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  1. Abstract

    Carbohydrate active enzymes (CAZymes) are made by various organisms for complex carbohydrate metabolism. Genome mining of CAZymes has become a routine data analysis in (meta-)genome projects, owing to the importance of CAZymes in bioenergy, microbiome, nutrition, agriculture, and global carbon recycling. In 2012, dbCAN was provided as an online web server for automated CAZyme annotation. dbCAN2 (https://bcb.unl.edu/dbCAN2) was further developed in 2018 as a meta server to combine multiple tools for improved CAZyme annotation. dbCAN2 also included CGC-Finder, a tool for identifying CAZyme gene clusters (CGCs) in (meta-)genomes. We have updated the meta server to dbCAN3 with the following new functions and components: (i) dbCAN-sub as a profile Hidden Markov Model database (HMMdb) for substrate prediction at the CAZyme subfamily level; (ii) searching against experimentally characterized polysaccharide utilization loci (PULs) with known glycan substates of the dbCAN-PUL database for substrate prediction at the CGC level; (iii) a majority voting method to consider all CAZymes with substrate predicted from dbCAN-sub for substrate prediction at the CGC level; (iv) improved data browsing and visualization of substrate prediction results on the website. In summary, dbCAN3 not only inherits all the functions of dbCAN2, but also integrates three new methods for glycan substrate prediction.

     
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  2. Abstract

    Carbohydrate Active EnZymes (CAZymes) are significantly important for microbial communities to thrive in carbohydrate rich environments such as animal guts, agricultural soils, forest floors, and ocean sediments. Since 2017, microbiome sequencing and assembly have produced numerous metagenome assembled genomes (MAGs). We have updated our dbCAN-seq database (https://bcb.unl.edu/dbCAN_seq) to include the following new data and features: (i) ∼498 000 CAZymes and ∼169 000 CAZyme gene clusters (CGCs) from 9421 MAGs of four ecological (human gut, human oral, cow rumen, and marine) environments; (ii) Glycan substrates for 41 447 (24.54%) CGCs inferred by two novel approaches (dbCAN-PUL homology search and eCAMI subfamily majority voting) (the two approaches agreed on 4183 CGCs for substrate assignments); (iii) A redesigned CGC page to include the graphical display of CGC gene compositions, the alignment of query CGC and subject PUL (polysaccharide utilization loci) of dbCAN-PUL, and the eCAMI subfamily table to support the predicted substrates; (iv) A statistics page to organize all the data for easy CGC access according to substrates and taxonomic phyla; and (v) A batch download page. In summary, this updated dbCAN-seq database highlights glycan substrates predicted for CGCs from microbiomes. Future work will implement the substrate prediction function in our dbCAN2 web server.

     
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  3. Abstract

    Pan-genome analyses of metagenome-assembled genomes (MAGs) may suffer from the known issues with MAGs: fragmentation, incompleteness and contamination. Here, we conducted a critical assessment of pan-genomics of MAGs, by comparing pan-genome analysis results of complete bacterial genomes and simulated MAGs. We found that incompleteness led to significant core gene (CG) loss. The CG loss remained when using different pan-genome analysis tools (Roary, BPGA, Anvi’o) and when using a mixture of MAGs and complete genomes. Contamination had little effect on core genome size (except for Roary due to in its gene clustering issue) but had major influence on accessory genomes. Importantly, the CG loss was partially alleviated by lowering the CG threshold and using gene prediction algorithms that consider fragmented genes, but to a less degree when incompleteness was higher than 5%. The CG loss also led to incorrect pan-genome functional predictions and inaccurate phylogenetic trees. Our main findings were supported by a study of real MAG-isolate genome data. We conclude that lowering CG threshold and predicting genes in metagenome mode (as Anvi’o does with Prodigal) are necessary in pan-genome analysis of MAGs. Development of new pan-genome analysis tools specifically for MAGs are needed in future studies.

     
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  4. Abstract Motivation

    Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.

    Results

    This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes.

    Availability and implementation

    https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  5. Abstract

    Dragon fruits are tropical fruits economically important for agricultural industries. As members of the family ofCactaceae, they have evolved to adapt to the arid environment. Here we report the draft genome ofHylocereus undatus, commercially known as the white-fleshed dragon fruit. The chromosomal level genome assembly contains 11 longest scaffolds corresponding to the 11 chromosomes ofH. undatus. Genome annotation ofH. undatusfound ~29,000 protein-coding genes, similar toCarnegiea gigantea(saguaro). Whole-genome duplication (WGD) analysis revealed a WGD event in the last common ancestor ofCactaceaefollowed by extensive genome rearrangements. The divergence time betweenH. undatusandC. giganteawas estimated to be 9.18 MYA. Functional enrichment analysis of orthologous gene clusters (OGCs) in sixCactaceaeplants found significantly enriched OGCs in drought resistance. Fruit flavor-related functions were overrepresented in OGCs that are significantly expanded inH. undatus. TheH. undatusdraft genome also enabled the discovery of carbohydrate and plant cell wall-related functional enrichment in dragon fruits treated with trypsin for a longer storage time. Lastly, genes of the betacyanin (a red-violet pigment and antioxidant with a very high concentration in dragon fruits) biosynthetic pathway were found to be co-localized on a 12 Mb region of one chromosome. The consequence may be a higher efficiency of betacyanin biosynthesis, which will need experimental validation in the future. TheH. undatusdraft genome will be a great resource to study various cactus plants.

     
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  6. Ercolini, Danilo (Ed.)
    ABSTRACT Dietary polyphenols can significantly benefit human health, but their bioavailability is metabolically controlled by human gut microbiota. To facilitate the study of polyphenol metabolism for human gut health, we have manually curated experimentally characterized polyphenol utilization proteins (PUPs) from published literature. This resulted in 60 experimentally characterized PUPs (named seeds) with various metadata, such as species and substrate. Further database search found 107,851 homologs of the seeds from UniProt and UHGP (unified human gastrointestinal protein) databases. All PUP seeds and homologs were classified into protein classes, families, and subfamilies based on Enzyme Commission (EC) numbers, Pfam (protein family) domains, and sequence similarity networks. By locating PUP homologs in the genomes of UHGP, we have identified 1,074 physically linked PUP gene clusters (PGCs), which are potentially involved in polyphenol metabolism in the human gut. The gut microbiome of Africans was consistently ranked the top in terms of the abundance and prevalence of PUP homologs and PGCs among all geographical continents. This reflects the fact that dietary polyphenols are consumed by the African population more commonly than by other populations, such as Europeans and North Americans. A case study of the Hadza hunter-gatherer microbiome verified the feasibility of using dbPUP to profile metagenomic data for biologically meaningful discovery, suggesting an association between diet and PUP abundance. A Pfam domain enrichment analysis of PGCs identified a number of putatively novel PUP families. Lastly, a user-friendly web interface ( https://bcb.unl.edu/dbpup/ ) provides all the data online to facilitate the research of polyphenol metabolism for improved human health. IMPORTANCE Long-term consumption of polyphenol-rich foods has been shown to lower the risk of various human diseases, such as cardiovascular diseases, cancers, and metabolic diseases. Raw polyphenols are often enzymatically processed by gut microbiome, which contains various polyphenol utilization proteins (PUPs) to produce metabolites with much higher bioaccessibility to gastrointestinal cells. This study delivered dbPUP as an online database for experimentally characterized PUPs and their homologs in human gut microbiome. This work also performed a systematic classification of PUPs into enzyme classes, families, and subfamilies. The signature Pfam domains were identified for PUP families, enabling conserved domain-based PUP annotation. This standardized sequence similarity-based PUP classification system offered a guideline for the future inclusion of new experimentally characterized PUPs and the creation of new PUP families. An in-depth data analysis was further conducted on PUP homologs and physically linked PUP gene clusters (PGCs) in gut microbiomes of different human populations. 
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  7. null (Ed.)
    Phytopathogenic Ascomycota are responsible for substantial economic losses each year, destroying valuable crops. The present study aims to provide new insights into phytopathogenicity in Ascomycota from a comparative genomic perspective. This has been achieved by categorizing orthologous gene groups (orthogroups) from 68 phytopathogenic and 24 non-phytopathogenic Ascomycota genomes into three classes: Core, (pathogen or non-pathogen) group-specific, and genome-specific accessory orthogroups. We found that (i) ~20% orthogroups are group-specific and accessory in the 92 Ascomycota genomes, (ii) phytopathogenicity is not phylogenetically determined, (iii) group-specific orthogroups have more enriched functional terms than accessory orthogroups and this trend is particularly evident in phytopathogenic fungi, (iv) secreted proteins with signal peptides and horizontal gene transfers (HGTs) are the two functional terms that show the highest occurrence and significance in group-specific orthogroups, (v) a number of other functional terms are also identified to have higher significance and occurrence in group-specific orthogroups. Overall, our comparative genomics analysis determined positive enrichment existing between orthogroup classes and revealed a prediction of what genomic characteristics make an Ascomycete phytopathogenic. We conclude that genes shared by multiple phytopathogenic genomes are more important for phytopathogenicity than those that are unique in each genome. 
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  8. null (Ed.)
    Zygnematophyceae green algae (ZGA) have been shown to be the closest relatives of land plants. Three nuclear genomes ( Spirogloea muscicola , Mesotaenium endlicherianum , and Penium margaritaceum ) of ZGA have been recently published, and more genomes are underway. Here we analyzed two Zygnema circumcarinatum strains SAG 698-1a (mating +) and SAG 698-1b (mating −) and found distinct cell sizes and other morphological differences. The molecular identities of the two strains were further investigated by sequencing their 18S rRNA, psaA and rbcL genes. These marker genes of SAG 698-1a were surprisingly much more similar to Z. cylindricum (SAG 698-2) than to SAG 698-1b. Phylogenies of these marker genes also showed that SAG 698-1a and SAG 698-1b were well separated into two different Zygnema clades, where SAG 698-1a was clustered with Z. cylindricum , while SAG 698-1b was clustered with Z. tunetanum . Additionally, physiological parameters like ETR max values differed between SAG 698-1a and SAG 698-1b after 2 months of cultivation. The de-epoxidation state (DEPS) of the xanthophyll cycle pigments also showed significant differences. Surprisingly, the two strains could not conjugate, and significantly differed in the thickness of the mucilage layer. Additionally, ZGA cell walls are highly enriched with sticky and acidic polysaccharides, and therefore the widely used plant nuclear extraction protocols do not work well in ZGA. Here, we also report a fast and simple method, by mechanical chopping, for efficient nuclear extraction in the two SAG strains. More importantly, the extracted nuclei were further used for nuclear genome size estimation of the two SAG strains by flow cytometry (FC). To confirm the FC result, we have also used other experimental methods for nuclear genome size estimation of the two strains. Interestingly, the two strains were found to have very distinct nuclear genome sizes (313.2 ± 2.0 Mb in SAG 698-1a vs. 63.5 ± 0.5 Mb in SAG 698-1b). Our multiple lines of evidence strongly indicate that SAG 698-1a possibly had been confused with SAG 698-2 prior to 2005, and most likely represents Z. cylindricum or a closely related species. 
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