Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism.

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.

Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans.

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.

Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies.

Whether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts.

The evolution, evolvability and engineering of gene regulatory DNA.

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces4-6. Here we build 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 such 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 illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution.

Longitudinal quantification of Bifidobacterium longum subsp. infantis reveals late colonization in the infant gut independent of maternal milk HMO composition.

Breast milk contains human milk oligosaccharides (HMOs) that cannot be digested by infants, yet nourish their developing gut microbiome. While Bifidobacterium are the best-known utilizers of individual HMOs, a longitudinal study examining the evolving microbial community at high-resolution coupled with mothers' milk HMO composition is lacking. Here, we developed a high-throughput method to quantify Bifidobacterium longum subsp. infantis (BL. infantis), a proficient HMO-utilizer, and applied it to a longitudinal cohort consisting of 21 mother-infant dyads. We observed substantial changes in the infant gut microbiome over the course of several months, while the HMO composition in mothers' milk remained relatively stable. Although Bifidobacterium species significantly influenced sample variation, no specific HMOs correlated with Bifidobacterium species abundance. Surprisingly, we found that BL. infantis colonization began late in the breastfeeding period both in our cohort and in other geographic locations, highlighting the importance of focusing on BL. infantis dynamics in the infant gut.

CRISPR-Cas-based identification of a sialylated human milk oligosaccharides utilization cluster in the infant gut commensal Bacteroides dorei.

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 poorly characterized. Here, we investigate Bacteroides dorei isolates from breastfed-infants and identify that polysaccharide utilization locus (PUL) 33 enables B. dorei to utilize sialylated HMOs. We perform transcriptional profiling and identity upregulated genes when growing on sialylated HMOs. Using CRISPR-Cas12 to knock-out four PUL33 genes, combined with complementation assays, we identify GH33 as the critical gene in PUL33 for sialylated HMO-utilization. This demonstration of an HMO-utilization system by Bacteroides species isolated from infants opens the way to further characterization of additional such systems, to better understand HMO-utilization in the infant gut.

Implementation of pooled saliva tests for universal screening of cCMV infection.

Congenital cytomegalovirus (cCMV) is the most common intrauterine infection, leading to neurodevelopmental disabilities. Universal newborn infant screening of cCMV has been increasingly advocated. In the absence of a high-throughput screening test, which can identify all infected newborn infants, the development of an accurate and efficient testing strategy has remained an ongoing challenge. Here we assessed the implementation of pooled saliva polymerase chain reaction (PCR) tests for universal screening of cCMV, in two hospitals of Jerusalem from April 2022 through April 2023. During the 13-month study period, 15,805 infants (93.6% of all live newborn infants) were screened for cCMV using the pooled approach that has since become our routine screening method. The empirical efficiency of the pooling was six (number of tested newborn infants per test), thereby sparing 83% of the saliva tests. Only a minor 3.05 PCR cycle loss of sensitivity was observed for the pooled testing, in accordance with the theoretical prediction for an eight-sample pool. cCMV was identified in 54 newborn infants, with a birth prevalence of 3.4 per 1,000; 55.6% of infants identified with cCMV were asymptomatic at birth and would not have been otherwise targeted for screening. The study demonstrates the wide feasibility and benefits of pooled saliva testing as an efficient, cost-sparing and sensitive approach for universal screening of cCMV.

High-resolution nucleosome mapping reveals transcription-dependent promoter packaging.

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.

Comprehensive comparative analysis of strand-specific RNA sequencing methods.

Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but 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 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 identified 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.

GENI: A web server to identify gene set enrichments in tumor samples.

The Cancer Genome Atlas (TCGA) and analogous projects have yielded invaluable tumor-associated genomic data. Despite several web-based platforms designed to enhance accessibility, certain analyses require prior bioinformatic expertise. To address this need, we developed Gene ENrichment Identifier (GENI, https://www.shaullab.com/geni), which is designed to promptly compute correlations for genes of interest against the entire transcriptome and rank them against well-established biological gene sets. Additionally, it generates comprehensive tables containing genes of interest and their corresponding correlation coefficients, presented in publication-quality graphs. Furthermore, GENI has the capability to analyze multiple genes simultaneously within a given gene set, elucidating their significance within a specific biological context. Overall, GENI's user-friendly interface simplifies the biological interpretation and analysis of cancer patient-associated data, advancing the understanding of cancer biology and accelerating scientific discoveries.

Comparison of vaginal microbiota between women with inflammatory bowel disease and healthy controls.

BACKGROUND: The gut microbiota in patients with inflammatory bowel disease are perturbed in both composition and function. The vaginal microbiome and its role in the reproductive health of women with inflammatory bowel disease is less well described. OBJECTIVE: We aim to compare the vaginal microbiota of women with inflammatory bowel disease to healthy controls. METHODS: Women with inflammatory bowel disease enrolled in a longitudinal cohort study provided self-collected vaginal swabs. Healthy controls underwent provider-collected vaginal swabs at routine gynecologic exams. All participants completed surveys on health history, vulvovaginal symptoms and gastrointestinal symptoms, if applicable. Microbiota were characterized by sequencing the V4 region of the 16S rRNA gene. Associations between patient characteristics and microbial community composition were evaluated by PERMANOVA and Principal Components Analysis. Lactobacillus dominance of the microbial community was compared between groups using chi-square and Poisson regression. RESULTS: The cohort included 54 women with inflammatory bowel disease (25 Ulcerative colitis, 25 Crohn's Disease) and 26 controls. A majority, 72 (90%) were White; 17 (31%) with inflammatory bowel disease and 7 (27%) controls were postmenopausal. The composition of the vaginal microbiota did not vary significantly by diagnosis or severity of inflammatory bowel disease but did vary by menopausal status (p = 0.042). There were no significant differences in Shannon Diversity Index between healthy controls and women with IBD in premenopausal participants. There was no difference in proportion of Lactobacillus dominance according to diagnosis in premenopausal participants. A subgroup of postmenopausal women with Ulcerative colitis showed a significant higher alpha diversity and a lack of Lactobacillus dominance in the vaginal microbiome. CONCLUSIONS: Menopausal status had a larger impact on vaginal microbial communities than inflammatory bowel disease diagnosis or severity.

Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing.

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.

EasyMap - An Interactive Web Tool for Evaluating and Comparing Associations of Clinical Variables and Microbiome Composition.

One of the most common tasks in microbiome studies is comparing microbial profiles across various groups of people (e.g., sick vs. healthy). Routinely, researchers use multivariate linear regression models to address these challenges, such as linear regression packages, MaAsLin2, LEfSe, etc. In many cases, it is unclear which metadata variables should be included in the linear model, as many human-associated variables are correlated with one another. Thus, multiple models are often tested, each including a different set of variables, however the challenge of selecting the metadata variables in the final model remains. Here, we present EasyMap, an interactive online tool allowing for (1) running multiple multivariate linear regression models, on the same features and metadata; (2) visualizing the associations between microbial features and clinical metadata found in each model; and (3) comparing across the various models to identify the critical metadata variables and select the optimal model. EasyMap provides a side-by-side visualization of association results across the various models, each with additional metadata variables, enabling us to evaluate the impact of each metadata variable on the associated feature. EasyMap's interface enables filtering associations by significance, focusing on specific microbes and finding the robust associations that are found across multiple models. While EasyMap was designed to analyze microbiome data, it can handle any other tabular data with numeric features and metadata variables. EasyMap takes the common task of multivariate linear regression to the next level, with an intuitive and simple user interface, allowing for wide comparisons of multiple models to identify the robust microbial feature associations. EasyMap is available at http://yassour.rcs.huji.ac.il/easymap.

Preemies going pro: How probiotic treatment matures the microbiome of extreme premature infants.

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

Human milk oligosaccharides and the infant gut microbiome from an eco-evolutionary perspective.

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.

The Infant Gut Commensal Bacteroides dorei Presents a Generalized Transcriptional Response to Various Human Milk Oligosaccharides.

Human milk oligosaccharides (HMOs) are a family of glycans found in breastmilk with over 200 identified structures. Despite being t​​he third-largest solid component in breastmilk, HMOs are indigestible by infants, and they serve as food for the infant gut bacteria. Most research thus far has focused on Bifidobacterium species that harbor many glycoside hydrolases (GHs) tailored to break the carbon bonds in HMO molecules. However, there are additional microbes in the infant gut, such as Bacteroides species, with increasing evidence that they, too, are able to break-down HMOs. To study the unbiased impact of breastfeeding on the infant gut microbiome, we need to investigate the underlying mechanisms of HMO utilization by all members of the infant gut. Here, we developed an optimized system for isolating Bacteroides strains from infant stool samples. We then examined the HMO utilization capacity of multiple Bacteroides isolates by performing growth curves on six common HMOs (2'-FL, DFL, 3'-SL, 6'-SL, LNT, LNnT). Isolates often displayed similar growth characteristics on similarly-structured HMOs, like sialylated or fucosylated sugars. We identified variation in HMO utilization across multiple strains of the same species, and chose to focus here on a Bacteroides dorei isolate that was able to utilize the test HMOs. We performed RNA sequencing on B. dorei cultures, comparing the transcriptional profile in minimal media supplemented with glucose or HMOs. We showed that B. dorei employs an extensive metabolic response to HMOs. Surprisingly, there was no clear up-regulation for most GH families previously known to break-down HMOs, possibly because they were almost exclusively described in Bifidobacterium species. Instead, B. dorei exhibits a generalized response to HMOs, markedly up-regulating several shared GH families across all conditions. Within each GH family, B. dorei displays a consistent pattern of up-regulation of some genes with down-regulation of the others. This response pattern to HMOs has yet to be described in other commensals of the infant gut. Our work highlights the importance of expanding the HMO-microbiome studies beyond Bifidobacterium species, sheds light on the differences across Bacteroides strains in terms of HMO utilization, and paves the way to understanding the mechanisms enabling Bacteroides HMO utilization.

Longitudinal disease-associated gut microbiome differences in infants with food protein-induced allergic proctocolitis.

BACKGROUND: 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-immunoglobulin E (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. RESULTS: 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 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. We saw some of these significant taxonomic differences even prior to symptom onset. There were no consistent longitudinal differences in richness or stability diversity metrics between infants with FPIAP and healthy controls. CONCLUSIONS: This study is the first to identify differences in the infant gut microbiome in children who develop FPIAP, some even before they develop symptoms, and provides a foundation for more mechanistic investigation into the pathogenesis of FPIAP and subsequent food allergic diseases in childhood. Video abstract.

Amniotic fluid biomarkers predict the severity of congenital cytomegalovirus infection.

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.

Lessons learned from the prenatal microbiome controversy.

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.