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Introduction and Aim: Diabetic foot ulcers (DFUs) are a common and debilitating diabetic consequence leading to lower-limb amputations, long-term disability, and reduced lifespan. There is a lack of clinical diagnosis expertise because of no adequate diagnostic signs for DFU. As a result, the current research aims to find out how differently expressed genes (DEGs) affect the DFU. Materials and Methods: Bioinformatics analysis was used to evaluate DEGs using the GSE132187 dataset of the NCBI-GEO database, which contained samples from three hyperglycemic and three normoglycemic macrophage-like cell lines. Following the discovery of DEGs, Gene Ontology (GO) and KEGG pathway enrichment analysis were used to investigate how genes are classified into preset bins based on their functional properties. To discover hub DEGs in DFU, a protein-protein interaction (PPI) network was built and five topological parameters such as degree, stress, Closeness centrality, betweenness centrality, and radiality were evaluated. Results: We found 547 DEGs using the GSE132187 dataset, comprising 79 upregulated DEGs and 468 downregulated DEGs. There were 434 nodes and 1724 edges in the PPI network. The giant network uncovered six modules that are significantly enriched in biological processes like positive JNK cascade regulation, positive interferon-gamma production regulation, negative cell proliferation regulation, cellular response to zinc ion, cellular response to lipopolysaccharide, wound healing, and inflammatory response. Conclusion: Bioinformatics analysis revealed the major differentially expressed hub-genes implicated in DFUs. These findings suggested that these genes could be used as a DFU prognostic, diagnostic, or therapeutic targets.

Limiting the spread of virus during the recent pandemic outbreak was a major challenge. Viral loads in saliva, nasopharyngeal and oropharyngeal swabs were the major cause for droplet transmission and aerosols. Saliva being the major contributor for the presence of viral load is the major key factor; various mouthwashes and their combination were analyzed and utilized in health care centers to hamper the spread of virus and decrease viral load. The compositions of these mouthwashes to an extent affected the viral load and thereby transmission, but there is always a scope for other protocols which may provide better results. Here we evaluated the potential of antimicrobial peptide LL-37 in decreasing the viral load of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through an in silico work and evidence from other studies. This narrative review highlighted a brief nonsystematic methodology to include the selected articles for discussion. Accessible electronic databases (Medline, Scopus, Web of Science, SciELO, and PubMed) were used to find studies that reported the salivary viral load of SARS-CoV-2 published between December 2019 and June 2021. The following keywords were utilized for brief searching of the databases: “saliva,” “viral load,” and “SARS-CoV-2.” Articles in English language, in vitro cell-line studies, ex vivo studies, and clinical trials explaining the viral load of SARS-CoV-2 in saliva and strategies to decrease viral load were included in this review. The search was complemented by manual searching of the reference lists of included articles and performing a citation search for any additional reviews. The antiviral potential of cationic host defense peptide LL-37 was evaluated using computational approaches providing in silico evidence. The analysis of docking studies and the display of positive interfacial hydrophobicity of LL-37 resulting in disruption of COVID-19 viral membrane elucidate the fact that LL-37 could be effective against all variants of SARS-CoV-2. Further experimental studies would be needed to confirm the binding of the receptor-binding domain with LL-37. The possibility of using it in many forms further to decrease the viral load by disrupting the viral membrane is seen.

Background: The bioactive constituents from Zingiber officinale (Z. officinale) have shown a positive effect on neurodegenerative diseases like Alzheimer's disease (AD), which manifests as progressive memory loss and cognitive impairment. Objective: This study investigates the binding ability and the pharmaco-therapeutic potential of Z. officinale with AD disease targets by molecular docking and molecular dynamic (MD) simulation approaches. Method: By coupling enormous available phytochemical data and advanced computational technologies, the possible molecular mechanism of action of these bioactive compounds was deciphered by evaluating phytochemicals, target fishing, and network biological analysis. Results: As a result, 175 bioactive compounds and 264 human target proteins were identified. The gene ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis and molecular docking were used to predict the basis of vital bioactive compounds and biomolecular mechanisms involved in the treatment of AD. Amongst selected bioactive compounds, 10- Gingerdione and 1-dehydro-[8]-gingerdione exhibited significant anti-neurological properties against AD targeting amyloid precursor protein with docking energy of -6.0 and -5.6, respectively. Conclusion: This study suggests that 10-Gingerdione and 1-dehydro-[8]-gingerdione strongly modulates the anti-neurological activity and are associated with pathological features like amyloid-β plaques and hyperphosphorylated tau protein are found to be critically regulated by these two target proteins. This comprehensive analysis provides a clue for further investigation of these natural compounds' inhibitory activity in drug discovery for AD treatment.

Background: Alzheimer's disease (AD) can go years without being undiagnosed due to a lack of biomarker identification with a growing incidence rate in the geriatric population. Identifying genes and their transcriptional factors and kinases that regulate the phosphorylation and pathogenesis of AD is a state-of-art approach to identifying novel diagnostic biomarkers. Methodology: Microarray dataset GSE140829 was retrieved from the GEO database to identify differentially expressed genes (DEGs) between AD and control samples. Furthermore, a protein interaction network was built using the String database, and DEGs were examined using Cytoscape based on high betweenness centrality (BC) and degree values. Additionally, the hub genes were identified via Cytohubba, and eight modules were then identified using molecular complex detection (MCODE). Results: Using a Venn diagram, we mined 1674 common DEGs from AD and control samples. The primary interaction data from the STRING consists of 1198 nodes and 1992 edges, which serve an extenuated network. Further, a core network was extracted from an extended network that consists of 676 nodes connected via 1955 edges and were analyzed based on high BC and Degree values. Based on the network topological analysis and network clustering, the hub genes were identified and further validated by coparing them with the backbone network. Compelling results from both the core network and backbone network HSP90AA1identified as a major blood biomarker, followed by HSPA5, CREBBP, UBC, GRB2, MAPK3, and TRAF6 are selected as the major biomarkers. Conclusion: This study shows the potential for predicting AD risk factors and identifies promising blood biomarkers for early AD diagnosis. Additionally, developing inhibitors for the identified transcriptional factors and kinases might improve future therapeutic applications.

SARS-CoV-2 has become a pandemic causing a serious global health concern. The absence of effective drugs for treatment of the disease has caused its rapid spread on a global scale. Similarly to the SARS-CoV, the SARS-CoV-2 is also involved in a complex interplay with the host cells. This infection is characterized by a diffused alveolar damage consistent with the Acute Respiratory Disease Syndrome (ARDS). To explore the complex mechanisms of the disease at the system level, we used a network medicine tools approach. The protein-protein interactions (PPIs) between the SARS-CoV and the associated human cell proteins are crucial for the viral pathogenesis. Since the cellular entry of SARS-CoV-2 is accomplished by binding of the spike glycoprotein binding domain (RBD) to the human angiotensin-converting enzyme 2 (hACE2), a molecule that can bind to the spike RDB-hACE2 interface could block the virus entry. Here, we performed a virtual screening of 55 compounds to identify potential molecules that can bind to the spike glycoprotein and spike-ACE2 complex interface. It was found that the compound ethyl 1-{3-[(2,4-dichlorobenzyl) carbamoyl]-1-ethyl-6-fluoro-4-oxo-1,4-dihydro-7-quinolinyl}-4-piperidine carboxylate (the S54 ligand) and ethyl 1-{3-[(2,4-dichlorobenzyl) carbamoyl]-1-ethyl-6-fluoro-4-oxo-1,4-dihydro-7-quinolinyl}-4 piperazine carboxylate (the S55 ligand) forms hydrophobic interactions with Tyr41A, Tyr505B and Tyr553B, Leu29A, Phe495B, respectively of the spike glycoprotein, the hotspot residues in the spike glycoprotein RBD-hACE2 binding interface. Furthermore, molecular dynamics simulations and free energy calculations using the MM-GBSA method showed that the S54 ligand is a stronger binder than a known SARS-CoV spike inhibitor SSAA09E3 (N-(9,10-dioxo-9, 10-dihydroanthracen-2-yl) benzamide). Communicated by Ramaswamy H. Sarma

This study aims to investigate the mechanism of natural antioxidant ferulic acid (FA) in reducing oxidative stress followed by its inhibitory effect on the Keap1-Nrf2 complex. FA was treated ex vivo with human blood for 30 min at 37 °C ± 1 °C and exposed to 1.5 Gy of γ- rays of 60Co (0.789 Gy/min) and allowed for repair for an hour at 37 °C ± 1 °C. FA's free radical scavenging capacity was measured using 2,7-dichlorofluorescein diacetate assay and cytogenetic assays. Further, a possible mechanism of protein-ligand interaction between FA and Keap1-Nrf2 pathway protein as a cellular drug target was studied using docking and molecular dynamics simulation. The 1.5 Gy of γ- rays exposed to pre-treated blood with FA showed a significant (p < 0.05) reduction in reactive oxygen species and DNA damage compared to the normal control blood group sample. The ligand-protein transient binding interaction in molecular dynamic simulation over a period of 100 ns was consistent and stable emphasizing complementary charge between the protein and ligand, speculating higher hydrophobic amino acid residues in the Keap1 active pocket. This might sway the Keap1 from interaction with Nrf2, and could lead to nuclear translocation of Nrf2 during radiation-induced oxidative stress. The present study emphasizes the radioprotective effect of FA against 1.5 Gy of γ- rays exposed to human blood and the application of in silico approaches helpful for the possible protective effect of FA. Communicated by Ramaswamy H. Sarma

Increased antibiotic resistance in Helicobacter pylori (H. pylori), a major human pathogen, constitutes a substantial threat to human health. Understanding the pathophysiology and development of antibiotic resistance can aid our battle with the infections caused by H. pylori. The aim of this study is to discover the high-impact key regulatory mechanisms and genes involved in antimicrobial drug resistance (AMR). In this study, we constructed a functional gene interaction network by integrating multiple sources of data related to antibiotic resistant genes (number-77) from H. pylori. The gene interaction network was assortative, with a hierarchical, scale-free topology enriched in a variety of gene ontology (GO) categories and KEGG pathways. Using an iterative clustering methodology, we identified a number of communities in the AMR gene network that comprised nine genes (sodB, groEL, gyrA, recA, polA, tuf, infB, rpsJ, and gyrB) that were present at the deepest level and hence were key regulators of AMR. Further, an antibiotic-resistant gene network-based centrality analysis revealed superoxide dismutase (sodB) as a bottleneck node in the network. Our findings suggested that sodB is critically enriched in the cellular response to oxidative stress, removal of superoxide radicals, cellular oxidant detoxification processes, cellular component biogenesis, response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process and reactive oxygen species metabolic process. We demonstrated how the sodB, which are involved in the response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process, reactive oxygen species metabolic process, regulated by Fur gene/proteins, claim a major authority over regulation and signal propagation in the AMR.

The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.

Amidst the surge in the prevalence of resistant H. pylori infections, WHO in 2017 has given a high priority to clarithromycin-resistant H. pylori for research and to develop new antibacterial agents. In this study, the Helicobacter pylori 26695 strain was investigated with extensive computational biology applications to identify novel therapeutic drug targets or vaccine candidates. During the proteomic functional annotation of an organism, it is crucial to determine the function of proteins. The pathogen-specific pathways were found to include only twelve proteins, paving the way further to determine drug or vaccine targets. Lipoprotein A-4’-phosphatase (LpxF) was found to be a novel vaccine target with the highest antigenicity. Having broad-spectrum conservancy with other H. pylori strains. Further, an immunoinformatic approach was used to predict an effective epitope-based vaccine against H. pylori. LpxF protein has been predicted to have linear and conformational B-cell epitopes and cytotoxic T-lymphocyte epitopes. Virtual screening of all the predicted 35 peptides against human TLR2 receptors resulted in identifying the top 5 peptides. Subsequent redocking with exhaustive parameters reported two peptides with docking energy of -6.9 kcal/mol with a good interaction pattern between the peptide-TLR2 complexes. Furthermore, a panel of two potent epitopes has been proposed that could be used to immunize populations against multiple H. pylori infections.

The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.

Purpose The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach. Materials and methods Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure. Results A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes. Conclusion This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.

EP300 is one of the putative tumor-suppressor genes and is mutated/deleted, under expressed/overexpressed in several types of cancer. The role of EP300 and its interactions during cancer is crucial to explore its reprogramming events that lead to malignant phenotype and acquisition of drug resistance. In this context, all the experimentally valid EP300 interactors were collected from the primary protein-protein interaction (PPI) databases and followed by tracing their subcellular location using the UniProtKB database. Further, all the EP300 interactors were categorized based on their subcellular location and functionally annotated with the DAVID gene ontology tool. Subsequently, the interactome of EP300 with its interactors was constructed and identified TP53, CREBBP, JUN, HDAC1, CTNNB1, MYC, PCNA, HDAC2, FOS, and KAT2B as the top first neighbors of EP300. Together, the present analysis gives a comprehensive overview on EP300 interactors located in different subcellular locations

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has infected >235 million people and killed over 4.8 million individuals worldwide. Although vaccines have been developed for prophylactic management, there are no clinically proven antivirals to treat the viral infection. Continuous efforts are being made all over the world to develop effective drugs but these are being delayed by periodic outbreak of mutated SARS-CoV-2 and a lack of knowledge of molecular mechanisms underlying viral pathogenesis and post-infection complications. In this regard, the involvement of Annexin A2 (AnxA2), a lipid-raft related phospholipid-binding protein, in SARS-CoV-2 attachment, internalization, and replication has been discussed. In addition to the evidence from published literature, we have performed in silico docking of viral spike glycoprotein and RNA-dependent RNA polymerase with human AnxA2 to find the molecular interactions. Overall, this review provides the molecular insights into a potential role of AnxA2 in the SARS-CoV-2 pathogenesis and post-infection complications, especially thrombosis, cytokine storm, and insulin resistance.