Bracken: estimating species abundance in metagenomics data

A Pilot Study of the Effect of Deployment on the Gut Microbiome and Traveler's Diarrhea Susceptibility

Traveler's diarrhea (TD) is a recurrent and significant issue for many travelers including the military. While many known enteric pathogens exist that are causative agents of diarrhea, our gut microbiome may also play a role in TD susceptibility. To this end, we conducted a pilot study of the microbiome of warfighters prior to- and after deployment overseas to identify marker taxa relevant to TD. This initial study utilized full-length 16S rRNA gene sequencing to provide additional taxonomic resolution toward identifying predictive taxa.16S rRNA analyses of pre- and post-deployment fecal samples identified multiple marker taxa as significantly differentially abundant in subjects that reported diarrhea, including Weissella, Butyrivibrio, Corynebacterium, uncultivated Erysipelotrichaceae, Jeotgallibaca, unclassified Ktedonobacteriaceae, Leptolinea, and uncultivated Ruminiococcaceae. The ability to identify TD risk prior to travel will inform prevention and mitigation strategies to influence diarrhea susceptibility while traveling.

Treatments of porcine fecal samples affect high-throughput virome sequencing results

The porcine enteric microbiota is currently extensively studied, taking advantage of developments in high-throughput sequencing technologies. However, the viral part of the microbiota, the virome, is being lightly explored, and the impact of the pretreatments used before sequencing the viruses is barely considered. In this study, the impacts of filtration, RNase and DNase treatments on virus reads recovery and diversity after sequencing on a MiSeq platform were assessed on fecal samples individually taken at <3, 5, 12 and 20 weeks from two piglets. None of the four pretreatment series affected the virus read averages or influenced diversity, but the samples with the higher proportion of reads corresponding to an entry in the "nt" database were those receiving the least number of pretreatments. The enzymatic pretreatments affected the detection of the single-stranded RNA viruses of Aichivirus C, porcine astrovirus, Sapovirus and posavirus, which is worrisome, as these viruses can be involved in swine diarrhea. If enzymatic pretreatments are used when sequencing using a high-throughput method, it may impact single-stranded RNA virus recovery, but not the overall virome diversity. Therefore, filtrated samples may be the better option, reducing the amount of bacterial genetic material while preserving the virus reads.

Evaluation of methods for the reduction of contaminating host reads when performing shotgun metagenomic sequencing of the milk microbiome

Shotgun metagenomic sequencing is a valuable tool for the taxonomic and functional profiling of microbial communities. However, this approach is challenging in samples, such as milk, where a low microbial abundance, combined with high levels of host DNA, result in inefficient and uneconomical sequencing. Here we evaluate approaches to deplete host DNA or enrich microbial DNA prior to sequencing using three commercially available kits. We compared the percentage of microbial reads obtained from each kit after shotgun metagenomic sequencing. Using bovine and human milk samples, we determined that host depletion with the MolYsis complete5 kit significantly improved microbial sequencing depth compared to other approaches tested. Importantly, no biases were introduced. Additionally, the increased microbial sequencing depth allowed for further characterization of the microbiome through the generation of metagenome-assembled genomes (MAGs). Furthermore, with the use of a mock community, we compared three common classifiers and determined that Kraken2 was the optimal classifier for these samples. This evaluation shows that microbiome analysis can be performed on both bovine and human milk samples at a much greater resolution without the need for more expensive deep-sequencing approaches.

Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data

Background

The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction.

Results

Comparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in normalized microbial abundance, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken, for taxonomic annotation, had higher detection sensitivity. As classification models are limited to labeling pre-trained origins, we took an alternative approach using Lasso-regularized multivariate regression to predict geographic coordinates for comparison. In both models, the prediction errors were much higher in Leave-1-city-out than in 10-fold cross validation, of which the former realistically forecasted the increased difficulty in accurately predicting samples from new origins. This challenge was further confirmed when applying the model to a set of samples obtained from new origins. Overall, the prediction performance of the regression and classification models, as measured by mean squared error, were comparable on mystery samples. Due to higher prediction error rates for samples from new origins, we provided an additional strategy based on prediction ambiguity to infer whether a sample is from a new origin. Lastly, we report increased prediction error when data from different sequencing protocols were included as training data.

Conclusions

Herein, we highlight the capacity of predicting sample origin accurately with pre-trained origins and the challenge of predicting new origins through both regression and classification models. Overall, this work provides a summary of the impact of sequencing technique, protocol, taxonomic analytical approaches, and machine learning approaches on the use of metagenomics for prediction of sample origin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13062-020-00287-y.

The distribution of microbiomes and resistomes across farm environments in conventional and organic dairy herds in Pennsylvania

BACKGROUND

Antimicrobial resistance is a serious concern. Although the widespread use of antimicrobials in livestock has exacerbated the emergence and dissemination of antimicrobial resistance genes (ARG) in farm environments, little is known about whether antimicrobial use affects distribution of ARG in livestock systems. This study compared the distribution of microbiomes and resistomes (collections of ARG) across different farm sectors in dairy herds that differed in their use of antimicrobials. Feces from heifers, non-lactating, and lactating cows, manure storage, and soil from three conventional (antimicrobials used to treat cows) and three organic (no antimicrobials used for at least four years) farms in Pennsylvania were sampled. Samples were extracted for genomic DNA, processed, sequenced on the Illumina NextSeq platform, and analyzed for microbial community and resistome profiles using established procedures.

RESULTS

Microbial communities and resistome profiles clustered by sample type across all farms. Overall, abundance and diversity of ARG in feces was significantly higher in conventional herds compared to organic herds. The ARG conferring resistance to betalactams, macrolide-lincosamide-streptogramin (MLS), and tetracyclines were significantly higher in fecal samples of dairy cows from conventional herds compared to organic herds. Regardless of farm type, all manure storage samples had greater diversity (albeit low abundance) of ARG conferring resistance to aminoglycosides, tetracyclines, MLS, multidrug resistance, and phenicol. All soil samples had lower abundance of ARG compared to feces, manure, and lagoon samples and were comprised of ARG conferring resistance to aminoglycosides, glycopeptides, and multi-drug resistance. The distribution of ARG is likely driven by the composition of microbiota in the respective sample types.

CONCLUSIONS

Antimicrobial use on farms significantly influenced specific groups of ARG in feces but not in manure storage or soil samples.

A shotgun metagenomic investigation of the microbiota of udder cleft dermatitis in comparison to healthy skin in dairy cows

Udder cleft dermatitis (UCD) is a skin condition affecting the fore udder attachment of dairy cows. UCD may be defined as mild (eczematous skin changes) or severe (open wounds, large skin changes). Our aims were to compare the microbiota of mild and severe UCD lesions with the microbiota of healthy skin from the fore udder attachment of control cows, and to investigate whether mastitis-causing pathogens are present in UCD lesions. Samples were obtained from cows in six dairy herds. In total, 36 UCD samples categorized as mild (n = 17) or severe (n = 19) and 13 control samples were sequenced using a shotgun metagenomic approach and the reads were taxonomically classified based on their k-mer content. The Wilcoxon rank sum test was used to compare the abundance of different taxa between different sample types, as well as to compare the bacterial diversity between samples. A high proportion of bacteria was seen in all samples. Control samples had a higher proportion of archaeal reads, whereas most samples had low proportions of fungi, protozoa and viruses. The bacterial microbiota differed between controls and mild and severe UCD samples in both composition and diversity. Subgroups of UCD samples were visible, characterized by increased proportion of one or a few bacterial genera or species, e.g. Corynebacterium, Staphylococcus, Brevibacterium luteolum, Trueperella pyogenes and Fusobacterium necrophorum. Bifidobacterium spp. were more common in controls compared to UCD samples. The bacterial diversity was higher in controls compared to UCD samples. Bacteria commonly associated with mastitis were uncommon. In conclusion, a dysbiosis of the microbiota of mild and severe UCD samples was seen, characterized by decreased diversity and an increased proportion of certain bacteria. There was no evidence of a specific pathogen causing UCD or that UCD lesions are important reservoirs for mastitis-causing bacteria.

A quasimetagenomics method for concerted detection and subtyping of Salmonella enterica and E. coli O157:H7 from romaine lettuce

Quasimetagenomics refers to the sequencing of a modified food microbiome to facilitate combined detection and subtyping of targeted pathogens in a single workflow. Through quasimetagenomic sequencing, pathogens are detected and subtyped in a shortened time frame compared to traditional culture enrichment and whole genome sequencing-based analyses. While this method was previously used to detect and subtype Salmonella enterica from chicken, iceberg lettuce, and black pepper, it has not been applied to investigate multiple pathogens in one workflow. A quasimetagenomic method to concertedly detect and subtype Salmonella enterica and Escherichia coli O157:H7 from artificially contaminated romaine lettuce in a single workflow was developed. All quasimetagenomic samples with initial target pathogen inoculum levels of ~1 CFU/g were detected and serotyped after co-enrichment of the two pathogens for 12 h. Single nucleotide polymorphism typing was achievable for some initial pathogen inoculum levels as low as ~0.1 CFU/g. Our results suggest that this method can be used for concerted detection and subtyping of multiple bacterial pathogens from romaine lettuce even at low contamination levels.

Assessment of In Vitro and In Silico Protocols for Sequence-Based Characterization of the Human Vaginal Microbiome

The vaginal microbiome has been connected to a wide range of health outcomes. This has led to a thriving research environment but also to the use of conflicting methodologies to study its microbial composition. Here, we systematically assessed best practices for the sequencing-based characterization of the human vaginal microbiome. As far as 16S rRNA gene sequencing is concerned, the V1-V3 region performed best in silico, but limitations of current sequencing technologies meant that the V3-V4 region performed equally well. Both approaches presented very good agreement with qPCR quantification of key taxa, provided that an appropriate bioinformatic pipeline was used. Shotgun metagenomic sequencing presents an interesting alternative to 16S rRNA gene amplification and sequencing but requires deeper sequencing and more bioinformatic expertise and infrastructure. We assessed different tools for the removal of host reads and the taxonomic annotation of metagenomic reads, including a new, easy-to-build and -use reference database of vaginal taxa. This curated database performed as well as the best-performing previously published strategies. Despite the many advantages of shotgun sequencing, none of the shotgun approaches assessed here agreed with the qPCR data as well as the 16S rRNA gene sequencing.IMPORTANCE The vaginal microbiome has been connected to various aspects of host health, including susceptibility to sexually transmitted infections as well as gynecological cancers and pregnancy outcomes. This has led to a thriving research environment but also to conflicting available methodologies, including many studies that do not report their molecular biological and bioinformatic methods in sufficient detail to be considered reproducible. This can lead to conflicting messages and delay progress from descriptive to intervention studies. By systematically assessing best practices for the characterization of the human vaginal microbiome, this study will enable past studies to be assessed more critically and assist future studies in the selection of appropriate methods for their specific research questions.

Microbiota composition data of imago and larval stage of the anhydrobiotic midge

The ability of larvae of a non-biting midge Polypedilum vanderplanki (Chironomidae) to withstand complete desiccation is a remarkable natural example of adaptation to extreme environment. In anhydrobiosis the larvae lose up to 99.2% of water and stay in a dry form until rainfall in natural environment or up to several decades in laboratory maintaining ability to restore activity soon after rehydration [1]. In the desiccated state, the larvae tolerate a variety of abiotic stresses, including high radiation exposure (7000Gry of ⁶⁰Co gamma rays) [2]. Such a cross-resistance to desiccation and ionizing radiation is a characteristic of many anhydrobiotic organisms and believed to be based on similar molecular mechanisms.

Microorganisms associated with the anhydrobiotic midge can also sustain desiccation and thus be radiation-resistant because desiccation-resistant prokaryotes are shown to be cross-resistant to ionizing radiation [3]. Microorganisms inhabiting larvae of the anhydrobiotic midge can also sustain desiccation and probably can sustain high doses of ionizing radiation.

Therefore, it would be of interest to analyze the taxonomic and functional composition of microbiome of the anhydrobiotic midge. Sequencing data for the total DNA of anhydrobiotic organisms, which also contain reads derived from symbiotic microorganisms provide a promising opportunity to identify microorganisms with remarkable adaptation. It is known that at least some protective genes, such as late embryogenesis abundant (LEA) genes appeared in the genome of the midge by probable horizontal gene transfer from bacteria [1]. We performed shotgun sequencing of imago and larvae DNA samples using HiSeq 2000 and Genome Analyzer IIX System platforms. To assess microbiome diversity specific to anhydrobiotic midges, we analyzed Pool-seq data of the natural population of imago and Pool-seq data of final instar larvae. Data has been deposited in NCBI BioProject repository at NCBI under the accession number PRJNA659554 and consists of raw sequence data.

The Role of the Gut Microbiome in Resisting Norovirus Infection as Revealed by a Human Challenge Study

Norovirus infections take a heavy toll on worldwide public health. While progress has been made toward understanding host responses to infection, the role of the gut microbiome in determining infection outcome is unknown. Moreover, data are lacking on the nature and duration of the microbiome response to norovirus infection, which has important implications for diagnostics and host recovery. Here, we characterized the gut microbiomes of subjects enrolled in a norovirus challenge study. We analyzed microbiome features of asymptomatic and symptomatic individuals at the genome (population) and gene levels and assessed their response over time in symptomatic individuals. We show that the preinfection microbiomes of subjects with asymptomatic infections were enriched in Bacteroidetes and depleted in Clostridia relative to the microbiomes of symptomatic subjects. These compositional differences were accompanied by differences in genes involved in the metabolism of glycans and sphingolipids that may aid in host resilience to infection. We further show that microbiomes shifted in composition following infection and that recovery times were variable among human hosts. In particular, Firmicutes increased immediately following the challenge, while Bacteroidetes and Proteobacteria decreased over the same time. Genes enriched in the microbiomes of symptomatic subjects, including the adenylyltransferase glgC, were linked to glycan metabolism and cell-cell signaling, suggesting as-yet unknown roles for these processes in determining infection outcome. These results provide important context for understanding the gut microbiome role in host susceptibility to symptomatic norovirus infection and long-term health outcomes.IMPORTANCE The role of the human gut microbiome in determining whether an individual infected with norovirus will be symptomatic is poorly understood. This study provides important data on microbes that distinguish asymptomatic from symptomatic microbiomes and links these features to infection responses in a human challenge study. The results have implications for understanding resistance to and treatment of norovirus infections.

Metagenomic Association Analysis of Gut Symbiont Limosilactobacillus reuteri Without Host-Specific Genome Isolation

Limosilactobacillus reuteri is a model symbiont that colonizes the guts of vertebrates in studies on host adaptation of the gut symbiont. Previous studies have investigated host-specific phylogenetic and functional properties by isolating the genomic sequence. This dependency on genome isolation is a significant bottleneck. Here, we propose a method to study the association between L. reuteri and its hosts directly from metagenomic reads without strain isolation using pan-genomes. We characterized the host-specificity of L. reuteri in metagenomic samples, not only in previously studied organisms (mice and pigs) but also in dogs. For each sample, two types of profiles were generated: (1) genome-based strain type abundance profiles and (2) gene composition profiles. Our profiles showed host-association of L. reuteri in both phylogenetic and functional aspects without depending on host-specific genome isolation. We observed not only the presence of host-specific lineages, but also the dominant lineages associated with the different hosts. Furthermore, we showed that metagenome-assembled genomes provide detailed insights into the host-specificity of L. reuteri. We inferred evolutionary trajectories of host-associative L. reuteri strains in the metagenomic samples by placing the metagenome-assembled genomes into a phylogenetic tree and identified novel host-specific genes that were unannotated in existing pan-genome databases. Our pan-genomic approach reduces the need for time-consuming and expensive host-specific genome isolation, while producing consistent results with previous host-association findings in mice and pigs. Additionally, we predicted associations that have not yet been studied in dogs.

Colony-Level Effects of Amygdalin on Honeybees and Their Microbes

Amygdalin, a cyanogenic glycoside, is found in the nectar and pollen of almond trees, as well as in a variety of other crops, such as cherries, nectarines, apples and others. It is inevitable that western honeybees (Apis mellifera) consistently consume amygdalin during almond pollination season because almond crops are almost exclusively pollinated by honeybees. This study tests the effects of a field-relevant concentration of amygdalin on honeybee microbes and the activities of key honeybee genes. We executed a two-month field trial providing sucrose solutions with or without amygdalin ad libitum to free-flying honeybee colonies. We collected adult worker bees at four time points and used RNA sequencing technology and our HoloBee database to assess global changes in microbes and honeybee transcripts. Our hypothesis was that amygdalin will negatively affect bee microbes and possibly immune gene regulation. Using a log2 fold-change cutoff at two and intraday comparisons, we show no large change of bacterial counts, fungal counts or key bee immune gene transcripts, due to amygdalin treatment in relation to the control. However, relatively large titer decreases in the amygdalin treatment relative to the control were found for several viruses. Chronic bee paralysis virus levels had a sharp decrease (-14.4) with titers then remaining less than the control, Black queen cell virus titers were lower at three time points (<-2) and Deformed wing virus titers were lower at two time points (<-6) in amygdalin-fed compared to sucrose-fed colonies. Titers of Lotmaria passim were lower in the treatment group at three of the four dates (<-4). In contrast, Sacbrood virus had two dates with relative increases in its titers (>2). Overall, viral titers appeared to fluctuate more so than bacteria, as observed by highly inconstant patterns between treatment and control and throughout the season. Our results suggest that amygdalin consumption may reduce several honeybee viruses without affecting other microbes or colony-level expression of immune genes.

Large-Scale Metagenome Assembly Reveals Novel Animal-Associated Microbial Genomes, Biosynthetic Gene Clusters, and Other Genetic Diversity

Large-scale metagenome assemblies of human microbiomes have produced a vast catalogue of previously unseen microbial genomes; however, comparatively few microbial genomes derive from other vertebrates. Here, we generated 5,596 metagenome-assembled genomes (MAGs) from the gut metagenomes of 180 predominantly wild animal species representing 5 classes, in addition to 14 existing animal gut metagenome data sets. The MAGs comprised 1,522 species-level genome bins (SGBs), most of which were novel at the species, genus, or family level, and the majority were enriched in host versus environment metagenomes. Many traits distinguished SGBs enriched in host or environmental biomes, including the number of antimicrobial resistance genes. We identified 1,986 diverse biosynthetic gene clusters; only 23 clustered with any MIBiG database references. Gene-based assembly revealed tremendous gene diversity, much of it host or environment specific. Our MAG and gene data sets greatly expand the microbial genome repertoire and provide a broad view of microbial adaptations to the vertebrate gut.IMPORTANCE Microbiome studies on a select few mammalian species (e.g., humans, mice, and cattle) have revealed a great deal of novel genomic diversity in the gut microbiome. However, little is known of the microbial diversity in the gut of other vertebrates. We studied the gut microbiomes of a large set of mostly wild animal species consisting of mammals, birds, reptiles, amphibians, and fish. Unfortunately, we found that existing reference databases commonly used for metagenomic analyses failed to capture the microbiome diversity among vertebrates. To increase database representation, we applied advanced metagenome assembly methods to our animal gut data and to many public gut metagenome data sets that had not been used to obtain microbial genomes. Our resulting genome and gene cluster collections comprised a great deal of novel taxonomic and genomic diversity, which we extensively characterized. Our findings substantially expand what is known of microbial genomic diversity in the vertebrate gut.

Difference in the rhizosphere microbiome of Melia azedarach during removal of benzo(a)pyrene from cadmium co-contaminated soil

Benzo(a)pyrene (BaP) is a highly persistent biohazard polyaromatic hydrocarbon and often reported to be present in soils co-contaminated with heavy metals. The present study explains the rhizodegradation of BaP using bacterial consortium in the rhizosphere of Melia azedarach, along with a change in taxonomical and functional properties of the rhizosphere microbiome. The relative abundance of most dominant phylum Proteobacteria was 2% higher with BaP, while in the presence of both BaP and Cd, its abundance was 2.2% lower. Functional metagenome analysis also revealed the shifting of microbial community and functional gene abundance in the favor of xenobiotic compound degradation upon augmentation of bacterial consortium. Interestingly, upon the addition of BaP the range of functional abundance for genes of PAH degradation (0.165-0.19%), was found to be decreasing. However, augmentation of a bacterial consortium led to an increase in its abundance including genes for degradation of benzoate (0.55-0.64%), toluene (0.2-0.22%), naphthalene (0.25-0.295%) irrespective of the addition of BaP and Cd. Moreover, under greenhouse condition, the application of M. azedarach-bacterial consortium enhanced the degradation of BaP in the rhizosphere (88%) after 60 days, significantly higher than degradation in bulk soil (68.22%). The analysis also showed an increase in degradation of BaP by 15% with plant-native microbe association than in bulk soil. Therefore, the association of M. azedarach-bacterial consortium enhanced the degradation of BaP in soil along with the taxonomical and functional attributes of the rhizosphere microbiome.

The trans-kingdom battle between donor and recipient gut microbiome influences fecal microbiota transplantation outcome

Fundamental restoration ecology and community ecology theories can help us better understand the underlying mechanisms of fecal microbiota transplantation (FMT) and to better design future microbial therapeutics for recurrent Clostridioides difficile infections (rCDI) and other dysbiosis-related conditions. In this study, stool samples were collected from donors and rCDI patients one week prior to FMT (pre-FMT), as well as from patients one week following FMT (post-FMT). Using metagenomic sequencing and machine learning, our results suggested that FMT outcome is not only dependent on the ecological structure of the recipients, but also the interactions between the donor and recipient microbiomes at the taxonomical and functional levels. We observed that the presence of specific bacteria in donors (Clostridioides spp., Desulfovibrio spp., Odoribacter spp. and Oscillibacter spp.) and the absence of fungi (Yarrowia spp.) and bacteria (Wigglesworthia spp.) in recipients prior to FMT could predict FMT success. Our results also suggested a series of interlocked mechanisms for FMT success, including the repair of the disturbed gut ecosystem by transient colonization of nexus species followed by secondary succession of bile acid metabolizers, sporulators, and short chain fatty acid producers.

RNA sequencing of whole blood in dogs with primary immune-mediated hemolytic anemia (IMHA) reveals novel insights into disease pathogenesis

Immune-mediated hemolytic anemia (IMHA) is a life-threatening autoimmune disorder characterized by a self-mediated attack on circulating red blood cells. The disease occurs naturally in both dogs and humans, but is significantly more prevalent in dogs. Because of its shared features across species, dogs offer a naturally occurring model for studying IMHA in people. In this study, we used RNA sequencing of whole blood from treatment-naïve dogs to study transcriptome-wide changes in gene expression in newly diagnosed animals compared to healthy controls. We found many overexpressed genes in pathways related to neutrophil function, coagulation, and hematopoiesis. In particular, the most highly overexpressed gene in cases was a phospholipase scramblase, which mediates the externalization of phosphatidylserine from the inner to the outer leaflet of cell membranes. This family of genes has been shown to be critically important for programmed cell death of erythrocytes as well as the initiation of the clotting cascade. Unexpectedly, we found marked underexpression of many genes related to lymphocyte function. We also identified groups of genes that are highly associated with the inflammatory response and red blood cell regeneration in affected dogs. We did not find any genes that distinguished dogs that lived vs. those that died at 30 days following diagnosis, nor did we find any relevant genomic signatures of microbial organisms in the blood of affected animals. Future studies are warranted to validate these findings and assess their implication in developing novel therapeutic approaches for dogs and humans with IMHA.

Gut bacterial deamination of residual levodopa medication for Parkinson's disease

Background

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by both motor and non-motor symptoms. Gastrointestinal tract dysfunction is one of the non-motor features, where constipation is reported as the most common gastrointestinal symptom. Aromatic bacterial metabolites are attracting considerable attention due to their impact on gut homeostasis and host’s physiology. In particular, Clostridium sporogenes is a key contributor to the production of these bioactive metabolites in the human gut.

Results

Here, we show that C. sporogenes deaminates levodopa, the main treatment in Parkinson’s disease, and identify the aromatic aminotransferase responsible for the initiation of the deamination pathway. The deaminated metabolite from levodopa, 3-(3,4-dihydroxyphenyl)propionic acid, elicits an inhibitory effect on ileal motility in an ex vivo model. We detected 3-(3,4-dihydroxyphenyl)propionic acid in fecal samples of Parkinson’s disease patients on levodopa medication and found that this metabolite is actively produced by the gut microbiota in those stool samples.

Conclusions

Levodopa is deaminated by the gut bacterium C. sporogenes producing a metabolite that inhibits ileal motility ex vivo. Overall, this study underpins the importance of the metabolic pathways of the gut microbiome involved in drug metabolism not only to preserve drug effectiveness, but also to avoid potential side effects of bacterial breakdown products of the unabsorbed residue of medication.

IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring

BACKGROUND

Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments.

FINDINGS

We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2.

CONCLUSION

The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

Big Data in metagenomics: Apache Spark vs MPI

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when dealing with increasing problems sizes, making in this way the usage of High Performance Computing (HPC) technologies such as the message passing interface (MPI) a promising alternative. Recently, MetaCacheSpark, an Apache Spark based software for detection and quantification of species composition in food samples has been proposed. This tool can be used to analyze high throughput sequencing data sets of metagenomic DNA and allows for dealing with large-scale collections of complex eukaryotic and bacterial reference genome. In this work, we propose MetaCache-MPI, a fast and memory efficient solution for computing clusters which is based on MPI instead of Apache Spark. In order to evaluate its performance a comparison is performed between the original single CPU version of MetaCache, the Spark version and the MPI version we are introducing. Results show that for 32 processes, MetaCache-MPI is 1.65× faster while consuming 48.12% of the RAM memory used by Spark for building a metagenomics database. For querying this database, also with 32 processes, the MPI version is 3.11× faster, while using 55.56% of the memory used by Spark. We conclude that the new MetaCache-MPI version is faster in both building and querying the database and uses less RAM memory, when compared with MetaCacheSpark, while keeping the accuracy of the original implementation.

Genomic characterization of a diazotrophic microbiota associated with maize aerial root mucilage

A geographically isolated maize landrace cultivated on nitrogen-depleted fields without synthetic fertilizer in the Sierra Mixe region of Oaxaca, Mexico utilizes nitrogen derived from the atmosphere and develops an extensive network of mucilage-secreting aerial roots that harbors a diazotrophic (N2-fixing) microbiota. Targeting these diazotrophs, we selected nearly 600 microbes of a collection obtained from mucilage and confirmed their ability to incorporate heavy nitrogen (15N2) metabolites in vitro. Sequencing their genomes and conducting comparative bioinformatic analyses showed that these genomes had substantial phylogenetic diversity. We examined each diazotroph genome for the presence of nif genes essential to nitrogen fixation (nifHDKENB) and carbohydrate utilization genes relevant to the mucilage polysaccharide digestion. These analyses identified diazotrophs that possessed the canonical nif gene operons, as well as many other operon configurations with concomitant fixation and release of >700 different 15N labeled metabolites. We further demonstrated that many diazotrophs possessed alternative nif gene operons and confirmed their genomic potential to derive chemical energy from mucilage polysaccharide to fuel nitrogen fixation. These results confirm that some diazotrophic bacteria associated with Sierra Mixe maize were capable of incorporating atmospheric nitrogen into their small molecule extracellular metabolites through multiple nif gene configurations while others were able to fix nitrogen without the canonical (nifHDKENB) genes.

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.

Microbiome-Informed Food Safety and Quality: Longitudinal Consistency and Cross-Sectional Distinctiveness of Retail Chicken Breast Microbiomes

Chicken has recently overtaken beef as the most-consumed meat in the United States. The growing popularity of chicken is accompanied by frequent occurrences of foodborne pathogens and increasing concerns over antibiotic usage. Our study represents a proof-of-concept investigation into the possibility and practicality of leveraging microbiome-informed food safety and quality. Through a longitudinal and cross-sectional survey, we established the chicken microbiome as a robust and multifaceted food microbiology attribute that could provide a variety of safety and quality information and retain systematic signals characteristic of overall processing environments.

Microorganisms and their communities on foods are important determinants and indicators of food safety and quality. Despite growing interests in studying food and food-related microbiomes, how effective and practical it is to glean various food safety and quality information from food commodity microbiomes remains underinvestigated. Microbiomes of retail chicken breast from 4 processing establishments in 3 major U.S. broiler production states displayed longitudinal consistency over 7 months and cross-sectional distinctiveness associated with individual processing environments. Packaging type and processing environment but not antibiotic usage and seasonality affected composition and diversity of the microbiomes. Low abundances of antimicrobial resistance genes were found on chicken breasts, and no significant resistome difference was observed between antibiotic-free and conventional products. Benchmarked by culture enrichment, shotgun metagenomics sequencing delivered sensitive and specific detection of Salmonella enterica from chicken breasts.

IMPORTANCE Chicken has recently overtaken beef as the most-consumed meat in the United States. The growing popularity of chicken is accompanied by frequent occurrences of foodborne pathogens and increasing concerns over antibiotic usage. Our study represents a proof-of-concept investigation into the possibility and practicality of leveraging microbiome-informed food safety and quality. Through a longitudinal and cross-sectional survey, we established the chicken microbiome as a robust and multifaceted food microbiology attribute that could provide a variety of safety and quality information and retain systematic signals characteristic of overall processing environments.

Tracking resistomes, virulence genes, and bacterial pathogens in long-term manure-amended greenhouse soils

Organic manure has been implicated as an important source of antibiotic resistance genes (ARGs) in agricultural soils. However, the profiles of biocide resistance genes (BRGs), metal resistance genes (MRGs) and virulence genes (VGs) and their bacterial hosts in manure-amended soils remain largely unknown. Herein, a systematic metagenome-based survey was conducted to comprehensively explore the changes in resistomes, VGs and their bacterial hosts, mobile genetic elements (MGEs), and pathogenic bacteria in manure-amended greenhouse soils. Many manure-borne ARGs, BRGs, MRGs, VGs, and bacterial pathogens could be transferred into soils by applying manures, and their abundance and diversity were markedly positively correlated with greenhouse planting years (manure amendment years). The main ARGs transferred from manures to soils conferred resistance to tetracycline, aminoglycoside, and macrolide-lincosamide-streptogramin. Both statistical analysis and gene arrangements showed a good positive co-occurrence pattern of ARGs/BRGs/MRGs/VGs and MGEs. Furthermore, bacterial hosts of resistomes and VGs were significantly changed in the greenhouse soils in comparison with the field soils. Our findings confirmed the migration and dissemination of resistomes, VGs, and bacterial pathogens, and their accumulation and persistence were correlated with the continuous application of manures.

The Nubeam reference-free approach to analyze metagenomic sequencing reads

We present Nubeam (nucleotide be a matrix) as a novel reference-free approach to analyze short sequencing reads. Nubeam represents nucleotides by matrices, transforms a read into a product of matrices, and assigns numbers to reads based on the product matrix. Nubeam capitalizes on the noncommutative property of matrix multiplication, such that different reads are assigned different numbers and similar reads similar numbers. A sample, which is a collection of reads, becomes a collection of numbers that form an empirical distribution. We demonstrate that the genetic difference between samples can be quantified by the distance between empirical distributions. Nubeam includes the k-mer method as a special case, but unlike the k-mer method, it is convenient for Nubeam to account for GC bias and nucleotide quality. As a reference-free approach, Nubeam avoids reference bias and mapping bias, and can work with organisms without reference genomes. Thus, Nubeam is ideal to analyze data sets from metagenomics whole genome shotgun (WGS) sequencing, where the amount of unmapped reads is substantial. When applied to a WGS sequencing data set to quantify distances between metagenomics samples from various human body habitats, Nubeam recapitulates findings made by mapping-based methods and sheds light on contributions of unmapped reads. Nubeam is also useful in analyzing 16S rRNA sequencing data, which is a more prevalent type of data set in metagenomics studies. In our analysis, Nubeam recapitulated the findings that natural microbiota in mouse gut are resilient under challenges, and Nubeam detected differences in vaginal microbiota between cases of polycystic ovary syndrome and healthy controls.

Metavirome and its functional diversity analysis through microbiome study of the Sikkim Himalayan hot spring solfataric mud sediments

Viruses are the most prodigious repertory of the genetic material on the earth. They are elusive, breakneck, evolutionary life particles that constitute a riveting concealed world. Environmental viruses have been obscurely explored, and hence, such an intriguing world of viruses was studied in the Himalayan Geothermal Belt of Indian peninsula at Sikkim corridor through hot springs. The hot springs located at the North Sikkim district were selected for the current study. The solfataric mud sediment samples were pooled from both the hot springs. The virus community showed significant diversity among the two hot springs of Yume Samdung. Reads for viruses among the mud sediments at Old Yume Samdung hot springs (OYS) was observed to be 11% and in the case of New Yume Samdung hot springs (NYS) it was 6%. Both the hot springs were abundant in dsDNA viromes. The metavirome reads in both the OYS and NYS hot spring mud sediments showed the predominance of Caudovirales; Herpesvirales; Ortervirales among which viral reads from Siphoviridae, Myoviridae, Phycodnaviridae and Podoviridae were abundantly present. Other viral communities belonged to families like Baculoviridae, Mimiviridae, Parvoviridae, Marseilleviridae etc. Interestingly, in the case of NYS, the unassigned group reads belonged to some unclassified giant DNA viruses like genera Pandoravirus and Pithovirus. Other interesting findings were - reads for Badnavirus having ds (RT-DNA) was exclusively found in NYS whereas Rubulavirus having ss(-)RNA was exclusively found in OYS sample. This is the first ever report on viruses from any hot springs of Sikkim till date.

Improving Oral Health and Modulating the Oral Microbiome to Reduce Bloodstream Infections from Oral Organisms in Pediatric and Young Adult Hematopoietic Stem Cell Transplantation Recipients: A Randomized Controlled Trial

Bloodstream infections (BSIs) from oral organisms are a significant cause of morbidity and mortality in hematopoietic stem cell transplantation (HSCT) recipients. There are no proven strategies to decrease BSIs from oral organisms. The aim of this study was to evaluate the impact of daily xylitol wipes in improving oral health, decreasing BSI from oral organisms, and modulating the oral microbiome in pediatric HSCT recipients. This was a single-center 1:1 randomized controlled trial in pediatric HSCT recipients age >2 years. Age-matched healthy children were enrolled to compare the oral microbiome. The oral hygiene standard of care (SOC) group continued to receive the standard oral hygiene regimen. The xylitol group received daily oral xylitol wipes (with .7 g xylitol) in addition to the SOC. The intervention started from the beginning of the transplantation chemotherapy regimen and extended to 28 days following transplantation. The primary outcome was oral health at interval time points, and secondary outcomes included BSIs from oral organisms in the first 30 days following transplantation, oral microbiome abundance, and diversity and oral pathogenic organism abundance. The study was closed early due to efficacy after an interim analysis of the first 30 HSCT recipients was performed (SOC group, n = 16; xylitol group, n = 14). The xylitol group had a significantly lower rate of gingivitis at days 7, 14, and 28 following transplantation (P = .031, .0039, and .0005, respectively); oral plaque at days 7 and 14 (P = .045 and .0023, respectively); and oral ulcers >10 mm at day 14 (P = .049) compared with the SOC group. The xylitol group had no BSI from oral organisms compared with the SOC group, which had 4 (P = .04). The xylitol group had significantly lower abundance of potential BSI pathogens, such as Staphylococcus aureus (P = .036), Klebsiella pneumoniae (P = .033), and Streptococcus spp (P = .011) at the day after transplantation compared with the SOC group. Healthy children and young adults had significantly increased oral microbiome diversity compared with all HSCT recipients (P < .001). The addition of xylitol to standard oral care significantly improves oral health, decreases BSI from oral organisms, and decreases the abundance of pathogenic oral organisms in pediatric and young adult HSCT recipients.

Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2

BACKGROUND

For decades, 16S ribosomal RNA sequencing has been the primary means for identifying the bacterial species present in a sample with unknown composition. One of the most widely used tools for this purpose today is the QIIME (Quantitative Insights Into Microbial Ecology) package. Recent results have shown that the newest release, QIIME 2, has higher accuracy than QIIME, MAPseq, and mothur when classifying bacterial genera from simulated human gut, ocean, and soil metagenomes, although QIIME 2 also proved to be the most computationally expensive. Kraken, first released in 2014, has been shown to provide exceptionally fast and accurate classification for shotgun metagenomics sequencing projects. Bracken, released in 2016, then provided users with the ability to accurately estimate species or genus relative abundances using Kraken classification results. Kraken 2, which matches the accuracy and speed of Kraken 1, now supports 16S rRNA databases, allowing for direct comparisons to QIIME and similar systems.

METHODS

For a comprehensive assessment of each tool, we compare the computational resources and speed of QIIME 2's q2-feature-classifier, Kraken 2, and Bracken in generating the three main 16S rRNA databases: Greengenes, SILVA, and RDP. For an evaluation of accuracy, we evaluated each tool using the same simulated 16S rRNA reads from human gut, ocean, and soil metagenomes that were previously used to compare QIIME, MAPseq, mothur, and QIIME 2. We evaluated accuracy based on the accuracy of the final genera read counts assigned by each tool. Finally, as Kraken 2 is the only tool providing per-read taxonomic assignments, we evaluate the sensitivity and precision of Kraken 2's per-read classifications.

RESULTS

For both the Greengenes and SILVA database, Kraken 2 and Bracken are up to 100 times faster at database generation. For classification, using the same data as previous studies, Kraken 2 and Bracken are up to 300 times faster, use 100x less RAM, and generate results that more accurate at 16S rRNA profiling than QIIME 2's q2-feature-classifier.

CONCLUSION

Kraken 2 and Bracken provide a very fast, efficient, and accurate solution for 16S rRNA metataxonomic data analysis. Video Abstract.

Detection of Antimicrobial Resistance Genes in the Milk Production Environment: Impact of Host DNA and Sequencing Depth

Over the past decades, antimicrobial resistance (AMR) has been recognized as one of the most serious threats to public health. Although originally considered a problem to human health, the emerging crisis of AMR requires a "One Health" approach, considering human, animal, and environmental reservoirs. In this regard, the extensive use of antibiotics in the livestock production systems to treat mastitis and other bacterial diseases can lead to the presence of AMR genes in bacteria that contaminate or naturally occur in milk and dairy products, thereby introducing them into the food chain. The recent development of high-throughput next-generation sequencing (NGS) technologies is improving the fast characterization of microbial communities and their functional capabilities. In this context, whole metagenome sequencing (WMS), also called shotgun metagenomic sequencing, allows the generation of a vast amount of data which can be interrogated to generate the desired evidence, including the resistome. However, the amount of host DNA poses a major challenge to metagenome analysis. Given the current absence of literature concerning the application of WMS on milk to detect the presence of AMR genes, in the present study, we evaluated the effect of different sequencing depths, host DNA depletion methods and matrices to characterize the resistome of a milk production environment. WMS was conducted on three aliquots of bulk tank milk and three aliquots of the in-line milk filter collected from a single dairy farm; a fourth aliquot of milk and milk filter was bioinformatically subsampled. Two commercially available host DNA depletion methods were applied, and metagenomic DNA was sequenced to two different sequencing depth. Milk filters proved to be the most suitable matrices to evaluate the presence of AMR genes; besides, the pre-extraction host DNA depletion method was the most efficient approach to remove host reads. To our knowledge, this is the first study to evaluate the limitations posed by the host DNA in investigating the milk resistome with a WMS approach, confirming the circulation of AMR genes in the milk production environment.

High throughput shotgun sequencing of eRNA reveals taxonomic and derived functional shifts across a benthic productivity gradient

Benthic macrofauna is regularly used in monitoring programmes, however the vast majority of benthic eukaryotic biodiversity lies mostly in microscopic organisms, such as meiofauna (invertebrates < 1 mm) and protists, that rapidly responds to environmental change. These communities have traditionally been hard to sample and handle in the laboratory, but DNA sequencing has made such work less time consuming. While DNA sequencing captures both alive and dead organisms, environmental RNA (eRNA) better targets living organisms or organisms of recent origin in the environment. Here, we assessed the biodiversity of three known bioindicator microeukaryote groups (nematodes, foraminifera, and ciliates) in sediment samples collected at seven coastal sites along an organic carbon (OC) gradient. We aimed to investigate if eRNA shotgun sequencing can be used to simultaneously detect differences in (i) biodiversity of multiple microeukaryotic communities; and (ii) functional feeding traits of nematodes. Results showed that biodiversity was lower for nematodes and foraminifera in high OC (6.2%-6.9%), when compared to low OC sediments (1.2%-2.8%). Dissimilarity in community composition increased for all three groups between Low OC and High OC, as well as the classified feeding type of nematode genera (with more nonselective deposit feeders in high OC sediment). High relative abundant genera included nematode Sabatieria and foraminifera Elphidium in high OC, and Cryptocaryon-like ciliates in low OC sediments. Considering that future sequencing technologies are likely to decrease in cost, the use of eRNA shotgun sequencing to assess biodiversity of benthic microeukaryotes could be a powerful tool in recurring monitoring programmes.

Consumption of a Western-Style Diet Modulates the Response of the Murine Gut Microbiome to Ciprofloxacin

Dietary composition and antibiotic use have major impacts on the structure and function of the gut microbiome, often resulting in dysbiosis. Despite this, little research has been done to explore the role of host diet as a determinant of antibiotic-induced microbiome disruption. Here, we utilize a multi-omic approach to characterize the impact of Western-style diet consumption on ciprofloxacin-induced changes to gut microbiome structure and transcriptional activity. We found that Western diet consumption dramatically increased Bacteroides abundances and shifted the community toward the metabolism of simple sugars and mucus glycoproteins. Mice consuming a Western-style diet experienced a greater expansion of Firmicutes following ciprofloxacin treatment than those eating a control diet. Transcriptionally, we found that ciprofloxacin reduced the abundance of tricarboxylic acid (TCA) cycle transcripts on both diets, suggesting that carbon metabolism plays a key role in the response of the gut microbiome to this antibiotic. Despite this, we observed extensive diet-dependent differences in the impact of ciprofloxacin on microbiota function. In particular, at the whole-community level we detected an increase in starch degradation, glycolysis, and pyruvate fermentation following antibiotic treatment in mice on the Western diet, which we did not observe in mice on the control diet. Similarly, we observed diet-specific changes in the transcriptional activity of two important commensal bacteria, Akkermansia muciniphila and Bacteroides thetaiotaomicron, involving diverse cellular processes such as nutrient acquisition, stress responses, and capsular polysaccharide (CPS) biosynthesis. These findings demonstrate that host diet plays a role in determining the impacts of ciprofloxacin on microbiome composition and microbiome function.IMPORTANCE Due to the growing incidence of disorders related to antibiotic-induced dysbiosis, it is essential to determine how our "Western"-style diet impacts the response of the microbiome to antibiotics. While diet and antibiotics have profound impacts on gut microbiome composition, little work has been done to examine their combined effects. Previous work has shown that nutrient availability, influenced by diet, plays an important role in determining the extent of antibiotic-induced disruption to the gut microbiome. Thus, we hypothesize that the Western diet will shift microbiota metabolism toward simple sugar and mucus degradation and away from polysaccharide utilization. Because of bacterial metabolism's critical role in antibiotic susceptibility, this change in baseline metabolism will impact how the structure and function of the microbiome are impacted by ciprofloxacin exposure. Understanding how diet modulates antibiotic-induced microbiome disruption will allow for the development of dietary interventions that can alleviate many of the microbiome-dependent complications of antibiotic treatment.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

A unified catalog of 204,938 reference genomes from the human gut microbiome

Comprehensive, high-quality reference genomes are required for functional characterization and taxonomic assignment of the human gut microbiota. We present the Unified Human Gastrointestinal Genome (UHGG) collection, comprising 204,938 nonredundant genomes from 4,644 gut prokaryotes. These genomes encode >170 million protein sequences, which we collated in the Unified Human Gastrointestinal Protein (UHGP) catalog. The UHGP more than doubles the number of gut proteins in comparison to those present in the Integrated Gene Catalog. More than 70% of the UHGG species lack cultured representatives, and 40% of the UHGP lack functional annotations. Intraspecies genomic variation analyses revealed a large reservoir of accessory genes and single-nucleotide variants, many of which are specific to individual human populations. The UHGG and UHGP collections will enable studies linking genotypes to phenotypes in the human gut microbiome.

More than 200,000 gut prokaryotic reference genomes and the proteins they encode are collated, providing comprehensive resources for microbiome researchers.

Enterobacteriaceae predominate in the endophytic microbiome and contribute to the resistome of strawberry

Antibiotic resistance genes (ARGs) harbored by plant microbiomes have been implicated as a potential risk to public health via food chain, especially directly edible fruits and vegetables. Here, we investigated the microbiome and antibiotic resistome in soil-strawberry ecosystem using shotgun metagenomic sequencing. The results showed that the enterobacterial population dominated the endophytes of strawberry fruits. Moreover, 85 subtypes of ARGs, including several clinically important ARGs, were detected in the strawberry fruit metagenomes. Additionally, host tracking analysis in combination with antibiotic-resistant bacterial isolate screening suggested that fruit-borne ARGs were mainly carried by members of the Enterobacteriaceae family. Unexpectedly, most of fruit-borne isolates were found to be resistant to several clinically important antimicrobials, e.g., erythromycin and cephalexin. Our findings provide broad insights into endophytic antibiotic resistomes of direct edible strawberry fruits and their potential hosts, and highlight the potential exposure risks of plant microbiomes to the human food chain.

Thermus thermophilus DNA can be used as internal control for process monitoring of clinical metagenomic next-generation sequencing of urine samples

INTRODUCTION

Metagenomics is increasingly considered for clinical diagnostics. In order for this technology to become integrated in the clinical microbiology laboratory, process controls are required. Molecular diagnostic tests typically integrate an internal control (IC) to detect potential sources of variation and technical failure. However, few studies report on the integration of an IC in metagenomics.

AIM

We aimed to develop an easy-to-use IC method for the process control of library preparation and sequencing applied to metagenomics in clinical microbiology diagnostics using Thermus thermophilus DNA.

METHODOLOGY

DNA was extracted from urine samples and sequenced on the Ion Torrent Proton in the absence and presence of incremental concentrations (0.5-2-5%) of IC. Between aliquots of each sample, we compared the IC relative abundance (RA), and after in silico subtraction of IC reads, analysed microbial composition and the RA of pathogens. The optimal IC concentration was defined as the lowest concentration still detectable in all samples with the smallest impact on the microbial composition.

RESULTS

The RA of IC correlated linearly with the spiked IC concentration (r² = 0.99). IC added in a concentration of 0.5% of the total DNA concentration was detectable in all sample aliquots, regardless of human-bacterial DNA proportion, and after in silico removal gave the smallest difference in RA of pathogens compared to the sample aliquot sequenced in the absence of IC. The microbial composition in the presence and absence of IC was highly similar after in silico removal of IC reads (median BC-dissimilarity per sample: 0.059), provided samples had a mean of >10,000 bacterial reads.

CONCLUSION

T. thermophilus DNA at a percentage of 0.5% of the total DNA concentration was successfully applied for the process control of metagenomics of urine samples. We demonstrated negligible alterations in sample microbial composition after in silico subtraction of IC reads. This approach contributes toward implementation of metagenomics in the clinical microbiology laboratory.

Shotgun-Metagenomics on Positive Blood Culture Bottles Inoculated With Prosthetic Joint Tissue: A Proof of Concept Study

Clinical metagenomics is actively moving from research to clinical laboratories. It has the potential to change the microbial diagnosis of infectious diseases, especially when detection and identification of pathogens can be challenging, such as in prosthetic joint infection (PJI). The application of metagenomic sequencing to periprosthetic joint tissue (PJT) specimens is often challenged by low bacterial load in addition to high level of inhibitor and contaminant host DNA, limiting pathogen recovery. Shotgun-metagenomics (SMg) performed directly on positive blood culture bottles (BCBs) inoculated with PJT may be a convenient approach to overcome these obstacles. The aim was to test if it is possible to perform SMg on PJT inoculated into BCBs for pathogen identification in PJI diagnosis. Our study was conducted as a laboratory method development. For this purpose, spiked samples (positive controls), negative control and clinical tissue samples (positive BCBs) were included to get a comprehensive overview. We developed a method for preparation of bacterial DNA directly from PJT inoculated in BCBs. Samples were processed using MolYsis5 kit for removal of human DNA and DNA extracted with BiOstic kit. High DNA quantity/quality was obtained, and no inhibition was observed during the library preparation, allowing further sequencing process. DNA sequencing reads obtained from the BCBs, presented a low proportion of human reads (<1%) improving the sensitivity of bacterial detection. We detected a 19-fold increase in the number of reads mapping to human in a sample untreated with MolYsis5. Taxonomic classification of clinical samples identified a median of 96.08% (IQR, 93.85-97.07%; range 85.7-98.6%) bacterial reads. Shotgun-metagenomics results were consistent with the results from a conventional BCB culture method, validating our approach. Overall, we demonstrated a proof of concept that it is possible to perform SMg directly on BCBs inoculated with PJT, with potential of pathogen identification in PJI diagnosis. We consider this a first step in research efforts needed to face the challenges presented in PJI diagnoses.

Low Abundance of Methanotrophs in Sediments of Shallow Boreal Coastal Zones With High Water Methane Concentrations

Coastal zones are transitional areas between land and sea where large amounts of organic and inorganic carbon compounds are recycled by microbes. Especially shallow zones near land have been shown to be the main source for oceanic methane (CH4) emissions. Water depth has been predicted as the best explanatory variable, which is related to CH4 ebullition, but exactly how sediment methanotrophs mediates these emissions along water depth is unknown. Here, we investigated the relative abundance and RNA transcripts attributed to methane oxidation proteins of aerobic methanotrophs in the sediment of shallow coastal zones with high CH4 concentrations within a depth gradient from 10-45 m. Field sampling consisted of collecting sediment (top 0-2 cm layer) from eight stations along this depth gradient in the coastal Baltic Sea. The relative abundance and RNA transcripts attributed to the CH4 oxidizing protein (pMMO; particulate methane monooxygenase) of the dominant methanotroph Methylococcales was significantly higher in deeper costal offshore areas (36-45 m water depth) compared to adjacent shallow zones (10-28 m). This was in accordance with the shallow zones having higher CH4 concentrations in the surface water, as well as more CH4 seeps from the sediment. Furthermore, our findings indicate that the low prevalence of Methylococcales and RNA transcripts attributed to pMMO was restrained to the euphotic zone (indicated by Photosynthetically active radiation (PAR) data, photosynthesis proteins, and 18S rRNA data of benthic diatoms). This was also indicated by a positive relationship between water depth and the relative abundance of Methylococcales and pMMO. How these processes are affected by light availability requires further studies. CH4 ebullition potentially bypasses aerobic methanotrophs in shallow coastal areas, reducing CH4 availability and limiting their growth. Such mechanism could help explain their reduced relative abundance and related RNA transcripts for pMMO. These findings can partly explain the difference in CH4 concentrations between shallow and deep coastal areas, and the relationship between CH4 concentrations and water depth.

LEMMI: a continuous benchmarking platform for metagenomics classifiers

Studies of microbiomes are booming, along with the diversity of computational approaches to make sense out of the sequencing data and the volumes of accumulated microbial genotypes. A swift evaluation of newly published methods and their improvements against established tools is necessary to reduce the time between the methods' release and their adoption in microbiome analyses. The LEMMI platform offers a novel approach for benchmarking software dedicated to metagenome composition assessments based on read classification. It enables the integration of newly published methods in an independent and centralized benchmark designed to be continuously open to new submissions. This allows developers to be proactive regarding comparative evaluations and guarantees that any promising methods can be assessed side by side with established tools quickly after their release. Moreover, LEMMI enforces an effective distribution through software containers to ensure long-term availability of all methods. Here, we detail the LEMMI workflow and discuss the performances of some previously unevaluated tools. We see this platform eventually as a community-driven effort in which method developers can showcase novel approaches and get unbiased benchmarks for publications, and users can make informed choices and obtain standardized and easy-to-use tools.

Multiple approaches for massively parallel sequencing of SARS-CoV-2 genomes directly from clinical samples

BACKGROUND

COVID-19 (coronavirus disease 2019) has caused a major epidemic worldwide; however, much is yet to be known about the epidemiology and evolution of the virus partly due to the scarcity of full-length SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genomes reported. One reason is that the challenges underneath sequencing SARS-CoV-2 directly from clinical samples have not been completely tackled, i.e., sequencing samples with low viral load often results in insufficient viral reads for analyses.

METHODS

We applied a novel multiplex PCR amplicon (amplicon)-based and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of SARS-CoV-2 from serials dilutions of a cultured isolate, and eight clinical samples covering a range of sample types and viral loads. We also examined and compared the sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner.

RESULTS

We demonstrated that both amplicon and capture methods efficiently enriched SARS-CoV-2 content from clinical samples, while the enrichment efficiency of amplicon outran that of capture in more challenging samples. We found that capture was not as accurate as meta and amplicon in identifying between-sample variations, whereas amplicon method was not as accurate as the other two in investigating within-sample variations, suggesting amplicon sequencing was not suitable for studying virus-host interactions and viral transmission that heavily rely on intra-host dynamics. We illustrated that meta uncovered rich genetic information in the clinical samples besides SARS-CoV-2, providing references for clinical diagnostics and therapeutics. Taken all factors above and cost-effectiveness into consideration, we proposed guidance for how to choose sequencing strategy for SARS-CoV-2 under different situations.

CONCLUSIONS

This is, to the best of our knowledge, the first work systematically investigating inter- and intra-individual variations of SARS-CoV-2 using amplicon- and capture-based whole-genome sequencing, as well as the first comparative study among multiple approaches. Our work offers practical solutions for genome sequencing and analyses of SARS-CoV-2 and other emerging viruses.

Developing standards for the microbiome field

BACKGROUND

Effective standardisation of methodologies to analyse the microbiome is essential to the entire microbiome community. Despite the microbiome field being established for over a decade, there are no accredited or certified reference materials available to the wider community. In this study, we describe the development of the first reference reagents produced by the National Institute for Biological Standards and Control (NIBSC) for microbiome analysis by next-generation sequencing. These can act as global working standards and will be evaluated as candidate World Health Organization International Reference Reagents.

RESULTS

We developed the NIBSC DNA reference reagents Gut-Mix-RR and Gut-HiLo-RR and a four-measure framework for evaluation of bioinformatics tool and pipeline bias. Using these reagents and reporting system, we performed an independent evaluation of a variety of bioinformatics tools by analysing shotgun sequencing and 16S rRNA sequencing data generated from the Gut-Mix-RR and Gut-HiLo-RR. We demonstrate that key measures of microbiome health, such as diversity estimates, are largely inflated by the majority of bioinformatics tools. Across all tested tools, biases were present, with a clear trade-off occurring between sensitivity and the relative abundance of false positives in the final dataset. Using commercially available mock communities, we investigated how the composition of reference reagents may impact benchmarking studies. Reporting measures consistently changed when the same bioinformatics tools were used on different community compositions. This was influenced by both community complexity and taxonomy of species present. Both NIBSC reference reagents, which consisted of gut commensal species, proved to be the most challenging for the majority of bioinformatics tools tested. Going forward, we recommend the field uses site-specific reagents of a high complexity to ensure pipeline benchmarking is fit for purpose.

CONCLUSIONS

If a consensus of acceptable levels of error can be agreed on, widespread adoption of these reference reagents will standardise downstream gut microbiome analyses. We propose to do this through a large open-invite collaborative study for multiple laboratories in 2020. Video Abstract.

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

Sorghum Growth Promotion by Paraburkholderia tropica and Herbaspirillum frisingense: Putative Mechanisms Revealed by Genomics and Metagenomics

Bacteria from the genera Paraburkholderia and Herbaspirillum can promote the growth of Sorghum bicolor, but the underlying mechanisms are not yet known. In a pot experiment, sorghum plants grown on sterilized substrate were inoculated with Paraburkholderia tropica strain IAC/BECa 135 and Herbaspirillum frisingense strain IAC/BECa 152 under phosphate-deficient conditions. These strains significantly increased Sorghum bicolor cultivar SRN-39 root and shoot biomass. Shotgun metagenomic analysis of the rhizosphere revealed successful colonization by both strains; however, the incidence of colonization was higher in plants inoculated with P. tropica strain IAC/BECa 135 than in those inoculated with H. frisingense strain IAC/BECa 152. Conversely, plants inoculated with H. frisingense strain IAC/BECa 152 showed the highest increase in biomass. Genomic analysis of the two inoculants implied a high degree of rhizosphere fitness of P. tropica strain IAC/BECa 135 through environmental signal processing, biofilm formation, and nutrient acquisition. Both genomes contained genes related to plant growth-promoting bacterial (PGPB) traits, including genes related to indole-3-acetate (IAA) synthesis, nitrogen fixation, nodulation, siderophore production, and phosphate solubilization, although the P. tropica strain IAC/BECa 135 genome contained a slightly more extensive repertoire. This study provides evidence that complementary mechanisms of growth promotion in Sorghum might occur, i.e., that P. tropica strain IAC/BECa 135 acts in the rhizosphere and increases the availability of nutrients, while H. frisingense strain IAC/BECa 152 influences plant hormone signaling. While the functional and taxonomic profiles of the rhizobiomes were similar in all treatments, significant differences in plant biomass were observed, indicating that the rhizobiome and the endophytic microbial community may play equally important roles in the complicated plant-microbial interplay underlying increased host plant growth.

Improved gastrointestinal health for irritable bowel syndrome with metagenome-guided interventions

Irritable bowel syndrome (IBS) is the most prevalent functional gastrointestinal disorder worldwide, and the most common reason for referral to gastroenterology clinics. However, the pathophysiology is still not fully understood and consequently current management guidelines are very symptom-specific, leading to mixed results. Here we present a study of 88 individuals with IBS who had baseline sequencing of their gut microbiome (stool samples), received targeted interventions that included dietary, supplement, prebiotic/probiotic, and lifestyle recommendations for a 30-day period, and a follow-up sequencing of their gut microbiome. The study's objectives were to demonstrate unique metagenomic signatures across the IBS phenotypes and to validate whether metagenomic-guided interventions could lead to improvement of symptom scores in individuals with IBS. Enrolled subjects also completed a baseline and post-intervention questionnaire that assessed their symptom scores. The average symptom score of an individual with IBS at baseline was 160 and at the endpoint of the study the average symptom score of the cohort was 100.9. The mixed IBS subtype showed the most significant reduction in symptom scores across the different subtypes (average decrease by 102 points, P = 0.005). The metagenomics analysis reveals shifts in the microbiome post-intervention that have been cross-validated with the literature as being associated with improvement of IBS symptoms. Given the complex nature of IBS, further studies with larger sample sizes, more targeted analyses, and a broader population cohort are needed to explore these results further.

Mutations in Mycobacterium tuberculosis Isolates with Discordant Results for Drug-Susceptibility Testing in Peru

Evaluation of resistance to antituberculosis drugs is routinely performed with genotypic or phenotypic methods; however, discordance can be seen between these different methodologies. Our objective was to identify mutations that could explain discordant results in the evaluation of susceptibility to rifampicin and isoniazid between molecular and phenotypic methods, using whole genome sequencing (WGS). Peruvian strains showing sensitive results in the GenoType MTBDRplus v2.0 test and resistant results in the proportions in the agar-plaque test for isoniazid or rifampin were selected. Discordance was confirmed by repeating both tests, and WGS was performed, using the Next Generation Sequencing methodology. Obtained sequences were aligned “through reference” (genomic mapping) using the program BWA with the algorithm “mem”, using as a reference the genome of the M. tuberculosis H37Rv strain. Discordance was confirmed in 14 strains for rifampicin and 21 for isoniazid, with 1 strain in common for both antibiotics, for a total of 34 unique strains. The most frequent mutation in the rpoB gene in the discordant strains for rifampicin was V170F. The most frequent mutations in the discordant strains for isoniazid were katG R463L, kasA G269S, and Rv1592c I322V. Several other mutations are reported. This is the first study in Latin America addressing mutations present in strains with discordant results between genotypic and phenotypic methods to rifampicin and isoniazid. These mutations could be considered as future potential targets for genotypic tests for evaluation of susceptibility to these drugs.

Soil microbial diversity drops with land-use change in a high mountain temperate forest: a metagenomics survey

Land-use change has been identified as the most severe threat to biodiversity. Soils are important biodiversity reservoirs, but to what extent conversion of high-altitude temperate forest to arable land affects taxonomic and functional soil biodiversity is still largely unknown. Shotgun metagenomics was used to determine the taxonomic and functional diversity of bacteria, archaea and DNA virus in terms of effective number of species in high-altitude temperate oak and pine-oak forest and arable soils from Mexico. Generally, the soil ecosystem maintained its microbial species richness notwithstanding land-use change. Archaea diversity was not affected by land-use change, but the bacterial diversity decreased with 45-55% when the oak forest was converted to arable land and 65-75% when the pine-oak forest was. Loss in bacterial diversity as a result of land-use change was positively correlated (R² = 0.41) with the 10-25% loss in functional diversity. The archaeal communities were evener than the bacterial ones, which might explain their different response to land-use change. We expected a decrease in DNA viral communities as the bacterial diversity decreased, i.e. their potential hosts. However, a higher viral diversity was found in the arable than in the forest soils. It was found that converting high altitude oak and pine-oak forests to arable land more than halved the bacterial diversity, but did not affect the archaeal and even increased the viral diversity.

Antimicrobial Resistance Gene Prevalence in a Population of Patients with Advanced Dementia Is Related to Specific Pathobionts

Long-term care facilities are significant reservoirs of antimicrobial-resistant organisms, and patients with advanced dementia are particularly vulnerable to multidrug-resistant organism (MDRO) acquisition and antimicrobial overuse. In this study, we longitudinally examined a group of patients with advanced dementia using metagenomic sequencing. We found significant inter- and intra-subject heterogeneity in microbiota composition, suggesting temporal instability. We also observed a link between the antimicrobial resistance gene density in a sample and the relative abundances of several pathobionts, particularly Escherichia coli, Proteus mirabilis, and Enterococcus faecalis, and used this relationship to predict resistance gene density in samples from additional subjects. Furthermore, we used metagenomic assembly to demonstrate that these pathobionts had higher resistance gene content than many gut commensals. Given the frequency and abundances at which these pathobionts were found in this population and the underlying vulnerability to MDRO of patients with advanced dementia, attention to microbial blooms of these species may be warranted.

Probabilistic model based on circular statistics for quantifying coverage depth dynamics originating from DNA replication

BACKGROUND

With the development of DNA sequencing technology, static omics profiling in microbial communities, such as taxonomic and functional gene composition determination, has become possible. Additionally, the recently proposed in situ growth rate estimation method allows the applicable range of current comparative metagenomics to be extended to dynamic profiling. However, with this method, the applicable target range is presently limited. Furthermore, the characteristics of coverage depth during replication have not been sufficiently investigated.

RESULTS

We developed a probabilistic model that mimics coverage depth dynamics. This statistical model explains the bias that occurs in the coverage depth due to DNA replication and errors that arise from coverage depth observation. Although our method requires a complete genome sequence, it involves a stable to low coverage depth (>0.01×). We also evaluated the estimation using real whole-genome sequence datasets and reproduced the growth dynamics observed in previous studies. By utilizing a circular distribution in the model, our method facilitates the quantification of unmeasured coverage depth features, including peakedness, skewness, and degree of density, around the replication origin. When we applied the model to time-series culture samples, the skewness parameter, which indicates the asymmetry, was stable over time; however, the peakedness and degree of density parameters, which indicate the concentration level at the replication origin, changed dynamically. Furthermore, we demonstrated the activity measurement of multiple replication origins in a single chromosome.

CONCLUSIONS

We devised a novel framework for quantifying coverage depth dynamics. Our study is expected to serve as a basis for replication activity estimation from a broader perspective using the statistical model.

A big data approach to metagenomics for all-food-sequencing

BACKGROUND

All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches.

RESULTS

We introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark).

CONCLUSIONS

We present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).

Improved Metagenomic Taxonomic Profiling Using a Curated Core Gene-Based Bacterial Database Reveals Unrecognized Species in the Genus Streptococcus

Shotgun metagenomics is of great importance in order to understand the composition of the microbial community associated with a sample and the potential impact it may exert on its host. For clinical metagenomics, one of the initial challenges is the accurate identification of a pathogen of interest and ability to single out that pathogen within a complex community of microorganisms. However, in absence of an accurate identification of those microorganisms, any kind of conclusion or diagnosis based on misidentification may lead to erroneous conclusions, especially when comparing distinct groups of individuals. When comparing a shotgun metagenomic sample against a reference genome sequence database, the classification itself is dependent on the contents of the database. Focusing on the genus Streptococcus, we built four synthetic metagenomic samples and demonstrated that shotgun taxonomic profiling using the bacterial core genes as the reference database performed better in both taxonomic profiling and relative abundance prediction than that based on the marker gene reference database included in MetaPhlAn2. Additionally, by classifying sputum samples of patients suffering from chronic obstructive pulmonary disease, we showed that adding genomes of genomospecies to a reference database offers higher taxonomic resolution for taxonomic profiling. Finally, we show how our genomospecies database is able to identify correctly a clinical stool sample from a patient with a streptococcal infection, proving that genomospecies provide better taxonomic coverage for metagenomic analyses.