Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions. In the pilot study, we proposed a host-specific in-silico tool for predicting, designing, and scanning TNF-α inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF-α inducing/non-inducing epitopes from human and mouse hosts. Firstly, we developed alignment-free (machine learning based models using composition-based features of peptides) methods for predicting TNF-α inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, an alignment-based (using BLAST) method has been used for predicting TNF-α inducing epitopes. Finally, a hybrid method (combination of alignment-free and alignment-based method) has been developed for predicting epitopes. Hybrid approach achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified potential TNF-α inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2, and human insulin. The best models developed in this study has been incorporated in the webserver TNFepitope, standalone package and GitLab.
Tumor Necrosis Factor alpha (TNF-α) is a classical, pleiotropic pro-inflammatory cytokine that functions by promoting the cellular signal activation and trafficking of leukocytes to the inflammatory sites. During acute inflammation, TNF-α cytokine is released by macrophages/monocytes or via other cell types (e.g., B cells, T cells, mast cells, fibroblasts), which further regulates hematopoiesis, immune responses, tumor regression and various infections. TNF-α is the first “adipokine” reported in the literature to be produced from the adipose tissue. It plays a significant role in various biological processes, including immunomodulation, fever, inflammatory response, inhibition of tumor formation, and inhibition of virus replication. TNF-α is involved in various physiological processes, for instance, the induction of pro-inflammatory interleukins (IL-1 and IL-6). It also interacts with various cytokines/chemokines and regulates signaling pathways in different disease states. Studies have demonstrated that peptide-based vaccines are used for the treatment of various diseases, including cancer. For instance, Probst et al., conducted a study in which peptide vaccination strategies and tumor-homing TNF fusion proteins are used for cancer treatment. Sluis et al., revealed that the vaccine induced TNF-α cytokine significantly causes tumoricidal effects and promotes cisplatin-mediated death of tumor cells. Moreover, TNF-receptor superfamily agonists are used as adjuvants for cancer vaccines.
Recent studies also showed that, the higher expression of TNF-α cytokine leads to the pathogenesis of numerous diseases including ischemia-reperfusion injury, sepsis, chronic heart failure, viral myocarditis, and cardiac allograft rejection. For example, Guo et al., reported that, the cytokine release syndrome in COVID-19 patients is associated with increased levels of TNF-α, IL-6, IL-2, IL-7, and IL-10 cytokines. Moreover, there is a direct relationship between TNF-α and IL-6 cytokines in the severity and survival of COVID-19 patients. Therefore, several anti-TNF inhibitors or drugs (including etanercept, infliximab, adalimumab, certolizumab pegol, and golimumab) are approved by FDA to treat a number of diseases. These inhibitors are used to block the overproduction of TNF-α in different disease conditions like ankylosing spondylitis, Crohn's disease, hidradenitis suppurativa, juvenile idiopathic arthritis, plaque psoriasis, polyarticular juvenile idiopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ulcerative colitis, and uveitis. Anti-TNF-α therapy has reported beneficial effects by not only restoring aberrant TNF mediated immune mechanisms but also by deactivating the pathogenic fibroblast-like mesenchymal cells.
As reported in the literature, TNF-α is a key cytokine involved in several diseases and their progression. Therefore, it can act as a primary target cytokine in disease progression. This creates a need to develop a computational tool for predicting TNF-α inducing peptides using sequence information. In the present study, we have developed an in-silico method to classify the TNF-α inducing and non-inducing epitopes. We have developed this tool using experimentally validated TNF-α inducing and non-inducing peptides from the human and mouse hosts. Additionally, we have also used randomly generated peptides from the SwissProt database as the negative dataset. We have developed prediction models using various machine learning classifiers and evaluated their performance on the independent dataset.
The complete workflow of the current study is illustrated in Fig. 1.
In this study, we have collected experimentally validated TNF-α inducing peptides from the immune epitope database (IEDB). After pre-processing, we observed that 3177 out of 3635 TNF-α inducing peptides are belong to human or mouse hosts, and only a few epitopes were available for other hosts. So, we worked with only two major hosts (i.e., human and mouse). We found most of the peptides lie within the range of 8–20
We have computed amino acid composition for the main and alternate datasets for human and mouse hosts. After that, we have calculated the average compositions for each amino acid residues in TNF-α inducing and non-inducing peptides. As depicted in Fig. 2A, in case of human dataset, amino acids such as leucine (L), valine (V), tyrosine (Y), and tryptophan (W) have higher composition in the TNF-α inducing peptides in comparison with the TNF-α non-inducing and random peptides. Similarly, the
Major histocompatibity complex region encodes numbers of proteins including human leukocyte antigen (HLAs) which are necessary for self-recognition, cytokine genes like TNF, LTA, LTB, which are responsible for the inflammations. TNF-α is a significant inflammatory cytokine produced by T cells and macrophages that regulates several immune cell signaling pathways that result in necrosis or cell death. These pathways are involved in a range of biological responses, such as cell
Designing a vaccine or immunotherapy against various diseases using peptide/epitope technology is a viable approach. TNF-α is a versatile cytokine plays major biological processes including cell survival, proliferation, differentiation, and death. Several clinical trials are being carried out to understand the effects of TNF-based therapy for the treatment of cancer patients. In the past, several in-silico approaches for predicting T cell epitopes were developed, however, there was no specific
The current work has received grant from the Department of Bio-Technology (DBT), Govt. of India, India.
AD and GPSR collected and processed the datasets. AD, SP, KN and GPSR implemented the algorithms and developed the prediction models. AD, SP and GPSR analysed the results. SC, AD and SP created the web server. AD, SJ, SP and SC and GPSR penned the manuscript. GPSR conceived and coordinated the project. All authors have read and approved the final manuscript.
The authors declare no competing financial and non-financial interests.
Authors are thankful to the Department of Bio-Technology (DBT) and Department of Science and Technology (DST-INSPIRE) for fellowships and the financial support and Department of Computational Biology, IIITD New Delhi for infrastructure and facilities.
Anjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
