Background-The composition of the gut microbiome is affected by host phenotype, genotype, immune function, and diet. Here we used the phenotype of RELMβ Knockout (KO) mice to assess the influence of these factors.

U1 snRNP (U1), in addition to its splicing role, protects pre-mRNAs from drastic premature termination by cleavage and polyadenylation (PCPA) at cryptic polyadenylation signals (PASs) in introns. Here, a high-throughput sequencing strategy of differentially expressed transcripts (HIDE-seq) mapped PCPA sites genome wide in divergent organisms. Surprisingly, whereas U1 depletion terminated most nascent gene transcripts within ~1 kb, moderate functional U1 level decreases, insufficient to inhibit splicing, dose-dependently shifted PCPA downstream and elicited mRNA 3' UTR shortening and proximal 3' exon switching characteristic of activated immune and neuronal cells, stem cells, and cancer. Activated neurons' signature mRNA shortening could be recapitulated by U1 decrease and antagonized by U1 overexpression. Importantly, we show that rapid and transient transcriptional upregulation inherent to neuronal activation physiology creates U1 shortage relative to pre-mRNAs. Additional experiments suggest cotranscriptional PCPA counteracted by U1 association with nascent transcripts, a process we term telescripting, ensuring transcriptome integrity and regulating mRNA length.

BackgroundRecent studies have suggested that bacteria associated with the placenta—a “placental microbiome”—may be important in reproductive health and disease. However, a challenge in working with specimens with low bacterial biomass, such as placental samples, is that some or all of the bacterial DNA may derive from contamination in dust or commercial reagents. To investigate this, we compared placental samples from healthy deliveries to a matched set of contamination controls, as well as to oral and vaginal samples from the same women.ResultsWe quantified total 16S rRNA gene copies using quantitative PCR and found that placental samples and negative controls contained low and indistinguishable copy numbers. Oral and vaginal swab samples, in contrast, showed higher copy numbers. We carried out 16S rRNA gene sequencing and community analysis and found no separation between communities from placental samples and contamination controls, though oral and vaginal samples showed characteristic, distinctive composition. Two different DNA purification methods were compared with similar conclusions, though the composition of the contamination background differed. Authentically present microbiota should yield mostly similar results regardless of the purification method used—this was seen for oral samples, but no placental bacterial lineages were (1) shared between extraction methods, (2) present at >1 % of the total, and (3) present at greater abundance in placental samples than contamination controls.ConclusionsWe conclude that for this sample set, using the methods described, we could not distinguish between placental samples and contamination introduced during DNA purification.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0172-3) contains supplementary material, which is available to authorized users.

Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should always be run with experimental samples. We introduce a convenient set of non-biological DNA sequences that can be useful as positive controls for high-volume analysis. Careful analysis of negative and positive controls is particularly important in studies of samples with low microbial biomass, where contamination can comprise most or all of a sample. Lastly, we summarize approaches to enhancing experimental robustness by careful control of multiple comparisons and to comparing discovery and validation cohorts. We hope the experimental tactics summarized here will help researchers in this exciting field advance their studies efficiently while avoiding errors.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0267-5) contains supplementary material, which is available to authorized users.