Modulation of in Vitro SARS-CoV-2 Infection by Stephania tetrandra and Its Alkaloid Constituents.

Botanical natural products have been widely consumed for their purported usefulness against COVID-19. Here, six botanical species from multiple sources and 173 isolated natural product compounds were screened for blockade of wild-type (WT) SARS-CoV-2 infection in human 293T epithelial cells overexpressing ACE-2 and TMPRSS2 protease (293TAT). Antiviral activity was demonstrated by an extract from Stephania tetrandra. Extract fractionation, liquid chromatography-mass spectrometry (LC-MS), antiviral assays, and computational analyses revealed that the alkaloid fraction and purified alkaloids tetrandrine, fangchinoline, and cepharanthine inhibited WT SARS-CoV-2 infection. The alkaloids and alkaloid fraction also inhibited the delta variant of concern but not WT SARS-CoV-2 in VeroAT cells. Membrane permeability assays demonstrate that the alkaloids are biologically available, although fangchinoline showed lower permeability than tetrandrine. At high concentrations, the extract, alkaloid fractions, and pure alkaloids induced phospholipidosis in 293TAT cells and less so in VeroAT cells. Gene expression profiling during virus infection suggested that alkaloid fraction and tetrandrine displayed similar effects on cellular gene expression and pathways, while fangchinoline showed distinct effects on cells. Our study demonstrates a multifaceted approach to systematically investigate the diverse activities conferred by complex botanical mixtures, their cell-context specificity, and their pleiotropic effects on biological systems.

Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus-host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems.

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high 'control centrality' of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.

Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians.

BACKGROUND: Multi-marker approaches for risk prediction in coronary artery disease (CAD) have been inconsistent due to biased selection of specific know biomarkers. We have assessed the global proteome of CAD-affected and unaffected subjects, and developed a pathway network model for elucidating the mechanism and risk prediction for CAD. MATERIALS AND METHODS: A total of 252 samples (112 CAD-affected without family history and 140 true controls) were analyzed by Surface-Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) by using CM10 cationic chips and bioinformatics tools. RESULTS: Out of 36 significant peaks in SELDI-TOF MS, nine peaks could do better discrimination of CAD subjects and controls (area under the curve (AUC) of 0.963) based on the Support Vector Machine (SVM) feature selection method. Of the nine peaks used in the model for discrimination of CAD-affected and unaffected, the m/z corresponding to 22,859 was identified as stress-related protein HSP27 and was shown to be highly associated with CAD (odds ratio of 3.47). The 36 biomarker peaks were identified and a network profile was constructed showing the functional association between different pathways in CAD. CONCLUSION: Based on our data, proteome profiling with SELDI-TOF MS and SVM feature selection methods can be used for novel network biomarker discovery and risk stratification in CAD. The functional associations of the identified novel biomarkers suggest that they play an important role in the development of disease.

Integrative bioinformatics analysis of genomic and proteomic approaches to understand the transcriptional regulatory program in coronary artery disease pathways.

Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

Relationship of adiponectin and leptin to coronary artery disease, classical cardiovascular risk factors and atherothrombotic biomarkers in the IARS cohort.

Adiponectin and leptin link metabolic disorders and coronary artery disease (CAD). We analysed their relationship with CAD, classical risk factors and biomarkers in 287 CAD patients (cases) and 477 unaffected family members (controls) selected from the Indian Atherosclerosis Research Study (IARS). Classical risk factors included diabetes, hypertension, dyslipidaemia and obesity markers. Novel biomarkers were measured according to manufacturer recommendations. Adverse clinical events were recorded through telephonic follow-up. Cases showed lower adiponectin levels (4684.62 ± 190.73 ng/ml) than controls (5768.86 ± 152.87 ng/ml) (p=1.58X10(-5)); Leptin levels were higher in affected males (12.47 ± 1.32 ng/ml) than in male controls (9.53 ± 1.19 ng/ml, p=0.017). Adiponectin 1st quartile showed significant protection against CAD in females when compared to 3rd (odds ratio [OR] 0.39, 0.16-0.92, p=0.032) or 4th (OR 0.32, 0.14-0.72; p=0.006) quartile group. Leptin 3rd quartile showed higher CAD risk in males as compared to 1st quartile group (OR 2.09, 1.09-4.01, p=0.028). Subjects with metabolic syndrome showed low adiponectin and high leptin levels. Adipokines showed opposing association trend with lipids, inflammatory and coagulation markers and strong correlation (r=-0.14 to 0.52) with obesity markers. Cases with recurrent event and controls who developed new cardiac event during follow up showed high adiponectin levels (p<0.05). A model that combined adiponectin, leptin and conventional risk factors yielded the best 'C' index (0.890, 0.067-0.912). CAD patients in the top adiponectin tertile showed relatively poor survival curve as compared to the bottom Adiponectin tertile group. In conclusion, our findings strengthen the reported association between low adiponectin, high leptin, obesity-related metabolic disturbances and incident CAD in Asian Indians.