Accurate read-based metagenome characterization using a hierarchical suite of unique signatures

Metatranscriptomics: A Tool for Clinical Metagenomics

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Metagenomic profiling of nasopharyngeal samples from adults with acute respiratory infection

Diagnosis of acute respiratory infections (ARIs) is challenging due to the broad diversity of potential microbial causes. We used metagenomic next-generation sequencing (mNGS) to analyze the nasopharyngeal virome of ARI patients, who had undergone testing with a clinical multiplex PCR panel (Amplisens ARVI-screen-FRT). We collected nasopharyngeal swabs from 49 outpatient adults, 32 of whom had ARI symptoms and were PCR-positive, and 4 asymptomatic controls in Kazakhstan during Spring 2021. We assessed the biodiversity of the mNGS-derived virome and concordance with PCR results. PCR identified common ARI viruses in 65% of the symptomatic cases. mNGS revealed viral taxa consisting of human, non-human eukaryotic and bacteriophage groups, comprising 15, 11 and 28 genera, respectively. Notable ARI-associated human viruses included rhinovirus (16.3%), betaherpesvirus 7 (14.3%) and Epstein-Barr virus (8.16%). The primary phage hosts were Streptococcus spp. (32.7%), Pseudomonas aeruginosa (24.5%) and Burkholderia spp. (20.4%). In total, 47% of ARIs were linked solely to bacterial pathogens, a third to viral-bacterial co-infections, and less than 10% to only viral infections by mNGS. PCR showed low concordance with mNGS, except for rhinovirus. These results underscore the importance of broad diagnostic methods and question the effectiveness of commonly used PCR panels in ARI diagnosis.

Genomic fingerprints of the world's soil ecosystems

Despite the explosion of soil metagenomic data, we lack a synthesized understanding of patterns in the distribution and functions of soil microorganisms. These patterns are critical to predictions of soil microbiome responses to climate change and resulting feedbacks that regulate greenhouse gas release from soils. To address this gap, we assay 1,512 manually curated soil metagenomes using complementary annotation databases, read-based taxonomy, and machine learning to extract multidimensional genomic fingerprints of global soil microbiomes. Our objective is to uncover novel biogeographical patterns of soil microbiomes across environmental factors and ecological biomes with high molecular resolution. We reveal shifts in the potential for (i) microbial nutrient acquisition across pH gradients; (ii) stress-, transport-, and redox-based processes across changes in soil bulk density; and (iii) greenhouse gas emissions across biomes. We also use an unsupervised approach to reveal a collection of soils with distinct genomic signatures, characterized by coordinated changes in soil organic carbon, nitrogen, and cation exchange capacity and in bulk density and clay content that may ultimately reflect soil environments with high microbial activity. Genomic fingerprints for these soils highlight the importance of resource scavenging, plant-microbe interactions, fungi, and heterotrophic metabolisms. Across all analyses, we observed phylogenetic coherence in soil microbiomes-more closely related microorganisms tended to move congruently in response to soil factors. Collectively, the genomic fingerprints uncovered here present a basis for global patterns in the microbial mechanisms underlying soil biogeochemistry and help beget tractable microbial reaction networks for incorporation into process-based models of soil carbon and nutrient cycling.IMPORTANCEWe address a critical gap in our understanding of soil microorganisms and their functions, which have a profound impact on our environment. We analyzed 1,512 global soils with advanced analytics to create detailed genetic profiles (fingerprints) of soil microbiomes. Our work reveals novel patterns in how microorganisms are distributed across different soil environments. For instance, we discovered shifts in microbial potential to acquire nutrients in relation to soil acidity, as well as changes in stress responses and potential greenhouse gas emissions linked to soil structure. We also identified soils with putative high activity that had unique genomic characteristics surrounding resource acquisition, plant-microbe interactions, and fungal activity. Finally, we observed that closely related microorganisms tend to respond in similar ways to changes in their surroundings. Our work is a significant step toward comprehending the intricate world of soil microorganisms and its role in the global climate.

Ongoing Cooperative Engagement Facilitates Agile Pandemic and Outbreak Response: Lessons Learned Through Cooperative Engagement Between Uganda and the United States

Pathogens threaten human lives and disrupt economies around the world. This has been clearly illustrated by the current COVID-19 pandemic and outbreaks in livestock and food crops. To manage pathogen emergence and spread, cooperative engagement programs develop and strengthen biosafety, biosecurity, and biosurveillance capabilities among local researchers to detect pathogens. In this case study, we describe the efforts of a collaboration between the Los Alamos National Laboratory and the Uganda Virus Research Institute, the primary viral diagnostic laboratory in Uganda, to implement and ensure the sustainability of sequencing for biosurveillance. We describe the process of establishing this capability along with the lessons learned from both sides of the partnership to inform future cooperative engagement efforts in low- and middle-income countries. We found that by strengthening sequencing capabilities at the Uganda Virus Research Institute before the COVID-19 pandemic, the institute was able to successfully sequence SARS-CoV-2 samples and provide data to the scientific community. We highlight the need to strengthen and sustain capabilities through in-country training, collaborative research projects, and trust.

Sea cucumber (Holothuria glaberrima) intestinal microbiome dataset from Puerto Rico, generated by shotgun sequencing

The sea cucumber (H. glaberrima) is a species found in the shallow waters near coral reefs and seagrass beds in Puerto Rico. To characterize the microbial taxonomic composition and functional profiles present in the sea cucumber, total DNA was obtained from their intestinal system, fosmid libraries constructed, and subsequent sequencing was performed. The diversity profile displayed that the most predominant domain was Bacteria (76.56 %), followed by Viruses (23.24 %) and Archaea (0.04 %). Within the 11 phyla identified, the most abundant was Proteobacteria (73.16 %), followed by Terrabacteria group (3.20 %) and Fibrobacterota, Chlorobiota, Bacteroidota (FCB) superphylum (1.02 %). The most abundant species were Porvidencia rettgeri (21.77 %), Pseudomonas stutzeri (14.78 %), and Alcaligenes faecalis (5.00 %). The functional profile revealed that the most abundant functions are related to transporters, MISC (miscellaneous information systems), organic nitrogen, energy, and carbon utilization. The data collected in this project on the diversity and functional profiles of the intestinal system of the H. glaberrima provided a detailed view of its microbial ecology. These findings may motivate comparative studies aimed at understanding the role of the microbiome in intestinal regeneration.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

Human mRNA in saliva can correctly identify individuals harboring acute infection

IMPORTANCE

There are a variety of clinical and laboratory criteria available to clinicians in controlled healthcare settings to help them identify whether an infectious disease is present. However, in situations such as a new epidemic caused by an unknown infectious agent, in health screening contexts performed within communities and outside of healthcare facilities or in battlefield or potential biowarfare situations, this gets more difficult. Pathogen-agnostic methods for rapid screening and triage of large numbers of people for infection status are needed, in particular methods that might work on an easily accessible biospecimen like saliva. Here, we identify a small, core set of approximately 70 human genes whose transcripts serve as saliva-based biomarkers of infection in the human body, in a way that is agnostic to the specific pathogen causing infection.

Mapping human pathogens in wastewater using a metatranscriptomic approach

The monitoring of cities' wastewaters for the detection of potentially pathogenic viruses and bacteria has been considered a priority during the COVID-19 pandemic to monitor public health in urban environments. The methodological approaches frequently used for this purpose include deoxyribonucleic acid (DNA)/Ribonucleic acid (RNA) isolation followed by quantitative polymerase chain reaction (qPCR) and reverse transcription (RT)‒qPCR targeting pathogenic genes. More recently, the application of metatranscriptomic has opened opportunities to develop broad pathogenic monitoring workflows covering the entire pathogenic community within the sample. Nevertheless, the high amount of data generated in the process requires an appropriate analysis to detect the pathogenic community from the entire dataset. Here, an implementation of a bioinformatic workflow was developed to produce a map of the detected pathogenic bacteria and viruses in wastewater samples by analysing metatranscriptomic data. The main objectives of this work was the development of a computational methodology that can accurately detect both human pathogenic virus and bacteria in wastewater samples. This workflow can be easily reproducible with open-source software and uses efficient computational resources. The results showed that the used algorithms can predict potential human pathogens presence in the tested samples and that active forms of both bacteria and virus can be identified. By comparing the computational method implemented in this study to other state-of-the-art workflows, the implementation analysis was faster, while providing higher accuracy and sensitivity. Considering these results, the processes and methods to monitor wastewater for potential human pathogens can become faster and more accurate. The proposed workflow is available at https://github.com/waterpt/watermonitor and can be implemented in currently wastewater monitoring programs to ascertain the presence of potential human pathogenic species.

Translational informatics for human microbiota: data resources, models and applications

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

Bioinformatic Tools for NGS-Based Metagenomics to Improve the Clinical Diagnosis of Emerging, Re-Emerging and New Viruses

Epidemics and pandemics have occurred since the beginning of time, resulting in millions of deaths. Many such disease outbreaks are caused by viruses. Some viruses, particularly RNA viruses, are characterized by their high genetic variability, and this can affect certain phenotypic features: tropism, antigenicity, and susceptibility to antiviral drugs, vaccines, and the host immune response. The best strategy to face the emergence of new infectious genomes is prompt identification. However, currently available diagnostic tests are often limited for detecting new agents. High-throughput next-generation sequencing technologies based on metagenomics may be the solution to detect new infectious genomes and properly diagnose certain diseases. Metagenomic techniques enable the identification and characterization of disease-causing agents, but they require a large amount of genetic material and involve complex bioinformatic analyses. A wide variety of analytical tools can be used in the quality control and pre-processing of metagenomic data, filtering of untargeted sequences, assembly and quality control of reads, and taxonomic profiling of sequences to identify new viruses and ones that have been sequenced and uploaded to dedicated databases. Although there have been huge advances in the field of metagenomics, there is still a lack of consensus about which of the various approaches should be used for specific data analysis tasks. In this review, we provide some background on the study of viral infections, describe the contribution of metagenomics to this field, and place special emphasis on the bioinformatic tools (with their capabilities and limitations) available for use in metagenomic analyses of viral pathogens.

Leveraging microbiome rediversification for the ecological rescue of soil function

BACKGROUND

Global biodiversity losses threaten ecosystem services and can impact important functional insurance in a changing world. Microbial diversity and function can become depleted in agricultural systems and attempts to rediversify agricultural soils rely on either targeted microbial introductions or retaining natural lands as biodiversity reservoirs. As many soil functions are provided by a combination of microbial taxa, rather than outsized impacts by single taxa, such functions may benefit more from diverse microbiome additions than additions of individual commercial strains. In this study, we measured the impact of soil microbial diversity loss and rediversification (i.e. rescue) on nitrification by quantifying ammonium and nitrate pools. We manipulated microbial assemblages in two distinct soil types, an agricultural and a forest soil, with a dilution-to-extinction approach and performed a microbiome rediversification experiment by re-introducing microorganisms lost from the dilution. A microbiome water control was included to act as a reference point. We assessed disruption and potential restoration of (1) nitrification, (2) bacterial and fungal composition through 16S rRNA gene and fungal ITS amplicon sequencing and (3) functional genes through shotgun metagenomic sequencing on a subset of samples.

RESULTS

Disruption of nitrification corresponded with diversity loss, but nitrification was successfully rescued in the rediversification experiment when high diversity inocula were introduced. Bacterial composition clustered into groups based on high and low diversity inocula. Metagenomic data showed that genes responsible for the conversion of nitrite to nitrate and taxa associated with nitrogen metabolism were absent in the low diversity inocula microcosms but were rescued with high diversity introductions.

CONCLUSIONS

In contrast to some previous work, our data suggest that soil functions can be rescued by diverse microbiome additions, but that the concentration of the microbial inoculum is important. By understanding how microbial rediversification impacts soil microbiome performance, we can further our toolkit for microbial management in human-controlled systems in order to restore depleted microbial functions.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. To realize the hope of capturing Earth's microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform ( http://www.kbase.us/ ). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).

Characterization of viral pathogens associated with symptomatic upper respiratory tract infection in adults during a low COVID-19 transmission period

Background

The epidemiology of respiratory tract infections (RTI) has dramatically changed over the course of the COVID-19 pandemic. A major effort in the clinical management of RTI has been directed toward diagnosing COVID-19, while the causes of other, common community RTI often remain enigmatic. To shed light on the etiological causes of RTI during a low COVID-19 transmission period in 2021, we did a pilot study using molecular testing for virologic causes of upper RTI among adults with respiratory symptoms from Almaty, Kazakhstan.

Methods

Adults presenting at two public hospitals with respiratory symptoms were screened using SARS-CoV-2 PCR on nasopharyngeal swabs. A subset of RTI+, COVID-19-negative adults (n = 50) was then tested for the presence of common RTI viruses and influenza A virus (IAV). Next generation virome sequencing was used to further characterize the PCR-detected RTI pathogens.

Results

Of 1,812 symptomatic adults, 21 (1.2%) tested SARS-CoV-2-positive. Within the COVID-19 negative outpatient subset, 33/50 subjects (66%) had a positive PCR result for a common community RTI virus, consisting of human parainfluenza virus 3-4 (hPIV 3-4) in 25/50 (50%), rhinovirus (hRV) in 2 (4%), hPIV4-hRV co-infection in four (8%) and adenovirus or the OCR43/HKU-1 coronavirus in two (4%) cases; no IAV was detected. Virome sequencing allowed to reconstruct sequences of most PCR-identified rhinoviruses and hPIV-3/human respirovirus-3.

Conclusions

COVID-19 was cause to a low proportion of symptomatic RTI among adults. Among COVID-negative participants, symptomatic RTI was predominantly associated with hPIV and hRV. Therefore, respiratory viruses other than SARS-CoV-2 should be considered in the clinical management and prevention of adult RTI in the post-pandemic era.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

The IMG/M data management and analysis system v.7: content updates and new features

The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) at the Department of Energy (DOE) Joint Genome Institute (JGI) continues to provide support for users to perform comparative analysis of isolate and single cell genomes, metagenomes, and metatranscriptomes. In addition to datasets produced by the JGI, IMG v.7 also includes datasets imported from public sources such as NCBI Genbank, SRA, and the DOE National Microbiome Data Collaborative (NMDC), or submitted by external users. In the past couple years, we have continued our effort to help the user community by improving the annotation pipeline, upgrading the contents with new reference database versions, and adding new analysis functionalities such as advanced scaffold search, Average Nucleotide Identity (ANI) for high-quality metagenome bins, new cassette search, improved gene neighborhood display, and improvements to metatranscriptome data display and analysis. We also extended the collaboration and integration efforts with other DOE-funded projects such as NMDC and DOE Biology Knowledgebase (KBase).

Genomic analysis and biochemical profiling of an unaxenic strain of Synechococcus sp. isolated from the Peruvian Amazon Basin region

Cyanobacteria are diverse photosynthetic microorganisms able to produce a myriad of bioactive chemicals. To make possible the rational exploitation of these microorganisms, it is fundamental to know their metabolic capabilities and to have genomic resources. In this context, the main objective of this research was to determine the genome features and the biochemical profile of Synechococcus sp. UCP002. The cyanobacterium was isolated from the Peruvian Amazon Basin region and cultured in BG-11 medium. Growth parameters, genome features, and the biochemical profile of the cyanobacterium were determined using standardized methods. Synechococcus sp. UCP002 had a specific growth rate of 0.086 ± 0.008 μ and a doubling time of 8.08 ± 0.78 h. The complete genome of Synechococcus sp. UCP002 had a size of ∼3.53 Mb with a high coverage (∼200x), and its quality parameters were acceptable (completeness = 99.29%, complete and single-copy genes = 97.5%, and contamination = 0.35%). Additionally, the cyanobacterium had six plasmids ranging from 24 to 200 kbp. The annotated genome revealed ∼3,422 genes, ∼ 3,374 protein-coding genes (with ∼41.31% hypothetical protein-coding genes), two CRISPR Cas systems, and 61 non-coding RNAs. Both the genome and plasmids had the genes for prokaryotic defense systems. Additionally, the genome had genes coding the transcription factors of the metalloregulator ArsR/SmtB family, involved in sensing heavy metal pollution. The biochemical profile showed primary nutrients, essential amino acids, some essential fatty acids, pigments (e.g., all-trans-β-carotene, chlorophyll a, and phycocyanin), and phenolic compounds. In conclusion, Synechococcus sp. UCP002 shows biotechnological potential to produce human and animal nutrients and raw materials for biofuels and could be a new source of genes for synthetic biological applications.

Lightella neohaematopini: A new lineage of highly reduced endosymbionts coevolving with chipmunk lice of the genus Neohaematopinus

Sucking lice (Anoplura) are known to have established symbiotic associations multiple times with different groups of bacteria as diverse as Enterobacteriales, Legionellales, and Neisseriales. This diversity, together with absence of a common coevolving symbiont (such as Buchnera, in aphids), indicates that sucking lice underwent a series of symbiont acquisitions, losses, and replacements. To better understand evolution and significance of louse symbionts, genomic and phylogenetic data are needed from a broader taxonomic diversity of lice and their symbiotic bacteria. In this study, we extend the known spectrum of the louse symbionts with a new lineage associated with Neohaematopinus pacificus, a louse species that commonly parasitizes North American chipmunks. The recent coevolutionary analysis showed that rather than a single species, these lice form a cluster of unique phylogenetic lineages specific to separate chipmunk species (or group of closely related species). Using metagenomic assemblies, we show that the lice harbor a bacterium which mirrors their phylogeny and displays traits typical for obligate mutualists. Phylogenetic analyses place this bacterium within Enterobacteriaceae on a long branch related to another louse symbiont, “Candidatus Puchtella pedicinophila.” We propose for this symbiotic lineage the name “Candidatus Lightella neohaematopini.” Based on the reconstruction of metabolic pathways, we suggest that like other louse symbionts, L. neohaematopini provides its host with at least some B vitamins. In addition, several samples harbored another symbiotic bacterium phylogenetically affiliated with the Neisseriales-related symbionts described previously from the lice Polyplax serrata and Hoplopleura acanthopus. Characterizing these bacteria further extend the known diversity of the symbiotic associations in lice and show unique complexity and dynamics of the system.

Nanopore sequencing of a monkeypox virus strain isolated from a pustular lesion in the Central African Republic

Monkeypox is an emerging and neglected zoonotic disease whose number of reported cases has been gradually increasing in Central Africa since 1980. This disease is caused by the monkeypox virus (MPXV), which belongs to the genus Orthopoxvirus in the family Poxviridae. Obtaining molecular data is particularly useful for establishing the relationships between the viral strains involved in outbreaks in countries affected by this disease. In this study, we evaluated the use of the MinION real-time sequencer as well as different polishing tools on MinION-sequenced genome for sequencing the MPXV genome originating from a pustular lesion in the context of an epidemic in a remote area of the Central African Republic. The reads corresponding to the MPXV genome were identified using two taxonomic classifiers, Kraken2 and Kaiju. Assembly of these reads led to a complete sequence of 196,956 bases, which is 6322 bases longer than the sequence previously obtained with Illumina sequencing from the same sample. The comparison of the two sequences showed mainly indels at the homopolymeric regions. However, the combined use of Canu with specific polishing tools such as Medaka and Homopolish was the best combination that reduced their numbers without adding mismatches. Although MinION sequencing is known to introduce a number of characteristic errors compared to Illumina sequencing, the new polishing tools allow a better-quality MinION-sequenced genome, thus to be used to help determine strain origin through phylogenetic analysis.

StrainGE: a toolkit to track and characterize low-abundance strains in complex microbial communities

Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify strains at 0.1x coverage and detect variants for multiple conspecific strains within a sample from coverages as low as 0.5x.

Antitumor Necrosis Factor-like Ligand 1A Therapy Targets Tissue Inflammation and Fibrosis Pathways and Reduces Gut Pathobionts in Ulcerative Colitis

BACKGROUND

The first-in-class treatment PF-06480605 targets the tumor necrosis factor-like ligand 1A (TL1A) molecule in humans. Results from the phase 2a TUSCANY trial highlighted the safety and efficacy of PF-06480605 in ulcerative colitis. Preclinical and in vitro models have identified a role for TL1A in both innate and adaptive immune responses, but the mechanisms underlying the efficacy of anti-TL1A treatment in inflammatory bowel disease (IBD) are not known.

METHODS

Here, we provide analysis of tissue transcriptomic, peripheral blood proteomic, and fecal metagenomic data from the recently completed phase 2a TUSCANY trial and demonstrate endoscopic improvement post-treatment with PF-06480605 in participants with ulcerative colitis.

RESULTS

Our results revealed robust TL1A target engagement in colonic tissue and a distinct colonic transcriptional response reflecting a reduction in inflammatory T helper 17 cell, macrophage, and fibrosis pathways in patients with endoscopic improvement. Proteomic analysis of peripheral blood revealed a corresponding decrease in inflammatory T-cell cytokines. Finally, microbiome analysis showed significant changes in IBD-associated pathobionts, Streptococcus salivarius, S. parasanguinis, and Haemophilus parainfluenzae post-therapy.

CONCLUSIONS

The ability of PF-06480605 to engage and inhibit colonic TL1A, targeting inflammatory T cell and fibrosis pathways, provides the first-in-human mechanistic data to guide anti-TL1A therapy for the treatment of IBD.

De Novo Assembly and Annotation of the Vaginal Metatranscriptome Associated with Bacterial Vaginosis

The vaginal microbiome plays an important role in women’s health and disease. Here we reanalyzed 40 vaginal transcriptomes from a previous study of de novo assembly (metaT-Assembly) followed by functional annotation. We identified 286,293 contigs and further assigned them to 25 phyla, 209 genera, and 339 species. Lactobacillus iners and Lactobacillus crispatus dominated the microbiome of non-bacterial vaginosis (BV) samples, while a complex of microbiota was identified from BV-associated samples. The metaT-Assembly identified a higher number of bacterial species than the 16S rRNA amplicon and metaT-Kraken methods. However, metaT-Assembly and metaT-Kraken exhibited similar major bacterial composition at the species level. Binning of metatranscriptome data resulted in 176 bins from major known bacteria and several unidentified bacteria in the vagina. Functional analyses based on Clusters of Orthologous Genes (COGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways suggested that a higher number of transcripts were expressed by the microbiome complex in the BV-associated samples than in non-BV-associated samples. The KEGG pathway analysis with an individual bacterial genome identified specific functions of the identified bacterial genome. Taken together, we demonstrated that the metaT-Assembly approach is an efficient tool to understand the dynamic microbial communities and their functional roles associated with the human vagina.

Multiple Cases of Bacterial Sequence Erroneously Incorporated Into Publicly Available Chloroplast Genomes

Public sequencing databases are invaluable resources to biological researchers, but assessing data veracity as well as the curation and maintenance of such large collections of data can be challenging. Genomes of eukaryotic organelles, such as chloroplasts and other plastids, are particularly susceptible to assembly errors and misrepresentations in these databases due to their close evolutionary relationships with bacteria, which may co-occur within the same environment, as can be the case when sequencing plants. Here, based on sequence similarities with bacterial genomes, we identified several suspicious chloroplast assemblies present in the National Institutes of Health (NIH) Reference Sequence (RefSeq) collection. Investigations into these chloroplast assemblies reveal examples of erroneous integration of bacterial sequences into chloroplast ribosomal RNA (rRNA) loci, often within the rRNA genes, presumably due to the high similarity between plastid and bacterial rRNAs. The bacterial lineages identified within the examined chloroplasts as the most likely source of contamination are either known associates of plants, or co-occur in the same environmental niches as the examined plants. Modifications to the methods used to process untargeted ‘raw’ shotgun sequencing data from whole genome sequencing efforts, such as the identification and removal of bacterial reads prior to plastome assembly, could eliminate similar errors in the future.

Presence and Persistence of Putative Lytic and Temperate Bacteriophages in Vaginal Metagenomes from South African Adolescents

The interaction between gut bacterial and viral microbiota is thought to be important in human health. While fluctuations in female genital tract (FGT) bacterial microbiota similarly determine sexual health, little is known about the presence, persistence, and function of vaginal bacteriophages. We conducted shotgun metagenome sequencing of cervicovaginal samples from South African adolescents collected longitudinally, who received no antibiotics. We annotated viral reads and circular bacteriophages, identified CRISPR loci and putative prophages, and assessed their diversity, persistence, and associations with bacterial microbiota composition. Siphoviridae was the most prevalent bacteriophage family, followed by Myoviridae, Podoviridae, Herelleviridae, and Inoviridae. Full-length siphoviruses targeting bacterial vaginosis (BV)-associated bacteria were identified, suggesting their presence in vivo. CRISPR loci and prophage-like elements were common, and genomic analysis suggested higher diversity among Gardnerella than Lactobacillus prophages. We found that some prophages were highly persistent within participants, and identical prophages were present in cervicovaginal secretions of multiple participants, suggesting that prophages, and thus bacterial strains, are shared between adolescents. The number of CRISPR loci and prophages were associated with vaginal microbiota stability and absence of BV. Our analysis suggests that (pro)phages are common in the FGT and vaginal bacteria and (pro)phages may interact.

Widespread bacterial diversity within the bacteriome of fungi

Knowledge of associations between fungal hosts and their bacterial associates has steadily grown in recent years as the number and diversity of examinations have increased, but current knowledge is predominantly limited to a small number of fungal taxa and bacterial partners. Here, we screened for potential bacterial associates in over 700 phylogenetically diverse fungal isolates, representing 366 genera, or a tenfold increase compared with previously examined fungal genera, including isolates from several previously unexplored phyla. Both a 16 S rDNA-based exploration of fungal isolates from four distinct culture collections spanning North America, South America and Europe, and a bioinformatic screen for bacterial-specific sequences within fungal genome sequencing projects, revealed that a surprisingly diverse array of bacterial associates are frequently found in otherwise axenic fungal cultures. We demonstrate that bacterial associations with diverse fungal hosts appear to be the rule, rather than the exception, and deserve increased consideration in microbiome studies and in examinations of microbial interactions.

Isolation and Characterization of Bacteriophages Infecting Burkholderia glumae, the Major Causal Agent of Bacterial Panicle Blight in Rice

Bacterial panicle blight (BPB), caused by Burkholderia glumae, is one of the most severe seed-borne bacterial diseases of rice in the world, which can decrease rice production by ≤75%. Nevertheless, there are few effective measures to manage this disease. In an attempt to develop an alternative management tool for BPB, we isolated and characterized phages from soil and water that are effective to lyse several strains of B. glumae. After tests of host ranges, the phages NBP1-1, NBP4-7, and NBP4-8 were selected for further comprehensive characterization, all of which could lyse B. glumae BGLa14-8 (phage sensitive) but not B. glumae 336gr-1 (phage insensitive). This result indicates that the phages killing B. glumae cells have specific host ranges at the strain level within the bacterial species. In the greenhouse condition of this study, foliar application of the phage NBP4-7 reduced the severity of BPB caused by B. glumae BGLa14-8 ≤62% but did not cause any significant effect on the infection by B. glumae 336gr-1. Electron microscopy and whole-genome sequencing were also performed to characterize the three selected phages. Transmission electron microscopy revealed that the selected phages belong to the family Myoviridae. Furthermore, whole-genome sequence analysis indicated that the three phages belong to a same species and are closely related to the Burkholderia phage KL3, a member of the Myoviridae family.

Accessing Dietary Effects on the Rumen Microbiome: Different Sequencing Methods Tell Different Stories

The current study employed both amplicon and shotgun sequencing to examine and compare the rumen microbiome in Angus bulls fed with either a backgrounding diet (BCK) or finishing diet (HG), to assess if both methods produce comparable results. Rumen digesta samples from 16 bulls were subjected for microbial profiling. Distinctive microbial profiles were revealed by the two methods, indicating that choice of sequencing approach may be a critical facet in studies of the rumen microbiome. Shotgun-sequencing identified the presence of 303 bacterial genera and 171 archaeal species, several of which exhibited differential abundance. Amplicon-sequencing identified 48 bacterial genera, 4 archaeal species, and 9 protozoal species. Among them, 20 bacterial genera and 5 protozoal species were differentially abundant between the two diets. Overall, amplicon-sequencing showed a more drastic diet-derived effect on the ruminal microbial profile compared to shotgun-sequencing. While both methods detected dietary differences at various taxonomic levels, few consistent patterns were evident. Opposite results were seen for the phyla Firmicutes and Bacteroidetes, and the genus Selenomonas. This study showcases the importance of sequencing platform choice and suggests a need for integrative methods that allow robust comparisons of microbial data drawn from various omic approaches, allowing for comprehensive comparisons across studies.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Detection of multiple viruses potentially infecting humans in sewage water from Xinjiang Uygur Autonomous Region, China

The progress of sequencing technologies has facilitated metagenomics projects on environmental samples like sewage water. The present study concerned the analysis of sewage samples collected from 3 locations in Xinjiang Uygur Autonomous Region in China. The analysis focused on RNA viruses known to infect humans and identified viruses from 10 families. The proportion of human virus species in the sewage samples was relatively stable with an average of 17%. Thirty virus species known to infect humans were identified and they belonged to 6 families: Picornaviridae (12), Astroviridae (11), Reoviridae (3), Caliciviridae (2), Papillomaviridae (1) and Picobirnaviridae (1). A total of 16 full-length genomes were generated from Astroviridae, Picornaviridae (Salivirus and Kobuvirus) and Picobirnaviridae. Astroviruses appeared to be the most present viruses and were detected in all sewage samples. Analyzing the virome of sewage samples should help to monitor any potential risks to public health.

Current challenges and best-practice protocols for microbiome analysis

Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

Cyanobacterial blooms in wastewater treatment facilities: Significance and emerging monitoring strategies

Municipal wastewater treatment facilities (WWTFs) are prone to the proliferation of cyanobacterial species which thrive in stable, nutrient-rich environments. Dense cyanobacterial blooms frequently disrupt treatment processes and the supply of recycled water due to their production of extracellular polymeric substances, which hinder microfiltration, and toxins, which pose a health risk to end-users. A variety of methods are employed by water utilities for the identification and monitoring of cyanobacteria and their toxins in WWTFs, including microscopy, flow cytometry, ELISA, chemoanalytical methods, and more recently, molecular methods. Here we review the literature on the occurrence and significance of cyanobacterial blooms in WWTFs and discuss the pros and cons of the various strategies for monitoring these potentially hazardous events. Particular focus is directed towards next-generation metagenomic sequencing technologies for the development of site-specific cyanobacterial bloom management strategies. Long-term multi-omic observations will enable the identification of indicator species and the development of site-specific bloom dynamics models for the mitigation and management of cyanobacterial blooms in WWTFs. While emerging metagenomic tools could potentially provide deep insight into the diversity and flux of problematic cyanobacterial species in these systems, they should be considered a complement to, rather than a replacement of, quantitative chemoanalytical approaches.

Evaluation of the upper airway microbiome and immune response with nasal epithelial lining fluid absorption and nasal washes

Despite being commonly used to collect upper airway epithelial lining fluid, nasal washes are poorly reproducible, not suitable for serial sampling, and limited by a dilution effect. In contrast, nasal filters lack these limitations and are an attractive alternative. To examine whether nasal filters are superior to nasal washes as a sampling method for the characterization of the upper airway microbiome and immune response, we collected paired nasal filters and washes from a group of 40 healthy children and adults. To characterize the upper airway microbiome, we used 16S ribosomal RNA and shotgun metagenomic sequencing. To characterize the immune response, we measured total protein using a BCA assay and 53 immune mediators using multiplex magnetic bead-based assays. We conducted statistical analyses to compare common microbial ecology indices and immune-mediator median fluorescence intensities (MFIs) between sample types. In general, nasal filters were more likely to pass quality control in both children and adults. There were no significant differences in microbiome community richness, α-diversity, or structure between pediatric samples types; however, these were all highly dissimilar between adult sample types. In addition, there were significant differences in the abundance of amplicon sequence variants between sample types in children and adults. In adults, total proteins were significantly higher in nasal filters than nasal washes; consequently, the immune-mediator MFIs were not well detected in nasal washes. Based on better quality control sequencing metrics and higher immunoassay sensitivity, our results suggest that nasal filters are a superior sampling method to characterize the upper airway microbiome and immune response in both children and adults.

Metalign: efficient alignment-based metagenomic profiling via containment min hash

Metagenomic profiling, predicting the presence and relative abundances of microbes in a sample, is a critical first step in microbiome analysis. Alignment-based approaches are often considered accurate yet computationally infeasible. Here, we present a novel method, Metalign, that performs efficient and accurate alignment-based metagenomic profiling. We use a novel containment min hash approach to pre-filter the reference database prior to alignment and then process both uniquely aligned and multi-aligned reads to produce accurate abundance estimates. In performance evaluations on both real and simulated datasets, Metalign is the only method evaluated that maintained high performance and competitive running time across all datasets.

Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data

Metagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.

Assembling Reads Improves Taxonomic Classification of Species

Most current approach to metagenomic classification employ short next generation sequencing (NGS) reads that are present in metagenomic samples to identify unique genomic regions. NGS reads, however, might not be long enough to differentiate similar genomes. This suggests a potential for using longer reads to improve classification performance. Presently, longer reads tend to have a higher rate of sequencing errors. Thus, given the pros and cons, it remains unclear which types of reads is better for metagenomic classification. We compared two taxonomic classification protocols: a traditional assembly-free protocol and a novel assembly-based protocol. The novel assembly-based protocol consists of assembling short-reads into longer reads, which will be subsequently classified by a traditional taxonomic classifier. We discovered that most classifiers made fewer predictions with longer reads and that they achieved higher classification performance on synthetic metagenomic data. Generally, we observed a significant increase in precision, while having similar recall rates. On real data, we observed similar characteristics that suggest that the classifiers might have similar performance of higher precision with similar recall with longer reads. We have shown a noticeable difference in performance between assembly-based and assembly-free taxonomic classification. This finding strongly suggests that classifying species in metagenomic environments can be achieved with higher overall performance simply by assembling short reads. Further, it also suggests that long-read technologies might be better for species classification.

Whole-Genome Sequence of an Orf Virus Isolate Derived from a Cell Culture Infected with Contagious Ecthyma Vaccine

This is a draft genome of an orf virus (ORFV) vaccine strain assembled via long- and short-read hybrid assembly. ORFV is a zoonotic pathogen that affects sheep and goats. The genome of the virus contained in the vaccine was found to have high similarity (98%) to those of other published strains.

This is a draft genome of an orf virus (ORFV) vaccine strain assembled via long- and short-read hybrid assembly. ORFV is a zoonotic pathogen that affects sheep and goats. The genome of the virus contained in the vaccine was found to have high similarity (98%) to those of other published strains.

Contribution to the ecology of the Italian hare (Lepus corsicanus)

The Italian hare (Lepus corsicanus) is endemic to Central-Southern Italy and Sicily, classified as vulnerable due to habitat alterations, low density and fragmented populations and ecological competition with the sympatric European hare (Lepus europaeus). Despite this status, only few and local studies have explored its ecological features. We provided some key traits of the ecological niche of the Italian hare as well as its potential distribution in the Italian peninsula. All data derived from genetically validated presences. We generated a habitat suitability model using maximum entropy distribution model for the Italian hare and its main competitor, the European hare. The dietary habits were obtained for the Italian hare with DNA metabarcoding and High-Throughput Sequencing on faecal pellets. The most relevant environmental variables affecting the potential distribution of the Italian hare are shared with the European hare, suggesting a potential competition. The variation in the observed altitudinal distribution is statistically significant between the two species.The diet of the Italian hare all year around includes 344 plant taxa accounted by 62 families. The Fagaceae, Fabaceae, Poaceae, Rosaceae and Solanaceae (counts > 20,000) represented the 90.22% of the total diet. Fabaceae (60.70%) and Fagaceae (67.47%) were the most abundant plant items occurring in the Spring/Summer and Autumn/Winter diets, respectively. The Spring/Summer diet showed richness (N = 266) and diversity index values (Shannon: 2.329, Evenness: 0.03858, Equitability: 0.4169) higher than the Autumn/Winter diet (N = 199, Shannon: 1.818, Evenness: 0.03096, Equitability: 0.3435). Our contribution adds important information to broaden the knowledge on the environmental (spatial and trophic) requirements of the Italian hare, representing effective support for fitting management actions in conservation planning.

An Observational Study of Sepsis in Takeo Province Cambodia: An in-depth examination of pathogens causing severe infections

The world's most consequential pathogens occur in regions with the fewest diagnostic resources, leaving the true burden of these diseases largely under-represented. During a prospective observational study of sepsis in Takeo Province Cambodia, we enrolled 200 patients over an 18-month period. By coupling traditional diagnostic methods such as culture, serology, and PCR to Next Generation Sequencing (NGS) and advanced statistical analyses, we successfully identified a pathogenic cause in 46.5% of our cohort. In all, we detected 25 infectious agents in 93 patients, including severe threat pathogens such as Burkholderia pseudomallei and viral pathogens such as Dengue virus. Approximately half of our cohort remained undiagnosed; however, an independent panel of clinical adjudicators determined that 81% of those patients had infectious causes of their hospitalization, further underscoring the difficulty of diagnosing severe infections in resource-limited settings. We garnered greater insight as to the clinical features of severe infection in Cambodia through analysis of a robust set of clinical data.

TAMA: improved metagenomic sequence classification through meta-analysis

BACKGROUND

Microorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. To understand their environments more deeply, the composition of microorganisms in environmental samples has been studied using metagenomes, which are the collections of genomes of the microorganisms. Although many tools have been developed for taxonomy analysis based on different algorithms, variability of analysis outputs of existing tools from the same input metagenome datasets is the main obstacle for many researchers in this field.

RESULTS

Here, we present a novel meta-analysis tool for metagenome taxonomy analysis, called TAMA, by intelligently integrating outputs from three different taxonomy analysis tools. Using an integrated reference database, TAMA performs taxonomy assignment for input metagenome reads based on a meta-score by integrating scores of taxonomy assignment from different taxonomy classification tools. TAMA outperformed existing tools when evaluated using various benchmark datasets. It was also successfully applied to obtain relative species abundance profiles and difference in composition of microorganisms in two types of cheese metagenome and human gut metagenome.

CONCLUSION

TAMA can be easily installed and used for metagenome read classification and the prediction of relative species abundance from multiple numbers and types of metagenome read samples. TAMA can be used to more accurately uncover the composition of microorganisms in metagenome samples collected from various environments, especially when the use of a single taxonomy analysis tool is unreliable. TAMA is an open source tool, and can be downloaded at https://github.com/jkimlab/TAMA.

CCMetagen: comprehensive and accurate identification of eukaryotes and prokaryotes in metagenomic data

There is an increasing demand for accurate and fast metagenome classifiers that can not only identify bacteria, but all members of a microbial community. We used a recently developed concept in read mapping to develop a highly accurate metagenomic classification pipeline named CCMetagen. The pipeline substantially outperforms other commonly used software in identifying bacteria and fungi and can efficiently use the entire NCBI nucleotide collection as a reference to detect species with incomplete genome data from all biological kingdoms. CCMetagen is user-friendly, and the results can be easily integrated into microbial community analysis software for streamlined and automated microbiome studies.

A Comparison of Two DNA Metagenomic Bioinformatic Pipelines While Evaluating the Microbial Diversity in Feces of Tanzanian Small Holder Dairy Cattle

Analysis of shotgun metagenomic data generated from next generation sequencing platforms can be done through a variety of bioinformatic pipelines. These pipelines employ different sets of sophisticated bioinformatics algorithms which may affect the results of this analysis. In this study, we compared two commonly used pipelines for shotgun metagenomic analysis: MG-RAST and Kraken 2, in terms of taxonomic classification, diversity analysis, and usability using their primarily default parameters. Overall, the two pipelines detected similar abundance distributions in the three most abundant taxa Proteobacteria, Firmicutes, and Bacteroidetes. Within bacterial domain, 497 genera were identified by both pipelines, while an additional 694 and 98 genera were solely identified by Kraken 2 and MG-RAST, respectively. 933 species were detected by the two algorithms. Kraken 2 solely detected 3550 species, while MG-RAST identified 557 species uniquely. For archaea, Kraken 2 generated 105 and 236 genera and species, respectively, while MG-RAST detected 60 genera and 88 species. 54 genera and 72 species were commonly detected by the two methods. Kraken 2 had a quicker analysis time (~4 hours) while MG-RAST took approximately 2 days per sample. This study revealed that Kraken 2 and MG-RAST generate comparable results and that a reliable high-level overview of sample is generated irrespective of the pipeline selected. However, Kraken 2 generated a more accurate taxonomic identification given the higher number of "Unclassified" reads in MG-RAST. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of the combined pipelines.

High-throughput phenotyping of cell-to-cell interactions in gel microdroplet pico-cultures

Microbiomes exert significant influence on our planet's ecology. Elucidating the identities of individual microbes within these communities and how they interact is a vital research imperative. Using traditional plating and culturing methods, it is impractical to assess even a small fraction of the interactions that exist within microbial communities. To address this technology gap, we integrated gel microdroplet technology with microfluidics to generate millions of microdroplet cultures (MDs) that sequester individual cells for phenotyping MDs, facilitating rapid analysis and viable recovery using flow cytometry. Herein, we describe a validated high-throughput phenotyping pipeline that elucidates cell-to-cell interactions for millions of combinations of microorganisms. Through iterative co-culturing of an algae and a pool of environmentally sourced microbes, we successfully isolated bacteria that improved algal growth.

Standardized phylogenetic and molecular evolutionary analysis applied to species across the microbial tree of life

There is growing interest in reconstructing phylogenies from the copious amounts of genome sequencing projects that target related viral, bacterial or eukaryotic organisms. To facilitate the construction of standardized and robust phylogenies for disparate types of projects, we have developed a complete bioinformatic workflow, with a web-based component to perform phylogenetic and molecular evolutionary (PhaME) analysis from sequencing reads, draft assemblies or completed genomes of closely related organisms. Furthermore, the ability to incorporate raw data, including some metagenomic samples containing a target organism (e.g. from clinical samples with suspected infectious agents), shows promise for the rapid phylogenetic characterization of organisms within complex samples without the need for prior assembly.

Unique k-mers as Strain-Specific Barcodes for Phylogenetic Analysis and Natural Microbiome Profiling

The need for a comparative analysis of natural metagenomes stimulated the development of new methods for their taxonomic profiling. Alignment-free approaches based on the search for marker k-mers turned out to be capable of identifying not only species, but also strains of microorganisms with known genomes. Here, we evaluated the ability of genus-specific k-mers to distinguish eight phylogroups of Escherichia coli (A, B1, C, E, D, F, G, B2) and assessed the presence of their unique 22-mers in clinical samples from microbiomes of four healthy people and four patients with Crohn's disease. We found that a phylogenetic tree inferred from the pairwise distance matrix for unique 18-mers and 22-mers of 124 genomes was fully consistent with the topology of the tree, obtained with concatenated aligned sequences of orthologous genes. Therefore, we propose strain-specific "barcodes" for rapid phylotyping. Using unique 22-mers for taxonomic analysis, we detected microbes of all groups in human microbiomes; however, their presence in the five samples was significantly different. Pointing to the intraspecies heterogeneity of E. coli in the natural microflora, this also indicates the feasibility of further studies of the role of this heterogeneity in maintaining population homeostasis.

Characterizing Mutational Signatures in Human Cancer Cell Lines Reveals Episodic APOBEC Mutagenesis

Multiple signatures of somatic mutations have been identified in cancer genomes. Exome sequences of 1,001 human cancer cell lines and 577 xenografts revealed most common mutational signatures, indicating past activity of the underlying processes, usually in appropriate cancer types. To investigate ongoing patterns of mutational-signature generation, cell lines were cultured for extended periods and subsequently DNA sequenced. Signatures of discontinued exposures, including tobacco smoke and ultraviolet light, were not generated in vitro. Signatures of normal and defective DNA repair and replication continued to be generated at roughly stable mutation rates. Signatures of APOBEC cytidine deaminase DNA-editing exhibited substantial fluctuations in mutation rate over time with episodic bursts of mutations. The initiating factors for the bursts are unclear, although retrotransposon mobilization may contribute. The examined cell lines constitute a resource of live experimental models of mutational processes, which potentially retain patterns of activity and regulation operative in primary human cancers.

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Annotation of mutational signatures across 1,001 cancer cell lines and 577 PDXs

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Activities of mutational processes determined over time in cancer cell lines

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APOBEC-associated mutagenesis is often ongoing and can be episodic

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Detection of mutational signatures by single-cell sequencing

Legionella pneumophila as Cause of Severe Community-Acquired Pneumonia, China

We report a case of community-acquired pneumonia in a patient in China. We verified Legionella pneumophila infection through next-generation sequencing of blood, sputum, and pleural effusion samples. Our results show the usefulness of next-generation sequencing and of testing different samples early in the course of illness to identify this bacterium.

A Robust Metatranscriptomic Technology for Population-Scale Studies of Diet, Gut Microbiome, and Human Health

A functional readout of the gut microbiome is necessary to enable precise control of the gut microbiome's functions, which support human health and prevent or minimize a wide range of chronic diseases. Stool metatranscriptomic analysis offers a comprehensive functional view of the gut microbiome, but despite its usefulness, it has rarely been used in clinical studies due to its complexity, cost, and bioinformatic challenges. This method has also received criticism due to potential intrasample variability, rapid changes, and RNA degradation. Here, we describe a robust and automated stool metatranscriptomic method, called Viomega, which was specifically developed for population-scale studies. Viomega includes sample collection, ambient temperature sample preservation, total RNA extraction, physical removal of ribosomal RNAs (rRNAs), preparation of directional Illumina libraries, Illumina sequencing, taxonomic classification based on a database of >110,000 microbial genomes, and quantitative microbial gene expression analysis using a database of ~100 million microbial genes. We applied this method to 10,000 human stool samples and performed several small-scale studies to demonstrate sample stability and consistency. In summary, Viomega is an inexpensive, high-throughput, automated, and accurate sample-to-result stool metatranscriptomic technology platform for large-scale studies and a wide range of applications.

SEPATH: benchmarking the search for pathogens in human tissue whole genome sequence data leads to template pipelines

BACKGROUND

Human tissue is increasingly being whole genome sequenced as we transition into an era of genomic medicine. With this arises the potential to detect sequences originating from microorganisms, including pathogens amid the plethora of human sequencing reads. In cancer research, the tumorigenic ability of pathogens is being recognized, for example, Helicobacter pylori and human papillomavirus in the cases of gastric non-cardia and cervical carcinomas, respectively. As of yet, no benchmark has been carried out on the performance of computational approaches for bacterial and viral detection within host-dominated sequence data.

RESULTS

We present the results of benchmarking over 70 distinct combinations of tools and parameters on 100 simulated cancer datasets spiked with realistic proportions of bacteria. mOTUs2 and Kraken are the highest performing individual tools achieving median genus-level F1 scores of 0.90 and 0.91, respectively. mOTUs2 demonstrates a high performance in estimating bacterial proportions. Employing Kraken on unassembled sequencing reads produces a good but variable performance depending on post-classification filtering parameters. These approaches are investigated on a selection of cervical and gastric cancer whole genome sequences where Alphapapillomavirus and Helicobacter are detected in addition to a variety of other interesting genera.

CONCLUSIONS

We provide the top-performing pipelines from this benchmark in a unifying tool called SEPATH, which is amenable to high throughput sequencing studies across a range of high-performance computing clusters. SEPATH provides a benchmarked and convenient approach to detect pathogens in tissue sequence data helping to determine the relationship between metagenomics and disease.