Biology subjects

Khan, S. A.

Publications and source records attributed to Khan, S. A..

25 records · Page 2Linked to original sources

bioRxiv2021

Designing Novel and Potent Inhibitors for Multi Drug-Resistant Tuberculosis: A Computational Approach

Tuberculosis is a major global health problem and is still among the top 10 causes of death. The increasing rate of drug resistance to infectious agents has provoked an urgent need to discover novel anti-tuberculosis agents with novel modes of action. In this study, small molecule inhibitors of the proteins encoded by the drug resistant genes, i.e., katG, gyrA, pncA and rpoB of Mycobacterium tuberculosis (M. tuberculosis), were identified using computational methods. In the ligand base pharmacophore, an already reported four ligands for the four proteins encoded by the resistant genes of M. tuberculosis were selected for the generation of pharmacophores. The validated pharmacophores model of all the four proteins, generated on the basis of ligand base, were selected for further screening of ZINC drug like database. As screening results 486 structurally diverse hits for katG, 542 for PncA, 112 for rpoB and 365 for gyrA were mapped and filtered via Lipinskis rule of five. Finally, on the basis of docking score and binding interactions, ten small molecules were selected for each protein as novel inhibitors. These selected novel inhibitors have significant interaction with the active site of the protein and a strong possibility to act as an additional starting opinion in the development of new and potential inhibitors. The result indicates that novel inhibitors could be a promising lead compound and be effective in treating sensitive as well as multi drug resistant tuberculosis.

bioinformatics↗

bioRxiv2021

A recombinant selective drug-resistant M. bovis BCG enhances the bactericidal activity of a second-line tuberculosis regimen

Drug-resistant tuberculosis (DR-TB) results from infection by Mycobacterium tuberculosis strains resistant to at least rifampin or isoniazid. To improve the treatment outcome in DR-TB, therapeutic vaccines are considered an ideal choice as they can enhance pathogen clearance and minimize disease sequelae. To date, there is no therapeutic vaccine reported to be effective when combined with a chemotherapy regimen against DR-TB. The only available TB vaccine, the M. bovis BCG (BCG) is susceptible to several anti-TB drugs hence not a perfect option for therapeutic vaccination. Herein, we developed a recombinant BCG (RdrBCG) overexpressing Ag85B and Rv2628 with resistance to selected anti-TB drugs. When administered three times adjunct to a second-line anti-TB regimen in a classical murine model of DR-TB, the RdrBCG lowered lung M. tuberculosis colony-forming units by 1 log10. Furthermore, vaccination with the RdrBCG adjunct to TB chemotherapy minimized lung tissue pathology in mice. Most importantly, the RdrBCG maintained the exogenously inserted genes and showed almost the same virulence as its parent BCG Tice strain in severe combined immune-deficient mice. All these suggested that the RdrBCG was stable, safe and effective. Hence, the "recombinant" plus "drug-resistant" BCG strategy could be a useful concept for developing therapeutic vaccines against DR-TB.

microbiology↗

bioRxiv2019

Molecular pathway analysis indicates a distinct metabolic and immune phenotype in women with right-sided colon cancer

Colon cancer is the third most commonly diagnosed cancer in the United States. Recent reports have shown that the location of the primary tumor is of clinical importance. Patients with right-sided cancers (RCCs) (tumors arising between the cecum and proximal transverse colon) have poorer clinical outcomes than those with left-sided colon cancers (LCCs) (tumors arising between the distal transverse colon and sigmoid colon, excluding the rectum). Interestingly, women have a lower incidence of colon cancer than men do. However, women have a higher propensity for RCC than men. Identification of gene expression differences between RCC and LCC is considered a potential means of prognostication. Furthermore, studying colon cancer sidedness could reveal important predictive markers for response to various treatments. This study provides a comprehensive bioinformatic analysis of various genes and molecular pathways that correlated with sex and anatomical location of colon cancer using four publicly available annotated datasets housed in the National Center for Biotechnology Informations Gene Expression Omnibus (GEO). We identified differentially expressed genes in tumor tissues from women with RCC, which showed attenuated energy and nutrient metabolism when compared to women with LCC. Specifically, we showed that the downregulation of 5 AMP-activated protein kinase alpha subunit (AMPK) and downregulated anti-tumor immune response in women with RCC. This difference was not seen when comparing tumor tissues from men with RCC to men with LCC. Therefore, women with RCC may have a specific metabolic and immune phenotype which accounts for differences in prognosis and treatment response.

cell biology↗

bioRxiv2019

Animal model and therapeutic prospects for left ventricular hypertrophy and associated renal complication

Cardiac and renal dysfunction often co-occur and considerably worsen the prognosis leading to difficulty in therapy in left ventricular hypertrophy (LVH). The aim of this study was to elucidate changes in expression of human ortholog genes of hypertension, vascular and cardiac remodeling and hypertensive nephropathy phenotypes under normal, disease and gasotransmitter, H2S (hydrogen sulphide) and NO (nitric oxide) and combined (H2S+NO), treatment of rat myocardium and renal tissues. LVH rat models were generated and were treated with H2S and NO. Relative gene expression was quantified. Heart and renal physical indices were significantly modified under individual as well as combined H2S+NO treatment in control and LVH rats. Expression analysis revealed, hypertension, vascular remodeling genes ACE, TNF and IGF1, mRNAs to be significantly increased (P<0.05) in myocardia and kidneys of LVH rats, while individual and combined H2S+NO treatment reduced gene expression to normal/near to normal values. The cardiac remodeling genes MYH7, TGF{beta}, SMAD4 and BRG1 expression were significantly up-regulated (P<0.05) in the myocardia of LVH and combined H2S+NO treatment, which recovered the normal/near to normal expression more effectively as compared to individual treatments. Interestingly, individual as well as combined H2S and NO treatment significantly decreased PKD1 expression in renal tissue, which was significantly up-regulated in LVH rats (P<0.05). The reduction in hemodynamic parameters and cardiac indices as well as alteration in gene expression on treatment in LVH rat model indicates important therapeutic potential of combined treatment with H2S+NO gasotransmitters in hypertension and cardiac hypertrophy associated with renal complications.

pharmacology and toxicology↗

bioRxiv2019

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

It is largely unknown how the risk of development of breast cancer is transduced by somatic genetic alterations. To address this lacuna of knowledge and acknowledging that benign breast disease (BBD) is an established risk factor for breast cancer, we established a case-control study: The Benign Breast & Cancer Risk (BBCAR) Study. Cases are women with BBD who developed subsequent invasive breast cancer (IBC) at least 3 years after the biopsy and controls are women with BBD who did not develop IBC (median follow-up 16.6 years). We selected 135 cases and individually matched controls (1:2) to cases based on age and type of benign disease: non-proliferative or proliferation without atypia. Whole exome sequencing was performed on DNA from the benign lesions and from subsets with available germline DNA or tumor DNA. Although the number of cases and controls with copy number variation data is limited, several amplifications and deletions are exclusive to the cases. In addition to two known mutational signatures, a novel signature was identified that is significantly (p=0.007) associated with triple negative breast cancer. The somatic mutation rate in benign lesions is similar to that of invasive breast cancer and does not differ between cases and controls. Two mutated genes are significantly associated with time to the diagnosis of breast cancer, and mutations shared between the benign biopsy tissue and the breast malignancy for the ten cases for which we had matched pairs were identified. BBD tissue is a rich source of clues to breast oncogenesis.\n\nOne Sentence SummaryGenetic aberrations in benign breast lesions distinguish breast cancer cases from controls and predict cancer risk.

genetics↗

bioRxiv2019

Deep learning for cancer type classification

Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. To address these limitations, we propose DeepCues, a deep learning model that utilizes convolutional neural networks to derive features from DNA sequencing data for disease classification and relevant gene discovery. Using whole-exome sequencing, germline variants and somatic mutations, including insertions and deletions, are interactively amalgamated as features. In this study, we applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p=8.8E-25). Using DeepCues, we found that the top 20 genes associated with breast cancer have a 40% overlap with the top 20 breast cancer genes in the COSMIC database. These data support DeepCues as a novel method to improve the representational resolution of both germline variants and somatic mutations interactively and their power in predicting cancer types, as well the genes involved in each cancer.

cancer biology↗

bioRxiv2016

tensorBF: an R package for Bayesian tensor factorization

With recent advancements in measurement technologies, many multi-way and tensor datasets have started to emerge. Exploiting the natural tensor structure in the data has been shown to be advantageous for both explorative and predictive studies in several application areas of bioinformatics and computational biology. Therefore, there has subsequently arisen a need for robust and flexible tools for effectively analyzing tensor data sets. We present the R package tensorBF, which is the first R package providing Bayesian factorization of a tensor. Our package implements a generative model that automatically identifies the number of factors needed to explain the tensor, overcoming a key limitation of traditional tensor factorizations. We also recommend best practices when using tensor factorizations for both, explorative and predictive analysis with an example application on drug response dataset. The package also implements tools related to the normalization of data, informative noise priors and visualization. Availability: The package is available at https://cran.r-project.org/package=tensorBF.

bioinformatics↗