A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome

Anti-adipogenesis effect of indole-3-acrylic acid on human preadipocytes and HFD-induced zebrafish

BACKGROUND

Obesity, defined as excessive or abnormal body fat accumulation, which could significantly increase the risk of cardiovascular disease, type 2 diabetes mellitus (T2DM) diseases and seriously affect people's quality of life. More than 2 billion people are overweight, and the incidence of obesity is increasing rapidly worldwide, it has become a widely concerned public health issue in the world. Diverse evidence show that active metabolites are involved in the pathophysiological processes of obesity.

AIMS

However, whether the downstream catabolite of tryptophan, 3-indole acrylic acid (IA), is involved in obesity remains unclear.

METHODS

We collected the samples of serum from peripheral blood of obesity and health controls, and liquid chromatography-mass spectrometry (LC-MS) was performed to identify the plasma levels of IA. Additionally, we verified the potential benefits of IA on human preadipocytes and HFD- induced zebrafish by cell viability assay, flow cytometry assay, Oil red O staining, total cholesterol (T-CHO), triglyceride (TG) and nonesterified free fatty acids (NEFA) measurements and Nile Red staining. RNA-Seq, functional analysis and western blot revealed the mechanisms underlying the function of IA.

RESULTS

We found that the content of IA in peripheral blood serum of overweight people was significantly lower than that of normal people. In addition, supplementation with IA in zebrafish larvae induced by a high fat diet (HFD) dramatically reduced HFD induced lipid accumulation. IA had no effect on proliferation and apoptosis of preadipocytes, but significantly inhibited adipogenesis of preadipocytes by down-regulate CEBPα and PPARγ. RNA-Seq and functional analysis revealed that IA regulated the adipogenesis of preadipocytes through stimulate the phosphorylation of STAT1.

CONCLUSIONS

Taken together, IA has been identified as a potent metabolite for the prevention or treatment of obesity.

Drug Mechanism: A bioinformatic update

A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.

The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Non-Genetic-Induced Zebrafish Model for Type 2 Diabetes with Emphasis on Tools in Model Validation

The unrelenting increase in the incidence of type 2 diabetes (T2D) necessitates the urgent need for effective animal models to mimic its pathophysiology. Zebrafish possess human-like metabolic traits and share significant genetic similarities, making them valuable candidates for studying metabolic disorders, including T2D. This review emphasizes the critical role of animal models in diabetes research, especially focusing on zebrafish as an alternative model organism. Different approaches to a non-genetic model of T2D in zebrafish, such as the glucose solution, diet-induced, chemical-induced, and combined diet-induced and glucose solution methods, with an emphasis on model validation using indicators of T2D, were highlighted. However, a significant drawback lies in the validation of these models. Some of these models have not extensively demonstrated persistent hyperglycemia or response to insulin resistance and glucose tolerance tests, depicted the morphology of the pancreatic β-cell, or showed their response to antidiabetic drugs. These tools are crucial in T2D pathology. Future research on non-genetic models of T2D in zebrafish must extensively focus on validating the metabolic deficits existing in the model with the same metabolic defects in humans and improve on the existing models for a better understanding of the molecular mechanisms underlying T2D and exploring potential therapeutic interventions.

Traumatic acid inhibits ACSL4 associated lipid accumulation in adipocytes to attenuate high-fat diet-induced obesity

Obesity is a major health concern that lacks effective intervention strategies. Traumatic acid (TA) is a potent wound-healing agent in plants, considered an antioxidant food ingredient. This study demonstrated that TA treatment significantly reduced lipid accumulation in human adipocytes and prevented high-fat diet induced obesity in zebrafish. Transcriptome sequencing revealed TA-activated fatty acid (FA) degradation and FA metabolism signaling pathways. Moreover, western blotting and quantitative polymerase chain reaction showed that TA inhibited the expression of long-chain acyl-CoA synthetase-4 (ACSL4). Overexpression of ACSL4 resulted in the reversal of TA beneficiary effects, indicating that the attenuated lipid accumulation of TA was regulated by ACSL4 expression. Limited proteolysis-mass spectrometry and microscale thermophoresis were then used to confirm hexokinase 2 (HK2) as a direct molecular target of TA. Thus, we demonstrated the molecular basis of TA in regulating lipid accumulation and gave the first evidence that TA may function through the HK2-ACSL4 axis.

Zebrafish facilitate non-alcoholic fatty liver disease research: Tools, models and applications

Non-alcoholic fatty liver disease (NAFLD) has become an increasingly epidemic metabolic disease worldwide. NAFLD can gradually deteriorate from simple liver steatosis, inflammation and fibrosis to liver cirrhosis and/or hepatocellular carcinoma. Zebrafish are vertebrate animal models that are genetically and metabolically conserved with mammals and have unique advantages such as high fecundity, rapid development ex utero and optical transparency. These features have rendered zebrafish an emerging model system for liver diseases and metabolic diseases favoured by many researchers in recent years. In the present review, we summarize a series of tools for zebrafish NAFLD research and the models established through different dietary feeding, hepatotoxic chemical treatments and genetic manipulations via transgenic or genome editing technologies. We also discuss how zebrafish models facilitate NAFLD studies by providing novel insights into NAFLD pathogenesis, toxicology research, and drug evaluation and discovery.

Isolation and proteomic study of fish liver lipid droplets

Lipid droplets (LDs) are a neutral lipid storage organelle that is conserved in almost all species. Excessive storage of neutral lipids in LDs is directly associated with many metabolic syndromes. Zebrafish is a better model animal for the study of LD biology due to its transparent embryonic stage compared to other organisms. However, the study of LDs in fish has been difficult due to the lack of specific LD marker proteins and the limitation of purification technology. In this paper, the purification and proteomic analysis of liver LDs of fish including zebrafish and Carassius auratus were performed for the first time. 259 and 267 proteins were identified respectively. Besides most of the identified proteins were reported in previous LD proteomes of mammals, indicating the similarity between mammal and fish LDs. We also identified many unique proteins of liver LDs in fish that are involved in the regulation of LD dynamics. Through morphological and biochemical analysis, we found that the marker protein Plin2 of zebrafish LD was located on LDs in Huh7 cells. These results will facilitate further study of LDs in fish and liver metabolic diseases using fish as a model animal.

Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans

Sarcopenia, characterized by age-related loss of muscle mass, strength, and decreased physical performance, is a growing public health challenge amid the rapidly ageing population. As there are no approved drugs that target sarcopenia, it has become increasingly urgent to identify promising pharmacological interventions. In this study, we conducted an integrative drug repurposing analysis utilizing three distinct approaches. Firstly, we analyzed skeletal muscle transcriptomic sequencing data in humans and mice using gene differential expression analysis, weighted gene co-expression analysis, and gene set enrichment analysis. Subsequently, we employed gene expression profile similarity assessment, hub gene expression reversal, and disease-related pathway enrichment to identify and repurpose candidate drugs, followed by the integration of findings with rank aggregation algorithms. Vorinostat, the top-ranking drug, was also validated in an in vitro study, which demonstrated its efficacy in promoting muscle fiber formation. Although still requiring further validation in animal models and human clinical trials, these results suggest a promising drug repurposing prospect in the treatment and prevention of sarcopenia.

Methionine sulfoxide suppresses adipogenic differentiation by regulating the mitogen-activated protein kinase signaling pathway

In this study, methionine sulfoxide (MetO) was identified as an active metabolite that suppresses adipogenesis after screening obese individuals versus the normal population. MetO suppressed the gene and protein expression of CCAAT/enhancer binding protein (C/EBP) α, adipocyte fatty acid binding protein 4 (FABP4), and the nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ) during human preadipocyte (HPA) differentiation. Adipogenesis decreased following MetO treatment; however, the preadipocyte number, proliferation, and apoptosis were unaffected. The activity of phosphorylated extracellular signal-related kinase (P-ERK) of the mitogen-activated protein kinase (MAPK) pathway was significantly inhibited in HPA after MetO treatment. Furthermore, treatment of preadipocytes with the selective P-ERK1/2 agonist Ro 67-7476 abolished the effect of MetO against adipogenesis suggesting that MetO function is dependent on the MAPK pathway. The mechanistic insights of adipogenesis suppression by MetO presented in this study shows its potential as an antiobesity drug.

Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks

Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.

Esculetin Alleviates Nonalcoholic Fatty Liver Disease on High-Cholesterol-Diet-Induced Larval Zebrafish and FFA-Induced BRL-3A Hepatocyte

Non-alcoholic fatty liver disease (NAFLD), defined in recent years as metabolic-associated fatty liver disease (MAFLD), is one of the most common liver diseases in the world, with no drugs on market. Esculetin (ESC) is an active compound discovered in a variety of natural products that modulates a wide range of metabolic diseases and is a potential drug for the treatment of NAFLD. In this study, we used an HCD-induced NAFLD larval zebrafish model in vivo and an FFA-induced BRL-3A hepatocyte model in vitro to evaluate the anti-NAFLD effect of ESC. Lipid lowering, anti-oxidation and anti-inflammation effects were revealed on ESC and related gene changes were observed. This study provides a reference for further study and development of ESC as a potential anti-NAFLD/MAFLD drug.

Single-cell-led drug repurposing for Alzheimer's disease

Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy.

Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis

INTRODUCTION

Non-alcoholic steatohepatitis (NASH), an advanced subtype of non-alcoholic fatty liver disease (NAFLD), has becoming the most important aetiology for end-stage liver disease, such as cirrhosis and hepatocellular carcinoma. This study were designed to explore novel genes associated with NASH.

METHODS

Here, five independent Gene Expression Omnibus (GEO) datasets were combined into a single cohort and analyzed using network biology approaches.

RESULTS

11 modules identified by weighted gene co-expression network analysis (WGCNA) showed significant association with the status of NASH. Further characterization of four gene modules of interest demonstrated that molecular pathology of NASH involves the upregulation of hub genes related to immune response, cholesterol and lipid metabolic process, extracellular matrix organization, and the downregulation of hub genes related to cellular amino acid catabolic, respectively. After DEGs enrichment analysis and module preservation analysis, the Turquoise module associated with immune response displayed a remarkably correlation with NASH status. Hub genes with high degree of connectivity in the module, including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN were further verified in clinical samples and mouse model of NASH. Moreover, single-cell RNA-seq analysis showed that those key genes were expressed by distinct immune cells such as microphages, natural killer, dendritic, T and B cells. Finally, the potential transcription factors of Turquoise module were characterized, including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which increased with NASH progression.

DISCUSSION

In conclusion, our integrative analysis will contribute to the understanding of NASH and may enable the development of potential biomarkers for NASH therapy.

Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing

The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.

Nutrigenetic Interaction of Spontaneously Hypertensive Rat Chromosome 20 Segment and High-Sucrose Diet Sensitizes to Metabolic Syndrome

Several corresponding regions of human and mammalian genomes have been shown to affect sensitivity to the manifestation of metabolic syndrome via nutrigenetic interactions. In this study, we assessed the effect of sucrose administration in a newly established congenic strain BN.SHR20, in which a limited segment of rat chromosome 20 from a metabolic syndrome model, spontaneously hypertensive rat (SHR), was introgressed into Brown Norway (BN) genomic background. We mapped the extent of the differential segment and compared the genomic sequences of BN vs. SHR within the segment in silico. The differential segment of SHR origin in BN.SHR20 spans about 9 Mb of the telomeric portion of the short arm of chromosome 20. We identified non-synonymous mutations e.g., in ApoM, Notch4, Slc39a7, Smim29 genes and other variations in or near genes associated with metabolic syndrome in human genome-wide association studies. Male rats of BN and BN.SHR20 strains were fed a standard diet for 18 weeks (control groups) or 16 weeks of standard diet followed by 14 days of high-sucrose diet (HSD). We assessed the morphometric and metabolic profiles of all groups. Adiposity significantly increased only in BN.SHR20 after HSD. Fasting glycemia and the glucose levels during the oral glucose tolerance test were higher in BN.SHR20 than in BN groups, while insulin levels were comparable. The fasting levels of triacylglycerols were the highest in sucrose-fed BN.SHR20, both compared to the sucrose-fed BN and the control BN.SHR20. The non-esterified fatty acids and total cholesterol concentrations were higher in BN.SHR20 compared to their respective BN groups, and the HSD elicited an increase in non-esterified fatty acids only in BN.SHR20. In a new genetically defined model, we have isolated a limited genomic region involved in nutrigenetic sensitization to sucrose-induced metabolic disturbances.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

Coix Seed-Based Milk Fermented With Limosilactobacillus reuteri Improves Lipid Metabolism and Gut Microbiota in Mice Fed With a High-Fat Diet

The aim of this study was to investigate the effects of coix seed-based milk (CSM) fermented with Limosilactobacillus reuteri (L. reuteri) on dyslipidemia and the composition of the intestinal microbiota in high fat diet (HFD)-fed mice. Changes in the body weight, serum lipid levels, activities of hepatic oxidative stress factors, expression of lipid-related genes, and composition of the intestinal microbiota of HFD-fed mice after supplementation with CSM were determined. The results showed that intake of CSM reduced the body weight gain as well as serum total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels, and increased the high-density lipoprotein cholesterol (HDL-C) levels in the mice. Meanwhile, supplementation with CSM could relieve liver oxidative stress, down-regulate the expression of genes related to lipid synthesis, and prevent liver fat accumulation in mice fed with HFD. The 16S rRNA sequencing of the intestinal microbiota showed that CSM regulated the gut microbiota community structure at different taxonomic levels, and reversed gut dysbiosis induced by HFD. The relative abundance of Muribaculaceae, Lachnospiraceae, Dubosiella and Akkermansia which are negatively correlated with blood lipid levels were significantly increased by the intervention of CSM, while the relative abundance of Desulfovibrionaceae, Ruminococca-ceae_UCG-014, Psychrobacter, and Staphylococcus which have positive correlation with blood lipid levels were significantly decreased. These results indicated that CSM might serve as a novel and promising dietary supplement for ameliorating hyperlipidemia and intestinal microbiota disorders caused by HFDs.

An Overview of the TRP-Oxidative Stress Axis in Metabolic Syndrome: Insights for Novel Therapeutic Approaches

Metabolic syndrome (MS) is a complex pathology characterized by visceral adiposity, insulin resistance, arterial hypertension, and dyslipidaemia. It has become a global epidemic associated with increased consumption of high-calorie, low-fibre food and sedentary habits. Some of its underlying mechanisms have been identified, with hypoadiponectinemia, inflammation and oxidative stress as important factors for MS establishment and progression. Alterations in adipokine levels may favour glucotoxicity and lipotoxicity which, in turn, contribute to inflammation and cellular stress responses within the adipose, pancreatic and liver tissues, in addition to hepatic steatosis. The multiple mechanisms of MS make its clinical management difficult, involving both non-pharmacological and pharmacological interventions. Transient receptor potential (TRP) channels are non-selective calcium channels involved in a plethora of physiological events, including energy balance, inflammation and oxidative stress. Evidence from animal models of disease has contributed to identify their specific contributions to MS and may help to tailor clinical trials for the disease. In this context, the oxidative stress sensors TRPV1, TRPA1 and TRPC5, play major roles in regulating inflammatory responses, thermogenesis and energy expenditure. Here, the interplay between these TRP channels and oxidative stress in MS is discussed in the light of novel therapies to treat this syndrome.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Pyroptosis-Related Signatures for Predicting Prognosis in Breast Cancer

Background

Female breast cancer (BC) has become the most common cancer in the world, and its mortality was considerably higher in transitioning vs. transitioned countries. Pyroptosis, an inflammation-dependent programmed cell death mediated by inflammasomes, has been observed in human colorectal tumors and gliomas. However, the characteristics of pyrolysis-related genes and their influence and mechanism on the tumorigenesis and progress of BC were unknown.

Methods

Based on the global public database, we used comprehensive bioinformatics analysis to systematically analyze the expression of pyroptosis-related genes in BC and their relationship in tumor progression. In addition, BC patients were divided into two groups, and the clinical features and outcomes could be better predicted by the consistent clustering of pyroptosis-related genes. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to establish a risk score. Then, we further explored the prognostic value and clinical features of pyroptosis genes. Finally, we used the Human Protein Atlas (HPA) platform to identify the expression at protein levels of the key genes.

Results

We confirmed that the expression of pyroptosis-related genes was different in BC and normal breast tissues. A high frequency of somatic mutations occurred in BC. In addition, 33 pyroptosis-related proteins interacted frequently. Based on univariate analysis and the LASSO Cox model, five pyroptosis-related genes [including GADMA, interleukin-6 (IL-6), NLR pyrin domain-containing protein 6 (NLRP6), caspase-1 (CASP1), and caspase-9 (CASP9)], were obtained to calculate a risk score. The risk score was identified as an independent risk factor for the prognosis of BC and might play an auxiliary role in clinical classification. The HPA platform confirmed that the expression trends of the key genes were consistent with our previous studies.

Conclusion

Pyroptosis had an important effect on the progression of BC. And the pyroptosis-related genes could be used as new prognostic biomarkers and therapeutic targets for BC.

Network-Based Approach to Repurpose Approved Drugs for COVID-19 by Integrating GWAS and Text Mining Data

The coronavirus disease 19 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a rapidly increasing prevalence and has caused significant morbidity/mortality. Despite the availability of many vaccines that can offer widespread immunization, it is also important to reach effective treatment for COVID-19 patients. However, the development of novel drug therapeutics is usually a time-consuming and costly process, and therefore, repositioning drugs that were previously approved for other purposes could have a major impact on the fight against COVID-19. Here, we first identified lung-specific gene regulatory/interaction subnetworks (COVID-19-related genes modules) enriched for COVID-19-associated genes obtained from GWAS and text mining. We then screened the targets of 220 approved drugs from DrugBank, obtained their drug-induced gene expression profiles in the LINCS database, and constructed lung-specific drug-related gene modules. By applying an integrated network-based approach to quantify the interactions of the COVID-19-related gene modules and drug-related gene modules, we prioritized 13 approved drugs (e.g., alitretinoin, clocortolone, terazosin, doconexent, and pergolide) that could potentially be repurposed for the treatment of COVID-19. These findings provide important and timely insights into alternative therapeutic options that should be further explored as COVID-19 continues to spread.

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

Diversity: current and prospective secondary metabolites for nutrition and medicine

Plants have been used as sources of food, feed and medicine for millennia. The ever-increasing population has, however, dramatically increased the burden on our arable land to meet nutritional demand. Concomitantly, and in part due to poor nutrition, we are faced with massive increases in chronic diseases, meaning the need for medicine has also increased. Here, we look back on research in these areas, surveying the polyphenols as a case study for health-conferring metabolites. We conclude that the tools that will allow us to breed more nutritious crops are all at hand. We stress that collaboration between plant and medical research needs to be intensified in order to improve our understanding of the bioactivities. In doing so, we attempt to draw a roadmap for the use of plants for mid-21st Century human health.

The Proapoptotic Gene Bad Regulates Brain Development via p53-Mediated Stress Signals in Zebrafish

Studies have shown that the BH3-only domain Bad regulates brain development via the control of programmed cell death (PCD), but very few studies have addressed its effect on the molecular signaling of brain development in the system. In this work, we examined the novel role of zebrafish Bad in initial programmed cell death for brain morphogenesis through the priming of p53-mediated stress signaling. In a biological function study on the knockdown of Bad by morpholino oligonucleotides, at 24 h post-fertilization (hpf) Bad defects induced abnormal hindbrain development, as determined in a tissue section by means of HE staining which traced the damaged hindbrain. Then, genome-wide approaches for monitoring either the upregulation of apoptotic-related genes (11.8%) or the downregulation of brain development-related genes (29%) at the 24 hpf stage were implemented. The p53/caspase-8-mediated apoptotic death pathway was strongly involved, with the pathway being strongly reversed in a p53 mutant (p53^M214K) line during Bad knockdown. Furthermore, we propose the involvement of a p53-mediated stress signal which is correlated with regulating Bad loss-mediated brain defects. We found that some major genes in brain development, such as crybb1, pva1b5, irx4a, pax7a, and fabp7a, were dramatically restored in the p53^M214K line, and brain development recovered to return movement behavior to normal. Our findings suggest that Bad is required for (PCD) control, exerting a p53 stress signal on caspase-8/tBid-mediated death signaling and brain development-related gene regulation.

Transgenic fluorescent zebrafish lines that have revolutionized biomedical research

Since its debut in the biomedical research fields in 1981, zebrafish have been used as a vertebrate model organism in more than 40,000 biomedical research studies. Especially useful are zebrafish lines expressing fluorescent proteins in a molecule, intracellular organelle, cell or tissue specific manner because they allow the visualization and tracking of molecules, intracellular organelles, cells or tissues of interest in real time and in vivo. In this review, we summarize representative transgenic fluorescent zebrafish lines that have revolutionized biomedical research on signal transduction, the craniofacial skeletal system, the hematopoietic system, the nervous system, the urogenital system, the digestive system and intracellular organelles.

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.

Methods

The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.

Results

A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.

Conclusion

Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Association of the Psoriatic Microenvironment With Treatment Response

Importance

The ability to predict the efficacy of systemic psoriasis therapy based on immune profiles in skin biopsies could reduce the use of inappropriate treatment and its associated costs and adverse events. It could considerably decrease drug development trial costs as well.

Objective

To develop a bioinformatic gene signature score derived from skin mRNA to predict psoriasis treatment outcomes for a variety of therapies.

Design, Setting, and Participants

In this decision analytical model using 1145 skin samples from different cohorts of 12 retrospective psoriasis studies, samples were analyzed using the CIBERSORT algorithm to define the immune landscape of psoriasis lesions and controls. Random forest classification and principal component analysis algorithms were used to estimate psoriatic microenvironment (PME) signature genes and construct a PME score. Overall, 85 and 421 psoriasis lesions from 1 and 4 independent cohorts were used as discovery and validation studies, respectively. Among them, 157, 71, 89, and 90 psoriasis lesions were treated with etanercept, tofacitinib, adalimumab, and methotrexate, respectively.

Main Outcomes and Measures

Number of weeks after treatment initiation when responders and nonresponders could be predicted.

Results

Overall, 22 immune cell subtypes formed infiltration patterns that differentiated psoriasis lesions from healthy skin. In psoriasis lesions, the expression of 33 PME signature genes defined 2 immune phenotypes and in aggregate could be simplified to a numerical PME score. A high PME score, characterized by keratinocyte differentiation, correlated with a better treatment response (Psoriasis Area and Severity Index [PASI] reduction, 75.8%; 95% CI, 69.4% to 82.2%; P = .03), whereas a low PME score exhibited an immune activation signature and was associated with a worse response (PASI reduction, 53.5%; 95% CI, 45.3% to 61.7%; P = .03). The PME score at week 4 after treatment initiation correlated with future responder vs nonresponder to treatment status 8 to 12 weeks earlier than PASI reduction for etanercept, methotrexate plus adalimumab, and tofacitinib.

Conclusions and Relevance

The PME score is a biometric score that may predict clinical efficacy of systemic psoriasis therapy in advance of clinical responses. As an application of personalized medicine, it may reduce the exposure of patients with psoriasis to ineffective and expensive therapies.

Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories

Single-cell RNA sequencing (scRNA-seq) is a widely used method for identifying cell types and trajectories in biologically heterogeneous samples, but it is limited in its detection and quantification of lowly expressed genes. This results in missing important biological signals, such as the expression of key transcription factors (TFs) driving cellular differentiation. We show that targeted sequencing of ∼1000 TFs (scCapture-seq) in iPSC-derived neuronal cultures greatly improves the biological information garnered from scRNA-seq. Increased TF resolution enhanced cell type identification, developmental trajectories, and gene regulatory networks. This allowed us to resolve differences among neuronal populations, which were generated in two different laboratories using the same differentiation protocol. ScCapture-seq improved TF-gene regulatory network inference and thus identified divergent patterns of neurogenesis into either excitatory cortical neurons or inhibitory interneurons. Furthermore, scCapture-seq revealed a role for of retinoic acid signaling in the developmental divergence between these different neuronal populations. Our results show that TF targeting improves the characterization of human cellular models and allows identification of the essential differences between cellular populations, which would otherwise be missed in traditional scRNA-seq. scCapture-seq TF targeting represents a cost-effective enhancement of scRNA-seq, which could be broadly applied to improve scRNA-seq resolution.

Quantifying the progression of non-alcoholic fatty liver disease in human biomimetic liver microphysiology systems with fluorescent protein biosensors

Metabolic syndrome is a complex disease that involves multiple organ systems including a critical role for the liver. Non-alcoholic fatty liver disease (NAFLD) is a key component of the metabolic syndrome and fatty liver is linked to a range of metabolic dysfunctions that occur in approximately 25% of the population. A panel of experts recently agreed that the acronym, NAFLD, did not properly characterize this heterogeneous disease given the associated metabolic abnormalities such as type 2 diabetes mellitus (T2D), obesity, and hypertension. Therefore, metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as the new term to cover the heterogeneity identified in the NAFLD patient population. Although many rodent models of NAFLD/NASH have been developed, they do not recapitulate the full disease spectrum in patients. Therefore, a platform has evolved initially focused on human biomimetic liver microphysiology systems that integrates fluorescent protein biosensors along with other key metrics, the microphysiology systems database, and quantitative systems pharmacology. Quantitative systems pharmacology is being applied to investigate the mechanisms of NAFLD/MAFLD progression to select molecular targets for fluorescent protein biosensors, to integrate computational and experimental methods to predict drugs for repurposing, and to facilitate novel drug development. Fluorescent protein biosensors are critical components of the platform since they enable monitoring of the pathophysiology of disease progression by defining and quantifying the temporal and spatial dynamics of protein functions in the biosensor cells, and serve as minimally invasive biomarkers of the physiological state of the microphysiology system experimental disease models. Here, we summarize the progress in developing human microphysiology system disease models of NAFLD/MAFLD from several laboratories, developing fluorescent protein biosensors to monitor and to measure NAFLD/MAFLD disease progression and implementation of quantitative systems pharmacology with the goal of repurposing drugs and guiding the creation of novel therapeutics.

Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug-disease pharmacological effect between drug-target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.

Zebrafish as an animal model for biomedical research

Zebrafish have several advantages compared to other vertebrate models used in modeling human diseases, particularly for large-scale genetic mutant and therapeutic compound screenings, and other biomedical research applications. With the impactful developments of CRISPR and next-generation sequencing technology, disease modeling in zebrafish is accelerating the understanding of the molecular mechanisms of human genetic diseases. These efforts are fundamental for the future of precision medicine because they provide new diagnostic and therapeutic solutions. This review focuses on zebrafish disease models for biomedical research, mainly in developmental disorders, mental disorders, and metabolic diseases.

Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a B lymphoid malignancy highly dependent on the microenvironment. Despite new targeted therapies such as ibrutinib and venetoclax, disease progression and relapse remain an issue. CLL cell interactions with the supportive tissue microenvironment play a critical role in disease pathogenesis. We used a platform for drug discovery based on systems biology and artificial intelligence, to identify drugs targeting key proteins described to have a role in the microenvironment. The selected compounds were screened in CLL cell lines in the presence of stromal cells to mimic the microenvironment and validated the best candidates in primary CLL cells. Our results showed that the commercial drug simvastatin was the most effective and selective out of the tested compounds. Simvastatin decreased CLL cell survival and proliferation as well as cell adhesion. Importantly, this drug enhanced the antitumor effect of venetoclax and ibrutinib. We proposed that systems biology approaches combined with pharmacological screening could help to find new drugs for CLL treatment and to predict new combinations with current therapies. Our results highlight the possibility of repurposing widely used drugs such as statins to target the microenvironment and to improve the efficacy of ibrutinib or venetoclax in CLL cells.

Integrative analyses of biomarkers and pathways for adipose tissue after bariatric surgery

We explored potential biomarkers and molecular mechanisms regarding multiple benefits after bariatric surgery. Differentially expressed genes (DEGs) for subcutaneous adipose tissue (AT) after bariatric surgery were identified by analyzing two expression profiles from the GEO. Subsequently, enrichment analysis, GSEA, PPI network, and gene-microRNAs and gene-TFs networks were interrogated to identify hub genes and associated pathways. Co-expressed DEGs included one that was up-regulated and 22 that were down-regulated genes. The enrichment analyses indicated that down-regulated DEGs were significantly involved in inflammatory responses. GSEA provided comprehensive evidence that most genes enriched in pro-inflammation pathways, while gene-sets after surgery enriched in metabolism. We identified nine hub genes in the PPI network, most of which were validated as highly expressed and hypomethylated in obesity by Attie Lab Diabetes and DiseaseMeth databases, respectively. DGIdb was also applied to predict potential therapeutic agents that might reverse abnormally high hub gene expression. Bariatric surgery induces a significant shift from an obese pro-inflammatory state to an anti-inflammatory state, with improvement in adipocyte metabolic function – representing key mechanisms whereby AT function improves after bariatric surgery. Our study deepens a mechanistic understanding of the benefits of bariatric surgery and provides potential biomarkers or treatment targets for further research.

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

High intensity interval training exercise-induced physiological changes and their potential influence on metabolic syndrome clinical biomarkers: a meta-analysis

BACKGROUND

Despite the current debate about the effects of high intensity interval training (HIIT), HIIT elicits big morpho-physiological benefit on Metabolic Syndrome (MetS) treatment. However, no review or meta-analysis has compared the effects of HIIT to non-exercising controls in MetS variables. The aim of this study was to determine through a systematic review, the effectiveness of HIIT on MetS clinical variables in adults.

METHODS

Studies had to be randomised controlled trials, lasting at least 3 weeks, and compare the effects of HIIT on at least one of the MetS clinical variables [fasting blood glucose (BG), high-density lipoprotein (HDL-C) triglyceride (TG), systolic (SBP) or diastolic blood pressure (DBP) and waist circumference (WC)] compared to a control group. The methodological quality of the studies selected was evaluated using the PEDro scale.

RESULTS

Ten articles fulfilled the selection criteria, with a mean quality score on the PEDro scale of 6.7. Compared with controls, HIIT groups showed significant and relevant reductions in BG (- 0.11 mmol/L), SBP (- 4.44 mmHg), DBP (- 3.60 mmHg), and WC (- 2.26 cm). Otherwise, a slight increase was observed in HDL-C (+ 0.02 mmol/L). HIIT did not produce any significant changes in TG (- 1.29 mmol/L).

CONCLUSIONS

HIIT improves certain clinical aspects in people with MetS (BG, SBP, DBP and WC) compared to people with MetS who do not perform physical exercise. Plausible physiological changes of HIIT interventions might be related with large skeletal muscle mass implication, improvements in the vasomotor control, better baroreflex control, reduction of the total peripheral resistance, increases in excess post-exercise oxygen consumption, and changes in appetite and satiety mechanisms.

Computational approaches to understanding nutrient metabolism and metabolic disorders

Computational methods are becoming more and more essential to elucidate biological systems. Many different approaches exist with pros and cons. This paper reviews the most useful technologies focusing on nutrient metabolism and metabolic disorders. Space limitation prevents from exploring the examples in details, but pointers to the relevant papers are reported.

Network pharmacology-based prediction of the active ingredients and mechanism of Shen Gui capsule for application to coronary heart disease

BACKGROUND

Shen Gui capsule (SGC) is a new national drug in China that is widely used in clinical practice and has significant therapeutic effects on coronary heart disease (CHD). However, its active ingredients and mechanism of action for treating coronary heart disease remain unknown. Therefore, the purpose of this paper is to systematically explore the mechanism and efficacy of SGC in the "multicomponent-multitarget- multipathway" treatment for CHD using network pharmacology technology.

METHODS

The potential active ingredients of SGC were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and screened by pharmacokinetic parameters. Their possible targets were predicted using the TCMSP and DrugBank database. The CHD-related targets were identified from Comparative Toxicogenomics Database (CTD), UniProt, and PharmGKB database. The compound-target-disease network was constructed using Cytoscape for visualization. Additionally, the protein functional annotation and identification of signaling pathways of potential targets were performed by Gene Ontology (GO) and KEGG enrichment analysis using the Metascape platform.

RESULTS

The 61 active ingredients of SGC were found to regulate neuroactive ligand-receptor interaction, fluid shear stress and atherosclerosis, estrogen signaling pathway and other pathways through 62 targets. SGC is involved in regulating the circulatory system, nervous system and immune system and other aspects of the body, and thus plays a significant role in the treatment of CHD and its complications, showing the mechanism of SGC's "multicomponent, multitarget, and multipathway" prevention and treatment of CHD. In addition, three predictive components were first found to have potential biological activity by this method.

CONCLUSION

The studies we have performed successfully predict the effective components and potential targets of SGC in the prevention and treatment of CHD, which helped to systematically clarify its mechanism of action and provided a direction for future research on the modern mechanism of SGC in the treatment of CHD.