Microplastic interactions in the agroecosystems: methodological advances and limitations in quantifying microplastics from agricultural soil

The instantaneous growth of the world population is intensifying the pressure on the agricultural sector. On the other hand, the critical climate changes and increasing load of pollutants in the soil are imposing formidable challenges on agroecosystems, affecting productivity and quality of the crops. Microplastics are among the most prevalent pollutants that have already invaded all terrestrial and aquatic zones. The increasing microplastic concentration in soil critically impacts crop plants growth and yield. The current review elaborates on the behaviors of microplastics in soil and their impact on soil quality and plant growth. The study shows that microplastics alter the soil's biophysical properties, including water-holding capacity, bulk density, aeration, texture, and microbial composition. In addition, microplastics interact with multiple pollutants, such as polyaromatic hydrocarbons and heavy metals, making them more bioavailable to crop plants. The study also provides a detailed insight into the current techniques available for the isolation and identification of soil microplastics, providing solutions to some of the critical challenges faced and highlighting the research gaps. In our study, we have taken a holistic, comprehensive approach by analysing and comparing various interconnected aspects to provide a deeper understanding of all research perspectives on microplastics in agroecosystems.

Assessment of polystyrene nano plastics effect on human salivary α-amylase structural alteration: Insights from an in vitro and in silico study

The study found that the enzyme activity of human salivary α-amylase (α-AHS) was competitively inhibited by nanoplastic polystyrene (PS-NPs), with a half-inhibitory concentration (IC50) of 92 μg/mL, while the maximum reaction rate (Vmax) remained unchanged at 909 μg/mL•min. An increase in the concentration of PS-NPs led to a quenching of α-AHS fluorescence with a slight red shift, indicating a static mechanism. The binding constant (Ka) and quenching constant (Kq) were calculated to be 2.92 × 1011 M-1 and 1.078 × 1019 M-1• S-1 respectively, with a hill coefficient (n) close to one and an apparent binding equilibrium constant (KA) of 1.54 × 1011 M-1. Molecular docking results suggested that the interaction between α-AHS and PS-NPs involved π-anion interactions between the active site Asp197, Asp300 residues, and van der Waals force interactions affecting the Tyr, Trp, and other residues. Fourier transform infrared (FT-IR) and circular dichroism (CD) analyses revealed conformational changes in α-AHS, including a loss of secondary structure α-helix and β-sheet. The study concludes that the interaction between α-AHS and PS-NPs leads to structural and functional changes in α-AHS, potentially impacting human health. This research provides a foundation for further toxicological analysis of MPs/NPs in the human digestive system.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Micro-nanoemulsion and nanoparticle-assisted drug delivery against drug-resistant tuberculosis: recent developments

Tuberculosis (TB) is a major global health problem and the second most prevalent infectious killer after COVID-19. It is caused by Mycobacterium tuberculosis (Mtb) and has become increasingly challenging to treat due to drug resistance. The World Health Organization declared TB a global health emergency in 1993. Drug resistance in TB is driven by mutations in the bacterial genome that can be influenced by prolonged drug exposure and poor patient adherence. The development of drug-resistant forms of TB, such as multidrug resistant, extensively drug resistant, and totally drug resistant, poses significant therapeutic challenges. Researchers are exploring new drugs and novel drug delivery systems, such as nanotechnology-based therapies, to combat drug resistance. Nanodrug delivery offers targeted and precise drug delivery, improves treatment efficacy, and reduces adverse effects. Along with nanoscale drug delivery, a new generation of antibiotics with potent therapeutic efficacy, drug repurposing, and new treatment regimens (combinations) that can tackle the problem of drug resistance in a shorter duration could be promising therapies in clinical settings. However, the clinical translation of nanomedicines faces challenges such as safety, large-scale production, regulatory frameworks, and intellectual property issues. In this review, we present the current status, most recent findings, challenges, and limiting barriers to the use of emulsions and nanoparticles against drug-resistant TB.

Identification of Potential Inhibitors Targeting GTPase-Kirsten RAt Sarcoma Virus (K-Ras) Driven Cancers via E-Pharmacophore-Based Virtual Screening and Drug Repurposing Approach

BACKGROUND

Mutations in the K-Ras gene are among the most frequent genetic alterations in various cancers, and inhibiting RAS signaling has shown promising results in treating solid tumors. However, finding effective drugs that can bind to the RAS protein remains challenging. This drove us to explore new compounds that could inhibit tumor growth, particularly in cancers that harbor K-Ras mutations.

METHODS

Our study used bioinformatic techniques such as E-pharmacophore virtual screening, molecular simulation, principal component analysis (PCA), extra precision (XP) docking, and ADMET analyses to identify potential inhibitors for K-Ras mutants G12C and G12D.

RESULTS

In our study, we discovered that inhibitors such as afatinib, osimertinib, and hydroxychloroquine strongly inhibit the G12C mutant. Similarly, hydroxyzine, zuclopenthixol, fluphenazine, and doxapram were potent inhibitors for the G12D mutant. Notably, all six of these molecules exhibit a high binding affinity for the H95 cryptic groove present in the mutant structure. These molecules exhibited a unique affinity mechanism at the molecular level, which was further enhanced by hydrophobic interactions. Molecular simulations and PCA revealed the formation of stable complexes within switch regions I and II. This was particularly evident in three complexes: G12C-osimertinib, G12D-fluphenazine, and G12D-zuclopenthixol. Despite the dynamic nature of switches I and II in K-Ras, the interaction of inhibitors remained stable. According to QikProp results, the properties and descriptors of the selected molecules fell within an acceptable range compared to sotorasib.

CONCLUSIONS

We have successfully identified potential inhibitors of the K-Ras protein, laying the groundwork for the development of targeted therapies for cancers driven by K-Ras mutations.

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the "SingleR." Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand-receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug-gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

In silico network pharmacology analysis and molecular docking validation of Swasa Kudori tablet for screening druggable phytoconstituents of asthma

Traditional medicines are impactful in treating a cluster of respiratory-related illnesses. This paper demonstrates screening active, druggable phytoconstituents from a classical Siddha-based poly-herbal formulation called Swasa Kudori Tablet to treat asthma. The phytoconstituents of Swasa Kudori are identified as Calotropis gigantea, Piper nigrum, and (Co-drug) Abies webbiana. Active chemical compounds are extracted with the Chemical Entities of Biological Interest (ChEBI) database. The gene targets of each compound are identified based on the pharmacological activity using the DIGEP-Pred database. Thirty-two genes showing Pa> 0.7 is screened, and the target markers are selected after performing gene overlap evaluation with the asthma genes reported in GeneCards and DisGeNET database. Ten markers are identified, such as ADIPOQ, CASP8, CAT, CCL2, CD86, FKBP5, HMOX1, NFE2L2, TIMP1, VDR, in common, listed as molecular targets. Pharmacokinetic assessment (ADME) revealed five natural drug compounds 2-5-7-trihydroxy-2-(4-hydroxyphenyl)-2,3-dihydro-4H-chromen-4-one, (+)-catechin-3'-methyl ether, futoenone, 5-hydroxy-4',7-dimethoxyflavanone, and pinocembrin showing better druggability. Further screening delineates the target (HMOX1) and drug (pinocembrin) for molecular docking evaluation. When docked with HO-1, Pinocembrin showed a binding affinity of -8.0 kcal/mol. MD simulation studies substantiate the docking studies as HO-1 in complex with pinocembrin remains stable in the simulated trajectory. The current findings exhibit the significance of traditional medicines as potential drug candidates against asthma.

Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease

Alzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11 years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP +  + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD.

SARS-CoV-2 vaccine breakthrough infections (VBI) by Omicron variant (B.1.1.529) and consequences in structural and functional impact

This study investigated the efficacy of existing vaccines against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of Johnson & Johnson vaccine or who were vaccinated more than five months before. A total of 36 variants in Omicron's spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. The genotyping of the SARS-CoV-2 viral sequence revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). A woman showed two of these mutations, indicating a potential risk of infection after successful immunization, as recently reported by Hacisuleyman (2021). We examine the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of the spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for ACE2 and found that the wild- and mutant-spike proteins were using atomistic molecular dynamics simulations. According to the binding free energies calculated during mutagenesis, the ACE2 bound Omicron spikes more strongly than the wild strain SARS-CoV-2. T95I, D614G, and E484K are three substitutions that significantly contribute to RBD, corresponding to ACE2 binding energies and a doubling of the electrostatic potential of Omicron spike proteins. The Omicron appears to bind to ACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

In silico network pharmacology study on Glycyrrhiza glabra: Analyzing the immune-boosting phytochemical properties of Siddha medicinal plant against COVID-19

Immunosenescence is a pertinent factor in the mortality rate caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The changes in the immune system are strongly associated with age and provoke the deterioration of the individual's health. Traditional medical practices in ancient India effectively deal with COVID-19 by boosting natural immunity through medicinal plants. The anti-inflammatory and antiviral properties of Glycyrrhiza glabra are potent in fighting against COVID-19 and promote immunity boost against the severity of the infection. Athimadhura Chooranam, a polyherbal formulation containing Glycyrrhiza glabra as the main ingredient, is recommended as an antiviral Siddha herb by the Ministry of AYUSH. This paper is intended to identify the phytoconstituents of Glycyrrhiza glabra that are actively involved in preventing individuals from COVID-19 transmission. The modulated pathways, enrichment study, and drug-likeness are calculated from the target proteins of the phytoconstituents at the pharmacological activity (Pa) of more than 0.7. Liquiritigenin and Isoliquiritin, the natural compounds in Glycyrrhiza glabra, belong to the flavonoid class and exhibit ameliorative effects against COVID-19. The latter compound displays a higher protein interaction to a maximum of six, out of which HMOX1, PLAU, and PGR are top-hub genes. ADMET screening further confirms the significance of the abovementioned components containing better drug-likeness. The molecular docking and molecular dynamics method identified liquiritigenin as a possible lead molecule capable of inhibiting the activity of the major protease protein of SARS-CoV-2. The findings emphasize the importance of in silico network pharmacological assessments in delivering cost-effective, time-bound clinical drugs.

Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer

TNBC is a highly malignant breast cancer known for its aggressive behavior affecting young female adults. The standard treatment for TNBC includes surgery, chemotherapy, and radiotherapy, which often have significant side effects. Therefore, novel preventive methods are required to combat TNBC effectively. In this study, we utilized immunoinformatics to construct an in-silico vaccine against TNBC using the TRIM25 molecule via the reverse vaccinology method. Four vaccines were designed by generating T and B-cell epitopes linked with four different linkers. The modeled vaccine was docked and the results showed that vaccine-3 exhibited the highest affinity with the immune receptors. The molecular dynamics results revealed that the binding affinity and stability of Vaccine-3 were greater than those of Vaccine 2 complexes. This study has great potential preventive measures for TNBC, and further research is warranted to evaluate its efficacy in preclinical settings. This study presents an innovative preventive strategy for triple-negative breast cancer (TNBC) through immunoinformatics and reverse vaccinology to develop an in-silico vaccine. Leveraging these innovative techniques offers a novel avenue for combating the complex challenges associated with TNBC. This approach demonstrates considerable potential as a significant breakthrough in preventive measures for this particularly aggressive and malignant form of breast cancer.

Evolving strategies and application of proteins and peptide therapeutics in cancer treatment

Several proteins and peptides have therapeutic potential and can be used for cancer therapy. By binding to cell surface receptors and other indicators uniquely linked with or overexpressed on tumors compared to healthy tissue, protein biologics enhance the active targeting of cancer cells, as opposed to the passive targeting of cells by conventional small-molecule chemotherapeutics. This study focuses on peptide medications that exist to slow or stop tumor growth and the spread of cancer, demonstrating the therapeutic potential of peptides in cancer treatment. As an alternative to standard chemotherapy, peptides that selectively kill cancer cells while sparing healthy tissue are developing. A mountain of clinical evidence supports the efficacy of peptide-based cancer vaccines. Since a single treatment technique may not be sufficient to produce favourable results in the fight against cancer, combination therapy is emerging as an effective option to generate synergistic benefits. One example of this new area is the use of anticancer peptides in combination with nonpeptidic cytotoxic drugs or the combination of immunotherapy with conventional therapies like radiation and chemotherapy. This review focuses on the different natural and synthetic peptides obtained and researched. Discoveries, manufacture, and modifications of peptide drugs, as well as their contemporary applications, are summarized in this review. We also discuss the benefits and difficulties of potential advances in therapeutic peptides.

Molecular mechanisms augmenting resistance to current therapies in clinics among cervical cancer patients

Cervical cancer (CC) is the fourth leading cause of cancer death (~ 324,000 deaths annually) among women internationally, with 85% of these deaths reported in developing regions, particularly sub-Saharan Africa and Southeast Asia. Human papillomavirus (HPV) is considered the major driver of CC, and with the availability of the prophylactic vaccine, HPV-associated CC is expected to be eliminated soon. However, female patients with advanced-stage cervical cancer demonstrated a high recurrence rate (50-70%) within two years of completing radiochemotherapy. Currently, 90% of failures in chemotherapy are during the invasion and metastasis of cancers related to drug resistance. Although molecular target therapies have shown promising results in the lab, they have had little success in patients due to the tumor heterogeneity fueling resistance to these therapies and bypass the targeted signaling pathway. The last two decades have seen the emergence of immunotherapy, especially immune checkpoint blockade (ICB) therapies, as an effective treatment against metastatic tumors. Unfortunately, only a small subgroup of patients (< 20%) have benefited from this approach, reflecting disease heterogeneity and manifestation with primary or acquired resistance over time. Thus, understanding the mechanisms driving drug resistance in CC could significantly improve the quality of medical care for cancer patients and steer them to accurate, individualized treatment. The rise of artificial intelligence and machine learning has also been a pivotal factor in cancer drug discovery. With the advancement in such technology, cervical cancer screening and diagnosis are expected to become easier. This review will systematically discuss the different tumor-intrinsic and extrinsic mechanisms CC cells to adapt to resist current treatments and scheme novel strategies to overcome cancer drug resistance.

Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridiumdifficile

Antimicrobial resistance has caused chaos worldwide due to the depiction of multidrug-resistant (MDR) infective microorganisms. A thorough examination of antimicrobial resistance (AMR) genes and associated resistant mechanisms is vital to solving this problem. Clostridium difficile (C. difficile) is an opportunistic nosocomial bacterial strain that has acquired exogenous AMR genes that confer resistance to antimicrobials such as erythromycin, azithromycin, clarithromycin, rifampicin, moxifloxacin, fluoroquinolones, vancomycin, and others. A network of interactions, including 20 AMR genes, was created and analyzed. In functional enrichment analysis, Cellular components (CC), Molecular Functions (MF), and Biological Processes (BP) were discovered to have substantial involvement. Mutations in the rpl genes, which encode ribosomal proteins, confer resistance in Gram-positive bacteria. Full erythromycin and azithromycin cross-resistance can be conferred if more than one of the abovementioned genes is present. In the enriched BP, rps genes related to transcriptional regulation and biosynthesis were found. The genes belong to the rpoB gene family, which has previously been related to rifampicin resistance. The genes rpoB, gyrA, gyrB, rpoS, rpl genes, rps genes, and Van genes are thought to be the hub genes implicated in resistance in C. difficile. As a result, new medications could be developed using these genes. Overall, our observations provide a thorough understanding of C. difficile AMR mechanisms.

Identification of potential circadian genes and associated pathways in colorectal cancer progression and prognosis using microarray gene expression analysis

Colorectal cancer (CRC) is third cancer causing death in the world. CRC is associated with disrupting the circadian rhythm (CR), closely associating the CRC progression and the dysregulation of genes involved in the biological clock. In this study, we aimed to understand the circadian rhythm changes in patients diagnosed with CRC. We used the GEO database with the ID GSE46549 for our analysis, which consists of 32 patients with CRC and one as normal control. Our study has identified five essential genes involved in CRC, HAPLN1, CDH12, IGFBP5, DCHS2, and DOK5, and had different enriched pathways, such as the Wnt-signaling pathway, at different time points of study. As a part of our study, we also identified various related circadian genes, such as CXCL12, C1QTNF2, MRC2, and GLUL, from the Circadian Gene Expression database, that played a role in circadian rhythm and CRC development. As circadian timing can influence the host tissue's ability to tolerate anticancer medications, the genes reported can serve as a potential drug target for treating CRC and become beneficial to translational settings.

Phage-based therapy against biofilm producers in gram-negative ESKAPE pathogens

Persistent antibiotic use results in the rise of antimicrobial resistance with limited or no choice for multidrug-resistant (MDR) and extensively drug resistant (XDR) bacteria. This necessitates a need for alternative therapy to effectively combat clinical pathogens that are resistant to last resort antibiotics. The study investigates hospital sewage as a potential source of bacteriophages to control resistant bacterial pathogens. Eighty-one samples were screened for phages against selected clinical pathogens. Totally, 10 phages were isolated against A. baumannii, 5 phages against K. pneumoniae, and 16 phages were obtained against P. aeruginosa. The novel phages were observed to be strain-specific with complete bacterial growth inhibition of up to 6 h as monotherapy without antibiotics. Phage plus colistin combinations reduced the minimum-biofilm eradication concentration of colistin up to 16 folds. Notably, a cocktail of phages exhibited maximum efficacy with complete killing at 0.5-1 μg/ml colistin concentrations. Thus, phages specific to clinical strains have a higher edge in treating nosocomial pathogens with their proven anti-biofilm efficacy. In addition, analysis of phage genomes revealed close phylogenetic relations with phages reported from Europe, China, and other neighbouring countries. This study serves as a reference and can be extended to other antibiotics and phage types to assess optimum synergistic combinations to combat various drug resistant pathogens in the ongoing AMR crisis.

Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

Molecular characterization of circadian gene expression and its correlation with survival percentage in colorectal cancer patients

Colorectal cancer (CRC) is a form of cancer characterized by many symptoms and readily metastasizes to different organs in the body. Circadian rhythm is one of the many processes that is observed to be dysregulated in CRC-affected patients. In this study, we aim to identify the dysregulated physiological processes in CRC-affected patients and correlate the expression profiles of the circadian clock genes with CRC-patients' survival rates. We performed an extensive microarray gene expression pipeline, whereby 471 differentially expressed genes (DEGs) were identified, following which, we streamlined our search to 43 circadian clock affecting DEGs. The Circadian Gene Database was accessed to retrieve the circadian rhythm-specific genes. The DEGs were then subjected to multi-level functional annotation, i.e., preliminary analysis using ClueGO/CluePedia and pathway enrichment using DAVID. The findings of our study were interesting, wherein we observed that the survival percentage of CRC-affected patients dropped significantly around the 100th-month mark. Furthermore, we identified hormonal activity, xenobiotic metabolism, and PI3K-Akt signaling pathway to be frequently dysregulated cellular functions. Additionally, we detected that the ZFYVE family of genes and the two genes, namely MYC and CDK4 were the significant DEGs that are linked to the pathogenesis and progression of CRC. This study sheds light on the importance of bioinformatics to simplify our understanding of the interactions of different genes that control different phenotypes.

Molecular docking and dynamic approach to screen the drug candidate against the Imipenem-resistant CarO porin in Acinetobacter baumannii

The multidrug-resistant Acinetobacter baumannii is an emerging nosocomial pathogen in the healthcare sector. Intrinsic resistance in A. baumannii is a significant problem framing a perfect treatment regimen. Also, this organism showed more resistance towards the carbapenem antibiotics, especially for imipenem and meropenem. The development of carbapenem-resistant Acinetobacter baumannii is mainly due to the alteration or loss of the porin region in the outer membrane. The most well-known porin in Acinetobacter baumannii is CarO (carbapenem-associated outer membrane protein). The CarO protein, which functions as a porin channel for carbapenem inflow, may contribute to carbapenem resistance. The current study identifies a potent drug candidate with a better binding affinity to the carbapenem-resistant outer membrane protein. We investigated the specificity of carbapenems such as imipenem, meropenem, ertapenem, biapenem, doripenem, and fluoroquinolone drugs such as sitafloxacin against the imipenem-resistant CarO protein was demonstrated using the computational approaches molecular docking and dynamic simulation for 50 ns. As a result, the high to low enzyme-ligand complex's binding affinity exhibited a greater binding affinity for ertapenem -7.76 kcal·mol^-1 and sitafloxacin -7.75 kcal·mol^-1 than biapenem, doripenem, meropenem, and imipenem. The molecular dynamic simulation and the MMPBSA analysis depicted ertapenem -55.431±25.908 kJ/mol and sitafloxacin -47.154 ± 11.052 kJ/mol with better binding affinity and more stability against the imipenem resistant CarO protein when it compared to other antibiotics.

Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach

The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics. This study aimed to use machine-learning techniques of expressed genes in patients with AD to identify potential biomarkers that can be used for future therapy. The dataset is accessed from the Gene Expression Omnibus (GEO) database (Accession Number: GSE36980). The subgroups (AD blood samples from frontal, hippocampal, and temporal regions) are individually investigated against non-AD models. Prioritized gene cluster analyses are conducted with the STRING database. The candidate gene biomarkers were trained with various supervised machine-learning (ML) classification algorithms. The interpretation of the model prediction is perpetrated with explainable artificial intelligence (AI) techniques. This experiment revealed 34, 60, and 28 genes as target biomarkers of AD mapped from the frontal, hippocampal, and temporal regions. It is identified ORAI2 as a shared biomarker in all three areas strongly associated with AD's progression. The pathway analysis showed that STIM1 and TRPC3 are strongly associated with ORAI2. We found three hub genes, TPI1, STIM1, and TRPC3, in the network of the ORAI2 gene that might be involved in the molecular pathogenesis of AD. Naive Bayes classified the samples of different groups by fivefold cross-validation with 100% accuracy. AI and ML are promising tools in identifying disease-associated genes that will advance the field of targeted therapeutics against genetic diseases.

HPV and molecular mimicry in systemic lupus erythematosus and an impact of compiling B-cell epitopes and MHC-class II binding profiles with in silico evidence

Epidemiological link between HPV and SLE is evolving. The possibility of HPV infection-induced molecular mimicry and systemic lupus erythematosus (SLE) was elucidated through detailed in silico analyses. Conserved regions in the structural protein sequences of high-risk HPV types were inferred, and sequence homologies between viral and human peptides were identified to delineate proteins implicated in SLE. B-cell epitopes and MHC-class II binding were compiled using Immune Epitope Database and ProPred II analysis tool. Molecular modeling and molecular dynamics/simulation (MDS) were performed using AutoDock Vina and GROMACS, respectively. Sequence alignment revealed 32 conserved regions, and 27/32 viral peptides showed varying similarities to human peptides, rich in B-cell epitopes with superior accessibility, high hydrophilicity, antigenicity and disposition to bind many class-II HLA alleles. Molecular docking of 13 viral peptides homologous (100%) to human peptides implicated in SLE showed that VIR-PEP1 (QLFNKPYWL) and VIR-PEP2 (DTYRFVTS) exhibited higher binding affinities than corresponding human peptides to SLE predisposing HLA-DRB1 allele. MDS of these peptides showed that the viral peptides had superior folding, compactness, and a higher number of hydrogen bonds than human peptides throughout the simulation period. SASA analysis revealed that the VIR-PEP1&2 fluctuated less frequently than corresponding human peptides. MM-PBSA revealed that the VIR-PEP2 complex exhibited higher binding energy than the human peptide complex. This suggests that highly conserved structural peptides of high-risk HPV types homologous to human peptides could compete and bind avidly to the HLA allele associated with SLE and predispose HPV-infected individuals to SLE through molecular mimicry.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening to identify potential lipase inhibitors to reduce lipid storage in Wolman disorder

Wolman disorder (WD) was first described in Iranian-Jewish (IJ) children, and it is caused by a deficiency of the lysosomal acid lipase (LAL). Newborns with WD are healthy and active at birth but soon develop severe malnutrition symptoms and often die before 1 year. In particular, spleens, livers, bone marrows, intestines, adrenal glands, and lymph nodes accumulate harmful amounts of lipids. G87V mutation in LIPA is responsible for Wolman disorder. Some reports suggest that δ-tocopherol can reduce lipid accumulation in cholesterol storage disorders. Hence, we used δ-tocopherol for the virtual screening process in this study. Initially, the lead compounds were docked with native and G87V mutant LIPA. Subsequently, the ADME and toxicity parameters for screened compounds were determined to ensure the safety profiles. Finally, the molecular dynamics simulations result indicated that dl-alpha-Tocopherol-13C3, a molecule obtained from the PubChem database, is identified as a potential and stable lead molecule that could be effective against the G87V mutant form of LIPA.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

Identification of dysregulated canonical pathways associated with pathogenesis and progression of Amyotrophic Lateral Sclerosis-An integrated bioinformatics approach

The mechanisms responsible for the pathogenesis and progression of Amyotrophic Lateral Sclerosis (ALS) remain poorly understood, making the diagnosis of ALS challenging. We aimed to find the novel gene biomarkers via computationally analyzing microarray expression studies, in three different cell lineages, namely myotube cells, astrocyte cells and oligodendrocyte cells. Microarray gene expression profiles were obtained and analyzed for three cell types: myotube cell lineage (GSE122261), astrocyte, and oligodendrocyte cell lineage (GSE87385). A comprehensive computational pipeline, tailored explicitly for microarray gene expression profiling studies, was devised to analyze the sample groups, wherein the myotube sample group comprised of six control (GSM3462697, GSM3462698, GSM3462699, GSM3462700, GSM3462701, GSM3462702) & six diseased (GSM3462691, GSM3462692, GSM3462693, GSM3462694, GSM3462695, GSM3462696) samples were considered. Similarly, for the astrocyte sample group two samples each for the control (GSM2330040, GSM2330042) and the diseased (GSM2330039, GSM2330041), and for the oligodendrocyte sample group, 2 control (GSM2330043, GSM2330045) samples and two diseased (GSM2330044, GSM2330046) samples were considered for the current study. The in-depth interaction of these DEGs was studied using MCODE and subjected to preliminary functional analysis using ClueGO/CluePedia plug-in. Qiagen's IPA software was employed for enrichment analysis, which generated the key canonical pathways and a list of potential biomarker molecules specific to each sample group. The preliminary analysis yielded 512 DEGs across all 3-sample groups, wherein 139 DEGs belonged to the myotube sample group, 216 DEGs for the astrocyte sample group, and 157 DEGs for the oligodendrocytes sample group. The data suggests growth hormone signaling and its activity, ErbB signaling pathway, and JAK/STAT signaling pathway are some of the pathways that are significantly dysregulated and play a crucial role in the development and progression of ALS. KISS1R and CSHL1 are potential genes that could act as diagnostic biomarkers in myotube cell types. Also, KRAS, TGFB2, JUN, and SMAD6 genes may be used as prognostic biomarkers to differentiate between early and late-stage ALS-diseased patients.

Analysis of signaling cascades from myeloma cells treated with pristimerin

Multiple myeloma (MM) is the 2nd most frequently diagnosed blood cancer after non-Hodgkin's lymphoma. The present study aimed to identify the differentially expressed genes (DEGs) between the control and pristimerin-treated MM cell lines. We examined the GSE14011 microarray dataset and screened DEGs with GEO2R statistical tool using the inbuilt limma package. We used a bioinformatics pipeline to identify the differential networks, signaling cascades, and the survival of the hub genes. We implemented two different enrichment analysis including ClueGO and Metacore™, to get accurate annotation for most significant DEGs. We screened the most significant 408 DEGs from the dataset based on p-values and logFC values. Using protein network analysis, we found the genes UBC, HSP90AB1, HSPH1, HSPA1B, HSPA1L, HSPA6, HSPD1, DNAJB1, HSPE1, DNAJC10, BAG3, and DNAJC7 had higher node degree distribution. In contrast, the functional annotation provided that the DEGs were predominantly enriched in B-cell receptor signaling, unfolded protein response, positive regulation of phagocytosis, HSP70, and HSP40-dependent folding, and ubiquitin-proteasomal proteolysis. Using network algorithms, and comparing enrichment analysis, we found the hub genes enriched were INHBE, UBC, HSPA1A, HSP90AB1, IKBKB, and BAG3. These DEGs were further validated with overall survival and gene expression analysis between the tumor and control groups. Finally, pristimerin effects were validated independently in a cell line model consisting of IM9 and U266 MM cells. Pristimerin induced in vitro cytotoxicity in MM cells in a dose-dependent manner. Pristimerin inhibited NF-κB, induced accumulation of ubiquitinated proteins and inhibited HSP60 in the validation of bioinformatics findings, while pristimerin-induced caspase-3 and PARP cleavage confirmed cell death. Taken together, we found that the identified DEGs were strongly associated with the apoptosis induced in MM cell lines due to pristimerin treatment, and combinatorial therapy derived from pristimerin could act as novel anti-myeloma multifunctional agents.

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol

AIMS

To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury.

BACKGROUND

Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects.

OBJECTIVE

To evaluate the role of machine learning algorithms (MLAs) and bioinformatics tools to delineate the predictor SNPs as well as to understand their molecular dynamics.

METHODS

A cross-sectional study was undertaken by recruiting critically ill patients with chronic renal failure and administering intravenous paracetamol as a standard of care. Serum concentrations of paracetamol and the principal metabolites were estimated. Following SNPs were evaluated: CYP2E1*2, CYP2E1_-1295G>C, CYP2D6*10, CYP3A4*1B, CYP3A4*2, CYP1A2*1K, CYP1A2*6, CYP3A4*3, and CYP3A5*7. MLAs were used to identify the predictor genetic variable for acute liver failure. Bioinformatics tools such as Predict SNP2 and molecular docking (MD) were undertaken to evaluate the impact of the above SNPs with binding affinity to paracetamol.

RESULTS

CYP2E1*2 and CYP1A2*1C genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that CYP1A2*3 was highly deleterious in all the tools. MD revealed binding energy of -5.5 Kcal/mol, -6.9 Kcal/mol, and -6.8 Kcal/mol for CYP1A2, CYP1A2*3, and CYP1A2*6 against paracetamol. MD simulations revealed that CYP1A2*3 and CYP1A2*6 missense variants in CYP1A2 affect the binding ability with paracetamol. In-silico techniques found that CYP1A2*2 and CYP1A2*6 are highly harmful. MD simulations revealed CYP3A4*2 (A>G) had decreased binding energy with paracetamol than CYP3A4, and CYP3A4*2(A>T) and CYP3A4*3 both have greater binding energy with paracetamol.

CONCLUSION

Polymorphisms in CYP2E1, CYP1A2, CYP3A4, and CYP3A5 significantly influence paracetamol's clinical outcomes or binding affinity. Robust clinical studies are needed to identify these polymorphisms' clinical impact on the pharmacokinetics or pharmacodynamics of paracetamol.

Computational screening and structural analysis of Gly201Arg and Gly201Asp missense mutations in human cyclin-dependent kinase 4 protein

The regulatory proteins, cyclins, and cyclin-dependent kinases (CDKs) control the cell cycle progression. CDK4 gene mutations are associated with certain cancers such as melanoma, breast cancer, and rhabdomyosarcoma. Therefore, understanding the mechanisms of cell cycle control and cell proliferation is essential in developing cancer treatment regimens. In this study, we obtained cancer-causing CDK4 mutations from the COSMIC database and subjected them to a series of in silico analyses to identify the most significant mutations. An overall of 238 mutations (119 missense mutations) retrieved from the COSMIC database were investigated for the pathogenic and destabilizing properties using the PredictSNP and iStable algorithms. Further, the amino acid position of the most pathogenic and destabilizing mutations were analyzed to understand the nature of amino acid conservation across the species during the evolution. We observed that the missense mutations G201R and G201D were more significant and the Glycine at position 201 was found to highly conserved. These significant mutations were subjected to molecular dynamics simulation analysis to understand the protein's structural changes. The results from molecular dynamics simulations revealed that both G201R and G201D of CDK4 are capable of altering the protein's native form. On comparison among the most significant mutations, G201R disrupted the protein structure higher than the protein with G201D.

CDK regulators—Cell cycle progression or apoptosis—Scenarios in normal cells and cancerous cells

Serine/threonine kinases called cyclin-dependent kinases (CDKs) interact with cyclins and CDK inhibitors (CKIs) to control the catalytic activity. CDKs are essential controllers of RNA transcription and cell cycle advancement. The ubiquitous overactivity of the cell cycle CDKs is caused by a number of genetic and epigenetic processes in human cancer, and their suppression can result in both cell cycle arrest and apoptosis. This review focused on CDKs, describing their kinase activity, their role in phosphorylation inhibition, and CDK inhibitory proteins (CIP/KIP, INK 4, RPIC). We next compared the role of different CDKs, mainly p21, p27, p57, p16, p15, p18, and p19, in the cell cycle and apoptosis in cancer cells with respect to normal cells. The current work also draws attention to the use of CDKIs as therapeutics, overcoming the pharmacokinetic barriers of pan-CDK inhibitors, analyze new chemical classes that are effective at attacking the CDKs that control the cell cycle (cdk4/6 or cdk2). It also discusses CDKI's drawbacks and its combination therapy against cancer patients. These findings collectively demonstrate the complexity of cancer cell cycles and the need for targeted therapeutic intervention. In order to slow the progression of the disease or enhance clinical outcomes, new medicines may be discovered by researching the relationship between cell death and cell proliferation.

Controlling cell proliferation by targeting cyclin-dependent kinase 6 using drug repurposing approach

Cyclin-dependent kinase 6 (CDK6) is an essential kinase in cell cycle progression, which is a viable target for inhibitors in various malignancies, including breast cancer. This study aimed to virtually screen efficient compounds as new leads in treating breast cancer using a drug repurposing approach. Apoptosis regulatory compounds were taken from the seleckchem database. Molecular docking experiments were carried out in the presence of abemaciclib, a routinely used FDA drug. Compared to conventional drugs, the two compounds demonstrated a higher binding affinity for CDK6. Compounds (N-benzyl-6-[(4-hydroxyphenyl)methyl]-8-(naphthalen-1-ylmethyl)-4,7-dioxo-3,6,9,9a-tetrahydro-2H-pyrazino[1,2-a]pyrimidine-1-carboxamide) and (1'-[4-[1-(4-fluorophenyl)indol-3-yl]butyl]spiro[1H-2-benzofuran-3,4'-piperidine]) were discovered to have an inhibitory effect against CDK6 at -8.49 and -6.78kcal/mol, respectively, compared to -8.09kcal/mol of the control molecule, the interacting residues of these two new compounds were found to fall within the binding site of the CDK6 molecule. Both compounds exhibited equal ADME features compared with abemaciclib and would be well distributed and metabolized by the body with an appropriate druglikeness range. Lastly, molecular dynamics was initiated for 200ns for the selected potent inhibitors and abemaciclib as complexed with CDK6. The RMSD, RMSF, Rg, H-Bond interactions, SASA, PCA, FEL, and MM/PBSA analysis were performed for the complexes to assess the stability, fluctuations, radius of gyration, hydrogen bond interaction, solvent accessibility, essential dynamics, free energy landscape, and MM/PBSA. The selected two compounds are small molecules in the appropriate druglikeness range. The results observed in molecular docking and molecular dynamics simulations were most promising for two compounds, suggesting their potent inhibitory effect against CDK6. We propose that these candidate compounds can undergo in vitro validation and in vivo testing for their further use against cancer.

Deciphering the effect of mutations in MMAA protein causing methylmalonic acidemia-A computational approach

Methylmalonic acidemia (MMA) is a rare genetic disorder affecting multiple body systems. We aimed to investigate the pathogenic mutations in MMAA that are associated with isolated methylmalonic acidemia to identify the structural behavior of MMAA upon mutation. The algorithms such as PredictSNP, iStable, ConSurf, and Align GVGD were employed to analyze the consequence of the mutations. Molecular docking was carried out for the native MMAA, L89P, G274D, and R359G to interpret its interactions with the GDP substrate. The docked complexes were simulated for 200ns aiding GROMACS in apprehending the behavior of MMAA upon mutation and GDP binding. After simulation, cα disruptions were observed using the RMSF plot, which indicated that several regions of mutant MMAAs have highly fluctuated. The gyration and H-bond plots were used to understand the compactness and intermolecular interaction with the GDP molecule. The MDS analysis showed that the mutations L89P, G274D, and R359G are highly unstable even after GDP binding, with the least compactness, fewer H-bonds, and larger conformational cα motions. Our study provided structural and dynamic insights into MMAA protein, which further helps to characterize these mutants and provide potential treatment strategies for MMA patients.

Computational and structural investigation of Palmitoyl-Protein Thioesterase 1 (PPT1) protein causing Neuronal Ceroid Lipofuscinoses (NCL)

The Neuronal Ceroid Lipofuscinoses (NCL) are a group of progressive neurodegenerative disorders, associated with 14 Ceroid Lipofuscinosis Neuronal genes (CLN1-14). The mutations in the Palmitoyl-Protein Thioesterase 1 (PPT1) protein serve as one of the major reasons for the causative of NCL. The PPT1 involves degrading and modifying cysteine residues in proteins or peptides by removing thioester-linked fatty acyl groups like palmitate prefers acyl chains of 14-18 carbons in length. In this study, we have analyzed the impact of PPT1 mutations on the deleteriousness, stability, conservative nature of amino acid, and impact of mutations on the protein structure. We have also used molecular dynamics simulations using GROMACS to perceive the alteration in the dynamic behavior of the PPT1 at the residual level. In this study, we have retrieved 23 PPT1 mutations from the UniProt database, and these were subjected to a series of analyses using varied computer algorithms. From these analyses, out of 23 mutations, 16 mutations were identified as deleterious. Among 16, eight mutations were identified to destabilize the protein structure, and finally, two mutations (W38C and L222P) were found to be positioned in the highly conserved region. The structural impact study observed that the mutant proline could disrupt the alpha helix formed by the leucine at position 222. Finally, from the molecular dynamics simulations, we observed that due to the mutations (W38C and L222P), the protein had experienced higher deviation, fluctuation, and lower compactness. These structural changes elucidate that these mutations can impact the structure and function of the PPT1 protein.

Integrative ontology and pathway-based approach identifies distinct molecular signatures in transcriptomes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) remains a serious concern globally due to many factors that including late diagnosis, lack of an ideal biomarker for diagnosis and prognosis, and high rate of mortality. In this study, we aimed to identify the essential dysregulated genes and molecular signatures associated with the progression and development of ESCC. The dataset with 15 ESCCs and the 15 adjacent normal tissue samples from the surrounding histopathologically tumor-free mucosa was selected. We applied bioinformatics pipelines including various topological parameters from MCODE, CytoNCA, and cytoHubba to prioritize the most significantly associated DEGs with ESCC. We performed functional enrichment annotation for the identified DEGs using DAVID and MetaCore™ GeneGo platforms. Furthermore, we validated the essential core genes in TCGA and GTEx datasets between the normal mucosa and ESCC for their expression levels. These DEGs were primarily enriched in positive regulation of transferase activity, negative regulation of organelle organization, cell cycle mitosis/S-phase transition, spindle organization/assembly, development, and regulation of angiogenesis. Subsequently, the DEGs were associated with the pathways such as oocyte meiosis, cell cycle, and DNA replication. Our study identified the eight-core genes (AURKA, AURKB, MCM2, CDC20, TPX2, PLK1, FOXM1, and MCM7) that are highly expressed among the ESCC, and TCGA dataset. The multigene comparison and principal component analysis resulted in elevated signals for the AURKA, MCM2, CDC20, TPX2, PLK1, and FOXM1. Overall, our study reported GO profiles and molecular signatures that might help researchers to grasp the pathological mechanisms underlying ESCC development and eventually provide novel therapeutic and diagnostic strategies.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

A computational model revealing the immune-related hub genes and key pathways involved in rheumatoid arthritis (RA)

Rheumatoid arthritis (RA) has one of the highest disability rates among inflammatory joint disorders. However, the reason and possible molecular events are still unclear. There are various treatment options available, but no complete cure. To obtain early diagnosis and successful medication in RA, it is necessary to explore gene susceptibility and pathogenic factors. The main intend of our work is to explore the immune-related hub genes with similar functions that are differentially expressed in RA patients. Three datasets such as GSE21959, GSE55457, and GSE77298, were taken to analyze the differently expressed genes (DEGs) among 55 RA and 33 control samples. We obtained 331 upregulated and 275 downregulated DEGs from three Gene Expression Omnibus (GEO) datasets using the R package. Furthermore, a protein-protein interaction network was built for upregulated and downregulated DEGs using Cytoscape. Subsequently, MCODE analysis was performed and obtained the top two modules in each DEG's upregulated and downregulated protein-protein interactions (PPIs) network. CytoNCA and cytoHubba were performed and identified overlapping DEGs. In addition, we narrowed down DEGs by filtering with immune-related genes and identified DE-IRGs. Gene ontology (GO) and KEGG pathway analysis in upregulated and downregulated DEGs were executed with the DAVID platform. Our study obtained the nine most significant DE-IRGs in RA such as CXCR4, CDK1, BUB1, BIRC5, AGTR1, EGFR, EDNRB, KALRN, and GHSR. Among them, CXCR4, CDK1, BUB1, and BIRC5 are overexpressed in RA and may contribute to the pathophysiology of the disease. Similarly, AGTR1, EGFR, EDNRB, KALRN, and GHSR are all low expressed in RA and may have a contribution to pathogenesis. GO, KEGG functional enrichment, and GeneMANIA showed that the dysregulated process of DE-IRGs causes RA development and progression. These findings may be helpful in future studies in RA diagnosis and therapy.

Computational structural assessment of BReast CAncer type 1 susceptibility protein (BRCA1) and BRCA1-Associated Ring Domain protein 1 (BARD1) mutations on the protein-protein interface

Breast cancer type 1 susceptibility protein (BRCA1) is closely related to the BRCA2 (breast cancer type 2 susceptibility protein) and BARD1 (BRCA1-associated RING domain-1) proteins. The homodimers were formed through their RING fingers; however they form more compact heterodimers preferentially, influencing BRCA1 residues 1-109 and BARD1 residues 26-119. We implemented an integrative computational pipeline to screen all the mutations in BRCA1 and identify the most significant mutations influencing the Protein-Protein Interactions (PPI) in the BRCA1-BARD1 protein complex. The amino acids involved in the PPI regions were identified from the PDBsum database with the PDB ID: 1JM7. We screened 2118 missense mutations in BRCA1 and none in BARD1 for pathogenicity and stability and analyzed the amino acid sequences for conserved residues. We identified the most significant mutations from these screenings as V11G, M18K, L22S, and T97R positioned in the PPI regions of the BRCA1-BARD1 protein complex. We further performed protein-protein docking using the ZDOCK server. The native protein-protein complex showed the highest binding score of 2118.613, and the V11G mutant protein complex showed the least binding score of 1992.949. The other three mutation protein complexes had binding scores between the native and V11G protein complexes. Finally, a molecular dynamics simulation study using GROMACS was performed to comprehend changes in the BRCA1-BARD1 complex's binding pattern due to the mutation. From the analysis, we observed the highest deviation with lowest compactness and a decrease in the intramolecular h-bonds in the BRCA1-BARD1 protein complex with the V11G mutation compared to the native complex or the complexes with other mutations.

Mutations in G6PC2 gene with increased risk for development of type 2 diabetes: Understanding via computational approach

An increase in the fast blood glucose (FBG) levels has been linked to an increased risk of developing a chronic condition, type 2 diabetes (T2D). The mutation in the G6PC2 gene was identified to have a lead role in the modulation of FBG levels. The abnormal regulation of this enzyme influences glucose-stimulated insulin secretion (GSIS), which controls the insulin levels corresponding to the system's glucose level. This study focuses on the mutations at the G6PC2 gene, which cause the variation from normal expression levels and increase the risk of T2D. We examined the non-synonymous single nucleotide polymorphisms (nsSNPs) present in the G6PC2 and subjected them to pathogenicity, stability, residue conservation, and membrane simulation. The individual representation of surrounding amino acids in the mutant (I63T) model showed the loss of hydrophobic interactions compared to the native G6PC2. In addition, the trajectory results from the membrane simulation exhibited reduced stability, and the least compactness was identified for the I63T mutant model. Our study shed light on the structural and conformational changes at the transmembrane region due to the I63T mutation in G6PC2. Additionally, the Gibbs free energy landscape analysis against the two principal components showed structural differences and decreased the conformational stability of the I63T mutant model compared to the native. Like those presented in this study, dynamical simulations may indeed be crucial to comprehending the structural insights of G6PC2 mutations in cardiovascular-associated mortality and T2D.

The role of photosynthesis related pigments in light harvesting, photoprotection and enhancement of photosynthetic yield in planta

Photosynthetic pigments are an integral and vital part of all photosynthetic machinery and are present in different types and abundances throughout the photosynthetic apparatus. Chlorophyll, carotenoids and phycobilins are the prime photosynthetic pigments which facilitate efficient light absorption in plants, algae, and cyanobacteria. The chlorophyll family plays a vital role in light harvesting by absorbing light at different wavelengths and allowing photosynthetic organisms to adapt to different environments, either in the long-term or during transient changes in light. Carotenoids play diverse roles in photosynthesis, including light capture and as crucial antioxidants to reduce photodamage and photoinhibition. In the marine habitat, phycobilins capture a wide spectrum of light and have allowed cyanobacteria and red algae to colonise deep waters where other frequencies of light are attenuated by the water column. In this review, we discuss the potential strategies that photosynthetic pigments provide, coupled with development of molecular biological techniques, to improve crop yields through enhanced light harvesting, increased photoprotection and improved photosynthetic efficiency.

Fruit ripening: dynamics and integrated analysis of carotenoids and anthocyanins

BACKGROUND

Fruits are vital food resources as they are loaded with bioactive compounds varying with different stages of ripening. As the fruit ripens, a dynamic color change is observed from green to yellow to red due to the biosynthesis of pigments like chlorophyll, carotenoids, and anthocyanins. Apart from making the fruit attractive and being a visual indicator of the ripening status, pigments add value to a ripened fruit by making them a source of nutraceuticals and industrial products. As the fruit matures, it undergoes biochemical changes which alter the pigment composition of fruits.

RESULTS

The synthesis, degradation and retention pathways of fruit pigments are mediated by hormonal, genetic, and environmental factors. Manipulation of the underlying regulatory mechanisms during fruit ripening suggests ways to enhance the desired pigments in fruits by biotechnological interventions. Here we report, in-depth insight into the dynamics of a pigment change in ripening and the regulatory mechanisms in action.

CONCLUSIONS

This review emphasizes the role of pigments as an asset to a ripened fruit as they augment the nutritive value, antioxidant levels and the net carbon gain of fruits; pigments are a source for fruit biofortification have tremendous industrial value along with being a tool to predict the harvest. This report will be of great utility to the harvesters, traders, consumers, and natural product divisions to extract the leading nutraceutical and industrial potential of preferred pigments biosynthesized at different fruit ripening stages.

Understanding the activating mechanism of the immune system against COVID-19 by Traditional Indian Medicine: Network pharmacology approach

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions are occurring rapidly; it is raising the alarm around the globe. Though vaccines are currently available, the evolution and mutations in the SARS-CoV-2 threaten available vaccines' significance. The drugs are still undergoing clinical trials, and certain medications are approved for “emergency use” or as an “off-label” drug during the pandemic. These drugs have been effective yet accommodating side effects, which also can be lethal. Complementary and alternative medicine is highly demanded since it embraces a holistic approach. Since ancient times, natural products have been used as drugs to treat various diseases in the medical field and are still widely practiced. Medicinal plants contain many active compounds that serve as the key to an effective drug design. The Kabasura kudineer and Nilavembu kudineer are the two most widely approved formulations to treat COVID-19. However, the mechanism of these formulations is not well known. The proposed study used a network pharmacology approach to understand the immune-boosting mechanism by the Kabasura kudineer, Nilavembu kudineer, and JACOM in treating COVID-19. The plants and phytochemical chemical compounds in the Kabasura kudineer, Nilavembu kudineer, and JACOM were obtained from the literature. The Swiss target prediction algorithm was used to predict the targets for these phytochemical compounds. The common genes for the COVID-19 infection and the drug targets were identified. The gene–gene interaction network was constructed to understand the interactions between these common genes and enrichment analyses to determine the biological process, molecular functions, cellular functions, pathways involved, etc. Finally, virtual screening and molecular docking studies were performed to identify the most potential targets and significant phytochemical compounds to treat the COVID-19. The present study identified potential targets as ACE, Cathepsin L, Cathepsin B, Cathepsin K, DPP4, EGFR, HDAC2, IL6, RIPK1, and VEGFA. Similarly, betulinic acid, 5″-(2⁗-Hydroxybenzyl) uvarinol, antofine, (S)-1′-methyloctyl caffeate, (Z)-3-phenyl-2-propenal, 7-oxo-10α-cucurbitadienol, and PLX-4720 collectively to be potential treatment agents for COVID-19.

Cardioprotective effects of phytopigments via multiple signaling pathways

BACKGROUND

Cardiovascular diseases (CVDs) are among the deadliest non-communicable diseases, and millions of dollars are spent every year to combat CVDs. Unfortunately, the multifactorial etiology of CVDs complicates the development of efficient therapeutics. Interestingly, phytopigments show significant pleiotropic cardioprotective effects both in vitro and in vivo.

PURPOSE

This review gives an overview of the cardioprotective effects of phytopigments based on in vitro and in vivo studies as well as clinical trials.

METHODS

A literature-based survey was performed to collect the available data on cardioprotective activities of phytopigments via electronic search engines such as PubMed, Google Scholar, and Scopus.

RESULTS

Different classes of phytopigments such as carotenoids, xanthophylls, flavonoids, anthocyanins, anthraquinones alleviate major CVDs (e.g., cardiac hypertrophy, atherosclerosis, hypertension, cardiotoxicities) via acting on signaling pathways related to AMPK, NF-κB, NRF2, PPARs, AKT, TLRs, MAPK, JAK/STAT, NLRP3, TNF-α, and RA.

CONCLUSION

Phytopigments represent promising candidates to develop novel and effective CVD therapeutics. More randomized, placebo-controlled clinical studies are recommended to establish the clinical efficacy of phytopigments.

Analysis and effective separation of toxic pollutants from water resources using MBBR: Pathway prediction using alkaliphilic P. mendocina

Azo dyes are highly toxic, which acts as a notable mutagen and carcinogen. This has a significant effect on human health, plants, animals, aquatic and terrestrial environments. Thus, the degradation of the azo dyes is exclusively studied using the conventional methods of which biodegradation is an eco-friendly approach. Hence, the present study is focused on the elucidation of reactive mixed azo dye degradation pathway using MBBR and laccase enzyme produced by an alkaliphilic bacterium P. mendocina. Synthetic wastewater treatment performed using MBBR was very effective which reduced the COD and BOD to 90 mg/L and 460 mg/L. The potential degrader P. mendocina was isolated and laccase enzyme was screened. Finally, the degradation pathway was elucidated. The in silico toxicity analysis predicted Reactive Red and Reactive Brown as developmental toxicants during Reactive Black as Developmental non-toxicant. Docking studies were performed to understand interaction of laccase with compounds evolved from dyes.

Computational validation of ABCB1 gene polymorphism and its effect on tacrolimus dose concentration/levels in renal transplant individuals of South India

Patients with end-stage renal failure require hemodialysis and peritoneal dialysis; however, kidney transplantation is considered a better treatment option for renal failure patients, improving their quality of life and longevity. Among several potent immunosuppressive agents, tacrolimus (TAC) has shown progressive improvement in the graft survival rates after renal transplantation. Fifty kidney transplant patients undergoing TAC immunosuppressive treatment were included. The human genomic DNA was isolated using the phenol-chloroform extraction procedure. CYP3A5*6, CYP3A5*2, and ABCB1 exon 21 G2677 T/A polymorphisms were genotyped using the polymerase chain reaction-restriction fragment length polymorphism method. Fisher's exact test and Chi-square analysis were performed to analyze the data, where p < 0.05 was considered statistically significant. In addition, we implemented bioinformatics studies on ABCB1 protein to determine the mutation's effect sequentially and structurally. Among the genotyped single nucleotide polymorphisms (SNPs), SNPs of CYP3A5*2 and CYP3A5*6 did not vary in the studied population. The concentration/dose (C/D) ratio of TT genotype of the ABCB1 gene was higher (95% CI: 177.38-269.46) when compared to TA and AA. However, there were no substantial differences between the ABCB1 genotypes and TAC C/D ratio (p = 0.953). The TAC dose mg/kg/day (p = 0.002) and C/D ratio (p = 0.004) exhibited a statistically significant difference. However, no significant difference was found with respect to the ABCB1 gene between the non-toxicity and toxicity groups. Mutation and residue interaction analysis results showed that the S893T mutation destabilizes the ABCB1 protein, thus reducing the protein's flexibility. The present study demonstrated a substantial relationship between the TAC dose and C/D ratio, including the non-toxicity and toxicity groups. However, no possible correlation was observed between the ABCB1 gene polymorphism and renal transplant.

The neuroprotective potential of carotenoids in vitro and in vivo

BACKGROUND

Despite advances in research on neurodegenerative diseases, the pathogenesis and treatment response of neurodegenerative diseases remain unclear. Recent studies revealed a significant role of carotenoids to treat neurodegenerative diseases. The aim of this study was to systematically review the neuroprotective potential of carotenoids in vivo and in vitro and the molecular mechanisms and pathological factors contributing to major neurodegenerative diseases (Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and stroke).

HYPOTHESIS

Carotenoids as therapeutic molecules to target neurodegenerative diseases.

RESULTS

Aggregation of toxic proteins, mitochondrial dysfunction, oxidative stress, the excitotoxic pathway, and neuroinflammation were the major pathological factors contributing to the progression of neurodegenerative diseases. Furthermore, in vitro and in vivo studies supported the beneficiary role of carotenoids, namely lycopene, β-carotene, crocin, crocetin, lutein, fucoxanthin and astaxanthin in alleviating disease progression. These carotenoids provide neuroprotection by inhibition of neuro-inflammation, microglial activation, excitotoxic pathway, modulation of autophagy, attenuation of oxidative damage and activation of defensive antioxidant enzymes. Additionally, studies conducted on humans also demonstrated that dietary intake of carotenoids lowers the risk of neurodegenerative diseases.

CONCLUSION

Carotenoids may be used as drugs to prevent and treat neurodegenerative diseases. Although, the in vitro and in vivo results are encouraging, further well conducted clinical studies on humans are required to conclude about the full potential of neurodegenerative diseases.

A computational overview on phylogenetic characterization, pathogenic mutations, and drug targets for Ebola virus disease

The World Health Organization declared Ebola virus disease (EVD) as the major outbreak in the 20th century. EVD was first identified in 1976 in South Sudan and the Democratic Republic of the Congo. EVD was transmitted from infected fruit bats to humans via contact with infected animal body fluids. The Ebola virus (EBOV) has a genome size of ∼18,959 bp. It encodes seven distinct proteins: nucleoprotein (NP), glycoprotein (GP), viral proteins VP24, VP30, VP35, matrix protein VP40, and polymerase L is considered a prime target for potential antiviral strategies. The current US FDA-approved anti-EVD vaccine, ERVERBO, and the other equally effective anti-EBOV combinations of three fully human monoclonal antibodies such as REGN-EB3, primarily target the envelope glycoprotein. This work elaborates on the EBOV's phylogenetic structure and the crucial mutations associated with viral pathogenicity.

Effect of shape and anthocyanin capping on antibacterial activity of CuI particles

The recent upsurge of antibiotic-resistant infections has posed to be a serious health concern worldwide. In the present paper, the effect of shape & capping agent on the antibacterial activity (on Skin and Urinary Tract Infection (UTI) causing bacteria) of copper iodide (CuI) particles was probed. CuI synthesized without a capping agent was leaf-like, and that with one was prismatic in shape. XRD of the synthesized CuI confirmed their high crystalline nature and purity. The average crystallite sizes of capped and uncapped CuI were calculated to be 91.10 nm and 89.01 nm respectively from X-Ray powder diffraction data. The average particle size of capped CuI was found to be 492.7 nm and that of uncapped CuI was found to be 2.96 μm using HR-SEM analysis. The crystals obtained were further characterized using EDAX, FTIR spectroscopy and UV-Visible spectroscopy. Antibacterial activity of prismatic CuI capped with the flower extract of Hibiscus rosa-sinensis showed better activity than that of uncapped CuI. AFM analysis was carried out to confirm the proposed mechanism for antibacterial activity through the morphological changes on the bacterial cell wall in the presence of capped CuI. Molecular docking studies were performed to reaffirm the enhanced antibacterial activity of prismatic CuI further. The present study demonstrates the superior antibacterial propensity of prismatic CuI, consequently making it a potent antibacterial agent.

Residue interaction networks of K-Ras protein with water molecules identifies the potential role of switch II and P-loop

The mutant K-Ras with aberrant signaling is the primary cause of several cancers. The proposed study investigated the influence of water molecules in K-Ras crystal structure, where they have a significant function by understanding their residue interaction networks (RINs). We analyzed the RINs of K-Ras with and without water molecules and determined their interaction properties. RINs were developed with the help of StructureViz2 and RINspector; further, the changes in K-Ras backbone flexibility were predicted with the DynaMine. We found that the residues K42, I142, and L159 are the hotspots from water, including the K-Ras-GTP complex with the highest residue centrality analysis (RCA) Z-score. The DynaMine prediction calculated the NMR S² value for the frequently mutated positions G12, G13, and Q61 showing a minor shift in flexibility, which make up the P-Loop and switch II of the K-Ras protein. This flexibility shift can account for changes in conformational activity and the protein's GTPase activity, making it difficult to recognize by the effectors and exchange factors. Taken together, our study helps in understanding the functional importance of the water molecules in K-Ras protein and the impact of mutation that modulate the conformational state of the protein.

Inhibition of MMP2-PEX by a novel ester of dihydroxy cinnamic and linoleic acid from the seagrass Cymodocea serrulata

Matrix metalloproteinases (MMPs) are pivotal for cancer cell migration and metastasis which are generally over-expressed in such cell types. Many drugs targeting MMPs do so by binding to the conserved catalytic domains and thus exhibit poor selectivity due to domain-similarities with other proteases. We report herein the binding of a novel compound [3-(E-3,4-dihydroxycinnamaoyloxyl)-2-hydroxypropyl 9Z, 12Z-octadeca-9, 12-dienoate; Mol. wt: 516.67 Da], (C₁), isolated from a seagrass, Cymodocea serrulata to the unconserved hemopexin-like (PEX) domain of MMP2 (- 9.258 kcal/mol). MD simulations for 25 ns, suggest stable ligand-target binding. In addition, C₁ killed an ovarian cancer cell line, PA1 at IC₅₀: 5.8 μM (lesser than Doxorubicin: 8.6 µM) and formed micronuclei, apoptotic bodies and nucleoplasmic bridges whilst causing DNA laddering, S and G2/M phase dual arrests and MMP disturbance, suggesting intrinsic apoptosis. The molecule increased mRNA transcripts of BAX and BAD and down-regulated cell survival genes, Bcl-xL, Bcl-2, MMP2 and MMP9. The chemical and structural details of C₁ were deduced through FT-IR, GC-MS, ESI-MS, ¹H and ¹³C NMR [both 1D and 2D] spectra.

An integrative analysis to distinguish between emphysema (EML) and alpha-1 antitrypsin deficiency-related emphysema (ADL)-A systems biology approach

Lung Emphysema is an abnormal enlargement of the air sacs followed by the destruction of alveolar walls without any prominent fibrosis. This study primarily identifies the differentially expressed genes (DEGs), interactions between them, and their significant involvement in the activated signaling cascades. The dataset with ID GSE1122 (five normal lung tissue samples, five of usual emphysema, and five of alpha-1 antitrypsin deficiency-related emphysema) from the gene expression omnibus (GEO) was analyzed using the GEO2R tool. The physical association between the DEGs were mapped using the STRING tool and was visualized in the Cytoscape software. The enriched functional processes were identified with the ClueGO plugin's help from Cytoscape. Further integrative functional annotation was performed by implying the GeneGo Metacore™ to distinguish the enriched pathway maps, process networks, and GO processes. The results from this analysis revealed the critical signaling cascades that have been either activated or inhibited due to identified DEGs. We found the activated pathways such as immune response IL-1 signaling pathway, positive regulation of smooth muscle migration, BMP signaling pathway, positive regulation of leukocyte migration, NIK/NF-kappB signaling, and cytochrome-c oxidase activity. Finally, we mapped four crucial genes (CCL5, ALK, TAC1, CD74, and HLA-DOA) by comparing the functional annotations that could be significantly influential in emphysema molecular pathogenesis. Our study provides insights into the pathogenesis of emphysema and helps in developing potential drug targets against emphysema.