An integrated in-silico Pharmaco-BioInformatics approaches to identify synergistic effects of COVID-19 to HIV patients

COVID-19-Associated Sepsis: Potential Role of Phytochemicals as Functional Foods and Nutraceuticals

The acute manifestations of coronavirus disease 2019 (COVID-19) exhibit the hallmarks of sepsis-associated complications that reflect multiple organ failure. The inflammatory cytokine storm accompanied by an imbalance in the pro-inflammatory and anti-inflammatory host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to severe and critical septic shock. The sepsis signature in severely afflicted COVID-19 patients includes cellular reprogramming and organ dysfunction that leads to high mortality rates, emphasizing the importance of improved clinical care and advanced therapeutic interventions for sepsis associated with COVID-19. Phytochemicals of functional foods and nutraceutical importance have an incredible impact on the healthcare system, which includes the prevention and/or treatment of chronic diseases. Hence, in the present review, we aim to explore the pathogenesis of sepsis associated with COVID-19 that disrupts the physiological homeostasis of the body, resulting in severe organ damage. Furthermore, we have summarized the diverse pharmacological properties of some potent phytochemicals, which can be used as functional foods as well as nutraceuticals against sepsis-associated complications of SARS-CoV-2 infection. The phytochemicals explored in this article include quercetin, curcumin, luteolin, apigenin, resveratrol, and naringenin, which are the major phytoconstituents of our daily food intake. We have compiled the findings from various studies, including clinical trials in humans, to explore more into the therapeutic potential of each phytochemical against sepsis and COVID-19, which highlights their possible importance in sepsis-associated COVID-19 pathogenesis. We conclude that our review will open a new research avenue for exploring phytochemical-derived therapeutic agents for preventing or treating the life-threatening complications of sepsis associated with COVID-19.

Insights into the dynamic interactions of RNase a and osmolytes through computational approaches

Osmolytes are small organic molecules that are known to stabilize proteins and other biological macromolecules under various stressful conditions. They belong to various categories such as amino acids, methylamines, and polyols. These substances are commonly known as 'compatible solutes' because they do not disrupt cellular processes and help regulate the osmotic balance within cells. In the case of ribonuclease A (RNase A), which is prone to aggregation, the presence of osmolytes can help to maintain its structural stability and prevent unwanted interactions leading to protein aggregation. In this study, we investigated the interaction between RNase A and several osmolytes using molecular docking and molecular dynamics (MD) simulations. We performed molecular docking to predict the binding mode and binding affinity of each osmolyte with RNase A. MD simulations were then carried out to investigate the dynamics and stability of the RNase A-osmolyte complexes. Our results show that two osmolytes, glucosylglycerol and sucrose have favorable binding affinities with RNase A. The possible role of these osmolytes in stabilizing the RNase A and prevention of aggregation is also explored. By providing computational insights into the interaction between RNase A and osmolytes, the study offers valuable information that could aid in comprehending the mechanisms by which osmolytes protect proteins and help in designing therapeutics for protein-related disorders based on osmolytes. These findings may have significant implications for the development of novel strategies aimed at preventing protein misfolding and aggregation in diverse disease conditions.Communicated by Ramaswamy H. Sarma.

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.

Molecular interactions between metformin and D-limonene inhibit proliferation and promote apoptosis in breast and liver cancer cells

BACKGROUND

Cancer is a fatal disease that severely affects humans. Designing new anticancer strategies and understanding the mechanism of action of anticancer agents is imperative.

HYPOTHESIS/PURPOSE

In this study, we evaluated the utility of metformin and D-limonene, alone or in combination, as potential anticancer therapeutics using the human liver and breast cancer cell lines HepG2 and MCF-7.

STUDY DESIGN

An integrated systems pharmacology approach is presented for illustrating the molecular interactions between metformin and D-limonene.

METHODS

We applied a systems-based analysis to introduce a drug-target-pathway network that clarifies different mechanisms of treatment. The combination treatment of metformin and D-limonene induced apoptosis in both cell lines compared with single drug treatments, as indicated by flow cytometric and gene expression analysis.

RESULTS

The mRNA expression of Bax and P53 genes were significantly upregulated while Bcl-2, iNOS, and Cox-2 were significantly downregulated in all treatment groups compared with normal cells. The percentages of late apoptotic HepG2 and MCF-7 cells were higher in all treatment groups, particularly in the combination treatment group. Calculations for the combination index (CI) revealed a synergistic effect between both drugs for HepG2 cells (CI = 0.14) and MCF-7 cells (CI = 0.22).

CONCLUSION

Our data show that metformin, D-limonene, and their combinations exerted significant antitumor effects on the cancer cell lines by inducing apoptosis and modulating the expression of apoptotic genes.

Berberine modulates cardiovascular diseases as a multitarget-mediated alkaloid with insights into its downstream signals using in silico prospective screening approaches

Atherosclerosis is potentially correlated with several cardiac disorders that are greatly associated with cellular oxidative stress generation, inflammation, endothelial cells dysfunction, and many cardiovascular complications. Berberine is a natural isoquinoline alkaloid compound that widely modulates pathogenesis of atherosclerosis through its different curative potentials. This in silico screening study was designed to confirm the potent restorative properties of berberine chloride as a multitarget-mediated alkaloid against the CVDs and their complications through screening, identifying, visualizing, and evaluating its binding models, affinities, and interactions toward several CVDs-related targets as direct and/or indirect-mediated signals via inhibiting cellular ER stress and apoptotic signals and activating autophagy pathway. The drug-likeness properties of berberine were predicted using the computational QSAR/ADMET and Lipinski's RO5 analyses as well as in silico molecular docking simulations. The potent berberine-binding modes, residues-interaction patterns, and free energies of binding scores towards several CVDs-related targets were estimated using molecular docking tools. Furthermore, the pharmacokinetic properties and toxicological features of berberine were clearly determined. According to this in silico virtual screening study, berberine chloride could restore cardiac function and improve pathogenic features of atherosclerotic CVDs through alleviating ER stress and apoptotic signals, activating autophagy, improving insulin sensitivity, decreasing hyperglycemia and dyslipidemia, increasing intracellular RCT signaling, attenuating oxidative stress and vascular inflammation, and upregulating cellular antioxidant defenses in many cardiovascular tissues. In this in silico study, berberine chloride greatly modulated several potent CVDs-related targets, including SIGMAR1, GRP78, CASP3, BECN1, PIK3C3, SQSTM1/p62, LC3B, GLUT3, INSR, LDLR, LXRα, PPARγ, IL1β, IFNγ, iNOS, COX-2, MCP-1, IL10, GPx1, and SOD3.

In silico and in vivo evaluation of the anti-cryptosporidial activity of eugenol

Background

Cryptosporidiosis is an opportunistic parasitic disease widely distributed worldwide. Although Cryptosporidium sp. causes asymptomatic infection in healthy people, it may lead to severe illness in immunocompromised individuals. Limited effective therapeutic alternatives are available against cryptosporidiosis in this category of patients. So, there is an urgent need for therapeutic alternatives for cryptosporidiosis. Recently, the potential uses of Eugenol (EUG) have been considered a promising novel treatment for bacterial and parasitic infections. Consequently, it is suggested to investigate the effect of EUG as an option for the treatment of cryptosporidiosis.

Materials and methods

The in silico bioinformatics analysis was used to predict and determine the binding affinities and intermolecular interactions of EUG and Nitazoxanide (NTZ) toward several Cryptosporidium parvum (C. parvum) lowa II target proteins. For animal study, five groups of immunosuppressed Swiss albino mice (10 mice each) were used. Group I was left uninfected (control), and four groups were infected with 1,000 oocysts of Cryptosporidium sp. The first infected group was left untreated. The remaining three infected groups received NTZ, EUG, and EUG + NTZ, respectively, on the 6th day post-infection (dpi). All mice were sacrificed 30 dpi. The efficacy of the used formulas was assessed by counting the number of C. parvum oocysts excreted in stool of infected mice, histopathological examination of the ileum and liver tissues and determination of the expression of iNOS in the ileum of mice in different animal groups.

Results

treatment with EUG resulted in a significant reduction in the number of oocysts secreted in stool when compared to infected untreated mice. In addition, oocyst excretion was significantly reduced in mice received a combination therapy of EUG and NTZ when compared with those received NTZ alone. EUG succeeded in reverting the histopathological alterations induced by Cryptosporidium infection either alone or in combination with NTZ. Moreover, mice received EUG showed marked reduction of the expression of iNOS in ileal tissues.

Conclusion

Based on the results, the present study signified a basis for utilizing EUG as an affordable, safe, and alternative therapy combined with NTZ in the management of cryptosporidiosis.

Network pharmacology and in vivo evidence of the pharmacological mechanism of geniposide in the treatment of atherosclerosis

BACKGROUND

Atherosclerosis (AS) is a fundamental pathological state in various cardiovascular diseases. Geniposide, which is the main active component of Gardenia jasminides, is effective against AS. However, the underlying molecular mechanisms remain unclear. Here, we sought to elucidate them.

METHODS

The targets of AS and geniposide were collected from online public databases. The potential mechanism of Geniposide in treating AS was predicted by constructing a protein-protein interaction (PPI) network and conducting Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hub proteins and core pathways were verified by molecular docking and in vivo experiments. Moreover, the effect of geniposide on AS was assessed by measuring the atherosclerotic plaque area in the thoracic aorta of mice. ApoE-/- mice were used to establish AS models and randomly divided into different groups. Two different doses of geniposide were administered to the mice. Hematoxylin and eosin (HE) staining was performed to evaluate the effects of geniposide on AS. Oil Red O and Sirius Red staining were used to evaluate plaque stability. The protein expression of key markers involved in the signalling pathways was examined using western blotting and immunofluorescence.

RESULTS

A total of 239 active targets, 3418 AS-related disease targets, and 129 overlapping targets were identified. Hub genes were detected, and molecular docking revealed that geniposide strongly interacted with hub proteins (AKT1, VEGFA, CTNNB1, MMP9, and EGFR). Moreover, 109 signalling pathways, including the Rap1 signalling pathway, were identified using enrichment analysis. The results of in vivo experiments demonstrated that geniposide reduced body weight and blood lipid levels, alleviated the formation of atherosclerotic plaques, enhanced plaque stability, and inhibited inflammation, at least partially, by activating the Rap1/PI3K/Akt signalling pathway in ApoE-/- mice.

CONCLUSION

Geniposide can alleviate AS and enhance the stability of atherosclerotic plaques by regulating the Rap1/PI3K/Akt signalling pathway.

Research progress of plant-derived natural products in thyroid carcinoma

Thyroid carcinoma (TC) is a prevalent malignancy of the endocrine system, with a notable rise in its detection rate in recent decades. The primary therapeutic approaches for TC now encompass thyroidectomy and radioactive iodine therapy, yielding favorable prognoses for the majority of patients. TC survivors may necessitate ongoing surveillance, remedial treatment, and thyroid hormone supplementation, while also enduring the adverse consequences of thyroid hormone fluctuations, surgical complications, or side effects linked to radioactive iodine administration, and encountering enduring physical, psychosocial, and economic hardships. In vitro and in vivo studies of natural products against TC are demonstrating the potential of these natural products as alternatives to the treatment of thyroid cancer. This therapy may offer greater convenience, affordability, and acceptability than traditional therapies. In the early screening of natural products, we mainly use a combination of database prediction and literature search. The pharmacological effects on TC of selected natural products (quercetin, genistein, apigenin, luteolin, chrysin, myricetin, resveratrol, curcumin and nobiletin), which hold promise for therapeutic applications in TC, are reviewed in detail in this article through most of the cell-level evidence, animal-level evidence, and a small amount of human-level evidence. In addition, this article explores possible issues, such as bioavailability, drug safety.

Mechanism of XiJiaQi in the treatment of chronic heart failure: Integrated analysis by pharmacoinformatics, molecular dynamics simulation, and SPR validation

OBJECTIVE

Chronic heart failure (CHF) is a complicated clinical syndrome with a high mortality rate. XiJiaQi (XJQ) is a traditional Chinese medicine used in the clinical treatment of CHF, but its bioactive components and their modes of action remain unknown. This study was designed to unravel the molecular mechanism of XJQ in the treatment of CHF using multiple computer-assisted and experimental methods.

METHODS

Pharmacoinformatics-based methods were used to explore the active components and targets of XJQ in the treatment of CHF. ADMETlab was then utilized to evaluate the pharmacokinetic and toxicological properties of core components. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were to explore the underlying mechanism of XJQ treatment. Molecular docking, surface plasmon resonance (SPR), and molecular dynamics (MD) were employed to evaluate the binding of active components to putative targets.

RESULTS

Astragaloside IV, formononetin, kirenol, darutoside, periplocin and periplocymarin were identified as core XJQ-related components, and IL6 and STAT3 were identified as core XJQ targets. ADME/T results indicated that periplocin and periplocymarin may have potential toxicity. GO and KEGG pathway analyses revealed that XJQ mainly intervenes in inflammation, apoptosis, diabetes, and atherosclerosis-related biological pathways. Molecular docking and SPR revealed that formononetin had a high affinity with IL6 and STAT3. Furthermore, MD simulation confirmed that formononetin could firmly bind to the site 2 region of IL6 and the DNA binding domain of STAT3.

CONCLUSION

This study provides a mechanistic rationale for the clinical application of XJQ. Modulation of STAT3 and IL-6 by XJQ can impact CHF, further guiding research efforts into the molecular underpinnings of CHF.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Integrating Computational Methods in Network Pharmacology and In Silico Screening to Uncover Multi-targeting Phytochemicals against Aberrant Protein Glycosylation in Lung Cancer

Glycoproteins are an underexploited drug target for cancer therapeutics. In this work, we integrated computational methods in network pharmacology and in silico docking approaches to identify phytochemical compounds that could potentially interact with several cancer-associated glycoproteins. We first created a database of phytochemicals from selected plant species, Manilkara zapota (sapodilla/chico), Mangifera indica (mango), Annona muricata (soursop/guyabano), Artocarpus heterophyllus (jackfruit/langka), Lansium domesticum (langsat/lanzones), and Antidesma bunius (bignay), and performed pharmacokinetic analysis to determine their drug-likeness properties. We then constructed a phytochemical-glycoprotein interaction network and characterized the degree of interactions between the phytochemical compounds and with cancer-associated glycoproteins and other glycosylation-related proteins. We found a high degree of interactions from α-pinene (Mangifera indica), cyanomaclurin (Artocarpus heterophyllus), genistein (Annona muricata), kaempferol (Annona muricata and Antidesma bunius), norartocarpetin (Artocarpus heterophyllus), quercetin (Annona muricata, Antidesma bunius, Manilkara zapota, Mangifera indica), rutin (Annona muricata, Antidesma bunius, Lansium domesticum), and ellagic acid (Antidesma bunius and Mangifera indica). Subsequent docking analysis confirmed that these compounds could potentially bind to EGFR, AKT1, KDR, MMP2, MMP9, ERBB2, IGF1R, MTOR, and HRAS proteins, which are known cancer biomarkers. In vitro cytotoxicity assays of the plant extracts showed that the n-hexane, ethyl acetate, and methanol leaf extracts from A. muricata, L. domesticum and M. indica gave the highest growth inhibitory activity against A549 lung cancer cells. These may help further explain the reported cytotoxic activities of select compounds from these plant species.