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Several ecological data types, especially microbiome count data, are commonly sample-wise normalized before analysis to correct for sampling bias and other technical artifacts. Recently, we developed an algorithm for the normalization of ecological count data called ‘scaling with ranked subsampling (SRS)’, which surpasses the widely adopted ‘rarefying’ (random subsampling without replacement) in reproducibility and in safeguarding the original community structure. Here, we describe an implementation of the SRS algorithm in the ‘SRS’ R package and the ‘q2-srs’ QIIME 2 plugin. We also provide accessory functions for dataset exploration to guide the choice of parameters for SRS.

Taxonomic marker gene analysis allows uncovering taxonomic profiles of microbial communities at low cost, making it omnipresent in microbiome research. There is an ever‐expanding set of tools to extract further biological information from this kind of data. In this perspective, we enunciate several concerns regarding the biological validity of predicting functional potential from taxonomic profiles, especially when they are generated by short‐read sequencing. The taxonomic resolution of marker genes, intragenomic variability of marker genes, and the compositional nature of microbiome data are discussed. Combining actual measurements of microbiome functions with predicted functional potentials is proposed as a powerful approach to better understand microbiome functioning. In this context, the significance of predicted functional potentials for generating and testing hypotheses is highlighted. We argue that functions of microbiomes predicted from microbiome DNA read count data generated by short‐read amplicon sequencing should not serve as the only basis to draw biological inferences. We raise concerns regarding the biological validity of predicting functional potential from microbiome taxonomic profiles generated by short‐read amplicon sequencing. We reason that predicted functional potential profiles can be improved by employing long‐read sequencing technologies, which in combination with independent measurements of actual functions can constitute a powerful approach to understanding microbiome functioning. Highlights Concerns regarding the biological validity of predicting functional potential from taxonomic profiles of microbiome data sets are enunciated. Taxonomic resolution of marker genes, intragenomic variability of marker genes, and the compositional nature of microbiome data are discussed. Combining measurements of actual functions and predicted functional potential profiles is a powerful approach to understanding microbial functioning.

Intestinal microbiota (IM) diversity and composition regulates host immunity and affects outcomes after allogeneic stem cell transplantation (allo-HSCT). We evaluated if the oral mucosa microbiota (OM) could impact the outcomes in patients who underwent allo-HSCT. Samples from the oral mucosa of 30 patients were collected at three time points: before the conditioning regimen, at aplasia, and at engraftment. We analyzed the associations of OM diversity and composition with allo-HSCT outcomes. Lower OM diversity at preconditioning was associated with a higher risk of relapse at 3 years (68% versus 33%, respectively; P  = 0.04). Dominance (relative abundance ≥ 30%) by a single genus at preconditioning was also associated with a higher risk of relapse (63% versus 36% at 3 years, respectively; P  = 0.04), as well as worse progression-free survival (PFS; 19% versus 55%, respectively; P  = 0.01), and overall survival (OS) at 3 years (38% versus 81%, respectively; P  = 0.02). In our study we observed that OM dysbiosis is associated with a higher risk of relapse and worse survival after allo-HSCT.

The link between the oral microbiome and neurodevelopmental disorders remains a compelling hypothesis, still requiring confirmation in large-scale datasets. Leveraging over 7000 whole-genome sequenced salivary samples from 2025 US families with children diagnosed with autism spectrum disorders (ASD), our cross-sectional study shows that the oral microbiome composition can discriminate ASD subjects from neurotypical siblings (NTs, AUC = 0.66), with 108 differentiating species ( q  < 0.005). The relative abundance of these species is highly correlated with cognitive impairment as measured by Full-Scale Intelligence Quotient (IQ). ASD children with IQ < 70 also exhibit lower microbiome strain sharing with parents ( p  < 10 −6 ) with respect to NTs. A two-pronged functional enrichment analysis suggests the contribution of enzymes from the serotonin, GABA, and dopamine degradation pathways to the distinct microbial community compositions observed between ASD and NT samples. Although measures of restrictive eating diet and proxies of oral hygiene show relatively minor effects on the microbiome composition, the observed associations with ASD and IQ may still represent unaccounted-for underlying differences in lifestyle among groups. While causal relationships could not be established, our study provides substantial support to the investigation of oral microbiome biomarkers in ASD. Here, Manghi et al. identify potential salivary microbial biomarkers for autism through a large-scale metagenomic analysis of 2,000 families, revealing shifts in neurotransmitter-related pathways

Oral mucositis (OM) is a complex acute cytotoxicity of antineoplastic treatment that affects 40–85% of patients undergoing hematopoietic stem-cell transplantation. OM is associated with prolonged hospitalization, increased extensive pharmacotherapy, need for parenteral nutrition, and elevated treatment costs. As OM onset relates to the mucosal microenvironment status, with a particular role for microbiota-driven inflammation, we aimed to investigate whether the oral mucosa microbiota was associated with the clinical course of OM in patients undergoing allogeneic hematopoietic stem-cell transplantation. We collected oral mucosa samples from 30 patients and analyzed the oral mucosa microbiota by 16S rRNA sequencing. A total of 13 patients (43%) developed ulcerative OM. We observed that specific taxa were associated with oral mucositis grade and time to oral mucositis healing. Porphyromonas relative abundance at preconditioning was positively correlated with ulcerative OM grade (Spearman ρ = 0.61, P = 0.028) and higher Lactobacillus relative abundance at ulcerative OM onset was associated with shortened ulcerative OM duration (P = 0.032). Additionally, we generated a machine-learning-based bacterial signature that uses pre-treatment microbial profiles to predict whether a patient will develop OM during treatment. Our findings suggest that further research should focus on host-microbiome interactions to better prevent and treat OM.

Type 2 diabetes mellitus (T2DM), the most common form of diabetes, is a progressive chronic metabolic disease that has increasingly spread worldwide, enhancing the mortality rate, particularly from cardiovascular diseases (CVD). Lifestyle improvement through diet and physical activity is, together with drug treatment, the cornerstone of T2DM management. The Mediterranean diet (MD), which favors a prevalence of unprocessed vegetable foods and a reduction in red meats and industrial foods, without excluding any food category, is usually recommended. Recently, scientific societies have promoted a very low-calorie ketogenic diet (VLCKD), a multiphasic protocol that limits carbohydrates and then gradually re-introduces them, with a favorable outcome on body weight and metabolic parameters. Indeed, gut microbiota (GM) modifications have been linked to overweight/obesity and metabolic alterations typical of T2DM. Diet is known to affect GM largely, but only a few studies have investigated the effects of VLCKD on GM, especially in T2DM. In this study, we have compared anthropometric, biochemical, lifestyle parameters, the quality of life, and the GM of eleven patients with recently diagnosed T2DM and overweight or obesity, randomly assigned to two groups of six and five patients who followed the VLCKD (KETO) or hypocaloric MD (MEDI) respectively; parameters were recorded at baseline (T0) and after two (T2) and three months (T3). The results showed that VLCKD had more significant beneficial effects than MD on anthropometric parameters, while biochemical improvements did not statistically differ. As for the GM, despite the lack of significant results regarding the alpha and beta diversity, and the Firmicutes/Bacteroidota ratio between the two groups, in the KETO group, a significant increase in beneficial microbial taxa such as Verrucomicrobiota phylum with its members Verrucomicrobiae, Verrucomicrobiales, Akkermansiaceae, and Akkermansia, Christensenellaceae family, Eubacterium spp., and a reduction in microbial taxa previously associated with obesity (Firmicutes and Actinobacteriota) or other diseases (Alistipes) was observed both at T2 and T3. With regards to the MEDI group, variations were limited to a significant increase in Actinobacteroidota phylum at T2 and T3 and Firmicutes phylum at T3. Moreover, a metagenomic alteration linked to some metabolic pathways was found exclusively in the KETO group. In conclusion, both dietary approaches allowed patients to improve their state of health, but VLCKD has shown better results on body composition as well as on GM profile.

Accessibility to next-generation sequencing (NGS) technologies has enabled the profiling of microbial communities living in distinct habitats. 16S ribosomal RNA (rRNA) gene sequencing is widely used for microbiota profiling with NGS technologies. Since most used NGS platforms generate short reads, sequencing the full-length 16S rRNA gene is impractical. Therefore, choosing which 16S rRNA hypervariable region to sequence is critical in microbiota profiling studies. All nine 16S rRNA hypervariable regions are taxonomically informative, but due to variability in profiling performance for specific clades, choosing the ideal 16S rRNA hypervariable region will depend on the bacterial composition of the habitat under study. Recently, NGS allowed the identification of microbes in the urinary tract, and urinary microbiota has become an active research area. However, there is no current study evaluating the performance of different 16S rRNA hypervariable regions for male urinary microbiota profiling. We collected urine samples from male volunteers and profiled their urinary microbiota by sequencing a panel of six amplicons encompassing all nine 16S rRNA hypervariable regions. Systematic comparisons of their performance indicate V1V2 hypervariable regions better assess the taxa commonly present in male urine samples, suggesting V1V2 amplicon sequencing is more suitable for male urinary microbiota profiling. We believe our results will be helpful to guide this crucial methodological choice in future male urinary microbiota studies.

Acute graft- versus -host disease (aGVHD) is one of the major causes of death after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Recently, aGVHD onset was linked to intestinal microbiota (IM) dysbiosis. However, other bacterial-rich gastrointestinal sites, such as the mouth, which hosts several distinctive microbiotas, may also impact the risk of GVHD. The dental biofilm microbiota (DBM) is highly diverse and, like the IM, interacts with host cells and modulates immune homeostasis. We characterized changes in the DBM of patients during allo-HSCT and evaluated whether the DBM could be associated with the risk of aGVHD. DBM dysbiosis during allo-HSCT was marked by a gradual loss of bacterial diversity and changes in DBM genera composition, with commensal genera reductions and potentially pathogenic bacteria overgrowths. High Streptococcus and high Corynebacterium relative abundance at preconditioning were associated with a higher risk of aGVHD (67% vs. 33%; HR = 2.89, P = 0.04 and 73% vs. 37%; HR = 2.74, P = 0.04, respectively), while high Veillonella relative abundance was associated with a lower risk of aGVHD (27% vs. 73%; HR = 0.24, P < 0.01). Enterococcus faecalis bloom during allo-HSCT was observed in 17% of allo-HSCT recipients and was associated with a higher risk of aGVHD (100% vs. 40%; HR = 4.07, P < 0.001) and severe aGVHD (60% vs. 12%; HR = 6.82, P = 0.01). To the best of our knowledge, this is the first study demonstrating that DBM dysbiosis is associated with the aGVHD risk after allo-HSCT.

This study focuses on a dedicated collection of diverse oral sites to comprehensively investigate microbial differences between patients who develop RRC and those who do not. RRC is a severe oral disease that profoundly impacts on the oral health and overall quality of life of cancer survivors. Leveraging shotgun metagenomics, we characterize the unique microbial variations in in vivo irradiated dental biofilms, unveiling novel insights into the microbial ecology of radiotherapy-treated patients. Furthermore, this research integrates extensive data on oral health and oncological profiles, providing a comprehensive understanding of the intricate relationship between oral microbial communities and the outcomes of radiotherapy-induced toxicity.

Understudied pet-associated microbiomes represent a rich source for the discovery of microbial taxa important for pet and human health. From a cohort of 23 dogs, we sampled and metagenomically sequenced 64 dental plaque microbiomes, generating 1945 metagenome-assembled genomes spanning 347 microbial species, including 277 undercharacterized species without cultivated representatives. Integration with human microbiome data revealed the dog plaque microbiome is more diverse than – and shows little overlap (5.9% species in common) with – the human plaque microbiome, even though some shared periodontal pathobionts arise as a potential concern.