Haploflow: Strain-resolved de novo assembly of viral genomes.

In viral infections often multiple related viral strains are present, due to coinfection or within-host evolution. We describe Haploflow, a de Bruijn graph-based assembler for de novo genome assembly of viral strains from mixed sequence samples using a novel flow algorithm. We assessed Haploflow across multiple benchmark data sets of increasing complexity, showing that Haploflow is faster and more accurate than viral haplotype assemblers and generic metagenome assemblers not aiming to reconstruct strains. Haplotype reconstructed high-quality strain-resolved assemblies from clinical HCMV samples and SARS-CoV-2 genomes from wastewater metagenomes identical to genomes from clinical isolates.

"Candidatus Paraporphyromonas polyenzymogenes" encodes multi-modular cellulases linked to the type IX secretion system.

BACKGROUND: In nature, obligate herbivorous ruminants have a close symbiotic relationship with their gastrointestinal microbiome, which proficiently deconstructs plant biomass. Despite decades of research, lignocellulose degradation in the rumen has thus far been attributed to a limited number of culturable microorganisms. Here, we combine meta-omics and enzymology to identify and describe a novel Bacteroidetes family ("Candidatus MH11") composed entirely of uncultivated strains that are predominant in ruminants and only distantly related to previously characterized taxa. RESULTS: The first metabolic reconstruction of Ca. MH11-affiliated genome bins, with a particular focus on the provisionally named "Candidatus Paraporphyromonas polyenzymogenes", illustrated their capacity to degrade various lignocellulosic substrates via comprehensive inventories of singular and multi-modular carbohydrate active enzymes (CAZymes). Closer examination revealed an absence of archetypical polysaccharide utilization loci found in human gut microbiota. Instead, we identified many multi-modular CAZymes putatively secreted via the Bacteroidetes-specific type IX secretion system (T9SS). This included cellulases with two or more catalytic domains, which are modular arrangements that are unique to Bacteroidetes species studied to date. Core metabolic proteins from Ca. P. polyenzymogenes were detected in metaproteomic data and were enriched in rumen-incubated plant biomass, indicating that active saccharification and fermentation of complex carbohydrates could be assigned to members of this novel family. Biochemical analysis of selected Ca. P. polyenzymogenes CAZymes further iterated the cellulolytic activity of this hitherto uncultured bacterium towards linear polymers, such as amorphous and crystalline cellulose as well as mixed linkage ß-glucans. CONCLUSION: We propose that Ca. P. polyenzymogene genotypes and other Ca. MH11 members actively degrade plant biomass in the rumen of cows, sheep and most likely other ruminants, utilizing singular and multi-domain catalytic CAZymes secreted through the T9SS. The discovery of a prominent role of multi-modular cellulases in the Gram-negative Bacteroidetes, together with similar findings for Gram-positive cellulosomal bacteria (Ruminococcus flavefaciens) and anaerobic fungi (Orpinomyces sp.), suggests that complex enzymes are essential and have evolved within all major cellulolytic dominions inherent to the rumen.

Novel Syntrophic Populations Dominate an Ammonia-Tolerant Methanogenic Microbiome.

Biogas reactors operating with protein-rich substrates have high methane potential and industrial value; however, they are highly susceptible to process failure because of the accumulation of ammonia. High ammonia levels cause a decline in acetate-utilizing methanogens and instead promote the conversion of acetate via a two-step mechanism involving syntrophic acetate oxidation (SAO) to H2 and CO2, followed by hydrogenotrophic methanogenesis. Despite the key role of syntrophic acetate-oxidizing bacteria (SAOB), only a few culturable representatives have been characterized. Here we show that the microbiome of a commercial, ammonia-tolerant biogas reactor harbors a deeply branched, uncultured phylotype (unFirm_1) accounting for approximately 5% of the 16S rRNA gene inventory and sharing 88% 16S rRNA gene identity with its closest characterized relative. Reconstructed genome and quantitative metaproteomic analyses imply unFirm_1's metabolic dominance and SAO capabilities, whereby the key enzymes required for acetate oxidation are among the most highly detected in the reactor microbiome. While culturable SAOB were identified in genomic analyses of the reactor, their limited proteomic representation suggests that unFirm_1 plays an important role in channeling acetate toward methane. Notably, unFirm_1-like populations were found in other high-ammonia biogas installations, conjecturing a broader importance for this novel clade of SAOB in anaerobic fermentations. IMPORTANCE The microbial production of methane or "biogas" is an attractive renewable energy technology that can recycle organic waste into biofuel. Biogas reactors operating with protein-rich substrates such as household municipal or agricultural wastes have significant industrial and societal value; however, they are highly unstable and frequently collapse due to the accumulation of ammonia. We report the discovery of a novel uncultured phylotype (unFirm_1) that is highly detectable in metaproteomic data generated from an ammonia-tolerant commercial reactor. Importantly, unFirm_1 is proposed to perform a key metabolic step in biogas microbiomes, whereby it syntrophically oxidizes acetate to hydrogen and carbon dioxide, which methanogens then covert to methane. Only very few culturable syntrophic acetate-oxidizing bacteria have been described, and all were detected at low in situ levels compared to unFirm_1. Broader comparisons produced the hypothesis that unFirm_1 is a key mediator toward the successful long-term stable operation of biogas production using protein-rich substrates.

Snowball: strain aware gene assembly of metagenomes.

MOTIVATION: Gene assembly is an important step in functional analysis of shotgun metagenomic data. Nonetheless, strain aware assembly remains a challenging task, as current assembly tools often fail to distinguish among strain variants or require closely related reference genomes of the studied species to be available. RESULTS: We have developed Snowball, a novel strain aware gene assembler for shotgun metagenomic data that does not require closely related reference genomes to be available. It uses profile hidden Markov models (HMMs) of gene domains of interest to guide the assembly. Our assembler performs gene assembly of individual gene domains based on read overlaps and error correction using read quality scores at the same time, which results in very low per-base error rates. AVAILABILITY AND IMPLEMENTATION: The software runs on a user-defined number of processor cores in parallel, runs on a standard laptop and is available under the GPL 3.0 license for installation under Linux or OS X at https://github.com/hzi-bifo/snowball CONTACT: AMC14@helmholtz-hzi.de,a.schoenhuth@cwi.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data.

DNA assembly is a core methodological step in metagenomic pipelines used to study the structure and function within microbial communities. Here we investigate the utility of Pacific Biosciences long and high accuracy circular consensus sequencing (CCS) reads for metagenomic projects. We compared the application and performance of both PacBio CCS and Illumina HiSeq data with assembly and taxonomic binning algorithms using metagenomic samples representing a complex microbial community. Eight SMRT cells produced approximately 94 Mb of CCS reads from a biogas reactor microbiome sample that averaged 1319 nt in length and 99.7% accuracy. CCS data assembly generated a comparative number of large contigs greater than 1 kb, to those assembled from a ~190x larger HiSeq dataset (~18 Gb) produced from the same sample (i.e approximately 62% of total contigs). Hybrid assemblies using PacBio CCS and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length and number of large contigs. The incorporation of CCS data produced significant enhancements in taxonomic binning and genome reconstruction of two dominant phylotypes, which assembled and binned poorly using HiSeq data alone. Collectively these results illustrate the value of PacBio CCS reads in certain metagenomics applications.

Adaptive Mutations That Occurred during Circulation in Humans of H1N1 Influenza Virus in the 2009 Pandemic Enhance Virulence in Mice.

UNLABELLED: During the 2009 H1N1 influenza pandemic, infection attack rates were particularly high among young individuals who suffered from pneumonia with occasional death. Moreover, previously reported determinants of mammalian adaptation and pathogenicity were not present in 2009 pandemic H1N1 influenza A viruses. Thus, it was proposed that unknown viral factors might have contributed to disease severity in humans. In this study, we performed a comparative analysis of two clinical 2009 pandemic H1N1 strains that belong to the very early and later phases of the pandemic. We identified mutations in the viral hemagglutinin (HA) and the nucleoprotein (NP) that occurred during pandemic progression and mediate increased virulence in mice. Lethal disease outcome correlated with elevated viral replication in the alveolar epithelium, increased proinflammatory cytokine and chemokine responses, pneumonia, and lymphopenia in mice. These findings show that viral mutations that have occurred during pandemic circulation among humans are associated with severe disease in mice. IMPORTANCE: In this study, novel determinants of 2009 pandemic H1N1 influenza pathogenicity were identified in the viral hemagglutinin (HA) and the nucleoprotein (NP) genes. In contrast to highly pathogenic avian influenza viruses, increased virulence in mice did not correlate with enhanced polymerase activity but with reduced activity. Lethal 2009 pandemic H1N1 infection in mice correlated with lymphopenia and severe pneumonia. These studies suggest that molecular mechanisms that mediate 2009 pandemic H1N1 influenza pathogenicity are distinct from those that mediate avian influenza virus pathogenicity in mice.

Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods.

MOTIVATION: Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows identifying the sequenced community members and reconstructing taxonomic bins with sequence data for the individual taxa. For the massive datasets generated by next-generation sequencing technologies, this cannot be performed with de-novo phylogenetic inference methods. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignment by fast approximate determination of evolutionary neighbors from sequence similarities. RESULTS: Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short as well as for long sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples because it identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6 Gb of sequence data per day on a standard multiprocessor system with 10 CPU cores and microbial RefSeq as the genomic reference data.

Computational prediction of vaccine strains for human influenza A (H3N2) viruses.

Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.

Isolation of Succinivibrionaceae implicated in low methane emissions from Tammar wallabies.

The Tammar wallaby (Macropus eugenii) harbors unique gut bacteria and produces only one-fifth the amount of methane produced by ruminants per unit of digestible energy intake. We have isolated a dominant bacterial species (WG-1) from the wallaby microbiota affiliated with the family Succinivibrionaceae and implicated in lower methane emissions from starch-containing diets. This was achieved by using a partial reconstruction of the bacterium's metabolism from binned metagenomic data (nitrogen and carbohydrate utilization pathways and antibiotic resistance) to devise cultivation-based strategies that produced axenic WG-1 cultures. Pure-culture studies confirm that the bacterium is capnophilic and produces succinate, further explaining a microbiological basis for lower methane emissions from macropodids. This knowledge also provides new strategic targets for redirecting fermentation and reducing methane production in livestock.

Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores.

Metagenomic and bioinformatic approaches were used to characterize plant biomass conversion within the foregut microbiome of Australia's "model" marsupial, the Tammar wallaby (Macropus eugenii). Like the termite hindgut and bovine rumen, key enzymes and modular structures characteristic of the "free enzyme" and "cellulosome" paradigms of cellulose solubilization remain either poorly represented or elusive to capture by shotgun sequencing methods. Instead, multigene polysaccharide utilization loci-like systems coupled with genes encoding beta-1,4-endoglucanases and beta-1,4-endoxylanases--which have not been previously encountered in metagenomic datasets--were identified, as were a diverse set of glycoside hydrolases targeting noncellulosic polysaccharides. Furthermore, both rrs gene and other phylogenetic analyses confirmed that unique clades of the Lachnospiraceae, Bacteroidales, and Gammaproteobacteria are predominant in the Tammar foregut microbiome. Nucleotide composition-based sequence binning facilitated the assemblage of more than two megabase pairs of genomic sequence for one of the novel Lachnospiraceae clades (WG-2). These analyses show that WG-2 possesses numerous glycoside hydrolases targeting noncellulosic polysaccharides. These collective data demonstrate that Australian macropods not only harbor unique bacterial lineages underpinning plant biomass conversion, but their repertoire of glycoside hydrolases is distinct from those of the microbiomes of higher termites and the bovine rumen.