Airway and Oral microbiome profiling of SARS-CoV-2 infected asthma and non-asthma cases revealing alterations-A pulmonary microbial investigation

New evidence strongly discloses the pathogenesis of host-associated microbiomes in respiratory diseases. The microbiome dysbiosis modulates the lung's behavior and deteriorates the respiratory system's effective functioning. Several exogenous and environmental factors influence the development of asthma and chronic lung disease. The relationship between asthma and microbes is reasonably understood and yet to be investigated for more substantiation. The comorbidities such as SARS-CoV-2 further exacerbate the health condition of the asthma-affected individuals. This study examines the raw 16S rRNA sequencing data collected from the saliva and nasopharyngeal regions of pre-existing asthma (23) and non-asthma patients (82) infected by SARS-CoV-2 acquired from the public database. The experiment is designed in a two-fold pattern, analyzing the associativity between the samples collected from the saliva and nasopharyngeal regions. Later, investigates the microbial pathogenesis, its role in exacerbations of respiratory disease, and deciphering the diagnostic biomarkers of the target condition. LEfSE analysis identified that Actinobacteriota and Pseudomonadota are enriched in the SARS-CoV-2-non-asthma group and SARS-CoV-2 asthma group of the salivary microbiome, respectively. Random forest algorithm is trained with amplicon sequence variants (ASVs) attained better classification accuracy, ROC scores on nasal (84% and 87%) and saliva datasets (93% and 97.5%). Rothia mucilaginosa is less abundant, and Corynebacterium tuberculostearicum showed higher abundance in the SARS-CoV-2 asthma group. The increase in Streptococcus at the genus level in the SARS-CoV-2-asthma group is evidence of discriminating the subgroups.Scopu

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

IntroductionMucormycosis 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 methodsA 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.ResultsAll 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.ConclusionWGS 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

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.Udhaya Kumar. S, one of the authors, gratefully acknowledges the Indian Council of Medical Research (ICMR), India, for providing him a Senior Research Fellowship [ISRM/11(93)/2019]. In addition, the authors would like to thank the Vellore Institute of Technology, Vel-lore, India, and Qatar University, Doha, Qatar for providing the necessary research facilities and encouragement to carry out this work. Open Access funding provided by the Qatar National Library.Scopu

Unraveling the Dysbiosis of Vaginal Microbiome to Understand Cervical Cancer Disease Etiology—An Explainable AI Approach

Microbial Dysbiosis is associated with the etiology and pathogenesis of diseases. The studies on the vaginal microbiome in cervical cancer are essential to discern the cause and effect of the condition. The present study characterizes the microbial pathogenesis involved in developing cervical cancer. Relative species abundance assessment identified Firmicutes, Actinobacteria, and Proteobacteria dominating the phylum level. A significant increase in Lactobacillus iners and Prevotella timonensis at the species level revealed its pathogenic influence on cervical cancer progression. The diversity, richness, and dominance analysis divulges a substantial decline in cervical cancer compared to control samples. The β diversity index proves the homogeneity in the subgroups’ microbial composition. The association between enriched Lactobacillus iners at the species level, Lactobacillus, Pseudomonas, and Enterococcus genera with cervical cancer is identified by Linear discriminant analysis Effect Size (LEfSe) prediction. The functional enrichment corroborates the microbial disease association with pathogenic infections such as aerobic vaginitis, bacterial vaginosis, and chlamydia. The dataset is trained and validated with repeated k-fold cross-validation technique using a random forest algorithm to determine the discriminative pattern from the samples. SHapley Additive exPlanations (SHAP), a game theoretic approach, is employed to analyze the results predicted by the model. Interestingly, SHAP identified that the increase in Ralstonia has a higher probability of predicting the sample as cervical cancer. New evidential microbiomes identified in the experiment confirm the presence of pathogenic microbiomes in cervical cancer vaginal samples and their mutuality with microbial imbalance.The authors acknowledge the Indian Council of Medical Research (ICMR), the Government of India agency, for the research grant No. BMI/12(13)/2021, ID No: 2021-6359, and grant No. VIR/COVID-19/31/2021/ECD-I, ID. NO: 2021-5570

Box plots of Alpha diversity indices.

The Shannon and Simpson indices depict the ASV diversity, whereas the Choa 1 represents the ASV abundance in the SARS-CoV-2-asthma and SARS-CoV-2-non-asthma conditions at gender level groupings. (Nasal Dataset–Top; Saliva Dataset—Bottom). The boxes show the interquartile range between the 25th and 75th percentile, whereas horizontal lines and colored dots represent the median and outliers. The SARS-CoV-2-asthma and SARS-CoV-2-Non-asthma groups were shown in green and orange colors. (Nasal Dataset–Top; Saliva Dataset—Bottom).</p

Pie chart with the distribution of microbiome at phylum level displaying top abundance (Nasal Dataset–Top; Saliva Dataset—Bottom).

Pie chart with the distribution of microbiome at phylum level displaying top abundance (Nasal Dataset–Top; Saliva Dataset—Bottom).</p

PCOA plots of Beta diversity based on the Bray-Curtis distance metric.

The ellipses represent SARS-CoV-2-asthma (green) and SARS-CoV-2-Non-asthma (orange) conditions (Nasal Dataset–Top; Saliva Dataset—Bottom).</p

Fig 7 -

Heatmap visualizes the difference between African-American, Asian, and White races at the class level (Nasal Dataset–Left; Saliva Dataset—Right). Each square in the heatmap represents the given ASVs relative abundance; the orange color’s intensity correlates with the class-level abundance.</p

The Area Under the Receiver Operational Characteristic (AU-ROC) curve for SARS-CoV-2-asthmac (green) and SARS-CoV-2-Non-asthma (orange) groups based on microbial composition.

The Area Under the Receiver Operational Characteristic (AU-ROC) curve for SARS-CoV-2-asthmac (green) and SARS-CoV-2-Non-asthma (orange) groups based on microbial composition.</p

Whole-exome sequencing analysis of NSCLC reveals the pathogenic missense variants from cancer-associated genes

Background: Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. NSCLC accounts for 84% of all lung cancer cases. In recent years, advances in pathway understanding, methods for discovering novel genetic biomarkers, and new drugs designed to inhibit the signaling cascades have enabled clinicians to personalize therapy for NSCLC. Objectives: The primary aim of this study is to identify the genes associated with NSCLC that harbor pathogenic variants that could be causative for NSCLC. The second aim is to investigate their roles in different pathways that lead to NSCLC. Methods: We examined exome-sequencing datasets from 54 NSCLC patients to characterize the variants associated with NSCLC. Results: Our findings revealed that 17 variants in 14 genes were considered highly pathogenic, including CDKN2A, ERBB2, FOXP1, IDH1, JAK3, KMT2D, K-Ras, MSH3, MSH6, POLE, RNF43, TCF7L2, TP53, and TSC1. Gene set enrichment analysis revealed the involvement of transmembrane receptor protein tyrosine kinase activity, protein binding, ATP binding, phosphatidylinositol-4,5-bisphosphate 3-kinase, and Ras guanyl-nucleotide exchange factor activity. Pathway analysis of these genes yielded different cancer-related pathways, including colorectal, prostate, endometrial, pancreatic, PI3K-Akt signaling pathways, and signaling pathways regulating pluripotency of stem cells. Module 1 from protein-protein interactions (PPIs) identified genes that harbor pathogenic SNPs. Three of the most deleterious SNPs are ERBB2 (rs1196929947), K-Ras (rs121913529), and POLE (rs751425952). Interestingly, one patient has a pathogenic K-Ras variant (rs121913529) co-occurred with the missense variant (rs752054698) inTSC1 gene. Conclusion: This study maps highly pathogenic variants associated with NSCLC and investigates their contributions to the pathogenesis of NSCLC. This study sheds light on the potential applications of precision medicine in patients with NSCLC. 2022 Elsevier LtdThe authors would like to take this opportunity to thank the management of Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India, and Qatar University, Doha, Qatar, for providing the necessary facilities and encouragement to carry out this work.Scopu