Microbial genomic island discovery, visualization and analysis

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.

Synteruptor: mining genomic islands for non-classical specialized metabolite gene clusters

Microbial specialized metabolite biosynthetic gene clusters (SMBGCs) are a formidable source of natural products of pharmaceutical interest. With the multiplication of genomic data available, very efficient bioinformatic tools for automatic SMBGC detection have been developed. Nevertheless, most of these tools identify SMBGCs based on sequence similarity with enzymes typically involved in specialised metabolism and thus may miss SMBGCs coding for undercharacterised enzymes. Here we present Synteruptor (https://bioi2.i2bc.paris-saclay.fr/synteruptor), a program that identifies genomic islands, known to be enriched in SMBGCs, in the genomes of closely related species. With this tool, we identified a SMBGC in the genome of Streptomyces ambofaciens ATCC23877, undetected by antiSMASH versions prior to antiSMASH 5, and experimentally demonstrated that it directs the biosynthesis of two metabolites, one of which was identified as sphydrofuran. Synteruptor is also a valuable resource for the delineation of individual SMBGCs within antiSMASH regions that may encompass multiple clusters, and for refining the boundaries of these SMBGCs.

Pathogenomes of Shiga Toxin Positive and Negative Escherichia coli O157:H7 Strains TT12A and TT12B: Comprehensive Phylogenomic Analysis Using Closed Genomes

Shiga toxin-producing Escherichia coli are zoonotic pathogens that cause food-borne human disease. Among these, the O157:H7 serotype has evolved from an enteropathogenic O55:H7 ancestor through the displacement of the somatic gene cluster and recurrent toxigenic conversion by Shiga toxin-converting bacteriophages. However, atypical strains that lack the Shiga toxin, the characteristic virulence hallmark, are circulating in this lineage. For this study, we analyzed the pathogenome and virulence inventories of the stx+ strain, TT12A, isolated from a patient with hemorrhagic colitis, and its respective co-isolated stx- strain, TT12B. Sequencing the genomes to closure proved critical to the cataloguing of subtle strain differentiating sequence and structural polymorphisms at a high-level of phylogenetic accuracy and resolution. Phylogenomic profiling revealed SNP and MLST profiles similar to the near clonal outbreak isolates. Their prophage inventories, however, were notably different. The attenuated atypical non-shigatoxigenic status of TT12B is explained by the absence of both the ΦStx1a- and ΦStx2a-prophages carried by TT12A, and we also recorded further alterations in the non-Stx prophage complement. Phenotypic characterization indicated that culture growth was directly impacted by the strains' distinct lytic phage complement. Altogether, our phylogenomic and phenotypic analyses show that these intimately related isogenic strains are on divergent Stx(+/stx-) evolutionary paths.

Pathogenomes and virulence profiles of representative big six non-O157 serogroup Shiga toxin-producing Escherichia coli

Shiga toxin (Stx)-producing Escherichia coli (STEC) of non-O157:H7 serotypes are responsible for global and widespread human food-borne disease. Among these serogroups, O26, O45, O103, O111, O121, and O145 account for the majority of clinical infections and are colloquially referred to as the "Big Six." The "Big Six" strain panel we sequenced and analyzed in this study are reference type cultures comprised of six strains representing each of the non-O157 STEC serogroups curated and distributed by the American Type Culture Collection (ATCC) as a resource to the research community under panel number ATCC MP-9. The application of long- and short-read hybrid sequencing yielded closed chromosomes and a total of 14 plasmids of diverse functions. Through high-resolution comparative phylogenomics, we cataloged the shared and strain-specific virulence and resistance gene content and established the close relationship of serogroup O26 and O103 strains featuring flagellar H-type 11. Virulence phenotyping revealed statistically significant differences in the Stx-production capabilities that we found to be correlated to the strain's individual stx-status. Among the carried Stx1a, Stx2a, and Stx2d phages, the Stx2a phage is by far the most responsive upon RecA-mediated phage mobilization, and in consequence, stx2a + isolates produced the highest-level of toxin in this panel. The availability of high-quality closed genomes for this "Big Six" reference set, including carried plasmids, along with the recorded genomic virulence profiles and Stx-production phenotypes will provide a valuable foundation to further explore the plasticity in evolutionary trajectories in these emerging non-O157 STEC lineages, which are major culprits of human food-borne disease.

Pan-Genome Plasticity and Virulence Factors: A Natural Treasure Trove for Acinetobacter baumannii

Acinetobacter baumannii is a Gram-negative pathogen responsible for a variety of community- and hospital-acquired infections. It is recognized as a life-threatening pathogen among hospitalized individuals and, in particular, immunocompromised patients in many countries. A. baumannii, as a member of the ESKAPE group, encompasses high genomic plasticity and simultaneously is predisposed to receive and exchange the mobile genetic elements (MGEs) through horizontal genetic transfer (HGT). Indeed, A. baumannii is a treasure trove that contains a high number of virulence factors. In accordance with these unique pathogenic characteristics of A. baumannii, the authors aim to discuss the natural treasure trove of pan-genome and virulence factors pertaining to this bacterial monster and try to highlight the reasons why this bacterium is a great concern in the global public health system.

Genomic islands and their role in fitness traits of two key sepsis-causing bacterial pathogens

To survive and establish a niche for themselves, bacteria constantly evolve. Toward that, they not only insert point mutations and promote illegitimate recombinations within their genomes but also insert pieces of 'foreign' deoxyribonucleic acid, which are commonly referred to as 'genomic islands' (GEIs). The GEIs come in several forms, structures and types, often providing a fitness advantage to the harboring bacterium. In pathogenic bacteria, some GEIs may enhance virulence, thus altering disease burden, morbidity and mortality. Hence, delineating (i) the GEIs framework, (ii) their encoded functions, (iii) the triggers that help them move, (iv) the mechanisms they exploit to move among bacteria and (v) identification of their natural reservoirs will aid in superior tackling of several bacterial diseases, including sepsis. Given the vast array of comparative genomics data, in this short review, we provide an overview of the GEIs, their types and the compositions therein, especially highlighting GEIs harbored by two important pathogens, viz. Acinetobacter baumannii and Klebsiella pneumoniae, which prominently trigger sepsis in low- and middle-income countries. Our efforts help shed some light on the challenges these pathogens pose when equipped with GEIs. We hope that this review will provoke intense research into understanding GEIs, the cues that drive their mobility across bacteria and the ways and means to prevent their transfer, especially across pathogenic bacteria.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5-2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

Comparative genomic analysis of two Arctic Pseudomonas strains reveals insights into the aerobic denitrification in cold environments

BACKGROUND

Biological denitrification has been commonly adopted for the removal of nitrogen from sewage effluents. However, due to the low temperature during winter, microorganisms in the wastewater biological treatment unit usually encounter problems such as slow cell growth and low enzymatic efficiency. Hence, the isolation and screening of cold-tolerant aerobic denitrifying bacteria (ADB) have recently drawn attention. In our previous study, two Pseudomonas strains PMCC200344 and PMCC200367 isolated from Arctic soil demonstrated strong denitrification ability at low temperatures. The two Arctic strains show potential for biological nitrogen removal from sewage in cold environments. However, the genome sequences of these two organisms have not been reported thus far.

RESULTS

Here, the basic characteristics and genetic diversity of strains PMCC200344 and PMCC200367 were described, together with the complete genomes and comparative genomic results. The genome of Pseudomonas sp. PMCC200344 was composed of a circular chromosome of 6,478,166 bp with a G + C content of 58.60% and contained a total of 5,853 genes. The genome of Pseudomonas sp. PMCC200367 was composed of a circular chromosome of 6,360,061 bp with a G + C content of 58.68% and contained 5,801 genes. Not only prophages but also genomic islands were identified in the two Pseudomonas strains. No plasmids were observed. All genes of a complete set of denitrification pathways as well as various putative cold adaptation and heavy metal resistance genes in the genomes were identified and analyzed. These genes were usually detected on genomic islands in bacterial genomes.

CONCLUSIONS

These analytical results provide insights into the genomic basis of microbial denitrification in cold environments, indicating the potential of Arctic Pseudomonas strains in nitrogen removal from sewage effluents at low temperatures.

Draft genome sequence and probiotic functional property analysis of Lactobacillus gasseri LM1065 for food industry applications

Probiotics are defined as live organisms in the host that contribute to health benefits. Lactobacillus gasseri LM1065, isolated from human breast milk, was investigated for its probiotic properties based on its genome. Draft genome map and de novo assembly were performed using the PacBio RS II system and hierarchical genome assembly process (HGAP). Probiotic properties were determined by the resistance to gastric conditions, adherence ability, enzyme production, safety assessment and mobile genetic elements. The fungistatic effect and inhibition of hyphae transition were studied using the cell-free supernatant (CFS). L. gasseri LM1065 showed high gastric pepsin tolerance and mild tolerance to bile salts. Auto-aggregation and hydrophobicity were measured to be 61.21% and 61.55%, respectively. The adherence to the human intestinal epithelial cells was measured to be 2.02%. Antibiotic-resistance genes and putative virulence genes were not predicted in the genomic analysis, and antibiotic susceptibility was satisfied by the criteria of the European Food Safety Authority. CFS showed a fungistatic effect and suppressed the tricarboxylic acid cycle in Candida albicans (29.02%). CFS also inhibited the transition to true hyphae and damaged the blastoconidia. This study demonstrates the essential properties of this novel probiotic, L. gasseri LM1065, and potential to inhibit vaginal C. albicans infection.

Distinctive microbial community and genome structure in coastal seawater from a human-made port and nearby offshore island in northern Taiwan facing the Northwestern Pacific Ocean

Pollution in human-made fishing ports caused by petroleum from boats, dead fish, toxic chemicals, and effluent poses a challenge to the organisms in seawater. To decipher the impact of pollution on the microbiome, we collected surface water from a fishing port and a nearby offshore island in northern Taiwan facing the Northwestern Pacific Ocean. By employing 16S rRNA gene amplicon sequencing and whole-genome shotgun sequencing, we discovered that Rhodobacteraceae, Vibrionaceae, and Oceanospirillaceae emerged as the dominant species in the fishing port, where we found many genes harboring the functions of antibiotic resistance (ansamycin, nitroimidazole, and aminocoumarin), metal tolerance (copper, chromium, iron and multimetal), virulence factors (chemotaxis, flagella, T3SS1), carbohydrate metabolism (biofilm formation and remodeling of bacterial cell walls), nitrogen metabolism (denitrification, N2 fixation, and ammonium assimilation), and ABC transporters (phosphate, lipopolysaccharide, and branched-chain amino acids). The dominant bacteria at the nearby offshore island (Alteromonadaceae, Cryomorphaceae, Flavobacteriaceae, Litoricolaceae, and Rhodobacteraceae) were partly similar to those in the South China Sea and the East China Sea. Furthermore, we inferred that the microbial community network of the cooccurrence of dominant bacteria on the offshore island was connected to dominant bacteria in the fishing port by mutual exclusion. By examining the assembled microbial genomes collected from the coastal seawater of the fishing port, we revealed four genomic islands containing large gene-containing sequences, including phage integrase, DNA invertase, restriction enzyme, DNA gyrase inhibitor, and antitoxin HigA-1. In this study, we provided clues for the possibility of genomic islands as the units of horizontal transfer and as the tools of microbes for facilitating adaptation in a human-made port environment.

Genomic Islands Identified in Highly Resistant Serratia sp. HRI: A Pathway to Discover New Disinfectant Resistance Elements

Molecular insights into the mechanisms of resistance to disinfectants are severely limited, together with the roles of various mobile genetic elements. Genomic islands are a well-characterised molecular resistance element in antibiotic resistance, but it is unknown whether genomic islands play a role in disinfectant resistance. Through whole-genome sequencing and the bioinformatic analysis of Serratia sp. HRI, an isolate with high disinfectant resistance capabilities, nine resistance islands were predicted and annotated within the genome. Resistance genes active against several antimicrobials were annotated in these islands, most of which are multidrug efflux pumps belonging to the MFS, ABC and DMT efflux families. Antibiotic resistance islands containing genes encoding for multidrug resistance proteins ErmB (macrolide and erythromycin resistance) and biclomycin were also found. A metal fitness island harbouring 13 resistance and response genes to copper, silver, lead, cadmium, zinc, and mercury was identified. In the search for disinfectant resistance islands, two genomic islands were identified to harbour smr genes, notorious for conferring disinfectant resistance. This suggests that genomic islands are capable of conferring disinfectant resistance, a phenomenon that has not yet been observed in the study of biocide resistance and tolerance.

Leveraging comparative genomics to uncover alien genes in bacterial genomes

A significant challenge in bacterial genomics is to catalogue genes acquired through the evolutionary process of horizontal gene transfer (HGT). Both comparative genomics and sequence composition-based methods have often been invoked to quantify horizontally acquired genes in bacterial genomes. Comparative genomics methods rely on completely sequenced genomes and therefore the confidence in their predictions increases as the databases become more enriched in completely sequenced genomes. Recent developments including in microbial genome sequencing call for reassessment of alien genes based on information-rich resources currently available. We revisited the comparative genomics approach and developed a new algorithm for alien gene detection. Our algorithm compared favourably with the existing comparative genomics-based methods and is capable of detecting both recent and ancient transfers. It can be used as a standalone tool or in concert with other complementary algorithms for comprehensively cataloguing alien genes in bacterial genomes.

Comparative genome analysis of Streptococcus strains to identify virulent genes causing neonatal meningitis

AIM

To determine Streptococcus agalactiae genes responsible for causing neonatal meningitis.

BACKGROUND

Streptococcus agalactiae strain 2603 V/R is causative agent of neonatal meningitis, maternal infection and sepsis in young children. World health organisation reported high burden of new born death caused by this bacterium. Streptococcus agalactiae colonizing epithelial cells of vagina and endothelial cells have high resistance to available antibiotic drugs which makes it essential to determine new drug targets.

OBJECTIVES

To compare the genome of selected strain with the non-pathogenic strains of streptococcus and identify the virulent and antibiotic resistant genes for adaptation in host environment.

METHOD

The whole genome of human pathogen Streptococcus agalactiae strain 2603 V/R was analysed and compared with Streptococcus dysgalactiae strains using visualization and annotation tools. Genomic islands, mobile genetic elements, virulent and resistant genes were studied.

RESULTS

Genetically pathogenic strain is most similar to Streptococcus dysgalactiae subsp. equisimilis strain NCTC 7136. Comparative analysis revealed the importance of capsular polysaccharides and surface proteins responsible for avoiding immune system attachment to host epithelial cells and virulent behaviour. High number of genes coding for antibiotics resistance may provide a competitive advantage for survival of pathogenic Streptococcus agalactiae strain 2603 V/R in its niche.

CONCLUSIONS

The comparative analysis of pathogenic strain Streptococcus agalactiae with non-pathogenic strains of Streptococcus dysgalactiae provided new insights in pathogenicity that could aid in recognization for new regions and genes for development of new drug development strategies considering presence of high number of resistance genes.

Comparative genomics of Trueperella pyogenes available in the genome database reveals multidrug resistance genomic islands

OBJECTIVE

An opportunistic pathogen, Trueperella pyogenes can infect cattle, buffalo, pig, goat, cat, dog, forest musk deer, etc., affecting various organs. The aim of this study was to identify the multidrug resistance genomic islands of T. pyogenes genomes available in NCBI database and also in the recently isolated strain TN_CUL_2020.

METHODS

The strain TN_CUL_2020 isolated from swine lung abscess was sequenced by Illumina platform, and all the available T. pyogenes genome in NCBI database was retrieved for the comparative analysis. The ABRicate searches was used to identify antimicrobial resistance genes, and genomic islands (GIs) were predicted using IslandViewer 4.

RESULTS

The strains SH01, SH02, and TP1 were predicted with maximum number of drug resistance genes. Genomic islands identified had multidrug resistance genes along with the class I integron and/or IS6100 elements in SH01, SH02, TP1. Composite transposons of IS6100 were noted in T2849, T4479, and TP3 intercalating tet(33) resistance genes. Several strains were predicted with phage elements, type IV secretion system, the toxin-antitoxin system in the GIs.

CONCLUSION

Swine strains SH01, SH02 were predicted with multidrug resistance genes along with class I integrons. The presence of class I integrons, insertional elements, type IV secretion system, toxin-antitoxin system, and phage elements may aid in the horizontal transfer of antimicrobial resistance genes.

Evolution of trimethoprim/sulfamethoxazole resistance in Shewanella algae from the perspective of comparative genomics and global phylogenic analysis

OBJECTIVE

Shewanella algae is a zoonotic marine bacterium that causes a variety of infections in immunocompromised patients or those who have been exposed to seawater. The development of trimethoprim/sulfamethoxazole (TMP/SMX) resistance in S. algae are found in human and environment isolates during the past ten years, and thus the treatment options are decreasing.

METHODOLOGY

In the study, we conduct a comparative genomic study to identify the resistant mechanism of TMP/SMX-resistance in S. algae.

RESULTS

We found the resistance of TMP/SMX in S. algae is associated with the existence of sul1 and dfrA12 within the class 1 integron. The gene cassette dfra12-aadA2-qacEΔ1/sul1 within the class 1 integron is highly conserved. In addition, the class 1 integron and encapsulated sul1 are significantly enriched in Enterobacteriaceae in NCBI and UniProt databases.

CONCLUSION

Our study suggests that the horizontal transfer of TMP/SMX resistance via class 1 integron is most frequently occurred within Enterobacteriaceae and has spread to a wide range of sources including soil, poultry, and marine water.

Reduced and Nonreduced Genomes in Paraburkholderia Symbionts of Social Amoebas

The social amoeba Dictyostelium discoideum is a predatory soil protist frequently used for studying host-pathogen interactions. A subset of D. discoideum strains isolated from soil persistently carry symbiotic Paraburkholderia, recently formally described as P. agricolaris, P. bonniea, and P. hayleyella. The three facultative symbiont species of D. discoideum present a unique opportunity to study a naturally occurring symbiosis in a laboratory model protist. There is a large difference in genome size between P. agricolaris (8.7 million base pairs [Mbp]) versus P. hayleyella and P. bonniea (4.1 Mbp). We took a comparative genomics approach and compared the three genomes of D. discoideum symbionts to 12 additional Paraburkholderia genomes to test for genome evolution patterns that frequently accompany host adaptation. Overall, P. agricolaris is difficult to distinguish from other Paraburkholderia based on its genome size and content, but the reduced genomes of P. bonniea and P. hayleyella display characteristics indicative of genome streamlining rather than deterioration during adaptation to their protist hosts. In addition, D. discoideum-symbiont genomes have increased secretion system and motility genes that may mediate interactions with their host. Specifically, adjacent BurBor-like type 3 and T6SS-5-like type 6 secretion system operons shared among all three D. discoideum-symbiont genomes may be important for host interaction. Horizontal transfer of these secretion system operons within the amoeba host environment may have contributed to the unique ability of these symbionts to establish and maintain a symbiotic relationship with D. discoideum. IMPORTANCE Protists are a diverse group of typically single cell eukaryotes. Bacteria and archaea that form long-term symbiotic relationships with protists may evolve in additional ways than those in relationships with multicellular eukaryotes such as plants, animals, or fungi. Social amoebas are a predatory soil protist sometimes found with symbiotic bacteria living inside their cells. They present a unique opportunity to explore a naturally occurring symbiosis in a protist frequently used for studying host-pathogen interactions. We show that one amoeba-symbiont species is similar to other related bacteria in genome size and content, while the two reduced-genome-symbiont species show characteristics of genome streamlining rather than deterioration during adaptation to their host. We also identify sets of genes present in all three amoeba-symbiont genomes that are potentially used for host-symbiont interactions. Because the amoeba symbionts are distantly related, the amoeba host environment may be where these genes were shared among symbionts.

Genome analysis and 2'-fucosyllactose utilization characteristics of a new Akkermansia muciniphila strain isolated from mice feces

Akkermansia muciniphila is considered to be a next-generation probiotic, and closely related to host metabolism and immune response. Compared with other probiotics, little is known about its genomic analysis. Therefore, further researches about isolating more A. muciniphila strains and exploring functional genes are needed. In the present study, a new strain isolated from mice feces was identified as A. muciniphila (Muc^X). Whole-genome sequencing and annotation revealed that Muc^X possesses key genes necessary for human milk oligosaccharides (HMO) utilization, including α-L-fucosidases, β-galactosidases, exo-α-sialidases, and β-acetylhexosaminidases. The complete metabolic pathways for γ-aminobutyric acid and squalene and genes encoding functional proteins, such as the outer membrane protein Amuc_1100, were annotated in the Muc^X genome. Comparative genome analysis was used to identify functional genes unique to Muc^X compared to six other A. muciniphila strains. Results showed Muc^X genome possesses unique genes, including sugar transporters and transferases. Single-strain incubation revealed faster utilization of 2'-fucosyllactose (2'-FL), galacto-oligosaccharides, and lactose by Muc^X than by A. muciniphila DSM 22959. This study isolated and identified an A. muciniphila strain that can utilize 2'-FL, and expolored the genes related to HMO utilization and special metabolites, which provided a theoretical basis for the further excavation of A. muciniphila function and the compound application with fucosylated oligosaccharides.

Comparative Genomic Analysis of Antarctic Pseudomonas Isolates with 2,4,6-Trinitrotoluene Transformation Capabilities Reveals Their Unique Features for Xenobiotics Degradation

The nitroaromatic explosive 2,4,6-trinitrotoluene (TNT) is a highly toxic and persistent environmental pollutant. Since physicochemical methods for remediation are poorly effective, the use of microorganisms has gained interest as an alternative to restore TNT-contaminated sites. We previously demonstrated the high TNT-transforming capability of three novel Pseudomonas spp. isolated from Deception Island, Antarctica, which exceeded that of the well-characterized TNT-degrading bacterium Pseudomonas putida KT2440. In this study, a comparative genomic analysis was performed to search for the metabolic functions encoded in the genomes of these isolates that might explain their TNT-transforming phenotype, and also to look for differences with 21 other selected pseudomonads, including xenobiotics-degrading species. Comparative analysis of xenobiotic degradation pathways revealed that our isolates have the highest abundance of key enzymes related to the degradation of fluorobenzoate, TNT, and bisphenol A. Further comparisons considering only TNT-transforming pseudomonads revealed the presence of unique genes in these isolates that would likely participate directly in TNT-transformation, and others involved in the β-ketoadipate pathway for aromatic compound degradation. Lastly, the phylogenomic analysis suggested that these Antarctic isolates likely represent novel species of the genus Pseudomonas, which emphasizes their relevance as potential agents for the bioremediation of TNT and other xenobiotics.

Insights into the Antimicrobial Resistance Profile of a Next Generation Probiotic Akkermansia muciniphila DSM 22959

Akkermansia muciniphila is a Gram-negative intestinal anaerobic bacterium recently proposed as a novel probiotic candidate to be incorporated in food and pharmaceutical forms. Despite its multiple health benefits, the data addressing its antimicrobial susceptibility profile remain scarce. However, the absence of acquired resistance in probiotic strains is a compulsory criterion for its approval in the qualified presumption of safety list. This study aimed at characterizing the A. muciniphila DSM 22959 strain’s antimicrobial susceptibility profile using phenotypic and in silico approaches. To establish the phenotypic antimicrobial susceptibility profile of this strain, minimum inhibitory concentrations of eight antimicrobials were determined using broth microdilution and E-test methods. Additionally, the A. muciniphila DSM 22959 genome was screened using available databases and bioinformatics tools to identify putative antimicrobial resistance genes (ARG), virulence factors (VF), genomic islands (GI), and mobile genetic elements (MGE). The same categorization was obtained for both phenotypic methods. Resistance phenotype was observed for gentamicin, kanamycin, streptomycin, and ciprofloxacin, which was supported by the genomic context. No evidence was found of horizontal acquisition or potential transferability of the identified ARG and VF. Thus, this study provides new insights regarding the phenotypic and genotypic antimicrobial susceptibility profiles of the probiotic candidate A. muciniphila DSM 22959.

Analysis of the Taxonomy, Synteny, and Virulence Factors for Soft Rot Pathogen Pectobacterium aroidearum in Amorphophallus konjac Using Comparative Genomics

Bacterial soft rot is a devastating disease for a wide range of crops, vegetables, and ornamental plants including konjac (Amorphophallus konjac). However, the pangenome and genomic plasticity of the konjac soft rot pathogens is little explored. In this study, we reported the complete genome sequences of 11 bacterial isolates that can cause typical soft rot symptoms in konjac by in vitro and in vivo pathogenicity tests. Based on in silico DNA-DNA hybridization, average nucleotide identity and phylogenomic analysis, all 11 isolates were determined to be Pectobacterium aroidearum. In addition, synteny analysis of these genomes revealed considerable chromosomal inversions, one of which is triggered by homologous recombination of ribose operon. Pangenome analysis and COG enrichment analysis showed that the pangenome of P. aroidearum is open and that accessory genes are enriched in replication, recombination, and repair. Variations in type IV secretion system and type VI secretion system were found, while plant cell wall degrading enzymes were conserved. Furthermore, sequence analyses also provided evidence for the presence of a type V secretion system in Pectobacterium. These findings advance our understanding of the pathogenicity determinants, genomic plasticity, and evolution of P. aroidearum.

CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction

N4-Acetylcytidine (ac4C) is a highly conserved post-transcriptional and an extensively existing RNA modification, playing versatile roles in the cellular processes. Due to the limitation of techniques and knowledge, large-scale identification of ac4C is still a challenging task. RNA sequences are like sentences containing semantics in the natural language. Inspired by the semantics of language, we proposed a hybrid model for ac4C prediction. The model used long short-term memory and convolution neural network to extract the semantic features hidden in the sequences. The semantic and the two traditional features (k-nucleotide frequencies and pseudo tri-tuple nucleotide composition) were combined to represent ac4C or non-ac4C sequences. The eXtreme Gradient Boosting was used as the learning algorithm. Five-fold cross-validation over the training set consisting of 1160 ac4C and 10,855 non-ac4C sequences obtained the area under the receiver operating characteristic curve (AUROC) of 0.9004, and the independent test over 469 ac4C and 4343 non-ac4C sequences reached an AUROC of 0.8825. The model obtained a sensitivity of 0.6474 in the five-fold cross-validation and 0.6290 in the independent test, outperforming two state-of-the-art methods. The performance of semantic features alone was better than those of k-nucleotide frequencies and pseudo tri-tuple nucleotide composition, implying that ac4C sequences are of semantics. The proposed hybrid model was implemented into a user-friendly web-server which is freely available to scientific communities: http://47.113.117.61/ac4c/ . The presented model and tool are beneficial to identify ac4C on large scale.

Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks

Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

Throughout the entirety of human history, bacterial pathogens have played an important role and even shaped the fate of civilizations. The application of genomics within the last 27 years has radically changed the way we understand the biology and evolution of these pathogens. In this review, we discuss how the short- (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing technologies have shaped the discipline of bacterial pathogen genomics, in terms of fundamental research (i.e., evolution of pathogenicity), forensics, food safety, and routine clinical microbiology. We have mined and discuss some of the most prominent data/bioinformatics resources such as NCBI pathogens, PATRIC, and Pathogenwatch. Based on this mining, we present some of the most popular sequencing technologies, hybrid approaches, assemblers, and annotation pipelines. A small number of bacterial pathogens are of very high importance, and we also present the wealth of the genomic data for these species (i.e., which ones they are, the number of antimicrobial resistance genes per genome, the number of virulence factors). Finally, we discuss how this discipline will probably be transformed in the near future, especially by transitioning into metagenome-assembled genomes (MAGs), thanks to long-read sequencing.

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.

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.

Horizontal gene transfer and adaptive evolution in bacteria

Horizontal gene transfer (HGT) is arguably the most conspicuous feature of bacterial evolution. Evidence for HGT is found in most bacterial genomes. Although HGT can considerably alter bacterial genomes, not all transfer events may be biologically significant and may instead represent the outcome of an incessant evolutionary process that only occasionally has a beneficial purpose. When adaptive transfers occur, HGT and positive selection may result in specific, detectable signatures in genomes, such as gene-specific sweeps or increased transfer rates for genes that are ecologically relevant. In this Review, we first discuss the various mechanisms whereby HGT occurs, how the genetic signatures shape patterns of genomic variation and the distinct bioinformatic algorithms developed to detect these patterns. We then discuss the evolutionary theory behind HGT and positive selection in bacteria, and discuss the approaches developed over the past decade to detect transferred DNA that may be involved in adaptation to new environments.

Revealing antimicrobial resistance profile of the novel probiotic candidate Faecalibacterium prausnitzii DSM 17677

Faecalibacterium prausnitzii, a resident anaerobic bacterium commonly found in healthy gut microbiota, has been proposed as a next generation probiotic with high potential for application in food matrices and pharmaceutical formulations. Despite its recognized health benefits, detailed information regarding its antimicrobial susceptibility profile is still lacking. However, this information is crucial to determine its safety, since the absence of acquired antimicrobial resistance is required to qualify a probiotic candidate as safe for human and animal consumption. Herein, the antimicrobial susceptibility profile of F. prausnitzii DSM 17677 strain was evaluated by integrating both phenotypic and in silico data. Phenotypic antimicrobial susceptibility was evaluated by determining minimum inhibitory concentrations of 9 antimicrobials using broth microdilution and E-test® methods. Also, the whole genome of F. prausnitzii DSM 17677 was analysed, using several databases and bioinformatics tools, to identify possible antibiotic resistance genes (ARG), genomic islands (GI) and mobile genetic elements (MGE). With exception of erythromycin, the same classification (susceptible or resistant) was obtained in both broth microdilution and E-test® methods. Phenotypic resistance to ampicillin, gentamycin, kanamycin and streptomycin were detected, which was supported by the genomic context. Other ARG were also identified but they seem not to be expressed under the tested conditions. F. prausnitzii DSM 17677 genome contains 24 annotated genes putatively involved in resistance against the following classes of antimicrobials: aminoglycosides (such as gentamycin, kanamycin and streptomycin), macrolides (such as erythromycin), tetracyclines and lincosamides. The presence of putative ARG conferring resistance to β-lactams could only be detected using a broader homology search. The majority of these genes are not encoded within GI or MGE and no plasmids were reported for this strain. Despite the fact that most genes are related with general resistance mechanisms, a streptomycin-specific ARG poses the only potential concern identified. This specific ARG is encoded within a GI and a MGE, meaning that it could have been laterally acquired and might be transferred to other bacteria present in the same environment. Thus, our findings provide relevant insights regarding the phenotypic and genotypic antimicrobial resistance profiles of the probiotic candidate F. prausnitzii DSM 17677.

Pan-Genome-Wide Analysis of Pantoea ananatis Identified Genes Linked to Pathogenicity in Onion

Pantoea ananatis, a gram negative and facultative anaerobic bacterium is a member of a Pantoea spp. complex that causes center rot of onion, which significantly affects onion yield and quality. This pathogen does not have typical virulence factors like type II or type III secretion systems but appears to require a biosynthetic gene-cluster, HiVir/PASVIL (located chromosomally comprised of 14 genes), for a phosphonate secondary metabolite, and the 'alt' gene cluster (located in plasmid and comprised of 11 genes) that aids in bacterial colonization in onion bulbs by imparting tolerance to thiosulfinates. We conducted a deep pan-genome-wide association study (pan-GWAS) to predict additional genes associated with pathogenicity in P. ananatis using a panel of diverse strains (n = 81). We utilized a red-onion scale necrosis assay as an indicator of pathogenicity. Based on this assay, we differentiated pathogenic (n = 51)- vs. non-pathogenic (n = 30)-strains phenotypically. Pan-genome analysis revealed a large core genome of 3,153 genes and a flexible accessory genome. Pan-GWAS using the presence and absence variants (PAVs) predicted 42 genes, including 14 from the previously identified HiVir/PASVIL cluster associated with pathogenicity, and 28 novel genes that were not previously associated with pathogenicity in onion. Of the 28 novel genes identified, eight have annotated functions of site-specific tyrosine kinase, N-acetylmuramoyl-L-alanine amidase, conjugal transfer, and HTH-type transcriptional regulator. The remaining 20 genes are currently hypothetical. Further, a core-genome SNPs-based phylogeny and horizontal gene transfer (HGT) studies were also conducted to assess the extent of lateral gene transfer among diverse P. ananatis strains. Phylogenetic analysis based on PAVs and whole genome multi locus sequence typing (wgMLST) rather than core-genome SNPs distinguished red-scale necrosis inducing (pathogenic) strains from non-scale necrosis inducing (non-pathogenic) strains of P. ananatis. A total of 1182 HGT events including the HiVir/PASVIL and alt cluster genes were identified. These events could be regarded as a major contributing factor to the diversification, niche-adaptation and potential acquisition of pathogenicity/virulence genes in P. ananatis.

Examining horizontal gene transfer in microbial communities

Bacteria acquire novel DNA through horizontal gene transfer (HGT), a process that enables an organism to rapidly adapt to changing environmental conditions, provides a competitive edge and potentially alters its relationship with its host. Although the HGT process is routinely exploited in laboratories, there is a surprising disconnect between what we know from laboratory experiments and what we know from natural environments, such as the human gut microbiome. Owing to a suite of newly available computational algorithms and experimental approaches, we have a broader understanding of the genes that are being transferred and are starting to understand the ecology of HGT in natural microbial communities. This Review focuses on these new technologies, the questions they can address and their limitations. As these methods are applied more broadly, we are beginning to recognize the full extent of HGT possible within a microbiome and the punctuated dynamics of HGT, specifically in response to external stimuli. Furthermore, we are better characterizing the complex selective pressures on mobile genetic elements and the mechanisms by which they interact with the bacterial host genome.

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/.

geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe geneshot, a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data. By applying geneshot to two independent previously published cohorts, we identify microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth.

Genome analysis provides insights into the biocontrol ability of Mitsuaria sp. strain TWR114

Mitsuaria sp. TWR114 is a biocontrol agent against tomato bacterial wilt (TBW). We aimed to gain genomic insights relevant to the biocontrol mechanisms and colonization ability of this strain. The draft genome size was found to be 5,632,523 bp, with a GC content of 69.5%, assembled into 1144 scaffolds. Genome annotation predicted a total of 4675 protein coding sequences (CDSs), 914 pseudogenes, 49 transfer RNAs, 3 noncoding RNAs, and 2 ribosomal RNAs. Genome analysis identified multiple CDSs associated with various pathways for the metabolism and transport of amino acids and carbohydrates, motility and chemotactic capacities, protection against stresses (oxidative, antibiotic, and phage), production of secondary metabolites, peptidases, quorum-quenching enzymes, and indole-3-acetic acid, as well as protein secretion systems and their related appendages. The genome resource will extend our understanding of the genomic features related to TWR114's biocontrol and colonization abilities and facilitate its development as a new biopesticide against TBW.

Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis

BACKGROUND

Mycobacterium leprae and Mycobacterium lepromatosis are gram-positive bacterial pathogens and the causative agents of leprosy in humans across the world. The elimination of leprosy cannot be achieved by multidrug therapy alone, and highlights the need for new tools and drugs to prevent the emergence of new resistant strains.

METHODS

In this study, our contribution includes the prediction of vaccine targets and new putative drugs against leprosy, using reverse vaccinology and subtractive genomics. Six strains of Mycobacterium leprae and Mycobacterium lepromatosis (4 and 2 strains, respectively) were used for comparison taking Mycobacterium leprae strain TN as the reference genome. Briefly, we used a combined reverse vaccinology and subtractive genomics approach.

RESULTS

As a result, we identified 12 common putative antigenic proteins as vaccine targets and three common drug targets against Mycobacterium leprae and Mycobacterium lepromatosis. Furthermore, the docking analysis using 28 natural compounds with three drug targets was done.

CONCLUSIONS

The bis-naphthoquinone compound Diospyrin (CID 308140) obtained from indigenous plant Diospyros spp. showed the most favored binding affinity against predicted drug targets, which can be a candidate therapeutic target in the future against leprosy.

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.

Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

Background

Genomic islands (GIs) play an important role in the chromosome diversity of Enterococcus. In the current study, we aimed to investigate the spread of GIs between Enterococcus strains and their correlation with antibiotic resistance genes (ARGs). Bitsliced Genomic Signature Indexes (BIGSI) were used to screen the NCBI Sequence Read Archive (SRA) for multiple resistant Enterococcus. A total of 37 pairs of raw reads were screened from 457,000 whole-genome sequences (WGS) in the SRA database, which come from 37 Enterococci distributed in eight countries. These raw reads were assembled for the prediction and analysis of GIs, ARGs, plasmids and prophages.

Results

The results showed that GIs were universal in Enterococcus, with an average of 3.2 GIs in each strain. Network analysis showed that frequent genetic information exchanges mediated by GIs occurred between Enterococcus strains. Seven antibiotic-resistant genomic islands (ARGIs) were found to carry one to three ARGs, mdtG, tetM, dfrG, lnuG, and fexA, in six strains. These ARGIs were involved in the spread of antibiotic resistance in 45.9% of the 37 strains, although there was no significant positive correlation between the frequency of GI exchanges and the number of ARGs each strain harboured (r = 0. 287, p = 0.085). After comprehensively analysing the genome data, we found that partial GIs were associated with multiple mobile genetic elements (transposons, integrons, prophages and plasmids) and had potential natural transformation characteristics.

Conclusions

All of these results based on genomic sequencing suggest that GIs might mediate the acquisition of some ARGs and might be involved in the high genome plasticity of Enterococcus through transformation, transduction and conjugation, thus providing a fitness advantage for Enterococcus hosts under complex environmental factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02114-4.

The environment drives microbial trait variability in aquatic habitats

A prerequisite to improve the predictability of microbial community dynamics is to understand the mechanisms of microbial assembly. To study factors that contribute to microbial community assembly, we examined the temporal dynamics of genes in five aquatic metagenome time-series, originating from marine offshore or coastal sites and one lake. With this trait-based approach we expected to find gene-specific patterns of temporal allele variability that depended on the seasonal metacommunity size of carrier-taxa and the variability of the milieu and the substrates to which the resulting proteins were exposed. In more detail, we hypothesized that a larger seasonal metacommunity size would result in increased temporal variability of functional units (i.e., gene alleles), as shown previously for taxonomic units. We further hypothesized that multicopy genes would feature higher temporal variability than single-copy genes, as gene multiplication can result from high variability in substrate quality and quantity. Finally, we hypothesized that direct exposure of proteins to the extracellular environment would result in increased temporal variability of the respective gene compared to intracellular proteins that are less exposed to environmental fluctuations. The first two hypotheses were confirmed in all data sets, while significant effects of the subcellular location of gene products was only seen in three of the five time-series. The gene with the highest allele variability throughout all data sets was an iron transporter, also representing a target for phage infection. Previous work has emphasized the role of phage-prokaryote interactions as a major driver of microbial diversity. Our finding therefore points to a potentially important role of iron transporter-mediated phage infections for the assembly and maintenance of diversity in aquatic prokaryotes.

Genome Complexity Browser: Visualization and quantification of genome variability

Comparative genomics studies may be used to acquire new knowledge regarding genome architecture, which defines the rules for combining sets of genes in the genome of living organisms. Hundreds of thousands of prokaryotic genomes have been sequenced and assembled. However, computational tools capable of simultaneously comparing large numbers of genomes are lacking. We developed the Genome Complexity Browser, a tool that allows the visualization of gene contexts, in a graph-based format, and the quantification of variability for different segments of a genome. The graph-based visualization allows the inspection of changes in gene contents and neighborhoods across hundreds of genomes, simultaneously, which may facilitate the identification of conserved and variable segments of operons or the estimation of the overall variability associated with a particular genome locus. We introduced a measure called complexity, to quantify genome variability. Intraspecies and interspecies comparisons revealed that regions with high complexity values tended to be located in areas that are conserved across different strains and species.

The comparison of genomes among different bacteria and archaea species has revealed that many species frequently exchange genes. Occasionally, such horizontal gene transfer events result in the acquisition of pathogenic properties or antibiotic resistance in the recipient organism. Previously, the probabilities of gene insertions were found to vary, with unequal distributions along a chromosome. At some loci, referred to as hotspots, changes occur with much higher frequencies compared with other regions of the chromosome. We developed a computational method and a software tool, called Genome Complexity Browser, that allows the identification of genome variability hotspots and the visualization of changes. We compared the localization of various hotspots and revealed that some demonstrate conserved localizations, even across species, whereas others are transient. Our tool allows users to visually inspect the patterns of gene changes in graph-based format, which presents the visualization in a format that is both compact and informative.

Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands

Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized. Given the association of antimicrobial resistance (AMR) genes and virulence factor (VF) genes with MGEs, studying their transmission is a public-health priority. The variable copy number and sequence composition of MGEs makes them potentially problematic for MAG binning methods. To systematically investigate this issue, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated. While 82-94 % of chromosomes could be correctly recovered and binned, only 38-44 % of GIs and 1-29 % of plasmid sequences were found. Strikingly, no plasmid-borne VF nor AMR genes were recovered, and only 0-45 % of AMR or VF genes within GIs. We conclude that short-read MAG approaches, without further optimization, are largely ineffective for the analysis of mobile genes, including those of public-health importance, such as AMR and VF genes. We propose that researchers should explore developing methods that optimize for this issue and consider also using unassembled short reads and/or long-read approaches to more fully characterize metagenomic data.

Is Long-Term Heavy Metal Exposure Driving Carriage of Antibiotic Resistance in Environmental Opportunistic Pathogens: A Comprehensive Phenomic and Genomic Assessment Using Serratia sp. SRS-8-S-2018

The carriage of both, heavy metal and antibiotic resistance appears to be a common trait in bacterial communities native to long-term contaminated habitats, including the Savannah River Site (SRS). There is widespread soil contamination at the SRS; a United States Department of Energy (DOE) facility with long-term contamination from past industrial and nuclear weapons production activities. To further evaluate the genomic and metabolic traits that underpin metal and antibiotic resistance, a robust mercury (Hg) and uranium (U)-resistant strain- SRS-8-S-2018, was isolated. Minimum inhibitory concentration of this strain revealed resistance to Hg (10 μg/ml) and U (5 mM), the two main heavy metal contaminants at the SRS. Metabolic assessment of strain SRS-8-S-2018 using Biolog metabolic fingerprinting analysis revealed preference for carbohydrate utilization followed by polymers, amino acids, carboxy acids, and esters; this physiological activity diminished when Hg stress was provided at 1 and 3 μg/ml and completely ceased at 5 μg/ml Hg, indicating that continued release of Hg will have negative metabolic impacts to even those microorganisms that possess high resistance ability. Development of antibiotic resistance in strain SRS-8-S-2018 was evaluated at a functional level using phenomics, which confirmed broad resistance against 70.8% of the 48 antibiotics tested. Evolutionary and adaptive traits of strain SRS-8-S-2018 were further assessed using genomics, which revealed the strain to taxonomically affiliate with Serratia marcescens species, possessing a genome size of 5,323,630 bp, 5,261 proteins (CDS), 55 genes for transfer RNA (tRNA), and an average G + C content of 59.48. Comparative genomics with closest taxonomic relatives revealed 360 distinct genes in SRS-8-S-2018, with multiple functions related to both, antibiotic and heavy metal resistance, which likely facilitates the strain’s survival in a metalliferous soil habitat. Comparisons drawn between the environmentally isolated Serratia SRS-8-S-2018 with 31 other strains revealed a closer functional association with medically relevant isolates suggesting that propensity of environmental Serratia isolates in acquiring virulence traits, as a function of long-term exposure to heavy metals, which is facilitating development, recruitment and proliferation of not only metal resistant genes (MRGs) but antibiotic resistant genes (ARGs), which can potentially trigger future bacterial pathogen outbreaks emanating from contaminated environmental habitats.

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.

Experimental approaches to tracking mobile genetic elements in microbial communities

Horizontal gene transfer is an important mechanism of microbial evolution and is often driven by the movement of mobile genetic elements between cells. Due to the fact that microbes live within communities, various mechanisms of horizontal gene transfer and types of mobile elements can co-occur. However, the ways in which horizontal gene transfer impacts and is impacted by communities containing diverse mobile elements has been challenging to address. Thus, the field would benefit from incorporating community-level information and novel approaches alongside existing methods. Emerging technologies for tracking mobile elements and assigning them to host organisms provide promise for understanding the web of potential DNA transfers in diverse microbial communities more comprehensively. Compared to existing experimental approaches, chromosome conformation capture and methylome analyses have the potential to simultaneously study various types of mobile elements and their associated hosts. We also briefly discuss how fermented food microbiomes, given their experimental tractability and moderate species complexity, make ideal models to which to apply the techniques discussed herein and how they can be used to address outstanding questions in the field of horizontal gene transfer in microbial communities.

Strains of Bradyrhizobium cosmicum sp. nov., isolated from contrasting habitats in Japan and Canada possess photosynthesis gene clusters with the hallmark of genomic islands

The taxonomic status of two previously characterized Bradyrhizobium strains (58S1T and S23321) isolated from contrasting habitats in Canada and Japan was verified by genomic and phenotypic analyses. Phylogenetic analyses of five and 27 concatenated protein-encoding core gene sequences placed both strains in a highly supported lineage distinct from named species in the genus Bradyrhizobium with Bradyrhizobium betae as the closest relative. Average nucleotide identity values of genome sequences between the test and reference strains were between 84.5 and 94.2 %, which is below the threshold value for bacterial species circumscription. The complete genomes of strains 58S1T and S23321 consist of single chromosomes of 7.30 and 7.23 Mbp, respectively, and do not have symbiosis islands. The genomes of both strains have a G+C content of 64.3 mol%. Present in the genome of these strains is a photosynthesis gene cluster (PGC) containing key photosynthesis genes. A tRNA gene and its partial tandem duplication were found at the boundaries of the PGC region in both strains, which is likely the hallmark of genomic island insertion. Key nitrogen-fixation genes were detected in the genomes of both strains, but nodulation and type III secretion system genes were not found. Sequence analysis of the nitrogen fixation gene, nifH, placed 58S1T and S23321 in a novel lineage distinct from described Bradyrhizobium species. Data for phenotypic tests, including growth characteristics and carbon source utilization, supported the sequence-based analyses. Based on the data presented here, a novel species with the name Bradyrhizobium cosmicum sp. nov. is proposed with 58S1T (=LMG 31545T=HAMBI 3725T) as the type strain.

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.

Potential evolutionary impact of integrative and conjugative elements (ICEs) and genomic islands in the Ralstonia solanacearum species complex

Ralstonia solanacearum, a soil-borne plant pathogen, encompasses a large number of strains known as R. solanacearum species complex (RSSC). Although it has been suggested that mobile genetic elements (MGEs) may play an important role in the RSSC genome, the evolutionary impact of these elements remains unknown. Here, we identified and analysed Integrative and Conjugative Elements (ICEs) and Genomic Islands (GIs) in the 121 genomes published for Ralstonia spp., including RSSC strains and three other non-plant pathogenic Ralstonia spp. Our results provided a dataset of 12 ICEs and 31 GIs distributed throughout Ralstonia spp. Four novel ICEs in RSSC were found. Some of these elements cover 5% of the host genome and carry accessory genes with a potential impact on the fitness and pathogenicity of RSSC. In addition, phylogenetic analysis revealed that these MGEs clustered to the same species, but there is evidence of strains from different countries that host the same element. Our results provide novel insight into the RSSC adaptation, opening new paths to a better understanding of how these elements affect this soil-borne plant pathogen.

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.

Salmonella Pathogenicity Island 1 (SPI-1): The Evolution and Stabilization of a Core Genomic Type Three Secretion System

Salmonella Pathogenicity Island 1 (SPI-1) encodes a type three secretion system (T3SS), effector proteins, and associated transcription factors that together enable invasion of epithelial cells in animal intestines. The horizontal acquisition of SPI-1 by the common ancestor of all Salmonella is considered a prime example of how gene islands potentiate the emergence of new pathogens with expanded niche ranges. However, the evolutionary history of SPI-1 has attracted little attention. Here, we apply phylogenetic comparisons across the family Enterobacteriaceae to examine the history of SPI-1, improving the resolution of its boundaries and unique architecture by identifying its composite gene modules. SPI-1 is located between the core genes fhlA and mutS, a hotspot for the gain and loss of horizontally acquired genes. Despite the plasticity of this locus, SPI-1 demonstrates stable residency of many tens of millions of years in a host genome, unlike short-lived homologous T3SS and effector islands including Escherichia ETT2, Yersinia YSA, Pantoea PSI-2, Sodalis SSR2, and Chromobacterium CPI-1. SPI-1 employs a unique series of regulatory switches, starting with the dedicated transcription factors HilC and HilD, and flowing through the central SPI-1 regulator HilA. HilA is shared with other T3SS, but HilC and HilD may have their evolutionary origins in Salmonella. The hilA, hilC, and hilD gene promoters are the most AT-rich DNA in SPI-1, placing them under tight control by the transcriptional repressor H-NS. In all Salmonella lineages, these three promoters resist amelioration towards the genomic average, ensuring strong repression by H-NS. Hence, early development of a robust and well-integrated regulatory network may explain the evolutionary stability of SPI-1 compared to T3SS gene islands in other species.

Pathogenomes of Atypical Non-shigatoxigenic Escherichia coli NSF/SF O157:H7/NM: Comprehensive Phylogenomic Analysis Using Closed Genomes

The toxigenic conversion of Escherichia coli strains by Shiga toxin-converting (Stx) bacteriophages were prominent and recurring events in the stepwise evolution of enterohemorrhagic E. coli (EHEC) O157:H7 from an enteropathogenic (EPEC) O55:H7 ancestor. Atypical, attenuated isolates have been described for both non-sorbitol fermenting (NSF) O157:H7 and SF O157:NM serotypes, which are distinguished by the absence of Stx, the characteristic virulence hallmark of Stx-producing E. coli (STEC). Such atypical isolates either never acquired Stx-phages or may have secondarily lost stx during the course of infection, isolation, or routine subculture; the latter are commonly referred to as LST (Lost Shiga Toxin)-isolates. In this study we analyzed the genomes of 15 NSF O157:H7 and SF O157:NM strains from North America, Europe, and Asia that are characterized by the absence of stx, the virulence hallmark of STEC. The individual genomic basis of the Stx (-) phenotype has remained largely undetermined as the majority of STEC genomes in public genome repositories were generated using short read technology and are in draft stage, posing a major obstacle for the high-resolution whole genome sequence typing (WGST). The application of LRT (long-read technology) sequencing provided us with closed genomes, which proved critical to put the atypical non-shigatoxigenic NSF O157:H7 and SF O157:NM strains into the phylogenomic context of the stepwise evolutionary model. Availability of closed chromosomes for representative Stx (-) NSF O157:H7 and SF O157:NM strains allowed to describe the genomic basis and individual evolutionary trajectories underlying the absence of Stx at high accuracy and resolution. The ability of LRT to recover and accurately assemble plasmids revealed a strong correlation between the strains' featured plasmid genotype and chromosomally inferred clade, which suggests the coevolution of the chromosome and accessory plasmids. The identified ancestral traits in the pSFO157 plasmid of NSF O157:H7 strain LSU-61 provided additional evidence for its intermediate status. Taken together, these observations highlight the utility of LRTs for advancing our understanding of EHEC O157:H7/NM pathogenome evolution. Insights into the genomic and phenotypic plasticity of STEC on a lineage- and genome-wide scale are foundational to improve and inform risk assessment, biosurveillance, and prevention strategies.

Typing methods based on whole genome sequencing data

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Genomic Islands in the Full-Genome Sequence of an NAD-Hemin-Independent Avibacterium paragallinarum Strain Isolated from Peru

Here, we report the full-genome sequence of an NAD-hemin-independent Avibacterium paragallinarum serovar C-2 strain, FARPER-174, isolated from layer hens in Peru. This genome contained 12 potential genomic islands that include ribosomal protein-coding genes, a nadR gene, hemocin-coding genes, sequences of fagos, an rtx operon, and drug resistance genes.