Species-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement. We have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our method does not need any training datasets specific for different taxonomic groups. Instead only a reference database is required for aligning to the query sequences, making our method easily applicable for different regions of the 16S rRNA gene or other phylogenetic marker genes. Reliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA .

Domestic combustion of biomass fuels, such as wood, charcoal, crop residue and dung causes Household Air Pollution (HAP). These inhaled particulates affect more than half of the world’s population, causing respiratory problems such as infection and inflammatory lung disease. We examined whether the presence of black carbon in alveolar macrophages was associated with alterations in the lung microbiome in a Malawi population. Bronchoalveolar lavage samples from 44 healthy adults were sequenced using 16S rDNA amplification to assess microbial diversity, richness and relative taxa abundance. Individuals were classified as high or low particulate exposure as determined by questionnaire and the percentage of black carbon within their alveolar macrophages. Subjects in the low and high particulate groups did not differ in terms of source of fuels used for cooking or lighting. There was no difference in alpha or beta diversity by particulate group. Neisseria and Streptococcus were significantly more abundant in samples from high particulate exposed individuals, and Tropheryma was found less abundant. Petrobacter abundance was higher in people using biomass fuel for household cooking and lighting, compared with exclusive use of electricity. Healthy adults in Malawi exposed to higher levels of particulates have higher abundances of potentially pathogenic bacteria (Streptococcus, Neisseria) within their lung microbiome. Domestic biomass fuel use was associated with an uncommon environmental bacterium (Petrobacter) associated with oil-rich niches.

Short chain fatty acids (SCFA) are key microbial metabolites that modulate intestinal homeostasis and may influence irritable bowel syndrome (IBS) pathophysiology. We aimed to assess microbial features associated with SCFA and determine if features varied across IBS subtypes and endophenotypes. We analyzed stool microbial metagenomes, stool SCFA, and measurable IBS traits (stool bile acids, colonic transit, stool form) in 41 patients with IBS (IBS with constipation [IBS-C] IBS with diarrhea [IBS-D]) and 17 healthy controls. We used partial canonical correspondence analyses (pCCA), conditioned on transit, to quantify microbe-SCFA associations across groups. We further compared gut microbiome-encoded potential for substrate utilization across groups and within a subset of participants selected by their stool characteristics as well as stool microbiomes of patients with and without clinical bile acid malabsorption (BAM). Microbe-SCFA associations differed across groups and revealed key taxa including Dorea sp. CAG:317 and Bifidobacterium pseudocatenulatum in IBS-D and Akkermansia muciniphila and Prevotella copri in IBS-C that that could underlie subtype-specific microbially-mediated mechanisms. The greatest number of microbe-SCFA associations were observed in IBS-D. Several SCFA-producing species demonstrated inverse correlations with SCFA. Fewer bacterial taxa were associated with acetate to butyrate ratios in IBS compared to health. In participants selected by stool form, we demonstrated differential abundances of microbial genes/pathways for SCFA metabolism and degradation of carbohydrates and mucin across groups. SCFA-producing taxa were reduced in IBS-D patients with BAM. Keystone taxa responsible for SCFA production differ by IBS subtype and traits. IBS microbiomes appear exhibit reduced functional redundancy. Differences in substrate preferences are also linked to bowel functions. Focusing on taxa that drive SCFA profiles and stool form may be a rational strategy for identifying relevant microbial targets in IBS.

Background: Dysmenorrhea is highly prevalent; it places women at risk for other chronic pain conditions. There is a high degree of individual variability in menstrual pain severity, the number of painful sites, and co-occurring gastrointestinal symptoms. Distinct dysmenorrhea symptom-based phenotypes were previously identified, but the biological underpinnings of these phenotypes are less known. One underexplored contributor is the vaginal microbiome. The vaginal microbiota differs significantly among reproductive-age women and may modulate as well as amplify reproductive tract inflammation, which may contribute to dysmenorrhea symptoms. Objectives: The objective of this study was to examine associations between dysmenorrhea symptom-based phenotypes and vaginal microbiome compositions on- and off-menses. Methods: We conducted a prospective, longitudinal, pilot study of 20 women (aged 15–24 years) grouped into three dysmenorrhea symptom-based phenotypes: “mild localized pain,” “severe localized pain,” and “severe multiple pain and gastrointestinal symptoms.” Over one menstrual cycle, participants provided vaginal swabs when they were on- and off-menses. We assayed the vaginal microbiome using 16S rRNA gene sequencing. Permutational multivariate analysis of variance tests were used to compare microbiome compositions across phenotypes, with heat maps generated to visualize the relative abundance of bacterial taxa. Results: The vaginal microbiome compositions (n = 40) were different across the three phenotypes. After separating the on-menses (n = 20) and off-menses (n = 20) specimens, the statistically significant difference was seen on-menses, but not off-menses. Compared to the “mild localized pain” phenotype, participants in the “multiple severe symptoms” phenotype had a lower lactobacilli level and a higher abundance of Prevotella, Atopobium, and Gardnerella when on-menses. We also observed trends of differences across phenotypes in vaginal microbiome change from off- to on-menses. Discussion: The study provides proof-of-concept data to support larger studies on associations between dysmenorrhea symptom-based phenotypes and vaginal microbiome that might lead to new intervention targets and/or biomarkers for dysmenorrhea. This line of research has the potential to inform precision dysmenorrhea treatment that can improve women’s quality of life.

Males often experience worse cardiac outcomes than females in sepsis. This study identified pyruvate dehydrogenase kinase 4 (PDK4) as a key mediator of this disparity. PDK4 regulates glucose utilization by inhibiting pyruvate dehydrogenase (PDH) in mitochondria. In a mouse endotoxemia model, a sublethal dose of lipopolysaccharide (LPS, 5 mg/kg) significantly upregulated myocardial PDK4 and induced cardiac dysfunction in males but not females. Cardiac-specific PDK4 overexpression promoted this cardiac dysfunction in both sexes, whereas PDK4 knockout provided protection. In WT males, LPS reduced PDH activity and fatty acid oxidation (FAO) while increasing lactate levels, suggesting a shift toward glycolysis. These effects were exacerbated by PDK4 overexpression but attenuated by knockout. In females, metabolic changes were minimal, aside from reduced FAO in LPS-challenged females overexpressing PDK4. Additionally, a higher LPS dose (8 mg/kg) triggered cardiac dysfunction in females, accompanied by modest upregulation of PDK4, but without changes in PDH or lactate. Dichloroacetate (DCA), restraining PDK-mediated PDH inhibition, improved cardiac function in males but not females during endotoxemia. PDK4 overexpression also exacerbated cardiac mitochondrial damage, reduced mitophagy, and increased oxidative stress and inflammation during endotoxemia — effects that were prevented by PDK4 knockout. These findings suggest that PDK4 drives sex-specific cardiac responses in sepsis.

Objective: Estimating the hospitalization risk for people with comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance. Traditional biostatistical methods for risk estimations require: (i) the number of infected people who were not hospitalized, which may be severely undercounted since many infected people were not tested; (ii) comorbidity information for people not hospitalized, which may not always be readily available. We aim to overcome these limitations by developing a Bayesian approach to estimate the risk ratio of hospitalization for COVID-19 patients with comorbidities. Materials and Methods: We derived a Bayesian approach to estimate the posterior distribution of the risk ratio using the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. We applied our approach to 2 large-scale datasets in the United States: 2491 patients in the COVID-NET, and 5700 patients in New York hospitals. Results: Our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively. Discussion: Our approach only needs (i) the number of hospitalized COVID-19 patients and their comorbidity information, which can be reliably obtained using hospital records, and (ii) the prevalence of the comorbidity of interest in the general population, which is regularly documented by public health agencies for common medical conditions. Conclusion: We developed a novel Bayesian approach to estimate the hospitalization risk for people with comorbidities infected with the SARS-CoV-2 virus.