Emerging evidence suggests a significant role of gut microbiota on neurodevelopmental disorders, including Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). Our study aimed to compare gut microbiota composition between these disorders and evaluate the effect of probiotic supplementation. We conducted a 12-week randomized, double-blind, placebo-controlled trial with 80 children aged 5-14 years (39 with ADHD, 41 with ASD). Baseline and post-intervention fecal samples were analyzed using 16S rRNA gene sequencing to identify changes in gut microbiota composition. We identified 22 taxa differentiating ADHD and ASD (AUC = 0.939), characterised by increased presence of Clostridia, Ruminococcaceae, and Lachnospiraceae in ADHD, and Bacteroides, Bacilli and Actinobacteria in ASD. These differences remained after accounting for potential confounders. ASD children receiving probiotics had significant increases in Chao 1, Fisher's alpha, and Shannon indices whereas no significant differences in α and β-diversity were found in ADHD. In ADHD, bacteria with potential adverse effects were under-represented. In ASD, the abundance of Eggerthellaceae, and other taxa associated with gastrointestinal problems and anxiety was decreased. Variations in gut microbiota may influence responses in ADHD and ASD. Probiotic supplementation favorably altered gut microbiota composition, offering insights for future therapeutic strategies targeting the microbiome in neurodevelopmental disorders. Recent research underscores the role of gut microbiota in ADHD and ASD, indicating that diet can significantly influence microbiota composition and potentially manage these neurodevelopmental disorders. This study reveals distinct differences in gut microbiota composition between children with ADHD and ASD and demonstrates that probiotic supplementation can modulate specific microbial genera in each disorder. These findings pave the way for the development of innovative microbiome-targeted therapies, offering a new avenue for the treatment of neurodevelopmental disorders. Understanding this relationship is crucial for designing future interventions.

Mutation research is crucial for detecting and treating SARS-CoV-2 and developing vaccines. Using over 5,300,000 sequences from SARS-CoV-2 genomes and custom Python programs, we analyzed the mutational landscape of SARS-CoV-2. Although almost every nucleotide in the SARS-CoV-2 genome has mutated at some time, the substantial differences in the frequency and regularity of mutations warrant further examination. C>U mutations are the most common. They are found in the largest number of variants, pangolin lineages, and countries, which indicates that they are a driving force behind the evolution of SARS-CoV-2. Not all SARS-CoV-2 genes have mutated in the same way. Fewer non-synonymous single nucleotide variations are found in genes that encode proteins with a critical role in virus replication than in genes with ancillary roles. Some genes, such as spike (S) and nucleocapsid (N), show more non-synonymous mutations than others. Although the prevalence of mutations in the target regions of COVID-19 diagnostic RT-qPCR tests is generally low, in some cases, such as for some primers that bind to the N gene, it is significant. Therefore, ongoing monitoring of SARS-CoV-2 mutations is crucial. The SARS-CoV-2 Mutation Portal provides access to a database of SARS-CoV-2 mutations.

Emerging evidence suggests a potential role of gut dysbiosis in neurodegenerative disorders and, in particular, Alzheimer’s disease (AD) pathology and cognitive decline. However, the role of gut microbiome in the early prodromal stages of AD and particularly in mild cognitive impairment (MCI) remains understudied and has been mostly explored in Asian populations with no representation of European populations. To address this research gap in the literature and to suggest novel microbiome features associated with MCI, we conducted a cross-sectional study in a European population sample and profiled gut microbiota in 99 individuals without dementia through 16s ribosomal RNA (rRNA) sequencing. Individuals were categorized by cognitive status based on standard clinical criteria to cognitively normal (n = 49) or individuals with MCI (n = 50). Differential abundance through Microbiome Multivariable Associations with Linear model (MaAsLin2) and elastic net logistic regression analyses were used to identify gut microbiome features associated with MCI. MCI group was older than the CN group and age was used as covariate in the differential abundance analysis. No differences in alpha and beta diversity were found between the two groups (p > 0.05). At false discovery rate (FDR) < 0.05, we identified specific genera associated with MCI, mostly linked to short chain fatty acids (SCFAs) production (e.g., Candidatus_Soleaferrea q = 0.027, MaAsLin2 coefficient = 1.65, Sellimonas q = 0.017, MaAsLin2 coefficient = −4.45), while we highlight nominal (p < 0.05, q > 0.05) correlations of genera (e.g., Hydrogenoanaerobacterium, Subdoligranulum) with metrics of cognitive assessment. Microbiota was shown to have a fairly good discriminative capacity for MCI status (area under the curve AUC = 0.77), with Rothia genus found as the top predictor for MCI (beta coefficient [95% confidence intervals] = 0.224 [0.216–0.233]). Overall, our findings add to current knowledge reporting gut microbiome alterations in MCI by suggesting novel associated microbiome features; however, larger scale longitudinal studies are needed to further elucidate the underlying biological pathways linked to the disease.

Insulin resistance (IR)-related miRNAs have been associated with the development and progression of Alzheimer’s disease (AD). The dietary modulation of these miRNAs could become a potential strategy to manage AD. The aim of this study was to evaluate the effect of a high-fat diet (HFD), which aggravates AD-related pathogenic processes, on serum, cortex and hippocampus IR-related miRNA expression. C57BL/6J WT and APP_Swe/PS1dE9 mice were fed either an HFD or a conventional diet till 6 months of age. The mice fed with the HFD showed a significant increase in body weight and worsening glucose and insulin metabolism. miR-19a-3p was found to be up-regulated in the cortex, hippocampus and serum of APP/PS1 mice and in the serum and hippocampus of WT mice fed with the HFD. miR-34a-5p and miR-146a-5p were up-regulated in the serum of both groups of mice after consuming the HFD. Serum miR-29c-3p was overexpressed after consuming the HFD, along with hippocampal miR-338-3p and miR-125b-5p, only in WT mice. The HFD modulated the expression of peripheral and brain miRNAs related to glucose and insulin metabolism, suggesting the potential role of these miRNAs not only as therapeutic targets of AD but also as peripheral biomarkers for monitoring AD.

Background: Dementia risk is influenced by metabolic and lifestyle factors, including hyperinsulinemia, chronic inflammation, and type 2 diabetes. Glycemic index (GI) and glycemic load (GL) reflect the quality and quantity of dietary carbohydrates and their impact on glucose metabolism and systemic health. However, their associations with the risk of dementia subtypes remain unclear. This study examined the associations between dietary GI and GL and the risk of all-cause and different dementia subtypes, such as Alzheimer’s disease (AD), vascular dementia (VD), and frontotemporal dementia. Methods: A prospective analysis was conducted by using data from 202 302 dementia-free participants in the UK Biobank cohort. Dietary GI and GL were estimated by using the Oxford WebQ 24-hour web-based dietary questionnaire. Cox proportional hazards regressions with restricted cubic splines were used to evaluate nonlinear relationships. Two-piece Cox models were used to identify inflection points, which served as reference values to estimate hazard ratios (HRs). Results: Dementia incidence was 0.89 per 1000 person-years. GI (mean±SD: 48.71 ± 7.68) showed an inverse J-shaped association with dementia risk, while GL (121.49 ± 39.00) showed a J-shaped pattern, with inflection points at 49.30 and 111.01, respectively. After adjusting for potential confounders, GI values of 111.01 were associated with higher dementia risk (HR, 1.145; 95% CI, 1.048–1.251). Similar findings were observed for AD and VD. Conclusion: Low-GI diets may offer protective effects against all-cause dementia, AD, and VD, whereas high-GL diets may increase the risk. These findings underscore the importance of considering both carbohydrate quality and quantity in dementia prevention and management strategies.

SARS-CoV-2 and the COVID-19 pandemic have marked a milestone in the history of scientific research worldwide. To ensure that treatments are successful in the mid-long term, it is crucial to characterize SARS-CoV-2 mutations, as they might lead to viral resistance. Data from >5,700,000 SARS-CoV-2 genomes available at GISAID was used to report SARS-CoV-2 mutations. Given the pivotal role of its main protease (M-pro) in virus replication, a detailed analysis of SARS-CoV-2 M-pro mutations was conducted, with particular attention to mutation-resistant residues or mutation coldspots, defined as those residues that have mutated in five or fewer genomes. 32 mutation coldspots were identified, most of which mediate interprotomer interactions or funneling interaction networks from the substrate-binding site towards the dimerization surface and vice versa. Besides, mutation coldspots were virtually conserved in all main proteases from other CoVs. Our results provide valuable information about key residues to M-pro structure that could be useful in rational target-directed drug design and establish a solid groundwork based on mutation analyses for the inhibition of M-pro dimerization, with a potential applicability to future coronavirus outbreaks.