No abstract

The human microbiota correlates closely with the health status of its host. This article analyzes the microbial composition of several subjects under different conditions over time spans that ranged from days to months. Using the Langevin equation as the basis of our mathematical framework to evaluate microbial temporal stability, we proved that stable microbiotas can be distinguished from unstable microbiotas. This initial step will help us to determine how temporal microbiota stability is related to a subject’s health status and to develop a more comprehensive framework that will provide greater insight into this complex system.

Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.

Metagenomic sequencing is becoming widespread in biomedical and environmental research, and the pace is increasing even more thanks to nanopore sequencing. With a rising number of samples and data per sample, the challenge of efficiently comparing results within a specimen and between specimens arises. Reagents, laboratory, and host related contaminants complicate such analysis. Contamination is particularly critical in low microbial biomass body sites and environments, where it can comprise most of a sample if not all. Recentrifuge implements a robust method for the removal of negative-control and crossover taxa from the rest of samples. With Recentrifuge, researchers can analyze results from taxonomic classifiers using interactive charts with emphasis on the confidence level of the classifications. In addition to contamination-subtracted samples, Recentrifuge provides shared and exclusive taxa per sample, thus enabling robust contamination removal and comparative analysis in environmental and clinical metagenomics. Regarding the first area, Recentrifuge’s novel approach has already demonstrated its benefits showing that microbiomes of Arctic and Antarctic solar panels display similar taxonomic profiles. In the clinical field, to confirm Recentrifuge’s ability to analyze complex metagenomes, we challenged it with data coming from a metagenomic investigation of RNA in plasma that suffered from critical contamination to the point of preventing any positive conclusion. Recentrifuge provided results that yielded new biological insight into the problem, supporting the growing evidence of a blood microbiota even in healthy individuals, mostly translocated from the gut, the oral cavity, and the genitourinary tract. We also developed a synthetic dataset carefully designed to rate the robust contamination removal algorithm, which demonstrated a significant improvement in specificity while retaining a high sensitivity even in the presence of cross-contaminants. Recentrifuge’s official website is www.recentrifuge.org . The data and source code are anonymously and freely available on GitHub and PyPI. The computing code is licensed under the AGPLv3. The Recentrifuge Wiki is the most extensive and continually-updated source of documentation for Recentrifuge, covering installation, use cases, testing, and other useful topics.

Gut microbiota is closely related to acute infectious diarrhea, one of the leading causes of mortality and morbidity in children worldwide. Understanding the dynamics of the recovery from this disease is of clinical interest. This work aims to correlate the dynamics of gut microbiota with the evolution of children who were suffering from acute infectious diarrhea caused by a rotavirus, and their recovery after the administration of a probiotic, Saccharomyces boulardii CNCM I-745. The experiment involved 10 children with acute infectious diarrhea caused by a rotavirus, and six healthy children, all aged between 3 and 4 years. The children who suffered the rotavirus infection received S. boulardii CNCM I-745 twice daily for the first 5 days of the experiment. Fecal samples were collected from each participant at 0, 3, 5, 10, and 30 days after probiotic administration. Microbial composition was characterized by 16S rRNA gene sequencing. Alpha and beta diversity were calculated, along with dynamical analysis based on Taylor's law to assess the temporal stability of the microbiota. All children infected with the rotavirus stopped having diarrhea at day 3 after the intervention. We observed low alpha diversities in the first 5 days (p-value < 0.05, Wilcoxon test), larger at 10 and 30 days after probiotic treatment. Canonical correspondence analysis (CCA) showed differences in the gut microbiota of healthy children and of those who suffered from acute diarrhea in the first days (p-value < 0.05, ADONIS test), but not in the last days of the experiment. Temporal variability was larger in children infected with the rotavirus than in healthy ones. In particular, Gammaproteobacteria class was found to be abundant in children with acute diarrhea. We identified the microbiota transition from a diseased state to a healthy one with time, whose characterization may lead to relevant clinical data. This work highlights the importance of using time series for the study of dysbiosis related to diarrhea.

We have performed a cosmological numerical simulation of primordial baryonic gas collapsing on to a 3 × 107  M⊙ dark matter (DM) halo. We show that the large scale baryonic accretion process and the merger of few ∼ 106 M⊙ DM haloes, triggered by the gravitational potential of the biggest halo, are enough to create supersonic () shocks and develop a turbulent environment. In this scenario, the post‐shocked regions are able to produce both H2 and deuterated H2 molecules very efficiently, reaching maximum abundances of and nHD∼ few × 10−6 nH, enough to cool the gas below 100 K in some regions. The kinetic energy spectrum of the turbulent primordial gas is close to a Burgers spectrum, , which could favour the formation of low‐mass primordial stars. The solenoidal‐to‐total kinetic energy ratio is 0.65 ≲Rk≲ 0.7 for a wide range of wavenumbers; this value is close to the Rk≈ 2/3 natural equipartition energy value of a random turbulent flow. In this way, turbulence and molecular cooling seem to work together in order to produce potential star formation regions of cold dense gas in primordial environments. We conclude that both the mergers and the collapse process on to the main DM halo provide enough energy to develop supersonic turbulence which favours the molecular coolant formation: this mechanism, which could be universal and the main route towards the formation of the first galaxies, is able to create potential star‐forming regions at high redshift.

Summary Solar panels located on high (Arctic and Antarctic) latitudes combine the harshness of the climate with that of the solar exposure. We report here that these polar solar panels are inhabited by similar microbial communities in taxonomic terms, dominated by Hymenobacter spp., Sphingomonas spp. and Ascomycota. Our results suggest that solar panels, even on high latitudes, can shape a microbial ecosystem adapted to irradiation and desiccation.

The term “bacterial dysbiosis” is being used quite extensively in metagenomic studies, however, the identification of harmful bacteria often fails due to large overlap between the bacterial species found in healthy volunteers and patients. We hypothesized that the pathogenic oral bacteria are individual-specific and they correlate with oxidative stress markers in saliva which reflect the inflammatory processes in the oral cavity. Temporally direct and lagged correlations between the markers and bacterial taxa were computed individually for 26 volunteers who provided saliva samples during one month (21.2 ± 2.7 samples/volunteer, 551 samples in total). The volunteers’ microbiomes differed significantly by their composition and also by their degree of microbiome temporal variability and oxidative stress markers fluctuation. The results showed that each of the marker-taxa pairs can have negative correlations in some volunteers while positive in others. Streptococcus mutans, which used to be associated with caries before the metagenomics era, had the most prominent correlations with the oxidative stress markers, however, these correlations were not confirmed in all volunteers. The importance of longitudinal samples collections in correlation studies was underlined by simulation of single sample collections in 1000 different combinations which produced contradictory results. In conclusion, the distinct intra-individual correlation patterns suggest that different bacterial consortia might be involved in the oxidative stress induction in each human subject. In the future, decreasing cost of DNA sequencing will allow to analyze multiple samples from each patient, which might help to explore potential diagnostic applications and understand pathogenesis of microbiome-associated oral diseases.