Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

Optimized model architectures for deep learning on genomic data.

The success of deep learning in various applications depends on task-specific architecture design choices, including the types, hyperparameters, and number of layers. In computational biology, there is no consensus on the optimal architecture design, and decisions are often made using insights from more well-established fields such as computer vision. These may not consider the domain-specific characteristics of genome sequences, potentially limiting performance. Here, we present GenomeNet-Architect, a neural architecture design framework that automatically optimizes deep learning models for genome sequence data. It optimizes the overall layout of the architecture, with a search space specifically designed for genomics. Additionally, it optimizes hyperparameters of individual layers and the model training procedure. On a viral classification task, GenomeNet-Architect reduced the read-level misclassification rate by 19%, with 67% faster inference and 83% fewer parameters, and achieved similar contig-level accuracy with ~100 times fewer parameters compared to the best-performing deep learning baselines.

Contribution of bacterial and host factors to pathogen "blooming" in a gnotobiotic mouse model for Salmonella enterica serovar Typhimurium-induced enterocolitis.

Inflammation has a pronounced impact on the intestinal ecosystem by driving an expansion of facultative anaerobic bacteria at the cost of obligate anaerobic microbiota. This pathogen "blooming" is also a hallmark of enteric Salmonella enterica serovar Typhimurium (S. Tm) infection. Here, we analyzed the contribution of bacterial and host factors to S. Tm "blooming" in a gnotobiotic mouse model for S. Tm-induced enterocolitis. Mice colonized with the Oligo-Mouse-Microbiota (OMM12), a minimal bacterial community, develop fulminant colitis by day 4 after oral infection with wild-type S. Tm but not with an avirulent mutant. Inflammation leads to a pronounced reduction in overall intestinal bacterial loads, distinct microbial community shifts, and pathogen blooming (relative abundance >50%). S. Tm mutants attenuated in inducing gut inflammation generally elicit less pronounced microbiota shifts and reduction in total bacterial loads. In contrast, S. Tm mutants in nitrate respiration, salmochelin production, and ethanolamine utilization induced strong inflammation and S. Tm "blooming." Therefore, individual Salmonella-specific inflammation-fitness factors seem to be of minor importance for competition against this minimal microbiota in the inflamed gut. Finally, we show that antibody-mediated neutrophil depletion normalized gut microbiota loads but not intestinal inflammation or microbiota shifts. This suggests that neutrophils equally reduce pathogen and commensal bacterial loads in the inflamed gut.

A self-supervised deep learning method for data-efficient training in genomics.

Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models.

Pulsed antibiotic treatments of gnotobiotic mice manifest in complex bacterial community dynamics and resistance effects.

Bacteria can evolve to withstand a wide range of antibiotics (ABs) by using various resistance mechanisms. How ABs affect the ecology of the gut microbiome is still poorly understood. We investigated strain-specific responses and evolution during repeated AB perturbations by three clinically relevant ABs, using gnotobiotic mice colonized with a synthetic bacterial community (oligo-mouse-microbiota). Over 80 days, we observed resilience effects at the strain and community levels, and we found that they were correlated with modulations of the estimated growth rate and levels of prophage induction as determined from metagenomics data. Moreover, we tracked mutational changes in the bacterial populations, and this uncovered clonal expansion and contraction of haplotypes and selection of putative AB resistance-conferring SNPs. We functionally verified these mutations via reisolation of clones with increased minimum inhibitory concentration (MIC) of ciprofloxacin and tetracycline from evolved communities. This demonstrates that host-associated microbial communities employ various mechanisms to respond to selective pressures that maintain community stability.

Strain Identification and Quantitative Analysis in Microbial Communities.

Microbiology has long studied the ways in which subtle genetic differences between closely related microbial strains can have profound impacts on their phenotypes and those of their surrounding environments and communities. Despite the growth in high-throughput microbial community profiling, however, such strain-level differences remain challenging to detect. Once detected, few quantitative approaches have been well-validated for associating strain variants from microbial communities with phenotypes of interest, such as medication usage, treatment efficacy, host environment, or health. First, the term "strain" itself is not used consistently when defining a highly-resolved taxonomic or genomic unit from within a microbial community. Second, computational methods for identifying such strains directly from shotgun metagenomics are difficult, with several possible reference- and assembly-based approaches available, each with different sensitivity/specificity tradeoffs. Finally, statistical challenges exist in using any of the resulting strain profiles for downstream analyses, which can include strain tracking, phylogenetic analysis, or genetic association studies. We provide an in depth discussion of recently available computational tools that can be applied for this task, as well as statistical models and gaps in performing and interpreting any of these three main types of studies using strain-resolved shotgun metagenomic profiling of microbial communities.

Rapid and accurate identification of ribosomal RNA sequences via deep learning.

Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal RNA (rRNA) sequences are highly abundant among cellular RNA, but if the target sequences do not include polyadenylation, these cannot be easily removed in library preparation, requiring their post-hoc removal with computational techniques to accelerate and improve downstream analyses. Here, we describe RiboDetector, a novel software based on a Bi-directional Long Short-Term Memory (BiLSTM) neural network, which rapidly and accurately identifies rRNA reads from transcriptomic, metagenomic, metatranscriptomic, noncoding RNA, and ribosome profiling sequence data. Compared with state-of-the-art approaches, RiboDetector produced at least six times fewer misclassifications on the benchmark datasets. Importantly, the few false positives of RiboDetector were not enriched in certain Gene Ontology (GO) terms, suggesting a low bias for downstream functional profiling. RiboDetector also demonstrated a remarkable generalizability for detecting novel rRNA sequences that are divergent from the training data with sequence identities of <90%. On a personal computer, RiboDetector processed 40M reads in less than 6 min, which was ∼50 times faster in GPU mode and ∼15 times in CPU mode than other methods. RiboDetector is available under a GPL v3.0 license at https://github.com/hzi-bifo/RiboDetector.

In vitro interaction network of a synthetic gut bacterial community.

A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM12) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM12 interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM12 interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.

APOS-antibiotic prophylaxis for preventing infectious complications in orthognathic surgery: study protocol for a phase III, multicentre, randomised, controlled, double blinded, clinical trial with two parallel study arms.

BACKGROUND: It is a constant debate among surgeons whether the use of prolonged postoperative antibiotics may reduce surgical site infection rates. As specific treatment guidelines are still lacking, many surgeons continue to use broad-spectrum antibiotics, causing not only increased costs but also contributing to the potential for antibiotic resistance. Hence, there is an urgent need for an appropriately designed prospective clinical trial, to investigate whether a prophylactic use of antibiotics after surgery actually decreases surgical site infections to a clinically relevant degree. METHODS: This study presents a multicentre, randomised, controlled, double-blinded, clinical trial with two parallel study arms to demonstrate that no postoperative antibiotic prophylaxis (AP) is not inferior to antibiotic prophylaxis with respect to surgical site infections in patients having undergone orthognathic surgery. The primary efficacy endpoint is defined as the occurrence of postoperative surgical site infections within 30 days of surgery. Secondary endpoints are further efficacy and subject-oriented parameters within 90 days after surgery. The entire trial is planned for 54 months, with an enrolment of 1420 patients over 39 months by 14 national participating centres. DISCUSSION: As a highly standardised procedure on an exceeding, healthy and young homogenous study population and identical processes all over the world, elective orthognathic surgery as clean-contaminated procedure provides comparable intervention groups with balanced baseline characteristics, comparable surgical duration, even when performed within multiple centres. Therefore, evaluating antibiotic prophylaxis after orthognathic surgery will be of high scientific value representable for other surgical procedures. TRIAL REGISTRATION: DRKS-German Clinical Trials Register- DRKS00022838 ; EudraCT No. 2020-001397-30. Registered on 29 March 2021.

Intrathecal activation of CD8+ memory T cells in IgG4-related disease of the brain parenchyma.

IgG4-related disease (IgG4-RD) is a fibroinflammatory disorder signified by aberrant infiltration of IgG4-restricted plasma cells into a variety of organs. Clinical presentation is heterogeneous, and pathophysiological mechanisms of IgG4-RD remain elusive. There are very few cases of IgG4-RD with isolated central nervous system manifestation. By leveraging single-cell sequencing of the cerebrospinal fluid (CSF) of a patient with an inflammatory intracranial pseudotumor, we provide novel insights into the immunopathophysiology of IgG4-RD. Our data illustrate an IgG4-RD-associated polyclonal T-cell response in the CSF and an oligoclonal T-cell response in the parenchymal lesions, the latter being the result of a multifaceted cell-cell interaction between immune cell subsets and pathogenic B cells. We demonstrate that CD8+ T effector memory cells might drive and sustain autoimmunity via macrophage migration inhibitory factor (MIF)-CD74 signaling to immature B cells and CC-chemokine ligand 5 (CCL5)-mediated recruitment of cytotoxic CD4+ T cells. These findings highlight the central role of T cells in sustaining IgG4-RD and open novel avenues for targeted therapies.

Evaluating assembly and variant calling software for strain-resolved analysis of large DNA viruses.

Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and 6 variant callers on 10 lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive variant calls, which were strongly enriched in T to G changes in a 'G.G' context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo), to enable full reproducibility and further benchmarking on these and other data.

Tryptophan metabolism drives dynamic immunosuppressive myeloid states in IDH-mutant gliomas.

The dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype: in isocitrate dehydrogenase (IDH)-mutant tumors, differentiation of infiltrating myeloid cells is blocked, resulting in an immature phenotype. In late-stage gliomas, monocyte-derived macrophages drive tolerogenic alignment of the microenvironment, thus preventing T cell response. We define the IDH-dependent tumor education of infiltrating macrophages to be causally related to a complex re-orchestration of tryptophan metabolism, resulting in activation of the aryl hydrocarbon receptor. We further show that the altered metabolism of IDH-mutant gliomas maintains this axis in bystander cells and that pharmacological inhibition of tryptophan metabolism can reverse immunosuppression. In conclusion, we provide evidence of a glioma genotype-dependent intratumoral network of resident and recruited myeloid cells and identify tryptophan metabolism as a target for immunotherapy of IDH-mutant tumors.

Identification of Natural CRISPR Systems and Targets in the Human Microbiome.

Many bacteria resist invasive DNA by incorporating sequences into CRISPR loci, which enable sequence-specific degradation. CRISPR systems have been well studied from isolate genomes, but culture-independent metagenomics provide a new window into their diversity. We profiled CRISPR loci and cas genes in the body-wide human microbiome using 2,355 metagenomes, yielding functional and taxonomic profiles for 2.9 million spacers by aligning the spacer content to each sample's metagenome and corresponding gene families. Spacer and repeat profiles agree qualitatively with those from isolate genomes but expand their diversity by approximately 13-fold, with the highest spacer load present in the oral microbiome. The taxonomy of spacer sequences parallels that of their source community, with functional targets enriched for viral elements. When coupled with cas gene systems, CRISPR-Cas subtypes are highly site and taxon specific. Our analysis provides a comprehensive collection of natural CRISPR-cas loci and targets in the human microbiome.

Evolutionary Stabilization of Cooperative Toxin Production through a Bacterium-Plasmid-Phage Interplay.

Colicins are toxins produced and released by Enterobacteriaceae to kill competitors in the gut. While group A colicins employ a division of labor strategy to liberate the toxin into the environment via colicin-specific lysis, group B colicin systems lack cognate lysis genes. In Salmonella enterica serovar Typhimurium (S. Tm), the group B colicin Ib (ColIb) is released by temperate phage-mediated bacteriolysis. Phage-mediated ColIb release promotes S. Tm fitness against competing Escherichia coli It remained unclear how prophage-mediated lysis is realized in a clonal population of ColIb producers and if prophages contribute to evolutionary stability of toxin release in S. Tm. Here, we show that prophage-mediated lysis occurs in an S. Tm subpopulation only, thereby introducing phenotypic heterogeneity to the system. We established a mathematical model to study the dynamic interplay of S. Tm, ColIb, and a temperate phage in the presence of a competing species. Using this model, we studied long-term evolution of phage lysis rates in a fluctuating infection scenario. This revealed that phage lysis evolves as bet-hedging strategy that maximizes phage spread, regardless of whether colicin is present or not. We conclude that the ColIb system, lacking its own lysis gene, is making use of the evolutionary stable phage strategy to be released. Prophage lysis genes are highly prevalent in nontyphoidal Salmonella genomes. This suggests that the release of ColIb by temperate phages is widespread. In conclusion, our findings shed new light on the evolution and ecology of group B colicin systems.IMPORTANCE Bacteria are excellent model organisms to study mechanisms of social evolution. The production of public goods, e.g., toxin release by cell lysis in clonal bacterial populations, is a frequently studied example of cooperative behavior. Here, we analyze evolutionary stabilization of toxin release by the enteric pathogen Salmonella The release of colicin Ib (ColIb), which is used by Salmonella to gain an edge against competing microbiota following infection, is coupled to bacterial lysis mediated by temperate phages. Here, we show that phage-dependent lysis and subsequent release of colicin and phage particles occurs only in part of the ColIb-expressing Salmonella population. This phenotypic heterogeneity in lysis, which represents an essential step in the temperate phage life cycle, has evolved as a bet-hedging strategy under fluctuating environments such as the gastrointestinal tract. Our findings suggest that prophages can thereby evolutionarily stabilize costly toxin release in bacterial populations.

Monitoring innate immune cell dynamics in the glioma microenvironment by magnetic resonance imaging and multiphoton microscopy (MR-MPM).

Rationale: Glioblastoma is the most frequent, primary brain tumor that is characterized by a highly immunosuppressive tumor microenvironment (TME). The TME plays a key role for tumor biology and the effectiveness of immunotherapies. Composition of the TME correlates with overall survival and governs therapy response. Non invasive assessment of the TME has been notoriously difficult. Methods: We have designed an in vivo imaging approach to non invasively visualize innate immune cell dynamics in the TME in a mouse glioma model by correlated MRI and multiphoton microscopy (MR-MPM) using a bimodal, fluorescently labeled iron oxide nanoparticle (NP). The introduction of Teflon cranial windows instead of conventional Titanium rings dramatically reduced susceptibility artifacts on MRI and allowed longitudinal MR-MPM imaging for innate immune cell tracking in the same animal. Results: We visualized tumor associated macrophage and microglia (TAM) dynamics in the TME and dissect the single steps of NP uptake by blood-born monocytes that give rise to tumor-associated macrophages. Next to peripheral NP-loading, we identified a second route of direct nanoparticle uptake via the disrupted blood-brain barrier to directly label tissue resident TAMs. Conclusion: Our approach allows innate immune cell tracking by MRI and multiphoton microscopy in the same animal to longitudinally investigate innate immune cell dynamics in the TME.

Susceptibility-weighted imaging in malignant melanoma brain metastasis.

BACKGROUND: The value of cerebral susceptibility-weighted imaging (SWI) in malignant melanoma (MM) patients remains controversial and the effect of melanin on SWI is not well understood. PURPOSE: To systematically analyze the spectrum of intracerebral findings in MM brain metastases (BM) on SWI and to determine the diagnostic value of SWI. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: In all, 100 patients with melanoma BM (69 having received radiotherapy [RT] and 31 RT-naïve) and a control group of 100 melanoma patients without BM were included. For detailed analysis of signal characteristics, 175 metastases were studied. FIELD STRENGTH/SEQUENCE: Gradient echo SWI sequence at 1.5, 3.0, and 9.4 T. ASSESSMENT: Signal characteristics from melanotic and amelanotic BMs on SWI with a focus on blooming artifacts were analyzed, as well as the presence and longitudinal dynamics of isolated SWI blooming artifacts in patients with and without BM. STATISTICAL TESTS: Chi-squared and Student's t-test were used for contingency table measures and group data of signal and clinical characteristics, respectively. RESULTS: Melanotic and amelanotic metastases did not show significant differences of SWI blooming artifacts (38% vs. 43%, P = 0.61). Most metastases without an initial SWI artifact developed a signal dropout during follow-up (80%; 65/81). Isolated SWI artifacts were detected more frequently in patients with BM (20 vs. 9, P = 0.03), of which the majority were found in patients who had received RT (17 vs. 3, P = 0.08). None of these isolated SWI blooming artifacts turned into overt metastases over time (median follow-up: 8.5 months). Similar findings persisted as remnants of successfully treated metastases (88%; 7/8). DATA CONCLUSION: We conclude that SWI provides little additional diagnostic benefit over standard T1 -weighted imaging, as melanin content alone does not cause diagnostically relevant SWI blooming. Signal transition of SWI may rather indicate secondary phenomena like microbleeding and/or metal scavenging. Our results suggest that isolated SWI artifacts do not constitute vital tumor tissue but represent unspecific microbleedings, RT-related parenchymal changes or posttherapeutic remnants of former metastatic lesions. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:1251-1259.

Publisher Correction: Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life.

In the version of this Article originally published, in the first sentence of the second paragraph of the Discussion section, the word "operingrationally" should have read "operationally". This has now been amended in all versions of the Article.