Immunomodulatory Effect of Phytoactive Compounds on Human Health: A Narrative Review Integrated with Bioinformatics Approach.

BACKGROUND: Immunomodulation is the modification of immune responses to control disease progression. While the synthetic immunomodulators have proven efficacy, they are coupled with toxicity and other adverse effects, and hence, the efforts were to identify natural phytochemicals with immunomodulatory potential. OBJECTIVE: The objective of this study is to understand the immunomodulatory properties of various phytochemicals and investigate them in Echinacea species extracts using an in silico approach. METHODOLOGY: Several scientific database repositories were searched using different keywords: "Phytochemicals," "Alkaloids," "Polyphenols," "Flavonoids," "Lectins," "Glycosides," "Tannins," "Terpenoids," "Sterols," "Immunomodulators," and "Human Immune System" without any language restriction. Additionally, the study specifically investigated the immunomodulatory properties of Echinacea species extracts using gene expression analysis of GSE12259 from NCBI-GEO through the Bioconductor package GEOquery and limma. RESULTS: A total of 182 studies were comprehensively analyzed to understand immunomodulatory phytochemicals. The in silico analysis highlighted key biological processes (positive regulation of cytokine production, response to tumor necrosis factor) and molecular functions (cytokine receptor binding, receptor-ligand activity, and cytokine activity) among Echinacea species extracts contributing to immune responses. Further, it also indicated the association of various metabolic pathways, i.e., pathways in cancer, cytokine-cytokine receptor interaction, NF-kappa B, PI3K-Akt, TNF, MAPK, and NOD-like receptor signaling pathways, with immune responses. The study revealed various hub targets, including CCL20, CCL4, GCH1, SLC7A11, SOD2, EPB41L3, TNFAIP6, GCLM, EGR1, and FOS. CONCLUSION: The present study presents a cumulative picture of phytochemicals with therapeutic benefits. Additionally, the study also reported a few novel genes and pathways in Echinacea extracts by re-analyzing GSE 12259, indicating its anti-inflammatory, anti-viral, and immunomodulatory properties.

Exploring the molecular underpinning of psoriasis and its associated comorbidities through network approach: cross talks of genes and pathways.

Patients with psoriasis often complain of several linked disorders including autoimmune and cardiometabolic diseases. Understanding of molecular link between psoriasis and associated comorbidities would be of great interest at the point of patient care management. Integrative unbiased network approach, indicates significant unidirectional gene overlap between psoriasis and its associated comorbid condition including obesity (31 upregulated and 26 downregulated), ischemic stroke (14 upregulated and 2 downregulated), dyslipidaemia (5 upregulated, 5 downregulated), atherosclerosis (8 upregulated and 1 downregulated) and type II diabetes (5 upregulated, 5 downregulated). The analysis revealed substantial gene sharing among the different psoriasis-associated comorbidities. Molecular comorbidity index determining the strength of the interrelation between psoriasis and its comorbidities indicates prevalence of dyslipidaemia followed by type II diabetes among psoriasis patients. The Jaccard coefficient indices revealed psoriasis shared maximum number of biological pathways with dyslipidaemia followed by type 2 diabetes, ischemic stroke, obesity and atherosclerosis. Moreover, pathway annotation highlighted nearly 45 shared pathways amongst psoriasis and its comorbidities and a substantial number of shared pathways was found among multi-morbidities. Overall, the present study established conceivable link between psoriasis and comorbid diseases. The shared genes and overlapped pathways may be explored as a common productive target for psoriasis and its comorbid conditions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03533-y.

The interaction analysis between human serum albumin with chlorpyrifos and its derivatives through sub-atomic docking and molecular dynamics simulation techniques.

Chlorpyrifos (CPF) is an extensively used organophosphate pesticide for crop protection. However, there are concerns about it contaminating the environment and human health, with estimated three lakh deaths annually. The molecular modeling protocol was assisted in redesigning thirteen well-known CPF linkers and inserting them at five selectable CPF (R1-R5) positions of CPF to get 258 CPF derivatives. CPF and its derivatives were optimized using LigPrep and docked to a grid centralized on Trp214 using extra precision glide docking. The Binding free energy of complexes was calculated using molecular mechanics/generalized born surface area (MM-GBSA). CPF and CPFD-225 have glide scores of - 3.08 and - 6.152 kcal/mol, respectively, with human serum albumin and ΔG bind for CPF (- 33.041817 kcal/mol) (- 52.825 kcal/mol) for CPF-D225. The top ten CPF derivatives showed at least ninefold better binding free energy than the CPF proposed for polyclonal antibody production. Subsequently, molecular docking studies revealed that CPF and its derivatives could bind to human serum albumin (HSA). Furthermore, using the Desmond package, a 100-ns molecular dynamics (MD) simulation was performed on the potential binding site. The final systems of CPF-HSA and CPF-222D complexes consist of 76,014 and 76,026 atoms, respectively. The physical stability of both the systems (CPF-HSA and CPF-222D) was analyzed by considering the overall potential energy, RMSF, RMSD, Hydrophobic interactions, and water-mediated patterns, which showed total energy of - 141,610 kcal/mol and - 140,150 kcal/mol, respectively. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03344-7.

A comparative in silico study to detect the effect of food-additives on metabolic protein and its perturbations compensated by osmolytes.

Since its inception, food additive has been an integral part of the food processing industry with various commercial roles. Besides its advantages, various studies have already highlighted its long-term adverse effects on human health. However, in terms of protein structures and functions, the innate mechanism that triggers these effects has not been elucidated in previously reported studies. Our work takes an in silico approach to delve into structural implications resulting from these additives with three well studied metabolic proteins-lysozyme, bovine serum albumin (BSA) and ribonuclease A. Three classes of food additives- synthetic color, preservatives, and phosphate-containing, are taken here to understand their effects on the aforementioned metabolic proteins. Conventional molecular docking and dynamics (MD) studies reveal that these additives induce significant structural perturbations. Among them, carmoisine brings about the most secondary structural changes for lysozyme and ribonuclease A, whereas sodium tripolyphosphate affects BSA the most. To restore the secondary structural loss, we further examine the roles of osmolytes through cross-docking and higher timescale MD simulations. These studies unravel that application of osmolytes like raffinose and trehalose triggers structural restoration for BSA, lysozyme and ribonuclease A, and highlight their roles as co-formulants to alleviate the adverse effects of food additives.

Protein-protein interaction and in silico mutagenesis studies on IL17A and its peptide inhibitor.

Protein-protein interactions of Interleukin-17 (IL17) play vital role in the autoimmune and inflammatory diseases, such as rheumatoid arthritis, multiple sclerosis, and psoriasis. Potent therapeutics for these diseases could be developed by blocking or modulating these interactions through biologics, peptide inhibitors and small molecule inhibitors. Unlike biologics, peptide inhibitors are cost effective and can be orally available. Peptide inhibitors do not require a binding groove as that of small molecules either. Therefore, crystal structure of IL17A in complex with a high affinity peptide inhibitor (HAP) (1-IHVTIPADLWDWIN-14) is investigated with an aim to find hot spots that could improve its potency. An in silico mutagenesis strategy was implemented using FoldX PSSM to scan for positions tolerant to amino acid substitution. Three positions T4, A7, and N14 showed improved stability when mutated with 'F/M/Y', 'P' and 'F/M/Y', respectively. A set of 31 mutant peptides are designed through combinations of these tolerant mutations using Build Model application of FoldX. Binding affinity and interactions of 31 peptides are assessed through protein-peptide docking and binding free energy calculations. Two peptides namely, P1 ("1-IHVTIPPDLWDWIY-14") and P2 ("1-IHVMIPPDLWDWIF-14") showed better binding affinity to IL17A dimerization site compared to HAP. Interactions of P1, P2 and HAP are also analyzed through 100 ns molecular dynamics simulations using GROMACS v5.0. The results revealed that the P2 peptide likely to offer better potency compared to HAP and P1. Therefore, the P2 peptide can be synthesized to develop oral therapies for autoimmune and inflammatory diseases with further experimental evaluations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02856-y.

Discovery of T-cell Driven Subunit Vaccines from Zika Virus Genome: An Immunoinformatics Approach.

The recent outbreaks of Zika virus and the absence of a specific therapy have necessitated to identify T-cell-stimulating antigenic peptides as potential subunit vaccine candidates. The translated ssRNA (+) genome of Zika virus was explored in EMBOSS antigenic and VaxiJen to predict 63 peptides as potential antigens. Three MHC-II binding peptide prediction tools, viz. NetMHCIIpan, PREDIVAC and immune epitope database (IEDB) were employed in consensus on 63 antigenic peptides to propose 14 T-helper cell epitopes. Similarly, analysis on 63 antigenic peptides through NetMHC, NetCTL and IEDB MHC-I binding peptide prediction tool led to identification of 14 CTL epitopes. Seven T-cell epitopes, C:44-66, M:135-149, NS2A:124-144, NS3:421-453, NS3:540-554, NS4B:90-134 and NS4B:171-188, are observed to share overlapping MHC-I and MHC-II binding motifs and hence, are being proposed to constitute minimum T-cell antigens to elicit protective T-cell immune response against Zika. Three of them, C:44-66, NS3:421-453 and NS3:540-554 are identified to be conserved across all the selected strains of Zika virus. Moreover, the 21 T-cell epitopes are non-self to humans and exhibited good coverage in variable populations of 14 geographical locations. Therefore, 21 T-cell epitopes are proposed as potential subunit vaccines against Zika virus.