Reference-free structural variant detection in microbiomes via long-read co-assembly graphs.

MOTIVATION: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. RESULTS: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux. AVAILABILITY AND IMPLEMENTATION: rhea is open source and available at: https://github.com/treangenlab/rhea.

Lightweight taxonomic profiling of long-read sequenced metagenomes with Lemur and Magnet.

Taxonomic profiling is a ubiquitous task in the analysis of clinical and environmental microbiomes. The advent of long-read sequencing of microbiomes necessitates the development of new taxonomic profilers tailored to long-read shotgun metagenomic datasets. Here, we introduce Lemur and Magnet, a pair of tools optimized for lightweight and accurate taxonomic profiling from long-read shotgun metagenomic datasets. Lemur is a marker-gene based method that leverages an EM algorithm to reduce false positive calls while preserving true positives; Magnet makes detailed presence/absence calls for bacterial genomes based on whole-genome read mapping. The tools work in sequence: Lemur estimates abundances conservatively, and Magnet operates on the genomes of identified organisms to filter out likely false positive taxa. The result is an increase in precision of as much as 70%, which far exceeds competing methods. By operating only on marker genes, Lemur is a comparatively lightweight software. We demonstrate that it can run in minutes to hours on a laptop with 32 GB of RAM, even for large inputs - a crucial feature given the portability of long-read sequencing machines. Furthermore, the marker gene database used by Lemur is only 4 GB and contains information from over 300,000 RefSeq genomes. The reference is available at https://zenodo.org/records/10802546, and the software is open-source and available at https://github.com/treangenlab/lemur.

Microbial Community Profiling Protocol with Full-length 16S rRNA Sequences and Emu.

16S rRNA targeted amplicon sequencing is an established standard for elucidating microbial community composition. While high-throughput short-read sequencing can elicit only a portion of the 16S rRNA gene due to their limited read length, third generation sequencing can read the 16S rRNA gene in its entirety and thus provide more precise taxonomic classification. Here, we present a protocol for generating full-length 16S rRNA sequences with Oxford Nanopore Technologies (ONT) and a microbial community profile with Emu. We select Emu for analyzing ONT sequences as it leverages information from the entire community to overcome errors due to incomplete reference databases and hardware limitations to ultimately obtain species-level resolution. This pipeline provides a low-cost solution for characterizing microbiome composition by exploiting real-time, long-read ONT sequencing and tailored software for accurate characterization of microbial communities. © 2024 Wiley Periodicals LLC. Basic Protocol: Microbial community profiling with Emu Support Protocol 1: Full-length 16S rRNA microbial sequences with Oxford Nanopore Technologies sequencing platform Support Protocol 2: Building a custom reference database for Emu.

Reference-free Structural Variant Detection in Microbiomes via Long-read Coassembly Graphs.

Bacterial genome dynamics are vital for understanding the mechanisms underlying microbial adaptation, growth, and their broader impact on host phenotype. Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to absence of clear reference genomes and presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing a single metagenome coassembly graph constructed from all samples in a series. The log fold change in graph coverage between subsequent samples is then calculated to call SVs that are thriving or declining throughout the series. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, which is particularly noticeable as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between subsequent time and temperature samples, suggesting host advantage. Our innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis, and thus provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial genome dynamics.

Ultra-high throughput mapping of genetic design space.

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 5 drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

Analysis of bronchoalveolar lavage fluid metatranscriptomes among patients with COVID-19 disease.

To better understand the potential relationship between COVID-19 disease and hologenome microbial community dynamics and functional profiles, we conducted a multivariate taxonomic and functional microbiome comparison of publicly available human bronchoalveolar lavage fluid (BALF) metatranscriptome samples amongst COVID-19 (n = 32), community acquired pneumonia (CAP) (n = 25), and uninfected samples (n = 29). We then performed a stratified analysis based on mortality amongst the COVID-19 cohort with known outcomes of deceased (n = 10) versus survived (n = 15). Our overarching hypothesis was that there are detectable and functionally significant relationships between BALF microbial metatranscriptomes and the severity of COVID-19 disease onset and progression. We observed 34 functionally discriminant gene ontology (GO) terms in COVID-19 disease compared to the CAP and uninfected cohorts, and 21 GO terms functionally discriminant to COVID-19 mortality (q < 0.05). GO terms enriched in the COVID-19 disease cohort included hydrolase activity, and significant GO terms under the parental terms of biological regulation, viral process, and interspecies interaction between organisms. Notable GO terms associated with COVID-19 mortality included nucleobase-containing compound biosynthetic process, organonitrogen compound catabolic process, pyrimidine-containing compound biosynthetic process, and DNA recombination, RNA binding, magnesium and zinc ion binding, oxidoreductase activity, and endopeptidase activity. A Dirichlet multinomial mixtures clustering analysis resulted in a best model fit using three distinct clusters that were significantly associated with COVID-19 disease and mortality. We additionally observed discriminant taxonomic differences associated with COVID-19 disease and mortality in the genus Sphingomonas, belonging to the Sphingomonadacae family, Variovorax, belonging to the Comamonadaceae family, and in the class Bacteroidia, belonging to the order Bacteroidales. To our knowledge, this is the first study to evaluate significant differences in taxonomic and functional signatures between BALF metatranscriptomes from COVID-19, CAP, and uninfected cohorts, as well as associating these taxa and microbial gene functions with COVID-19 mortality. Collectively, while this data does not speak to causality nor directionality of the association, it does demonstrate a significant relationship between the human microbiome and COVID-19. The results from this study have rendered testable hypotheses that warrant further investigation to better understand the causality and directionality of host-microbiome-pathogen interactions.

Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data.

16S ribosomal RNA-based analysis is the established standard for elucidating the composition of microbial communities. While short-read 16S rRNA analyses are largely confined to genus-level resolution at best, given that only a portion of the gene is sequenced, full-length 16S rRNA gene amplicon sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate often observed in long-read data. Here we present Emu, an approach that uses an expectation-maximization algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from simulated datasets and mock communities show that Emu is capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of Emu by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow with those returned by full-length 16S rRNA gene sequences processed with Emu.

Fecal Microbiota Transplantation Derived from Alzheimer's Disease Mice Worsens Brain Trauma Outcomes in Wild-Type Controls.

Traumatic brain injury (TBI) causes neuroinflammation and neurodegeneration, both of which increase the risk and accelerate the progression of Alzheimer's disease (AD). The gut microbiome is an essential modulator of the immune system, impacting the brain. AD has been related with reduced diversity and alterations in the community composition of the gut microbiota. This study aimed to determine whether the gut microbiota from AD mice exacerbates neurological deficits after TBI in control mice. We prepared fecal microbiota transplants from 18 to 24 month old 3×Tg-AD (FMT-AD) and from healthy control (FMT-young) mice. FMTs were administered orally to young control C57BL/6 (wild-type, WT) mice after they underwent controlled cortical impact (CCI) injury, as a model of TBI. Then, we characterized the microbiota composition of the fecal samples by full-length 16S rRNA gene sequencing analysis. We collected the blood, brain, and gut tissues for protein and immunohistochemical analysis. Our results showed that FMT-AD administration stimulates a higher relative abundance of the genus Muribaculum and a decrease in Lactobacillus johnsonii compared to FMT-young in WT mice. Furthermore, WT mice exhibited larger lesion, increased activated microglia/macrophages, and reduced motor recovery after FMT-AD compared to FMT-young one day after TBI. In summary, we observed gut microbiota from AD mice to have a detrimental effect and aggravate the neuroinflammatory response and neurological outcomes after TBI in young WT mice.

Alterations to the gut microbiome after sport-related concussion in a collegiate football players cohort: A pilot study.

Concussions, both single and repetitive, cause brain and body alterations in athletes during contact sports. The role of the brain-gut connection and changes in the microbiota have not been well established after sports-related concussions or repetitive subconcussive impacts. We recruited 33 Division I Collegiate football players and collected blood, stool, and saliva samples at three time points throughout the athletic season: mid-season, following the last competitive game (post-season), and after a resting period in the off-season. Additional samples were collected from four athletes that suffered from a concussion. 16S rRNA sequencing of the gut microbiome revealed a decrease in abundance for two bacterial species, Eubacterium rectale, and Anaerostipes hadrus, after a diagnosed concussion. No significant differences were found regarding the salivary microbiome. Serum biomarker analysis shows an increase in GFAP blood levels in athletes during the competitive season. Additionally, S100ß and SAA blood levels were positively correlated with the abundance of Eubacterium rectale species among the group of athletes that did not suffer a diagnosed concussion during the sports season. These findings provide initial evidence that detecting changes in the gut microbiome may help to improve concussion diagnosis following head injury.

It takes guts to learn: machine learning techniques for disease detection from the gut microbiome.

Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.

Association of Rituximab Use With Adverse Events in Children, Adolescents, and Young Adults.

Importance: Rituximab is among the most frequently used immunotherapies in pediatrics. Few studies have reported long-term adverse events associated with its use for children. Objective: To describe the use of rituximab and to assess whether its use is associated with short- or long-term adverse events, infections, or time to immune reconstitution in a diverse group of young people. Design, Setting, and Participants: This retrospective cohort study included 468 patients aged younger than 21 years who received rituximab for diverse indications between October 1, 2010, and December 31, 2017, at Texas Children's Hospital, a large pediatric referral hospital. Patterns of adverse events, infections, and immune recovery are described. Data analyses were conducted from December 2019 to June 2020. Exposure: One or more doses of rituximab. Main Outcomes and Measures: Adverse drug events (eg, anaphylaxis), incidence of mild and severe infections, and time to recovery of B lymphocyte subset counts and immunoglobulin levels. Survival models and logistic regression analyses and were used to identify associated risk factors of infectious and noninfectious adverse drug events. Results: We identified 468 patients receiving at least 1 dose of rituximab. The total follow-up time was 11 713 person-months. Of the 468 patients, 293 (62.6%) were female, the median (interquartile range) age at receipt of dose was 14.3 (9.9-16.8) years, and 209 (44.7%) were self-reported White Hispanic. Adverse events associated with rituximab infusion occurred in 72 patients (15.4%), and anaphylaxis occurred in 17 patients (3.6%). Long-term adverse events, such as prolonged neutropenia and leukoencephalopathy, were absent. Infections occurred in 224 patients (47.9%); 84 patients (17.9%) had severe infections, and 3 patients (0.6%) had lethal infections. Concurrent use of intravenous chemotherapy, treatment of systemic lupus erythematosus, neutropenia, and use of intravenous immunoglobulin were associated with increased risk of infection. Among 135 patients (28.8%) followed up to B cell count recovery, CD19+ or CD20+ cell numbers normalized in a median of 9.0 months (interquartile range, 5.9-14.4 months) following rituximab use; 48 of 95 patients (51%) evaluated beyond a year had low-for-age B cell counts. Recovery of CD27+ memory B cell number occurred in a median of 15.7 months (interquartile range, 6.0-22.7 months). Among patients with normal baseline values, low immunoglobulin G (IgG) levels developed in 67 of 289 patients (23.2%) and low IgM levels in 118 of 255 patients (40.8%); of these patients evaluated beyond 12 months from rituximab, 16 of 117 (13.7%) had persistently low IgG and 37 (33.9%) of 109 had persistently low IgM. Conclusions and Relevance: Rituximab is well tolerated among young people and is associated with few serious adverse events, but infections are common, corresponding to a prolonged period of B cell count recovery often lasting for longer than a year. Further examination of strategies to prevent infections following rituximab should be pursued.

Effects of the synthetic psychedelic 2,5-dimethoxy-4-iodoamphetamine (DOI) on ethanol consumption and place conditioning in male mice.

RATIONALE: Approximately 20 million adults in the USA have an alcohol use disorder (AUD). There are clinical and preclinical data suggesting that psychedelics may have benefits for AUD. OBJECTIVE: To investigate the effects of the synthetic psychedelic 2,5-dimethoxy-4-iodoamphetamine (DOI) on the behavioral effects of ethanol. METHODS: The effects of DOI were examined using ethanol-induced place conditioning (1.8 g/kg ethanol) and 2-bottle choice ethanol drinking (20% v/v), using a dose of DOI (3 mg/kg) that produced the maximal response in the serotonin 2A (5-HT2A) receptor-dependent head-twitch assay. Interactions between DOI and ethanol (3 g/kg) were examined using the ethanol-induced loss of righting reflex procedure and blood-ethanol analysis. To examine additional mechanisms by which psychedelics may interact with ethanol, we determined whether DOI reverses ethanol-induced nitric oxide release in macrophages, a marker of inflammation. RESULTS: DOI significantly attenuated ethanol-induced place conditioning and ethanol drinking. DOI-induced suppression of alcohol drinking depended upon 5-HT2A receptors, was selective for alcohol over water, and was selective for high alcohol-preferring subjects. DOI had no apparent pharmacokinetic interactions with ethanol, and DOI reduced ethanol-induced nitric oxide release. CONCLUSIONS: Our findings demonstrate that DOI blocks ethanol place conditioning and selectively reduces voluntary ethanol consumption. This may be related to modulation of the effects of ethanol in the reward circuitry of the brain, ethanol-induced neuroinflammation, or a combination of both. Additional studies to elucidate the mechanisms through which psychedelics attenuate the effects of ethanol would inform the pathophysiology of AUD and potentially provide new treatment options.