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Microbial Biotechnology
Published in Nwadiuto (Diuto) Esiobu, James Chukwuma Ogbonna, Charles Oluwaseun Adetunji, Olawole O. Obembe, Ifeoma Maureen Ezeonu, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Microbiomes and Emerging Applications, 2022
Olawole O. Obembe, Nwadiuto (Diuto) Esiobu, O. S. Aworunse, Nneka R. Agbakoba
Autoimmune disorders are those diseases whereby an individual’s immune system attacks self‐tissues. In an autoimmune disease, the immune system does not recognize self-cells, which it sees as foreign, and so releases autoantibodies that attack the healthy cells. Apart from aberrant autoantibodies production, genetic, immunologic, and environmental factors also play contributory roles in autoimmune diseases. A significant function of the gut microbiota is the maintenance of homeostasis of the human immune system, and so, any dysbiosis in the gut can adversely affect the host. Dysbiosis of the gut microbiome, which can come in the form of excessive growth of potentially pathogenic organisms, loss of beneficial organisms, or the entire loss of microbial diversity, can induce autoimmune disease in people (De Gruttola et al., 2016). Several autoimmune diseases are known, and some of them include type 1 diabetes mellitus (T1DM), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and inflammatory bowel disease.
AI and Autoimmunity
Published in Louis J. Catania, AI for Immunology, 2021
Autoimmune diseases are mostly characterized by autoantibodies in the patients’ serum or cerebrospinal fluid, representing diagnostic or prognostic biomarkers. Research has focused on single autoantigens or panels of single autoantigens. Unsupervised machine learning can broaden the focus by addressing the entire autoantigen repertoire in a systemic “omics-like” way. This approach aims to capture the enormous biodiversity in the sets of targeted antigens and pave the way toward a more holistic understanding of the concerted character of antibody-related humoral immune responses permitting high-throughput screenings of thousands of autoantigens in parallel. Clusters of autoantigens can be identified that share certain functional or spatial properties, or clusters of patients comprising clinical subgroups potentially useful for patient stratification. This may enhance the understanding of autoimmune diseases in a more comprehensive way compared to current single or panel autoantibodies approaches.9
Auto-antibodies as Biomarkers for Disease Diagnosis
Published in Raj Bawa, János Szebeni, Thomas J. Webster, Gerald F. Audette, Immune Aspects of Biopharmaceuticals and Nanomedicines, 2019
Angelika Lueking, Heike Göhler, Peter Schulz-Knappe
Auto-antibodies are a class of biomarkers suitable for risk assessment, screening, prognosis, disease stratification, and therapy monitoring. Auto-antibodies, i.e., antibodies directed against certain human proteins, are induced by immune system activity in response to a disease process. Auto-antibody production reflects the immune response to a continuous remodeling of cells or tissues caused by protein turnover and chronic disease processes. In this context, the immune system fails to properly distinguish between self and nonself, and attacks its own cells and tissues. However, in so-called autoimmune diseases, the auto-antibodies present in blood are indicative for the clinical symptoms and the state of the disease. Prominent examples of autoimmune diseases are rheumatoid arthritis (RA), multiple sclerosis (MS), coeliac disease, diabetes mellitus type 1, systemic lupus erythematosus (SLE), Sjogren’s syndrome, inflammatory bowel disease, and Hashimoto’s thyroiditis.
Autoantibodies and cancer among asbestos-exposed cohorts in Western Australia
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Renee N Carey, Jean C Pfau, Marvin J Fritzler, Jenette Creaney, Nicholas de Klerk, Arthur W (Bill) Musk, Peter Franklin, Nita Sodhi-Berry, Fraser Brims, Alison Reid
In this study, the relationship between ANA and asbestos-associated cancers was examined, since amphibole asbestos exposures were shown to induce ANA (Pfau et al. 2005), and because of some evidence that autoimmune responsiveness is associated with, and may play roles in, development of some cancers (Chapman et al. 2008; Macdonald, Parsy-Kowalska, and Chapman 2017; Noble et al. 2016). Anti-nuclear autoantibodies are strongly associated with, and sometimes diagnostic for, systemic autoimmune diseases including systemic lupus erythematosus (SLE), systemic sclerosis, Sjōgren Syndrome, and mixed connective tissue disease. Several investigators reported that ANA also occur in the serum of cancer patients (Abu-Shakra et al. 2001; Tan 2012; Vlagea et al. 2018), and the possibility that these autoantibodies may be related to DNA damage and cancer etiology was proposed (Noble et al. 2016; Vlagea et al. 2018). However, the plethora of different autoantibody specificities and challenges in their detection, plus inconsistencies with different cancer types, has made it difficult to test this hypothesis. A standardized method of ANA detection, the HEp-2000 indirect immunofluorescence test, was employed due to its reliability in detecting a wide range of autoantibodies, particularly in asbestos-exposed populations. The ALBIA method was also utilized to detect specific autoantibody targets associated in general with ANA in systemic autoimmune diseases (SAID), including those associated with amphibole exposure (Diegel et al. 2018).
Analysis of autoantibody profiles in two asbestiform fiber exposure cohorts
Published in Journal of Toxicology and Environmental Health, Part A, 2018
Jean C. Pfau, Christopher Barbour, Brad Black, Kinta M. Serve, Marvin J. Fritzler
The history of epidemiological studies exploring an association between asbestos exposure and autoantibody responses has recently been reviewed (Pfau, Serve, and Noonan 2014). Since the mid- 20th century, cross-sectional investigations noted the following in association with asbestos exposures: B cell humoral responses, including rheumatoid factor (RF) and antinuclear autoantibodies (ANA), increased serum IgG/IgA, and circulating immune complexes (Pfau, Serve, and Noonan 2014). After it was revealed that the population of Libby, Montana, had been exposed to asbestiform amphibole fibers through mining and widespread use of asbestiform fiber-laden vermiculite, subjects exposed to Libby Asbestiform Amphiboles (LAA) were found to display elevated frequency and titers of ANA compared to an age- and sex-matched reference population (Pfau et al. 2005). The most frequent autoantibodies detected in the serum of those subjects were against common systemic lupus erythematosus (SLE) autoantigens, including dsDNA, histone, SSA/Ro52, and ribonucleoproteins (RNP) (Pfau, Blake, and Fritzler 2009; Pfau et al. 2005). The material present in the Libby vermiculite was originally described as enriched in tremolite, a federally-regulated form of amphibole asbestos (Meeker et al. 2003). However, subsequent analyses revealed that the bulk of the material consists of “unregulated” asbestiform amphibole fibers, drawing questions as to whether it could be called “asbestos” (Boettcher 1967). In this study, the term LAA is used for the exposure from the Libby vermiculite, since clearly LAA is a collection asbestiform fibers and produces asbestos-related diseases (Cyphert et al. 2016; Kodavanti et al. 2014; Larson et al. 2012; Sullivan 2007; Whitehouse et al. 2008).