Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea

Constructing phylogenetic trees for microbiome data analysis: A mini-review

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes. In contrast, metagenomic and whole genome shotgun (WGS) sequencing involve sequencing from random fragments of the entire gene, making identification of consistent marker genes challenging owing to the vast diversity of genomic regions, resulting in a scarcity of robust tools for constructing phylogenetic trees. Although bacterial sequence tree construction tools exist for upstream bioinformatics, many downstream researchers-those integrating these trees into statistical models or machine learning-are either unaware of these tools or find them difficult to use due to the steep learning curve of processing raw sequences. This is compounded by the fact that public datasets often lack phylogenetic trees, providing only abundance tables and taxonomic classifications. To address this, we present a comprehensive review of phylogenetic tree construction techniques for microbiome data (16S rRNA or whole-genome shotgun sequencing). We outline the strengths and limitations of current methods, offering expert insights and step-by-step guidance to make these tools more accessible and widely applicable in quantitative microbiome data analysis.

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Reticulate evolution: Detection and utility in the phylogenomics era

Phylogenomics has enriched our understanding that the Tree of Life can have network-like or reticulate structures among some taxa and genes. Two non-vertical modes of evolution - hybridization/introgression and horizontal gene transfer - deviate from a strictly bifurcating tree model, causing non-treelike patterns. However, these reticulate processes can produce similar patterns to incomplete lineage sorting or recombination, potentially leading to ambiguity. Here, we present a brief overview of a phylogenomic workflow for inferring organismal histories and compare methods for distinguishing modes of reticulate evolution. We discuss how the timing of coalescent events can help disentangle introgression from incomplete lineage sorting and how horizontal gene transfer events can help determine the relative timing of speciation events. In doing so, we identify pitfalls of certain methods and discuss how to extend their utility across the Tree of Life. Workflows, methods, and future directions discussed herein underscore the need to embrace reticulate evolutionary patterns for understanding the timing and rates of evolutionary events, providing a clearer view of life's history.

MosAIC: An annotated collection of mosquito-associated bacteria with high-quality genome assemblies

Mosquitoes transmit medically important human pathogens, including viruses like dengue virus and parasites such as Plasmodium spp., the causative agent of malaria. Mosquito microbiomes are critically important for the ability of mosquitoes to transmit disease-causing agents. However, while large collections of bacterial isolates and genomic data exist for vertebrate microbiomes, the vast majority of work in mosquitoes to date is based on 16S rRNA gene amplicon data that provides limited taxonomic resolution and no functional information. To address this gap and facilitate future studies using experimental microbiome manipulations, we generated a bacterial Mosquito-Associated Isolate Collection (MosAIC) consisting of 392 bacterial isolates with extensive metadata and high-quality draft genome assemblies that are publicly available, both isolates and sequence data, for use by the scientific community. MosAIC encompasses 142 species spanning 29 bacterial families, with members of the Enterobacteriaceae comprising 40% of the collection. Phylogenomic analysis of 3 genera, Enterobacter, Serratia, and Elizabethkingia, reveal lineages of mosquito-associated bacteria isolated from different mosquito species in multiple laboratories. Investigation into species' pangenomes further reveals clusters of genes specific to these lineages, which are of interest for future work to test for functions connected to mosquito host association. Altogether, we describe the generation of a physical collection of mosquito-associated bacterial isolates, their genomic data, and analyses of selected groups in context of genome data from closely related isolates, providing a unique, highly valuable resource for research on bacterial colonisation and adaptation within mosquito hosts. Future efforts will expand the collection to include broader geographic and host species representation, especially from individuals collected from field populations, as well as other mosquito-associated microbes, including fungi, archaea, and protozoa.

The microbiome diversifies N -acyl lipid pools - including short-chain fatty acid-derived compounds

N -acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N -acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N -acyl lipid patterns from 2,700 public datasets, identifying 851 N -acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N -acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

Gut microbiota regulates stress responsivity via the circadian system

Stress and circadian systems are interconnected through the hypothalamic-pituitary-adrenal (HPA) axis to maintain responses to external stimuli. Yet, the mechanisms of how such signals are orchestrated remain unknown. Here, we uncover the gut microbiota as a regulator of HPA-axis rhythmicity. Microbial depletion disturbs the brain transcriptome and metabolome in stress-responding pathways in the hippocampus and amygdala across the day. This is coupled with a dysregulation of the circadian pacemaker in the brain that results in perturbed glucocorticoid rhythmicity. The resulting hyper-activation of the HPA axis at the sleep/wake transition drives time-of-day-specific impairments of the stress response and stress-sensitive behaviors. Finally, microbiota transplantation confirmed that diurnal oscillations of gut microbes underlie altered glucocorticoid secretion and that L. reuteri is a candidate strain for such effects. Our data offer compelling evidence that the microbiota regulates stress responsiveness in a circadian manner and is necessary to respond adaptively to stressors throughout the day.

BEAN and HABAS: Polyphyletic insertions in the DNA-directed RNA polymerase

The β and β' subunits of the RNA polymerase (RNAP) are large proteins with complex multi-domain architectures that include several insertional domains. Here, we analyze the domain organizations of RNAP-β and RNAP-β' using sequence, experimentally determined structures and AlphaFold structure predictions. We observe that lineage-specific insertional domains in bacterial RNAP-β belong to a group that we call BEAN (broadly embedded annex). We observe that lineage-specific insertional domains in bacterial RNAP-β' belong to a group that we call HABAS (hammerhead/barrel-sandwich hybrid). The BEAN domain has a characteristic three-dimensional structure composed of two square bracket-like elements that are antiparallel relative to each other. The HABAS domain contains a four-stranded open β-sheet with a GD-box-like motif in one of the β-strands and the adjoining loop. The BEAN domain is inserted not only in the bacterial RNAP-β', but also in the archaeal version of universal ribosomal protein L10. The HABAS domain is inserted in several metabolic proteins. The phylogenetic distributions of bacterial lineage-specific insertional domains of β and β' subunits of RNAP follow the Tree of Life. The presence of insertional domains can help establish a relative timeline of events in the evolution of a protein because insertion is inferred to post-date the base domain. We discuss mechanisms that might account for the discovery of homologous insertional domains in non-equivalent locations in bacteria and archaea.

Microbiome-metabolite linkages drive greenhouse gas dynamics over a permafrost thaw gradient

Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This contradicts common assumptions in trait-based microbial models and highlights the limitations of measuring microbial community-level data alone. Furthermore, feature-scale analysis revealed connections between microbial taxa, metabolites and observed CO2 and CH4 porewater variations. Our study showcases insights gained by using feature-level data and microorganism-metabolite interactions to better understand metabolic processes that drive greenhouse gas emissions during ecosystem changes.

Order of amino acid recruitment into the genetic code resolved by Last Universal Common Ancestor's protein domains

The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria, such as absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. More broadly, abiotic abundance might not reflect biotic abundance in the organisms in which the genetic code evolved. Here, we instead identify which protein domains date to the last universal common ancestor (LUCA), then infer the order of recruitment from deviations of their ancestrally reconstructed amino acid frequencies from the still-ancient post-LUCA controls. We find that smaller amino acids were added to the code earlier, with no additional predictive power in the previous "consensus" order. Metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Methionine and histidine were added to the code earlier than expected from their molecular weights, and glutamine later. Early methionine availability is compatible with inferred early use of S-adenosylmethionine, and early histidine with its purine-like structure and the demand for metal-binding. Even more ancient protein sequences - those that had already diversified into multiple distinct copies prior to LUCA - have significantly higher frequencies of aromatic amino acids (tryptophan, tyrosine, phenylalanine and histidine), and lower frequencies of valine and glutamic acid than single copy LUCA sequences. If at least some of these sequences predate the current code, then their distinct enrichment patterns provide hints about earlier, alternative genetic codes.

Incomplete human reference genomes can drive false sex biases and expose patient-identifying information in metagenomic data

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insufficient host filtration using prior human genome references can introduce false sex biases and inadvertently permit flow-through of host-specific DNA during bioinformatic analyses, which could be exploited for individual identification. To address these issues, we introduce and benchmark three host filtration methods of varying throughput, with concomitant applications across low biomass samples such as skin and high microbial biomass datasets including fecal samples. We find that these methods are important for obtaining accurate results in low biomass samples (e.g., tissue, skin). Overall, we demonstrate that rigorous host filtration is a key component of privacy-minded analyses of patient microbiomes and provide computationally efficient pipelines for accomplishing this task on large-scale datasets.

Memory-bound k-mer selection for large and evolutionarily diverse reference libraries

Using k-mers to find sequence matches is increasingly used in many bioinformatic applications, including metagenomic sequence classification. The accuracy of these downstream applications relies on the density of the reference databases, which are rapidly growing. Although the increased density provides hope for improvements in accuracy, scalability is a concern. Reference k-mers are kept in the memory during the query time, and saving all k-mers of these ever-expanding databases is fast becoming impractical. Several strategies for subsampling have been proposed, including minimizers and finding taxon-specific k-mers. However, we contend that these strategies are inadequate, especially when reference sets are taxonomically imbalanced, as are most microbial libraries. In this paper, we explore approaches for selecting a fixed-size subset of k-mers present in an ultra-large data set to include in a library such that the classification of reads suffers the least. Our experiments demonstrate the limitations of existing approaches, especially for novel and poorly sampled groups. We propose a library construction algorithm called k-mer RANKer (KRANK) that combines several components, including a hierarchical selection strategy with adaptive size restrictions and an equitable coverage strategy. We implement KRANK in highly optimized code and combine it with the locality-sensitive hashing classifier CONSULT-II to build a taxonomic classification and profiling method. On several benchmarks, KRANK k-mer selection significantly reduces memory consumption with minimal loss in classification accuracy. We show in extensive analyses based on CAMI benchmarks that KRANK outperforms k-mer-based alternatives in terms of taxonomic profiling and comes close to the best marker-based methods in terms of accuracy.

Challenges in Assembling the Dated Tree of Life

The assembly of a comprehensive and dated Tree of Life (ToL) remains one of the most formidable challenges in evolutionary biology. The complexity of life's history, involving both vertical and horizontal transmission of genetic information, defies its representation by a simple bifurcating phylogeny. With the advent of genome and metagenome sequencing, vast amounts of data have become available. However, employing this information for phylogeny and divergence time inference has introduced significant theoretical and computational hurdles. This perspective addresses some key methodological challenges in assembling the dated ToL, namely, the identification and classification of homologous genes, accounting for gene tree-species tree mismatch due to population-level processes along with duplication, loss, and horizontal gene transfer, and the accurate dating of evolutionary events. Ultimately, the success of this endeavor requires new approaches that integrate knowledge databases with optimized phylogenetic algorithms capable of managing complex evolutionary models.

The nature of the last universal common ancestor and its impact on the early Earth system

The nature of the last universal common ancestor (LUCA), its age and its impact on the Earth system have been the subject of vigorous debate across diverse disciplines, often based on disparate data and methods. Age estimates for LUCA are usually based on the fossil record, varying with every reinterpretation. The nature of LUCA's metabolism has proven equally contentious, with some attributing all core metabolisms to LUCA, whereas others reconstruct a simpler life form dependent on geochemistry. Here we infer that LUCA lived ~4.2 Ga (4.09-4.33 Ga) through divergence time analysis of pre-LUCA gene duplicates, calibrated using microbial fossils and isotope records under a new cross-bracing implementation. Phylogenetic reconciliation suggests that LUCA had a genome of at least 2.5 Mb (2.49-2.99 Mb), encoding around 2,600 proteins, comparable to modern prokaryotes. Our results suggest LUCA was a prokaryote-grade anaerobic acetogen that possessed an early immune system. Although LUCA is sometimes perceived as living in isolation, we infer LUCA to have been part of an established ecological system. The metabolism of LUCA would have provided a niche for other microbial community members and hydrogen recycling by atmospheric photochemistry could have supported a modestly productive early ecosystem.

Impact of diet change on the gut microbiome of common marmosets (Callithrix jacchus)

Gastrointestinal diseases are the most frequently reported clinical problems in captive common marmosets (Callithrix jacchus), often affecting the health and welfare of the animal and ultimately their use as a research subject. The microbiome has been shown to be intimately connected to diet and gastrointestinal health. Here, we use shotgun metagenomics and untargeted metabolomics in fecal samples of common marmosets collected before, during, and after a dietary transition from a biscuit to a gel diet. The overall health of marmosets, measured as weight recovery and reproductive outcome, improved after the diet transition. Moreover, each marmoset pair had significant shifts in the microbiome and metabolome after the diet transition. In general, we saw a decrease in Escherichia coli and Prevotella species and an increase in Bifidobacterium species. Untargeted metabolic profiles indicated that polyamine levels, specifically cadaverine and putrescine, were high after diet transition, suggesting either an increase in excretion or a decrease in intestinal reabsorption at the intestinal level. In conclusion, our data suggest that Bifidobacterium species could potentially be useful as probiotic supplements to the laboratory marmoset diet. Future studies with a larger sample size will be beneficial to show that this is consistent with the diet change.

IMPORTANCE

Appropriate diet and health of the common marmoset in captivity are essential both for the welfare of the animal and to improve experimental outcomes. Our study shows that a gel diet compared to a biscuit diet improves the health of a marmoset colony, is linked to increases in Bifidobacterium species, and increases the removal of molecules associated with disease. The diet transition had an influence on the molecular changes at both the pair and time point group levels, but only at the pair level for the microbial changes. It appears to be more important which genes and functions present changed rather than specific microbes. Further studies are needed to identify specific components that should be considered when choosing an appropriate diet and additional supplementary foods, as well as to validate the benefits of providing probiotics. Probiotics containing Bifidobacterium species appear to be useful as probiotic supplements to the laboratory marmoset diet, but additional work is needed to validate these findings.

SkinCom, a synthetic skin microbial community, enables reproducible investigations of the human skin microbiome

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

The impact of breastfeeding on the preterm infant's microbiome and metabolome: a pilot study

BACKGROUND

Human milk is unquestionably beneficial for preterm infants. We investigated how the transition from tube to oral/breastfeeding impacts the preterm infants' oral and gut microbiome and metabolome.

METHODS

We analyzed stool, saliva, and milk samples collected from a cohort of preterm infants enrolled in the MAP Study, a prospective observational trial. The microbiome and metabolome of the samples were analyzed from 4 longitudinal sample time points, 2 during tube feeds only and 2 after the initiation of oral/breastfeeding.

RESULTS

We enrolled 11 mother-infant dyads (gestational age = 27.9 (23.4-32.2)) and analyzed a total of 39 stool, 44 saliva, and 43 milk samples over 4 timepoints. In saliva samples, there was a shift towards increased Streptococcus and decreased Staphylococcus after oral feeding/breastfeeding initiation (p < 0.05). Milk sample metabolites were strongly influenced by the route of feeding and milk type (p < 0.05) and represented the pathways of Vitamin E metabolism, Vitamin B12 metabolism, and Tryptophan metabolism.

CONCLUSION

Our analysis demonstrated that the milk and preterm infant's saliva microbiome and metabolome changed over the course of the first four to 5 months of life, coinciding with the initiation of oral/breastfeeds.

IMPACT

The microbiome and metabolome is altered in the infant's saliva but not their stool, and in mother's milk when feeds are transitioned from tube to oral/breastfeeding. We assessed the relationship between the gut and oral microbiome/metabolome with the milk microbiome/metabolome over a longitudinal period of time in preterm babies. Metabolites that changed in the infants saliva after the initiation of oral feeds have the potential to be used as biomarkers for disease risk.

Advancements in prokaryotic systematics and the role of Bergey's International Society for Microbial Systematicsin addressing challenges in the meta-data era

Prokaryotes are ubiquitous in the biosphere, important for human health and drive diverse biological and environmental processes. Systematics of prokaryotes, whose origins can be traced to the discovery of microorganisms in the 17th century, has transitioned from a phenotype-based classification to a more comprehensive polyphasic taxonomy and eventually to the current genome-based taxonomic approach. This transition aligns with a foundational shift from studies focused on phenotypic traits that have limited comparative value to those using genome sequences. In this context, Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB) and Bergey's International Society for Microbial Systematics (BISMiS) play a pivotal role in guiding prokaryotic systematics. This review focuses on the historical development of prokaryotic systematics with a focus on the roles of BMSAB and BISMiS. We also explore significant contributions and achievements by microbiologists, highlight the latest progress in the field and anticipate challenges and opportunities within prokaryotic systematics. Additionally, we outline five focal points of BISMiS that are aimed at addressing these challenges. In conclusion, our collaborative effort seeks to enhance ongoing advancements in prokaryotic systematics, ensuring its continued relevance and innovative characters in the contemporary landscape of genomics and bioinformatics.

Analyzing microbial evolution through gene and genome phylogenies

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies.

Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

An evolutionary optimum amid moderate heritability in prokaryotic cell size

We investigate the distribution and evolution of prokaryotic cell size based on a compilation of 5,380 species. Size spans four orders of magnitude, from 100 nm (Mycoplasma) to more than 1 cm (Thiomargarita); however, most species congregate heavily around the mean. The distribution approximates but is distinct from log normality. Comparative phylogenetics suggests that size is heritable, yet the phylogenetic signal is moderate, and the degree of heritability is independent of taxonomic scale (i.e., fractal). Evolutionary modeling indicates the presence of an optimal cell size to which most species gravitate. The size is equivalent to a coccus of 0.70 μm in diameter. Analyses of 1,361 species with sequenced genomes show that genomic traits contribute to size evolution moderately and synergistically. Given our results, scaling theory, and empirical evidence, we discuss potential drivers that may expand or shrink cells around the optimum and propose a stability landscape model for prokaryotic cell size.

Multisubstrate specificity shaped the complex evolution of the aminotransferase family across the tree of life

Aminotransferases (ATs) are an ancient enzyme family that play central roles in core nitrogen metabolism, essential to all organisms. However, many of the AT enzyme functions remain poorly defined, limiting our fundamental understanding of the nitrogen metabolic networks that exist in different organisms. Here, we traced the deep evolutionary history of the AT family by analyzing AT enzymes from 90 species spanning the tree of life (ToL). We found that each organism has maintained a relatively small and constant number of ATs. Mapping the distribution of ATs across the ToL uncovered that many essential AT reactions are carried out by taxon-specific AT enzymes due to wide-spread nonorthologous gene displacements. This complex evolutionary history explains the difficulty of homology-based AT functional prediction. Biochemical characterization of diverse aromatic ATs further revealed their broad substrate specificity, unlike other core metabolic enzymes that evolved to catalyze specific reactions today. Interestingly, however, we found that these AT enzymes that diverged over billion years share common signatures of multisubstrate specificity by employing different nonconserved active site residues. These findings illustrate that AT family enzymes had leveraged their inherent substrate promiscuity to maintain a small yet distinct set of multifunctional AT enzymes in different taxa. This evolutionary history of versatile ATs likely contributed to the establishment of robust and diverse nitrogen metabolic networks that exist throughout the ToL. The study provides a critical foundation to systematically determine diverse AT functions and underlying nitrogen metabolic networks across the ToL.

Principles of bacterial genome organization, a conformational point of view

Bacterial chromosomes are large molecules that need to be highly compacted to fit inside the cells. Chromosome compaction must facilitate and maintain key biological processes such as gene expression and DNA transactions (replication, recombination, repair, and segregation). Chromosome and chromatin 3D-organization in bacteria has been a puzzle for decades. Chromosome conformation capture coupled to deep sequencing (Hi-C) in combination with other "omics" approaches has allowed dissection of the structural layers that shape bacterial chromosome organization, from DNA topology to global chromosome architecture. Here we review the latest findings using Hi-C and discuss the main features of bacterial genome folding.

Illuminating the coevolution of photosynthesis and Bacteria

Life harnessing light energy transformed the relationship between biology and Earth-bringing a massive flux of organic carbon and oxidants to Earth's surface that gave way to today's organotrophy- and respiration-dominated biosphere. However, our understanding of how life drove this transition has largely relied on the geological record; much remains unresolved due to the complexity and paucity of the genetic record tied to photosynthesis. Here, through holistic phylogenetic comparison of the bacterial domain and all photosynthetic machinery (totally spanning >10,000 genomes), we identify evolutionary congruence between three independent biological systems-bacteria, (bacterio)chlorophyll-mediated light metabolism (chlorophototrophy), and carbon fixation-and uncover their intertwined history. Our analyses uniformly mapped progenitors of extant light-metabolizing machinery (reaction centers, [bacterio]chlorophyll synthases, and magnesium-chelatases) and enzymes facilitating the Calvin-Benson-Bassham cycle (form I RuBisCO and phosphoribulokinase) to the same ancient Terrabacteria organism near the base of the bacterial domain. These phylogenies consistently showed that extant phototrophs ultimately derived light metabolism from this bacterium, the last phototroph common ancestor (LPCA). LPCA was a non-oxygen-generating (anoxygenic) phototroph that already possessed carbon fixation and two reaction centers, a type I analogous to extant forms and a primitive type II. Analyses also indicate chlorophototrophy originated before LPCA. We further reconstructed evolution of chlorophototrophs/chlorophototrophy post-LPCA, including vertical inheritance in Terrabacteria, the rise of oxygen-generating chlorophototrophy in one descendant branch near the Great Oxidation Event, and subsequent emergence of Cyanobacteria. These collectively unveil a detailed view of the coevolution of light metabolism and Bacteria having clear congruence with the geological record.

Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach

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/.

Timing the evolution of phosphorus-cycling enzymes through geological time using phylogenomics

Phosphorus plays a crucial role in controlling biological productivity, but geological estimates of phosphate concentrations in the Precambrian ocean, during life's origin and early evolution, vary over several orders of magnitude. While reduced phosphorus species may have served as alternative substrates to phosphate, their bioavailability on the early Earth remains unknown. Here, we reconstruct the phylogenomic record of life on Earth and find that phosphate transporting genes (pnas) evolved in the Paleoarchean (ca. 3.6-3.2 Ga) and are consistent with phosphate concentrations above modern levels ( > 3 µM). The first gene optimized for low phosphate levels (pstS; <1 µM) appeared around the same time or in the Mesoarchean depending on the reconstruction method. Most enzymatic pathways for metabolising reduced phosphorus emerged and expanded across the tree of life later. This includes phosphonate-catabolising CP-lyases, phosphite-oxidising pathways and hypophosphite-oxidising pathways. CP-lyases are particularly abundant in dissolved phosphate concentrations below 0.1 µM. Our results thus indicate at least local regions of declining phosphate levels through the Archean, possibly linked to phosphate-scavenging Fe(III), which may have limited productivity. However, reduced phosphorus species did not become widely used until after the Paleoproterozoic Great Oxidation Event (2.3 Ga), possibly linked to expansion of the biosphere at that time.

Generation of accurate, expandable phylogenomic trees with uDance

Phylogenetic trees provide a framework for organizing evolutionary histories across the tree of life and aid downstream comparative analyses such as metagenomic identification. Methods that rely on single-marker genes such as 16S rRNA have produced trees of limited accuracy with hundreds of thousands of organisms, whereas methods that use genome-wide data are not scalable to large numbers of genomes. We introduce updating trees using divide-and-conquer (uDance), a method that enables updatable genome-wide inference using a divide-and-conquer strategy that refines different parts of the tree independently and can build off of existing trees, with high accuracy and scalability. With uDance, we infer a species tree of roughly 200,000 genomes using 387 marker genes, totaling 42.5 billion amino acid residues.

Greengenes2 unifies microbial data in a single reference tree

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

Reclassification of Aestuariicella albida as Pseudomaricurvus albidus comb. nov. and Aestuariicella hydrocarbonica as Pseudomaricurvus hydrocarbonicus comb. nov. Based on Comparative Genomics and Molecular Synapomorphies

The genus Aestuariicella has been recently reclassified as a member of the family Cellvibrionaceae. However, the taxonomic position of the genus as a distinct member of the family has not been clarified. In the present study, we performed multilayered analyses anchored on genome sequences to clarify the relationship between the genera Aestuariicella and Pseudomaricurvus within the family Cellvibrionaceae. Phylogenetic analyses based on 16S rRNA gene, RNA polymerase beta subunit (RpoB) protein, and core gene sequences showed a well-supported tight cluster formed by the members of the two genera. Moreover, the analysis of the average amino acid identity (AAI) revealed that the members of the two genera shared 68.16-79.48% AAI, values which were within the range of observed AAI (≥ 67.23%) among the members of the same genus within the family Cellvibrionaceae. Members of the two genera also shared several common characteristics. Furthermore, molecular synapomorphies in a form of conserved signature indels were identified in six protein sequences that were exclusively shared by the members of the two genera. Based on the phylogenetic and molecular evidence presented here, we propose the reclassification of the species Aestuariicella albida and Aestuariicella hydrocarbonica as Pseudomaricurvus albidus comb. nov. and Pseudomaricurvus hydrocarbonicus comb. nov., respectively.

Structural and biochemical analysis of family 92 carbohydrate-binding modules uncovers multivalent binding to β-glucans

Carbohydrate-binding modules (CBMs) are non-catalytic proteins found appended to carbohydrate-active enzymes. Soil and marine bacteria secrete such enzymes to scavenge nutrition, and they often use CBMs to improve reaction rates and retention of released sugars. Here we present a structural and functional analysis of the recently established CBM family 92. All proteins analysed bind preferentially to β-1,6-glucans. This contrasts with the diversity of predicted substrates among the enzymes attached to CBM92 domains. We present crystal structures for two proteins, and confirm by mutagenesis that tryptophan residues permit ligand binding at three distinct functional binding sites on each protein. Multivalent CBM families are uncommon, so the establishment and structural characterisation of CBM92 enriches the classification database and will facilitate functional prediction in future projects. We propose that CBM92 proteins may cross-link polysaccharides in nature, and might have use in novel strategies for enzyme immobilisation.

The microbiota drives diurnal rhythms in tryptophan metabolism in the stressed gut

Chronic stress disrupts microbiota-gut-brain axis function and is associated with altered tryptophan metabolism, impaired gut barrier function, and disrupted diurnal rhythms. However, little is known about the effects of acute stress on the gut and how it is influenced by diurnal physiology. Here, we used germ-free and antibiotic-depleted mice to understand how microbiota-dependent oscillations in tryptophan metabolism would alter gut barrier function at baseline and in response to an acute stressor. Cecal metabolomics identified tryptophan metabolism as most responsive to a 15-min acute stressor, while shotgun metagenomics revealed that most bacterial species exhibiting rhythmicity metabolize tryptophan. Our findings highlight that the gastrointestinal response to acute stress is dependent on the time of day and the microbiome, with a signature of stress-induced functional alterations in the ileum and altered tryptophan metabolism in the colon.

Peripheral neuronal activation shapes the microbiome and alters gut physiology

The gastrointestinal (GI) tract is innervated by intrinsic neurons of the enteric nervous system (ENS) and extrinsic neurons of the central nervous system and peripheral ganglia. The GI tract also harbors a diverse microbiome, but interactions between the ENS and the microbiome remain poorly understood. Here, we activate choline acetyltransferase (ChAT)-expressing or tyrosine hydroxylase (TH)-expressing gut-associated neurons in mice to determine effects on intestinal microbial communities and their metabolites as well as on host physiology. The resulting multi-omics datasets support broad roles for discrete peripheral neuronal subtypes in shaping microbiome structure, including modulating bile acid profiles and fungal colonization. Physiologically, activation of either ChAT+ or TH+ neurons increases fecal output, while only ChAT+ activation results in increased colonic contractility and diarrhea-like fluid secretion. These findings suggest that specific subsets of peripherally activated neurons differentially regulate the gut microbiome and GI physiology in mice without involvement of signals from the brain.

Hot springs viruses at Yellowstone National Park have ancient origins and are adapted to thermophilic hosts

Geothermal springs house unicellular red algae in the class Cyanidiophyceae that dominate the microbial biomass at these sites. Little is known about host-virus interactions in these environments. We analyzed the virus community associated with red algal mats in three neighboring habitats (creek, endolithic, soil) at Lemonade Creek, Yellowstone National Park (YNP), USA. We find that despite proximity, each habitat houses a unique collection of viruses, with the giant viruses, Megaviricetes, dominant in all three. The early branching phylogenetic position of genes encoded on metagenome assembled virus genomes (vMAGs) suggests that the YNP lineages are of ancient origin and not due to multiple invasions from mesophilic habitats. The existence of genomic footprints of adaptation to thermophily in the vMAGs is consistent with this idea. The Cyanidiophyceae at geothermal sites originated ca. 1.5 Bya and are therefore relevant to understanding biotic interactions on the early Earth.

Dermal injury drives a skin to gut axis that disrupts the intestinal microbiome and intestinal immune homeostasis in mice

The composition of the microbial community in the intestine may influence the functions of distant organs such as the brain, lung, and skin. These microbes can promote disease or have beneficial functions, leading to the hypothesis that microbes in the gut explain the co-occurrence of intestinal and skin diseases. Here, we show that the reverse can occur, and that skin directly alters the gut microbiome. Disruption of the dermis by skin wounding or the digestion of dermal hyaluronan results in increased expression in the colon of the host defense genes Reg3 and Muc2, and skin wounding changes the composition and behavior of intestinal bacteria. Enhanced expression Reg3 and Muc2 is induced in vitro by exposure to hyaluronan released by these skin interventions. The change in the colon microbiome after skin wounding is functionally important as these bacteria penetrate the intestinal epithelium and enhance colitis from dextran sodium sulfate (DSS) as seen by the ability to rescue skin associated DSS colitis with oral antibiotics, in germ-free mice, and fecal microbiome transplantation to unwounded mice from mice with skin wounds. These observations provide direct evidence of a skin-gut axis by demonstrating that damage to the skin disrupts homeostasis in intestinal host defense and alters the gut microbiome.

Microbial Diversity and Open Questions about the Deep Tree of Life

In this perspective, we explore the transformative impact and inherent limitations of metagenomics and single-cell genomics on our understanding of microbial diversity and their integration into the Tree of Life. We delve into the key challenges associated with incorporating new microbial lineages into the Tree of Life through advanced phylogenomic approaches. Additionally, we shed light on enduring debates surrounding various aspects of the microbial Tree of Life, focusing on recent advances in some of its deepest nodes, such as the roots of bacteria, archaea, and eukaryotes. We also bring forth current limitations in genome recovery and phylogenomic methodology, as well as new avenues of research to uncover additional key microbial lineages and resolve the shape of the Tree of Life.

Comparative genomics of the genus Halioglobus reveals the genetic basis for the reclassification of Halioglobus pacificus as Parahalioglobus pacificus gen. nov. comb. nov

The genus Halioglobus is one of the environmentally relevant members of the family Halieaceae, class Gammaproteobacteria. At present, the genus is composed of three validly published species. However, in the recent study of the family Halieaceae, the species Halioglobus pacificus was observed to branch outside of the main clade formed by the members of Halioglobus, suggesting its distinct taxonomic placement within the family. In the present study, the taxonomic placement of H. pacificus was reassessed using comparative genomics. Phylogenomic analysis revealed the paraphyletic relationship of H. pacificus with the type species of the genus Halioglobus, and further demonstrated its genus-level placement. This phylogenetic relationship was reinforced by the average nucleotide and amino acid identity values shared by H. pacificus with the members of the family Halieaceae. Moreover, the results of the pan-genome analysis, together with the phenotype data, further supported the exclusion of H. pacificus from the genus Halioglobus. Based on these findings, the species H. pacificus is thereby assigned to a new genus Parahalioglobus gen. nov. as Parahalioglobus pacificus comb. nov.

Robustness of cancer microbiome signals over a broad range of methodological variation

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing

MOTIVATION

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity.

RESULTS

Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling.

AVAILABILITY AND IMPLEMENTATION

CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

Genome-scale community modelling reveals conserved metabolic cross-feedings in epipelagic bacterioplankton communities

Marine microorganisms form complex communities of interacting organisms that influence central ecosystem functions in the ocean such as primary production and nutrient cycling. Identifying the mechanisms controlling their assembly and activities is a major challenge in microbial ecology. Here, we integrated Tara Oceans meta-omics data to predict genome-scale community interactions within prokaryotic assemblages in the euphotic ocean. A global genome-resolved co-activity network revealed a significant number of inter-lineage associations across diverse phylogenetic distances. Identified co-active communities include species displaying smaller genomes but encoding a higher potential for quorum sensing, biofilm formation, and secondary metabolism. Community metabolic modelling reveals a higher potential for interaction within co-active communities and points towards conserved metabolic cross-feedings, in particular of specific amino acids and group B vitamins. Our integrated ecological and metabolic modelling approach suggests that genome streamlining and metabolic auxotrophies may act as joint mechanisms shaping bacterioplankton community assembly in the global ocean surface.

The evolutionary genomics of adaptation to stress in wild rhizobium bacteria

Microbiota comprise the bulk of life's diversity, yet we know little about how populations of microbes accumulate adaptive diversity across natural landscapes. Adaptation to stressful soil conditions in plants provides seminal examples of adaptation in response to natural selection via allelic substitution. For microbes symbiotic with plants however, horizontal gene transfer allows for adaptation via gene gain and loss, which could generate fundamentally different evolutionary dynamics. We use comparative genomics and genetics to elucidate the evolutionary mechanisms of adaptation to physiologically stressful serpentine soils in rhizobial bacteria in western North American grasslands. In vitro experiments demonstrate that the presence of a locus of major effect, the nre operon, is necessary and sufficient to confer adaptation to nickel, a heavy metal enriched to toxic levels in serpentine soil, and a major axis of environmental soil chemistry variation. We find discordance between inferred evolutionary histories of the core genome and nreAXY genes, which often reside in putative genomic islands. This suggests that the evolutionary history of this adaptive variant is marked by frequent losses, and/or gains via horizontal acquisition across divergent rhizobium clades. However, different nre alleles confer distinct levels of nickel resistance, suggesting allelic substitution could also play a role in rhizobium adaptation to serpentine soil. These results illustrate that the interplay between evolution via gene gain and loss and evolution via allelic substitution may underlie adaptation in wild soil microbiota. Both processes are important to consider for understanding adaptive diversity in microbes and improving stress-adapted microbial inocula for human use.

Modeling the mosaic structure of bacterial genomes to infer their evolutionary history

The chronology and phylogeny of bacterial evolution are difficult to reconstruct due to a scarce fossil record. The analysis of bacterial genomes remains challenging because of large sequence divergence, the plasticity of bacterial genomes due to frequent gene loss, horizontal gene transfer, and differences in selective pressure from one locus to another. Therefore, taking advantage of the rich and rapidly accumulating genomic data requires accurate modeling of genome evolution. An important technical consideration is that loci with high effective mutation rates may diverge beyond the detection limit of the alignment algorithms used, biasing the genome-wide divergence estimates toward smaller divergences. In this article, we propose a novel method to gain insight into bacterial evolution based on statistical properties of genome comparisons. We find that the length distribution of sequence matches is shaped by the effective mutation rates of different loci, by the horizontal transfers, and by the aligner sensitivity. Based on these inputs, we build a model and show that it accounts for the empirically observed distributions, taking the Enterobacteriaceae family as an example. Our method allows to distinguish segments of vertical and horizontal origins and to estimate the time divergence and exchange rate between any pair of taxa from genome-wide alignments. Based on the estimated time divergences, we construct a time-calibrated phylogenetic tree to demonstrate the accuracy of the method.

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Cell-lysis sensing drives biofilm formation in Vibrio cholerae

Matrix-encapsulated communities of bacteria, called biofilms, are ubiquitous in the environment and are notoriously difficult to eliminate in clinical and industrial settings. Biofilm formation likely evolved as a mechanism to protect resident cells from environmental challenges, yet how bacteria undergo threat assessment to inform biofilm development remains unclear. Here we find that population-level cell lysis events induce the formation of biofilms by surviving Vibrio cholerae cells. Survivors detect threats by sensing a cellular component released through cell lysis, which we identify as norspermidine. Lysis sensing occurs via the MbaA receptor with genus-level specificity, and responsive biofilm cells are shielded from phage infection and attacks from other bacteria. Thus, our work uncovers a connection between bacterial lysis and biofilm formation that may be broadly conserved among microorganisms.

Update on the proposed minimal standards for the use of genome data for the taxonomy of prokaryotes

The field of microbial taxonomy is dynamic, aiming to provide a stable and contemporary classification system for prokaryotes. Traditionally, reliance on phenotypic characteristics limited the comprehensive understanding of microbial diversity and evolution. The introduction of molecular techniques, particularly DNA sequencing and genomics, has transformed our perception of prokaryotic diversity. In the past two decades, advancements in genome sequencing have transitioned from traditional methods to a genome-based taxonomic framework, not only to define species, but also higher taxonomic ranks. As technology and databases rapidly expand, maintaining updated standards is crucial. This work seeks to revise the 2018 guidelines for applying genome sequencing data in microbial taxonomy, adapting minimal standards and recommendations to reflect technological progress during this period.

Evaluation of the safety and efficacy of fecal microbiota transplantations in bottlenose dolphins (Tursiops truncatus) using metagenomic sequencing

AIMS

Gastrointestinal disease is a leading cause of morbidity in bottlenose dolphins (Tursiops truncatus) under managed care. Fecal microbiota transplantation (FMT) holds promise as a therapeutic tool to restore gut microbiota without antibiotic use. This prospective clinical study aimed to develop a screening protocol for FMT donors to ensure safety, determine an effective FMT administration protocol for managed dolphins, and evaluate the efficacy of FMTs in four recipient dolphins.

METHODS AND RESULTS

Comprehensive health monitoring was performed on donor and recipient dolphins. Fecal samples were collected before, during, and after FMT therapy. Screening of donor and recipient fecal samples was accomplished by in-house and reference lab diagnostic tests. Shotgun metagenomics was used for sequencing. Following FMT treatment, all four recipient communities experienced engraftment of novel microbial species from donor communities. Engraftment coincided with resolution of clinical signs and a sustained increase in alpha diversity.

CONCLUSION

The donor screening protocol proved to be safe in this study and no adverse effects were observed in four recipient dolphins. Treatment coincided with improvement in clinical signs.

Phylogenetic reconciliation: making the most of genomes to understand microbial ecology and evolution

In recent years, phylogenetic reconciliation has emerged as a promising approach for studying microbial ecology and evolution. The core idea is to model how gene trees evolve along a species tree and to explain differences between them via evolutionary events including gene duplications, transfers, and losses. Here, we describe how phylogenetic reconciliation provides a natural framework for studying genome evolution and highlight recent applications including ancestral gene content inference, the rooting of species trees, and the insights into metabolic evolution and ecological transitions they yield. Reconciliation analyses have elucidated the evolution of diverse microbial lineages, from Chlamydiae to Asgard archaea, shedding light on ecological adaptation, host-microbe interactions, and symbiotic relationships. However, there are many opportunities for broader application of the approach in microbiology. Continuing improvements to make reconciliation models more realistic and scalable, and integration of ecological metadata such as habitat, pH, temperature, and oxygen use offer enormous potential for understanding the rich tapestry of microbial life.

CoCoPyE: feature engineering for learning and prediction of genome quality indices

BACKGROUND

The exploration of the microbial world has been greatly advanced by the reconstruction of genomes from metagenomic sequence data. However, the rapidly increasing number of metagenome-assembled genomes has also resulted in a wide variation in data quality. It is therefore essential to quantify the achieved completeness and possible contamination of a reconstructed genome before it is used in subsequent analyses. The classical approach for the estimation of quality indices solely relies on a relatively small number of universal single-copy genes. Recent tools try to extend the genomic coverage of estimates for an increased accuracy.

RESULTS

We developed CoCoPyE, a fast tool based on a novel 2-stage feature extraction and transformation scheme. First, it identifies genomic markers and then refines the marker-based estimates with a machine learning approach. In our simulation studies, CoCoPyE showed a more accurate prediction of quality indices than the existing tools. While the CoCoPyE web server offers an easy way to try out the tool, the freely available Python implementation enables integration into existing genome reconstruction pipelines.

CONCLUSIONS

CoCoPyE provides a new approach to assess the quality of genome data. It complements and improves existing tools and may help researchers to better distinguish between low-quality draft and high-quality genome assemblies in metagenome sequencing projects.

Robustness of Felsenstein's Versus Transfer Bootstrap Supports With Respect to Taxon Sampling

The bootstrap method is based on resampling sequence alignments and re-estimating trees. Felsenstein's bootstrap proportions (FBP) are the most common approach to assess the reliability and robustness of sequence-based phylogenies. However, when increasing taxon sampling (i.e., the number of sequences) to hundreds or thousands of taxa, FBP tend to return low support for deep branches. The transfer bootstrap expectation (TBE) has been recently suggested as an alternative to FBP. TBE is measured using a continuous transfer index in [0,1] for each bootstrap tree, instead of the binary {0,1} index used in FBP to measure the presence/absence of the branch of interest. TBE has been shown to yield higher and more informative supports while inducing a very low number of falsely supported branches. Nonetheless, it has been argued that TBE must be used with care due to sampling issues, especially in datasets with a high number of closely related taxa. In this study, we conduct multiple experiments by varying taxon sampling and comparing FBP and TBE support values on different phylogenetic depths, using empirical datasets. Our results show that the main critique of TBE stands in extreme cases with shallow branches and highly unbalanced sampling among clades, but that TBE is still robust in most cases, while FBP is inescapably negatively impacted by high taxon sampling. We suggest guidelines and good practices in TBE (and FBP) computing and interpretation.

r/K selection of GC content in prokaryotes

The guanine/cytosine (GC) content of prokaryotic genomes is species-specific, taking values from 16% to 77%. This diversity of selection for GC content remains contentious. We analyse the correlations between GC content and a range of phenotypic and genotypic data in thousands of prokaryotes. GC content integrates well with these traits into r/K selection theory when phenotypic plasticity is considered. High GC-content prokaryotes are r-strategists with cheaper descendants thanks to a lower average amino acid metabolic cost, colonize unstable environments thanks to flagella and a bacillus form and are generalists in terms of resource opportunism and their defence mechanisms. Low GC content prokaryotes are K-strategists specialized for stable environments that maintain homeostasis via a high-cost outer cell membrane and endospore formation as a response to nutrient deprivation, and attain a higher nutrient-to-biomass yield. The lower proteome cost of high GC content prokaryotes is driven by the association between GC-rich codons and cheaper amino acids in the genetic code, while the correlation between GC content and genome size may be partly due to functional diversity driven by r/K selection. In all, molecular diversity in the GC content of prokaryotes may be a consequence of ecological r/K selection.

A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.