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

The human gastrointestinal tract contains a complex community of microbes, fulfilling important health-promoting functions. However, this vast complexity of species hampers the assignment of responsible organisms to these functions. Recently, Akkermansia muciniphila, a new species from the deeply branched phylum Verrucomicrobia, was isolated from the human intestinal tract based on its capacity to efficiently use mucus as a carbon and nitrogen source. This anaerobic resident is associated with the protective mucus lining of the intestines. In order to uncover the functional potential of A. muciniphila, its genome was sequenced and annotated. It was found to contain numerous candidate mucinase-encoding genes, but lacking genes encoding canonical mucus-binding domains. Numerous phage-associated sequences found throughout the genome indicate that viruses have played an important part in the evolution of this species. Furthermore, we mined 37 GI tract metagenomes for the presence, and genetic diversity of Akkermansia sequences. Out of 37, eleven contained 16S ribosomal RNA gene sequences that are >95% identical to that of A. muciniphila. In addition, these libraries were found to contain large amounts of Akkermansia DNA based on average nucleotide identity scores, which indicated in one subject co-colonization by different Akkermansia phylotypes. An additional 12 libraries also contained Akkermansia sequences, making a total of ∼16 Mbp of new Akkermansia pangenomic DNA. The relative abundance of Akkermansia DNA varied between <0.01% to nearly 4% of the assembled metagenomic reads. Finally, by testing a large collection of full length 16S sequences, we find at least eight different representative species in the genus Akkermansia. These large repositories allow us to further mine for genetic heterogeneity and species diversity in the genus Akkermansia, providing novel insight towards the functionality of this abundant inhabitant of the human intestinal tract.

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad’s speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad . geNomad identifies mobile genetic elements in sequencing data.

High-throughput sequencing is expanding our knowledge of microbial diversity in the environment. Still, understanding the metabolic potentials and ecological roles of rare and uncultured microbes in natural communities remains a major challenge. To this end, we applied a ‘divide and conquer’ strategy that partitioned a massive metagenomic data set (>100 Gbp) into subsets based on K-mer frequency in sequence assembly to a low-diversity acid mine drainage (AMD) microbial community and, by integrating with an additional metatranscriptomic assembly, successfully obtained 11 draft genomes most of which represent yet uncultured and/or rare taxa (relative abundance <1%). We report the first genome of a naturally occurring Ferrovum population (relative abundance >90%) and its metabolic potentials and gene expression profile, providing initial molecular insights into the ecological role of these lesser known, but potentially important, microorganisms in the AMD environment. Gene transcriptional analysis of the active taxa revealed major metabolic capabilities executed in situ , including carbon- and nitrogen-related metabolisms associated with syntrophic interactions, iron and sulfur oxidation, which are key in energy conservation and AMD generation, and the mechanisms of adaptation and response to the environmental stresses (heavy metals, low pH and oxidative stress). Remarkably, nitrogen fixation and sulfur oxidation were performed by the rare taxa, indicating their critical roles in the overall functioning and assembly of the AMD community. Our study demonstrates the potential of the ‘divide and conquer’ strategy in high-throughput sequencing data assembly for genome reconstruction and functional partitioning analysis of both dominant and rare species in natural microbial assemblages.

The Deepwater Horizon oil spill in the Gulf of Mexico resulted in a deep-sea hydrocarbon plume that caused a shift in the indigenous microbial community composition with unknown ecological consequences. Early in the spill history, a bloom of uncultured, thus uncharacterized, members of the Oceanospirillales was previously detected, but their role in oil disposition was unknown. Here our aim was to determine the functional role of the Oceanospirillales and other active members of the indigenous microbial community using deep sequencing of community DNA and RNA, as well as single-cell genomics. Shotgun metagenomic and metatranscriptomic sequencing revealed that genes for motility, chemotaxis and aliphatic hydrocarbon degradation were significantly enriched and expressed in the hydrocarbon plume samples compared with uncontaminated seawater collected from plume depth. In contrast, although genes coding for degradation of more recalcitrant compounds, such as benzene, toluene, ethylbenzene, total xylenes and polycyclic aromatic hydrocarbons, were identified in the metagenomes, they were expressed at low levels, or not at all based on analysis of the metatranscriptomes. Isolation and sequencing of two Oceanospirillales single cells revealed that both cells possessed genes coding for n-alkane and cycloalkane degradation. Specifically, the near-complete pathway for cyclohexane oxidation in the Oceanospirillales single cells was elucidated and supported by both metagenome and metatranscriptome data. The draft genome also included genes for chemotaxis, motility and nutrient acquisition strategies that were also identified in the metagenomes and metatranscriptomes. These data point towards a rapid response of members of the Oceanospirillales to aliphatic hydrocarbons in the deep sea.

Cancer is the leading cause of death around the world. While some types of cancer have become manageable due to advancements in medicine, most cancers still lack available cures and treatments. Recent studies have shown that changes in the human microbiome, especially in the bacteriome, are associated with some cancers. Certain bacterial strains have been reported to promote the initiation and progression of cancer in humans. Other studies have used sequencing to observe changes in the bacteriome of healthy and cancer patients. However, studies that investigate the interactions between cancer cells and the complex bacteriome as a whole remain scarce. This is due to the absence of experimental methods to study the interactions between cancer cells and complex bacterial populations, which has delayed the progress in identifying cancer-causing and cancer-inhibiting bacteria, and in understanding the bacterial interactions and their influence on host cells. Here, we review approaches to studying cancer cell interactions with complex bacteriomes and suggest possible routes to overcome this problem, highlighting the need for interdisciplinary studies that may help advance this field. We speculate that a good understanding of cancer-bacteriome interactions may open the door to new lines of holistic bacteriotherapy for cancer that is otherwise unavailable.

As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how “microbiomes within microbiomes” affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales. 1R11iZ3xLrWDQ_XwZ4bfc5 Video Abstract

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM (“intrahost SARS-CoV-2 k-mer identification method”) to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response.

Palmerolide A has potential as a chemotherapeutic agent to target melanoma. We interrogated the microbiome of the Antarctic ascidian, Synoicum adareanum , using a cultivation-independent high-throughput sequencing and bioinformatic strategy. The Antarctic marine ecosystem harbors a wealth of biological and chemical innovation that has risen in concert over millennia since the isolation of the continent and formation of the Antarctic circumpolar current. Scientific inquiry into the novelty of marine natural products produced by Antarctic benthic invertebrates led to the discovery of a bioactive macrolide, palmerolide A, that has specific activity against melanoma and holds considerable promise as an anticancer therapeutic. While this compound was isolated from the Antarctic ascidian Synoicum adareanum , its biosynthesis has since been hypothesized to be microbially mediated, given structural similarities to microbially produced hybrid nonribosomal peptide-polyketide macrolides. Here, we describe a metagenome-enabled investigation aimed at identifying the biosynthetic gene cluster (BGC) and palmerolide A-producing organism. A 74-kbp candidate BGC encoding the multimodular enzymatic machinery (hybrid type I- trans -AT polyketide synthase-nonribosomal peptide synthetase and tailoring functional domains) was identified and found to harbor key features predicted as necessary for palmerolide A biosynthesis. Surveys of ascidian microbiome samples targeting the candidate BGC revealed a high correlation between palmerolide gene targets and a single 16S rRNA gene variant ( R  = 0.83 to 0.99). Through repeated rounds of metagenome sequencing followed by binning contigs into metagenome-assembled genomes, we were able to retrieve a nearly complete genome (10 contigs) of the BGC-producing organism, a novel verrucomicrobium within the Opitutaceae family that we propose here as “ Candidatus Synoicihabitans palmerolidicus.” The refined genome assembly harbors five highly similar BGC copies, along with structural and functional features that shed light on the host-associated nature of this unique bacterium. IMPORTANCE Palmerolide A has potential as a chemotherapeutic agent to target melanoma. We interrogated the microbiome of the Antarctic ascidian, Synoicum adareanum , using a cultivation-independent high-throughput sequencing and bioinformatic strategy. The metagenome-encoded biosynthetic machinery predicted to produce palmerolide A was found to be associated with the genome of a member of the S. adareanum core microbiome. Phylogenomic analysis suggests the organism represents a new deeply branching genus, “ Candidatus Synoicihabitans palmerolidicus,” in the Opitutaceae family of the Verrucomicrobia phylum. The Ca . Synoicihabitans palmerolidicus 4.29-Mb genome encodes a repertoire of carbohydrate-utilizing and transport pathways, a chemotaxis system, flagellar biosynthetic capacity, and other regulatory elements enabling its ascidian-associated lifestyle. The palmerolide producer’s genome also contains five distinct copies of the large palmerolide biosynthetic gene cluster that may provide structural complexity of palmerolide variants.