The role of <i>Candida albicans</i> in the severity of multiple sclerosis

Saroukolaei, Shahla Amri; Ghabaee, Mojdeh; Shokri, Hojjatollah; Badiei, Alireza; Ghourchian, Shadi

doi:10.1111/myc.12489

Cited by 11 publications

(13 citation statements)

References 43 publications

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“…Analytical models which considered both specific enzyme activity (SEA) and EDSS were applied. The SEA of C. albicans in MS was significantly higher than controls and although positive correlations with EDSS were observed ( p < 0.001, r = 0.65), similar correlation coefficients were found when EDSS was replaced with either disease duration or age (Saroukolaei et al, 2016).…”

Section: Gut Mycobiome and Disease Susceptibilitymentioning

confidence: 67%

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A Fungal World: Could the Gut Mycobiome Be Involved in Neurological Disease?

et al. 2019

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The human microbiome has received decades of attention from scientific and medical research communities. The human gastrointestinal tract is host to immense populations of microorganisms including bacteria, viruses, archaea, and fungi (the gut microbiota). High-throughput sequencing and computational advancements provide unprecedented ability to investigate the structure and function of microbial communities associated with the human body in health and disease. Most research to date has largely focused on elucidating the bacterial component of the human gut microbiota. Study of the gut “mycobiota,” which refers to the diverse array of fungal species, is a relatively new and rapidly progressing field. Though omnipresent, the number and abundance of fungi occupying the human gut is orders of magnitude smaller than that of bacteria. Recent insights however, have suggested that the gut mycobiota may be intricately linked to health and disease. Evaluation of the gut mycobiota has shown that not only are the fungal communities altered in disease, but they also play a role in maintaining intestinal homeostasis and influencing systemic immunity. In addition, it is now widely accepted that host-fungi and bacteria-fungi associations are critical to host health. While research of the gut mycobiota in health and disease is on the rise, little research has been performed in the context of neuroimmune and neurodegenerative conditions. Gut microbiota dysbiosis (specifically bacteria and archaea) have been reported in neurological diseases such as multiple sclerosis, amyotrophic lateral sclerosis, and Alzheimer's, among others. Given the widely accepted bacteria-fungi associations and paucity of mycobiota-specific studies in neurological disease, this review discusses the potential role fungi may play in multiple sclerosis and other neurological diseases. Herein, we provide an overview of recent advances in gut mycobiome research and discuss the plausible role of both intestinal and non-intestinal fungi in the context of neuroimmune and neurodegenerative conditions.

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Section: Gut Mycobiome and Disease Susceptibilitymentioning

confidence: 67%

“…Other research has investigated the role of C. albicans via the specific activity of proteinase A in the severity of MS (Saroukolaei et al, 2016). C. albicans strains were isolated from superficial surfaces of MS patients and healthy controls.…”

Section: Gut Mycobiome and Disease Susceptibilitymentioning

confidence: 99%

A Fungal World: Could the Gut Mycobiome Be Involved in Neurological Disease?

et al. 2019

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“…In addition, the oral colonization of Candida spp. in MS patients was higher than in healthy individuals [27], and C. albicans isolated from MS patients presented a higher specific enzyme activity compared to C. albicans isolated from healthy individuals and had a positive correlation with the severity of MS [28]. Using the experimental autoimmune encephalomyelitis (EAE) model, which is extensively employed to study the immunopathogenesis of MS [29][30][31], our research group previously demonstrated that C. albicans spreads to the CNS and aggravates the development of EAE due to the increased production of encephalitogenic cytokines [32].…”

Section: Introductionmentioning

confidence: 79%

Systemic Infection by Non-albicans Candida Species Affects the Development of a Murine Model of Multiple Sclerosis

Fraga-Silva

Munhoz-Alves

Mimura

et al. 2022

JoF

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Candidiasis may affect the central nervous system (CNS), and although Candida albicans is predominant, non-albicans Candida species can also be associated with CNS infections. Some studies have suggested that Candida infections could increase the odds of multiple sclerosis (MS) development. In this context, we investigated whether systemic infection by non-albicans Candida species would affect, clinically or immunologically, the severity of experimental autoimmune encephalomyelitis (EAE), which is an animal model used to study MS. For this, a strain of C. glabrata, C. krusei, and C. parapsilosis was selected and characterized using different in vitro and in vivo models. In these analysis, all the strains exhibited the ability to form biofilms, produce proteolytic enzymes, and cause systemic infections in Galleria mellonella, with C. glabrata being the most virulent species. Next, C57BL/6 mice were infected with strains of C. glabrata, C. krusei, or C. parapsilosis, and 3 days later were immunized with myelin oligodendrocyte glycoprotein to develop EAE. Mice from EAE groups previously infected with C. glabrata and C. krusei developed more severe and more prevalent paralysis, while mice from the EAE group infected with C. parapsilosis developed a disease comparable to non-infected EAE mice. Disease aggravation by C. glabrata and C. krusei strains was concomitant to increased IL-17 and IFN-γ production by splenic cells stimulated with fungi-derived antigens and with increased percentage of T lymphocytes and myeloid cells in the CNS. Analysis of interaction with BV-2 microglial cell line also revealed differences among these strains, in which C. krusei was the strongest activator of microglia concerning the expression of MHC II and CD40 and pro-inflammatory cytokine production. Altogether, these results indicated that the three non-albicans Candida strains were similarly able to reach the CNS but distinct in terms of their effect over EAE development. Whereas C. glabrata and C. Krusei aggravated the development of EAE, C. parapsilosis did not affect its severity. Disease worsening was partially associated to virulence factors in C. glabrata and to a strong activation of microglia in C. krusei infection. In conclusion, systemic infections by non-albicans Candida strains exerted influence on the experimental autoimmune encephalomyelitis in both immunological and clinical aspects, emphasizing their possible relevance in MS development.

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“… Kiiski et al [ 43 ] ERPs Linear Regression; Visual task is better than auditory task. Saroukolaei et al [ 44 ] Enzymes Linear Regression; NN; Higher CA. Sun et al [ 45 ] Postural sway RF; Accuracy in [92.3%, 95.6%].…”

Section: Resultsmentioning

confidence: 99%

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

Hossain

Daskalaki

Brüstle

et al. 2022

BMC Med Inform Decis Mak

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Background Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. Methods Systematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms. Results Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. Conclusions ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01985-5.

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The role of Candida albicans in the severity of multiple sclerosis

Cited by 11 publications

References 43 publications

A Fungal World: Could the Gut Mycobiome Be Involved in Neurological Disease?

A Fungal World: Could the Gut Mycobiome Be Involved in Neurological Disease?

Systemic Infection by Non-albicans Candida Species Affects the Development of a Murine Model of Multiple Sclerosis

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

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