Results for “protein annotation” — Page 2

DOE OSTI2022

Expansion of the global RNA virome reveals diverse clades of bacteriophages

High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum Lenarviricota, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

DR-BERT: A protein language model to annotate disordered regions

Not Available

Nambiar, Ananthan (ORCID:0000000297679456)↗

DOE OSTI2021

A General Framework to Learn Tertiary Structure for Protein Sequence Characterization

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Machine learning approaches for integrating multi-omics data to expand microbiome annotation

Preliminary: This final report corresponds to a grant (DE-SC0021216) that was awarded to the University of Montana. Mid-way through the grant period, I relocated from the University of Montana to the University of Arizona. The grant was ended at University of Montana in late 2022, with all efforts concluding on 08/26/22; the remaining funds supporting the project were relinquished by University of Montana, and were later awarded to University of Arizona under a new grant, with start date 04/01/23. This report focuses on results of research efforts at UMontana through 08/26/22. Results: We made progress in each of the three aims of the proposal. We released software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes. We made substantial progress in developing software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we made notable progress in developing AI methods (specifically: a neural embedding model) for identifying similarities between protein sequences based on amino-wise latent vectors. These efforts were supplemented by development of methods for protein modeling in support of predicting protein-drug binding activity, and by my leadership of a team in the NIH/DOE 2021 Petabyte-Scale Sequence Search hack-a-thon.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Gao, Mu↗

DOE OSTI2023

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Deep Green: Structural and Functional Genomic Characterization of Conserved Unannotated Green Lineage Proteins

Our overall objective is to improve and increase functional and structural predictions for a growing number of plant proteins of unknown structure and function (the Deep Green proteins), and to make our predictive data useful and accessible to the larger research community. The project is divided into five major objectives which include 1) Assembly and curation of Deep Green candidate protein sets; 2) in silico structural and functional predictions and network analyses; 3) assembly and validation of reverse genetic resources in Chlamydomonas reinhardtii (Chlamydomonas); 4) high throughput functional genomics characterization and prioritization in Chlamydomonas; and 5) structural validation of selected candidates and functional validation in two important reference plant species, Arabidopsis thaliana (Arabidopsis) and Setaria viridis (Setaria).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Developing High-Flux Ion Soft Landing with Mass-Selection for Improved Cryo-Electron Microscopy

The goal of this project is to redesign and repurpose existing high-flux ion soft-landing (SL) instrumentation developed at PNNL for new structural biology applications by integrating SL, mass-selection/separation of high mass-to-charge species, native mass spectrometry (MS), and cryo-electron microscopy (EM) to generate a workflow enabling structural and functional annotation of proteins and other complex biological samples. Ultimately, this work will result in a new instrument for mass-selection and SL based cryo-EM sample preparation and potentially increase the impact of PNNL and the broader microscopy community through commercial licensing.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Machine learning approaches for integrating multi-omics data to expand microbiome annotation (Final Technical Report)

We fulfilled all original three aims of the proposal. Following the earlier release (during the first phase of the project at Montana) of software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes, we have nearly completed a second gap-filling tool that improves accuracy and explainability. We completed software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we completed a neural embedding model for identifying similarities between protein sequences based on amino-wise latent vectors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Galba: genome annotation with miniprot and AUGUSTUS

The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes. Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments. Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae

Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.

09 BIOMASS FUELS↗

DOE OSTI2023

Structural Models and Sequence Alignment Results of the Rhodospirillum rubrum Proteome

This dataset contains the structural models for the primary transcripts of the Rhodospirillum rubrum proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. rubrum proteome to those available in the AlphaFold Protein Structure Database. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHBlits: https://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: https://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

KBase Narrative - Porphyromonadaceae sp. W3.11 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

DOE OSTI2024

KBase Narrative - Lachnospiraceae sp. C1.1 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

DOE OSTI2026

A Comment on “Deep Proteogenomics of a Photosynthetic Cyanobacterium”

Proteomic researchers strive to achieve complete annotation of protein-coding DNA sequences to provide a foundational context for their relevant biological data. A recent deep proteogenomic study using a photosynthetic cyanobacterium Synechocystis sp. PCC 6803 by Spät et al. proposed 64 refined open reading frames (ORFs). By searching LC-MS/MS data from affinity chromatography-isolated protein complexes, our laboratory identified that six of these high-abundance ORFs possess Nterminal initiation start sites that differ than those proposed in the alternative models. Our findings are supported by highly confident MS2 data, phylogenetic analysis, chemical labeling, and established data from two independent research groups. Based on these highquality experimental identifications, we subsequently propose a standardized strategy and set of criteria for future deep proteogenomic efforts to ensure accurate and stringent proteogenomic annotation.

cyanobacteria↗

DOE OSTI2024

Bactericidal effectors of the Stenotrophomonas maltophilia type IV secretion system: functional definition of the nuclease TfdA and structural determination of TfcB

ABSTRACT Stenotrophomonas maltophilia expresses a type IV protein secretion system (T4SS) that promotes contact-dependent killing of other bacteria and does so partly by secreting the effector TfcB. Here, we report the structure of TfcB, comprising an N-terminal domain similar to the catalytic domain of glycosyl hydrolase (GH-19) chitinases and a C-terminal domain for recognition and translocation by the T4SS. Utilizing a two-hybrid assay to measure effector interactions with the T4SS coupling protein VirD4, we documented the existence of five more T4SS substrates. One of these was protein 20845, an annotated nuclease. A S. maltophilia mutant lacking the gene for 20845 was impaired for killing Escherichia coli , Klebsiella pneumoniae , and Pseudomonas aeruginosa . Moreover, the cloned 20845 gene conferred robust toxicity, with the recombinant E. coli being rescued when 20845 was co-expressed with its cognate immunity protein. The 20845 effector was an 899 amino-acid protein, comprised of a GHH-nuclease domain in its N-terminus, a large central region of indeterminant function, and a C-terminus for secretion. Engineered variants of the 20845 gene that had mutations in the predicted catalytic site did not impede E. coli , indicating that the antibacterial effect of 20845 involves its nuclease activity. Using flow cytometry with DNA staining, we determined that 20845, but not its mutant variants, confers a loss in DNA content of target bacteria. Database searches revealed that uncharacterized homologs of 20845 occur within a range of bacteria. These data indicate that the S. maltophilia T4SS promotes interbacterial competition through the action of multiple toxic effectors, including a potent, novel DNase. IMPORTANCE Stenotrophomonas maltophilia is a multi-drug-resistant, Gram-negative bacterium that is an emerging pathogen of humans. Patients with cystic fibrosis are particularly susceptible to S. maltophilia infection. In hospital water systems and various types of infections, S. maltophilia co-exists with other bacteria, including other pathogens such as Pseudomonas aeruginosa . We previously demonstrated that S. maltophilia has a functional VirB/D4 type VI protein secretion system (T4SS) that promotes contact-dependent killing of other bacteria. Since most work on antibacterial systems involves the type VI secretion system, this observation remains noteworthy. Moreover, S. maltophilia currently stands alone as a model for a human pathogen expressing an antibacterial T4SS. Using biochemical, genetic, and cell biological approaches, we now report both the discovery of a novel antibacterial nuclease (TfdA) and the first structural determination of a bactericidal T4SS effector (TfcB).

59 BASIC BIOLOGICAL SCIENCES↗