The immune system as a biomonitor: explorations in innate and adaptive immunity

Abstract

One contribution of 25 to a Theme Issue 'The virtual physiological human: integrative approaches to computational biomedicine'. The human immune system has a highly complex, multi-layered structure which has evolved to detect and respond to changes in the internal microenvironment of the body. Recognition occurs at the molecular or submolecular scale, via classical reversible receptor-ligand interactions, and can lead to a response with great sensitivity and speed. Remarkably, recognition is coupled to memory, such that responses are modulated by events which occurred years or even decades before. Although the immune system in general responds differently and more vigorously to stimuli entering the body from the outside (e.g. infections), this is an emergent property of the system: many of the recognition molecules themselves have no inherent bias towards external stimuli (non-self ) but also bind targets found within the body (self ). It is quite clear that the immune response registers pathophysiological changes in general. Cancer, wounding and chronic tissue injury are some obvious examples. Against this background, the immune system 'state' tracks the internal processes of the body, and is likely to encode information regarding both current and past disease processes. Moreover, the distributed nature of most immune responses (e.g. typically involving lymphoid tissue, non-lymphoid tissue, bone marrow, blood, extracellular interstitial spaces, etc.) means that many of the changes associated with immune responses are manifested systemically, and specifically can be detected in blood. This provides a very convenient route to sampling immune cells. We consider two different and complementary ways of querying the human immune 'state' using high-dimensional genomic screening methodologies, and discuss the potentials of these approaches and some of the technological and computational challenges to be overcome.

Characterization of Gene Expression Patterns among Artificially Developed Cancer Stem Cells Using Spherical Self-Organizing Map

“… 28 They clearly showed a relationship between T-cell population and T-cell signature score obtained from various microarray data provided by independent groups. 28 …”

“…Since all microarray data were obtained by Agilent microarray system in this study, Bioconductor package agilp, which was developed by Thomas et al to normalize the microarray data, was employed. 28 They clearly showed a relationship between T-cell population and T-cell signature score obtained from various microarray data provided by independent groups. 28 Through the data mining procedure described earlier, FAM19A5 was significantly upregulated in iPS-CC1, MT2A in GC1-iPS, and TMED9 in all the CSCs including OCC-hiPS.…”

“…28 They clearly showed a relationship between T-cell population and T-cell signature score obtained from various microarray data provided by independent groups. 28 Through the data mining procedure described earlier, FAM19A5 was significantly upregulated in iPS-CC1, MT2A in GC1-iPS, and TMED9 in all the CSCs including OCC-hiPS. FAM19A5 has been reported as cholangiocarcinoma marker by protein analysis.…”

The Role of Nutrients in Supporting The Immune System Against Viral Infection; Newly Emerged Coronavirus (COVID19): A Narrative Review

“…They are compelling in checking the scatter of infection and killing the attacking organisms. However its action is slower than nonspecific antigen, but it is responsible for producing immunological "memory", following reinfection and exposed with a similar pathogen it will generate an incredible, quick antigenspecific reaction [13], [15]. With the information regarding the novel coronavirus (SARS-CoV-2) outbreak, supporting our immune system is of the highest priority.…”

Evolving cellular automata for diversity generation and pattern recognition: deterministic versus random strategy

“…Each B cell expresses its own unique receptor and a human can make about 10 12 different receptors [16,14,15], an astonishing number if compared to the number of genes in its whole genome (about 50, 000) making it impossible to have antibody genes encoded on DNA. Instead, a relatively small number of disjoint gene segments is inherited and the antibody region relevant for binding is assembled during B cell development by rearrangement of some gene segments [17,15,18]. After stimulation by antigen, B cells reproduce and introduce further mutations on the antibody binding region greatly increasing diversity [14,19,20].…”