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Abstract Copepods are among the most abundant organisms on the planet and play critical functions in aquatic ecosystems. Among copepods, populations of the Eurytemora affinis species complex are numerically dominant in many coastal habitats and serve as food sources for major fisheries. Intriguingly, certain populations possess the unusual capacity to invade novel salinities on rapid time scales. Despite their ecological importance, high-quality genomic resources have been absent for calanoid copepods, limiting our ability to comprehensively dissect the genome architecture underlying the highly invasive and adaptive capacity of certain populations. Here, we present the first chromosome-level genome of a calanoid copepod, from the Atlantic clade (Eurytemora carolleeae) of the E. affinis species complex. This genome was assembled using high-coverage PacBio long-read and Hi-C sequences of an inbred line, generated through 30 generations of full-sib mating. This genome, consisting of 529.3 Mb (contig N50 = 4.2 Mb, scaffold N50 = 140.6 Mb), was anchored onto four chromosomes. Genome annotation predicted 20,262 protein-coding genes, of which ion transport-related gene families were substantially expanded based on comparative analyses of 12 additional arthropod genomes. Also, we found genome-wide signatures of historical gene body methylation of the ion transport-related genes and the significant clustering of these genes on each chromosome. This genome represents one of the most contiguous copepod genomes to date and is among the highest quality marine invertebrate genomes. As such, this genome provides an invaluable resource to help yield fundamental insights into the ability of this copepod to adapt to rapidly changing environments. 

Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and accurate classification of metagenomic reads without needing to construct a reference database. The program DL-TODA presented here aims to classify metagenomic reads using a deep learning model trained on over 3000 bacterial species. A convolutional neural network architecture originally designed for computer vision was applied for the modeling of species-specific features. Using synthetic testing data simulated with 2454 genomes from 639 species, DL-TODA was shown to classify nearly 75% of the reads with high confidence. The classification accuracy of DL-TODA was over 0.98 at taxonomic ranks above the genus level, making it comparable with Kraken2 and Centrifuge, two state-of-the-art taxonomic classification tools. DL-TODA also achieved an accuracy of 0.97 at the species level, which is higher than 0.93 by Kraken2 and 0.85 by Centrifuge on the same test set. Application of DL-TODA to the human oral and cropland soil metagenomes further demonstrated its use in analyzing microbiomes from diverse environments. Compared to Centrifuge and Kraken2, DL-TODA predicted distinct relative abundance rankings and is less biased toward a single taxon. 

Abstract The Alpine goat ( Capra aegagrus hircus ) is parasitized by the barber pole worm ( Haemonchus contortus ). Hematological parameters from transcript and metagenome analysis in the host are reflective of infestation. We explored comparisons between blood samples of control, infected, infected zoledronic acid-treated, and infected antibody (anti-γδ T cells) treated wethers under controlled conditions. Seven days post-inoculation (dpi), we identified 7,627 transcripts associated with the different treatment types. Microbiome measurements at 7 dpi revealed fewer raw read counts across all treatments and a less diverse microbial flora than at 21 dpi. This study identifies treatment specific transcripts and an increase in microflora abundance and diversity as wethers age. Further, F / B ratio reflect health, based on depression or elevation above thresholds defined by the baseline of non-infected controls. Forty Alpine wethers were studied where blood samples were collected from five goats in four treatment groups on 7 dpi and 21 dpi. Transcript and microbiome profiles were obtained using the Partek Flow (St. Louis, Missouri, USA) software suites pipelines. Inflammation comparisons were based on the Firmicutes / Bacteriodetes ratios that are calculated as well as the reduction of microbial diversity. 

Abstract Fungi have evolved over millions of years and their species diversity is predicted to be the second largest on the earth. Fungi have cross-kingdom interactions with many organisms that have mutually shaped their evolutionary trajectories. Zygomycete fungi hold a pivotal position in the fungal tree of life and provide important perspectives on the early evolution of fungi from aquatic to terrestrial environments. Phylogenomic analyses have found that zygomycete fungi diversified into two separate clades, the Mucoromycota which are frequently associated with plants and Zoopagomycota that are commonly animal-associated fungi. Genetic elements that contributed to the fitness and divergence of these lineages may have been shaped by the varied interactions these fungi have had with plants, animals, bacteria, and other microbes. To investigate this, we performed comparative genomic analyses of the two clades of zygomycetes in the context of Kingdom Fungi, benefiting from our generation of a new collection of zygomycete genomes, including nine produced for this study. We identified lineage-specific genomic content that may contribute to the disparate biology observed in these zygomycetes. Our findings include the discovery of undescribed diversity in CotH, a Mucormycosis pathogenicity factor, which was found in a broad set of zygomycetes. Reconciliation analysis identified multiple duplication events and an expansion of CotH copies throughout the Mucoromycotina, Mortierellomycotina, Neocallimastigomycota, and Basidiobolus lineages. A kingdom-level phylogenomic analysis also identified new evolutionary relationships within the subphyla of Mucoromycota and Zoopagomycota, including supporting the sister-clade relationship between Glomeromycotina and Mortierellomycotina and the placement of Basidiobolus as sister to other Zoopagomycota lineages. 

Summary The ectomycorrhizal (ECM) symbiosis has independently evolved from diverse types of saprotrophic ancestors. In this study, we seek to identify genomic signatures of the transition to the ECM habit within the hyperdiverse Russulaceae.We present comparative analyses of the genomic architecture and the total and secreted gene repertoires of 18 species across the order Russulales, of which 13 are newly sequenced, including a representative of a saprotrophic member of Russulaceae,Gloeopeniophorella convolvens.The genomes of ECM Russulaceae are characterized by a loss of genes for plant cell wall‐degrading enzymes (PCWDEs), an expansion of genome size through increased transposable element (TE) content, a reduction in secondary metabolism clusters, and an association of small secreted proteins (SSPs) with TE ‘nests’, or dense aggregations of TEs. Some PCWDEs have been retained or even expanded, mostly in a species‐specific manner. The genome ofG. convolvenspossesses some characteristics of ECM genomes (e.g. loss of some PCWDEs, TE expansion, reduction in secondary metabolism clusters).Functional specialization in ECM decomposition may drive diversification. Accelerated gene evolution predates the evolution of the ECM habit, indicating that changes in genome architecture and gene content may be necessary to prime the evolutionary switch. 

Most of the described species in kingdom Fungi are contained in two phyla, the Ascomycota and the Basidiomycota (subkingdom Dikarya). As a result, our understanding of the biology of the kingdom is heavily influenced by traits observed in Dikarya, such as aerial spore dispersal and life cycles dominated by mitosis of haploid nuclei. We now appreciate that Fungi comprises numerous phylum-level lineages in addition to those of Dikarya, but the phylogeny and genetic characteristics of most of these lineages are poorly understood due to limited genome sampling. Here, we addressed major evolutionary trends in the non-Dikarya fungi by phylogenomic analysis of 69 newly generated draft genome sequences of the zoosporic (flagellated) lineages of true fungi. Our phylogeny indicated five lineages of zoosporic fungi and placed Blastocladiomycota, which has an alternation of haploid and diploid generations, as branching closer to the Dikarya than to the Chytridiomyceta. Our estimates of heterozygosity based on genome sequence data indicate that the zoosporic lineages plus the Zoopagomycota are frequently characterized by diploid-dominant life cycles. We mapped additional traits, such as ancestral cell-cycle regulators, cell-membrane– and cell-wall–associated genes, and the use of the amino acid selenocysteine on the phylogeny and found that these ancestral traits that are shared with Metazoa have been subject to extensive parallel loss across zoosporic lineages. Together, our results indicate a gradual transition in the genetics and cell biology of fungi from their ancestor and caution against assuming that traits measured in Dikarya are typical of other fungal lineages. 

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0, a modern data standard for collaborative science across the neurophysiology community.