Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox.

BACKGROUND: Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets. METHODS: GEO Expression datasets characterizing mRNA profile changes in different host responses to poxviruses were analyzed for shared pathway identification, and then, the Protein-protein interaction (PPI) maps were built. The viral gene expression datasets of Monkeypox virus (MPXV) and Vaccinia virus (VACV) were used to identify the significant viral genes and further investigated for their binding to the library of targeting molecules. RESULTS: Infection with MPXV interferes with various cellular pathways, including interleukin and MAPK signaling. While most host differentially expressed genes (DEGs) are predominantly downregulated upon infection, marked enrichments in histone modifiers and immune-related genes were observed. PPI analysis revealed a set of novel virus-specific protein interactions for the genes in the above functional clusters. The viral DEGs exhibited variable expression patterns in three studied cell types: primary human monocytes, primary human fibroblast, and HeLa, resulting in 118 commonly deregulated proteins. Poxvirus proteins C6R derived protein K7 and K7R of MPXV and VACV were prioritized as targets for potential therapeutic interventions based on their histone-regulating and immunosuppressive properties. In the computational docking and Molecular Dynamics (MD) experiments, these proteins were shown to bind the candidate small molecule S3I-201, which was further prioritized for lead development. RESULTS: MPXV circumvents cellular antiviral defenses by engaging histone modification and immune evasion strategies. C6R-derived protein K7 binding candidate molecule S3I-201 is a priority promising candidate for treating Mpox.

Interaction of eugenol-based anti-tuberculosis nanoemulsion with bovine serum albumin: A spectroscopic study including Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol.

Tuberculosis (TB), a deadly infectious disease, is primarily caused by the bacterium Mycobacterium tuberculosis. The misuse of antibiotics has led to the development of drug resistance, prompting researchers to explore new technologies to combat multidrug-resistant Tuberculosis (MDR TB). Phospholipid-based nanotherapeutics, such as nanoemulsions, are gaining traction as they enhance drug solubility, stability, and bioavailability. Our study focuses on the interaction between Bovine Serum Albumin (BSA) and a drug-loaded nanoemulsion based on Eugenol. This nanoemulsion incorporates Eugenol, Clove, cinnamon oil, and first-line anti-tuberculosis drugs like Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol. The primary objective is to assess the biosafety profile of the nanoemulsion upon interaction with BSA. We employed Fluorescence, UV-visible, and Fourier Transform Infrared Spectroscopy (FTIR) to analyze this interaction. UV-visible spectroscopy detected changes in hydrophobicity due to structural alterations in BSA near the tryptophan residue, leading to the formation of ground-state complexes. Fluorescence spectroscopy demonstrated that the nanoemulsion effectively quenched fluorescence originating from tryptophan and tyrosine residues. Studies using synchronous and three-dimensional spectroscopy point to a potential modification of the aromatic environment of BSA by the nanoemulsion. Resonance light scattering spectra indicated the formation of large aggregates due to the interaction with the nanoemulsion. The second derivative FTIR spectra showed an increase in the magnitude of secondary structure bands, suggesting a conformational shift. This research has significant pharmacological implications for developing safer, more targeted drug delivery systems. The information obtained from the interaction of the nanoemulsion with the blood carrier protein is vital for the future development of superior carriers with minimal adverse effects on patients. It is crucial to remember that conformational changes brought on by drug-ligand complexes attaching to carrier proteins may have negative consequences. Therefore, this study enhances the in vitro evaluation of potential adverse effects of the nanoemulsion on serum proteins.

Emerging strategies and therapeutic innovations for combating drug resistance in Staphylococcus aureus strains: A comprehensive review.

In recent years, antibiotic therapy has encountered significant challenges due to the rapid emergence of multidrug resistance among bacteria responsible for life-threatening illnesses, creating uncertainty about the future management of infectious diseases. The escalation of antimicrobial resistance in the post-COVID era compared to the pre-COVID era has raised global concern. The prevalence of nosocomial-related infections, especially outbreaks of drug-resistant strains of Staphylococcus aureus, have been reported worldwide, with India being a notable hotspot for such occurrences. Various virulence factors and mutations characterize nosocomial infections involving S. aureus. The lack of proper alternative treatments leading to increased drug resistance emphasizes the need to investigate and examine recent research to combat future pandemics. In the current genomics era, the application of advanced technologies such as next-generation sequencing (NGS), machine learning (ML), and quantum computing (QC) for genomic analysis and resistance prediction has significantly increased the pace of diagnosing drug-resistant pathogens and insights into genetic intricacies. Despite prompt diagnosis, the elimination of drug-resistant infections remains unattainable in the absence of effective alternative therapies. Researchers are exploring various alternative therapeutic approaches, including phage therapy, antimicrobial peptides, photodynamic therapy, vaccines, host-directed therapies, and more. The proposed review mainly focuses on the resistance journey of S. aureus over the past decade, detailing its resistance mechanisms, prevalence in the subcontinent, innovations in rapid diagnosis of the drug-resistant strains, including the applicants of NGS and ML application along with QC, it helps to design alternative novel therapeutics approaches against S. aureus infection.

Rational design of a multivalent vaccine targeting arthropod-borne viruses using reverse vaccinology strategies.

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants ß-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.

Exploring natural products library to identify potential inhibitors targeting isoniazid-resistant tuberculosis.

Mycobacterium tuberculosis (MTB) causing tuberculosis (TB) infection is a leading source of illness and death in developing nations, and the emergence of drug-resistant TB remains a significant global threat and a challenge in treating the disease. Mutations in the inhA and katG genes are connected to the principal molecular mechanism of isoniazid (INH) resistance, and continuous treatment of INH for more than a decade led to the evolution of INH resistant-TB (inhR-TB). Structure-based drug discovery approaches on traditional natural compounds are the contemporary source to identify significant lead molecules. This work focuses on discovering effective small compounds from natural compound libraries and applying pharmacophore-based virtual screening to filter out the molecules. The best-identified hit complexes were used for molecular dynamics simulations (MDS) to observe their stability and compactness. A three-dimensional e-pharmacophore hypothesis and screening generated 62 hits based on phase fitness scores from the pharmacophore-based virtual screening. Molecular docking experiments in Maestro's GLIDE module indicated that ZINC000002383126 and ASN22022 may be potential inhibitors of inhA and katG (native, inhA mutants S94A, Y158A, Y158F and Y158S and D137S, Y229F, S315T, W321F, and R418L mutants of katG). In addition, MDS analysis indicated that the native and mutant docked complexes of inhA and katG had good stability and remained compact in the binding pocket of the targets. In vitro studies can further validate the compounds that can act as INH competitive inhibitors.Communicated by Ramaswamy H. Sarma.

Whole genome analysis of Rhizopus species causing rhino-cerebral mucormycosis during the COVID-19 pandemic.

Introduction: Mucormycosis is an acute invasive fungal disease (IFD) seen mainly in immunocompromised hosts and in patients with uncontrolled diabetes. The incidence of mucormycosis increased exponentially in India during the SARS-CoV-2 (henceforth COVID-19) pandemic. Since there was a lack of data on molecular epidemiology of Mucorales causing IFD during and after the COVID-19 pandemic, whole genome analysis of the Rhizopus spp. isolated during this period was studied along with the detection of mutations that are associated with antifungal drug resistance. Materials and methods: A total of 50 isolates of Rhizopus spp. were included in this prospective study, which included 28 from patients with active COVID-19 disease, 9 from patients during the recovery phase, and 13 isolates from COVID-19-negative patients. Whole genome sequencing (WGS) was performed for the isolates, and the de novo assembly was done with the Spades assembler. Species identification was done by extracting the ITS gene sequence from each isolate followed by searching Nucleotide BLAST. The phylogenetic trees were made with extracted ITS gene sequences and 12 eukaryotic core marker gene sequences, respectively, to assess the genetic distance between our isolates. Mutations associated with intrinsic drug resistance to fluconazole and voriconazole were analyzed. Results: All 50 patients presented to the hospital with acute fungal rhinosinusitis. These patients had a mean HbA1c of 11.2%, and a serum ferritin of 546.8 ng/mL. Twenty-five patients had received steroids. By WGS analysis, 62% of the Rhizopus species were identified as R. delemar. Bayesian analysis of population structure (BAPS) clustering categorized these isolates into five different groups, of which 28 belong to group 3, 9 to group 5, and 8 to group 1. Mutational analysis revealed that in the CYP51A gene, 50% of our isolates had frameshift mutations along with 7 synonymous mutations and 46% had only synonymous mutations, whereas in the CYP51B gene, 68% had only synonymous mutations and 26% did not have any mutations. Conclusion: WGS analysis of Mucorales identified during and after the COVID-19 pandemic gives insight into the molecular epidemiology of these isolates in our community and establishes newer mechanisms for intrinsic azole resistance.

Interaction of antidiabetic formulation with nanoplastics and its binary influence on plasma protein.

Nanoplastics exposure to humans becomes inevitable due to its prevalence and permanence. Adsorption of emerging pollutant metformin hydrochloride (Met-HCl) -antidiabetic drug, on polystyrene nanoplastics (PSNPs) and influence on plasma protein binding was investigated. Fluorescence studies were carried out for human serum albumin (HSA) binding. Adsorption follows pseudo-second-order kinetics, intraparticle-diffusion, and Langmuir isotherm, undergoing both physisorption and chemisorption which was validated by FE-SEM, FTIR, and HRMS measurements. Complex, experiences static quenching mechanism by hydrogen bonding and VanderWaals force of attraction to HSA. FTIR confirms the secondary structural alteration of HSA. Since Met-HCl covers the NPs' surface, NPs' affinity for HSA is reduced and they might reach the target organs of Met-HCl, disrupt antidiabetic mechanisms and cause far-reaching implications. Results from molecular docking and simulation studies backed up these results as hydrophobic and hydrogen bonds dominate the binding process of the HSA-Met-HCl-PSNPs complex. This work will aid in understanding of the toxico-kinetics/dynamics of binary contaminants.

A new horizon in the phosphorylated sites of AGA: the structural impact of C163S mutation in aspartylglucosaminuria through molecular dynamics simulation.

Aspartylglucosaminuria (AGU) is a lysosomal storage disorder caused by insufficient aspartylglucosaminidase (AGA) activity leading to chronic neurodegeneration. We utilized the PhosphoSitePlus tool to identify the AGA protein's phosphorylation sites. The phosphorylation was induced on the specific residue of the three-dimensional AGA protein, and the structural changes upon phosphorylation were studied via molecular dynamics simulation. Furthermore, the structural behaviour of C163S mutation and C163S mutation with adjacent phosphorylation was investigated. We have examined the structural impact of phosphorylated forms and C163S mutation in AGA. Molecular dynamics simulations (200 ns) exposed patterns of deviation, fluctuation, and change in compactness of Y178 phosphorylated AGA protein (Y178-p), T215 phosphorylated AGA protein (T215-p), T324 phosphorylated AGA protein (T324-p), C163S mutant AGA protein (C163S), and C163S mutation with Y178 phosphorylated AGA protein (C163S-Y178-p). Y178-p, T215-p, and C163S mutation demonstrated an increase in intramolecular hydrogen bonds, leading to greater compactness of the AGA forms. Principle component analysis (PCA) and Gibbs free energy of the phosphorylated/C163S mutation structures exhibit transition in motion/orientation than Wild type (WT). T215-p may be more dominant among these than the other studied phosphorylated forms. It might contribute to hydrolyzing L-asparagine functioning as an asparaginase, thereby regulating neurotransmitter activity. This study revealed structural insights into the phosphorylation of Y178, T215, and T324 in AGA protein. Additionally, it exposed the structural changes of the C163S mutation and C163S-Y178-p of AGA protein. This research will shed light on a better understanding of AGA's phosphorylated mechanism.Communicated by Ramaswamy H. Sarma.

Determination of potential combination of non-ß-lactam, ß-lactam, and ß-lactamase inhibitors/ß-lactam enhancer against class D oxacillinases producing Acinetobacter baumannii: Evidence from in-vitro, molecular docking and dynamics simulation.

Carbapenem-resistant Acinetobacter baumannii, a predominant nosocomial pathogen in hospitals of intensive care units, is associated with bacteremia and ventilator-associated pneumonia with a high-risk mortality rate. To increase the effectiveness of the ß-lactam (BL) antibiotics, the use of ß-lactamase inhibitors (BLI) acts as a booster when given in combination with BL antibiotics. To this aspect, we selected BL antibiotics of cefiderocol, cefepime, non-BL antibiotic eravacycline, BLI of durlobactam, avibactam, and a ß-lactam enhancer (BLE) of zidebactam. To prove our hypothesis, we determined the minimum inhibitory concentration (MIC) of various BL or non-BL/BLI or BLE combinations using broth microdilution method followed by in silico analysis of molecular docking, molecular dynamics (MD) simulation, and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) identifies the potential combination. In MIC testing, eravacycline, cefepime/zidebactam, cefiderocol/zidebactam, and eravacycline in combination with zidebactam or durlobactam were found to be effective against oxacillinases (OXAs) (OXA-23/24/58 like) expressing A. baumannii isolates. The docking results of the selected ligands toward OXA-23, OXA-24, and OXA-58 had an excellent binding score ranging from -5.8 to -9.3 kcal/mol. Further, the docked complexes were subjected and evaluated using gromacs for molecular dynamics simulation of 50 ns toward selected class D OXAs. The binding energies obtained from MM-PBSA shed light on the binding efficiencies of each non-BL, BL, and BLI/BLE, thereby helping us to propose the drug combinations. Based on the MD trajectories scoring acquired, we propose using eravacycline, cefepime/zidebactam, cefiderocol/zidebactam, and eravacycline in combination with durlobactam or zidebactam would be promising for treating OXA-23, OXA-24, and OXA-58 like expressing A. baumannii infections.

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.

Therapeutic and diagnostic applications of exosomal circRNAs in breast cancer.

Circular RNAs (circRNAs) are regulatory elements that are involved in orchestrating gene expression and protein functions and are implicated in various biological processes including cancer. Notably, breast cancer has a significant mortality rate and is one of the most common malignancies in women. CircRNAs have been demonstrated to contribute to the pathogenesis of breast cancer including its initiation, progression, metastasis, and resistance to drugs. By acting as miRNA sponges, circRNAs can indirectly influence gene expression by disrupting miRNA regulation of their target genes, ultimately altering the course of cancer development and progression. Additionally, circRNAs can interact with proteins and modulate their functions including signaling pathways involved in the initiation and development of cancer. Recently, circRNAs can encode peptides that play a role in the pathophysiology of breast cancer and other diseases and their potential as diagnostic biomarkers and therapeutic targets for various cancers including breast cancer. CircRNAs possess biomarkers that differentiate, such as stability, specificity, and sensitivity, and can be detected in several biological specimens such as blood, saliva, and urine. Moreover, circRNAs play an important role in various cellular processes including cell proliferation, differentiation, and apoptosis, all of which are integral factors in the development and progression of cancer. This review synthesizes the functions of circRNAs in breast cancer, scrutinizing their contributions to the onset and evolution of the disease through their interactions with exosomes and cancer-related intracellular pathways. It also delves into the potential use of circRNA as a biomarker and therapeutic target against breast cancer. It discusses various databases and online tools that offer crucial circRNA information and regulatory networks. Lastly, the challenges and prospects of utilizing circRNAs in clinical settings associated with breast cancer are explored.

The targeted next-generation sequence revealed SMAD4, AKT1, and TP53 mutations from circulating cell-free DNA of breast cancer and its effect on protein structure - A computational approach.

Breast cancer biomarkers that detect marginally advanced stages are still challenging. The detection of specific abnormalities, targeted therapy selection, prognosis, and monitoring of treatment effectiveness over time are all made possible by circulating free DNA (cfDNA) analysis. The proposed study will detect specific genetic abnormalities from the plasma cfDNA of a female breast cancer patient by sequencing a cancer-related gene panel (MGM455 - Oncotrack Ultima), including 56 theranostic genes (SNVs and small INDELs). Initially, we determined the pathogenicity of the observed mutations using PredictSNP, iStable, Align-GVGD, and ConSurf servers. As a next step, molecular dynamics (MD) was implemented to determine the functional significance of SMAD4 mutation (V465M). Lastly, the mutant gene relationships were examined using the Cytoscape plug-in GeneMANIA. Using ClueGO, we determined the gene's functional enrichment and integrative analysis. The structural characteristics of SMAD4 V465M protein by MD simulation analysis further demonstrated that the mutation was deleterious. The simulation showed that the native structure was more significantly altered by the SMAD4 (V465M) mutation. Our findings suggest that SMAD4 V465M mutation might be significantly associated with breast cancer, and other patient-found mutations (AKT1-E17K and TP53-R175H) are synergistically involved in the process of SMAD4 translocate to nuclease, which affects the target gene translation. Therefore, this combination of gene mutations could alter the TGF-ß signaling pathway in BC. We further proposed that the SMAD4 protein loss may contribute to an aggressive phenotype by inhibiting the TGF-ß signaling pathway. Thus, breast cancer's SMAD4 (V465M) mutation might increase their invasive and metastatic capabilities.Communicated by Ramaswamy H. Sarma.

Multi-drug loaded eugenol-based nanoemulsions for enhanced anti-mycobacterial activity.

Tuberculosis is one of the oldest bacterial infections known to mankind caused by Mycobacterium tuberculosis. The aim of this research is to optimize and formulate a multi-drug loaded eugenol based nanoemulsion system and to evaluate its ability as an antimycobacterial agent and its potential to be a low cost and effective drug delivery system. All the three eugenol based drug loaded nano-emulsion systems were optimized using response surface methodology (RSM)-central composite design (CCD) and were found stable at a ratio of 1 : 5 (oil : surfactant) when ultrasonicated for 8 minutes. The minimum inhibitory concentration (MIC) values against strains of Mycobacterium tuberculosis highly proved that these essential oil-based nano-emulsions showed more promising results and an even improved anti-mycobacterium activity on the addition of a combination of drugs. The absorbance of 1st line anti-tubercular drugs from release kinetics studies showed a controlled and sustained release in body fluids. Thus, we can conclude that this is a much more efficient and desirable method in treating infections caused by Mycobacterium tuberculosis and even its MDR/XDR strains. All these nano-emulsion systems were stable for more than 3 months.

In silico analysis revealed the potential circRNA-miRNA-mRNA regulative network of non-small cell lung cancer (NSCLC).

BACKGROUND: The primary source of death in the world is non-small cell lung cancer (NSCLC). However, NSCLCs pathophysiology is still not completely understood. The current work sought to study the differential expression of mRNAs involved in NSCLC and their interactions with miRNAs and circRNAs. METHODS: We utilized three microarray datasets (GSE21933, GSE27262, and GSE33532) from the GEO NCBI database to identify the differentially expressed genes (DEGs) in NSCLC. We employed DAVID Functional annotation tool to investigate the underlying GO biological process, molecular functions, and KEGG pathways involved in NSCLC. We performed the Protein-protein interaction (PPI) network, MCODE, and CytoHubba analysis from Cytoscape software to identify the significant DEGs in NSCLC. We utilized miRnet to anticipate and build interaction between miRNAs and mRNAs in NSCLC and ENCORI to predict the miRNA-circRNA relationships and build the ceRNA regulatory network. Finally, we executed the gene expression and Kaplan-Meier survival analysis to validate the significant DEGs in the ceRNA network utilizing TCGA NSCLC and GEPIA data. RESULTS: We revealed a total of 156 overlapped DEGs (47 upregulated and 109 downregulated genes) in NSCLC. The PPI network, MCODE, and CytoHubba analysis revealed 12 hub genes (cdkn3, rrm2, ccnb1, aurka, nuf2, tyms, kif11, hmmr, ccnb2, nek2, anln, and birc5) that are associated with NSCLC. We identified that these 12 genes encode 12 mRNAs that are strongly linked with 8 miRNAs, and further, we revealed that 1 circRNA was associated with this 5 miRNA. We constructed the ceRNAs network that contained 1circRNA-5miRNAs-7mRNAs. The expression of these seven significant genes in LUAD & LUSC (NSCLC) was considerably higher in the TCGA database than in normal tissues. Kaplan-Meier survival plot reveals that increased expression of these hub genes was related to a poor survival rate in LUAD. CONCLUSION: Overall, we developed a circRNA-miRNA-mRNA regulation network to study the probable mechanism of NSCLC.

An integrated gene network analysis to decode the multi-drug resistance mechanism in Klebsiella pneumoniae.

Antimicrobial resistance (AMR) among microorganisms has become one of the worldwide concerns of this century and continues to challenge us. To properly understand this problem, it is essential to know the genes that cause AMR and their resistance mechanisms. Our present study focused on Klebsiella pneumoniae, which possesses AMR genes conferring resistance against multiple antibiotics. A gene interaction network of 42 functional partners was constructed and analyzed to broaden our understanding. Three closely related clusters (C1-C3) having an association with multi-drug resistance mechanisms were identified by clustering analysis. The enrichment analysis illustrated 30 genes in biological processes, 24 genes in molecular function, and 25 genes in cellular components having a significant role. The analysis of the gene interaction network revealed genes birA2, folP, pabC, folA, gyrB, glmM, gyrA, thyA_2 had maximum no. of interactions with their functional partners viz. 26, 25, 25, 24, 23, 23, 23, 23 respectively and can be considered as hub genes. Analyzing the enriched pathways and Gene Ontologies provides insight into AMR's molecular basis. In addition, the proposed study could aid the researchers in developing new treatment options to combat multi-drug resistant K. pneumoniae.

Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review.

The emergence of large-scale human genome projects, advances in DNA sequencing technologies, and the massive volume of electronic medical records [EMR] shift the transformation of healthcare research into the next paradigm, namely 'Precision Medicine.' This new clinical system model uses patients' genomic profiles and disparate healthcare data sources to a greater extent and provides personalized deliverables. As an advanced analytical technique, deep learning models significantly impact precision medicine because they can process voluminous amounts of diversified data with improved accuracy. Two salient features of deep learning models, namely processing a massive volume of multi-model data at multiple levels of abstraction and the ability to identify inherent features from the input data on their own, attract the implication of deep learning techniques in precision medicine research. The proposed review highlights the importance of deep learning-based analytical models in handling diversified and disparate big data sources of precision medicine. To augment further, state-of-the-art precision medicine research based on the taxonomy of deep learning models has been reviewed along with their research outcomes. The diversified data inputs used in research attempts, their applications, benchmarking data repositories, and usage of various evaluation measures for accuracy estimations are highlighted in this review. This review also brings out some promising analytical avenues of precision medicine research that give directions for future exploration.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis.

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.

Unraveling the versatility of CCD4: Metabolic engineering, transcriptomic and computational approaches.

Carotenoids are economically valuable isoprenoids synthesized by plants and microorganisms, which play a paramount role in their overall growth and development. Carotenoid cleavage dioxygenases are a vast group of enzymes that specifically cleave thecarotenoids to produce apocarotenoids. Recently, CCDs are a subject of talk because of their contributions to different aspects of plant growth and due to their significance in the production of economically valuable apocarotenoids. Among them, CCD4 stands unique because of its versatility in performing metabolic roles. This review focuses on the multiple functionalities of CCD4 like pigmentation, volatile apocarotenoid production, stress responses, etc. Interestingly, through our literature survey we arrived at a conclusion that CCD4 could perform functions of other carotenoid cleaving enzymes.The metabolic engineering, transcriptomic, and computational approaches adopted to reveal the contributions of CCD4 were also considered here for the study.Phylogenetic analysis was performed to delve into the evolutionary relationships of CCD4 in different plant groups. A tree of 81CCD genes from 64 plant species was constructed, signifying the presence of well-conserved families. Gene structures were illustrated and the difference in the number and position of exons could be considered as a factor behind functional versatility and substrate tolerance of CCD4 in different plants.

Deciphering the Role of Filamin B Calponin-Homology Domain in Causing the Larsen Syndrome, Boomerang Dysplasia, and Atelosteogenesis Type I Spectrum Disorders via a Computational Approach.

Filamins (FLN) are a family of actin-binding proteins involved in regulating the cytoskeleton and signaling phenomenon by developing a network with F-actin and FLN-binding partners. The FLN family comprises three conserved isoforms in mammals: FLNA, FLNB, and FLNC. FLNB is a multidomain monomer protein with domains containing an actin-binding N-terminal domain (ABD 1-242), encompassing two calponin-homology domains (assigned CH1 and CH2). Primary variants in FLNB mostly occur in the domain (CH2) and surrounding the hinge-1 region. The four autosomal dominant disorders that are associated with FLNB variants are Larsen syndrome, atelosteogenesis type I (AOI), atelosteogenesis type III (AOIII), and boomerang dysplasia (BD). Despite the intense clustering of FLNB variants contributing to the LS-AO-BD disorders, the genotype-phenotype correlation is still enigmatic. In silico prediction tools and molecular dynamics simulation (MDS) approaches have offered the potential for variant classification and pathogenicity predictions. We retrieved 285 FLNB missense variants from the UniProt, ClinVar, and HGMD databases in the current study. Of these, five and 39 variants were located in the CH1 and CH2 domains, respectively. These variants were subjected to various pathogenicity and stability prediction tools, evolutionary and conservation analyses, and biophysical and physicochemical properties analyses. Molecular dynamics simulation (MDS) was performed on the three candidate variants in the CH2 domain (W148R, F161C, and L171R) that were predicted to be the most pathogenic. The MDS analysis results showed that these three variants are highly compact compared to the native protein, suggesting that they could affect the protein on the structural and functional levels. The computational approach demonstrates the differences between the FLNB mutants and the wild type in a structural and functional context. Our findings expand our knowledge on the genotype-phenotype correlation in FLNB-related LS-AO-BD disorders on the molecular level, which may pave the way for optimizing drug therapy by integrating precision medicine.

First hybrid complete genome of Aeromonas veronii reveals chromosome-mediated novel structural variant mcr-3.30 from a human clinical sample.

Recent findings demonstrate the origin of the plasmid-mediated colistin resistance gene mcr-3 from aeromonads. The present study aimed to screen for plasmid-mediated colistin resistance among 30 clinical multidrug-resistant (MDR) Aeromonas spp. PCR was used to screen for the presence of mcr-1, mcr-2, mcr-3 and mcr-4, which revealed mcr-3 in a colistin-susceptible isolate (FC951). All other isolates were negative for mcr. Sequencing of FC951 revealed that the mcr-3 (mcr-3.30) identified was different from previously reported variants and had 95.62 and 95.28 % nucleotide similarity with mcr-3.3 and mcr-3.10. Hybrid assembly using IonTorrent and MinION reads revealed structural genetic information for mcr-3.30 with an insertion of ISAs18 within the gene. Due to this, mcr-3.30 was non-expressive, which makes FC951 susceptible to colistin. Further, in silico sequence and protein structural analysis confirmed the new variant. To the best of our knowledge, this is the first report on a novel mcr-3 variant from India. The significant role of mcr-like genes in different Aeromonas species remains unknown and requires additional investigation to obtains insights into the mechanism of colistin resistance.