Microbiota modulation by teriflunomide therapy in people with multiple sclerosis: An observational case-control study.

Multiple sclerosis (MS) is a chronic immune-mediated and heterogeneous disease characterized by demyelination, axonal damage, and physical and cognitive impairment. Recent studies have highlighted alterations in the microbiota of people with MS (pwMS). However, the intricate nature of the disease and the wide range of treatments available make it challenging to identify specific microbial populations or functions associated with MS symptoms and disease progression. This study aimed to characterize the microbiota of pwMS treated with the oral drug teriflunomide (TF) and compare it with that of pwMS treated with beta interferons (IFNß), pwMS treated with no previous disease modifying therapies (naïve), and healthy controls. Our findings demonstrate significant alterations in both the composition and function of the gut microbiota in pwMS that are further influenced by disease-modifying therapies. Specifically, oral treatment with TF had a notable impact on the gut microbiota of pwMS. Importantly, the dysregulated microbial environment within the gut was associated with symptoms commonly experienced by pwMS, including fatigue, anxiety, and depression.

Bacteria-Fungi Interactions in Multiple Sclerosis.

Multiple sclerosis (MS) arises from a complex interplay between host genetic factors and environmental components, with the gut microbiota emerging as a key area of investigation. In the current study, we used ion torrent sequencing to delve into the bacteriome (bacterial microbiota) and mycobiome (fungal microbiota) of people with MS (pwMS), and compared them to healthy controls (HC). Through principal coordinate, diversity, and abundance analyses, as well as clustering and cross-kingdom microbial correlation assessments, we uncovered significant differences in the microbial profiles between pwMS and HC. Elevated levels of the fungus Torulaspora and the bacterial family Enterobacteriaceae were observed in pwMS, whereas beneficial bacterial taxa, such as Prevotelladaceae and Dialister, were reduced. Notably, clustering analysis revealed overlapping patterns in the bacteriome and mycobiome data for 74% of the participants, with weakened cross-kingdom interactions evident in the altered microbiota of pwMS. Our findings highlight the dysbiosis of both bacterial and fungal microbiota in MS, characterized by shifts in biodiversity and composition. Furthermore, the distinct disease-associated pattern of fungi-bacteria interactions suggests that fungi, in addition to bacteria, contribute to the pathogenesis of MS. Overall, our study sheds light on the intricate microbial dynamics underlying MS, paving the way for further investigation into the potential therapeutic targeting of the gut microbiota in MS management.

Early biochemical analysis of COVID-19 patients helps severity prediction.

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

Microbial dysbiosis and lack of SCFA production in a Spanish cohort of patients with multiple sclerosis.

Background: Multiple sclerosis (MS) is a chronic, demyelinating, and immune-mediated disease of the central nervous system caused by a combination of genetic, epigenetic, and environmental factors. The incidence of MS has increased in the past several decades, suggesting changes in the environmental risk factors. Much effort has been made in the description of the gut microbiota in MS; however, little is known about the dysbiosis on its function. The microbiota produces thousands of biologically active substances among which are notable the short-chain fatty acid (SCFA) excretion. Objectives: Analyze the interaction between microbiota, SCFAs, diet, and MS. Methods: 16S, nutritional questionnaires, and SCFAS quantification have been recovered from MS patients and controls. Results: Our results revealed an increment in the phylum Proteobacteria, especially the family Enterobacteriaceae, a lack in total SCFA excretion, and an altered profile of SCFAs in a Spanish cohort of MS patients. These alterations are more evident in patients with higher disability. Conclusions: The abundance of Proteobacteria and acetate and the low excretion of total SCFAs, especially butyrate, are common characteristics of MS patients, and besides, both are associated with a worse prognosis of the disease.

O group is a protective factor for COVID19 in Basque population.

ABO blood groups have recently been related to COVID19 infection. In the present work, we performed this analysis using data from 412 COVID19 patients and 17796 blood donors, all of them from Gipuzkoa, a region in Northern Spain. The results obtained confirmed this relation, in addition to showing a clear importance of group O as a protective factor in COVID19 disease, with an OR = 0.59 (CI95% 0.481-0.7177, p<0.0001) while A, B and AB are risk factors. ABO blood groups are slightly differently distributed in the populations and therefore these results should be replicated in the specific areas with a proper control population.