Early life is a critical period for immune and metabolic development, but these patterns remain underexplored in populations from low- and middle-income countries. Here, we profile the microbiome and metabolome of 55 Bangladeshi mother-infant dyads over the first 6 months of life. Importantly, we observe an increase in microbially derived bile amidates and N-acyl lipids with age in conjunction with reads matching the bile salt hydrolase/transferase (bsh) gene. Although microbial source tracking confirms maternal fecal seeding, a substantial environmental contribution is also highlighted. Differences in infant fecal metabolic profiles are associated with delivery mode, maternal milk composition, household assets, and household-level water treatment. Cesarean section (C-section) delivery and untreated drinking water are linked to transient metabolic differences, including increases in bile amidates, N-acyl lipids, and other host-microbe co-metabolic products, including acylcarnitines. Multi-omics analysis reveals specific microbial-metabolite relationships, highlighting how early environmental and maternal living circumstances influence gut metabolic development through the microbiome.

Motivation Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, the training phase of DEPP does not scale to more than roughly 10 000 backbone species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331 270 species.Results This paper explores divide-and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP. While divide-and-conquer has been extensively used in phylogenetics, applying divide-and-conquer to data-hungry machine-learning methods needs nuance. C-DEPP uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing 20 million 16S fragments on the GG2 reference tree in 41 h of computation.Availability and implementation The dataset and C-DEPP software are freely available at https://github.com/yueyujiang/dataset_cdepp/.

Little is understood about how the two major types of heterochromatin domains (HP1 and Polycomb) are kept separate. In the yeast Cryptococcus neoformans , the Polycomb-like protein Ccc1 prevents deposition of H3K27me3 at HP1 domains. Here we show that phase separation propensity underpins Ccc1 function. Mutations of the two basic clusters in the intrinsically disordered region or deletion of the coiled-coil dimerization domain alter phase separation behavior of Ccc1 in vitro and have commensurate effects on formation of Ccc1 condensates in vivo, which are enriched for PRC2. Notably, mutations that alter phase separation trigger ectopic H3K27me3 at HP1 domains. Supporting a direct condensate-driven mechanism for fidelity, Ccc1 droplets efficiently concentrate recombinant C. neoformans PRC2 in vitro whereas HP1 droplets do so only weakly. These studies establish a biochemical basis for chromatin regulation in which mesoscale biophysical properties play a key functional role.

Fish are the most diverse and widely distributed vertebrates, yet little is known about the microbial ecology of fishes nor the biological and environmental factors that influence fish microbiota. To identify factors that explain microbial diversity patterns in a geographical subset of marine fish, we analyzed the microbiota (gill tissue, skin mucus, midgut digesta and hindgut digesta) from 101 species of Southern California marine fishes, spanning 22 orders, 55 families and 83 genera, representing ~25% of local marine fish diversity. We compare alpha, beta and gamma diversity while establishing a method to estimate microbial biomass associated with these host surfaces. We show that body site is the strongest driver of microbial diversity while microbial biomass and diversity is lowest in the gill of larger, pelagic fishes. Patterns of phylosymbiosis are observed across the gill, skin and hindgut. In a quantitative synthesis of vertebrate hindguts (569 species), we also show that mammals have the highest gamma diversity when controlling for host species number while fishes have the highest percent of unique microbial taxa. The composite dataset will be useful to vertebrate microbiota researchers and fish biologists interested in microbial ecology, with applications in aquaculture and fisheries management.

Fish are the most diverse and widely distributed vertebrates, yet little is known about the microbial ecology of fishes nor the biological and environmental factors that influence the fish microbiome. The microbiota from 101 species of Southern California marine fishes, spanning 22 orders, 55 families, and 83 genera representing ~ 25% of local marine fish diversity, was analyzed to identify patterns that explain microbial diversity patterns in a geographical subset of marine fish biodiversity. We compared fish microbiomes (gill, skin, midgut, and hindgut) using alpha, beta, and gamma diversity along with establishing a novel method to estimate microbial biomass (Qiime2 plugin katharoseq). For oceanic fishes from the neritic zone, host size and distance from shore were negatively associated with microbial biomass densities and diversity in the gills. Body site was the strongest driver for beta diversity with strong evidence of phylosymbiosis observed across the gill, skin, and hindgut, but not midgut. The majority of microbes from all fish body sites were of unknown origin but overall sea water generally contributes more microbes to fish mucus compared to marine sediment. In a meta-analysis of vertebrate hindguts (569 species), mammals had the highest gamma diversity when controlling for host species number while fishes had the highest percent of unique microbial taxa (92%). In fishes, the midgut, gill, and skin contains the majority of microbial diversity which collectively can be 5.5 times higher than the hindgut. The composite dataset will be useful to vertebrate microbiome researchers and fish biologists interested in microbial ecology with applications in aquaculture and fisheries management.

We determined that over 40 spliceosomal proteins are conserved between many fungal species and humans but were lost during the evolution of S. cerevisiae, an intron-poor yeast with unusually rigid splicing signals. We analyzed null mutations in a subset of these factors, most of which had not been investigated previously, in the intron-rich yeast Cryptococcus neoformans. We found they govern splicing efficiency of introns with divergent spacing between intron elements. Importantly, most of these factors also suppress usage of weak nearby cryptic/alternative splice sites. Among these, orthologs of GPATCH1 and the helicase DHX35 display correlated functional signatures and copurify with each other as well as components of catalytically active spliceosomes, identifying a conserved G-patch/helicase pair that promotes splicing fidelity. We propose that a significant fraction of spliceosomal proteins in humans and most eukaryotes are involved in limiting splicing errors, potentially through kinetic proofreading mechanisms, thereby enabling greater intron diversity.

Qsp1 is a secreted quorum sensing peptide required for virulence of the fungal meningitis pathogen Cryptococcus neoformans. Qsp1 functions to control cell wall integrity in vegetatively growing cells and also functions in mating. Rather than acting on a cell surface receptor, Qsp1 is imported to act intracellularly via the predicted oligopeptide transporter Opt1. Here, we identify a transcription factor network as a target of Qsp1. Using whole-genome chromatin immunoprecipitation, we find Qsp1 controls the genomic associations of three transcription factors to genes whose outputs are regulated by Qsp1. One of these transcription factors, Cqs2, is also required for the action of Qsp1 during mating, indicating that it might be a shared proximal target of Qsp1. Consistent with this hypothesis, deletion of CQS2 impacts the binding of other network transcription factors specifically to Qsp1-regulated genes. These genetic and genomic studies illuminate mechanisms by which an imported peptide acts to modulate eukaryotic gene expression.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder that affects up to 20% of women, however diagnosis is commonly unreliable and un-quantitative. Here we use supervised machine learning and measurements of 51 cytokines from a large cohort of patients to identify a low-dimensional set of potential biomarkers for diagnosis of PCOS. Both whole blood and individual follicular fluid (FF) aspirates were collected women during pre- intracytoplasmic sperm injection with in vitro fertilization (ICSI/IVF) oocyte retrieval and linked with patients' PCOS status as diagnosed by the Rotterdam criteria (n = 69 PCOS, n = 222 non-PCOS). We trained a binary support vector machine (SVM) using a random subset of patient data to determine cytokine profile associated with PCOS. Our resultant model includes 3 variables and is 76% accurate. This provides insight into the immunological basis of PCOS and may define a potential non-invasive quantitative strategy for diagnosis.