The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn's disease.

Reciprocal interactions between host T helper cells and gut microbiota enforce local immunological tolerance and modulate extra-intestinal immunity. However, our understanding of antigen-specific tolerance to the microbiome is limited. Here, we developed a systematic approach to predict HLA class-II-specific epitopes using the humanized bacteria-originated T cell antigen (hBOTA) algorithm. We identified a diverse set of microbiome epitopes spanning all major taxa that are compatible with presentation by multiple HLA-II alleles. In particular, we uncovered an immunodominant epitope from the TonB-dependent receptor SusC that was universally recognized and ubiquitous among Bacteroidales. In healthy human subjects, SusC-reactive T cell responses were characterized by IL-10-dominant cytokine profiles, whereas in patients with active Crohn's disease, responses were associated with elevated IL-17A. Our results highlight the potential of targeted antigen discovery within the microbiome to reveal principles of tolerance and functional transitions during inflammation.

The ubiquitin ligase RFWD3 is required for translesion DNA synthesis.

Lesions on DNA uncouple DNA synthesis from the replisome, generating stretches of unreplicated single-stranded DNA (ssDNA) behind the replication fork. These ssDNA gaps need to be filled in to complete DNA duplication. Gap-filling synthesis involves either translesion DNA synthesis (TLS) or template switching (TS). Controlling these processes, ubiquitylated PCNA recruits many proteins that dictate pathway choice, but the enzymes regulating PCNA ubiquitylation in vertebrates remain poorly defined. Here we report that the E3 ubiquitin ligase RFWD3 promotes ubiquitylation of proteins on ssDNA. The absence of RFWD3 leads to a profound defect in recruitment of key repair and signaling factors to damaged chromatin. As a result, PCNA ubiquitylation is inhibited without RFWD3, and TLS across different DNA lesions is drastically impaired. We propose that RFWD3 is an essential coordinator of the response to ssDNA gaps, where it promotes ubiquitylation to drive recruitment of effectors of PCNA ubiquitylation and DNA damage bypass.

SCAI promotes error-free repair of DNA interstrand crosslinks via the Fanconi anemia pathway.

DNA interstrand crosslinks (ICLs) are cytotoxic lesions that threaten genome integrity. The Fanconi anemia (FA) pathway orchestrates ICL repair during DNA replication, with ubiquitylated FANCI-FANCD2 (ID2) marking the activation step that triggers incisions on DNA to unhook the ICL. Restoration of intact DNA requires the coordinated actions of polymerase ζ (Polζ)-mediated translesion synthesis (TLS) and homologous recombination (HR). While the proteins mediating FA pathway activation have been well characterized, the effectors regulating repair pathway choice to promote error-free ICL resolution remain poorly defined. Here, we uncover an indispensable role of SCAI in ensuring error-free ICL repair upon activation of the FA pathway. We show that SCAI forms a complex with Polζ and localizes to ICLs during DNA replication. SCAI-deficient cells are exquisitely sensitive to ICL-inducing drugs and display major hallmarks of FA gene inactivation. In the absence of SCAI, HR-mediated ICL repair is defective, and breaks are instead re-ligated by polymerase θ-dependent microhomology-mediated end-joining, generating deletions spanning the ICL site and radial chromosomes. Our work establishes SCAI as an integral FA pathway component, acting at the interface between TLS and HR to promote error-free ICL repair.

Adversarial and variational autoencoders improve metagenomic binning.

Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime.

Global within-species phylogenetics of sewage microbes suggest that local adaptation shapes geographical bacterial clustering.

Most investigations of geographical within-species differences are limited to focusing on a single species. Here, we investigate global differences for multiple bacterial species using a dataset of 757 metagenomics sewage samples from 101 countries worldwide. The within-species variations were determined by performing genome reconstructions, and the analyses were expanded by gene focused approaches. Applying these methods, we recovered 3353 near complete (NC) metagenome assembled genomes (MAGs) encompassing 1439 different MAG species and found that within-species genomic variation was in 36% of the investigated species (12/33) coherent with regional separation. Additionally, we found that variation of organelle genes correlated less with geography compared to metabolic and membrane genes, suggesting that the global differences of these species are caused by regional environmental selection rather than dissemination limitations. From the combination of the large and globally distributed dataset and in-depth analysis, we present a wide investigation of global within-species phylogeny of sewage bacteria. The global differences found here emphasize the need for worldwide data sets when making global conclusions.

Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan.

Distinct gut microbiome ecology may be implicated in the prevention of aging-related diseases as it influences systemic immune function and resistance to infections. Yet, the viral component of the microbiome throughout different stages in life remains unexplored. Here we present a characterization of the centenarian gut virome using previously published metagenomes from 195 individuals from Japan and Sardinia. Compared with gut viromes of younger adults (>18 yr) and older individuals (>60 yr), centenarians had a more diverse virome including previously undescribed viral genera, such as viruses associated with Clostridia. A population shift towards higher lytic activity was also observed. Finally, we investigated phage-encoded auxiliary functions that influence bacterial physiology, which revealed an enrichment of genes supporting key steps in sulfate metabolic pathways. Phage and bacterial members of the centenarian microbiome displayed an increased potential for converting methionine to homocysteine, sulfate to sulfide and taurine to sulfide. A greater metabolic output of microbial hydrogen sulfide in centenarians may in turn support mucosal integrity and resistance to pathobionts.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Machine learning and deep learning applications in microbiome research.

The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data - being compositional, sparse, and high-dimensional - necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

Genome binning of viral entities from bulk metagenomics data.

Despite the accelerating number of uncultivated virus sequences discovered in metagenomics and their apparent importance for health and disease, the human gut virome and its interactions with bacteria in the gastrointestinal tract are not well understood. This is partly due to a paucity of whole-virome datasets and limitations in current approaches for identifying viral sequences in metagenomics data. Here, combining a deep-learning based metagenomics binning algorithm with paired metagenome and metavirome datasets, we develop Phages from Metagenomics Binning (PHAMB), an approach that allows the binning of thousands of viral genomes directly from bulk metagenomics data, while simultaneously enabling clustering of viral genomes into accurate taxonomic viral populations. When applied on the Human Microbiome Project 2 (HMP2) dataset, PHAMB recovered 6,077 high-quality genomes from 1,024 viral populations, and identified viral-microbial host interactions. PHAMB can be advantageously applied to existing and future metagenomes to illuminate viral ecological dynamics with other microbiome constituents.

Improved metagenome binning and assembly using deep variational autoencoders.

Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a program that uses deep variational autoencoders to encode sequence coabundance and k-mer distribution information before clustering. We show that a variational autoencoder is able to integrate these two distinct data types without any previous knowledge of the datasets. VAMB outperforms existing state-of-the-art binners, reconstructing 29-98% and 45% more near-complete (NC) genomes on simulated and real data, respectively. Furthermore, VAMB is able to separate closely related strains up to 99.5% average nucleotide identity (ANI), and reconstructed 255 and 91 NC Bacteroides vulgatus and Bacteroides dorei sample-specific genomes as two distinct clusters from a dataset of 1,000 human gut microbiome samples. We use 2,606 NC bins from this dataset to show that species of the human gut microbiome have different geographical distribution patterns. VAMB can be run on standard hardware and is freely available at https://github.com/RasmussenLab/vamb .

Determining the risk of calcium oxide (CaO) particle exposure to marine organisms.

Calcium oxide (CaO) is being considered as a possible treatment for both the control of echinoderm populations and the treatment against sea lice infestation in Norwegian salmon farms. CaO particles produce an exothermal reaction when in contact with water, which can cause epidermal burns and lesions to certain target organisms leading to death. The aim of the present study was to determine the effects of fine (<0.8 mm) and coarse (<2.5 mm) CaO particles to a range of marine species from different taxonomic groups: two echinoderms (Asterias ruben and Strongylocentrotus droebachiensis); two crustaceans (Carcinus maenas and Tisbe battagliai); two molluscs (Mytilus edulis and Hinia reticulata); a polychaete (Nereis pelagica); a fish (Cyclopterus sp.); and seaweed germlings (Fucus vesiculosus). Overall, the fine CaO particles were more toxic to the selected marine species than the coarse particles. Coarse CaO particle effects were only observed in four of the nine species tested (A. rubens, S. droebachiensis, N. pelagica, T. battagliai) with similar LC50 values between 207 and 268 g/m2. For the fine CaO particles, the lowest LC50 was for the epibenthic copepod (T. battagliai) at 3.14 g/m2, followed by the sea urchin (20.1 g/m2), starfish (22.2 g/m2), ragworm (29.6 g/m2), and netted dog whelk (41.9 g/m2). Lump sucker fish exhibited significant mortalities only at the highest fine CaO concentration tested (320 g/m2) and recorded an LC50 of 226 g/m2. The toxicity data were used to generate species sensitivity distributions (SSDs) for both fine and coarse CaO particles. The hazard concentrations for 5% of the species (HC5) calculated from the SSDs, based on NOEC values, for the coarse and fine particles were 35.5 and 1.5 g/m2 respectively. Using a recommended assessment factor of 5, the Predicted No Effect Concentration (PNEC) was calculated as 7.1 and 0.3 g/m2 for coarse and fine CaO particles respectively.

Effects of submarine mine tailings on macrobenthic community structure and ecosystem processes.

A mesocosm experiment with intact benthic communities was conducted to evaluate the effects of mine tailings on benthic community structure and biogeochemical processes. Two types of tailings were supplied from process plants using flotation and flocculation chemicals, while a third type was absent of added chemicals. All tailings impacted the sediment community at thin layers, and through more mechanisms than merely hypersedimentation. In general, the strongest impact was observed in a very fine-grained tailings containing flotation chemicals. The second strongest occurred in tailings with no process chemicals. The tailings with flocculation chemicals initiated the weakest response. Fluxes of oxygen, nitrate and ammonium provided some indications on biodegradation of organic phases. Release of phosphate and silicate decreased with increasing layer thickness of all three tailings. A threshold level of 2cm was identified both for faunal responses and for fluxes of phosphate and silicate. The particular impact mechanisms should receive more attention in future studies in order to minimize the environmental risk associated with tailings disposal.

Effects of cnidarian biofouling on salmon gill health and development of amoebic gill disease.

This study examines the potential implications of biofouling management on the development of an infectious disease in Norwegian farmed salmon. The hydroid Ectopleura larynx frequently colonises cage nets at high densities (thousands of colonies per m2) and is released into the water during regular in-situ net cleaning. Contact with the hydroids' nematocysts has the potential to cause irritation and pathological damage to salmon gills. Amoebic gill disease (AGD), caused by the amoeba Paramoeba perurans, is an increasingly international health challenge in Atlantic salmon farming. AGD often occurs concomitantly with other agents of gill disease. This study used laboratory challenge trials to: (1) characterise the gill pathology resulting from the exposure of salmon to hydroids, and (2) investigate if such exposure can predispose the fish to secondary infections-using P. perurans as an example. Salmon in tanks were exposed either to freshly 'shredded' hydroids resembling waste material from net cleaning, or to authentic concentrations of free-living P. perurans, or first to 'shredded' hydroids and then to P. perurans. Gill health (AGD gill scores, non-specific gill scores, lamellar thrombi, epithelial hyperplasia) was monitored over 5 weeks and compared to an untreated control group. Nematocysts of E. larynx contained in cleaning waste remained active following high-pressure cleaning, resulting in higher non-specific gill scores in salmon up to 1 day after exposure to hydroids. Higher average numbers of gill lamellar thrombi occurred in fish up to 7 days after exposure to hydroids. However, gill lesions caused by hydroids did not affect the infection rates of P. perurans or the disease progression of AGD. This study discusses the negative impacts hydroids and current net cleaning practices can have on gill health and welfare of farmed salmon, highlights existing knowledge gaps and reiterates the need for alternative approaches to net cleaning.