Islander: a database of precisely mapped genomic islands in tRNA and tmRNA genes

Hot Spots of Site-Specific Integration into the Sinorhizobium meliloti Chromosome

The diversity of phage-related sequences (PRSs) and their site-specific integration into the genomes of nonpathogenic, agriculturally valuable, nitrogen-fixing root nodule bacteria, such as Sinorhizobium meliloti, were evaluated in this study. A total of 314 PRSs, ranging in size from 3.24 kb to 88.98 kb, were identified in the genomes of 27 S. meliloti strains. The amount of genetic information foreign to S. meliloti accumulated in all identified PRSs was 6.30 Mb. However, more than 53% of this information was contained in prophages (Phs) and genomic islands (GIs) integrated into genes encoding tRNAs (tRNA genes) located on the chromosomes of the rhizobial strains studied. It was found that phiLM21-like Phs were predominantly abundant in the genomes of S. meliloti strains of distant geographical origin, whereas RR1-A- and 16-3-like Phs were much less common. In addition, GIs predominantly contained fragments of phages infecting bacteria of distant taxa, while rhizobiophage-like sequences were unique. A site-specific integration analysis revealed that not all tRNA genes in S. meliloti are integration sites, but among those in which integration occurred, there were "hot spots" of integration into which either Phs or GIs were predominantly inserted. For the first time, it is shown that at these integration "hot spots", not only is the homology of attP and attB strictly preserved, but integrases in PRSs similar to those of phages infecting the Proteobacteria genera Azospirillum or Pseudomonas are also present. The data presented greatly expand the understanding of the fate of phage-related sequences in host bacterial genomes and also raise new questions about the role of phages in bacterial-phage coevolution.

Genomic diversity, antibiotic resistance, and virulence in South African Enterococcus faecalis and Enterococcus lactis isolates

This study presents the empirical findings of an in-depth genomic analysis of Enterococcus faecalis and Enterococcus lactis isolates from South Africa. It offers valuable insights into their genetic characteristics and their significant implications for public health. The study uncovers nuanced variations in the gene content of these isolates, despite their similar GC contents, providing a comprehensive view of the evolutionary diversity within the species. Genomic islands are identified, particularly in E. faecalis, emphasizing its propensity for horizontal gene transfer and genetic diversity, especially in terms of antibiotic resistance genes. Pangenome analysis reveals the existence of a core genome, accounting for a modest proportion of the total genes, with 2157 core genes, 1164 shell genes, and 4638 cloud genes out of 7959 genes in 52 South African E. faecalis genomes (2 from this study, 49 south Africa genomes downloaded from NCBI, and E. faecalis reference genome). Detecting large-scale genomic rearrangements, including chromosomal inversions, underscores the dynamic nature of bacterial genomes and their role in generating genetic diversity. The study uncovers an array of antibiotic resistance genes, with trimethoprim, tetracycline, glycopeptide, and multidrug resistance genes prevalent, raising concerns about the effectiveness of antibiotic treatment. Virulence gene profiling unveils a diverse repertoire of factors contributing to pathogenicity, encompassing adhesion, biofilm formation, stress resistance, and tissue damage. These empirical findings provide indispensable insights into these bacteria's genomic dynamics, antibiotic resistance mechanisms, and virulence potential, underlining the pressing need to address antibiotic resistance and implement robust control measures.

Discovering genomic islands in unannotated bacterial genomes using sequence embedding

Motivation

Genomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. GEIs play a crucial role in the evolution of bacteria by rapidly introducing genetic diversity and thus helping them adapt to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is, therefore, an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs. Still, most of these studies rely on detecting anomalies in the unannotated nucleotide sequences or on a fixed set of known features on annotated nucleotide sequences.

Results

Here, we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high-precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland's accuracy rivals other GEI predictors, enabling efficient and faster identification of GEIs in unannotated bacterial genomes.

Availability and implementation

TreasureIsland is available under an MIT license at: https://github.com/FriedbergLab/GenomicIslandPrediction.

DICEP: An integrative approach to augmenting genomic island detection

Mobilization of clusters of genes called genomic islands (GIs) across bacterial lineages facilitates dissemination of traits, such as, resistance against antibiotics, virulence or hypervirulence, and versatile metabolic capabilities. Robust delineation of GIs is critical to understanding bacterial evolution that has a vast impact on different life forms. Methods for identification of GIs exploit different evolutionary features or signals encoded within the genomes of bacteria, however, the current state-of-the-art in GI detection still leaves much to be desired. Here, we have taken a combinatorial approach that accounted for GI specific features such as compositional bias, aberrant phyletic pattern, and marker gene enrichment within an integrative framework to delineate GIs in bacterial genomes. Our GI prediction tool, DICEP, was assessed on simulated genomes and well-characterized bacterial genomes. DICEP compared favorably with current GI detection tools on real and synthetic datasets.

A comprehensive comparative genomic analysis revealed that plant growth promoting traits are ubiquitous in strains of Stenotrophomonas

Stenotrophomonas strains, which are often described as plant growth promoting (PGP) bacteria, are ubiquitous in many environments. A total of 213 genomes of strains of Stenotrophomonas were analyzed using comparative genomics to better understand the ecological roles of these bacteria in the environment. The pan-genome of the 213 strains of Stenotrophomonas consists of 27,186 gene families, including 710 core gene families, 11,039 unique genes and 15,437 accessory genes. Nearly all strains of Stenotrophomonas harbor the genes for GH3-family cellulose degradation and GH2- and GH31-family hemicellulose hydrolase, as well as intact glycolysis and tricarboxylic acid cycle pathways. These abilities suggest that the strains of this genus can easily obtain carbon and energy from the environment. The Stenotrophomonas strains can respond to oxidative stress by synthesizing catalase, superoxide dismutase, methionine sulfoxide reductase, and disulfide isomerase, as well as managing their osmotic balance by accumulating potassium and synthesizing compatible solutes, such as betaine, trehalose, glutamate, and proline. Each Stenotrophomonas strain also contains many genes for resistance to antibiotics and heavy metals. These genes that mediate stress tolerance increase the ability of Stenotrophomonas strains to survive in extreme environments. In addition, many functional genes related to attachment and plant colonization, growth promotion and biocontrol were identified. In detail, the genes associated with flagellar assembly, motility, chemotaxis and biofilm formation enable the strains of Stenotrophomonas to effectively colonize host plants. The presence of genes for phosphate-solubilization and siderophore production and the polyamine, indole-3-acetic acid, and cytokinin biosynthetic pathways confer the ability to promote plant growth. These strains can produce antimicrobial compounds, chitinases, lipases and proteases. Each Stenotrophomonas genome contained 1-9 prophages and 17-60 genomic islands, and the genes related to antibiotic and heavy metal resistance and the biosynthesis of polyamines, indole-3-acetic acid, and cytokinin may be acquired by horizontal gene transfer. This study demonstrates that strains of Stenotrophomonas are highly adaptable for different environments and have strong potential for use as plant growth-promoting bacteria.

From whole genomes to probiotic candidates: A study of potential lactobacilli strains selection for vaginitis treatment

Vaginitis, characterized by pathogenic invasion and a deficiency in beneficial lactobacilli, has recognized lactobacilli supplementation as a novel therapeutic strategy. However, due to individual differences in vaginal microbiota, identifying universally effective Lactobacillus strains is challenging. Traditional methodologies for probiotic selection, which heavily depend on extensive in vitro experiments, are both time-intensive and laborious. The aim of this study was to pinpoint possible vaginal probiotic candidates based on whole-genome screening. We sequenced the genomes of 98 previously isolated Lactobacillus strains, annotating their genes involved in probiotic metabolite biosynthesis, adherence, acid/bile tolerance, and antibiotic resistance. A scoring system was used to assess the strains based on their genomic profiles. The highest-scoring strains underwent further in vitro evaluation. Consequently, two strains, Lactobacillus crispatus LG55-27 and Lactobacillus gasseri TM13-16, displayed an outstanding ability to produce d-lactate and adhere to human vaginal epithelial cells. They also showed higher antimicrobial activity against Gardnerella vaginalis, Escherichia coli, Candida albicans, Staphylococcus aureus, and Pseudomonas aeruginosa compared to reference Lactobacillus strains. Their resilience to acid and bile environments highlights the potential for oral supplementation. Oral and vaginal administration of these two strains were tested in a bacterial vaginosis (BV) rat model at various doses. Results indicated that combined vaginal administration of these strains at 1 × 106 CFU/day significantly mitigated BV in rats. This research offers a probiotic dosage guideline for vaginitis therapy, underscoring an efficient screening process for probiotics using genome sequencing, in vitro testing, and in vivo BV model experimentation.

Speeding genomic island discovery through systematic design of reference database composition

BACKGROUND

Genomic islands (GIs) are mobile genetic elements that integrate site-specifically into bacterial chromosomes, bearing genes that affect phenotypes such as pathogenicity and metabolism. GIs typically occur sporadically among related bacterial strains, enabling comparative genomic approaches to GI identification. For a candidate GI in a query genome, the number of reference genomes with a precise deletion of the GI serves as a support value for the GI. Our comparative software for GI identification was slowed by our original use of large reference genome databases (DBs). Here we explore smaller species-focused DBs.

RESULTS

With increasing DB size, recovery of our reliable prophage GI calls reached a plateau, while recovery of less reliable GI calls (FPs) increased rapidly as DB sizes exceeded ~500 genomes; i.e., overlarge DBs can increase FP rates. Paradoxically, relative to prophages, FPs were both more frequently supported only by genomes outside the species and more frequently supported only by genomes inside the species; this may be due to their generally lower support values. Setting a DB size limit for our SMAll Ranked Tailored (SMART) DB design speeded runtime ~65-fold. Strictly intra-species DBs would tend to lower yields of prophages for small species (with few genomes available); simulations with large species showed that this could be partially overcome by reaching outside the species to closely related taxa, without an FP burden. Employing such taxonomic outreach in DB design generated redundancy in the DB set; as few as 2984 DBs were needed to cover all 47894 prokaryotic species.

CONCLUSIONS

Runtime decreased dramatically with SMART DB design, with only minor losses of prophages. We also describe potential utility in other comparative genomics projects.

A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures

Genomic islands (GIs) play a crucial role in the spread of antibiotic resistance, virulence factors and antiviral defense systems in a broad range of bacterial species. However, the characterization and classification of GIs are challenging due to their relatively small size and considerable genetic diversity. Predicting their intercellular mobility is of utmost importance in the context of the emerging crisis of multidrug resistance. Here, we propose a large-scale classification method to categorize GIs according to their mobility profile and, subsequently, analyze their gene cargo. We based our classification decision scheme on a collection of mobility protein motif definitions available in publicly accessible databases. Our results show that the size distribution of GI classes correlates with their respective structure and complexity. Self-transmissible GIs are usually the largest, except in Bacillota and Actinomycetota, accumulate antibiotic and phage resistance genes, and favour the use of a tyrosine recombinase to insert into a host's replicon. Non-mobilizable GIs tend to use a DDE transposase instead. Finally, although tRNA genes are more frequently targeted as insertion sites by GIs encoding a tyrosine recombinase, most GIs insert in a protein-encoding gene. This study is a stepping stone toward a better characterization of mobile GIs in bacterial genomes and their mechanism of mobility.

A comprehensive genomic analysis provides insights on the high environmental adaptability of Acinetobacter strains

Acinetobacter is ubiquitous, and it has a high species diversity and a complex evolutionary pattern. To elucidate the mechanism of its high ability to adapt to various environment, 312 genomes of Acinetobacter strains were analyzed using the phylogenomic and comparative genomics methods. It was revealed that the Acinetobacter genus has an open pan-genome and strong genome plasticity. The pan-genome consists of 47,500 genes, with 818 shared by all the genomes of Acinetobacter, while 22,291 are unique genes. Although Acinetobacter strains do not have a complete glycolytic pathway to directly utilize glucose as carbon source, most of them harbored the n-alkane-degrading genes alkB/alkM (97.1% of tested strains) and almA (96.7% of tested strains), which were responsible for medium-and long-chain n-alkane terminal oxidation reaction, respectively. Most Acinetobacter strains also have catA (93.3% of tested strains) and benAB (92.0% of tested strains) genes that can degrade the aromatic compounds catechol and benzoic acid, respectively. These abilities enable the Acinetobacter strains to easily obtain carbon and energy sources from their environment for survival. The Acinetobacter strains can manage osmotic pressure by accumulating potassium and compatible solutes, including betaine, mannitol, trehalose, glutamic acid, and proline. They respond to oxidative stress by synthesizing superoxide dismutase, catalase, disulfide isomerase, and methionine sulfoxide reductase that repair the damage caused by reactive oxygen species. In addition, most Acinetobacter strains contain many efflux pump genes and resistance genes to manage antibiotic stress and can synthesize a variety of secondary metabolites, including arylpolyene, β-lactone and siderophores among others, to adapt to their environment. These genes enable Acinetobacter strains to survive extreme stresses. The genome of each Acinetobacter strain contained different numbers of prophages (0-12) and genomic islands (GIs) (6-70), and genes related to antibiotic resistance were found in the GIs. The phylogenetic analysis revealed that the alkM and almA genes have a similar evolutionary position with the core genome, indicating that they may have been acquired by vertical gene transfer from their ancestor, while catA, benA, benB and the antibiotic resistance genes could have been acquired by horizontal gene transfer from the other organisms.

Comparative genome analyses uncovered the cadmium resistance mechanism of enterobacter cloacae

Cadmium (Cd) can be transported into plants from polluted soils and may cause animal and human diseases through food chains, which requires the development of highly efficient methods for soil Cd remediation. Although we isolated an Enterobacter cloacae strain Cu6 with Cd resistance, this strain cannot be used for soil Cd remediation due to its lower resistance. Here, we domesticated Cu6 and obtained a highly Cd-resistant strain, LPY6, and found that this strain can attenuate the toxic effects of Cd on wheat seedling growth. We deciphered the high Cd-resistance mechanism of LPY6 by genome comparative and genetic analysis. Compared with Cu6, 75 genes were mutated in LPY6. Thirty-four of these genes were deleted, and 41 had single nucleotide polymorphisms (SNPs). Most of these mutated proteins are involved in basic metabolism, substrate transport, stress response and formate and hydrogen metabolism. RNA quantitative analysis and promoter activity assays showed that the transcription or mRNA levels of two operons (cadA and norVW) in these mutated genes were regulated by Cd, zinc (Zn) or lead (Pb) ions, suggesting that these two operons might be required for Cd, Zn or Pb resistance. Expression of cadA and norVW operons in LPY6 partially recovered Cd susceptibility, demonstrating that CadA and NorVW are involved in Cd resistance in E. cloacae. Our findings illustrate that E. cloacae acquires Cd resistance through different pathways and lay a foundation for developing highly efficient methods for soil Cd remediation.

On distinguishing between canonical tRNA genes and tRNA gene fragments in prokaryotes

Automated genome annotation is essential for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed for selected genomes in the Genomic tRNA database (GtRNAdb). Here, we highlight a pervasive error in prokaryotic tRNA gene sets on GtRNAdb: the mis-categorization of partial, non-canonical tRNA genes as standard, canonical tRNA genes. Firstly, we demonstrate the issue using the tRNA gene sets of 20 organisms from the archaeal taxon Thermococcaceae. According to GtRNAdb, these organisms collectively deviate from the expected set of tRNA genes in 15 instances, including the listing of eleven putative canonical tRNA genes. However, after detailed manual annotation, only one of these eleven remains; the others are either partial, non-canonical tRNA genes resulting from the integration of genetic elements or CRISPR-Cas activity (seven instances), or attributable to ambiguities in input sequences (three instances). Secondly, we show that similar examples of the mis-categorization of predicted tRNA sequences occur throughout the prokaryotic sections of GtRNAdb. While both canonical and non-canonical prokaryotic tRNA gene sequences identified by tRNAscan-SE are biologically interesting, the challenge of reliably distinguishing between them remains. We recommend employing a combination of (i) screening input sequences for the genetic elements typically associated with non-canonical tRNA genes, and ambiguities, (ii) activating the tRNAscan-SE automated pseudogene detection function, and (iii) scrutinizing predicted tRNA genes with low isotype scores. These measures greatly reduce manual annotation efforts, and lead to improved prokaryotic tRNA gene set predictions.

The pan-genome of the emerging multidrug-resistant pathogen Corynebacterium striatum

Corynebacterium striatum, a common constituent of the human skin microbiome, is now considered an emerging multidrug-resistant pathogen of immunocompromised and chronically ill patients. However, little is known about the molecular mechanisms in the transition from colonization to the multidrug-resistant (MDR) invasive phenotype in clinical isolates. This study performed a comprehensive pan-genomic analysis of C. striatum, including isolates from "normal skin microbiome" and from MDR infections, to gain insights into genetic factors contributing to pathogenicity and multidrug resistance in this species. For this, three novel genome sequences were obtained from clinical isolates of C. striatum of patients from Brazil, and other 24 complete or draft C. striatum genomes were retrieved from GenBank, including the ATCC6940 isolate from the Human Microbiome Project. Analysis of C. striatum strains demonstrated the presence of an open pan-genome (α = 0.852803) containing 3816 gene families, including 15 antimicrobial resistance (AMR) genes and 32 putative virulence factors. The core and accessory genomes included 1297 and 1307 genes, respectively. The identified AMR genes are primarily associated with resistance to aminoglycosides and tetracyclines. Of these, 66.6% are present in genomic islands, and four AMR genes, including aac(6')-ib7, are located in a class 1-integron. In conclusion, our data indicated that C. striatum possesses genomic characteristics favorable to the invasive phenotype, with high genomic plasticity, a robust genetic arsenal for iron acquisition, and important virulence determinants and AMR genes present in mobile genetic elements.

Pan-genomic analysis of Corynebacterium amycolatum gives insights into molecular mechanisms underpinning the transition to a pathogenic phenotype

Corynebacterium amycolatum is a nonlipophilic coryneform which is increasingly being recognized as a relevant human and animal pathogen showing multidrug resistance to commonly used antibiotics. However, little is known about the molecular mechanisms involved in transition from colonization to the MDR invasive phenotype in clinical isolates. In this study, we performed a comprehensive pan-genomic analysis of C. amycolatum, including 26 isolates from different countries. We obtained the novel genome sequences of 8 of them, which are multidrug resistant clinical isolates from Spain and Tunisia. They were analyzed together with other 18 complete or draft C. amycolatum genomes retrieved from GenBank. The species C. amycolatum presented an open pan-genome (α = 0.854905), with 3,280 gene families, being 1,690 (51.52%) in the core genome, 1,121 related to accessory genes (34.17%), and 469 related to unique genes (14.29%). Although some classic corynebacterial virulence factors are absent in the species C. amycolatum, we did identify genes associated with immune evasion, toxin, and antiphagocytosis among the predicted putative virulence factors. Additionally, we found genomic evidence for extensive acquisition of antimicrobial resistance genes through genomic islands.

Function-based classification of hazardous biological sequences: Demonstration of a new paradigm for biohazard assessments

Bioengineering applies analytical and engineering principles to identify functional biological building blocks for biotechnology applications. While these building blocks are leveraged to improve the human condition, the lack of simplistic, machine-readable definition of biohazards at the function level is creating a gap for biosafety practices. More specifically, traditional safety practices focus on the biohazards of known pathogens at the organism-level and may not accurately consider novel biodesigns with engineered functionalities at the genetic component-level. This gap is motivating the need for a paradigm shift from organism-centric procedures to function-centric biohazard identification and classification practices. To address this challenge, we present a novel methodology for classifying biohazards at the individual sequence level, which we then compiled to distinguish the biohazardous property of pathogenicity at the whole genome level. Our methodology is rooted in compilation of hazardous functions, defined as a set of sequences and associated metadata that describe coarse-level functions associated with pathogens (e.g., adherence, immune subversion). We demonstrate that the resulting database can be used to develop hazardous “fingerprints” based on the functional metadata categories. We verified that these hazardous functions are found at higher levels in pathogens compared to non-pathogens, and hierarchical clustering of the fingerprints can distinguish between these two groups. The methodology presented here defines the hazardous functions associated with bioengineering functional building blocks at the sequence level, which provide a foundational framework for classifying biological hazards at the organism level, thus leading to the improvement and standardization of current biosecurity and biosafety practices.

High-quality pan-genome of Escherichia coli generated by excluding confounding and highly similar strains reveals an association between unique gene clusters and genomic islands

The pan-genome analysis of bacteria provides detailed insight into the diversity and evolution of a bacterial population. However, the genomes involved in the pan-genome analysis should be checked carefully, as the inclusion of confounding strains would have unfavorable effects on the identification of core genes, and the highly similar strains could bias the results of the pan-genome state (open versus closed). In this study, we found that the inclusion of highly similar strains also affects the results of unique genes in pan-genome analysis, which leads to a significant underestimation of the number of unique genes in the pan-genome. Therefore, these strains should be excluded from pan-genome analysis at the early stage of data processing. Currently, tens of thousands of genomes have been sequenced for Escherichia coli, which provides an unprecedented opportunity as well as a challenge for pan-genome analysis of this classical model organism. Using the proposed strategies, a high-quality E. coli pan-genome was obtained, and the unique genes was extracted and analyzed, revealing an association between the unique gene clusters and genomic islands from a pan-genome perspective, which may facilitate the identification of genomic islands.

Orrella daihaiensis sp. nov., a bacterium isolated from Daihai Lake in Inner Mongolia

A novel aerobic, Gram-staining-negative, non-motile, short-rod-shaped strain, designated f23^T, was obtained from Daihai Lake, Inner Mongolia, Republic of China. 16S rRNA gene sequences analysis showed that f23^T belongs to the genus Orrella and is most closely related to Orrella marina H-Z20^T with 98.35% sequence similarity. The strain was oxidase positive, catalase positive and had well growth at pH 6.5-8.5, at temperature 28-40 °C and at 0-4.5% (w/v) NaCl. Colonies incubated at 37 °C on marine 2216 agar for 3 days were white, smooth, transparent, circular and less than 1.0 mm in diameter. The total genome size of f23^T was 2,803,849 bp with a G + C content of 52.79%. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain f23^T and O. marina H-Z20^T were 69.62% and 20.5%, which both below the species delineation threshold. Chemotaxonomic analysis showed that C_16:0, cyclo-C_17:0, C_18:0, Sum Feature 3 (C_16:1ω7c and/or C_16:1ω6c) and Sum Feature 8 (C_18:1ω6c and C_18:1ω7c) as the major fatty acids, ubiquinone-8 as the major isoprenoid quinone, phosphatidylethanolamine as the major cellular polar lipids. Based on the polyphasic analysis, f23^T represents a novel species within the genus Orrella, for which the name Orrella daihaiensis sp. nov. is proposed. The type strain is f23^T (= CGMCC 1.18761 ^T = KCTC 82425 ^T).

Performance assessment of genomic island prediction tools with an improved version of Design-Island

Genomic Islands (GIs) play an important role in the evolution and adaptation of prokaryotes. The origin and extent of ecological diversity of prokaryotes can be analyzed by comparing GIs across closely or distantly related prokaryotes. Understanding the importance of GI and to study the bacterial evolution, several GI prediction tools have been generated. An unsupervised method, Design-Island, was developed to identify GIs using Monte-Carlo statistical test on randomly selected segments of a chromosome. Here, in the present study Design-Island was modified with the incorporation of majority voting, multiple hypothesis testing correction. The performance of the modified version, Design-Island-II was tested and compared with the existing GI prediction tools. The performance assessment and benchmarking of the GI prediction tools require experimentally validated dataset, which is lacking. So, different datasets, generated or taken from literature were utilized to compare the sensitivity (SN), specificity (SP), precision (PPV) and accuracy (AC) of Design-Island-II. It showed substantial enhancement in term of SN, SP, PPV and AC, and significantly reduced the computation time of the algorithm. The performance of Design-Island-II has also been compared with several GI prediction tools using curated dataset of putative horizontally transferred genes. Design-Island-II showed the highest sensitivity and F1 score, comparable specificity, precision and accuracy in comparison to the other available methods. IslandViewer4 and Islander outperformed all the available methods in terms of AC and PPV respectively. Our study suggested Design-Island-II, IslandViewer4 and GIHunter among the top performing GI prediction tools considering both sensitivity and specificity of the methods.

Pangenome Evolution Reconciles Robustness and Instability of Rhizobial Symbiosis

Root nodulating rhizobia are nearly ubiquitous in soils and provide the critical service of nitrogen fixation to thousands of legume species, including staple crops. However, the magnitude of fixed nitrogen provided to hosts varies markedly among rhizobia strains, despite host legumes having mechanisms to selectively reward beneficial strains and to punish ones that do not fix sufficient nitrogen. Variation in the services of microbial mutualists is considered paradoxical given host mechanisms to select beneficial genotypes. Moreover, the recurrent evolution of non-fixing symbiont genotypes is predicted to destabilize symbiosis, but breakdown has rarely been observed. Here, we deconstructed hundreds of genome sequences from genotypically and phenotypically diverse Bradyrhizobium strains and revealed mechanisms that generate variation in symbiotic nitrogen fixation. We show that this trait is conferred by a modular system consisting of many extremely large integrative conjugative elements and few conjugative plasmids. Their transmissibility and propensity to reshuffle genes generate new combinations that lead to uncooperative genotypes and make individual partnerships unstable. We also demonstrate that these same properties extend beneficial associations to diverse host species and transfer symbiotic capacity among diverse strains. Hence, symbiotic nitrogen fixation is underpinned by modularity, which engenders flexibility, a feature that reconciles evolutionary robustness and instability. These results provide new insights into mechanisms driving the evolution of mobile genetic elements. Moreover, they yield a new predictive model on the evolution of rhizobial symbioses, one that informs on the health of organisms and ecosystems that are hosts to symbionts and that helps resolve the long-standing paradox.

Improved Mobilome Delineation in Fragmented Genomes

The mobilome of a microbe, i.e., its set of mobile elements, has major effects on its ecology, and is important to delineate properly in each genome. This becomes more challenging for incomplete genomes, and even more so for metagenome-assembled genomes (MAGs), where misbinning of scaffolds and other losses can occur. Genomic islands (GIs), which integrate into the host chromosome, are a major component of the mobilome. Our GI-detection software TIGER, unique in its precise mapping of GI termini, was applied to 74,561 genomes from 2,473 microbial species, each species containing at least one MAG and one isolate genome. A species-normalized deficit of ∼1.6 GIs/genome was measured for MAGs relative to isolates. To test whether this undercount was due to the higher fragmentation of MAG genomes, TIGER was updated to enable detection of split GIs whose termini are on separate scaffolds or that wrap around the origin of a circular replicon. This doubled GI yields, and the new split GIs matched the quality of single-scaffold GIs, except that highly fragmented GIs may lack central portions. Cross-scaffold search is an important upgrade to GI detection as fragmented genomes increasingly dominate public databases. TIGER2 better captures MAG microdiversity, recovering niche-defining GIs and supporting microbiome research aims such as virus-host linking and ecological assessment.

Complete Genome of Sinorhizobium meliloti AK76, a Symbiont of Wild Diploid Medicago lupulina from the Mugodgary Mountain Region

Sinorhizobium meliloti is a symbiotic bacterial species forming nitrogen-fixing nodules on roots of annual and perennial Medicago spp. We report the full genome sequence of S. meliloti strain AK76, an effective symbiont of the wild diploid plant Medicago lupulina grown in the Mugodgary Mountain region, Kazakhstan.

Deploying Viruses against Phytobacteria: Potential Use of Phage Cocktails as a Multifaceted Approach to Combat Resistant Bacterial Plant Pathogens

Plants in nature are under the persistent intimidation of severe microbial diseases, threatening a sustainable food production system. Plant-bacterial pathogens are a major concern in the contemporary era, resulting in reduced plant growth and productivity. Plant antibiotics and chemical-based bactericides have been extensively used to evade plant bacterial diseases. To counteract this pressure, bacteria have evolved an array of resistance mechanisms, including innate and adaptive immune systems. The emergence of resistant bacteria and detrimental consequences of antimicrobial compounds on the environment and human health, accentuates the development of an alternative disease evacuation strategy. The phage cocktail therapy is a multidimensional approach effectively employed for the biocontrol of diverse resistant bacterial infections without affecting the fauna and flora. Phages engage a diverse set of counter defense strategies to undermine wide-ranging anti-phage defense mechanisms of bacterial pathogens. Microbial ecology, evolution, and dynamics of the interactions between phage and plant-bacterial pathogens lead to the engineering of robust phage cocktail therapeutics for the mitigation of devastating phytobacterial diseases. In this review, we highlight the concrete and fundamental determinants in the development and application of phage cocktails and their underlying mechanism, combating resistant plant-bacterial pathogens. Additionally, we provide recent advances in the use of phage cocktail therapy against phytobacteria for the biocontrol of devastating plant diseases.

Mongoliitalea daihaiensis sp. nov., isolated from Daihai Lake in Inner Mongolia

A novel Gram-negative strain, designated X100-76^T, was isolated from Daihai Lake in Inner Mongolia, Republic of China. The strain was non-motile, non-spore-forming, long-rod-shaped, oxidase positive and catalase positive. Colonies incubated at 33 °C on 2216 marine agar medium for 3 days were circular, smooth, transparent, convex with clear edges, orange-red in colour and approximately 1.0 mm in diameter. Growth occurred at pH 6.5-12 (optimum pH 7.5), in 0-5.0% (w/v) NaCl (optimum 1.5%, w/v) and at 4-40 °C (optimum 28-33 °C). Phylogenetic analysis based on 16S rRNA gene sequences showed that X100-76^T belongs to the genus Mongoliitalea and is most closely related to Mongoliitalea lutea MIM18^T with 98.3% sequence similarity. The total genome size of X100-76^T was 4,816,617 bp with a G + C content of 39.6%. The digital DNA-DNA hybridization and average nucleotide identity values between strain X100-76^T and M. lutea MIM18^T were both below the recommended cut-off values. Chemotaxonomic analysis revealed iso-C_15:0, Sum Feature 4 (anteiso-C_17:1 B and/or iso-C_17:1 I), C_15:1ω6c, Sum Feature 9 (C_16:0 10-methyl and/or iso-C_17:1ω9c), C_16:0 and iso-C_15:1 G as the major fatty acids, menaquinone MK-7 as the major isoprenoid quinone, diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine and phosphatidylinositol as the major cellular polar lipids. The results of polyphasic analysis demonstrated that X100-76^T represents a novel species within the genus Mongoliitalea, for which the name Mongoliitalea daihaiensis sp. nov. is proposed. The type strain is X100-76^T (= CGMCC 1.18762^T = KCTC 82458^T).

Differential Expression Pattern of Pathogenicity-Related Genes of Ralstonia pseudosolanacearum YQ Responding to Tissue Debris of Casuarina equisetifolia

Ralstonia solanacearum species complex (RSSC) contains a group of destructive plant pathogenic bacteria, causing bacterial wilt of >200 species of crops and trees, such as Casuarina equisetifolia, worldwide. RSSC can survive in the soil environment for a long time and start infection after activation by host plants. This study conducted a transcriptome analysis on the expression pattern of the pathogenicity-related genes of a new isolated RSSC strain YQ (Ralstonia pseudosolanacearum phylotype I-16) in response to C. equisetifolia cladophyll (a branch of a stem that resembles and functions as a leaf) and root debris under in vitro culture. The cladophyll debris induced more genes up-regulated than the root debris, including pathogenicity-related genes involved in motility, effectors, type III secretion systems, quorum sensing, and plant cell wall degradation. Besides, many differentially expressed genes were related to transcriptional regulator such as cyclic dimeric guanosine monophosphate. Moreover, the cultures with cladophyll debris induced a faster wilting in bioassays, and the cell swimming was enhanced by cladophyll exudate. C. equisetifolia cladophylls could activate the expression of pathogenicity-related genes of strain YQ and accelerate infection. Our findings suggest that litterfall management in C. equisetifolia forests, or even other plantations, should receive attention to prevent the induction of bacterial wilt disease caused by RSSC.

Genomic and Phenotypic Biology of Novel Strains of Dickeya zeae Isolated From Pineapple and Taro in Hawaii: Insights Into Genome Plasticity, Pathogenicity, and Virulence Determinants

Dickeya zeae, a bacterial plant pathogen of the family Pectobacteriaceae, is responsible for a wide range of diseases on potato, maize, rice, banana, pineapple, taro, and ornamentals and significantly reduces crop production. D. zeae causes the soft rot of taro (Colocasia esculenta) and the heart rot of pineapple (Ananas comosus). In this study, we used Pacific Biosciences single-molecule real-time (SMRT) sequencing to sequence two high-quality complete genomes of novel strains of D. zeae: PL65 (size: 4.74997 MB; depth: 701x; GC: 53.6%) and A5410 (size: 4.7792 MB; depth: 558x; GC: 53.5%) isolated from economically important Hawaiian crops, taro, and pineapple, respectively. Additional complete genomes of D. zeae representing three additional hosts (philodendron, rice, and banana) and other species used for a taxonomic comparison were retrieved from the NCBI GenBank genome database. Genomic analyses indicated the truncated type III and IV secretion systems (T3SS and T4SS) in the taro strain, which only harbored one and two genes of T3SS and T4SS, respectively, and showed high heterogeneity in the type VI secretion system (T6SS). Unlike strain EC1, which was isolated from rice and recently reclassified as D. oryzae, neither the genome PL65 nor A5410 harbors the zeamine biosynthesis gene cluster, which plays a key role in virulence of other Dickeya species. The percentages of average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) between the two genomes were 94.47 and 57.00, respectively. In this study, we compared the major virulence factors [plant cell wall-degrading extracellular enzymes and protease (Prt)] produced by D. zeae strains and evaluated the virulence on taro corms and pineapple leaves. Both strains produced Prts, pectate lyases (Pels), and cellulases but no significant quantitative differences were observed (p > 0.05) between the strains. All the strains produced symptoms on taro corms and pineapple leaves, but the strain PL65 produced symptoms more rapidly than others. Our study highlights the genetic constituents of pathogenicity determinants and genomic heterogeneity that will help to understand the virulence mechanisms and aggressiveness of this plant pathogen.

Identifying genomic islands with deep neural networks

Background

Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types.

Results

We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools.

Conclusions

We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm

BACKGROUND

Genomic Islands (GIs) are clusters of genes that are mobilized through horizontal gene transfer. GIs play a pivotal role in bacterial evolution as a mechanism of diversification and adaptation to different niches. Therefore, identification and characterization of GIs in bacterial genomes is important for understanding bacterial evolution. However, quantifying GIs is inherently difficult, and the existing methods suffer from low prediction accuracy and precision-recall trade-off. Moreover, several of them are supervised in nature, and thus, their applications to newly sequenced genomes are riddled with their dependency on the functional annotation of existing genomes.

RESULTS

We present SSG-LUGIA, a completely automated and unsupervised approach for identifying GIs and horizontally transferred genes. SSG-LUGIA is a novel method based on unsupervised anomaly detection technique, accompanied by further refinement using cues from signal processing literature. SSG-LUGIA leverages the atypical compositional biases of the alien genes to localize GIs in prokaryotic genomes. SSG-LUGIA was assessed on a large benchmark dataset `IslandPick' and on a set of 15 well-studied genomes in the literature and followed by a thorough analysis on the well-understood Salmonella typhi CT18 genome. Furthermore, the efficacy of SSG-LUGIA in identifying horizontally transferred genes was evaluated on two additional bacterial genomes, namely, those of Corynebacterium diphtheria NCTC13129 and Pseudomonas aeruginosa LESB58. SSG-LUGIA was examined on draft genomes and was demonstrated to be efficient as an ensemble method.

CONCLUSIONS

Our results indicate that SSG-LUGIA achieved superior performance in comparison to frequently used existing methods. Importantly, it yielded a better trade-off between precision and recall than the existing methods. Its nondependency on the functional annotation of genomes makes it suitable for analyzing newly sequenced, yet uncharacterized genomes. Thus, our study is a significant advance in identification of GIs and horizontally transferred genes. SSG-LUGIA is available as an open source software at https://nibtehaz.github.io/SSG-LUGIA/.

Mobile genetic elements mediate the mixotrophic evolution of novel Alicyclobacillus species for acid mine drainage adaptation

Alicyclobacillus species inhabit diverse environments and have adapted to broad ranges of pH and temperature. However, their adaptive evolutions remain elusive, especially regarding the role of mobile genetic elements (MGEs). Here, we characterized the distributions and functions of MGEs in Alicyclobacillus species across five environments, including acid mine drainage (AMD), beverages, hot springs, sediments, and soils. Nine Alicyclobacillus strains were isolated from AMD and possessed larger genome sizes and more genes than those from other environments. Four AMD strains evolved to be mixotrophic and fell into distinctive clusters in phylogenetic tree. Four types of MGEs including genomic island (GI), insertion sequence (IS), prophage, and integrative and conjugative element (ICE) were widely distributed in Alicyclobacillus species. Further, AMD strains did not possess CRISPR-Cas systems, but had more GI, IS, and ICE, as well as more MGE-associated genes involved in the oxidation of iron and sulfide and the resistance of heavy metal and low temperature. These findings highlight the differences in phenotypes and genotypes between strains isolated from AMD and other environments and the important role of MGEs in rapid environment niche expansions.

panRGP: a pangenome-based method to predict genomic islands and explore their diversity

MOTIVATION

Horizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of genome plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to genomic islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date, no methods are available to identify GIs using hundreds of genomes to explore their diversity.

RESULTS

We present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on metagenome assembled genomes by redefining the borders of the leuX tRNA hotspot, a well-studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies.

AVAILABILITY AND IMPLEMENTATION

The methods presented in the current work are available through the following software: https://github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https://github.com/axbazin/panrgp_supdata.

Phylogenomic analysis of Clostridioides difficile ribotype 106 strains reveals novel genetic islands and emergent phenotypes

Clostridioides difficile infection (CDI) is a major healthcare-associated diarrheal disease. Consistent with trends across the United States, C. difficile RT106 was the second-most prevalent molecular type in our surveillance in Arizona from 2015 to 2018. A representative RT106 strain displayed robust virulence and 100% lethality in the hamster model of acute CDI. We identified a unique 46 KB genomic island (GI1) in all RT106 strains sequenced to date, including those in public databases. GI1 was not found in its entirety in any other C. difficile clade, or indeed, in any other microbial genome; however, smaller segments were detected in Enterococcus faecium strains. Molecular clock analyses suggested that GI1 was horizontally acquired and sequentially assembled over time. GI1 encodes homologs of VanZ and a SrtB-anchored collagen-binding adhesin, and correspondingly, all tested RT106 strains had increased teicoplanin resistance, and a majority displayed collagen-dependent biofilm formation. Two additional genomic islands (GI2 and GI3) were also present in a subset of RT106 strains. All three islands are predicted to encode mobile genetic elements as well as virulence factors. Emergent phenotypes associated with these genetic islands may have contributed to the relatively rapid expansion of RT106 in US healthcare and community settings.

Whole genome sequencing and genome annotation of Dermacoccus abyssi strain HZAU 226 isolated from spoiled eggs

Dermacoccus abyssi strain HZAU 226 is a spoilage bacterium isolated from eggs. So far, there are still few genomic resources available on the Dermacoccus abyssi. Here, we reported the complete genome sequence of Dermacoccus abyssi strain HZAU 226. High-quality DNA was extracted using the Qiagen kit, then single-molecule sequencing was performed by GridION sequencer. The raw data was quality-controlled and assembled to obtain the final genome, which consisted of a complete genome of 2,992,060 bp circular chromosome and a 64,524 bp plasmid. The structural and functional annotations of the genome were achieved through the analysis of different available databases, including antibiotic resistance genes, secondary metabolite synthesis genes and stress-related genes. Meanwhile, comparative genomic analyses of the strains were also performed. This is the first report on the complete genome of Dermacoccus abyssi, which will provide genomic resources for the study of spoilage bacteria in eggs.

Identification of the conjugative and mobilizable plasmid fragments in the plasmidome using sequence signatures

Plasmids are the key element in horizontal gene transfer in the microbial community. Recently, a large number of experimental and computational methods have been developed to obtain the plasmidomes of microbial communities. Distinguishing transmissible plasmid sequences, which are derived from conjugative or at least mobilizable plasmids, from non-transmissible plasmid sequences in the plasmidome is essential for understanding the diversity of plasmids and how they regulate the microbial community. Unfortunately, due to the highly fragmented characteristics of DNA sequences in the plasmidome, effective identification methods are lacking. In this work, we used information entropy from information theory to assess the randomness of synonymous codon usage over 4424 plasmid genomes. The results showed that for all amino acids, the choice of a synonymous codon in conjugative and mobilizable plasmids is more random than that in non-transmissible plasmids, indicating that transmissible plasmids have different sequence signatures from non-transmissible plasmids. Inspired by this phenomenon, we further developed a novel algorithm named PlasTrans. PlasTrans takes the triplet code sequences and base sequences of plasmid DNA fragments as input and uses the convolutional neural network of the deep learning technique to further extract the more complex signatures of the plasmid sequences and identify the conjugative and mobilizable DNA fragments. Tests showed that PlasTrans could achieve an AUC of as high as 84-91%, even though the fragments only contained hundreds of base pairs. To the best of our knowledge, this is the first quantitative analysis of the difference in sequence signatures between transmissible and non-transmissible plasmids, and we developed the first tool to perform transferability annotation for DNA fragments in the plasmidome. We expect that PlasTrans will be a useful tool for researchers who analyse the properties of novel plasmids in the microbial community and horizontal gene transfer, especially the spread of resistance genes and virulence factors associated with plasmids. PlasTrans is freely available via https://github.com/zhenchengfang/PlasTrans.

Pathogenicity Factors of Genomic Islands in Intestinal and Extraintestinal Escherichia coli

Escherichia coli is a versatile bacterial species that includes both harmless commensal strains and pathogenic strains found in the gastrointestinal tract in humans and warm-blooded animals. The growing amount of DNA sequence information generated in the era of "genomics" has helped to increase our understanding of the factors and mechanisms involved in the diversification of this bacterial species. The pathogenic side of E. coli that is afforded through horizontal transfers of genes encoding virulence factors enables this bacterium to become a highly diverse and adapted pathogen that is responsible for intestinal or extraintestinal diseases in humans and animals. Many of the accessory genes acquired by horizontal transfers form syntenic blocks and are recognized as genomic islands (GIs). These genomic regions contribute to the rapid evolution, diversification and adaptation of E. coli variants because they are frequently subject to rearrangements, excision and transfer, as well as to further acquisition of additional DNA. Here, we review a subgroup of GIs from E. coli termed pathogenicity islands (PAIs), a concept defined in the late 1980s by Jörg Hacker and colleagues in Werner Goebel's group at the University of Würzburg, Würzburg, Germany. As with other GIs, the PAIs comprise large genomic regions that differ from the rest of the genome by their G + C content, by their typical insertion within transfer RNA genes, and by their harboring of direct repeats (at their ends), integrase determinants, or other mobility loci. The hallmark of PAIs is their contribution to the emergence of virulent bacteria and to the development of intestinal and extraintestinal diseases. This review summarizes the current knowledge on the structure and functional features of PAIs, on PAI-encoded E. coli pathogenicity factors and on the role of PAIs in host-pathogen interactions.

Pan-genome analysis of Paenibacillus polymyxa strains reveals the mechanism of plant growth promotion and biocontrol

Rapid development of gene sequencing technologies has led to an exponential increase in microbial sequencing data. Genome research of a single organism does not capture the changes in the characteristics of genetic information within a species. Pan-genome analysis gives us a broader perspective to study the complete genetic information of a species. Paenibacillus polymyxa is a Gram-positive bacterium and an important plant growth-promoting rhizobacterium with the ability to produce multiple antibiotics, such as fusaricidin, lantibiotic, paenilan, and polymyxin. Our study explores the pan-genome of 14 representative P. polymyxa strains isolated from around the world. Heap's law model and curve fitting confirmed an open pan-genome of P. polymyxa. The phylogenetic and collinearity analyses reflected that the evolutionary classification of P. polymyxa strains are not associated with geographical area and ecological niches. Few genes related to phytohormone synthesis and phosphate solubilization were conserved; however, the nif cluster gene associated with nitrogen fixation exists only in some strains. This finding is indicative of nitrogen fixing ability is not stable in P. polymyxa. Analysis of antibiotic gene clusters in P. polymyxa revealed the presence of these genes in both core and accessory genomes. This observation indicates that the difference in living environment led to loss of ability to synthesize antibiotics in some strains. The current pan-genomic analysis of P. polymyxa will help us understand the mechanisms of biological control and plant growth promotion. It will also promote the use of P. polymyxa in agriculture.

Complete Genome Sequencing and Transcriptome Analysis of Nitrogen Metabolism of Succinivibrio dextrinosolvens Strain Z6 Isolated From Dairy Cow Rumen

The unclassified Succinivibrionaceae lineages are abundant in high yielding multiparous cows, and their presence is positively correlated with milk yield and fat percentage and reduces methane emissions. However, it is still unclear which species are associated with the most efficient feed nutrient utilization and productivity. Here, we used integrated whole genome sequencing and matrix-assisted laser desorption/ionization mass spectrometry, coupled with phenotypic and chemotaxonomic analysis, to characterize S. dextrinosolvens Z6, a species in Succinivibrionaceae isolated from the rumen. To assess the role of S. dextrinosolvens Z6 in nitrogen metabolism, cells grown in different nitrogen sources were analyzed by RNA sequencing. The whole genome sequence result revealed a genome size of 3.47 Mbp with 38.9% of G + C content. A total of 2993 encoding sequences account for 98%. The genes for regulating carbohydrate (10.6%) and amino acid (9%) transport and metabolism were the most abundant. ANI (Average nucleotide identity) showed that SD-Z6 was most closely related to SD-22B (99.96%). The whole genome alignment of SD-Z6 with SD-22B showed a more than 0.34 Mb nucleotide difference. Growth of SD-Z6 occurred at a temperature 36–42°C with an optimum at 39.7°C, pH 6–8; the optimum pH was 6.9 and with 0–1% (w/v) NaCl. The maximum growth (OD₆₀₀ 0.825 ± 0.12) and microbial crude protein (MCP) (178.2 μg/ml) were observed in cells grown in amino acid. The maximum concentration of ammonia (3.96 ± 1.2) was observed in urea containing media and 1.06 mM (26.7% of the produced) remained after 24 h incubation. Activities of urease and glutamine synthase (P < 0.01) and glutamate dehydrogenase (P < 0.05) were significantly different in nitrogen and growth phase. Glutamate synthetase (P < 0.01) was significantly different only at different growth phases. In total, 1246 differentially expressed genes (DEGs) were identified in all nitrogen. Among DEGs, 33 were related to nitrogen metabolism. Their expression correlated with nitrogen sources and the intensity of enzyme activity. This result enhances our understanding of the roles of Succinivibrionaceae in the efficient nitrogen utilization and on environmental protection.

Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails

The antibiotic resistance crisis has led to renewed interest in phage therapy as an alternative means of treating infection. However, conventional methods for isolating pathogen-specific phage are slow, labor-intensive, and frequently unsuccessful. We have demonstrated that computationally identified prophages carried by near-neighbor bacteria can serve as starting material for production of engineered phages that kill the target pathogen. Our approach and technology platform offer new opportunity for rapid development of phage therapies against most, if not all, bacterial pathogens, a foundational advance for use of phage in treating infectious disease.

New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two complementary computational tools that identify and precisely map prophages and other integrative genetic elements in bacterial genomes are used to identify prophage-laden bacteria that are close relatives of the target strain. Phage genomes are engineered to disable lysogeny, through use of long amplicon PCR and Gibson assembly. Finally, the engineered phage genomes are introduced into host bacteria for phage production. As an initial demonstration, we used this approach to produce a phage cocktail against the opportunistic pathogen Pseudomonas aeruginosa PAO1. Two prophage-laden P. aeruginosa strains closely related to PAO1 were identified, ATCC 39324 and ATCC 27853. Deep sequencing revealed that mitomycin C treatment of these strains induced seven phages that grow on P. aeruginosa PAO1. The most diverse five phages were engineered for nonlysogeny by deleting the integrase gene (int), which is readily identifiable and typically conveniently located at one end of the prophage. The Δint phages, individually and in cocktails, killed P. aeruginosa PAO1 in liquid culture as well as in a waxworm (Galleria mellonella) model of infection.

IMPORTANCE The antibiotic resistance crisis has led to renewed interest in phage therapy as an alternative means of treating infection. However, conventional methods for isolating pathogen-specific phage are slow, labor-intensive, and frequently unsuccessful. We have demonstrated that computationally identified prophages carried by near-neighbor bacteria can serve as starting material for production of engineered phages that kill the target pathogen. Our approach and technology platform offer new opportunity for rapid development of phage therapies against most, if not all, bacterial pathogens, a foundational advance for use of phage in treating infectious disease.

Microbial genomic island discovery, visualization and analysis

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Genome Sequences of Burkholderia thailandensis Strains E421, E426, and DW503

We present the draft genome sequences of three Burkholderia thailandensis strains, E421, E426, and DW503. E421 consists of 90 contigs of 6,639,935 bp and 67.73% GC content. E426 consists of 106 contigs of 6,587,853 bp and 67.73% GC content. DW503 consists of 102 contigs of 6,458,767 bp and 67.64% GC content.

We present the draft genome sequences of three Burkholderia thailandensis strains, E421, E426, and DW503. E421 consists of 90 contigs of 6,639,935 bp and 67.73% GC content. E426 consists of 106 contigs of 6,587,853 bp and 67.73% GC content. DW503 consists of 102 contigs of 6,458,767 bp and 67.64% GC content.

New candidates for regulated gene integrity revealed through precise mapping of integrative genetic elements

Integrative genetic elements (IGEs) are mobile multigene DNA units that integrate into and excise from host bacterial genomes. Each IGE usually targets a specific site within a conserved host gene, integrating in a manner that preserves target gene function. However, a small number of bacterial genes are known to be inactivated upon IGE integration and reactivated upon excision, regulating phenotypes of virulence, mutation rate, and terminal differentiation in multicellular bacteria. The list of regulated gene integrity (RGI) cases has been slow-growing because IGEs have been challenging to precisely and comprehensively locate in genomes. We present software (TIGER) that maps IGEs with unprecedented precision and without attB site bias. TIGER uses a comparative genomic, ping-pong BLAST approach, based on the principle that the IGE integration module (i.e. its int-attP region) is cohesive. The resultant IGEs from 2168 genomes, along with integrase phylogenetic analysis and gene inactivation tests, revealed 19 new cases of genes whose integrity is regulated by IGEs (including dut, eccCa1, gntT, hrpB, merA, ompN, prkA, tqsA, traG, yifB, yfaT and ynfE), as well as recovering previously known cases (in sigK, spsM, comK, mlrA and hlb genes). It also recovered known clades of site-promiscuous integrases and identified possible new ones.

Potent CRISPR-Cas9 inhibitors from Staphylococcus genomes

Anti-CRISPRs (Acrs) are small proteins that inhibit the RNA-guided DNA targeting activity of CRISPR-Cas enzymes. Encoded by bacteriophage and phage-derived bacterial genes, Acrs prevent CRISPR-mediated inhibition of phage infection and can also block CRISPR-Cas-mediated genome editing in eukaryotic cells. To identify Acrs capable of inhibiting Staphylococcus aureus Cas9 (SauCas9), an alternative to the most commonly used genome editing protein Streptococcus pyogenes Cas9 (SpyCas9), we used both self-targeting CRISPR screening and guilt-by-association genomic search strategies. Here we describe three potent inhibitors of SauCas9 that we name AcrIIA13, AcrIIA14, and AcrIIA15. These inhibitors share a conserved N-terminal sequence that is dispensable for DNA cleavage inhibition and have divergent C termini that are required in each case for inhibition of SauCas9-catalyzed DNA cleavage. In human cells, we observe robust inhibition of SauCas9-induced genome editing by AcrIIA13 and moderate inhibition by AcrIIA14 and AcrIIA15. We also find that the conserved N-terminal domain of AcrIIA13-AcrIIA15 binds to an inverted repeat sequence in the promoter of these Acr genes, consistent with its predicted helix-turn-helix DNA binding structure. These data demonstrate an effective strategy for Acr discovery and establish AcrIIA13-AcrIIA15 as unique bifunctional inhibitors of SauCas9.

Horizontally Acquired Homologs of Xenogeneic Silencers: Modulators of Gene Expression Encoded by Plasmids, Phages and Genomic Islands

Acquisition of mobile elements by horizontal gene transfer can play a major role in bacterial adaptation and genome evolution by providing traits that contribute to bacterial fitness. However, gaining foreign DNA can also impose significant fitness costs to the host bacteria and can even produce detrimental effects. The efficiency of horizontal acquisition of DNA is thought to be improved by the activity of xenogeneic silencers. These molecules are a functionally related group of proteins that possess affinity for the acquired DNA. Binding of xenogeneic silencers suppresses the otherwise uncontrolled expression of genes from the newly acquired nucleic acid, facilitating their integration to the bacterial regulatory networks. Even when the genes encoding for xenogeneic silencers are part of the core genome, homologs encoded by horizontally acquired elements have also been identified and studied. In this article, we discuss the current knowledge about horizontally acquired xenogeneic silencer homologs, focusing on those encoded by genomic islands, highlighting their distribution and the major traits that allow these proteins to become part of the host regulatory networks.

Genome-Wide Analyses Revealed Remarkable Heterogeneity in Pathogenicity Determinants, Antimicrobial Compounds, and CRISPR-Cas Systems of Complex Phytopathogenic Genus Pectobacterium

The Pectobacterium genus comprises pectolytic enterobacteria defined as the causal agents of soft rot, blackleg, and aerial stem rot diseases of potato and economically important crops. In this study, we undertook extensive genome-wide comparative analyses of twelve species that conform the Pectobacterium genus. Bioinformatics approaches outlined a low nucleotide identity of P. parmentieri and P. wasabiae with other species, while P. carotovorum subsp. odoriferum was shown to harbor numerous pseudogenes, which suggests low coding capacity and genomic degradation. The genome atlases allowed for distinguishing distinct DNA structures and highlighted suspicious high transcription zones. The analyses unveiled a noteworthy heterogeneity in the pathogenicity determinants. Specifically, phytotoxins, polysaccharides, iron uptake systems, and the type secretion systems III-V were observed in just some species. Likewise, a comparison of gene clusters encoding antimicrobial compounds put in evidence for high conservation of carotovoricin, whereas a few species possessed the phenazine, carbapenem, and carocins. Moreover, three clustered regularly interspaced short palindromic repeats-Cas (CRISPR-Cas) systems: I-E, I-F, and III-A were identified. Surrounding some CRISPR-Cas regions, different toxin and antitoxin systems were found, which suggests bacterial suicide in the case of an immune system failure. Multiple whole-genome alignments shed light on to the presence of a novel cellobiose phosphotransferase system (PTS) exclusive to P. parmenteri, and an unreported T5SS conserved in almost all species. Several regions that were associated with virulence, microbe antagonism, and adaptive immune systems were predicted within genomic islands, which underscored the essential role that horizontal gene transfer has imparted in the dynamic evolution and speciation of Pectobacterium species. Overall, the results decipher the different strategies that each species has developed to infect their hosts, outcompete for food resources, and defend against bacteriophages. Our investigation provides novel genetic insights that will assist in understanding the pathogenic lifestyle of Pectobacterium, a genus that jeopardizes the agriculture sustainability of important crops worldwide.

Pan-genome analysis of Riemerella anatipestifer reveals its genomic diversity and acquired antibiotic resistance associated with genomic islands

Riemerella anatipestifer is a gram-negative bacterium that leads to severe contagious septicemia in ducks, turkeys, chickens, and wild waterfowl. Here, a pan-genome with 32 R. anatipestifer genomes is re-established, and the mathematical model is calculated to evaluate the expansion of R. anatipestifer genomes, which were determined to be open. Average nucleotide identity (ANI) and phylogenetic analysis preliminarily clarify intraspecies variation and distance. Comparative genomic analysis of R. anatipestifer found that horizontal gene transfer events, which provide an expressway for the recruitment of novel functionalities and facilitate genetic diversity in microbial genomes, play a key role in the process of acquiring and transmitting antibiotic-resistance genes in R. anatipestifer. Furthermore, a new antibiotic-resistance gene cluster was identified in the same loci in 14 genomes. The uneven distribution of virulence factors was also confirmed by our results. Our study suggests that the ability to acquire foreign genes (such as antibiotic-resistance genes) increases the adaptability of R. anatipestifer, and the virulence genes with little mobility are highly conserved in R. anatipestifer.

Cumulative acquisition of pathogenicity islands has shaped virulence potential and contributed to the emergence of LEE-negative Shiga toxin-producing Escherichia coli strains

Shiga toxin-producing Escherichia coli (STEC) are foodborne pathogens causing severe gastroenteritis, which may lead to hemolytic uremic syndrome. The Locus of Enterocyte Effacement (LEE), a Pathogenicity Island (PAI), is a major determinant of intestinal epithelium attachment of a group of STEC strains; however, the virulence repertoire of STEC strains lacking LEE, has not been fully characterized. The incidence of LEE-negative STEC strains has increased in several countries, highlighting the relevance of their study. In order to gain insights into the basis for the emergence of LEE-negative STEC strains, we performed a large-scale genomic analysis of 367 strains isolated worldwide from humans, animals, food and the environment. We identified uncharacterized genomic islands, including two PAIs and one Integrative Conjugative Element. Additionally, the Locus of Adhesion and Autoaggregation (LAA) was the most prevalent PAI among LEE-negative strains and we found that it contributes to colonization of the mice intestine. Our comprehensive and rigorous comparative genomic and phylogenetic analyses suggest that the accumulative acquisition of PAIs has played an important, but currently unappreciated role, in the evolution of virulence in these strains. This study provides new knowledge on the pathogenicity of LEE-negative STEC strains and identifies molecular markers for their epidemiological surveillance.

Improved genomic island predictions with IslandPath-DIMOB

Motivation

Genomic islands (GIs) are clusters of genes of probable horizontal origin that play a major role in bacterial and archaeal genome evolution and microbial adaptability. They are of high medical and industrial interest, due to their enrichment in virulence factors, some antimicrobial resistance genes and adaptive metabolic pathways. The development of more sensitive but precise prediction tools, using either sequence composition-based methods or comparative genomics, is needed as large-scale analyses of microbial genomes increase.

Results

IslandPath-DIMOB, a leading GI prediction tool in the IslandViewer webserver, has now been significantly improved by modifying both the decision algorithm to determine sequence composition biases, and the underlying database of HMM profiles for associated mobility genes. The accuracy of IslandPath-DIMOB and other major software has been assessed using a reference GI dataset predicted by comparative genomics, plus a manually curated dataset from literature review. Compared to the previous version (v0.2.0), this IslandPath-DIMOB v1.0.0 achieves 11.7% and 5.3% increase in recall and precision, respectively. IslandPath-DIMOB has the highest Matthews correlation coefficient among individual prediction methods tested, combining one of the highest recall measures (46.9%) at high precision (87.4%). The only method with higher recall had notably lower precision (55.1%). This new IslandPath-DIMOB v1.0.0 will facilitate more accurate studies of GIs, including their key roles in microbial adaptability of medical, environmental and industrial interest.

Availability and implementation

IslandPath-DIMOB v1.0.0 is freely available through the IslandViewer webserver {{http://www.pathogenomics.sfu.ca/islandviewer/}} and as standalone software {{https://github.com/brinkmanlab/islandpath/}} under the GNU-GPLv3.

Supplementary information

Supplementary data are available at Bioinformatics online.

IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets

IslandViewer (http://www.pathogenomics.sfu.ca/islandviewer/) is a widely-used webserver for the prediction and interactive visualization of genomic islands (GIs, regions of probable horizontal origin) in bacterial and archaeal genomes. GIs disproportionately encode factors that enhance the adaptability and competitiveness of the microbe within a niche, including virulence factors and other medically or environmentally important adaptations. We report here the release of IslandViewer 4, with novel features to accommodate the needs of larger-scale microbial genomics analysis, while expanding GI predictions and improving its flexible visualization interface. A user management web interface as well as an HTTP API for batch analyses are now provided with a secured authentication to facilitate the submission of larger numbers of genomes and the retrieval of results. In addition, IslandViewer's integrated GI predictions from multiple methods have been improved and expanded by integrating the precise Islander method for pre-computed genomes, as well as an updated IslandPath-DIMOB for both pre-computed and user-supplied custom genome analysis. Finally, pre-computed predictions including virulence factors and antimicrobial resistance are now available for 6193 complete bacterial and archaeal strains publicly available in RefSeq. IslandViewer 4 provides key enhancements to facilitate the analysis of GIs and better understand their role in the evolution of successful environmental microbes and pathogens.

PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning

BACKGROUND

Phages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromosome-derived fragments in sequenced metagenomic data. However, there is a lack of efficient methods that can simultaneously identify phages and plasmids in metagenomic data, and the existing tools identifying either phages or plasmids have not yet presented satisfactory performance.

FINDINGS

We present PPR-Meta, a 3-class classifier that allows simultaneous identification of both phage and plasmid fragments from metagenomic assemblies. PPR-Meta consists of several modules for predicting sequences of different lengths. Using deep learning, a novel network architecture, referred to as the Bi-path Convolutional Neural Network, is designed to improve the performance for short fragments. PPR-Meta demonstrates much better performance than currently available similar tools individually for phage or plasmid identification, while testing on both artificial contigs and real metagenomic data. PPR-Meta is freely available via http://cqb.pku.edu.cn/ZhuLab/PPR_Meta or https://github.com/zhenchengfang/PPR-Meta.

CONCLUSIONS

To the best of our knowledge, PPR-Meta is the first tool that can simultaneously identify phage and plasmid fragments efficiently and reliably. The software is optimized and can be easily run on a local PC by non-computer professionals. We developed PPR-Meta to promote the research on mobile genetic elements and horizontal gene transfer.

Horizontal acquisition of hydrogen conversion ability and other habitat adaptations in the Hydrogenovibrio strains SP-41 and XCL-2

BACKGROUND

Obligate sulfur oxidizing chemolithoauthotrophic strains of Hydrogenovibrio crunogenus have been isolated from multiple hydrothermal vent associated habitats. However, a hydrogenase gene cluster (encoding the hydrogen converting enzyme and its maturation/assembly machinery) detected on the first sequenced H. crunogenus strain (XCL-2) suggested that hydrogen conversion may also play a role in this organism. Yet, numerous experiments have underlined XCL-2's inability to consume hydrogen under the tested conditions. A recent study showed that the closely related strain SP-41 contains a homolog of the XCL-2 hydrogenase (a group 1b [NiFe]-hydrogenase), but that it can indeed use hydrogen. Hence, the question remained unresolved, why SP-41 is capable of using hydrogen, while XCL-2 is not.

RESULTS

Here, we present the genome sequence of the SP-41 strain and compare it to that of the XCL-2 strain. We show that the chromosome of SP-41 codes for a further hydrogenase gene cluster, including two additional hydrogenases: the first appears to be a group 1d periplasmic membrane-anchored hydrogenase, and the second a group 2b sensory hydrogenase. The region where these genes are located was likely acquired horizontally and exhibits similarity to other Hydrogenovibrio species (H. thermophilus MA2-6 and H. marinus MH-110 T) and other hydrogen oxidizing Proteobacteria (Cupriavidus necator H16 and Ghiorsea bivora TAG-1 T). The genomes of XCL-2 and SP-41 show a strong conservation in gene order. However, several short genomic regions are not contained in the genome of the other strain. These exclusive regions are often associated with signs of DNA mobility, such as genes coding for transposases. They code for transport systems and/or extend the metabolic potential of the strains.

CONCLUSIONS

Our results suggest that horizontal gene transfer plays an important role in shaping the genomes of these strains, as a likely mechanism for habitat adaptation, including, but not limited to the transfer of the hydrogen conversion ability.

MTGIpick allows robust identification of genomic islands from a single genome

Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showing multiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizes missed by existing methods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.