Little is known about how variables, such as carcass mass, affect the succession pattern of microbes in soils during decomposition. To investigate the effects of carcass mass on the soil microbial community, soils associated with swine (Sus scrofa domesticus) carcasses of four different masses were sampled until the 15th day of decomposition during the month of June in a pasture near Lincoln, Nebraska. Soils underneath swine of 1, 20, 40, and 50 kg masses were investigated in triplicate, as well as control sites not associated with a carcass. Soil microbial communities were characterized by sequencing the archaeal, bacterial (16S), and eukaryotic (18S) rRNA genes in soil samples. We conclude that time of decomposition was a significant influence on the microbial community, but carcass mass was not. The gravesoil associated with 1 kg mass carcasses differs most compared to the gravesoil associated with other carcass masses. We also identify the 15 most abundant bacterial and eukaryotic taxa, and discuss changes in their abundance as carcass decomposition progressed. Finally, we show significant decreases in alpha diversity for carcasses of differing mass in pre-carcass rupture (days 0, 1, 2, 4, 5, and 6 postmortem) versus post-carcass rupture (days 9 and 15 postmortem) microbial communities.

A recent report warns that DNA extraction kits and other laboratory reagents are considerable sources of contamination in microbiome experiments. The issue of contamination is particularly problematic for samples of low biomass.

Chemical genomics expands our understanding of microbial tolerance to inhibitory chemicals, but its scope is often limited by the throughput of genome-scale library construction and genotype-phenotype mapping. Here we report a method for rapid, parallel, and deep characterization of the response to antibiotics in Escherichia coli using a barcoded genome-scale library, next-generation sequencing, and streamlined bioinformatics software. The method provides quantitative growth data (over 200,000 measurements) and identifies contributing antimicrobial resistance and susceptibility alleles. Using multivariate analysis, we also find that subtle differences in the population responses resonate across multiple levels of functional hierarchy. Finally, we use machine learning to identify a unique allelic and proteomic fingerprint for each antibiotic. The method can be broadly applied to tolerance for any chemical from toxic metabolites to next-generation biofuels and antibiotics.

Hsaio and colleagues link gut microbes to autism spectrum disorders (ASD) in a mouse model. They show that ASD symptoms are triggered by compositional and structural shifts of microbes and associated metabolites, but symptoms are relieved by a Bacteroides fragilis probiotic. Thus probiotics may provide therapeutic strategies for neurodevelopmental disorders.

The composition of tick microbiomes varies both within and among tick species. Whether this variation is intrinsic (related to tick characteristics) or extrinsic (related to vertebrate host and habitat) is poorly understood but important, as microbiota can influence the reproductive success and vector competence of ticks. We aimed to uncover what intrinsic and extrinsic factors best explain the microbial composition and taxon richness of 11 species of neotropical ticks collected from eight species of small mammals in 18 forest fragments across central Panama. Microbial richness varied among tick species, life stages, and collection sites but was not related to host blood source. Microbiome composition was best explained by tick life stage, with bacterial assemblages of larvae being a subset of those of nymphs. Collection site explained most of the bacterial taxa with differential abundance across intrinsic and extrinsic factors. Francisella and Rickettsia were highly prevalent, but their proportional abundance differed greatly among tick species, and we found both positive and negative cooccurrence between members of these two genera. Other tick endosymbionts (e.g., Coxiella and Rickettsiella) were associated with specific tick species. In addition, we detected Anaplasma and Bartonella in several tick species. Our results indicate that the microbial composition and richness of neotropical ticks are principally related to intrinsic factors (tick species and life stage) and collection site. Taken together, our analysis informs how tick microbiomes are structured and can help anchor our understanding of tick microbiomes from tropical environments more broadly.IMPORTANCE Blood-feeding arthropod microbiomes often play important roles in disease transmission, yet the factors that structure tick microbial communities in the Neotropics are unknown. Utilizing ticks collected from live animals in neotropical forest fragments, this study teases apart the contributions of intrinsic and extrinsic tick-associated factors on tick microbial composition as well as which specific microbes contribute to differences across tick species, tick life stages, the mammals they fed on, and the locations from where they were sampled. Furthermore, this study provides revelations of how notable tick-associated bacterial genera are interacting with other tick-associated microbes as well as the forest animals they encounter.

The oral microbiome is linked to oral and systemic health, but its fluctuation under frequent daily activities remains elusive. Here, we sampled saliva at 10- to 60-min intervals to track the high-resolution microbiome dynamics during the course of human activities. This dense time series data showed that eating activity markedly perturbed the salivary microbiota, with tongue-specific Campylobacter concisus and Oribacterium sinus and dental plaque-specific Lautropia mirabilis, Rothia aeria, and Neisseria oralis increased after every meal in a temporal order. The observation was reproducible in multiple subjects and across an 11-mo period. The microbiome composition showed significant diurnal oscillation patterns at different taxonomy levels with Prevotella/Alloprevotella increased at night and Bergeyella HMT 206/Haemophilus slowly increased during the daytime. We also identified microbial co-occurring patterns in saliva that are associated with the intricate biogeography of the oral microbiome. Microbial source tracking analysis showed that the contributions of distinct oral niches to the salivary microbiome were dynamically affected by daily activities, reflecting the role of saliva in exchanging microbes with other oral sites. Collectively, our study provides insights into the temporal microbiome variation in saliva and highlights the need to consider daily activities and diurnal factors in design of oral microbiome studies.

Background

Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics.

Methods

A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death.

Results

Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk.

Conclusions

Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients.

Clinical trial registration

URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264.

Ticks are important vectors for many emerging pathogens. However, they are also infected with many symbionts and commensals, often competing for the same niches. In this paper, we characterize the microbiome of Amblyomma americanum (Acari: Ixodidae), the lone star tick, in order to better understand the evolutionary relationships between pathogens and nonpathogens. Multitag pyrosequencing of prokaryotic 16S rRNA genes (16S rRNA) was performed on 20 lone star ticks (including males, females, and nymphs). Pyrosequencing of the rickettsial sca0 gene (also known as ompA or rompA) was performed on six ticks. Female ticks had less diverse microbiomes than males and nymphs, with greater population densities of Rickettsiales. The most common members of Rickettsiales were "Candidatus Rickettsia amblyommii" and "Candidatus Midichloria mitochondrii." "Ca. Rickettsia amblyommii" was 2.6-fold more common in females than males, and there was no sequence diversity in the sca0 gene. These results are consistent with a predominantly vertical transmission pattern for "Ca. Rickettsia amblyommii."

Background

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.

Results

Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.

Conclusions

These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

The mammal gut microbiome, which includes host microbes and their respective genes, is now recognized as an essential second genome that provides critical functions to the host. In humans, studies have revealed that lifestyle strongly influences the composition and diversity of the gastrointestinal microbiome. We hypothesized that these trends in humans may be paralleled in mammals subjected to anthropogenic forces such as domestication and captivity, in which diets and natural life histories are often greatly modified. We investigated fecal microbiomes of Przewalski's horse (PH; Equus ferus przewalskii), the only horses alive today not successfully domesticated by humans, and herded, domestic horse (E. f. caballus) living in adjacent natural grasslands. We discovered PH fecal microbiomes hosted a distinct and more diverse community of bacteria compared to domestic horses, which is likely partly explained by different plant diets as revealed by trnL maker data. Within the PH population, four individuals were born in captivity in European zoos and hosted a strikingly low diversity of fecal microbiota compared to individuals born in natural reserves in France and Mongolia. These results suggest that anthropogenic forces can dramatically reshape equid gastrointestinal microbiomes, which has broader implications for the conservation management of endangered mammals.