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The infant gut microbiome is impacted by early-life feeding, as human milk oligosaccharides (HMOs) found in breastmilk, cannot be digested by infants, and serve as nutrients for their gut bacteria. While the vast majority of HMO-utilization research has focused on Bifidobacterium species, recent studies have suggested additional HMO-utilizers, mostly Bacteroides, yet their utilization mechanism is completely unknown. Here, we investigated Bacteroides dorei isolates from breastfed-infants and identified that polysaccharide utilization locus (PUL) 32 enables B. dorei to utilize sialylated HMOs. We performed transcriptional profiling and identified upregulated genes when growing on sialylated HMOs. Using CRISPR-Cas12 to knock-out four PUL32 genes, with combined complementation assays, we identified GH33 as the critical gene in PUL32 for sialylated HMO-utilization. This is the first demonstration of an HMO-utilization system by a Bacteroides species isolated from infants, opening the way to further characterization of additional such systems, to better understand HMO-utilization in the infant gut.

Preterm birth can have long-term health consequences, and the gut microbiome is an important contributor to infant health. In this issue of Cell HostMicrobe, Samara et al. explore the effects of probiotics treatment on the infant gut microbiome of extremely premature infants.

The perinatal period represents a critical window for cognitive and immune system development, promoted by maternal and infant gut microbiomes and their metabolites. Here, we tracked the co-development of microbiomes and metabolomes from late pregnancy to 1 year of age using longitudinal multi-omics data from a cohort of 70 mother-infant dyads. We discovered large-scale mother-to-infant interspecies transfer of mobile genetic elements, frequently involving genes associated with diet-related adaptations. Infant gut metabolomes were less diverse than maternal but featured hundreds of unique metabolites and microbe-metabolite associations not detected in mothers. Metabolomes and serum cytokine signatures of infants who received regular-but not extensively hydrolyzed-formula were distinct from those of exclusively breastfed infants. Taken together, our integrative analysis expands the concept of vertical transmission of the gut microbiome and provides original insights into the development of maternal and infant microbiomes and metabolomes during late pregnancy and early life.

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype, and fitness(1–3). Constructing complete fitness landscapes, mapping DNA sequences to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces(4–6). Here, we construct sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly-sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis, that conflicting expression objectives in different environments constrain expression adaptation, and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using our models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and highlight the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for addressing fundamental questions in regulatory evolution.

BACKGROUNDCytomegalovirus (CMV) is the most common intrauterine infection, leading to infant brain damage. Prognostic assessment of CMV-infected fetuses has remained an ongoing challenge in prenatal care, in the absence of established prenatal biomarkers of congenital CMV (cCMV) infection severity. We aimed to identify prognostic biomarkers of cCMV-related fetal brain injury.METHODSWe performed global proteome analysis of mid-gestation amniotic fluid samples, comparing amniotic fluid of fetuses with severe cCMV with that of asymptomatic CMV-infected fetuses. The levels of selected differentially excreted proteins were further determined by specific immunoassays.RESULTSUsing unbiased proteome analysis in a discovery cohort, we identified amniotic fluid proteins related to inflammation and neurological disease pathways, which demonstrated distinct abundance in fetuses with severe cCMV. Amniotic fluid levels of 2 of these proteins - the immunomodulatory proteins retinoic acid receptor responder 2 (chemerin) and galectin-3-binding protein (Gal-3BP) - were highly predictive of the severity of cCMV in an independent validation cohort, differentiating between fetuses with severe (n = 17) and asymptomatic (n = 26) cCMV, with 100%-93.8% positive predictive value, and 92.9%-92.6% negative predictive value (for chemerin and Gal-3BP, respectively). CONCLUSIONAnalysis of chemerin and Gal-3BP levels in mid-gestation amniotic fluids could be used in the clinical setting to profoundly improve the prognostic assessment of CMV-infected fetuses.FUNDINGIsrael Science Foundation (530/18 and IPMP 3432/19); Research Fund - Hadassah Medical Organization.

Human milk oligosaccharides (HMOs) are a family of glycans found in breastmilk with over 200 identified structures. Despite being the third-largest component in breastmilk, HMOs are indigestible by infants, which raises an intriguing question: we would expect evolutionary dynamics to have shaped breastmilk to efficiently fulfill the baby's nutritional needs; what, then, could be the role of HMOs? Tracking their fate offers an answer: they are metabolized by certain gut bacteria, suggesting that breastmilk has been structured to shape the developing infant microbiome. We suggest that ecological paradigms, in particular, the notion of priority effects, can help contextualize the importance of HMOs as agents shaping the gut microbiome. The fitness consequences of this process provide insight regarding the evolutionary forces that have shaped the composition of breastmilk. In this review, we offer an eco-evolutionary perspective and present empirical data associating the compositions of mothers' milk and their infants' gut microbiomes.

Complex interactions between the gut microbiome and immune cells in infancy are thought to be part of the pathogenesis for the marked rise in pediatric allergic diseases, particularly food allergies. Food protein-induced allergic proctocolitis (FPIAP) is commonly the earliest recognized non-IgE-mediated food allergy in infancy and is associated with atopic dermatitis and subsequent IgE-mediated food allergy later in childhood. Yet, a large prospective longitudinal study of the microbiome of infants with FPIAP (including samples prior to symptom onset) has not been done. Here we analyzed 954 longitudinal samples from 160 infants in a nested case-control study (81 who developed FPIAP, and 79 matched controls) from 1 week to 1 year of age by 16S rRNA ribosomal gene sequencing as part of the Gastrointestinal Microbiome and Allergic Proctocolitis (GMAP) Study. We confirmed that vaginally delivered infants had a greater abundance of Bacteroides, infants who received any breast milk had a greater abundance of Bifidobacterium, and that overall bacterial richness rose over the first year. We found key differences in the microbiome of infants with FPIAP, most strongly a higher abundance of a genus of Enterobacteriaceae and a lower abundance of a family of Clostridiales during the symptomatic period, as well as other key taxonomic differences across symptom states including prior to symptom onset. This study contributes to the larger body of literature examining structural development of the early life gut microbiome and provides a foundation for more mechanistic investigation into the pathogenesis and microbial effects on FPIAP and subsequent food allergic diseases in childhood.

The human gut microbiome matures towards the adult composition during the first years of life and is implicated in early immune development. Here, we investigate the effects of microbial genomic diversity on gut microbiome development using integrated early childhood data sets collected in the DIABIMMUNE study in Finland, Estonia and Russian Karelia. We show that gut microbial diversity is associated with household location and linear growth of children. Single nucleotide polymorphism- and metagenomic assembly-based strain tracking revealed large and highly dynamic microbial pangenomes, especially in the genus Bacteroides, in which we identified evidence of variability deriving from Bacteroides-targeting bacteriophages. Our analyses revealed functional consequences of strain diversity; only 10% of Finnish infants harboured Bifidobacterium longum subsp. infantis, a subspecies specialized in human milk metabolism, whereas Russian infants commonly maintained a probiotic Bifidobacterium bifidum strain in infancy. Groups of bacteria contributing to diverse, characterized metabolic pathways converged to highly subject-specific configurations over the first two years of life. This longitudinal study extends the current view of early gut microbial community assembly based on strain-level genomic variation. Integration of longitudinal gut metagenomic datasets from children in Finland, Estonia and Russian Karelia reveals high strain-level diversity, which consequently impacts the functional capabilities of the early life microbiome.

Adaptive sample pooling for SARS-CoV-2 testing based on positivity rates spares 76% of test reactions without compromising specificity or sensitivity., Batch testing for SARS-CoV-2 Frequent and accurate RT-PCR–based testing is essential for preventing and managing SARS-CoV-2 infection; however, active infection surveillance is still often limited by time or resources. Cleary et al. demonstrate that considering population-level viral prevalence and individual viral loads allows for efficiency gains upon pooled testing with minimal loss of sensitivity, both theoretically and as validated in vitro using human swab and sputum samples. Barak et al. show that pooled testing of 133,816 hospital-collected patient nasopharyngeal samples eliminated three quarters of testing reactions with only a minor reduction in sensitivity, demonstrating the efficacy of the approach in the field. Both studies suggest that considered pooling of individual samples before testing could reliably increase SARS-CoV-2 testing throughput., Pooling multiple swab samples before RNA extraction and real-time reverse transcription polymerase chain reaction (RT-PCR) analysis has been proposed as a strategy to reduce costs and increase throughput of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests. However, reports on practical large-scale group testing for SARS-CoV-2 have been scant. Key open questions concern reduced sensitivity due to sample dilution, the rate of false positives, the actual efficiency (number of tests saved by pooling), and the impact of infection rate in the population on assay performance. Here, we report an analysis of 133,816 samples collected between April and September 2020 and tested by Dorfman pooling for the presence of SARS-CoV-2. We spared 76% of RNA extraction and RT-PCR tests, despite the frequently changing prevalence (0.5 to 6%). We observed pooling efficiency and sensitivity that exceeded theoretical predictions, which resulted from the nonrandom distribution of positive samples in pools. Overall, our findings support the use of pooling for efficient large-scale SARS-CoV-2 testing.

Mutations in non-coding cis-regulatory DNA sequences can alter gene expression, organismal phenotype, and fitness. Fitness landscapes, which map DNA sequence to organismal fitness, are a long-standing goal in biology, but have remained elusive because it is challenging to generalize accurately to the vast space of possible sequences using models built on measurements from a limited number of endogenous regulatory sequences. Here, we construct a sequence-to-expression model for such a landscape and use it to decipher principles of cis-regulatory evolution. Using tens of millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Sacccharomyces cerevisiae, we construct a deep transformer neural network model that generalizes with exceptional accuracy, and enables sequence design for gene expression engineering. Using our model, we predict and experimentally validate expression divergence under random genetic drift and strong selection weak mutation regimes, show that conflicting expression objectives in different environments constrain expression adaptation, and find that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for detecting selective constraint on gene expression using our model and natural sequence variation, and validate it using observed cis-regulatory diversity across 1,011 yeast strains, cross-species RNA-seq from three different clades, and measured expression-to-fitness curves. Finally, we develop a characterization of regulatory evolvability, use it to visualize fitness landscapes in two dimensions, discover evolvability archetypes, quantify the mutational robustness of individual sequences and highlight the mutational robustness of extant natural regulatory sequence populations. Our work provides a general framework that addresses key questions in the evolution of cis-regulatory sequences.

For more than a century, the prenatal environment was considered sterile. Over the last few years, findings obtained with next-generation sequencing approaches from samples of the placenta, the amniotic fluid, meconium, and even fetal tissues have challenged the dogma of a sterile womb, and additional reports have emerged that used culture, microscopy, and quantitative PCR to support the presence of a low-biomass microbial community at prenatal sites. Given the substantial implications of prenatal exposure to microbes for the development and health of the host, the findings have gathered substantial interest from academics, high impact journals, the public press, and funding agencies. However, an increasing number of studies have challenged the prenatal microbiome identifying contamination as a major issue, and scientists that remained skeptical have pointed to inconsistencies with in utero colonization, the impact of c-sections on early microbiome assembly, and the ability to generate germ-free mammals. A lively academic controversy has emerged on the existence of the wider importance of prenatal microbial communities. Microbiome has asked experts to discuss these issues and provide their thoughts on the implications. To allow for a broader perspective of this discussion, we have specifically selected scientists, who have a long-standing expertise in microbiome sciences but who have not directly been involved in the debate so far.

As the infant transitions from the isolated womb of its mother to the outside word, there are suddenly countless new sources for the acquisition of microbes. By studying which microbes succeed in making their home in the infant gut and which ones are lost, we can improve our understanding of why and how this selection process takes place. Research that includes longitudinal and dense sampling has allowed for the observation of the succession of bacterial populations throughout early development, and by utilizing sequencing tools that enable analysis at the strain level, we have found distinct patterns of transmission from the mother to the infant. Although many questions remain to be answered, current findings suggest that a balance of positive and negative selection drives the formation of ecological niches, where groups of bacteria are selected to perform tasks, creating a cost-effective community in the infant gut that benefits both the microbiota and their host.

Delivery mode is the variable with the greatest influence on the infant gut microbiome composition in the first few months of life. Children born by Cesarean section (C-section) lack species from the Bacteroides genus in their gut microbial community, and this difference can be detectable until 6-18 months of age. One hypothesis is that these differences stem from lack of exposure to the maternal vaginal microbiome, as children born by C-section do not pass through the birth canal; however, Bacteroides species are not common members of the vaginal microbiome, thus this explanation seems inadequate. Here, we set out to re-evaluate this hypothesis by collecting rectal and vaginal samples before delivery from 73 mothers with paired stool from their infants in the first two weeks of life. We compared microbial profiles of infants born by planned, pre-labor C-section to those born by emergent, post-labor surgery (where the child was in the birth canal, but eventually delivered through an abdominal incision), and found no significant differences in the microbiome between these two groups. Both groups showed the characteristic signature lack of Bacteroides species, despite their difference in exposure to the birth canal. Surprisingly, this signature was only evident in samples from week two of life, but not in the first week. Children born by C-section often had high abundance of Bacteroides in their first few days of life, but these were not stable colonizers of the infant gut, as they were not detectable by week two. Finally, we used metagenomic sequencing to compare microbial strains in maternal vaginal and rectal samples and samples from their infants; we found evidence for mother-to-child transmission of rectal rather than vaginal strains. These results challenge birth canal exposure as the dominant factor in infant gut microbiome establishment and implicate colonization efficiency rather than exposure as a dictating factor of the newborn gut microbiome composition.

Summary Mode of delivery strongly influences the early infant gut microbiome. Children born by cesarean section (C-section) lack Bacteroides species until 6–18 months of age. One hypothesis is that these differences stem from lack of exposure to the maternal vaginal microbiome. Here, we re-evaluate this hypothesis by comparing the microbial profiles of 75 infants born vaginally or by planned versus emergent C-section. Multiple children born by C-section have a high abundance of Bacteroides in their first few days of life, but at 2 weeks, both C-section groups lack Bacteroides (primarily according to 16S sequencing), despite their difference in exposure to the birth canal. Finally, a comparison of microbial strain profiles between infants and maternal vaginal or rectal samples finds evidence for mother-to-child transmission of rectal rather than vaginal strains. These results suggest differences in colonization stability as an important factor in infant gut microbiome composition rather than birth canal exposure., Graphical Abstract, Highlights Week 1 gut microbiota does not differ between infants born vaginally versus C-section Week 2 gut microbiota of C-section infants lacks Bacteroides Microbiota of infants born by C-section after labor resembles scheduled C-section Bacterial strains in infants match maternal rectal rather than vaginal strains, Mitchell et al. compare early-life infant gut microbiota by delivery mode, suggesting early colonization by Bacteroides regardless of delivery mode, but loss of Bacteroides by 2 weeks in C-section-delivered infants, whether or not exposed to the vagina in labor. Infant strains matched maternal rectal rather vaginal strains.

According to the hygiene hypothesis, the increasing incidence of autoimmune diseases in western countries may be explained by changes in early microbial exposure, leading to altered immune maturation. We followed gut microbiome development from birth until age three in 222 infants in Northern Europe, where early-onset autoimmune diseases are common in Finland and Estonia but are less prevalent in Russia. We found that Bacteroides species are lowly abundant in Russians but dominate in Finnish and Estonian infants. Therefore, their lipopolysaccharide (LPS) exposures arose primarily from Bacteroides rather than from Escherichia coli, which is a potent innate immune activator. We show that Bacteroides LPS is structurally distinct from E. coli LPS and inhibits innate immune signaling and endotoxin tolerance; furthermore, unlike LPS from E. coli, B. dorei LPS does not decrease incidence of autoimmune diabetes in non-obese diabetic mice. Early colonization by immunologically silencing microbiota may thus preclude aspects of immune education.

Identifying immunodominant T cell epitopes remains a significant challenge in the context of infectious disease, autoimmunity, and immuno-oncology. To address the challenge of antigen discovery, we developed a quantitative proteomic approach that enabled unbiased identification of major histocompatibility complex class II (MHCII)-associated peptide epitopes and biochemical features of antigenicity. On the basis of these data, we trained a deep neural network model for genome-scale predictions of immunodominant MHCII-restricted epitopes. We named this model bacteria originated T cell antigen (BOTA) predictor. In validation studies, BOTA accurately predicted novel CD4 T cell epitopes derived from the model pathogen Listeria monocytogenes and the commensal microorganism Muribaculum intestinale. To conclusively define immunodominant T cell epitopes predicted by BOTA, we developed a high-throughput approach to screen DNA-encoded peptide-MHCII libraries for functional recognition by T cell receptors identified from single-cell RNA sequencing. Collectively, these studies provide a framework for defining the immunodominance landscape across a broad range of immune pathologies.

Bacterial community acquisition in the infant gut impacts immune education and disease susceptibility. We compared bacterial strains across and within families in a prospective birth cohort of 44 infants and their mothers, sampled longitudinally in the first months of each child's life. We identified mother-to-child bacterial transmission events and describe the incidence of family-specific antibiotic resistance genes. We observed two inheritance patterns across multiple species, where often the mother's dominant strain is transmitted to the child, but occasionally her secondary strains colonize the infant gut. In families where the secondary strain of B. uniformis was inherited, a starch utilization gene cluster that was absent in the mother's dominant strain was identified in the child, suggesting the selective advantage of a mother's secondary strain in the infant gut. Our findings reveal mother-to-child bacterial transmission events at high resolution and give insights into early colonization of the infant gut. Using longitudinal metagenomic sequencing from 44 mother/child pairs, Yassour et al. characterized mother-to-child strain transmission patterns. While mothers' dominant strains were often inherited, nondominant secondary strain transmissions were also observed. Microbial functional analysis reveals that inherited maternal secondary strains may have a selective advantage to colonize infant guts., National Institutes of Health (Grant 1DP3DK094338–01)

Summary The acquisition and development of the infant microbiome are key to establishing a healthy host-microbiome symbiosis. The maternal microbial reservoir is thought to play a crucial role in this process. However, the source and transmission routes of the infant pioneering microbes are poorly understood. To address this, we longitudinally sampled the microbiome of 25 mother-infant pairs across multiple body sites from birth up to 4 months postpartum. Strain-level metagenomic profiling showed a rapid influx of microbes at birth followed by strong selection during the first few days of life. Maternal skin and vaginal strains colonize only transiently, and the infant continues to acquire microbes from distinct maternal sources after birth. Maternal gut strains proved more persistent in the infant gut and ecologically better adapted than those acquired from other sources. Together, these data describe the mother-to-infant microbiome transmission routes that are integral in the development of the infant microbiome., Graphical Abstract, Highlights • Strain-resolved metagenomics was used to track mother-to-infant microbiome transfer • Microbial strains from multiple maternal body sites transfer to the infant microbiome • The early microbial diversity in the infant gut is rapidly shaped by niche selection • The maternal gut microbiome is the source of the majority of transmitted strains, Ferretti et al. use metagenomics with strain-resolved computational profiling to characterize the transfer of microbes from mothers to their infants during their first 4 months of life. Multiple maternal body sites contribute to the developing infant microbiome, with maternal gut strains providing the largest contribution of colonizing microorganisms.

Background Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract that is associated with changes in the gut microbiome. Here, we sought to identify strain-specific functional correlates with IBD outcomes. Methods We performed metagenomic sequencing of monthly stool samples from 20 IBD patients and 12 controls (266 total samples). These were taxonomically profiled with MetaPhlAn2 and functionally profiled using HUMAnN2. Differentially abundant species were identified using MaAsLin and strain-specific pangenome haplotypes were analyzed using PanPhlAn. Results We found a significantly higher abundance in patients of facultative anaerobes that can tolerate the increased oxidative stress of the IBD gut. We also detected dramatic, yet transient, blooms of Ruminococcus gnavus in IBD patients, often co-occurring with increased disease activity. We identified two distinct clades of R. gnavus strains, one of which is enriched in IBD patients. To study functional differences between these two clades, we augmented the R. gnavus pangenome by sequencing nine isolates from IBD patients. We identified 199 IBD-specific, strain-specific genes involved in oxidative stress responses, adhesion, iron-acquisition, and mucus utilization, potentially conferring an adaptive advantage for this R. gnavus clade in the IBD gut. Conclusions This study adds further evidence to the hypothesis that increased oxidative stress may be a major factor shaping the dysbiosis of the microbiome observed in IBD and suggests that R. gnavus may be an important member of the altered gut community in IBD. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0490-5) contains supplementary material, which is available to authorized users.

In the version of this Article originally published, in the first sentence of the second paragraph of the Discussion section, the word "operingrationally" should have read "operationally". This has now been amended in all versions of the Article.

The gut microbial community is dynamic during the first 3 years of life, before stabilizing to an adult-like state. However, little is known about the impact of environmental factors on the developing human gut microbiome. We report a longitudinal study of the gut microbiome based on DNA sequence analysis of monthly stool samples and clinical information from 39 children, about half of whom received multiple courses of antibiotics during the first 3 years of life. Whereas the gut microbiome of most children born by vaginal delivery was dominated by Bacteroides species, the four children born by cesarean section and about 20% of vaginally born children lacked Bacteroides in the first 6 to 18 months of life. Longitudinal sampling, coupled with whole-genome shotgun sequencing, allowed detection of strain-level variation as well as the abundance of antibiotic resistance genes. The microbiota of antibiotic-treated children was less diverse in terms of both bacterial species and strains, with some species often dominated by single strains. In addition, we observed short-term composition changes between consecutive samples from children treated with antibiotics. Antibiotic resistance genes carried on microbial chromosomes showed a peak in abundance after antibiotic treatment followed by a sharp decline, whereas some genes carried on mobile elements persisted longer after antibiotic therapy ended. Our results highlight the value of high-density longitudinal sampling studies with high-resolution strain profiling for studying the establishment and response to perturbation of the infant gut microbiome., National Human Genome Research Institute (U.S.) (grant 2U54HG003067-10), Juvenile Diabetes Research Foundation International, National Institutes of Health (U.S.) (grant U54 DK102557), National Institutes of Health (U.S.) (grant R01 DK092405), National Institutes of Health (U.S.) (grant P30 DK043351), Leona M. and Harry B. Helmsley Charitable Trust, Crohn's and Colitis Foundation of America

Monitoring Meiosis During meiosis, or in yeast sporulation, haploid cells are generated from diploid cells. Brar et al. (p. 552 , published online 22 December) performed a detailed analysis of messenger RNA (mRNA) abundance and protein synthesis over the course of sporulation. The production of most proteins was tightly regulated both at the mRNA level and by translational control. An unexpected complexity was observed as the cell passed through this key developmental transition, including increases in noncanonical translation from upstream regions of known RNA transcripts, which appear to be important in translational control.

Background Obesity and type 2 diabetes (T2D) are linked both with host genetics and with environmental factors, including dysbioses of the gut microbiota. However, it is unclear whether these microbial changes precede disease onset. Twin cohorts present a unique genetically-controlled opportunity to study the relationships between lifestyle factors and the microbiome. In particular, we hypothesized that family-independent changes in microbial composition and metabolic function during the sub-clinical state of T2D could be either causal or early biomarkers of progression. Methods We collected fecal samples and clinical metadata from 20 monozygotic Korean twins at up to two time points, resulting in 36 stool shotgun metagenomes. While the participants were neither obese nor diabetic, they spanned the entire range of healthy to near-clinical values and thus enabled the study of microbial associations during sub-clinical disease while accounting for genetic background. Results We found changes both in composition and in function of the sub-clinical gut microbiome, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases. Finally, our unique study design allowed us to examine the strain similarity between twins, and we found that twins demonstrate strain-level differences in composition despite species-level similarities. Conclusions These changes in the microbiome might be used for the early diagnosis of an inflamed gut and T2D prior to clinical onset of the disease and will help to advance toward microbial interventions. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0271-6) contains supplementary material, which is available to authorized users.

Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.

Genome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression, and has led to mechanistic hypotheses regarding the rules by which chromatin structure is established. High-throughput sequencing has recently become the technology of choice for chromatin mapping studies, yet analysis of these experiments is still in its infancy. Here, we introduce a pipeline for analyzing deep sequencing maps of chromatin structure and apply it to data from S. cerevisiae. We analyze a digestion series where nucleosomes are isolated from under- and overdigested chromatin. We find that certain classes of nucleosomes are unusually susceptible or resistant to overdigestion, with promoter nucleosomes easily digested and mid-coding region nucleosomes being quite stable. We find evidence for highly sensitive nucleosomes located within “nucleosome-free regions,” suggesting that these regions are not always completely naked but instead are likely associated with easily digested nucleosomes. Finally, since RNA polymerase is the dominant energy-consuming machine that operates on the chromatin template, we analyze changes in chromatin structure when RNA polymerase is inactivated via a temperature-sensitive mutation. We find evidence that RNA polymerase plays a role in nucleosome eviction at promoters and is also responsible for retrograde shifts in nucleosomes during transcription. Loss of RNA polymerase results in a relaxation of chromatin structure to more closely match in vitro nucleosome positioning preferences. Together, these results provide analytical tools and experimental guidance for nucleosome mapping experiments, and help disentangle the interlinked processes of transcription and chromatin packaging.

Motivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5–10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed. Results: In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions. We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA. Contact: nir@cs.huji.ac.il

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

This article is part of the supplement: Beyond the Genome: The true gene count, human evolution and disease genomics, RNA-seq provides insights at multiple levels into the transcription of the genome as it yields sequence, splicing and expression-level information. We have been developing and comparing a wide range of RNA-seq methods for their ability to annotate transcribed genomic regions, identify differences between normal and cancer states, and quantify mRNA expression levels. We will present results in two areas: (i) strand-specific RNA-seq; and (ii) RNA-seq starting from total RNA. Strand-specific RNA-seq is a powerful tool for novel transcript discovery and genome annotation because it enables the identification of the strand of origin for non-coding RNA and anti-sense RNA, as well as defining the ends of adjacent or overlapping transcripts transcribed in different directions. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we directly compared seven library construction protocols, including both published and our own novel methods. We found marked differences in strand-specificity, library complexity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing the performance and ease of conducting each method, we identified the dUTP second strand marking [1] and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms. RNA-seq methods that do not require the purification of mRNA will be valuable for several applications, including samples with low input amounts and/or partial degradation. In these experiments, it is necessary to reduce the fraction of sequencing reads derived from ribosomal RNA. We will present results from multiple approaches, including the use of Not-So-Random (NSR) primers for reverse transcription [2] and the NuGEN Ovation RNA-Seq kit.

The fission yeast clade-comprising Schizosaccharomyces pombe, S. octosporus, S. cryophilus, and S. japonicus-occupies the basal branch of Ascomycete fungi and is an important model of eukaryote biology. A comparative annotation of these genomes identified a near extinction of transposons and the associated innovation of transposon-free centromeres. Expression analysis established that meiotic genes are subject to antisense transcription during vegetative growth, which suggests a mechanism for their tight regulation. In addition, trans-acting regulators control new genes within the context of expanded functional modules for meiosis and stress response. Differences in gene content and regulation also explain why, unlike the budding yeast of Saccharomycotina, fission yeasts cannot use ethanol as a primary carbon source. These analyses elucidate the genome structure and gene regulation of fission yeast and provide tools for investigation across the Schizosaccharomyces clade.

Background The use of genome-wide RNA abundance profiling by microarrays and deep sequencing has spurred a revolution in our understanding of transcriptional control. However, changes in mRNA abundance reflect the combined effect of changes in RNA production, processing, and degradation, and thus, mRNA levels provide an occluded view of transcriptional regulation. Results To partially disentangle these issues, we carry out genome-wide RNA polymerase II (PolII) localization profiling in budding yeast in two different stress response time courses. While mRNA changes largely reflect changes in transcription, there remains a great deal of variation in mRNA levels that is not accounted for by changes in PolII abundance. We find that genes exhibiting 'excess' mRNA produced per PolII are enriched for those with overlapping cryptic transcripts, indicating a pervasive role for nonproductive or regulatory transcription in control of gene expression. Finally, we characterize changes in PolII localization when PolII is genetically inactivated using the rpb1-1 temperature-sensitive mutation. We find that PolII is lost from chromatin after roughly an hour at the restrictive temperature, and that there is a great deal of variability in the rate of PolII loss at different loci. Conclusions Together, these results provide a global perspective on the relationship between PolII and mRNA production in budding yeast.

Strand-specific, massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in strand-specificity, library complexity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.

Background Recent studies in budding yeast have shown that antisense transcription occurs at many loci. However, the functional role of antisense transcripts has been demonstrated only in a few cases and it has been suggested that most antisense transcripts may result from promiscuous bi-directional transcription in a dense genome. Results Here, we use strand-specific RNA sequencing to study anti-sense transcription in Saccharomyces cerevisiae. We detect 1,103 putative antisense transcripts expressed in mid-log phase growth, ranging from 39 short transcripts covering only the 3' UTR of sense genes to 145 long transcripts covering the entire sense open reading frame. Many of these antisense transcripts overlap sense genes that are repressed in mid-log phase and are important in stationary phase, stress response, or meiosis. We validate the differential regulation of 67 antisense transcripts and their sense targets in relevant conditions, including nutrient limitation and environmental stresses. Moreover, we show that several antisense transcripts and, in some cases, their differential expression have been conserved across five species of yeast spanning 150 million years of evolution. Divergence in the regulation of antisense transcripts to two respiratory genes coincides with the evolution of respiro-fermentation. Conclusions Our work provides support for a global and conserved role for antisense transcription in yeast gene regulation., Canadian Friends of the Hebrew University, Howard Hughes Medical Institute, Human Frontier Science Program (Strasbourg, France), Burroughs Wellcome Fund (Career Award at the Scientific Interface), National Institutes of Health (U.S.). Pioneer Award, Broad Institute of MIT and Harvard, U.S.-Israel Binational Science Foundation (BSF), National Human Genome Research Institute (U.S.), Alfred P. Sloan Foundation

Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a challenging experimental task. Current approaches, relying on sequencing of ESTs or cDNA libraries, are expensive and labor-intensive. Here, we present a general approach for ab initio discovery of the complete transcriptome of the budding yeast, based only on the unannotated genome sequence and millions of short reads from a single massively parallel sequencing run. Using novel algorithms, we automatically construct a highly accurate transcript catalog. Our approach automatically and fully defines 86% of the genes expressed under the given conditions, and discovers 160 previously undescribed transcription units of 250 bp or longer. It correctly demarcates the 5′ and 3′ UTR boundaries of 86 and 77% of expressed genes, respectively. The method further identifies 83% of known splice junctions in expressed genes, and discovers 25 previously uncharacterized introns, including 2 cases of condition-dependent intron retention. Our framework is applicable to poorly understood organisms, and can lead to greater understanding of the transcribed elements in an explored genome.