In-silico annotation of innate immune system for identification of cancer prognostic biomarkers

Abstract

Innate immune system response is the initial/first line of defense against invading pathogens. It is non-specific and involves various cells like macrophages, neutrophils, natural killer cells, dendritic cells. The response is quicker than adaptive immunity. Unlikely there is no antibodies generation and memory after exposure with any type of infection. Innate immunity consist of physical and chemical barriers such as epithelia and antimicrobial chemicals produced at epithelial surfaces. The system is intricate and consists of blood proteins including member of the complement system and other mediators of inflammation. Phagocytic cells like neutrophils and macrophages, dendritic cells, natural killer cells and other lymphoid cells are essential part of the system. The recruitment and activation of neutrophils at the site of infection to eliminate pathogens is a key aspect of the innate response. The innate immune system also expresses a wide range of Pattern Recognition Receptors (PRRs) which are specialised in the recognition of evolutionary conserved structures known as Pathogen Associated Molecular patterns (PAMPs). Toll like Receptors (TLRs), C-lectin Type Receptors (CLRs), Mannose Binding Receptors (MBRs) and Nucleotide-binding Oligomerization Domain (NOD)-Like Receptor (NLRs) are some of the major PRRs. TLRs are being the most extensively studied PRRs. PAMPs are distinguished by being invariant across whole classes of pathogens, required for pathogen survival, and separate from "self." However, PRRs perceive host factors as "danger" signals in some situations, such as when they are present in atypical locations or abnormal molecular complexes as a result of infection, inflammation, or other forms of cellular stress. These are known as Damage Associated Molecular patterns (DAMPs). PRRs are specialised to recognise these DAMPs as well. PRRs present on the cell surface or intracellularly, signal the presence of infection to the host and initiate proinflammatory and antimicrobial responses by activating hundreds of new intracellular signalling pathways that include adaptor molecules, kinases, and transcription factors in response to PAMP recognition. PRR-induced signal transduction pathways eventually result in the activation of gene expression and the synthesis of a diverse range of molecules, including cytokines, chemokines, cell adhesion molecules, and immunoreceptors, which together facilitate the early host response to infection while also serves as a vital link to the adaptive immune response. Innate immunity is rapidly evolving, with novel cell types and molecular pathways being discovered and paradigms changing continuously. Over the last decade, our understanding of the processes by which pathogens are identified has improved significantly. This field has previously been thoroughly investigated. Still, the appropriate annotation of data, as well as the development of more efficient computing resources and diverse methodologies, remains a significant problem. To handle this, we have created a comprehensive knowledge base on PRRs and their corresponding ligands Pattern Recognition Receptor Database 2.0 (PRRDB2.0), which is an updated version of PRRDB. The database consists of more than 2700 entries data from 2008-2018. It provides a user-friendly all-device compatible webserver known as PRRDB2.0. This webserver includes detailed information on numerous classes of PRRs as well as their respective ligands/agonists. The database contains information such as the name, source, origin, role, sequence, length, and assay utilised for both elements. Proper annotation and adequate computational resources can help to understand and design the immune cells, the inflammasome, and DNA sensing. All of these are crucial for the activation and orchestration of innate immunity, which might lead to new treatment options for autoimmune, autoinflammatory, and infectious diseases. We developed “PRRpred” and “DefPred” tools that will help in the annotation of the innate immune system molecules. ‘PRRpred’ is an in-silico prediction of PRRs. It can predict whether the given protein sequence is PRR or not. It consists of two modules for prediction the first one based on composition of the protein sequence and the other one is based on evolutionary information. The best performing model is a hybrid model of both with Basic Local Alignment Search Tool (BLAST). User can download the prediction result in csv format with the result whether the provided input is PRR/Non-PRR. It is also accompanied by a user friendly, all device compatible web server. Whereas “DefPred” is an in-silico tool for scanning, predicting, and designing defensins. Defensins are host specific defense molecules, and are one of the class of Anti-microbial Peptides (AMPs). In this study, we described a reliable method developed for predicting defensins with high precision. We systematically collected defensins, AMPs and non-defensins from various resources to create the largest possible datasets. Developing new defensins can be a very effective alternative to drug resistance, and they are less toxic since they are host specific and produced in the host body. Each year, cancer alone claims the lives of millions of people all over the world. Despite advancements in cancer treatments, patient survival rates are still below average. The study of the innate immune system has led to the identification of key regulators and the development of chemo-therapeutics that can target them and reverse the state of a cancer patient. We tried to find out the relation between the gene expression of PRRs and the survival of patients with cancer. Firstly, we identified the prognostic gene signature from the expression profile of PRRs genes in case of endometrial cancer. Later on, we identified the most effective drugs from existing drugs using prognostic gene signature and did repurposing of FDA approved drugs. Our next goal was to design a universal biomarker corresponding to all types of cancer-based on PRR gene expressions. We tried and developed a 12 gene biomarkers. Although, the biomarker signatures’s efficiency is seen to differ among different cancer types, a substantial stratification is achieved in all cases. Lastly to check our hypothesis when there is a change in biological insight, is their any change in the performance of the prognostic biomarker across multiple cancer. For this we have compared two major pathways apoptotic and PRR biomarker genes in case of THCA, MESO and SKCM. We found both the pathways are highly interlinked and there is dependency of their genes in case of cancer. Altogether, the work discussed here in this thesis recommends some novel approach for the proper annotation of innate system molecules. Also, these molecules related signaling genes were utilized to create prognostic biomarker in various cancer. We anticipate that clinicians and researchers will use the findings of our investigations to develop advanced cancer treatment approaches.