We evaluated the effect of DMTs on Covid‐19 severity in patients with MS, with a pooled‐analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid‐19 severity was assessed by multivariate ordinal‐logistic models and pooled by a fixed‐effect meta‐analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti‐CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid‐19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled‐analysis confirms an increased risk of severe Covid‐19 in patients on anti‐CD20 therapies and supports the protective role of interferon.

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Hendra virus (HeV) is a recently identified paramyxovirus that is fatal in humans and could be used as an agent of bioterrorism. The HeV receptor-binding protein (G) is required in order for the fusion protein (F) to mediate fusion, and analysis of the triggering/activation of HeV F by G should lead to strategies for interfering with this key step in viral entry. HeV F, once triggered by the receptor-bound G, by analogy with other paramyxovirus F proteins, undergoes multistep conformational changes leading to a six-helix bundle (6HB) structure that accomplishes fusion of the viral and cellular membranes. The ectodomain of paramyxovirus F proteins contains two conserved heptad repeat regions (HRN and HRC) near the fusion peptide and the transmembrane domains, respectively. Peptides derived from the HRN and HRC regions of F are proposed to inhibit fusion by preventing F, after the initial triggering step, from forming the 6HB structure that is required for fusion. HeV peptides have previously been found to be effective at inhibiting HeV fusion. However, we found that a human parainfluenza virus 3 F-peptide is more effective at inhibiting HeV fusion than the comparable HeV-derived peptide.

Background: The MuSC-19 project is an Italian cohort study open to international partners that collects data on multiple sclerosis (MS) patients with COVID-19. During the second wave of the pandemic, serological tests became routinely available. Objective: To evaluate the seroprevalence of anti-SARS-CoV-2 antibodies according to the use of disease-modifying therapy (DMT) in a subset of patients included in the MuSC-19 data set who had undergone a serological test. Methods: We evaluated the association between positive serological test results and time elapsed since infection onset, age, sex, Expanded Disability Status Scale score, comorbidities and DMT exposure using a multivariable logistic model. Results: Data were collected from 423 patients (345 from Italy, 61 from Turkey and 17 from Brazil) with a serological test performed during follow-up. Overall, 325 out of 423 tested patients (76.8%) had a positive serological test. At multivariate analysis, therapy with anti-CD20 was significantly associated with a reduced probability of developing antibodies after COVID-19 (odds ratio (OR) = 0.20, p = 0.002). Conclusion: Patients with MS maintain the capacity to develop humoral immune response against SARS-COV-2, although to a lesser extent when treated with anti-CD20 drugs. Overall, our results are reassuring with respect to the possibility to achieve sufficient immunization with vaccination.

BackgroundThe detection of regions that affect quantitative traits (QTL), to implement selection assisted by molecular information, remains of particular interest in dairy sheep for which genetic gain is constrained by the high costs of large-scale phenotype and pedigree recording. QTL detection based on the combination of linkage disequilibrium and linkage analysis (LDLA) is the most suitable approach in family-structured populations. The main issue in performing LDLA mapping is the handling of the identity-by-descent (IBD) probability matrix. Here, we propose the use of principal component analysis (PCA) to perform LDLA mapping for milk traits in Sarda dairy sheep.MethodsA resource population of 3731 ewes belonging to 161 sire families and genotyped with the OvineSNP50 Beadchip was used to map genomic regions that affect five milk traits. The paternally and maternally inherited gametes of genotyped individuals were reconstructed and IBD probabilities between them were defined both at each SNP position and at the genome level. A QTL detection model fitting fixed effects of principal components that summarize IBD probabilities was tested at each SNP position. Genome-wide (GW) significance thresholds were determined by within-trait permutations.ResultsPCA resulted in substantial dimensionality reduction, in fact 137 and 32 (on average) principal components were able to capture 99% of the IBD variation at the locus and genome levels, respectively. Overall, 2563 positions exceeded the 0.05 GW significance threshold for at least one trait, which clustered into 75 QTL regions most of which affected more than one trait. The strongest signal was obtained for protein content on Ovis aries (OAR) chromosome 6 and overlapped with the region that harbours the casein gene cluster. Additional interesting positions were identified on OAR4 for fat content and on OAR11 for the three yield traits.ConclusionsPCA is a good strategy to summarize IBD probabilities. A large number of regions associated to milk traits were identified. The outputs provided by the proposed method are useful for the selection of candidate genes, which need to be further investigated to identify causative mutations or markers in strong LD with them for application in selection programs assisted by molecular information.