The microbial communities inhabiting the upper respiratory tract protect from respiratory infection. The maturity of the immune system is a major influence on the composition of the microbiome and, in youth, the microbiota and immune system are believed to mature in tandem. With age, immune function declines and susceptibility to respiratory infection increases. Whether these changes contribute to the microbial composition of the respiratory tract is unknown. Our goal was to determine whether the microbes of the upper respiratory tract differ between mid-aged adults (18-40 yr) and the elderly (>65 yr). Microbiomes of the anterior nares and oropharynx of elderly individuals were evaluated by 16S rRNA gene sequencing. These communities were compared with data on mid-aged adults obtained from the Human Microbiome Project. The microbiota of the elderly showed no associations with sex, comorbidities, residence, or vaccinations. Comparisons of mid-aged adults and the elderly demonstrated significant differences in the composition of the anterior nares and oropharynx, including a population in the anterior nares of the elderly that more closely resembled the oropharynx than the anterior nares of adults. The elderly oropharyngeal microbiota were characterized by increased abundance of streptococci, specifically, Streptococcus salivarius group species, but not Streptococcus pneumoniae, carriage of which was low (<3% of participants), as demonstrated by PCR (n = 4/123). Microbial populations of the upper respiratory tract in mid-aged adults and the elderly differ; it is possible that these differences contribute to the increased risk of respiratory infections experienced by the elderly.

The human gut microbiota has been implicated in most aspects of health and disease; however, most of the bacteria in this community are considered unculturable, so studies have relied on molecular-based methods. These methods generally do not permit the isolation of organisms, which is required to fully explore the functional roles of bacteria for definitive association with host phenotypes. Using a combination of culture and 16S rRNA gene sequencing, referred to as culture-enriched molecular profiling, we show that the majority of the bacteria identified by 16S sequencing of the human gut microbiota can be cultured. Five fresh, anaerobic fecal samples were cultured using 33 media and incubation of plates anaerobically and aerobically resulted in 66 culture conditions for culture-enriched molecular profiling. The cultivable portion of the fecal microbiota was determined by comparing the operational taxonomic units (OTUs) recovered by 16S sequencing of the culture plates to OTUs from culture-independent sequencing of the fecal sample. Targeted isolation of Lachnospiraceae strains using conditions defined by culture-enriched molecular profiling was carried out on two fresh stool samples. We show that culture-enriched molecular profiling, utilizing 66 culture conditions combined with 16S rRNA gene sequencing, allowed for the culturing of an average of 95 % of the OTUs present at greater than 0.1 % abundance in fecal samples. Uncultured OTUs were low abundance in stool. Importantly, comparing culture-enrichment to culture-independent sequencing revealed that the majority of OTUs were detected only by culture, highlighting the advantage of culture for studying the diversity of the gut microbiota. Applying culture-enriched molecular profiling to target Lachnospiraceae strains resulted in the recovery of 79 isolates, 12 of which are on the Human Microbiome Project’s “Most Wanted” list. We show that, through culture-enriched molecular profiling, the majority of the bacteria in the human gut microbiota can be cultured and this method revealed greater bacterial diversity compared to culture-independent sequencing. Additionally, this method could be applied for the targeted recovery of a specific bacterial group. This approach allows for the isolation of bacteria of interest from the gut microbiota, providing new opportunities to explore mechanisms of microbiota–host interactions and the diversity of the human microbiota.

Shifts in the maternal gut microbiome have been implicated in metabolic adaptations to pregnancy. We investigated how pregnancy and diet interact to influence the composition of the maternal gut microbiota. Female C57BL/6 mice were fed either a control or a high fat diet for 8 weeks prior to mating. After confirmation of pregnancy, maternal weight gain and food intake were recorded. Fecal pellets were collected at 2 timepoints prior to mating (at the beginning of the experiment, and after 6 weeks of the specified diet) and at 4 timepoints during pregnancy (gestation day 0.5, 5.5, 10.5, and 15.5). The microbial composition and predicted metabolic functionality of the non-pregnant and pregnant gut was determined via sequencing of the variable 3 region of the 16S rRNA gene. Upon conception, differences in gut microbial communities were observed in both control and high fat-fed mice, including an increase in mucin-degrading bacteria. Control versus high fat-fed pregnant mice possessed the most profound changes to their maternal gut microbiota as indicated by statistically significant taxonomic differences. High fat-fed pregnant mice, when compared to control-fed animals, were found to be significantly enriched in microbes involved in metabolic pathways favoring fatty acid, ketone, vitamin, and bile synthesis. We show that pregnancy-induced changes in the female gut microbiota occur immediately at the onset of pregnancy, are vulnerable to modulation by diet, but are not dependent upon increases in maternal weight gain during pregnancy. High fat diet intake before and during pregnancy results in distinctive shifts in the pregnant gut microbiota in a gestational-age dependent manner and these shifts predict significant differences in the abundance of genes that favor lipid metabolism, glycolysis and gluconeogenic metabolic pathways over the course of pregnancy.

The accessory genes of prokaryote and eukaryote pangenomes accumulate by horizontal gene transfer, differential gene loss, and the effects of selection and drift. We have developed Coinfinder, a software program that assesses whether sets of homologous genes (gene families) in pangenomes associate or dissociate with each other (i.e. are ‘coincident’) more often than would be expected by chance. Coinfinder employs a user-supplied phylogenetic tree in order to assess the lineage-dependence (i.e. the phylogenetic distribution) of each accessory gene, allowing Coinfinder to focus on coincident gene pairs whose joint presence is not simply because they happened to appear in the same clade, but rather that they tend to appear together more often than expected across the phylogeny. Coinfinder is implemented in C++, Python3 and R and is freely available under the GNU license from https://github.com/fwhelan/coinfinder.

Advances in next-generation sequencing technologies have allowed for detailed, molecular-based studies of microbial communities such as the human gut, soil, and ocean waters. Sequencing of the 16S rRNA gene, specific to prokaryotes, using universal PCR primers has become a common approach to studying the composition of these microbiota. However, the bioinformatic processing of the resulting millions of DNA sequences can be challenging, and a standardized protocol would aid in reproducible analyses. The short-read library 16S rRNA gene sequencing pipeline (sl1p, pronounced “slip”) was designed with the purpose of mitigating this lack of reproducibility by combining pre-existing tools into a computational pipeline. This pipeline automates the processing of raw 16S rRNA gene sequencing data to create human-readable tables, graphs, and figures to make the collected data more readily accessible. Data generated from mock communities were compared using eight OTU clustering algorithms, two taxon assignment approaches, and three 16S rRNA gene reference databases. While all of these algorithms and options are available to sl1p users, through testing with human-associated mock communities, AbundantOTU+, the RDP Classifier, and the Greengenes 2011 reference database were chosen as sl1p’s defaults based on their ability to best represent the known input communities. sl1p promotes reproducible research by providing a comprehensive log file, and reduces the computational knowledge needed by the user to process next-generation sequencing data. sl1p is freely available at https://bitbucket.org/fwhelan/sl1p .