Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon
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59 BASIC BIOLOGICAL SCIENCES↗
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Inverse Regression for Extraction of Tumor Site from Cancer Pathology Reports
Pathology reports are the primary source of information for cancer diagnosis of millions of the cancer patients across the United States. Cancer registries label these reports every year. The coded labels incorporate pertinent information such as cancer location, behavior, and histology. This information when combined with clinical information, medical imaging and even genomic information have a great potential to fuel discoveries in cancer research. The coding process is manual and requires many human experts to label the large volume of pathology reports in a timely manner. In this study, we have developed a supervised inverse regression based auto-labeler to automate the task. The experiments were conducted on a set of 942 pathology reports with human expert labels as the ground truth. We observed that the inverse regression based auto-labeler consistently performed better than or comparable to the best performing state-of-the-art method. These results demonstrate the potential of inverse regression for reliable information extraction from the pathology reports.
Dubey, Abhishek↗
Comparative functional pan-genome analyses to build connections between genomic dynamics and phenotypic evolution in polycyclic aromatic hydrocarbon metabolism in the genus Mycobacterium
Background: The bacterial genus Mycobacterium is of great interest in the medical and biotechnological fields. Despite a flood of genome sequencing and functional genomics data, significant gaps in knowledge between genome and phenome seriously hinder efforts toward the treatment of mycobacterial diseases and practical biotechnological applications. In this study, we propose the use of systematic, comparative functional pan-genomic analysis to build connections between genomic dynamics and phenotypic evolution in polycyclic aromatic hydrocarbon (PAH) metabolism in the genus Mycobacterium. Results: Phylogenetic, phenotypic, and genomic information for 27 completely genome-sequenced mycobacteria was systematically integrated to reconstruct a mycobacterial phenotype network (MPN) with a pan-genomic concept at a network level. In the MPN, mycobacterial phenotypes show typical scale-free relationships. PAH degradation is an isolated phenotype with the lowest connection degree, consistent with phylogenetic and environmental isolation of PAH degraders. A series of functional pan-genomic analyses provide conserved and unique types of genomic evidence for strong epistatic and pleiotropic impacts on evolutionary trajectories of the PAH-degrading phenotype. Under strong natural selection, the detailed gene gain/loss patterns from horizontal gene transfer (HGT)/deletion events hypothesize a plausible evolutionary path, an epistasis-based birth and pleiotropy-dependent death, for PAH metabolism in the genus Mycobacterium. This study generated a practical mycobacterial compendium of phenotypic and genomic changes, focusing on the PAH-degrading phenotype, with a pan-genomic perspective of the evolutionary events and the environmental challenges. Conclusions: Our findings suggest that when selection acts on PAH metabolism, only a small fraction of possible trajectories is likely to be observed, owing mainly to a combination of the ambiguous phenotypic effects of PAHs and the corresponding pleiotropy- and epistasis-dependent evolutionary adaptation. Evolutionary constraints on the selection of trajectories, like those seen in PAH-degrading phenotypes, are likely to apply to the evolution of other phenotypes in the genus Mycobacterium.
59 BASIC BIOLOGICAL SCIENCES↗
Targeted isolation and cultivation of uncultivated bacteria by reverse genomics
The majority of microorganisms from all taxonomic levels are uncultured. Single-cell genomes and metagenomes continue to increase the known diversity of Bacteria and Archaea; however, while ’omics can be used to infer physiological or ecological roles for species in a community, most of these hypothetical roles remain unvalidated. Here, we report a technique to capture specific microorganisms from complex communities into pure cultures using genome-informed antibody engineering. We apply our reverse genomics approach to isolate and sequence single cells and to cultivate three different species-level lineages of human oral Saccharibacteria (TM7). Using our pure cultures, we show that all three Saccharibacteria species are epibionts of diverse Actinobacteria. We also isolate and cultivate human oral SR1 bacteria, which are members of a lineage of previously uncultured bacteria. Reverse-genomics-enabled cultivation of microorganisms can be applied to any species from any environment and has the potential to unlock the isolation, cultivation and characterization of species from as-yet-uncultured branches of the microbial tree of life.
59 BASIC BIOLOGICAL SCIENCES↗
Chromosome-level genome assembly of Quercus variabilis provides insights into the molecular mechanism of cork thickness
Quercus variabilis is a deciduous woody species with high ecological and economic value and is a major source of cork in East Asia. Cork from thick softwood sheets have higher commercial value than those from thin sheets. It is extremely difficult to genetically improve Q. variabilis to produce high quality softwood due to the lack of genomic information. Here, we present a high-quality chromosomal genome assembly for Q. variabilis with length of 791,89 Mb and 54,606 predicted genes. Comparative analysis of protein sequences of Q. variabilis with 11 other species revealed that specific and expanded gene families were significantly enriched in the "fatty acid biosynthesis" pathway in Q. variabilis, which may contribute to the formation of its unique cork. Additionally, based on weighted correlation network analysis of time-course (i.e., five important developmental ages) gene expression data in thick-cork versus thin-cork genotypes of Q. variabilis, we identified one co-expression gene module associated with the thick-cork trait. Within this co-expression gene module, 10 hub genes were associated with suberin biosynthesis. Furthermore, we identified a total of 198 suberin biosynthesis-related new candidate genes that were up-regulated in trees with a thick cork layer relative to those with a thin cork layer. Also, we found that some genes related to cell expansion and cell division were highly expressed in trees with a thick cork layer. Collectively, our results revealed that two metabolic pathways (i.e., suberin biosynthesis, fatty acid biosynthesis), along with other genes involved in cell expansion, cell division, and transcriptional regulation, were associated with the thick-cork trait in Q. variabilis, providing insights into the molecular basis of cork development and knowledge for informing genetic improvement of cork thickness in Q. variabilis and closely related species.
59 BASIC BIOLOGICAL SCIENCES↗
Microbial metaproteomics: Identifying the repertoire of proteins that microorganisms use to compete and cooperate in complex environmental communities
The availability of genome information for microbial consortia, including unculturable species, from environmental samples has enabled systems-biology interrogation by providing a means to access genomic, transcriptomic, and proteomic information. This provides a unique opportunity to characterize the molecular activities and interactions of these microbial systems at a comprehensive level never before possible. Such information not only provides details about the organizational, functional, and metabolic activities of such systems, but also the untapped reserve of molecular activities that might be invoked and exploited under certain environmental conditions. Since bacteria naturally exist in complex ecosystems, it is imperative to develop and utilize analytical approaches that can provide molecular level details on systems consisting of mixed microbial membership. This is the realm of metaproteomics - the characterization of the complement of proteins expressed by a microbial community in an environmental sample
59 BASIC BIOLOGICAL SCIENCES↗
A single-cell genome perspective on studying intracellular associations in unicellular eukaryotes
Single-cell genomics (SCG) methods provide a unique opportunity to analyse whole genome information at the resolution of an individual cell. While SCG has been extensively used to investigate bacterial and archaeal genomes, the technique has been rarely used to access the genetic makeup of uncultivated microbial eukaryotes. In this regard, the use of SCG can provide a wealth of information; not only do the methods allow exploration of the genome, they can also help elucidate the relationship between the cell and intracellular entities extant in nearly all eukaryotes. SCG enables the study of total eukaryotic cellular DNA, which in turn allows us to better understand the evolutionary history and diversity of life, and the physiological interactions that define complex organisms.
59 BASIC BIOLOGICAL SCIENCES↗
Comparative genomics in acid mine drainage biofilm communities reveals metabolic and structural differentiation of co-occurring archaea
Background: Metal sulfide mineral dissolution during bioleaching and acid mine drainage (AMD) formation creates an environment that is inhospitable to most life. Despite dominance by a small number of bacteria, AMD microbial biofilm communities contain a notable variety of coexisting and closely related Euryarchaea, most of which have defied cultivation efforts. For this reason, we used metagenomics to analyze variation in gene content that may contribute to niche differentiation among co-occurring AMD archaea. Our analyses targeted members of the Thermoplasmatales and related archaea. These results greatly expand genomic information available for this archaeal order. Results: We reconstructed near-complete genomes for uncultivated, relatively low abundance organisms A-, E-, and Gplasma, members of Thermoplasmatales order, and for a novel organism, Iplasma. Genomic analyses of these organisms, as well as Ferroplasma type I and II, reveal that all are facultative aerobic heterotrophs with the ability to use many of the same carbon substrates, including methanol. Most of the genomes share genes for toxic metal resistance and surface-layer production. Only Aplasma and Eplasma have a full suite of flagellar genes whereas all but the Ferroplasma spp. have genes for pili production. Cryogenic-electron microscopy (cryo-EM) and tomography (cryo-ET) strengthen these metagenomics-based ultrastructural predictions. Notably, only Aplasma, Gplasma and the Ferroplasma spp. have predicted iron oxidation genes and Eplasma and Iplasma lack most genes for cobalamin, valine, (iso)leucine and histidine synthesis. Conclusion: The Thermoplasmatales AMD archaea share a large number of metabolic capabilities. All of the uncultivated organisms studied here (A-, E-, G-, and Iplasma) are metabolically very similar to characterized Ferroplasma spp., differentiating themselves mainly in their genetic capabilities for biosynthesis, motility, and possibly iron oxidation. These results indicate that subtle, but important genomic differences, coupled with unknown differences in gene expression, distinguish these organisms enough to allow for co-existence. Overall this study reveals shared features of organisms from the Thermoplasmatales lineage and provides new insights into the functioning of AMD communities.
59 BASIC BIOLOGICAL SCIENCES↗
Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage
Bacteria in the 16S rRNA clade SAR86 are among the most abundant uncultivated constituents of microbial assemblages in the surface ocean for which little genomic information is currently available. Bioinformatic techniques were used to assemble two nearly complete genomes from marine metagenomes and single-cell sequencing provided two more partial genomes. Recruitment of metagenomic data shows that these SAR86 genomes substantially increase our knowledge of non-photosynthetic bacteria in the surface ocean. Phylogenomic analyses establish SAR86 as a basal and divergent lineage of c-proteobacteria, and the individual genomes display a temperature dependent distribution. Modestly sized at 1.25–1.7 Mbp, the SAR86 genomes lack several pathways for amino-acid and vitamin synthesis as well as sulfate reduction, trends commonly observed in other abundant marine microbes. SAR86 appears to be an aerobic chemoheterotroph with the potential for proteorhodopsin-based ATP generation, though the apparent lack of a retinal biosynthesis pathway may require it to scavenge exogenously-derived pigments to utilize proteorhodopsin. The genomes contain an expanded capacity for the degradation of lipids and carbohydrates acquired using a wealth of tonB-dependent outer membrane receptors. Like the abundant planktonic marine bacterial clade SAR11, SAR86 exhibits metabolic streamlining, but also a distinct carbon compound specialization, possibly avoiding competition.
tonB receptors↗
Complete genome sequencing and evolutionary analysis of Indian isolates of Dengue virus type 2
Highlights: •Complete genome of Indian DENV-2 was deciphered for the first time in this study. •The recent Indian DENV-2 revealed presence of many unique amino acid residues. •Genotype shift (American to Cosmopolitan) characterizes evolution of DENV-2 in India. •Circulation of a unique clade of DENV-2 in South Asia was identified. -- Abstract: Dengue is the most important arboviral infection of global public health significance. It is now endemic in most parts of the South East Asia including India. Though Dengue virus type 2 (DENV-2) is predominantly associated with major outbreaks in India, complete genome information of Indian DENV-2 is not available. In this study, the full-length genome of five DENV-2 isolates (four from 2001 to 2011 and one from 1960), from different parts of India was determined. The complete genome of the Indian DENV-2 was found to be 10,670 bases long with an open reading frame coding for 3391 amino acids. The recent Indian DENV-2 (2001–2011) revealed a nucleotide sequence identity of around 90% and 97% with an older Indian DENV-2 (1960) and closely related Sri Lankan and Chinese DENV-2 respectively. Presence of unique amino acid residues and non-conservative substitutions in critical amino acid residues of major structural and non-structural proteins was observed in recent Indian DENV-2. Selection pressure analysis revealed positive selection in few amino acid sites of the genes encoding for structural and non-structural proteins. The molecular phylogenetic analysis based on comparison of both complete coding region and envelope protein gene with globally diverse DENV-2 viruses classified the recent Indian isolates into a unique South Asian clade within Cosmopolitan genotype. A shift of genotype from American to Cosmopolitan in 1970s characterized the evolution of DENV-2 in India. Present study is the first report on complete genome characterization of emerging DENV-2 isolates from India and highlights the circulation of a unique clade in South Asia.
60 APPLIED LIFE SCIENCES↗
A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations
The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.
60 APPLIED LIFE SCIENCES↗
A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates
ABSTRACT: Variation in the genome of Pseudomonas aeruginosa , an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.
59 BASIC BIOLOGICAL SCIENCES↗
DRAM for distilling microbial metabolism to automate the curation of microbiome function
Abstract Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.
59 BASIC BIOLOGICAL SCIENCES↗
Single-virus genomics reveals hidden cosmopolitan and abundant viruses
Microbes drive ecosystems under constraints imposed by viruses. However, a lack of virus genome information hinders our ability to answer fundamental, biological questions concerning microbial communities. Here we apply single-virus genomics (SVGs) to assess whether portions of marine viral communities are missed by current techniques. The majority of the here-identified 44 viral single-amplified genomes (vSAGs) are more abundant in global ocean virome data sets than published metagenome-assembled viral genomes or isolates. This indicates that vSAGs likely best represent the dsDNA viral populations dominating the oceans. Species-specific recruitment patterns and virome simulation data suggest that vSAGs are highly microdiverse and that microdiversity hinders the metagenomic assembly, which could explain why their genomes have not been identified before. Altogether, SVGs enable the discovery of some of the likely most abundant and ecologically relevant marine viral species, such as vSAG 37-F6, which were overlooked by other methodologies.
59 BASIC BIOLOGICAL SCIENCES↗
Tools and Models for Integrating Multiple Cellular Networks
In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed CRIT for correlation analysis in systems biology [5]. For Aim 3, we have further investigated the scaling relationship that the number of Transcription Factors (TFs) in a genome is proportional to the square of the total number of genes. We have extended the analysis from transcription factors to various classes of functional categories, and from individual categories to joint distribution [6]. By introducing a new analytical framework, we have generalized the original toolbox model to take into account of metabolic network with arbitrary network topology [7].
09 BIOMASS FUELS↗
Genome and evolution of the arbuscular mycorrhizal fungus Diversispora epigaea (formerly Glomus versiforme ) and its bacterial endosymbionts
Arbuscular mycorrhizal (AM) fungi form endosymbioses with most plants, and they themselves are hosts for Mollicutes/Mycoplasma-related endobacteria (MRE). Despite their significance, genomic information for AM fungi and their MRE are relatively sparse, which hinders our understanding of their biology and evolution. We assembled the genomes of the AM fungus Diversispora epigaea (formerly Glomus versiforme) and its MRE and performed comparative genomics and evolutionary analyses. The D. epigaea genome showed a pattern of substantial gene duplication and differential evolution of gene families, including glycosyltransferase family 25, whose activities are exclusively lipopolysaccharide biosynthesis. Genes acquired by horizontal transfer from bacteria possibly function in defense against foreign DNA or viruses. The MRE population was diverse, with multiple genomes displaying characteristics of differential evolution and encoding many MRE-specific genes as well as genes of AM fungal origin. Gene family expansion in D. epigaea may enhance adaptation to both external and internal environments, such as expansion of kinases for signal transduction upon external stimuli and expansion of nucleoside salvage pathway genes potentially for competition with MRE, whose genomes lack purine and pyrimidine biosynthetic pathways. Collectively, this metagenome provides high-quality references and begins to reveal the diversity within AM fungi and their MRE.
59 BASIC BIOLOGICAL SCIENCES↗
Draft genome sequence of the cellulolytic endophyte Chitinophaga costaii A37T2T
Abstract Here we report the draft genome sequence of Chitinophaga costai A37T2 T (=CIP 110584 T , =LMG 27458 T ), which was isolated from the endophytic community of Pinus pinaster tree. The total genome size of C. costaii A37T2 T is 5.07 Mbp, containing 4204 coding sequences. Strain A37T2 T encoded multiple genes likely involved in cellulolytic, chitinolytic and lipolytic activities. This genome showed 1145 unique genes assigned into 109 Cluster of Orthologous Groups in comparison with the complete genome of C. pinensis DSM 2588 T . The genomic information suggests the potential of the strain A37T2 T to interact with the plant metabolism. As there are only a few bacterial genomes related to Pine Wilt Disease, this work provides a contribution to the field.
Genetics & Heredity↗
Development of Computational Tools for Metabolic Model Curation, Flux Elucidation and Strain Design
An overarching goal of the Department of Energy mission is the efficient deployment and engineering of microbial and plant systems to enable biomass conversion in pursuit of high energy density liquid biofuels. This has spurred the pace at which new organisms are sequenced and annotated. This torrent of genomic information has opened the door to understanding metabolism in not just skeletal pathways and a handful of microorganisms but for truly genome-scale reconstructions derived for hundreds of microbes and plants. Understanding and redirecting metabolism is crucial because metabolic fluxes are unique descriptors of cellular physiology that directly assess the current cellular state and quantify the effect of genetic engineering interventions. At the same time, however, trying to keep pace with the rate of genomic data generation has ushered in a number of modeling and computational challenges related to (i) the automated assembly, testing and correction of genome-scale metabolic models, (ii) metabolic flux elucidation using labeled isotopes, and (iii) comprehensive identification of engineering interventions leading to the desired metabolism redirection.
97 MATHEMATICS AND COMPUTING↗