The role of mRNA processing in gene expression variability is poorly characterized. This study investigates the extent of cell-to-cell variability of alternative RNA splicing in mammalian cells using single-molecule imaging of CAPRIN1 and MKNK2 splice isoforms.

Background: Recent studies of various human microbiome habitats have revealed thousands of bacterial species and the existence of large variation in communities of microorganisms in the same habitats across individual human subjects. Previous efforts to summarize this diversity, notably in the human gut and vagina, have categorized microbiome profiles by clustering them into community state types (CSTs). The functional relevance of specific CSTs has not been established. Objective: We investigate whether CSTs can be used to assess dynamics in the microbiome. Design: We conduct a re-analysis of five sequencing-based microbiome surveys derived from vaginal samples with repeated measures. Results: We observe that detection of a CST transition is largely insensitive to choices in methods for normalization or clustering. We find that healthy subjects persist in a CST for two to three weeks or more on average, while those with evidence of dysbiosis tend to change more often. Changes in CST can be gradual or occur over less than one day. Upcoming CST changes and switches to high-risk CSTs can be predicted with high accuracy in certain scenarios. Finally, we observe that presence of Gardnerella vaginalis is a strong predictor of an upcoming CST change. Conclusion: Overall, our results show that the CST concept is useful for studying microbiome dynamics.

Although many proteins are known to function in microRNA (miRNA)-based translational repression, we lack a comprehensive understanding of temporal relationships between the mRNA, miRNA and their constituent proteins. To understand the dynamics of miRNA and protein interactions, we created a synthetic inducible miRNA system in mammalian cells. By visualizing single mRNAs and observing their co-localization with proteins over time, we produced a temporal association map of miRNA-associated factors. Argonaute2, Dcp1a, hedls and Rck co-localize with miRNA-regulated mRNA after 24 h of miRNA induction, and RNAi knockdown of any one of these proteins affected the co-localization of any of the other proteins with miRNA-regulated mRNA, demonstrating that these proteins could interact with each other in a complex. We identified Argonaute2 and hedls as proteins that co-localize and interact with miRNA-regulated mRNA, indicating that processing body components are involved in long-term storage of miRNA-regulated mRNA.

We designed and experimentally validated an in silico gene deletion strategy for engineering endogenous one-carbon (C1) metabolism in yeast. We used constraint-based metabolic modeling and computer-aided gene knockout simulations to identify five genes (ALT2, FDH1, FDH2, FUM1, and ZWF1), which, when deleted in combination, predicted formic acid secretion in Saccharomyces cerevisiae under aerobic growth conditions. Once constructed, the quintuple mutant strain showed the predicted increase in formic acid secretion relative to a formate dehydrogenase mutant ( fdh1 fdh2), while formic acid secretion in wild-type yeast was undetectable. Gene expression and physiological data generated post hoc identified a retrograde response to mitochondrial deficiency, which was confirmed by showing Rtg1-dependent NADH accumulation in the engineered yeast strain. Formal pathway analysis combined with gene expression data suggested specific modes of regulation that govern C1 metabolic flux in yeast. Specifically, we identified coordinated transcriptional regulation of C1 pathway enzymes and a positive flux control coefficient for the branch point enzyme 3-phosphoglycerate dehydrogenase (PGDH).Together, these results demonstrate that constraint-based models can identify seemingly unrelated mutations, which interact at a systems level across subcellular compartments to modulate flux through nonfermentative metabolic pathways.

BackgroundA growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson’s disease (PD) and Huntington disease (HD).MethodsPatient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson’s Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home.ResultsWhile compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (− 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home.ConclusionsOur findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies.Trial registrationTrials described in this study include a sub-study of the Open PRIDE-HD Huntington’s disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015–000904-24.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0714-7) contains supplementary material, which is available to authorized users.

Molecular hydrogen produced biologically from renewable biomass is an attractive replacement for fossil fuels. One potential route for biological hydrogen production is the conversion of biomass into formate, which can subsequently be processed into hydrogen by Escherichia coli. Formate is also a widely used commodity chemical, making its bioproduction even more attractive. Here we demonstrate the implementation of a formate-overproducing pathway in Saccharomyces cerevisiae, a well-established industrial organism. By expressing the anaerobic enzyme pyruvate formate lyase from E. coli, we engineered a strain of yeast that overproduced formate relative to undetectable levels in the wild type. The addition of a downstream enzyme, AdhE of E. coli, resulted in an additional 4.5-fold formate production increase as well as an increase in growth rate and biomass yield. Overall, an 18-fold formate increase was achieved in a strain background whose formate degradation pathway had been deleted. Finally, as a proof of concept, we were able to produce hydrogen from this formate-containing medium by using E. coli as a catalyst in a two-step process. With further optimizations, it may be feasible to use S. cerevisiae on a larger scale as the foundation for yeast-based biohydrogen.

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

During the past decade, it has become increasingly evident that there is variation in the transcriptome of genetically identical cells, even when grown in homogenous environments. This cell-to-cell variability has been shown to have a central role in processes ranging from stem cell differentiation to chemotherapy resistance. Given that many genes display extensive heterogeneity in their messenger RNA (mRNA) abundance on a per cell basis, understanding the nuclear sources of this variability is important for our fundamental grasp of nuclear function and stands to have clinical manifestations. In this chapter, we assess the contribution of different transcription regimes, nuclear architecture dynamics, RNA polymerase elongation, and gene copy number to transcriptome heterogeneity. We also discuss techniques that can be used to quantify single-cell mRNA abundance and conclude by commenting on future research directions.