Comprehensive overview of COVID-19 clinical trials.

Here, we present an overview of the clinical trials that are currently being conducted or have concluded to date on COVID-19 globally. A comprehensive search was conducted to present 16 trial registries from around the world. Collectively, there are 1,528 trials reported for COVID-19 to date. Out of them, 50 studies included paediatric age group from day 0 to less than or equal to 18 years of age. A few 18 studies involve only females and 20 only males. There are 2 trials currently underway in Bangladesh, 4 in Pakistan and 13 in India. Overall, 940 trials are related to medicines and/or interventions. They include standard of care for any viral illness, antivirals, anti-inflammatory and immune altering medications. Two out of 10 vaccine trials are novel vaccines. It is knowledgeable and resourceful to reach out to the concerned sponsor if a physician thinks his patient can benefit from the trials in the region.

Visualizing the regulatory role of Angiopoietin-like protein 8 (ANGPTL8) in glucose and lipid metabolic pathways.

ANGPTL8 (Angiopoietin-like protein 8) is a newly identified hormone emerging as a novel drug target for treatment of diabetes mellitus and dyslipidemia due to its unique metabolic nature. With increasing number of studies targeting the regulation of ANGPTL8, integration of their findings becomes indispensable. This study has been conducted with the aim to collect, analyze, integrate and visualize the available knowledge in the literature about ANGPTL8 and its regulation. We utilized this knowledge to construct a regulatory pathway of ANGPTL8 which is available at WikiPathways, an open source pathways database. It allows us to visualize ANGPTL8's regulation with respect to other genes/proteins in different pathways helping us to understand the complex interplay of novel hormones/genes/proteins in metabolic disorders. To the best of our knowledge, this is the first attempt to present an integrated pathway view of ANGPTL8's regulation and its associated pathways and is important resource for future omics-based studies.

Trackable and scalable LC-MS metabolomics data processing using asari.

Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination.

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

A Computational Systems Analyses to Identify Biomarkers and Mechanistic Link in Psoriasis and Cutaneous Squamous Cell Carcinoma.

Psoriasis is the most common and chronic skin disease that affects individuals from every age group. The rate of psoriasis is increasing over the time in both developed and developing countries. Studies have revealed the possibility of association of psoriasis with skin cancers, particularly non-melanoma skin cancers (NMSC), which, include basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC). There is a need to analyze the disease at molecular level to propose potential biomarkers and therapeutic targets in comparison to cSCC. Therefore, the second analyzed disease of this study is cSCC. It is the second most common prevalent skin cancer all over the world with the potential to metastasize and recur. There is an urge to validate the proposed biomarkers and discover new potential biomarkers as well. In order to achieve the goals and objectives of the study, microarray and RNA-sequencing data analyses were performed followed by network analysis. Afterwards, quantitative systems biology was implemented to analyze the results at a holistic level. The aim was to predict the molecular patterns that can lead psoriasis to cancer. The current study proposed potential biomarkers and therapeutic targets for psoriasis and cSCC. IL-17 signaling pathway is also identified as significant pathway in both diseases. Moreover, the current study proposed that autoimmune pathology, neutrophil recruitment, and immunity to extracellular pathogens are sensitive towards MAPKs (MAPK13 and MAPK14) and genes for AP-1 (FOSL1 and FOS). Therefore, these genes should be further studied in gene knock down based studies as they may play significant role in leading psoriasis towards cancer.

Long non-coding RNAs and their targets as potential biomarkers in breast cancer.

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

Deciphering the expression dynamics of ANGPTL8 associated regulatory network in insulin resistance using formal modelling approaches.

ANGPTL8 is a recently identified novel hormone which regulates both glucose and lipid metabolism. The increase in ANGPTL8 during compensatory insulin resistance has been recently reported to improve glucose tolerance and a part of cytoprotective metabolic circuit. However, the exact signalling entities and dynamics involved in this process have remained elusive. Therefore, the current study was conducted with a specific aim to model the regulation of ANGPTL8 with emphasis on its role in improving glucose tolerance during insulin resistance. The main contribution of this study is the construction of a discrete model (based on kinetic logic of René Thomas) and its equivalent Stochastic Petri Net model of ANGPTL8 associated Biological Regulatory Network (BRN) which can predict its dynamic behaviours. The predicted results of these models are in-line with the previous experimental observations and provide comprehensive insights into the signalling dynamics of ANGPTL8 associated BRN. The authors' results support the hypothesis that ANGPTL8 plays an important role in supplementing the insulin signalling pathway during insulin resistance and its loss can aggravate the pathogenic process by quickly leading towards Diabetes Mellitus. The results of this study have potential therapeutic implications for treatment of Diabetes Mellitus and are suggestive of its potential as a glucose-lowering agent.

Petri Net modelling approach for analysing the behaviour of Wnt/[inline-formula removed]-catenin and Wnt/Ca2+ signalling pathways in arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that may result in arrhythmia, heart failure and sudden death. The hallmark pathological findings are progressive myocyte loss and fibro fatty replacement, with a predilection for the right ventricle. This study focuses on the adipose tissue formation in cardiomyocyte by considering the signal transduction pathways including Wnt/[inline-formula removed]-catenin and Wnt/Ca2+ regulation system. These pathways are modelled and analysed using stochastic petri nets (SPN) in order to increase our comprehension of ARVC and in turn its treatment regimen. The Wnt/[inline-formula removed]-catenin model predicts that the dysregulation or absence of Wnt signalling, inhibition of dishevelled and elevation of glycogen synthase kinase 3 along with casein kinase I are key cytotoxic events resulting in apoptosis. Moreover, the Wnt/Ca2+ SPN model demonstrates that the Bcl2 gene inhibited by c-Jun N-terminal kinase protein in the event of endoplasmic reticulum stress due to action potential and increased amount of intracellular Ca2+ which recovers the Ca2+ homeostasis by phospholipase C, this event positively regulates the Bcl2 to suppress the mitochondrial apoptosis which causes ARVC.

Tryptophan catabolism reflects disease activity in human tuberculosis.

There is limited understanding of the role of host metabolism in the pathophysiology of human tuberculosis (TB). Using high-resolution metabolomics with an unbiased approach to metabolic pathway analysis, we discovered that the tryptophan pathway is highly regulated throughout the spectrum of TB infection and disease. This regulation is characterized by increased catabolism of tryptophan to kynurenine, which was evident not only in active TB disease but also in latent TB infection (LTBI). Further, we found that tryptophan catabolism is reversed with effective treatment of both active TB disease and LTBI in a manner commensurate with bacterial clearance. Persons with active TB and LTBI also exhibited increased expression of indoleamine 2,3-dioxygenase-1 (IDO-1), suggesting IDO-1 mediates observed increases in tryptophan catabolism. Together, these data indicate IDO-1-mediated tryptophan catabolism is highly preserved in the human response to Mycobacterium tuberculosis and could be a target for biomarker development as well as host-directed therapies.

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma.

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.

Biological Pathways Leading From ANGPTL8 to Diabetes Mellitus-A Co-expression Network Based Analysis.

Angiopoietin like protein 8 (ANGPTL8) is a newly identified hormone with unique nature due to its ability to regulate both glucose and lipid metabolic pathways. It is characterized as an important molecular player of insulin induced nutrient storage and utilization pathway during fasting to re-feeding metabolic transition. Several studies have contributed to increase our knowledge regarding its function and mechanism of action. Moreover, its altered expression levels have been observed in Insulin Resistance, Diabetes Mellitus (Types I & II) and Non Alcohlic Fatty Liver Disease emphasizing its assessment as a drug target. However, there is still a great deal of information that remains to be investigated including its associated biological processes, partner proteins in these processes, its regulators and its association with metabolic pathogenesis. In the current study, the analysis of a transcriptomic data set was performed for functional assessment of ANGPTL8 in liver. Weighted Gene Co-expression Network Analysis coupled with pathway analysis tools was performed to identify genes that are significantly co-expressed with ANGPTL8 in liver and investigate their presence in biological pathways. Gene ontology term enrichment analysis was performed to select the gene ontology classes that over-represent the hepatic ANGPTL8-co-expressed genes. Moreover, the presence of diabetes linked SNPs within the genes set co-expressed with ANGPTL8 was investigated. The co-expressed genes of ANGPTL8 identified in this study (n = 460) provides narrowed down list of molecular targets which are either co-regulated with it and/or might be regulation partners at different levels of interaction. These results are coherent with previously demonstrated roles and regulators of ANGPTL8. Specifically, thirteen co-expressed genes (MAPK8, CYP3A4, PIK3R2, PIK3R4,PRKAB2, G6PC, MAP3K11, FLOT1, PIK3C2G, SHC1, SLC16A2, and RAPGEF1) are also present in the literature curated pathway of ANGPTL8 (WP3915). Moreover, the gene-SNP analysis of highly associated biological processes with ANGPTL8 revealed significant genetic signals associated to Diabetes Mellitus and similar phenotypic traits. It provides meaningful insights on the influencing genes involved and co-expressed in these pathways. Findings of this study have implications in functional characterization of ANGPTL8 with emphasis on the identified genes and pathways and their possible involvement in the pathogenesis of Diabetes Mellitus and Insulin Resistance.

Formal Modeling of mTOR Associated Biological Regulatory Network Reveals Novel Therapeutic Strategy for the Treatment of Cancer.

Cellular homeostasis is a continuous phenomenon that if compromised can lead to several disorders including cancer. There is a need to understand the dynamics of cellular proliferation to get deeper insights into the prevalence of cancer. Mechanistic Target of Rapamycin (mTOR) is implicated as the central regulator of the metabolic pathway involved in growth whereas its two distinct complexes mTORC1 and mTORC2 perform particular functions in cellular propagation. To date, mTORC1 is a well defined therapeutic target to inhibit uncontrolled cell division, while the role of mTORC2 is not well characterized. Therefore, the current study is designed to understand the signaling dynamics of mTOR and its partner proteins such as PI3K, PTEN, mTORC2, PKB (Akt), mTORC1, and FOXO. For this purpose, a qualitative model of mTOR-associated Biological Regulatory Network (BRN) is constructed to predict its regulatory behaviors which may not be predictable otherwise. The depleted expression of PTEN and FOXO along with the overexpression of PI3K, mTORC2, mTORC1 and Akt is predicted as a stable steady state which is in accordance with their observed expression levels in the progression of various cancers. The qualitative model also predicts the homeostasis of all the entities in the form of qualitative cycles. The significant qualitative (discrete) cycle is identified by analyzing betweenness centralities of the qualitative (discrete) states. This cycle is further refined as a linear hybrid automaton model with the production (activation) and degradation (inhibition) time delays in order to analyze the real-time constraints for its existence. The analysis of the hybrid model provides a formal proof that during homeostasis the inhibition time delay of Akt is less than the inhibition time delay of mTORC2. In conclusion, our observations characterize that in homeostasis Akt is degraded with a faster rate than mTORC2 which suggests that the inhibition of Akt along with the activation of mTORC2 may be a better therapeutic strategy for the treatment of cancer.

Structural characterization of ANGPTL8 (betatrophin) with its interacting partner lipoprotein lipase.

Angiopoietin-like protein 8 (ANGPTL8) (also known as betatrophin) is a newly identified secretory protein with a potential role in autophagy, lipid metabolism and pancreatic beta-cell proliferation. Its structural characterization is required to enhance our current understanding of its mechanism of action which could help in identifying its receptor and/or other binding partners. Based on the physiological significance and necessity of exploring structural features of ANGPTL8, the present study is conducted with a specific aim to model the structure of ANGPTL8 and study its possible interactions with Lipoprotein Lipase (LPL). To the best of our knowledge, this is the first attempt to predict 3-dimensional (3D) structure of ANGPTL8. Three different approaches were used for modeling of ANGPTL8 including homology modeling, de-novo structure prediction and their amalgam which is then proceeded by structure verification using ERRATT, PROSA, Qmean and Ramachandran plot scores. The selected models of ANGPTL8 were further evaluated for protein-protein interaction (PPI) analysis with LPL using CPORT and HADDOCK server. Our results have shown that the crystal structure of iSH2 domain of Phosphatidylinositol 3-kinase (PI3K) p85ß subunit (PDB entry: 3mtt) is a good candidate for homology modeling of ANGPTL8. Analysis of inter-molecular interactions between the structure of ANGPTL8 and LPL revealed existence of several non-covalent interactions. The residues of LPL involved in these interactions belong from its lid region, thrombospondin (TSP) region and heparin binding site which is suggestive of a possible role of ANGPTL8 in regulating the proteolysis, motility and localization of LPL. Besides, the conserved residues of SE1 region of ANGPTL8 formed interactions with the residues around the hinge region of LPL. Overall, our results support a model of inhibition of LPL by ANGPTL8 through the steric block of its catalytic site which will be further explored using wet lab studies in future.

Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer.

BACKGROUND: Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. METHODS: A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. RESULTS: In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. CONCLUSION: The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

Formal modeling and analysis of the hexosamine biosynthetic pathway: role of O-linked N-acetylglucosamine transferase in oncogenesis and cancer progression.

The alteration of glucose metabolism, through increased uptake of glucose and glutamine addiction, is essential to cancer cell growth and invasion. Increased flux of glucose through the Hexosamine Biosynthetic Pathway (HBP) drives increased cellular O-GlcNAcylation (hyper-O-GlcNAcylation) and contributes to cancer progression by regulating key oncogenes. However, the association between hyper-O-GlcNAcylation and activation of these oncogenes remains poorly characterized. Here, we implement a qualitative modeling framework to analyze the role of the Biological Regulatory Network in HBP activation and its potential effects on key oncogenes. Experimental observations are encoded in a temporal language format and model checking is applied to infer the model parameters and qualitative model construction. Using this model, we discover step-wise genetic alterations that promote cancer development and invasion due to an increase in glycolytic flux, and reveal critical trajectories involved in cancer progression. We compute delay constraints to reveal important associations between the production and degradation rates of proteins. O-linked N-acetylglucosamine transferase (OGT), an enzyme used for addition of O-GlcNAc during O-GlcNAcylation, is identified as a key regulator to promote oncogenesis in a feedback mechanism through the stabilization of c-Myc. Silencing of the OGT and c-Myc loop decreases glycolytic flux and leads to programmed cell death. Results of network analyses also identify a significant cycle that highlights the role of p53-Mdm2 circuit oscillations in cancer recovery and homeostasis. Together, our findings suggest that the OGT and c-Myc feedback loop is critical in tumor progression, and targeting these mediators may provide a mechanism-based therapeutic approach to regulate hyper-O-GlcNAcylation in human cancer.