Abstract:The global control of tuberculosis (TB) presents a continuous health challenge to mankind. Despite having effective drugs, TB still has a devastating impact on human health. Contributing reasons include the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), the AIDS-pandemic, and the absence of effective vaccines against the disease. Indeed, alternative and effective methods of TB treatment and control are urgently needed. One such approach may be to more effectively engage the immune sys… Show more
“…Adaptive immunity comprises of receptors which are highly specific to antigens (61). In contrast, innate immunity consists of specialized receptors known as PRRs that recognize infectious pathogens and initiate inflammatory responses for their eradication (62). Several critical implications of PRRs have been reported in the past in the context of adjuvant designing, therapeutic targets, immunomodulator design, cancer immunotherapy, etc.…”
This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learningbased models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/ raghava/prrpred/).
“…Adaptive immunity comprises of receptors which are highly specific to antigens (61). In contrast, innate immunity consists of specialized receptors known as PRRs that recognize infectious pathogens and initiate inflammatory responses for their eradication (62). Several critical implications of PRRs have been reported in the past in the context of adjuvant designing, therapeutic targets, immunomodulator design, cancer immunotherapy, etc.…”
This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learningbased models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/ raghava/prrpred/).
“…Adaptive immunity comprises of receptors which are highly specific to antigens (52). In contrast, innate immunity consists of specialized receptors known as pattern recognition receptors (PRRs) that recognize infectious pathogens and initiate inflammatory responses for their eradication (53). Several critical implications of PRRs have been reported in the past in the context of adjuvant designing, therapeutic targets, immunomodulator design, cancer immunotherapy, etc.…”
This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, and non-pattern recognition receptors (Non-PRRs) obtained from PRRDB 2.0. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learningbased models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).
“…The natural immune system is the initial defense against TB, if it fails then TB bacilli will spread through macrophages to lymph nodes and blood flow to many organs. 4,9 The initial response to the tissue that has never been infected is in the form of an inflammatory cell, both polymorphonuclear neutrophils (PMN) cells and phagocyte cells mononuclear. Bacilli that enter alveoli will be ingested and destroyed by alveolar macrophages.…”
Section: Immunity To Tuberculosismentioning
confidence: 99%
“…Th1 cells produce type 1 cytokines, including IL-2, IL-12, IFN-γ, and tumor necrosis factor-alpha (TNF-α). 4,12 Cytokines released by Th1 are effective activators to generate cellular immune responses. Th2 cells make and release type 2 cytokines including IL-4, IL-5, IL-6, IL-9, and IL-10.…”
Section: Immunity To Tuberculosismentioning
confidence: 99%
“…Active TB has an association with suppression of T cell responses and increased production of immunosuppressed cytokines, namely IL-10 which can inhibit T cell proliferation and IFN-γ production. 4 MTB infection can induce oxidative stress. 5 Oxidative stress results from an imbalance between reactive oxygen species (ROS) and antioxidants.…”
Pulmonary tuberculosis is a chronic infection that is caused by Mycobacterium tuberculosis (MTB) infection and it isstill the major health problem worldwide. MTB infection can induce oxidative stress. Some studies have proved that active TB patients have an association with excessive oxidative stress which causes glutathione (GSH) levels to decrease and free radicals to increase. GSH facilitates the control of MTB intracellular bacterial growth in macrophages and has direct antimicrobial activity. N-acetyl cysteine (NAC) is a thiol, a precursor of L-cysteine and GSH that has been used for decades as a mucolytic agent in the treatment of respiratory diseases. Some studies have reported a beneficial role of NAC as an immunomodulator, besides, it also has anti-inflammatory and antimicrobial effect in TB management.
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