment study of interacting partners as well as functional disease ontology annotation (FunDO) of the enriched genes.

Results:

This study enabled the identification of key genes involved in these stress-related lifestyle diseases by prioritizing candidate genes based on their degree of involvement. In this systematic analysis, we have found key genes for these diseases based on their involvement and association at the gene network level and PPI.

Conclusion:

We have deciphered a group of genes that in combination play a crucial role and may impact the function of the whole genome in the four lifestyle disorders mentioned.

Mechanistic Role of MicroRNAs in Coupling Lipid Metabolism and Atherosclerosis

MicroRNAs (miRNAs, miRs) represent a group of powerful and versatile posttranscriptional regulators of gene expression being involved in the fine control of a plethora of physiological and pathological processes. Besides their well-established crucial roles in the regulation of cell cycle, embryogenesis or tumorigenesis, these tiny molecules have also been shown to participate in the regulation of lipid metabolism. In particular, miRs orchestrate cholesterol and fatty acids synthesis, transport, and degradation and low-density and high-density lipoprotein (LDL and HDL) formation. It is thus not surprising that they have also been reported to affect the development and progression of several lipid metabolism-related disorders including liver steatosis and atherosclerosis. Mounting evidence suggests that miRs might represent important "posttranscriptional hubs" of lipid metabolism, which means that one miR usually targets 3'-untranslated regions of various mRNAs that are involved in different steps of one precise metabolic/signaling pathway, e.g., one miR targets mRNAs of enzymes important for cholesterol synthesis, degradation, and transport. Therefore, changes in the levels of one key miR affect various steps of one pathway, which is thereby promoted or inhibited. This makes miRs potent future diagnostic and even therapeutic tools for personalized medicine. Within this chapter, the most prominent microRNAs involved in lipid metabolism, e.g., miR-27a/b, miR-33/33*, miR-122, miR-144, or miR-223, and their intracellular and extracellular functions will be extensively discussed, in particular focusing on their mechanistic role in the pathophysiology of atherosclerosis. Special emphasis will be given on miR-122, the first microRNA currently in clinical trials for the treatment of hepatitis C and on miR-223, the most abundant miR in lipoprotein particles.

Peroxisome proliferator activator receptor gamma (PPARG) regulates conceptus elongation in sheep

The ovine blastocyst hatches from the zona pellucida by Day 8 and develops into an ovoid or tubular conceptus (embryo and associated extraembryonic membranes) that grows and elongates into a filamentous form between Days 12 and 16. The trophectoderm of the elongating conceptus synthesizes and secretes interferon tau (IFNT) as well as prostaglandins (PGs) via prostaglandin synthase two (PTGS2). Intrauterine infusion of a PTGS2 inhibitor prevents conceptus elongation in sheep. Although many PGs are secreted, PGI2 and PGJ2 can activate nuclear peroxisome proliferator activator receptors (PPARs) that heterodimerize with retinoic X receptors (RXRs) to regulate gene expression and cellular function. Expression of PPARD, PPARG, RXRA, RXRB, and RXRG is detected in the elongating ovine conceptus, and nuclear PPARD and PPARG are present in the trophectoderm. Consequently, PPARD and PPARG are hypothesized to have essential roles in conceptus elongation in ruminants. In utero loss-of-function studies of PPARD and PPARG in the ovine conceptus trophectoderm were conducted using morpholino antisense oligonucleotides (MAOs) that inhibit mRNA translation. Elongating, filamentous-type conceptuses were recovered from ewes infused with a control morpholino or PPARD MAO. In contrast, PPARG MAO resulted in severely growth-retarded conceptuses or conceptus fragments with apoptotic trophectoderm. In order to identify PPARG-regulated genes, PPARG chromatin immunoprecipitation sequencing and RNA sequencing were conducted using Day 14 ovine conceptuses. These analyses revealed candidate PPARG-regulated genes involved in biological pathways, including lipid and glucose uptake, transport, and metabolism. Collectively, results support the hypothesis that PTGS2-derived PGs and PPARG are essential regulators of conceptus elongation, with specific roles in trophectoderm survival and proliferation.

Computationally Modeling Lipid Metabolism and Aging: A Mini-review

One of the greatest challenges in biology is to improve the understanding of the mechanisms which underpin aging and how these affect health. The need to better understand aging is amplified by demographic changes, which have caused a gradual increase in the global population of older people. Aging western populations have resulted in a rise in the prevalence of age-related pathologies. Of these diseases, cardiovascular disease is the most common underlying condition in older people. The dysregulation of lipid metabolism due to aging impinges significantly on cardiovascular health. However, the multifaceted nature of lipid metabolism and the complexities of its interaction with aging make it challenging to understand by conventional means. To address this challenge computational modeling, a key component of the systems biology paradigm is being used to study the dynamics of lipid metabolism. This mini-review briefly outlines the key regulators of lipid metabolism, their dysregulation, and how computational modeling is being used to gain an increased insight into this system.

Systematic transcriptome analysis of the zebrafish model of diamond-blackfan anemia induced by RPS24 deficiency

BACKGROUND

Diamond-Blackfan anemia (DBA) is a class of human diseases linked to defective ribosome biogenesis that results in clinical phenotypes. Genetic mutations in ribosome protein (RP) genes lead to DBA phenotypes, including hematopoietic defects and physical deformities. However, little is known about the global regulatory network as well as key miRNAs and gene pathways in the zebrafish model of DBA.

RESULTS

In this study, we establish the DBA model in zebrafish using an RPS24 morpholino and found that RPS24 is required for both primitive hematopoiesis and definitive hematopoiesis processes that are partially mediated by the p53 pathway. Several deregulated genes and miRNAs were found to be related to hematopoiesis, vascular development and apoptosis in RPS24-deficient zebrafish via RNA-seq and miRNA-seq data analysis, and a comprehensive regulatory network was first constructed to identify the mechanisms of key miRNAs and gene pathways in the model. Interestingly, we found that the central node genes in the network were almost all targeted by significantly deregulated miRNAs. Furthermore, the enforced expression of miR-142-3p, a uniquely expressed miRNA, causes a significant decrease in primitive erythrocyte progenitor cells and HSCs.

CONCLUSIONS

The present analyses demonstrate that the comprehensive regulatory network we constructed is useful for the functional prediction of new and important miRNAs in DBA and will provide insights into the pathogenesis of mutant rps24-mediated human DBA disease.

Transcriptomics profiling of human SGBS adipogenesis

Obesity is an ever-growing epidemic where tissue homeostasis is influenced by the differentiation of adipocytes that function in lipid metabolism, endocrine and inflammatory processes. While this differentiation process has been well-characterized in mice, limited data is available from human cells. Applying microarray expression profiling in the human SGBS pre-adipocyte cell line, we identified genes with differential expression during differentiation in combination with constraint-based modeling of metabolic pathway activity. Here we describe the experimental design and quality controls in detail for the gene expression and related results published by Galhardo et al. in Nucleic Acids Research 2014 associated with the data uploaded to NCBI Gene Expression Omnibus (GSE41352).

ChIP-seq profiling of the active chromatin marker H3K4me3 and PPARγ, CEBPα and LXR target genes in human SGBS adipocytes

Transcription factors (TFs) represent key factors to establish a cellular phenotype. It is known that several TFs could play a role in disease, yet less is known so far how their targets overlap. We focused here on identifying the most highly induced TFs and their putative targets during human adipogenesis. Applying chromatin immunoprecipitation coupled with deep sequencing (ChIP-Seq) in the human SGBS pre-adipocyte cell line, we identified genes with binding sites in their vicinity for the three TFs studied, PPARγ, CEBPα and LXR. Here we describe the experimental design and quality controls in detail for the deep sequencing data and related results published by Galhardo et al. in Nucleic Acids Research 2014 [1] associated with the data uploaded to NCBI Gene Expression Omnibus (GSE41578).

Promise and reality in the expanding field of network interaction analysis: metabolic networks

In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.

Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease

Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.

Combinatorial regulation of lipoprotein lipase by microRNAs during mouse adipogenesis

MicroRNAs (miRNAs) regulate gene expression directly through base pairing to their targets or indirectly through participating in multi-scale regulatory networks. Often miRNAs take part in feed-forward motifs where a miRNA and a transcription factor act on shared targets to achieve accurate regulation of processes such as cell differentiation. Here we show that the expression levels of miR-27a and miR-29a inversely correlate with the mRNA levels of lipoprotein lipase (Lpl), their predicted combinatorial target, and its key transcriptional regulator peroxisome proliferator-activated receptor gamma (Pparg) during 3T3-L1 adipocyte differentiation. More importantly, we show that Lpl, a key lipogenic enzyme, can be negatively regulated by the two miRNA families in a combinatorial fashion on the mRNA and functional level in maturing adipocytes. This regulation is mediated through the Lpl 3'UTR as confirmed by reporter gene assays. In addition, a small mathematical model captures the dynamics of this feed-forward motif and predicts the changes in Lpl mRNA levels upon network perturbations. The obtained results might offer an explanation to the dysregulation of LPL in diabetic conditions and could be extended to quantitative modeling of regulation of other metabolic genes under similar regulatory network motifs.