Inflammatory is a typical reaction to infection and damage, whether it be local or systemic. A good immune system response adopts a certain pattern, with the first reaction being powerful but brief, ending in the prohibiting of the injury and a return to equilibrium. This distinctive inflammatory route is necessary for tissue repair and remodeling, as well as regaining a strong homeostasis and serious function (Hotamisligil and Erbay, 2008). The evolutionary advantages of an optimally functioning immune response are clear in defending against harmful invaders. Because immunological responses are connected to energy metabolism, it may be claimed that integrating these systems and collaborating to respond to variations in the energy and dietetic environment would be advantageous. To maintain a healthy homeostasis, these reactions must be managed both temporally and spatially.
Chronic disturbance of metabolic balance, such as overnutrition, may result in altered immunological responses. persistent inflammatory induction, particularly in through metabolism important organs such as the liver and adipose tissue, stimulates the release of cytokines that are proinflammatory, severe phase including proteins, inflammatory lipids, and additional physiological mediators of inflammation into bloodstream, resulting in a systemic inflammation situation (Hotamisligil, 2017; Gregor and Hotamisligil, 2011; Minihane et al., 2015). These progressions play an important part in the development of long-lasting metabolic diseases such like overweightness, diabetes, fatty liver disease, and heart related disease (Hotamisligil, 2006), as well as providing the mechanism for risk factors for virus infectious disease like coronavirus disease − 2019 (Stefan et al., 2021). To support treatment efforts for metabolically illnesses, it is critical to get a greater knowledge of the inflammation to anticipate the individual's homeostatic inflammatory condition. To this goal, tissue-derived plasma biomarkers that represent tissue-specific and systemic inflammatory changes across time would be extremely useful.
Various systemically inflammatory markers, such as C-reactivity protein (CRP), IL (interleukin) 6, Interleukin 18, fibrinogen, as well as adhesion proteins vasculature cell adherence protein 1 (VCAM-1). (Shibata et al., 2009; Pradhan, 2001; Li et al., 2009; Thorand et al., 2005; Kelesidis et al., 2006) have been shown to identify "end-stage" chronic poor-quality inflammatory diseases that include type 2 diabetes, cardiovascular illness, and carcinoma. Conversely, elevated plasma concentrations of a protein called a substance that reduces inflammation, were shown to be adversely linked with Cardiovascular (Shibata et al., 2009), type 2 diabetes (Li et al., 2009), and obesity-related malignancies (Kelesidis et al., 2006).
To determine how much such and additional markers indicate the initial phases of illness development at the level of tissue rather than at the level of the system, an extensive evaluation of pro-inflammatory markers at the systemic threshold is required to select markers that allow evaluation of tissue-derived mild inflammation of low grade. The latest advances in a high-through technologies have enabled the generation, analysis, and integration of huge multiomics databases at both the cellular and molecular levels in order to uncover genetic markers of disease progression. The growing use of in computational methods and bioinformatics has motivated investigators to combine multiple datasets with prior information and databases to provide a systems-level picture of illness progression (Mcdermott et al., 2013; Vafaee et al., 2018). The analysis of gene set enrichment has emerged as the premier method for analysing omics information, decreasing intricacy and offering a systems perspective on the biochemical mechanisms associated with the progression of diseases (Khatri et al., 2012). There have been several ways offered in the research for this purpose. Most strategies rely on levels of expression and biological processes (Khatri et al., 2012; García Campos et al., 2015).
The technique reported here reverses this paradigm, starting with known pathways and tissue-derived biomolecules, resulting in readily interpretable potential biomarkers that may aid in disease monitoring in the early stages and treatment regimens. By focusing on the main processes that contribute to persistent low-grade inflammation, we were able to identify common traits across a wide spectrum of metabolic illnesses. Jolanda et al. (2021) developed an in-silico technique that predicts blood-based candidate biomarkers using prior knowledge of dysregulated process in long-lasting inflammation. This technique was validated using biomarker databases, experimental data, and scientific literature to find blood-based biomarkers indicating the dynamic inflammatory state throughout the subclinical process of chronic low-grade inflammation in the liver and adipose tissue. Several researches have recently looked at the underlying processes that cause hepatic inflammation to develop. First, immune cell recruitment to the liver was thought to be critical for the onset of HBV-related hepatic inflammation and subsequently longterm persistent infection. (Zhu et al., 2017; Hou et al., 2018). The various cytokine expression levels were also discussed during different stages of HBV infection. (Trehanpati and Vyas, 2017; Li et al., 2016). For example, Interleukin-22 is a cytokine implicated in the aetiology of liver disease but has a contentious function in hepatic inflammation in HBV patients. Cobleigh and Robek (2013); Gao et al. (2013). Second, endoplasmic reticulum and mitochondrial disorders have been linked to the pathophysiology of hepatic inflammation, which leads to liver damage (Kim et al., 2017; Mansouri et al., 2018). Third, structural alterations in the gut microbes, translocation of bacteria, and subsequent immunological damage may influence the onset and progression of chronic HBV-induced liver inflammation (Yang et al., 2018).
Phylogenetic Analysis
For phylogenetic analysis in Homo sapiens and Pan troglodytes, using amino acid sequences. Phylogenetic and molecular evolutionary analyses were conducted using MEGA version X (Kumar, Stecher, Li, Knyaz, and Tamura 2018) and further annotation and visualization made through Interactive Tree of Life (iTOL) (https://itol.embl.de/upload.cgi). Phylogenetic tree provided in circular form and similar sequences were grouped (Kalpan-Levy et al., 2012). Evolutionary relationship among the Homo sapiens and Pan troglodytes was explored based on Neighbour Joining method via the Molecular Evolution Genetic Analysis (MEGA).
Gene expression analysis
Transcription analysis and putative functions of 5 genes relating to diabetes mellitus associated genes in different organs of the body of H. sapiens. The expression levels in different organs or tissues based on RNA-seq data (found at https://www.ebi.ac.uk/gxa/home in the Expression Atlas database) were downloaded (FPKM values only) (Cook et al. 2019) and analyzed by using Heat mapper at http://www.heatmapper.ca/ (Babicki et al. 2016). Data was comprising RNA sequences from two different experiments showing the expression values in FPKM or TPM for liver inflammation associated genes in H. sapiens (Gonzalez et al., 2018).