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While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance.

While immunologic correlates of COVID-19 have been widely reported, their associations with post-acute sequelae of COVID-19 (PASC) remain less clear. Due to the wide array of PASC presentations, understanding if specific disease features associate with discrete immune processes and therapeutic opportunities is important. Here we profile patients in the recovery phase of COVID-19 via proteomics screening and machine learning to find signatures of ongoing antiviral B cell development, immune-mediated fibrosis, and markers of cell death in PASC patients but not in controls with uncomplicated recovery. Plasma and immune cell profiling further allow the stratification of PASC into inflammatory and non-inflammatory types. Inflammatory PASC, identifiable through a refined set of 12 blood markers, displays evidence of ongoing neutrophil activity, B cell memory alterations, and building autoreactivity more than a year post COVID-19. Our work thus helps refine PASC categorization to aid in both therapeutic targeting and epidemiological investigation of PASC. Post-acute sequelae of COVID-19 (PASC) has heterogenous presentation and complex etiology. Here the authors profile peripheral blood of patients with PASC and analyze by machine-learning to identify immune and serology features that allow the stratification of PASC into inflammatory and non-inflammatory types for better diagnosis and therapy-planning.

Acquisition of genes through horizontal gene transfer (HGT) allows microbes to rapidly gain new capabilities and adapt to new or changing environments. Identifying widespread HGT regions within multispecies microbiomes can pinpoint the molecular mechanisms that play key roles in microbiome assembly. We sought to identify horizontally transferred genes within a model microbiome, the cheese rind. Comparing 31 newly sequenced and 134 previously sequenced bacterial isolates from cheese rinds, we identified over 200 putative horizontally transferred genomic regions containing 4733 protein coding genes. The largest of these regions are enriched for genes involved in siderophore acquisition, and are widely distributed in cheese rinds in both Europe and the US. These results suggest that HGT is prevalent in cheese rind microbiomes, and that identification of genes that are frequently transferred in a particular environment may provide insight into the selective forces shaping microbial communities. From the depths of the ocean to the lining of the human gut, almost every environment on Earth is home to a unique community of microorganisms referred to as a microbiome. Within these communities, unrelated microorganisms can exchange genetic information through a process known as horizontal gene transfer. For example, genes linked to antibiotic resistance are often transferred between different microorganisms, which can create increasingly drug resistant microbes and has important implications for human health. Horizontal gene transfer has been studied for almost 100 years, but examining it directly is challenging because, almost by definition, it requires studying a community of microbes rather than one microbe in isolation. As such, researchers are looking for simple models of microbial communities that can be easily manipulated in experiments. Bonham et al. have now turned to the outer surface of cheese, also known as cheese rind, to better understand horizontal gene transfer. As a model system, the cheese rind microbiome is relatively simple to work with because cheese rind is easy to replicate in the laboratory, and the microbes growing on cheese can be grown on their own or in combinations with other microbes. By comparing the genetic material of 165 cheese-associated bacteria to one another, Bonham et al. identified over 4,000 genes that were shared between the bacteria, including several large clusters of genes that were shared by many species. Many of the identified genes (about 23% to be precise) appear to help the microorganisms acquire nutrients that are known to be in short supply on the surface of cheese surface, including iron. Bacteria typically use specialized molecules called siderophores to scavenge for iron and uptake systems to carry the iron-bound siderophore back into the cell. Notably, only the genes associated with the uptake systems were found in some of the shared gene clusters. This finding suggests that horizontal gene transfer has allowed some microbes to “cheat” and take up iron-bound siderophores without expending energy to produce the siderophores themselves. Using the cheese rind microbiome as a model system, it becomes possible to explore how horizontal gene transfer works in more detail than before. A better understanding of this process can then be applied to other important microbiomes, including those where genes conferring antibiotic resistance are commonly exchanged.

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life. Using 3154 samples from 1827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.56 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of “microbiome age” for assessing early gut maturation that may be used alongside other measures of child development. Here, the authors perform a global analysis of over 3000 infant gut samples revealing a universal pattern of microbial changes over the first 1.5 years, with declines in Bifidobacterium and increases in Faecalibacterium , providing a standard for early gut development.

Bifidobacterium longum subsp. infantis (B. infantis) is one of a few microorganisms capable of metabolizing human breast milk and is a pioneer colonizer in the guts of breastfed infants. One current challenge is differentiating B. infantis from its close relatives, B. longum and B. suis. All three organisms are classified in the same species group but only B. infantis can metabolize human milk oligosaccharides (HMOs). We compared HMO-metabolizing genes across different Bifidobacterium genomes and developed B. infantis-specific primers to determine if the genes alone or the primers can be used to quickly characterize B. infantis. We showed that B. infantis is uniquely identified by the presence of five HMO-metabolizing gene clusters, tested for its prevalence in infant gut metagenomes, and validated the results using the B. infantis-specific primers. We observed that only 15 of 203 (7.4%) children under 2 years old from a cohort of US children harbored B. infantis. These results highlight the importance of developing and improving approaches to identify B. infantis. A more accurate characterization may provide insights into regional differences of B. infantis prevalence in infant gut microbiota.

Inflammatory bowel diseases, which include Crohn’s disease and ulcerative colitis, affect several million individuals worldwide. Crohn’s disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study’s infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi’omics Database ( http://ibdmdb.org ), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases. The Inflammatory Bowel Disease Multi’omics Database includes longitudinal data encompassing a multitude of analyses of stool, blood and biopsies of more than 100 individuals, and provides a comprehensive description of host and microbial activities in inflammatory bowel diseases.

Over the past decade, extensive research has revealed strong links between the gut microbiome and the brain, at least in adults or those with neuropsychiatric disorders. This study explores how these associations emerge in early development using a longitudinal sample of 194 infants with repeated microbiome metabolism and electroencephalography (EEG) measures during the critical early period of visual cortex neuroplasticity. We examined microbial genes encoding enzymes for neuroactive compounds (e.g., GABA, glutamate, tryptophan, and short-chain fatty acids) and their association with the visual-evoked potential (VEP). Genes from 4-month stool samples strongly correlated with VEP features between 9 and 14 months, suggesting that early microbial metabolism influences later visual neurodevelopment. These prospective associations were more numerous than the concurrent ones. Our findings suggest that early gut microbiome metabolic potential plays a crucial role in shaping neural plasticity and visual neurodevelopment.

Background Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. Methods Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. Results Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. Limitations The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. Conclusions Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories.

Background: While early life exposures such as mode of birth, breastfeeding, and antibiotic use are established regulators of microbiome composition in early childhood, recent research suggests that the social environment may also exert influence. Two recent studies in adults demonstrated associations between socioeconomic factors and microbiome composition. This study expands on this prior work by examining the association between family socioeconomic status (SES) and host genetics with microbiome composition in infants and children. Methods: Family SES was used to predict a latent variable representing six genera abundances generated from whole-genome shotgun sequencing. A polygenic score derived from a microbiome genome-wide association study was included to control for potential genetic associations. Associations between family SES and microbiome diversity were assessed. Results: Anaerostipes, Bacteroides, Eubacterium, Faecalibacterium, and Lachnospiraceae spp. significantly loaded onto a latent factor, which was significantly predicted by SES (p < 0.05) but not the polygenic score (p > 0.05). Our results indicate that SES did not predict alpha diversity but did predict beta diversity (p < 0.001). Conclusions: Our results demonstrate that modifiable environmental factors influence gut microbiome composition at an early age. These results are important as our understanding of gut microbiome influences on health continue to expand.

1. The general importance of metacommunity and metapopulation theories is poorly understood because few studies have examined responses of the suite of species that occupy the same fragmented landscape. In this study, we examined the importance of spatial ecological theories using a large-scale, naturally fragmented landscape. 2. We measured the occurrence and abundance of 44 common beetle species in 31 natural rainforest fragments in Tasmania, Australia. We tested for an effect on beetle distribution of geographic variables (patch area, patch isolation and amount of surrounding habitat) and of environmental variables based on plant species, after first accounting for spatial autocorrelation using principal coordinates of neighbour matrices. The environmental variables described a productivity gradient and a post-fire succession from eucalypt-dominated forest to late-successional rainforest. 3. Few species had distributions consistent with a metapopulation. However, the amount of surrounding habitat and patch isolation influenced the occurrence or abundance of 30% of beetle species, implying that dispersal into or out of patches was an important process. 4. Three species showed a distribution that could arise by interactions with dominant competitors or predators with higher occurrence in small patches. 5. Environmental effects were more commonly observed than spatial effects. Twenty-three per cent of species showed evidence of habitat-driven, deterministic metapopulations. Furthermore, almost half of the species were influenced by the plant succession or productivity gradient, including effects at the within-patch, patch and regional scales. The beetle succession involved an increase in the frequency of many species, and the addition of new species, with little evidence of species turnover. Niche-related ecological theory such as the species-sorting metacommunity theory was therefore the most broadly applicable concept. 6. We conclude that classic and source-sink metapopulations are probably rare in this large-scale, naturally fragmented system, although dispersal processes like those occurring in metapopulations may have a substantial influence on community composition. However, deterministic processes (niche specialisation, species-sorting metacommunities and deterministic metapopulations) drive the occurrence or frequency of the majority of species. We urge further research into the prevalence of spatial ecological processes in large-scale natural ecosystems to expand our understanding of the processes that may be important in nature.