Ehtishamul Haq
@kashmiruniversity.ac.in
Professor, Biotechnology
University of Kashmir
RESEARCH, TEACHING, or OTHER INTERESTS
Biochemistry, Genetics and Molecular Biology, Biochemistry, Bioengineering, Biotechnology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Asma Rafiq, Sabreena Aashaq, Iqra Jan, Mahvish Ali, Rabia Rakshan, Asma Bashir, Ehtishamul Haq, and Mushtaq A. Beigh
Elsevier BV
Qamar Taban, Syed Mudasir Ahmad, Peerzada Tajamul Mumtaz, Basharat Bhat, Ehtishamul Haq, Suhail Magray, Sahar Saleem, Nadeem Shabir, Amatul Muhee, Zahid Amin Kashoo,et al.
Springer Science and Business Media LLC
AbstractSCARB1 belongs to class B of Scavenger receptors (SRs) that are known to be involved in binding and endocytosis of various pathogens. SRs have emerging role in regulating innate immunity and host–pathogen interactions by acting in co-ordination with Toll-like receptors.Query Little is known about the function of SCARB1 in milk-derived mammary epithelial cells (MECs). This study reports the role of SCARB1 in infection and its potential association in TLR4 signaling on bacterial challenge in Goat mammary epithelial cells (GMECs). The novelty in the establishment of MEC culture lies in the method that aims to enhance the viability of the cells with intact characteristics upto a higher passage number. We represent MEC culture to be used as a potential infection model for deeper understanding of animal physiology especially around the mammary gland. On E.coli challenge the expression of SCARB1 was significant in induced GMECs at 6 h. Endoribonuclease-esiRNA based silencing of SCARB1 affects the expression of TLR4 and its pathways i.e. MyD88 and TRIF pathways on infection. Knockdown also affected the endocytosis of E.coli in GMECs demonstrating that E.coli uses SCARB1 function to gain entry in cells. Furthermore, we predict 3 unique protein structures of uncharacterized SCARB1 (Capra hircus) protein. Overall, we highlight SCARB1 as a main participant in host defence and its function in antibacterial advances to check mammary gland infections.
Irfan Gul, Amreena Hassan, Ehtishamul Haq, Syed Mudasir Ahmad, Riaz Ahmad Shah, Nazir Ahmad Ganai, Naveed Anjum Chikan, Mohamed Faizal Abdul-Careem, and Nadeem Shabir
MDPI AG
Vaccination is widely used to control Infectious Bronchitis in poultry; however, the limited cross-protection and safety issues associated with these vaccines can lead to vaccination failures. Keeping these limitations in mind, the current study explored the antiviral potential of phytocompounds against the Infectious Bronchitis virus using in silico approaches. A total of 1300 phytocompounds derived from fourteen botanicals were screened for their potential ability to inhibit the main protease, papain-like protease or RNA-dependent RNA–polymerase of the virus. The study identified Methyl Rosmarinate, Cianidanol, Royleanone, and 6,7-Dehydroroyleanone as dual-target inhibitors against any two of the key proteins. At the same time, 7-alpha-Acetoxyroyleanone from Rosmarinus officinalis was found to be a multi-target protein inhibitor against all three proteins. The potential multi-target inhibitor was subjected to molecular dynamics simulations to assess the stability of the protein–ligand complexes along with the corresponding reference ligands. The findings specified stable interactions of 7-alpha-Acetoxyroyleanone with the protein targets. The results based on the in silico study indicate that the phytocompounds can potentially inhibit the essential proteins of the Infectious Bronchitis virus; however, in vitro and in vivo studies are required for validation. Nevertheless, this study is a significant step in exploring the use of botanicals in feed to control Infectious Bronchitis infections in poultry.
Irfan Gul, Amreena Hassan, Jan Mohd Muneeb, Towseef Akram, Ehtishamul Haq, Riaz Ahmad Shah, Nazir Ahmad Ganai, Syed Mudasir Ahmad, Naveed Anjum Chikan, and Nadeem Shabir
Frontiers Media SA
Infectious bursal disease virus is the causative agent of infectious bursal disease (Gumboro disease), a highly contagious immunosuppressive disease of chicken with a substantial economic impact on small- and large-scale poultry industries worldwide. Currently, live attenuated vaccines are widely used to control the disease in chickens despite their issues with safety (immunosuppression and bursal atrophy) and efficiency (breaking through the maternally-derived antibody titer). To overcome the drawbacks, the current study has, for the first time, attempted to construct a computational model of a multiepitope based vaccine candidate against infectious bursal disease virus, which has the potential to overcome the safety and protection issues found in the existing live-attenuated vaccines. The current study used a reverse vaccinology based immunoinformatics approach to construct the vaccine candidate using major and minor capsid proteins of the virus, VP2 and VP3, respectively. The vaccine construct was composed of four CD8+ epitopes, seven CD4+ T-cell epitopes, 11 B-cell epitopes and a Cholera Toxin B adjuvant, connected using appropriate flexible peptide linkers. The vaccine construct was evaluated as antigenic with VaxiJen Score of 0.6781, immunogenic with IEDB score of 2.89887 and non-allergenic. The 55.64 kDa construct was further evaluated for its physicochemical characteristics, which revealed that it was stable with an instability index of 16.24, basic with theoretical pI of 9.24, thermostable with aliphatic index of 86.72 and hydrophilic with GRAVY score of −0.256. The docking and molecular dynamics simulation studies of the vaccine construct with Toll-like receptor-3 revealed fair structural interaction (binding affinity of −295.94 kcal/mol) and complex stability. Further, the predicted induction of antibodies and cytokines by the vaccine construct indicated the possible elicitation of the host's immune response against the virus. The work is a significant attempt to develop next-generation vaccines against the infectious bursal disease virus though further experimental studies are required to assess the efficacy and protectivity of the proposed vaccine candidate in vivo.
Ashaq Hussain Mir, Mir Khurshid Iqbal, Mujeeb Zafar Banday, Henah Mehraj Balkhi, and Ehtishamul Haq
Elsevier BV
Qamar Taban, Peerzada Tajamul Mumtaz, Khalid Z. Masoodi, Ehtishamul Haq, and Syed Mudasir Ahmad
Springer Science and Business Media LLC
AbstractScavenger receptors belong to a superfamily of proteins that are structurally heterogeneous and encompass the miscellaneous group of transmembrane proteins and soluble secretory extracellular domain. They are functionally diverse as they are involved in various disorders and biological pathways and their major function in innate immunity and homeostasis. Numerous scavenger receptors have been discovered so far and are apportioned in various classes (A-L). Scavenger receptors are documented as pattern recognition receptors and known to act in coordination with other co-receptors such as Toll-like receptors in generating the immune responses against a repertoire of ligands such as microbial pathogens, non-self, intracellular and modified self-molecules through various diverse mechanisms like adhesion, endocytosis and phagocytosis etc. Unlike, most of the scavenger receptors discussed below have both membrane and soluble forms that participate in scavenging; the role of a potential scavenging receptor Angiotensin-Converting Enzyme-2 has also been discussed whereby only its soluble form might participate in preventing the pathogen entry and replication, unlike its membrane-bound form. This review majorly gives an insight on the functional aspect of scavenger receptors in host defence and describes their mode of action extensively in various immune pathways involved with each receptor type.
Syed Nisar Hussain Bukhari, Amit Jain, Ehtishamul Haq, Abolfazl Mehbodniya, and Julian Webber
MDPI AG
The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety—they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.
Syed Nisar Hussain Bukhari, Amit Jain, and Ehtishamul Haq
Springer Singapore
Syed Nisar Hussain Bukhari, Amit Jain, Ehtishamul Haq, Abolfazl Mehbodniya, and Julian Webber
MDPI AG
An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.
Syed Nisar Hussain Bukhari, Amit Jain, Ehtishamul Haq, Moaiad Ahmad Khder, Rahul Neware, Jyoti Bhola, and Moslem Lari Najafi
Hindawi Limited
Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.
Sajad Mohd Wani, F.A. Masoodi, Ehtishamul Haq, Mukhtar Ahmad, and S.A. Ganai
Elsevier BV
Insha Zahoor, Amrina Shafi, Khalid Majid Fazili, and Ehtishamul Haq
Springer International Publishing
Amrina Shafi, Insha Zahoor, Ehtishamul Haq, and Khalid Majid Fazili
Springer International Publishing
Syed Sana Mehraj, Azra N. Kamili, Ruqeya Nazir, Ehtishamul Haq, and Henah Mehraj Balkhi
Elsevier BV
Henah Mehraj Balkhi, Ehtishamul Haq, Taseen Gul, and Syed Sana
Bentham Science Publishers Ltd.
Background: Caffeic acid phenethyl ester and Dasatinib in combination, when used incongruous proportions and durations, present an antitumor potential for glioma in vitro, suggesting a high therapeutic potential for glioma treatment. Objective: In the present study, we addressed the question whether CAPE and Dasatinib target multiple pathways involved in tumor growth, proliferation and development on an in vivo rat model of glioma. Method: Expression analysis of proteins thought to be mediating proliferation, cell motility, angiogenesis, and invasion was carried out to delineate the antineoplastic action of CAPE and Dasatinib. Results: CAPE and Dasatinib modulate the expression of proteins having potential interactive crosstalk with major oncogenic pathways involved in glioma progression. Our results showed that combination treatment modulates the expression of p53 in group co-administered with CAPE and Dasatinib after glioma induction in comparison to the group induced with glioma only. EGFR and PCNA expression were significantly altered in the co-treated group in comparison with the glioma-induced group. The effects of CAPE and Dasatinib treatment were further evaluated on the AKT pathway by Western blot analysis. The co-treated group showed a significant reduction in the expression of AKT. The histopathological analysis further backed the antiproliferative and anti invasive effects of CAPE and Dasatinib. Conclusion: This study in totality suggests that the combinational therapy remarkably reduces the proliferation of glioma cells in vivo, suggesting that CAPE and Dasatinib therapy could be exploited for the management of gliomas without showing drug-related resistances and side effects, suggesting a high therapeutic potential of the therapy in glioma.
Younis Mohammad Hazari, Arif Bashir, Mudasir Habib, Samirul Bashir, Huma Habib, M. Abul Qasim, Naveed Nazir Shah, Ehtishamul Haq, Jeffrey Teckman, and Khalid Majid Fazili
Elsevier BV
Insha Zahoor and Ehtishamul Haq
Elsevier BV
Mujeeb Zafar Banday, Ashaq Hussain Mir, Aga Syed Sameer, Nissar A Chowdri, and Ehtishamul Haq
Elsevier BV
Insha Zahoor, Ravouf Asimi, Ehtishamul Haq, and Irfan Yousuf Wani
Elsevier BV
Mujeeb Zafar Banday, Henah Mehraj Balkhi, Aga Syed Sameer, Nissar A Chowdri, and Ehtishamul Haq
IOS Press
Chronic inflammation increases the risk of development of various cancers, including colorectal cancer. Interleukin-6 has been described as a key regulator of colorectal cancer development and is important in the process of colorectal tumorigenesis largely through the regulation of tumor-promoting inflammation. Several studies have reported the association of various polymorphisms in human interleukin-6 gene including IL-6 −174G/C single nucleotide polymorphism with various cancers, including colorectal cancer, but the results are mixed and inconclusive. The aim of this study was to analyze the association of IL-6 −174G/C promoter single nucleotide polymorphism with colorectal cancer risk and also to evaluate the modifying effects of possible IL-6 −174G/C single nucleotide polymorphism genotypes on different risk factors of colorectal cancer or the reciprocal effect in ethnic Kashmiri population through a case control setup. The genotype frequencies of IL-6 −174G/C promoter single nucleotide polymorphism were compared between 142 colorectal cancer patients and 184 individually matched healthy controls by using polymerase chain reaction–restriction fragment length polymorphism method. The association between the IL-6 −174G/C single nucleotide polymorphism and colorectal cancer risk was examined through conditional logistic regression models adjusted for multiple possible confounding (third) variables. The possible effect measure modification of the association between the relevant single nucleotide polymorphism genotypes and colorectal cancer risk by various colorectal cancer risk factors including age, gender, and smoking status was also evaluated. Furthermore, the associations between these single nucleotide polymorphisms and various clinicopathological parameters, demographic variables, and environmental factors within the case group subjects with regard to colorectal cancer risk were also analyzed. The overall association between the IL-6 −174G/C single nucleotide polymorphism and the modulation of colorectal cancer risk was found to be highly significant (p = 0.001). The variant genotype (CC) was significantly associated with a decreased risk of colorectal cancer (odds ratio, 0.15 (95% confidence interval, 0.04–0.54); p = 0.004). Furthermore, the less common IL-6-174C allele was associated with a decreased risk of colorectal cancer (odds ratio, 0.49 (95% confidence interval, 0.33–0.73); p = 0.0006). The combined variant genotype (GC + CC) was also significantly associated with a decreased risk of colorectal cancer (odds ratio, 0.54 (95% confidence interval, 0.33–0.89); p = 0.015). This study demonstrates that there is a strong and highly significant association between the IL-6 −174G/C promoter single nucleotide polymorphism and a decreased risk of colorectal cancer in ethnic Kashmiri population. However, in order to substantiate our findings, this study needs to be replicated with larger sample size and with other ethnically defined populations with comparable colorectal cancer incidence.
Insha Zahoor, Ehtishamul Haq, and Ravouf Asimi
Elsevier BV
Mujeeb Z. Banday, Aga S. Sameer, Nissar A. Chowdri, and Ehtishamul Haq
Ovid Technologies (Wolters Kluwer Health)
Chronic inflammation influences the development of various cancers including colorectal cancer (CRC). Interleukin-10 (IL-10), an anti-inflammatory cytokine, plays a vital role in several homeostatic physiological processes occurring in the human gastrointestinal tract including intestinal inflammation and is a key regulator of several gastrointestinal tract pathophysiological processes such as inflammatory bowel diseases that are associated with an increased predisposition to CRC. Several studies have reported the association of various polymorphisms in the human IL-10 gene including IL-10 −592C/A and IL-10 −1082A/G single nucleotide polymorphisms (SNPs) with various cancers including CRC, but these SNPs are yet to be studied in a Kashmiri population with respect to CRC risk. The aim of this study was to analyze the association of IL-10 −592C/A and IL-10 −1082A/G promoter SNPs with CRC risk in an ethnic Kashmiri population through a case–control design. The genotype frequencies of IL-10 −592C/A and IL-10 −1082A/G promoter SNPs were compared between 142 CRC patients and 184 individually matched healthy controls using the PCR and restriction fragment length polymorphism method. The association between the IL-10 −592C/A and IL-10 −1082A/G SNPs and CRC risk was examined through conditional logistic regression models adjusted for multiple possible confounding (third) variables. The possible effect measure modification of the association between the relevant SNP genotypes and CRC risk by various CRC risk factors including age, sex, and smoking status was also evaluated. Further, the associations between these SNPs and various clinicopathological parameters, demographic variables, and environmental factors in the case group patients with respect to CRC risk were also analyzed. The overall association between the IL-10 −592C/A SNP and the modulation of CRC risk was found to be significant (P=0.001). The variant genotype (AA) was significantly associated with a decreased risk of CRC (odds ratio: 0.25; 95% confidence interval: 0.11–0.61; P=0.002). Further, the less common IL-10 −592A allele was associated with a decreased risk of CRC (odds ratio: 0.64; 95% confidence interval: 0.46–0.88; P=0.0092). The overall association between the IL-10 −1082A/G SNP and the modulation of CRC risk was not found to be significant (P=0.141). This study has shown that there is a significant association between the IL-10 −592C/A promoter SNP and a decreased risk of CRC in an ethnic Kashmiri population, but the association between IL-10 −1082A/G SNP and the risk of CRC in the population under study is not significant. However, to substantiate our findings, this study needs to be replicated with a larger sample size and with other ethnically defined populations with comparable CRC incidence.
Insha Zahoor, Ehtishamul Haq, and Ravouf Asimi
Springer International Publishing
Younis Mohammad Hazari, Arif Bashir, Ehtisham ul Haq, and Khalid Majid Fazili
Springer Science and Business Media LLC
Mujeeb Zafar Banday, Aga Syed Sameer, Ashaq Hussain Mir, Taseem A. Mokhdomi, Nissar A. Chowdri, and Ehtishamul Haq
Elsevier BV