Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)

Determining the incidence, risk factors and biological drivers of irritable bowel syndrome (IBS) as part of the constellation of postacute sequelae of SARS-CoV-2 infection (PASC) outcomes in the Arizona CoVHORT-GI: a longitudinal cohort study

INTRODUCTION

Postacute sequelae of SARS-CoV-2 infection (PASC) are extensive. Also known as long COVID, primary outcomes reported are neurologic, cardiac and respiratory in nature. However, several studies have also reported an increase in gastrointestinal (GI) symptoms and syndromes following COVID-19. This study of PASC will include extensive analyses of GI symptoms, determine if people with pre-existing irritable bowel syndrome (IBS) are at higher risk of developing PASC generally or PASC-GI, and which biomarkers are impacted and to what degree. This R01 study is being funded by the National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK135483-01) from 2023 to 2028.

METHODS AND ANALYSES

This study combines a longitudinal epidemiologic cohort study and in-depth, novel biologic analyses. In collaboration with a pre-existing study, the Arizona CoVID-19 Cohort (CoVHORT)-GI will recruit participants based on the history of COVID infection(s), new or ongoing GI symptoms 3-6 months postinfection, and pre-existing or incident IBS diagnosis to represent five study groups for comparison and analyses. A subset (n=1000) of those recruited will submit both stool and blood samples. Both samples will undergo a novel method to quantitate humoral and mucosal immune responses to host-derived faecal communities in conjunction with magnetic bead-based separation and high-depth shotgun microbial sequencing. Stool samples will also undergo traditional microbiome analyses (diversity and abundance) and faecal calprotectin assays. Additional serum analyses will aim to determine if a proteomics-based signature exists that differentiates a unique biomarker compositional signature discriminating PASC-GI versus no PASC. All laboratory data will be linked with in-depth epidemiologic data on demographics, symptoms and chronic conditions.

ETHICS AND DISSEMINATION

This study involves human participants and was approved by the University of Arizona Institutional Review Board (IRB (#00002332) and has been deemed minimal risk. Participants gave informed consent to participate in the study before taking part. All publications from the study will be shared back to participants along with alternative lay summaries and webinars to communicate key findings. The data management plan has been published and is publicly available online, including protocols for data requests.

Epigenetic phase variation in the gut microbiome enhances bacterial adaptation

The human gut microbiome within the gastrointestinal tract continuously adapts to variations in diet, medications, and host physiology. A central strategy for genetic adaptation is epigenetic phase variation (ePV) mediated by bacterial DNA methylation, which can regulate gene expression, enhance clonal heterogeneity, and enable a single bacterial strain to exhibit variable phenotypic states. Genome-wide and site-specific ePV have been well characterized in human pathogens' antigenic variation and virulence factor production. However, the role of ePV in facilitating adaptation within the human microbiome remains poorly understood. Here, we comprehensively cataloged genome-wide and site-specific ePV in human infant and adult gut microbiomes. First, using long-read metagenomic sequencing, we detected genome-wide ePV mediated by complex structural variations of DNA methyltransferases, highlighting the ones associated with antibiotics or fecal microbiota transplantation. Second, we analyzed an extensive collection of public short-read metagenomic sequencing datasets, uncovering a greater prevalence of genome-wide ePV in the human gut microbiome. Third, we quantitatively detected site-specific ePVs using single-molecule methylation analysis to identify dynamic variations associated with antibiotic treatment or probiotic engraftment. Finally, we performed an in-depth assessment of an Akkermansia muciniphila isolate from an infant, highlighting that ePV can regulate gene expression and enhance the bacterial adaptive capacity by employing a bet-hedging strategy to increase tolerance to differing antibiotics. Our findings indicate that epigenetic modifications are a common and broad strategy used by bacteria in the human gut to adapt to their environment.

Microbial communities associated with marine sponges from diverse geographic locations harbor biosynthetic novelty

Marine sponges are a prolific source of biologically active small molecules, many of which originate from sponge-associated bacteria. Identifying the producing bacteria is a key step in developing sustainable routes for the production of these metabolites. To facilitate the required computational analyses, we developed MetaSing, a reproducible singularity-based pipeline for assembly, identification of high-quality metagenome-assembled genomes (MAGs), and analysis of biosynthetic gene clusters (BGCs) from metagenomic short-read data. We applied this pipeline to metagenomic sequencing data from 16 marine sponges collected from New Zealand, Tonga, and the Mediterranean Sea. This analysis yielded 643 MAGs representing 510 species. Of the 2,670 BGCs identified across all samples, 70.8% were linked to a MAG. Comparison of BGCs to those identified from previously sequenced bacteria revealed high biosynthetic novelty in variety of underexplored phyla, including Poribacteria, Acidobacteriota, and Dadabacteria. Alongside the observation that each sample contains unique biosynthetic potential, this holds great promise for natural product discovery and for furthering the understanding of different sponge holobionts.IMPORTANCEDiscovery of new chemical compounds such as natural products is a crucial endeavor to combat the increasing resistance to antibiotics and other drugs. This manuscript demonstrates that microbial communities associated with marine sponges investigated in this work encode the potential to produce novel chemistry. Lesser studied bacterial taxa that are often difficult to cultivate are particularly rich in potential.

Nanopore Sequencing Allows Recovery of High-Quality Completely Closed Genomes of All Cronobacter Species from Powdered Infant Formula Overnight Enrichments

Precision metagenomic approaches using Oxford Nanopore Technology (ONT) sequencing has been shown to allow recovery of complete genomes of foodborne bacteria from overnight enrichments of agricultural waters. This study tests the applicability of a similar approach for Cronobacter genome recovery from powdered infant formula (PIF) overnight enrichments, where Cronobacter typically dominates the overall microbiome (>90%). This study aimed to test whether using ONT sequencing of overnight PIF enrichments could recover a completely closed Cronobacter genome for further genomic characterization. Ten PIF samples, each inoculated with different Cronobacter strains, covering Cronobacter sakazakii, C. muytjensii, C. dublinensis, C. turicensis, and C. universalis, were processed according to the Bacteriological Analytical Manual (BAM) protocol. Real-time quantitative PCR (qPCR) was used for initial screening (detection and quantification) of the overnight enrichments and confirmed that the inoculated PIF samples after the overnight enrichment had high levels of Cronobacter (107 to 109 CFU/mL). DNA from overnight PIF enrichments was extracted from the enrichment broth and sequenced using ONT. Results showed that ONT sequencing could accurately identify, characterize, and close the genomes of Cronobacter strains from overnight PIF enrichments in 3 days, much faster than the nearly 2 weeks required by the current BAM method. Complete genome recovery and species differentiation were achieved. This suggests that combining qPCR with ONT sequencing provides a rapid, cost-effective alternative for detecting and characterizing Cronobacter in PIF, enabling timely corrective actions during outbreaks.

Simple, reference-independent assessment to empirically guide correction and polishing of hybrid microbial community metagenomic assembly

Hybrid metagenomic assembly of microbial communities, leveraging both long- and short-read sequencing technologies, is becoming an increasingly accessible approach, yet its widespread application faces several challenges. High-quality references may not be available for assembly accuracy comparisons common for benchmarking, and certain aspects of hybrid assembly may benefit from dataset-dependent, empiric guidance rather than the application of a uniform approach. In this study, several simple, reference-free characteristics-particularly coding gene content and read recruitment profiles-were hypothesized to be reliable indicators of assembly quality improvement during iterative error-fixing processes. These characteristics were compared to reference-dependent genome- and gene-centric analyses common for microbial community metagenomic studies. Two laboratory-scale bioreactors were sequenced with short- and long-read platforms, and assembled with commonly used software packages. Following long read assembly, long read correction and short read polishing were iterated up to ten times to resolve errors. These iterative processes were shown to have a substantial effect on gene- and genome-centric community compositions. Simple, reference-free assembly characteristics, specifically changes in gene fragmentation and short read recruitment, were robustly correlated with advanced analyses common in published comparative studies, and therefore are suitable proxies for hybrid metagenome assembly quality to simplify the identification of the optimal number of correction and polishing iterations. As hybrid metagenomic sequencing approaches will likely remain relevant due to the low added cost of short-read sequencing for differential coverage binning or the ability to access lower abundance community members, it is imperative that users are equipped to estimate assembly quality prior to downstream analyses.

MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads

BACKGROUND

Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms.

METHOD

In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests.

RESULTS

We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC.

CONCLUSION

The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

Metagenome-assembled genome sequences of two bacterial species from polyvinyl alcohol-degrading co-colonies

We present the metagenome-assembled genome sequences of two polyvinyl alcohol-degrading co-colony-derived bacterial species relative to Rhodanobacter sp. DHB23 and Priestia megaterium ATCC 14581. We estimated the genomes of these species to be 3,476,996- and 5,169,587-bp long (for Rhodanobacter sp. DHB23 and Priestia megaterium ATCC 14581, respectively).

Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis

Metagenomic sequencing has emerged as a transformative tool in infectious disease diagnosis, offering a comprehensive and unbiased approach to pathogen detection. Leveraging international standards and guidelines is essential for ensuring the quality and reliability of metagenomic sequencing in clinical practice. This review explores the implications of international standards and guidelines for the application of metagenomic sequencing in infectious disease diagnosis. By adhering to established standards, such as those outlined by regulatory bodies and expert consensus, healthcare providers can enhance the accuracy and clinical utility of metagenomic sequencing. The integration of international standards and guidelines into metagenomic sequencing workflows can streamline diagnostic processes, improve pathogen identification, and optimize patient care. Strategies in implementing these standards for infectious disease diagnosis using metagenomic sequencing are discussed, highlighting the importance of standardized approaches in advancing precision infectious disease diagnosis initiatives.

Metagenomic Analysis of Anaerobic Microbial Communities Degrading Short-Chain Fatty Acids as Sole Carbon Sources

Analyzing microbial communities using metagenomes is a powerful approach to understand compositional structures and functional connections in anaerobic digestion (AD) microbiomes. Whereas short-read sequencing approaches based on the Illumina platform result in highly fragmented metagenomes, long-read sequencing leads to more contiguous assemblies. To evaluate the performance of a hybrid approach of these two sequencing approaches we compared the metagenome-assembled genomes (MAGs) resulting from five AD microbiome samples. The samples were taken from reactors fed with short-chain fatty acids at different feeding regimes (continuous and discontinuous) and organic loading rates (OLR). Methanothrix showed a high relative abundance at all feeding regimes but was strongly reduced in abundance at higher OLR, when Methanosarcina took over. The bacterial community composition differed strongly between reactors of different feeding regimes and OLRs. However, the functional potential was similar regardless of feeding regime and OLR. The hybrid sequencing approach using Nanopore long-reads and Illumina MiSeq reads improved assembly statistics, including an increase of the N50 value (on average from 32 to 1740 kbp) and an increased length of the longest contig (on average from 94 to 1898 kbp). The hybrid approach also led to a higher share of high-quality MAGs and generated five potentially circular genomes while none were generated using MiSeq-based contigs only. Finally, 27 hybrid MAGs were reconstructed of which 18 represent potentially new species-15 of them bacterial species. During pathway analysis, selected MAGs revealed similar gene patterns of butyrate degradation and might represent new butyrate-degrading bacteria. The demonstrated advantages of adding long reads to metagenomic analyses make the hybrid approach the preferable option when dealing with complex microbiomes.

Nanopore-based enrichment of antimicrobial resistance genes - a case-based study

Rapid screening of hospital admissions to detect asymptomatic carriers of resistant bacteria can prevent pathogen outbreaks. However, the resulting isolates rarely have their genome sequenced due to cost constraints and long turn-around times to get and process the data, limiting their usefulness to the practitioner. Here we used real-time, on-device target enrichment ("adaptive") sequencing as a highly multiplexed assay covering 1,147 antimicrobial resistance genes. We compared its utility against standard and metagenomic sequencing, focusing on an isolate of Raoultella ornithinolytica harbouring three carbapenemases (NDM, KPC, VIM). Based on this experimental data, we then modelled the influence of several variables on the enrichment results and predicted the large effect of nucleotide identity (higher is better) and read length (shorter is better). Lastly, we showed how all relevant resistance genes are detected using adaptive sequencing on a miniature ("Flongle") flow cell, motivating its use in a clinical setting to monitor similar cases and their surroundings.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Introduction to the principles and methods underlying the recovery of metagenome-assembled genomes from metagenomic data

The rise of metagenomics offers a leap forward for understanding the genetic diversity of microorganisms in many different complex environments by providing a platform that can identify potentially unlimited numbers of known and novel microorganisms. As such, it is impossible to imagine new major initiatives without metagenomics. Nevertheless, it represents a relatively new discipline with various levels of complexity and demands on bioinformatics. The underlying principles and methods used in metagenomics are often seen as common knowledge and often not detailed or fragmented. Therefore, we reviewed these to guide microbiologists in taking the first steps into metagenomics. We specifically focus on a workflow aimed at reconstructing individual genomes, that is, metagenome-assembled genomes, integrating DNA sequencing, assembly, binning, identification and annotation.

MIntO: A Modular and Scalable Pipeline For Microbiome Metagenomic and Metatranscriptomic Data Integration

Omics technologies have revolutionized microbiome research allowing the characterization of complex microbial communities in different biomes without requiring their cultivation. As a consequence, there has been a great increase in the generation of omics data from metagenomes and metatranscriptomes. However, pre-processing and analysis of these data have been limited by the availability of computational resources, bioinformatics expertise and standardized computational workflows to obtain consistent results that are comparable across different studies. Here, we introduce MIntO (Microbiome Integrated meta-Omics), a highly versatile pipeline that integrates metagenomic and metatranscriptomic data in a scalable way. The distinctive feature of this pipeline is the computation of gene expression profile through integrating metagenomic and metatranscriptomic data taking into account the community turnover and gene expression variations to disentangle the mechanisms that shape the metatranscriptome across time and between conditions. The modular design of MIntO enables users to run the pipeline using three available modes based on the input data and the experimental design, including de novo assembly leading to metagenome-assembled genomes. The integrated pipeline will be relevant to provide unique biochemical insights into microbial ecology by linking functions to retrieved genomes and to examine gene expression variation. Functional characterization of community members will be crucial to increase our knowledge of the microbiome’s contribution to human health and environment. MIntO v1.0.1 is available at https://github.com/arumugamlab/MIntO.

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install-all dependencies are provided within containers-portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All codes are hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

Salvaging high-quality genomes of microbial species from a meromictic lake using a hybrid sequencing approach

Most of Earth's bacteria have yet to be cultivated. The metabolic and functional potentials of these uncultivated microorganisms thus remain mysterious, and the metagenome-assembled genome (MAG) approach is the most robust method for uncovering these potentials. However, MAGs discovered by conventional metagenomic assembly and binning are usually highly fragmented genomes with heterogeneous sequence contamination. In this study, we combined Illumina and Nanopore data to develop a new workflow to reconstruct 233 MAGs-six novel bacterial orders, 20 families, 66 genera, and 154 species-from Lake Shunet, a secluded meromictic lake in Siberia. With our workflow, the average N50 of reconstructed MAGs greatly increased 10-40-fold compared to when the conventional Illumina assembly and binning method were used. More importantly, six complete MAGs were recovered from our datasets. The recovery of 154 novel species MAGs from a rarely explored lake greatly expands the current bacterial genome encyclopedia.

Nanopore sequencing and its application to the study of microbial communities

Since its introduction, nanopore sequencing has enhanced our ability to study complex microbial samples through the possibility to sequence long reads in real time using inexpensive and portable technologies. The use of long reads has allowed to address several previously unsolved issues in the field, such as the resolution of complex genomic structures, and facilitated the access to metagenome assembled genomes (MAGs). Furthermore, the low cost and portability of platforms together with the development of rapid protocols and analysis pipelines have featured nanopore technology as an attractive and ever-growing tool for real-time in-field sequencing for environmental microbial analysis. This review provides an up-to-date summary of the experimental protocols and bioinformatic tools for the study of microbial communities using nanopore sequencing, highlighting the most important and recent research in the field with a major focus on infectious diseases. An overview of the main approaches including targeted and shotgun approaches, metatranscriptomics, epigenomics, and epitranscriptomics is provided, together with an outlook to the major challenges and perspectives over the use of this technology for microbial studies.