PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

BackgroundArtificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models’ fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets.ResultsTo address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of four replicate artificial human guts over the course of 1 month. The resulting analyses revealed how the ratio of biological variation to technical variation from sample processing depends on sampling frequency. In particular, we find that at hourly sampling frequencies, 76% of observed variation could be ascribed to technical sources, which could also skew the observed covariation between taxa. We also found that the artificial guts demonstrated replicable trajectories even after a recovery from a transient feed disruption. Additionally, we observed irregular sub-daily oscillatory dynamics associated with the bacterial family Enterobacteriaceae within all four replicate vessels.ConclusionsOur analyses suggest that, beyond variation due to sequence counting, technical variation from sample processing can obscure temporal variation from biological sources in artificial gut studies. Our analyses also supported hypotheses that human gut microbiota fluctuates on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0584-3) contains supplementary material, which is available to authorized users.

Genome-wide association studies (GWAS) have strongly implicated MIR137 (the gene encoding the microRNA miR-137) in schizophrenia. A parsimonious hypothesis is that a pathway regulated by miR-137 is important in the etiology of schizophrenia. Full evaluation of this hypothesis requires more definitive knowledge about biological targets of miR-137, which is currently lacking. Our goals were to expand knowledge of the biology of miR-137 by identifying its empirical targets, and to test whether the resulting lists of direct and indirect targets were enriched for genes and pathways involved in risk for schizophrenia. We overexpressed miR-137 in a human neural stem cell line and analyzed gene expression changes at 24 and 48 h using RNA sequencing. Following miR-137 overexpression, 202 and 428 genes were differentially expressed after 24 and 48 h. Genes differentially expressed at 24 h were enriched for transcription factors and cell cycle genes, and differential expression at 48 h affected a wider variety of pathways. Pathways implicated in schizophrenia were upregulated in the 48 h findings (major histocompatibility complex, synapses, FMRP interacting RNAs and calcium channels). Critically, differentially expressed genes at 48 h were enriched for smaller association P-values in the largest published schizophrenia GWAS. This work provides empirical support for a role of miR-137 in the etiology of schizophrenia.

Culture and screening of gut bacteria enable testing of microbial function and therapeutic potential. However, the diversity of human gut microbial communities (microbiota) impedes comprehensive experimental studies of individual bacterial taxa. Here, we combine advances in droplet microfluidics and high-throughput DNA sequencing to develop a platform for separating and assaying growth of microbiota members in picoliter droplets (MicDrop). MicDrop enabled us to cultivate 2.8 times more bacterial taxa than typical batch culture methods. We then used MicDrop to test whether individuals possess similar abundances of carbohydrate-degrading gut bacteria, using an approach which had previously not been possible due to throughput limitations of traditional bacterial culture techniques. Single MicDrop experiments allowed us to characterize carbohydrate utilization among dozens of gut bacterial taxa from distinct human stool samples. Our aggregate data across nine healthy stool donors revealed that all of the individuals harbored gut bacterial species capable of degrading common dietary polysaccharides. However, the levels of richness and abundance of polysaccharide-degrading species relative to monosaccharide-consuming taxa differed by up to 2.6-fold and 24.7-fold, respectively. Additionally, our unique dataset suggested that gut bacterial taxa may be broadly categorized by whether they can grow on single or multiple polysaccharides, and we found that this lifestyle trait is correlated with how broadly bacterial taxa can be found across individuals. This demonstration shows that it is feasible to measure the function of hundreds of bacterial taxa across multiple fecal samples from different people, which should in turn enable future efforts to design microbiota-directed therapies and yield new insights into microbiota ecology and evolution. IMPORTANCE Bacterial culture and assay are components of basic microbiological research, drug development, and diagnostic screening. However, community diversity can make it challenging to comprehensively perform experiments involving individual microbiota members. Here, we present a new microfluidic culture platform that makes it feasible to measure the growth and function of microbiota constituents in a single set of experiments. As a proof of concept, we demonstrate how the platform can be used to measure how hundreds of gut bacterial taxa drawn from different people metabolize dietary carbohydrates. Going forward, we expect this microfluidic technique to be adaptable to a range of other microbial assay needs.

We analyzed the artificial gut dataset described below using a MALLARD model that is generative and assumes there exists an unobserved microbial composition ( " ; the state) that evolves through time ( Fig. 1A) due to stochastic biological variations ( " ). 115We regard the state sequence ( $ , … , " , … , ' ) as the true microbial dynamics in a timeseries. Random technical variations ( " ) are then added to the true system state ( " ) resulting in the composition " (Fig. 1B). We observe " through a multinomial counting process. This formulation is similar to the constant level model commonly used in Bayesian time-series analysis (34). By separately modeling the process generating " 120 and " with distinct covariance matrices ( and , respectively), we can decouple biological and technical variations in artificial gut datasets (Fig. 1C). Visually, we found this model provided a good fit to artificial gut data (Fig. S1). Daily and hourly gut microbiota time-series in an artificial human gut 125We applied our model to an artificial gut that was constructed using continuousflow anaerobic bioreactor systems that have been validated as models of human gut microbiota (1,6,15, 35,36). The same starting human fecal inoculum was seeded into replicate ex vivo vessels (n=4) and cultured for 1 month (Fig. 2 and S2). Throughout the experiment pH, temperature, media input rates, and oxygen concentration were all 130 fixed (Methods). To introduce microbial dynamics into our systems, a single bolus of Bacteroides ovatus isolated from the stool donor was administered to the system on Day 23 (Methods). The B. ovatus bolus did not have discernable effects on microbial dynamics, but the media it was suspended in appeared to induce minor shifts in the relative abundances of select bacterial taxa that were visible with hourly sampling (Fig. 135

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Copy number variation plays a clear role in the etiology of many psychiatric disorders, particularly schizophrenia. We performed array-CGH to look for copy number variants between five pairs of monozygotic twins discordant for bipolar disorder or schizophrenia. Our study found no differences in copy number variants between the sets of twins. Although alluring, realistic accounting for heterogeneity and chimerism highlight the technological limitations in studying monozygotic twins discordant for psychiatric disorders.

PCR amplification plays a central role in the measurement of mixed microbial communities via high-throughput sequencing. Yet PCR is also known to be a common source of bias in microbiome data. Here we present a paired modeling and experimental approach to characterize and mitigate PCR bias in microbiome studies. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR bias under real-world conditions. Our results suggest that PCR can bias estimates of microbial relative abundances by a factor of 2-4 but that this bias can be mitigated using simple Bayesian multinomial logistic-normal linear models.