Marina Estella De León,Harriet S. Wilson,Guillaume Jospin,Jonathan A. Eisen
Genome sequencing and multifaceted taxonomic analysis of novel strains of violacein-producing bacteria and non-violacein-producing close relatives
applied microbiology,bacterial species concept,classification and taxonomy,phylogenomics,Proteobacteria,microbial diversity,violacein,whole genome sequencing,Janthinobacterium
Violacein is a water-insoluble violet pigment produced by various Gram-negative bacteria. The compound and the bacteria that produce it have been gaining attention due to the antimicrobial and proposed antitumour properties of violacein and the possibility that strains producing it may have broad industrial uses. Bacteria that produce violacein have been isolated from diverse environments including fresh and ocean waters, glaciers, tropical soils, trees, fish and the skin of amphibians. We report here the isolation and characterization of six violacein-producing bacterial strains and three non-violacein-producing close relatives, each isolated from either an aquatic environment or moist food materials in northern California, USA. For each isolate, we characterized traditional phenotypes, generated and analysed draft genome sequences, and carried out multiple types of taxonomic, phylogenetic and phylogenomic analyses. Based on these analyses we assign putative identifications to the nine isolates, which include representatives of the genera Chromobacterium , Aquitalea , Iodobacter , Duganella , Massilia and Janthinobacterium . In addition, we discuss the utility of various metrics for taxonomic assignment in these groups including average nucleotide identity, whole genome phylogenetic analysis and extent of recent homologous recombination using the software program PopCOGenT.
Genome sequencing and multifaceted taxonomic analysis of novel strains of violacein-producing bacteria and non-violacein-producing close relatives

Violacein is a water-insoluble violet pigment produced by various Gram-negative bacteria. The compound and the bacteria that produce it have been gaining attention due to the antimicrobial and proposed antitumour properties of violacein and the possibility that strains producing it may have broad industrial uses. Bacteria that produce violacein have been isolated from diverse environments including fresh and ocean waters, glaciers, tropical soils, trees, fish and the skin of amphibians. We report here the isolation and characterization of six violacein-producing bacterial strains and three non-violacein-producing close relatives, each isolated from either an aquatic environment or moist food materials in northern California, USA. For each isolate, we characterized traditional phenotypes, generated and analysed draft genome sequences, and carried out multiple types of taxonomic, phylogenetic and phylogenomic analyses. Based on these analyses we assign putative identifications to the nine isolates, which include representatives of the genera Chromobacterium , Aquitalea , Iodobacter , Duganella , Massilia and Janthinobacterium . In addition, we discuss the utility of various metrics for taxonomic assignment in these groups including average nucleotide identity, whole genome phylogenetic analysis and extent of recent homologous recombination using the software program PopCOGenT.

Raw reads and genome assemblies for strains HSC-31F16, HSC-77S12, HSC-21Su07, HSC-16F04, HSC-15S17, HSC-2F05, HSC-65S10 and HSC-3S05 are available through NCBI under BioProject accession number PRJNA603836 and those for strain UCD_MED1 are available under BioProject accession number PRJNA546016. Sequence Read Archive (SRA) accession numbers are as follows: HSC-31F16=SRX11406228, HSC-77S12=SRX11406255, HSC-21Su07=SRX12453551, HSC-16F04=SRX11406350, HSC-15S17=SRX11355670, HSC-2F05=SRX11489442, HSC-65S10=SRX11406257, HSC-3S05=SRX11489443 and UCD_MED1=SRR17805880. These Whole Genome Shotgun projects have been deposited at DDBJ/ENA/GenBank under the following accessions (versions described in this paper in parentheses): HSC-31F16, JAAOMA000000000 (JAAOMA010000000); HSC-77S12, JAAOLZ000000000 (JAAOLZ010000000); HSC-21Su07, JACERN000000000 (JACERN010000000); HSC-16F04, JAAOLX000000000 (JAAOLX010000000); HSC-15S17, JAHTGR000000000 (JAHTGR010000000); HSC-2F05, JAHYBX000000000 (JAHYBX0100000000; HSC-65S10, JAAOLY000000000 (JAAOLY010000000); HSC-3S05, JAHYBY000000000 (JAHYBY010000000); UCD_MED1, VDGE00000000 (VDGE01000000). Commands and scripts are available at https://github.com/JonathanEisensLab/9Genomes_paper. The complete ANI output file as well as all supplementary files are also available from the github data repository. Table S1 and Figs S1 and S2 are available with the online version of this article.

Bacterial pigments are a subject of investigation that can have a positive impact on economies, the environment and human health. Bacterial pigments are becoming important pharmaceutical and industrial chemicals because the production process of synthetic dyes used for textiles and foods requires hazardous chemicals generating hazardous wastes and dangerous working conditions for employees. Due to their promising applications, the discovery of new bacteria and new bacterial pigments is at the forefront of scientific research and economics. The violet pigment violacein is gaining attention from the scientific community because of its various antibiotic activities, which can be used for human and animal health, and its brilliant colour which can be used for industrial purposes. Pigmented bacteria can be isolated from natural environments, especially freshwater and marine habitats, and should be studied in greater detail because out of all the secondary metabolites having antibiotic activity, pigments are an understudied group. Nine new strains of bacteria were isolated from the environment, including a purple Janthinobacterium sp. strain which was observed growing on tofu. Of the nine strains characterized, six showed purple growth and genomic analysis revealed that at least one copy of all of the five genes (vioABCDEF) needed to produce violacein were present in each of these genomes. The remaining three strains were closely related to violacein producers, but they do not contain violacein genes nor did they exhibit pigmented colonies. The taxonomy and evolution of bacterial strains that are capable of expressing violacein are still not well understood even though the biosynthesis of the compound has been clearly demonstrated. There is currently no standardized method of taxonomic classification of bacteria to the species level, making correct species identification difficult. Classical methods include metabolic phenotyping, and 16S rRNA gene sequence analysis. Current classification methods include whole genome sequence analysis using average nucleotide identity, and phylogenomic methods based on established gene marker sets. We used classical methods and the currently accepted genomic methods with additional genomic population ecology approaches to maximize confidence in taxonomic placement of the nine novel strains and to accurately assign strains to existing species and describe five novel species of bacteria. This approach contributed to a better understanding of evolutionary relationships between pigmented bacteria and how pigment genes such as for violacein are transferred and retained between and within populations.

The violet pigment violacein is a secondary metabolite produced by bacteria from at least 11 known genera including Chromobacterium, Janthinobacterium, Iodobacter, Duganella, Collimonas, Pseudoalteromonas, Massilia, Pseudoduganella, Archangium, Microbulbifer and Chitinimonas [1–7]. Such violacein-producing bacteria have been isolated from environments including fresh and ocean waters, soil, ice, and the surfaces of plants, fish and amphibians [3]. Violacein is known to have multiple antimicrobial properties, including antifungal effects [1, 8–10]. Violacein is not essential to cell growth; however, its production probably gives the bacteria some competitive advantages due to its antibacterial, antifungal and antiprotozoal properties [2, 11]. In an effort to better understand the diversity and evolution of violacein-producing bacteria, we sequenced the genomes of nine bacterial isolates. Eight isolates were selected from a culture collection created and maintained at Sierra College; these were chosen because they either produced violet-coloured colonies (five isolates) or were inferred, based on 16S rRNA gene sequence analysis, to be closely related to known violacein-producing strains (three isolates). Additional criteria for selection of these specific isolates included culture viability and access to metadata about the strains. In addition to these eight isolates from the Sierra College collection, one violet-coloured strain isolated from refrigerated tofu (purchased fresh from a farmers market, not packaged) was also selected for whole genome sequencing and analysis. In addition to genome sequencing, observation of cell and colony morphology, plus metabolic testing was used to assist in characterizing the nine isolates. Proper bacterial species designation can be critical for many purposes including public health, clinical, food safety and biosafety applications because recognizing and mapping out evolutionary relationships can lead to a better understanding of the metabolic potential and survivability of organisms in a given niche [12]. Analysis of the genome data of the nine new isolates revealed that some of the strains could be relatively easily placed into a taxonomic and phylogenetic context with regard to other known bacteria. However, for some of the isolates, taxonomic and phylogenetic placement was not straightforward. In particular, it was challenging to make lower level assignments for these isolates – and especially challenging to make any type of clear species identification. We discuss various approaches to taxonomic determination for these new isolates including using BLASTn analysis of 16S rRNA genes, average nucleotide identity (ANI), whole genome phylogenies and measures of horizontal versus vertical gene flow (using the PopCOGenT program). Based on a combination of these diverse approaches, we propose novel species designations with high confidence for five of the isolates: Chromobacterium perflumen strain HSC-31F16, Aquitalea aquatica strain HSC-21Su07, Iodobacter violacea strain HSC-16F04, Massilia hydrophila strain HSC-2F05 and Duganella violaceicalia strain HSC-15S17. We tentatively classify the three new Janthinobacterium lividum strains HSC-65S10, HSC-3S05 and UCD_MED1 because this group presents problems in both taxonomy and annotations that require continued investigation, and we discuss the use of a mixed method approach for more accurate taxonomic classification within this group.

Sierra College students and staff collected and isolated bacteria from various local environments over many years. A subset of these were then characterized in more detail and chosen for genome sequencing and analysis as a part of this study (the selection process is described in the Results and discussion section). General information about the selected isolates is presented in Table 1. The general growth conditions used for obtaining these isolates is summarized here. Bacterial growth conditions were similar to those previously used to isolate violacein-producing bacteria [13]. Specifically, environmental samples were inoculated onto media containing tryptophan such as tryptic soy agar (TSA), nutrient agar (NA) and/or Reasoner’s 2A agar (R2A agar). One exception was strain HSC-15S17 that was initially isolated from mannitol-rich, nitrogen-free agar and subcultured on yeast extract malt (YEM) agar. Some water samples were collected in new Ziploc bags and transferred to TSA or NA plates using sterile pipettes (50–100 µl of water per plate). Other samples were collected with sterile cotton swabs that were used to inoculate plate media directly. Violet-coloured growth was taken from cooked rice using a sterile wire loop and inoculated onto NA plates. Violet-coloured growth was taken from tofu using a sterile loop and inoculated onto R2A plates. Cultures were incubated at room temperature resembling outdoor temperatures (18–20 °C). After incubation at room temperature for 2–3 days, colonies were subcultured for isolation. The subculturing of selected colonies on new agar plates was repeated several times until the strains were purified. All strains were routinely maintained, grown on TSA, NA, R2A or YEM agar media, and preserved as glycerol stocks with a final concentration of 20–25 % at −80 °C. The bacteria maintained during this investigation were frequently sub-cultured either from cryo-vials or isolated colonies and plated on TSA, NA or YEM media. Selective and differential plate media (5 % sheep blood agar, MacConkey’s agar and Tergitol-7 agar) were used for determining specific characteristics, while Mueller–Hinton agar (MHA) was used to conduct antimicrobial susceptibility testing as described below. All cultures were allowed to grow at room temperature until isolated colonies became visible (typically 48–72 h). Refrigeration or incubation at 37 °C was utilized under some circumstances. Plate cultures were examined visually to determine purity before samples were taken for sub-culturing or enzymatic testing. Colony form, margin, elevation, surface texture, optical character, pigmentation and size (mm) were recorded and variations developing over time were noted. Cell samples were aseptically transferred from isolated colonies on agar plates to new media (broth, agar deeps, agar slants or agar plates) using sterile wire loops. Specific media used for metabolic testing are described below (Bacterial phenotyping and metabolic testing). Tryptic soy broth (TSB) and yeast extract malt broth were used to establish broth cultures for antimicrobial susceptibility testing. Lawn cultures on the appropriate plate media (MHA or YEM) were prepared from these using sterile cotton swabs.

Chromosomal DNA for PCR amplification was extracted from fresh plate cultures by mixing visible cell masses with 500 µl Tris buffer (1 mM, pH 8.5) and boiling for 10 min to lyse the cells, denature enzymes present and liberate the template DNA. DNA samples were then used for PCR amplification of 16S rRNA genes as follows: 5 µl aliquots of template DNA were added directly to PCR mixtures containing 25 µl Taq Master Mix (Qiagen), 5 µl primer mix (10 µM Bacteria 8-forward 5′-AGAGTTTGATCMTGGCTCAG-3′ and Enteric 1530-reverse 5′-AGGAGGTGATCCARCCGCA-3′) and 15 µl sterile water. The PCR specifications were: cycle 1, 94 °C for 4 min; cycles 2–36, 55 °C for 45 s, 72 °C for 2 min and 94 °C for 30 s; cycle 37, 55 °C for 45 s followed by 20 min at 72 °C and then 0 °C until tubes were removed from the device and placed in a freezer. Following PCR amplification and gel electrophoresis, the PCR products were purified using Qiagen DNA purification kits. Sanger sequencing was performed on a 3730 Capillary Electrophoresis Genetic Analyzer and ABI BigDye Terminator v3.1 chemistry at the UC Davis DNA Sequencing Facility. Sequencing primers included the PCR primers listed above as well as Internal 533-forward (5′-CCAGCACGCCGCGGTAA-3′) and 907-reverse (5′-CCGTCAATTCMTTTRAGTTT-3′). Raw sequence data (.ab1 files) were opened with 4Peaks (RRID:SCR_000015) and traces were observed to determine quality and length (typically 800–900 nt). Reads generated with reverse primers were ‘flipped’ to obtain reverse complementary sequences and overlapping regions were compared visually. Nucleotide sequences were copied to text files for additional comparison and editing. The four sequences were concatenated and overlapping sections deleted. These 16S rRNA gene sequences were compared with reference RNA sequences (refseq_rna) available in the NCBI database using BLASTn to tentatively assign each isolate to a genus [14].

DNA for genome sequencing was obtained by transferring single colonies from plate cultures into 5 ml aliquots of TSB and incubating for 24 h on an orbital shaker (220 r.p.m.) at room temperature. DNA was extracted from these cultures using a Qiagen DNeasy Blood and Tissue kit following the manufacturer’s instructions. The resulting DNA samples were quantified using a Qubit 4 Fluorometer. Due to the highly viscous biofilm produced by strain HSC-15S17, this strain was submitted to Microbes NG for DNA extraction and sequencing as a subculture on a YEM agar plate. The genomes of all the HSC strains were sequenced by Microbes NG (http://www.microbesng.uk) using Illumina sequencing (HiSeq 2500 or NovaSeq 6000) with 2×250 bp paired-end reads. These genomes were put through a standard analysis pipeline by Microbes NG. Microbes NG identifies the closest available reference genome using Kraken and maps the reads to this using BWA mem to assess the quality of the data. Microbes NG performed a de novo assembly of the reads using SPAdes v. 3.7 (December 2019) and mapped the reads back to the resultant contigs using BWA mem again to obtain additional quality metrics. UCD_MED1 was sequenced at the UC Davis DNA Technologies Core Facility using Illumina Hiseq 4000 technology with a library preparation of PE150. Reads were trimmed for quality using BBDuk v. 37.02, and assembly was performed using the A5-miseq v. May-2019 assembly pipeline [15], which includes a universal Illumina adapter trimming step. CheckM was used to assess assembly quality and contamination. All genomes were eventually annotated by the Prokaryotic Genome Annotation Pipeline (PGAP) when submitted to NCBI [16–18].

For phylogenetic analysis we created a data set containing the following: the nine new genomes introduced here, all Gammaproteobacteria genomes from the NCBI genome assembly database that were listed as being in the genera Chromobacterium (n=85), Janthinobacterium (n=85), Duganella (n=77), Massilia (n=71), Aquitalea (n=15) and Iodobacter (n=6). This set includes all the genera that the nine new genomes were tentatively assigned to based on 16S rRNA gene sequencing, and one genome to serve as an outgroup. The genome chosen to serve as an outgroup was that of Archangium violaceum (formerly Cystobacter violaceus ) strain Cbvi76, in the order Myxococcales , which was selected because this bacterium is a distantly related member of the Deltaproteobacteria that is also known to produce violacein (and thus we were examining its genome for features related to violacein production). For this set of genomes, gene marker alignments were generated using GTDB-tk 1.3.0 [19]. Details of the GTDB marker set are available for download at https://data.ace.uq.edu.au/public/gtdb/data/releases/latest/. Then, a maximum likelihood tree was inferred using RAxML 8.2.11 [20], with the following settings: 250 bootstraps, PROTGAMMABLOSUM62 substitution matrix done on amino acids, random seeds for parsimony inference, and rapid bootstrap set to 8 and 47, respectively. Phylogenetic trees were viewed using Dendroscope version 3.5.9 [21].

The whole-genome similarity metric ANI was calculated for all genomes using Fast ANI v1.32 [22]. Fast ANI estimated the ANI between each new genome represented here and all genomes in the NCBI database that share the same genus by using alignment-free approximate sequence mapping. ANI was used with caution as only one part of the entire process of taxonomic classification because ANI measure does not strictly represent core genome evolutionary relatedness, as orthologous genes can vary widely between pairs of genomes compared [23]. A heat map of the ANI results was generated using the R packages gplots v3.1.1 [24] and Ape v5.4.1 [25] to incorporate the phylogenetic tree, and dplyr v1.0.5 [26] for data preparation and manipulation.

To help delineate populations based on gene flow, an analysis using PopCOGenT was performed on a select subset of clades within the genus Janthinobacterium (https://github.com/philarevalo/PopCOGenT) [27]. For the PopCOGenT analysis, 68 Janthinobacterium genomes were chosen based on their close phylogenetic similarity to the type strain. After creating a phylogenetic tree, all of the genomes within the two major clades encompassing the three Janthinobacterium genomes presented in this study were included in the analysis. Only the phylogenetically most distantly related Janthinobacterium clades were excluded. The subpopulation structure of genomes was investigated based on horizontal gene flow. The program PopCOGenT was used to compare the length of identical DNA sequences in genome pairs. The length bias parameter was calculated for each pair of genomes and a genome network was created to estimate horizontal gene flow between the nodes. The network was then clustered with Infomap to identify subpopulations with higher rates of horizontal gene flow. Specifically, in PopCOGent (using the default window length of 1000 bp) the magnitude of the sum of squared differences (SSD) is measured to estimate genome homogenization. In addition to SSD, the ‘initial divergence’ is calculated, which is a measure of the total diversity seen in each genome comparison. The log of the SSD was plotted against the log of the initial divergence in ggplot2 (R version 4.2.1 [28] 3+492 ‘Prairie Trillium’) to view distinct clusters of genomes with high and low rates of recombination [24, 28]. Log10 values were used to address skewness towards large values because compared to the large dataset very few points were much larger than the bulk of the data. We then ran a cluster module using Infomap [29] within PopCOGent to observe network formations between clonal pairs or unknown groups. The cluster output was viewed and manipulated using the network visualization software Gephi, with the parameters: layout=ForceAtlas2 [30].

Bacterial morphology includes both cellular (shape, endospore, flagella, inclusion bodies, Gram staining) and colonial (colour, dimensions, form) characteristics. The physiological and biochemical features include data on growth at different temperatures, pH values, salt concentrations or atmospheric conditions, growth in the presence of various substances such as antimicrobial agents, and data on the presence or activity of various enzymes, metabolization of compounds, etc. [31]. All media were initially maintained at room temperature, and data were collected and results determined according to published protocols [32]. Cell morphology was observed by using light microscopy with cells grown in sulfide indole motility medium (SIM) for 24–72 h at 18–24 °C. Observation of violet pigment production was used to infer violacein biosynthesis. Gram staining and KOH testing were performed as previously described [32, 33]. Motility was observed in wet mounts (cells in deionized water) magnified 450× and by stab inoculation in SIM medium (where growth away from the inoculation line indicates motility). Metabolic testing for all isolates was conducted in triplicate using media and reagents prepared for a general microbiology teaching laboratory and as indicated by previously published species descriptions. Oxidase reactions were performed on filter paper moistened with a solution of N,N,N′,N′-tetramethyl-p-phenylenediamine and catalase activity was demonstrated using 3 % hydrogen peroxide on glass slides. Mode of metabolism was determined using the oxidation/fermentation (O/F) test. Citrate utilization, urea hydrolysis and aesculin hydrolysis were determined on agar slants (Simmon’s citrate agar, Christensen’s urea agar and aesculin agar respectively). Gelatin hydrolysis was determined in nutrient gelatin deeps, starch hydrolysis was determined on starch agar plates with the addition of Gram’s iodine, and methyl-red-Voges–Proskauer (MR-VP) tests were conducted in tubes of MR-VP broth media followed by transfer of 1 ml aliquots of 48–72 h growth to screw-top tubes, application of Barrit’s reagents and vortex mixing to test for acetoin. Testing for acid production in MR-VP broth involved addition of six drops of methyl red indicator to cultures minus sample removed for the VP test. The formation of hydrogen sulphide was determined through observation of cultures stabbed into SIM medium, and indole formation was determined in SIM medium with the addition of Kovac’s reagent. The reduction of nitrate to nitrite or to nitrogenous gasses was determined in nitrate agar deeps using nitrate reagent A (sulphanilic acid), reagent B (alpha-naphthylamine) and zinc powder (when required). The formation of acid aerobically from carbohydrates (arabinose, glucose, inositol, lactose, maltose, mannitol, raffinose, rhamnose, sorbitol, sucrose and xylose) was determined on the surface of agar deeps containing a bromothymol blue agar base with peptone [32]. Haemolysis reactions and growth on selective and differential media were determined on agar plates (5 % sheep blood, MacConkey’s, Tergitol-7 and nitrogen-free agar). The presence/absence of genes associated with selected metabolic processes was investigated using the Protein Family Sorter tool of PATRIC-BRC (now BV-BRC) [34]. Antimicrobial susceptibility was determined using the disc diffusion method (Kirby–Bauer test) conducted on MHA plates inoculated with lawn cultures. Because strain HSC-15S17 would not grow on MHA, antimicrobial susceptibility testing for this strain was conducted on YEM. Ampicillin resistance was also determined on TSA plates containing ampicillin. Antimicrobial discs containing ampicillin (AM-10), bacitracin (B-10), kanamycin (K-30), penicillin G (P10), polymyxin (PB-300), rifampin (RA-5), streptomycin (S-10), tetracycline (TE-30) and vancomycin (VA-30) were applied with a disc dispenser (BD) or with sterile forceps. Following metabolic testing as described above, single or sole carbon source assimilation was investigated using BiOLOG EcoPlates [35]. Inocula were prepared for each strain by suspending one loopful of cell material (3 mm ball) from a 48 h plate culture in 15 ml of sterile water within a screw-top tube. The cellular material was thoroughly suspended by alternately shaking, vortex-mixing and allowing the tubes to stand over a period of 10–15 min. Each EcoPlate was warmed to room temperature, inoculated under aseptic conditions (100 µl of cell suspension per well), closed, sealed inside a clean Ziploc bag and stored at room temperature. Carbon source assimilation was determined by directly observing each EcoPlate placed on the surface of a portable LED lightbox (plate lid and plastic bag removed) at daily intervals over a period of 4 weeks.

Eight strains out of many opportunistically collected environmental isolates were selected. These isolates were considered candidates for potential violacein production. Five were selected because they formed violet- or deep violet-coloured colonies. Three isolates that did not form violet- coloured colonies were selected because 16S rRNA gene sequence analysis indicated they were closely related to known violacein producers (see rRNA analysis details in the next section). In addition, one strain isolated at UC Davis that produced violet-coloured colonies was also included in this study. Table 1 provides additional details about the selected isolates. The isolates chosen came from a diversity of freshwater environments and prepared foods (Table 1).

The cultures grown on plate media all formed colonies that were initially well isolated (Fig. 1); for some strains colony expansion and fusion occurred over time. HSC-31F16 formed circular, entire, raised, smooth-shiny, semi-opaque, pale pinkish-cream colonies, 1–3 mm in diameter after 48 h on TSA. Colony surfaces became wrinkled with age. This strain also grew well on MAC and T-7 media but colonies were smaller and more translucent. HSC-77S12 formed circular, entire, low-convexity, smooth-shiny, opaque, deep violet colonies, 1–3 mm in diameter after 48 h on TSA. It also grew well on MAC and T-7 media, but colonies were somewhat smaller. HSC-21Su07 formed circular, entire, low-convexity, smooth-shiny, semi-opaque, milky-tan colonies 1–3 mm in diameter after 48 h on TSA. It also grew well on MAC and T-7 media but colonies were more translucent and smaller. HSC-16F04 formed circular, entire, flat, smooth-shiny opaque to semi-translucent, violet colonies 2–4 mm in diameter after 48 h onNA (evidence of swarming appeared over time). It also grew well on TSA, MAC and T-7 media. Colonies formed on TSA were darker violet, more opaque and tended to swarm less than those grown on NA. HSC-15S17 formed circular, entire, high-convexity, smooth-shiny, semi-opaque, milky-beige colonies that became violet with age and were 1–3 mm in diameter after 48 h on YEM. Colonies became rubbery and difficult to sample over time. This strain grew poorly on NA and not at all on MAC, T-7, MHA or TSA. HSC-2F05 formed circular, entire, low-convexity, smooth-shiny, semi-opaque, milky-beige colonies 1–3 mm in diameter after 48 h on TSA. It also grew well on MAC and T-7 media but colonies were more translucent and smaller. HSC-65S10 formed circular to irregular, entire to undulate, low-convexity, wrinkled-shiny, opaque, deep violet colonies 1–3 mm in diameter after 48 h on NA. It also grew well on MAC and T-7 media but new growth was cream-coloured when sub-cultured repeatedly on TSA. HSC-3S05 formed circular, entire, convex, wrinkled-shiny, opaque, deep violet colonies 1–3 mm in diameter after 48 h on NA. It also grew well on MAC and T-7 media but new growth was cream-coloured when sub-cultured repeatedly on TSA. UCD_MED1 formed circular to irregular, entire to undulate, low-convexty, wrinkled-shiny, opaque, deep violet colonies 1–3 mm in diameter after 48 h on NA. This strain also grew well on MAC and T-7 media but new growth became cream-coloured when sub-cultured repeatedly on TSA. Each bacterial strain is cryopreserved at Sierra College and University of California, Davis. The strains are available from the corresponding author upon request. Strain HSC-15S17 is also cryopreserved in the ATCC US National Park Service Special Collection as TSD-229 and with the Culture Collection of Switzerland (CCOS). The other eight strains are available through the Leibniz Institute DSMZ and through the North Regional Research Laboratory (NRRL). Strains HSC-31F16, HSC-21Su07, HSC-16F04, HSC-15S17 and HSC-2F05 represent novel species for which the names Chromobacterium perflumen (type strain HSC-31F16=DSM 114492=NRRL B-65644), Aquitalea aquatica (type strain HSC-21Su07=DSM 114499=NRRL B-65646), Iodobacter violacea (type strain HSC-16F04=DSM 114489=NRRL B-65647), Duganella violaceicalia (type strain HSC-15S17=ATCC TSD-229 = CCOS 2056) and Massilia hydrophila (type strain HSC-2F05=DSM 114498=NRRL B-65648) are proposed. The strains of Chromobacterium piscinae (HSC-77S12=DSM 114490=NRRL B-65645), Janthinobacterium lividum (HSC-65S10=DSM 114488=NRRL B-65650), Janthinobacterium lividum (HSC-3S05=DSM 114487=NRRL B-65649) and Janthinobacterium lividum (UCD_MED1=DSM 114491=NRRL B-65651) represent previously described taxa.

A summary of metabolic capabilities including antimicrobial susceptibility and single carbon-source assimilation as determined through the application of BiOLOG EcoPlates is presented in Table 2.

Cells were motile, Gram-negative (KOH-positive), non-sporing, fermentative bacilli, 0.7–1×2–5 µm in size. This strain was positive for catalase, oxidase, citrate utilization, aesculin and gelatin hydrolysis, reduction of nitrate to nitrite (but not to nitrogenous gases) plus beta-haemolysis on 5 % sheep blood. It was negative for violacein formation, urea and starch hydrolysis, indole and hydrogen sulphide formation (SIM), formation of acid and acetoin in MR-VP medium and growth on nitrogen-free media. Acid was formed aerobically from inositol but none of the other carbohydrates provided; acid was formed through the fermentation of glucose (O/F).

Cells were motile, Gram-negative (KOH-positive), non-sporing, fermentative bacilli, 1–1.2×2–3 µm in size. This strain was positive for violacein formation, catalase, oxidase, citrate utilization, gelatin hydrolysis, the ability to reduce nitrate to nitrite (but not to nitrogenous gasses) and beta-haemolysis on 5 % sheep blood. It was negative for urea, starch and aesculin hydrolysis, indole and hydrogen sulphide formation (SIM), formation of acid and acetoin in MR-VP medium and growth on nitrogen-free media. Acid was not formed aerobically from any of the carbohydrates utilized, but acid was formed through the fermentation of glucose and maltose (O/F).

Cells were motile, Gram-negative (KOH-positive), non-sporing, fermentative bacilli, 0.5–1.0×1.5–2.5 µm in size. This strain was positive for catalase, oxidase, citrate utilization, urea hydrolysis, the reduction of nitrate to nitrite (but not to nitrogenous gasses) and haemolysis on 5 % sheep blood. It was negative for violacein formation, aesculin, starch and gelatin hydrolysis, indole and hydrogen sulphide formation (SIM), the formation of acid and acetoin in MR-VP medium and growth on nitrogen-free media. Acid was formed aerobically from inositol but none of the other carbohydrates utilized; acid was formed through the fermentation of glucose in O/F medium.

Cells were motile, Gram-negative (KOH-positive), non-sporing, fermentative bacilli, 0.7–1.0×3–5 µm in size. This strain was positive for violacein formation, catalase, gelatin hydrolysis, reduction of nitrate to nitrite (but not to nitrogenous gasses) and beta-haemolysis on 5 % sheep blood. It was negative for oxidase, citrate utilization, urea, starch and aesculin hydrolysis, indole and hydrogen sulphide formation (SIM), the formation of acid and acetoin in MR-VP medium and growth on nitrogen-free media. Acid was formed aerobically from maltose but none of the other carbohydrates utilized; acid was formed through the fermentation of glucose in O/F medium.

Cells were motile, Gram-negative (KOH-positive), non-sporing, oxidative coccobacilli, 1.0–1.5×1.5–2 µm in size. This strain was positive for violacein formation, catalase, oxidase, gelatin liquefaction, starch hydrolysis, the reduction of nitrate to nitrite (but not to nitrogenous gases) and growth on nitrogen-free media. It was negative for citrate utilization, urea and aesculin hydrolysis, indole and hydrogen sulphide production (SIM) and the formation of acid and acetoin from glucose fermentation (O/F and MR-VP). It was not haemolytic on sheep blood agar and acid was not formed aerobically from any of the carbohydrates utilized.

Cells were motile, Gram-negative (KOH-positive), non-sporing, oxidative bacilli, typically 0.7–1.0×2–5 µm in size (some reached 20 µm in length). This strain was positive for catalase, oxidase, gelatin liquefaction, starch hydrolysis, the reduction of nitrate to nitrite (but not to nitrogenous gasses) and haemolysis on sheep blood agar. It was negative for violacein formation, citrate utilization, urea and aesculin hydrolysis, indole and hydrogen sulphide production (SIM), the formation of acid and acetoin from glucose fermentation (O/F and MR-VP) and growth on nitrogen-free media. Acid was not formed aerobically from any of the carbohydrates utilized.

Cells were motile, Gram-negative (KOH-positive), non-sporing, oxidative bacilli, typically 0.5–1.0×3–5 µm in size (some reached 20 µm in length). This strain was positive for violacein formation, catalase, oxidase, citrate utilization, aesculin and gelatin hydrolysis, the ability to reduce nitrate to nitrite (but not to nitrogenous gases), beta-haemolysis on 5 % sheep blood and growth on nitrogen-free media. It was negative for urea and starch hydrolysis, indole and hydrogen sulphide formation (SIM), and the formation of acid and acetoin in MR-VP medium. Acid was formed aerobically from arabinose, glucose, inositol, maltose, mannitol, rhamnose, sorbitol, sucrose and xylose in O/F-type media, but not from lactose or raffinose.

Cells were motile, Gram-negative (KOH-positive), non-sporing, oxidative bacilli, typically 0.5–1.0×3–5 µm in size (some reached 15–20 µm in length). This strain was positive for violacein formation, catalase, oxidase, citrate utilization, aesculin, gelatin and urea hydrolysis, the ability to reduce nitrate and nitrite, beta-haemolysis on 5 % sheep blood and growth on nitrogen-free media. It was negative for starch hydrolysis, indole and hydrogen sulphide formation (SIM) and the formation of acid and acetoin in MR-VP medium. Acid was formed aerobically from arabinose, glucose, inositol, maltose, mannitol, rhamnose, sorbitol, sucrose and xylose in O/F-type media, but not from lactose or raffinose.

Cells were motile, Gram-negative (KOH-positive), non-sporing, oxidative bacilli, typically 0.5–1.0×3–5 µm in size (some reached 20 µm in length). This strain was positive for violacein formation, catalase, oxidase, citrate utilization, aesculin, gelatin and urea hydrolysis, the ability to reduce nitrate and nitrite, beta-haemolysis on 5 % sheep blood and growth on nitrogen-free media. It was negative for starch hydrolysis, indole and hydrogen sulphide formation (SIM), and the formation of acid and acetoin in MR-VP medium. Acid was formed aerobically from arabinose, glucose, inositol, maltose, mannitol, rhamnose, sorbitol, sucrose and xylose in O/F-type media, but not from lactose or raffinose.

Nearly complete 16S rRNA gene sequences were obtained for strains HSC-31F16 (1527 bp), HSC-77S12 (1527 bp), HSC-21Su07 (1527 bp), HSC-16F04 (1524 bp), HSC-15S17 (1520 bp), HSC-2F05 (1526 bp), HSC-65S10 (1522 bp), HSC-3S05 (1522 bp) and UCD_MED1 (1522 bp). The 16S gene in bacteria is generally about 1550 bp. These sequences were then compared using BLASTn searches to 16S rRNA gene sequences in the NCBI RefSeq database (Table 3). The results from 16S rRNA gene BLASTn comparisons showed that, though they did not produce violet-coloured colonies, strains HSC-31F16, HSC-21Su07 and HSC-2F05 were closely related to known violacein producers and thus merited further analysis. The results also showed that the six isolates that produced violet colonies (HSC-77S12, HSC-16F04, HSC-15S17, HSC-65S10, HSC-3S05 and UCD_MED1) were in groups recognized as violacein producers. In addition, rRNA gene sequence analysis was used to give each isolate a tentative genus assignment. The 16S rRNA gene sequences were submitted to the NCBI GenBank and assigned the following accession numbers: strain HSC-31F16=MW242637, strain HSC-77S12=ON012846, strain HSC-21Su07=ON013926, strain HSC-16F04=ON013928, strain HSC-15S17=MT579559, strain HSC-2F05=ON013927, strain HSC-65S10=ON012847, strain HSC-3S05=ON013781 and strain UCD_MED1=ON012958.

All isolates were sequenced by Microbes NG except UCD_MED1 which was sequenced by UC Davis (see Methods for details). Results from the whole genome shotgun sequencing and genome assembly for each isolate are shown in Table 4.

We created a phylogenetic tree that shows the placement of each of the nine new genomes within the phylogenetic context of a total of 349 genome assemblies. The tree consists of all bacterial strains of the same genera as the new strains presented here which are available within the NCBI database, plus one outgroup. This phylogenetic assemblage included genomes from 87 Chromobacterium strains, 16 Aquitalea strains, seven Iodobacter strains, 78 Duganella strains, 72 Massilia strains, 88 Janthinobactertium strains (incorporating three strains labelled as Janthinobacterium but are of inconclusive genus identity) and one Archangium strain. Here we show relevant subsets of each major clade a new strain is grouped within (Fig. 2a–g). Taxonomic annotation (incorrect/misidentified taxonomic annotations are placed in parentheses), strain ID and the GenBank accession number for each genome assembly are included in the branch labels. Bootstrap values are indicated on the branch nodes, and the scale bar (upper left corner) defines the scale of branch lengths. In this case, a line segment with the number ‘0.01’ or '0.001' shows the length of a branch that represents an amount of genetic change of 0.01 (1.0 %) or 0.001 (0.1 %). Type strains which are descendants of the original isolates used in species and subspecies descriptions, as defined by the Bacteriological Code, and exhibit all of the relevant phenotypic and genotypic properties, and are cited in the original published taxonomic circumscriptions are indicated as [TYPE] in each phylogenetic tree. The entire phylogenetic tree containing all publicly available genomes from the six genera in NCBI plus the outgroup used to root the tree, Archangium violaceum (349 taxa in total), can be found in Fig. S1. Implications of the patterns seen in the whole genome phylogeny are discussed in the Discussion section below.

ANI values were determined for the nine draft genome sequences by comparison with all available genome assemblies annotated as bacteria classified within the same genera. Table 5 shows the strains with the highest ANI values against the strains in this study as well as the type strains with the highest ANI value. The full ANI dataset is available in Table S2.

A heatmap of the ANI values, with strains ordered by their position in the whole genome phylogeny, was generated (Figs 3a–f, and S2). This ANI heatmap dendrogram shows the association between ANI and each pairwise comparison of genomes in the dendrogram. Average nucleotide identity percentage (ANI%) for genome assemblies within the six genera representing the strains introduced in this study were determined, where colouring is used to represent increments of ANI percentage. Maroon/brown=95 % and above, indicating the standard species-level differentiation. Black=99–99.9 %, dark brown=98–98.9 %, light brown=97–97.9 %, dark orange=96–96.9 %, dark pink=95–95.9 %, orange=94–94.9 %, light orange=93–93.9 %, yellow=92–92.9 %, light blue=91–91.9 %, periwinkle=90–90.9 % and white=<90 %. Horizontal black lines indicate the position of a new strain introduced here.

For the strains that had been assigned to the genera Aquitalea , Chromobacterium , Iodobacter , Duganella , and Massilia , ANI analysis and whole genome phylogenetics was considered sufficient for making taxonomic assignments (see Discussion). However, for the strains assigned to the genus Janthinobacterium , ANI analysis and whole genome phylogenetics was inconclusive due to ambiguous patterns of ANI% versus clusters in the tree. We therefore applied an additional type of analysis, using the program PopCOGenT to augment characterization of the Janthinobacterium group. PopCOGenT measures recent gene flow and recombination among genomes and thus serves as a ‘reverse ecology’ approach that can aid in the identification of genetic boundaries between isolates that are not discernible from standard ANI and phylogenomic methods [27]. PopCOGenT is designed to assess population structure under the assumption that organisms within the same population, compared to organisms that are genetically isolated from each other, should have higher rates of recombination and genomes with higher percentages of regions that have undergone recent recombination. It assesses the rates and extent of recombination by measuring levels of divergence between genomes as well as the lengths of identical stretches of DNA. Recent recombination between lineages should lead to an overabundance in number and length of identical regions between two genomes from those lineages (because there will not have been as much time to accumulate substitutions as compared to non-recombining regions). The method estimates recombination by comparing the lengths of identical regions between genome pairs versus an expectation under a Poisson model of just clonal mutation. This parameter is referred to as ‘length bias’ and is reported as ‘Observed SSD’ with higher values indicating more recombination. Generally, length bias is positively (although not linearly) correlated with the amount of gene flow between each pairwise genome comparison [27]. In addition, PopCOGenT measures the ‘initial divergence’, which is a measure of the total diversity seen in each genome comparison. One way of assessing the results of PopCOGenT analysis is to examine the relationship between initial divergence and SSD, which are shown for the Janthinobacterium genomes in Fig. 4. Another way of assessing PopCOGenT analysis is to infer networks of recent gene flow, which are shown in Fig. 5. The implications of the PopCOGenT analysis are addressed in the Discussion. For all pairs of Janthinobacterium genomes, the log10 transformation of initial divergence (a measure of the total similarity of the genomes) is plotted versus the log of the SSD score (a measure of the length bias towards longer stretches of identical DNA, which is used as a measure of the extent of recombination). The pairwise comparisons represented by the points in the upper left quadrant of the plot are genomes that are very similar to each other and in some cases may be separate sequences of the same strain. They thus have low levels of initial divergence and very high levels of inferred SSD. The pairwise comparisons represented by the points in the lower right of the plot represent the genome pairs that are significantly more distantly related (note the large jump in log initial divergence) and these generally have lower SSD scores.

A multifaceted approach, including a combination of chemotaxonomic, phenotypic and genotypic data, was used to determine the taxonomic and phylogenetic positions of the nine isolates introduced here. Initial 16S rRNA gene sequence analysis allowed strains to be assigned a putative genus-level taxonomic classification. Species-level classification was then confirmed or designated using a combination of whole genome phylogenetic placement and ANI analysis. ANI values of approximately 95 % are proposed by some to represent an accurate threshold for demarcating a species boundary for some bacteria [36, 37]. However, measures of similarity, even when taken across a whole genome, can be misleading regarding relatedness due to factors such as selection, mutation bias, unequal rates of evolution and gene loss; therefore, ANI should be used with caution as a measure of possible species boundaries. It is thus critically important to supplement ANI measures with actual phylogenetic analysis, such as that based on whole genome sequences. We used the GTDB-tk-based system here to provide such a phylogenomic analysis. In addition, morphological features, and the results of metabolic tests and other phenotypic characteristics have been used extensively to characterize bacteria, and although these alone cannot be used for classification or phylogenetic placement they do provide information about new strains and so were included. For most of the taxa found in this study, 16S rRNA gene sequence comparisons, ANI values and whole genome phylogenetic analyses provided a reasonable basis for inference of taxonomy and phylogenetic position. For the taxa determined to be in the genus Janthinobacterium , these methods alone were not fully sufficient, and we used PopCOGenT to provide additional information about evolutionary relationships as indicated by rate of gene flow. We discuss the isolates and their species designations in greater detail below. In addition, we also discuss possible needs for taxonomic reassignment of some of the genomes currently in NCBI.

Results indicate that Chromobacterium strain HSC-31F16 and the reference strain H4137_1 represent a novel species. Although strain HSC-31F16 initially appeared most similar to C. aquaticum strain CC-SEYA-1 [38] based on 16S rRNA gene sequences, it presented seven metabolic differences. It was catalase-positive, susceptible to rifampin, unable to assimilate galacturonic acid, glucosaminic acid, itaconic acid and phenylethylamine as single carbon sources, but able to assimilate γ-hydroxybutyric acid; these are all in opposition to the responses shown by C. aquaticum . Although genome data for C. aquaticum strain CC-SEYA-1 were not available for comparison, it is unlikely that strain HSC-31F16 is a representative of the same species. The whole genome-based phylogenetic tree Figs 2a and S1) indicated strain HSC-31F16 might represent a C. haemolyticum strain; however, this designation was not strongly supported by ANI values. The genome from strain HSC-31F16 shared only 93.92 % ANI with the C. haemolyticum type strain MDA0585=DSM 19808 [36]. It also shared only 94.03 % ANI with a complete genome from C. haemolyticum strain Bb2. Because the genome from strain HSC-31F16 is located on a long branch with only one other genome, that from C. haemolyticum strain H4137_1, and because it shares 97.46 % ANI with that genome, both strains probably represent a species other than C. haemolyticum . Based on results from all methods, we propose that strain HSC-31F16 be designated as representing a novel species. Specifically, we propose that this strain and strain H4137_1 be named Chromobacterium perflumen (per.flu’.men per L. prep. through or by means of, flumen L. n. river, perflumen through the river); HSC-31F16 was isolated from water collected along a major river in northern California. Strain HSC-77S12 should be taxonomically classified as the species Chromobacterium piscinae . This assignment is based on the whole genome phylogeny, which shows that it groups into a clade with those from other C. piscinae strains, including the type strain C. piscinae DSM 23278 (Figs 2b and S1). In addition, this genome shares >95 % ANI with those of other members of the species, including 98.82 % ANI with the type strain DSM 23278. We also suggest that the Chromobacterium ‘vaccinii’ strains A7 (GCF_019733135.1), A8 (GCF_019733095.1) and A15 (GCF_019732845.1) should be renamed as C. piscinae due to the 98.85, 98.72 and 98.82 % ANI similarity scores, respectively, of their genomes with that of HSC-77S12 and the phylogenetic grouping of these genomes within the C. piscinae clade containing the C. piscinae type strain. In addition, C. ‘piscinae’ strain ND17 (GCF_000812585.1) should probably be renamed as C. amazonense because the genome of this strain is in a clade with that of the C. amazonense type strain DSM 26508 (GCF_001855565.1). The metabolic characteristics of HSC-77S12 were generally consistent with the description provided for Chromobacterium piscinae LMG 3947 [39], with a few exceptions. Strain HSC-77S12 was positive for catalase and citrate utilization, while C. piscinae was described as negative for these traits. In addition, C. piscinae was described as being aerobic while strain HSC-77S12 was facultative (able to ferment glucose). Chromobacterium piscinae was also described as being negative for putrescine and l-phenylalanine assimilation while HSC-77S12 demonstrated weak assimilation of putrescine and positive assimilation of l-phenylalanine.

Results suggest that Aquitalea strain HSC-21Su07 and the Aquitalea strains MWU13-2470, MWU14-2217 and ASV1 (that lack species designations) also represent a nvel species. Although the 16S rRNA gene sequences of strain HSC-21Su07 showed high similarity (99.22 and 99.00 %) with those of the Aquitalea denitrifican s type strain 5YN1-3 (NR_044535) and Aquitalea magnusonii strain TRO-001DR8 (NR_043475) respectively, phylogenetic tree placement and ANI values indicate a lower degree of relatedness. Within the phylogenetic tree, the genomes of strains HSC-21Su07, MWU13-2470, MWU14-2217 and ASV1 form a small but separate clade, and when compared to one another have ANI values (~94 %), just below the current standard species threshold of 95 %. The sister clade to this group includes the genome of the type strain Aquitalea aquatilis THG-DN7_12 but the ANI value with this strain is moderately low (~92 %) and HSC-21Su07 should not be assigned to this species. Strain HSC-21Su07 appears even more distantly related and has lower ANI values when compared to the other Aquitalea species for which genomes are available ( A. denitrificans , A. magnusonii and A. pelogenes )and (Figs 2c and S1) and is not assigned to these species. The metabolic features of strain HSC-21Su07 were most consistent with those described for A. denitrificans [40], with the exception of glucose fermentation and urease formation (negative for strain 5YN1-3 and positive for HSC-21Su07). Features were less similar to those of A. magnusonii, which was described as being positive for urea hydrolysis and glycogen utilization, resistant to ampicillin, rifampin and streptomycin, but susceptible to tetracycline [41]. Strain HSC-21Su07 was also urease-positive but could not utilize glycogen, and was susceptible to ampicillin, rifampin and streptomycin but resistant to tetracycline. Based on these findings we propose a novel species name for strain HSC-21Su07 and the other strains within the same clade, namely MWU13-2470, MWU14-2217 and ASV1: Aquitalea aquatica (a.qua’.ti.ca. L. adj. aquatica living, growing or found in water, aquatic). HSC-21Su07 was isolated from spring water.

Results indicate that Iodobacter strain HSC-16F04 and Iodobacter sp. strain BJB302 also merit a novel species designation. The strain HSC-16F04 was initially assigned to the genus Iodobacter based on 16S rRNA gene sequence comparisons including 98.56 % identities with the same genes of both Iodobacter arcticus strain AsdM4-16 (NR_108462) and Iodobacter fluviatilis strain Logan C009 (NR_104727). Although this genus assignment is supported by whole genome phylogenetics and ANI, the genome of strain HSC-16F04 does not group closely with those of any named species within the limited number of genomes in the NCBI genome assembly database (there are only seven). Many more Iodobacter strains have been reported in the literature and in other databases such as the Ribosomal Database Project (RDP), but whole genome sequencing and publication for many Iodobacter strains are lacking. Phylogenetically, the genome from Iodobacter strain HSC-16F04 is sister to that from the violet-coloured strain Iodobacter sp. BJB302 and these shared an ANI value of 94.89 %. These two genomes form a sister clade to that of the effectively published but not validly published (under the rules of the International Code of Nomenclature of Bacteria), violet coloured Iodobacter fluviatilis type strain DSM 3764 (Figs 2d and S1), but the genome of strain HSC-21Su07 shared an ANI value of only 92.1 % with that of this type strain. Not all existing Iodobacter species are represented in this phylogeny due to lack of genome availability; unrepresented Iodobacter species include I. limnosediminis and the proposed novel species I. arcticus . The characteristics of strain HSC-16F04 were generally consistent with those described for I. fluviatilis [42], except that unlike I. fluviatilis, Iodobacter strain HSC-16F04 was oxidase-negative, did not assimilate malic acid and showed weak assimilation of Tween 40. Characteristics were less similar to those of Iodobacter arcticus , which was described as being strictly aerobic (respiratory), non-motile, citrate-positive and gelatinase-negative [43]. Strain HSC-16F04 was facultative (fermentative), motile, citrate-negative and gelatinase-positive. Since the genome from HSC-16F04 does not group with existing I. fluviatilis or I. ciconiae genomes phylogenetically and shares only low ANI values with these, we suggest that strain HSC-16F04 represents a novel Iodobacter species. The genome from this violet pigment producer and that of Iodobacter strain BJB302 appear closely related and are significantly unlike other Iodobacter whole genomes reported to date. The species name we propose for these two strains is Iodobacter violacea (vi.o.la.ce.a. L. adj. violaceus -a -um violet; the colonies are deep violet when formed on TSA or R2A agar).

Evidence suggests that Duganella strain HSC-15S17 represents a novel species. This strain was tentatively assigned to the genus Duganella based on 16S rRNA gene sequence similarity [98.55 % with Duganella zoogloeoides strain IAM 12670 (NR_025833) and 98.28 % with Duganella zoogloeoides strain NBRC 102465 (NR_114106)]. This assignment was supported by whole genome phylogeny (Figs 2e and S1) and ANI scores of >90 % with genomes of other Duganella strains. In terms of species-level assignment, the genome of HSC-15S17 is in a clade with genomes from two violet-coloured strains classified only to genus: Duganella sp. BJB475 and BJB476. A genome from the closest named relative to these is from D. aceris SAP-35; however, this taxonomic name has been effectively published but not validly published under the rules of the International Code of Nomenclature of Bacteria (Bacteriological Code). In terms of metabolism, the metabolic features of strain HSC-15S17 were only partially consistent with those of described species within this genus and characteristics described for the related species Duganella zoogloeoides [44]. Strain HSC-15S17 produces large, gelatinous, violet-coloured colonies when grown on YEM while the colonies of D. zoogloeoides were described as pale yellow to straw-coloured. In addition, D. zoogloeoides was described as forming acid aerobically from glucose and urease enzymes which strain HSC-15S17 did not. Strain HSC-15S17 should be considered as representing a novel species and propose that this and the Duganella strains BJB475 and BJB476 be given the name Duganella violaceicalia (vi.o.la.ce.i.cal.i.a. L. adj. violaceus -a -um violet; the purple pigment violacein is produced by this strain; Gr. adj. kal or kall beauty or beautiful; N.L. fem. adj. violaceicalia violet beauty). The striking violet colour of Duganella strain HSC-15S17 colonies develops over time as they grow.

Results indicate that the Massilia strains HSC-2F05 and MS-15 represent a novel species. Strain HSC-2F05 was assigned to the genus Massilia based on 16S rRNA gene sequence comparison and showed 98.32 % identity with the same genes from Massilia varians strain CCUG 35299 (NR_042652). This assignment was supported by its placement within the GTDB-tk-aligned whole genome phylogeny and the ANI values (Figs 2f and S1; Table 5). At the time of writing, the NCBI genome assembly database contained 98 public genomes with the genus annotation Massilia , and these were incorporated into the phylogenetic tree and heatmap dendrogram. Analysis of the GTDB-tk-aligned heatmap dendrogram verified that almost every one of the Massilia genome assemblies is not closely related to its nearest phylogenetic neighbours (<90 % ANI). Many individual genomes can be considered to represent unique species. The genome of Massilia strain HSC-2F05 has a 96.37 % ANI value with that of its nearest phylogenetic neighbour Massilia sp. strain MS-15, a strain without species-level classification, but has less than 89 % ANI values with sister taxa within the whole genome phylogenetic tree. The closest genome with species-level classification shares 84.52 % ANI with Massilia oculi strain CCUG_43427 (GCF_003143515.1). The metabolic features of strain HSC-2F05 were only partially consistent with characteristics described for the closest 16S rRNA gene match Massilia varians, previously known as Naxibacter varians [45, 46]. Morphological features were similar, but Massilia varians was described as being H2S-positive and negative for nitrate reduction while strain HSC-2F05 was negative for H2S production and positive for nitrate reduction. In addition, M. varians was described as being positive for aesculin hydrolysis while HSC-2F05 was negative, and as susceptible to polymyxin while HSC-2F05 was resistant to that antibiotic. Massilia strains HSC-2F05 and MS-15 should be designated as Massilia hydrophila (hy.dro’.phi.la. Gr. n. hydr water; Gr. adj. philos loving; M.L. adj. hydrophila water loving). Massilia strain HSC-2F05 was isolated from pond water.

Strains HSC-65S10, HSC-3S05 and UCD_MED1 were tentatively assigned to the genus Janthinobacterium based on analysis of 16S rRNA gene sequences. This was confirmed by the whole genome phylogenetic analysis Figs 2g and S1); however, making species-level assignments for these strains was complicated for multiple reasons discussed here. We describe some of the challenges presented by this genus and possible solutions below.

One major challenge in making specieslevel assignments for our strains related to problems in the naming of organisms with genomes listed as being from the genus Janthinobacterium in NCBI. There are currently five formally recognized species in the genus Janthinobacterium , including the original type strain: J. lividum DSM 1522T=NCTC 9796=H-24=ATCC 12473 [47]; the only pathogenic strain: J. agaricidamnosum DSM 9628T=NBRC 102515 [48]; and three species introduced by Lu et al. [49]: J. violaceinigrum strain FT13WT=GDMCC 1.1638=KACC 21319, J. aquaticum strain FT58WT=GDMCC 1.1676=KACC 21468 and J. rivuli strain FT68WT=GDMCC 1.1677=KACC 21469 [49]. One issue with this group is that there is some confusion in the NCBI database regarding the last two genomes. The 16S rRNA gene sequences for strains FT58W and FT68W are listed in the NCBI taxonomy database as J. aquaticum and J. rivuli but their genomes are not included under their taxonomic entries. Instead, the genomes are listed as being J. sp. with strain designations but not species identification. Another complicating factor for this group is that, according to the NCBI bacterial taxonomy database, three additional species names have been effectively published but not validly published under the rules of the International Code of Nomenclature of Bacteria (Bacteriological Code). These are J. [tructae] strain SNU WT3T [50], J. svalbardensis strain JA-1=DSM 25734 [51] and J. psychrotolerans strain S3−2=DSM 102223 [52]. In addition to these general naming issues, there are also many genome assemblies in the NCBI database that are included in the genus Janthinobacterium but for which the taxonomic assignment is almost certainly incorrect. We note, for taxa which we have inferred the names are probably incorrect, we write them with square brackets (e.g. [misannotated name]) around what we have found to be the incorrect parts of the names. One type of incorrect taxonomic naming involves multiple genomes that in the GTDB-tk phylogeny clearly do not belong in the genus Janthinobacterium . For example, the genome from the isolate listed as [ Janthinobacterium ] sp. strain CG23_25 (RefSeq ID: GCF_001485665.1) falls within the Massilia branch of the whole genome phylogenetic tree (Fig. S1). Similarly, the genome labelled as [ Janthinobacterium ] sp. strain B9-8 (RefSeq ID GCF_000969645.2) falls within the Iodobacter clade, and the genome labelled as [ Janthinobacterium ] strain HH01 (RefSeq ID: GCF_000335815.1) falls within the genus Duganella (Fig. S1). In addition, there are three genome assemblies annotated as ‘ Janthinobacterium ’ that are placed in the GTDB-tk tree outside of the genus but in a group for which no clear genus can be assigned. These genomes are: [ Janthinobacterium ] sp. 17J80-10 (GCF_004114795.1), [Janthinobacterium marseille] P9896 (GCF_903469655.1) and [Janthinobacterium marseille] (GCF_000013625.1). In addition to mis-assignment to the genus Janthinobacterium , there are many genomes correctly assigned to the genus Janthinobacterium but which have species names assigned that are inconsistent with the placement of type strains in the GTDB-tk tree. For example, the genome assembly labelled J. [agaricidamnosum] strain BHSEK (RefSeq ID: GCF_003667705.1) shares a more recent branch node with J. lividum strains than with the type strain of J. agaricidamnosum . The genome assemblies labelled J. svalbardensis PAMC 27463 and J. [tructae] strain SNU WT3T are sister taxa that group together within a clade consisting of strains named only to genus or labelled as J. lividum . These incorrect taxonomic assignments and the ambiguity regarding formal species names, as well as some taxa for which the 16S rRNA gene data are listed under one name and the genomes are listed under another, all serve to make assigning names to new isolates within this group challenging.

Another key challenge in making species-level assignments for the genus Janthinobacterium is that results from analysis of 16S rRNA gene sequences, ANI and whole genome phylogeny were somewhat ambiguous and contradictory; we summarize some of these results here. First, the 16S rRNA genes of all three of our strains showed >99.80 % identity to 16S rRNA genes of the Janthinobacterium lividum type strain DSM 1522 (NR_026365), and equally high percentage identity scores with many other Janthinobacterium lividum strains in the NCBI database. Such high percentage identity of 16S rRNA genes is frequently used to indicate that strains are from the same species. However, the results of ANI analysis and whole genome phylogenetics did not support a simple assignment of these strains to J. lividum . In particular, while ANI overall generally tracked well with the whole genome phylogeny for the genus Janthinobacterium , the specific ANI levels for the new strains and for strains that could potentially be assigned to J. lividum were lower than the standard 95 % cutoff used by many to delineate species. For example, if we use a 95 % ANI cutoff to delineate species, only the 12 closest strains to the J. lividum type strain NCTC 9796 can be classified as J. lividum . These include: EB271-G4-7A, YR213, BJB446, 1246, BJB301, MP5059B, EIF1, SLB01, PLB04, PLB02, MTR and EIF2. If we use 94 % as the species cutoff, we can include strain SP_13. And if we use 93 % as an ANI cutoff species threshold for this species, 34 additional strains could be included in the species J. lividum . If we focus solely on the three new strains of Janthinobacterium described in this paper, we also see some complexities in species boundary identification. The genome of each of the three new strains reported here has an ANI value of about 93 % with each other, and each of them has a 93.2–93.6% ANI to the J. lividum type strain. An additional 60 genomes have ANI values between 90 and 95 % (averaging 93 %) with the J. lividum type strain. Included in these 60 are many that have been published as ‘J. lividum’. Individual genomes forming distinct clades such as groups with short branches compared to each other, and separated by a long branch connecting the group to the rest of the tree have around 95 % or better ANI similarity with their nearest neighbours. Using strict ANI value cutoffs can be insufficient to identify species boundaries because of issues including unequal rates of evolution, different gene loss and gain, and the fact that species boundaries are determined by ecological and genetic factors and not percentage identity. In addition, it is important to note that the 95 % species threshold is based on analysis of a limited number of genomes generally with high levels of similarity [23]. Regardless of these caveats, the ANI values within this group suggest that drawing species boundaries using ANI in the same way as has been sometimes used for other taxa is potentially invalid.

Because the genus Janthinobacterium does not clearly fit into the standard ANI model, and the strains investigated here have nearly identical phenotypes (Tables 1 and 2), we used PopCOGenT to assist in investigation of the evolutionary history and species-level classification of this group in greater detail. As mentioned above, PopCOGenT assesses population structure by inferring the extent of recombination relative to the amount of divergence between genomes and this in turn can help determine where natural genetic boundaries exist between strains [27]. PopCOGenT has been used to distinguish recent gene flow events from historical ones and to identify ancestral nodes in phylogenomic trees that lead to speciation events [53]. PopCOGenT can be used to predict the recombination pattern and population structure within bacterial species and reveal distinct populations. Directionality of gene flow from population to population can also be determined [54]. We used PopCOGenT to analyse all genomes for a clade within the genus Janthinobacterium that included the type strains, the three new Janthinobacterium genomes presented in this study, and other related strains (68 genomes in total, Table S1). The results were assessed in two ways: by examining the relationships between initial divergence and length bias (Fig. 4) and by inferring networks of recent gene flow (Fig. 5). The results shown in Fig. 4 highlight that the Janthinobacterium genome comparisons come in two main categories. First, there are genome comparisons where the two genomes are very closely related (on the left along the ‘initial divergence’ x-axis). Some of these pairs appear to represent resequencing of the same strains, either because they were isolated from the same sample or because groups separately sequenced strains obtained from a common source. Other pairs appear to represent just quite closely related separate isolates. One application of PopCOGenT is to detect gene flow in coexisting microbes of the same species. The program can also be applied to examine gene flow for ancestral strains. PopCoGenT can be used to measure the amount of relatively recent horizontal gene flow compared to the level of divergence among many sets of microbes, whether or not they coexist, and data can provide information about historical genetic boundaries between lineages. In this study, PopCOGenT was used to provide information on patterns of gene flow in lineages of Janthinobacterium strains in order to supplement analysis of phylogeny, genomes and ANI. In addition, there are genome comparisons where the genome pairs are significantly more distantly related (along the right side of the x-axis) such that the initial divergence scores ‘jump’ multiple log values. The discontinuity along the x-axis is caused by the genomes that form distinct subpopulations (upper left side) having lower initial divergence than the genomes that do not form populations. This confirms that subpopulation clusters have lower rates of genetic divergence and genomes with higher rates of genetic divergence do not form subpopulations with other genomes in the dataset. The genome pairs with high levels of initial divergence almost always have lower SSD scores (and thus less inferred gene flow) than the genome pairs with low levels of initial divergence. Within this dataset genomes that form subpopulations must have an initial divergence score of <−4, and those that do not form subpopulations have an initial divergence score of >−3. To infer specific subpopulation structure from the initial divergence and SSD scores shown in Fig. 4, we generated a gene-flow network diagram (Fig. 5). Each genome is represented by a node in this diagram and the edges represent inferred gene flow with the thickness of the edges representing the amount of gene flow. Genomes that do not have any inferred gene flow with other genomes here are coloured in grey. Genomes that are part of gene flow networks are coloured in other colours with each specific colour used representing a distinct gene flow network. Overall, the only genome groups that formed strong gene flow networks were ones that were very closely related to each other as indicated by low initial divergence scores and being very close in the whole genome phylogenetic tree. Of note, no networks were seen that included pairs of strains with ANIs <95 %. Overall, the PopCOGenT analysis shows that for the strains in the genus Janthinobacterium analysed here, significant amounts of gene flow only occurs among some (but not all) of the strains for which the genome tree shows they are very closely related. In some cases, there is no detectable gene flow even with strains that are quite close. This possibly could be due to physical or ecological separation of strains that are closely related, which could prevent gene flow from occurring. We note that the strains included in this analysis were isolated from a wide diversity of locations and ecosystems and it is not surprising that they may be genetically isolated from each other.

In this section we discuss the implications for the taxonomic assignments of the new Janthinobacterium isolates by integrating the PopCOGenT results with the analysis of 16S rRNA sequences, ANI, whole genome phylogeny and phenotype. Phylogenetically, HSC-3S05 groups in a clade in the whole genome tree with three other strains classified only to the genus level. HSC-3S05 has an ANI >95 % with those strains. This clade is a sister group to a clade that includes both the J. lividum type strain and the type species for J. violaceinigrum . HSC-3S05 has 93.67 % ANI with the J. lividum type strain and 92.39 % ANI with the J. violaceinigrum type species. The metabolic characteristics of HSC-3S05 are generally consistent with the description provided for ‘typical’ Janthinobacterium lividum [55], except that the assimilation of N-acetyl-d-glucosamine, cellobiose, α-lactose and l-phenylalanine were found to be negative in published strains while HSC-3S05 was positive or weakly positive for these. HSC-65S10 groups in a clade in the whole genome tree with four other strains classified only to the genus level. HSC-65S10 has an ANI >95 % with those strains. HSC-65S10 is phylogenetically more distant from the J. lividum and J. violaceinigrum type strains than HSC-3S05 is. However, the ANI of HSC-65S10 with these two type strains is 93.27 and 92.29 respectively, similar to that of HSC-3S05 with these strains. The PopCOGenT results show that HSC-65S10 is not part of a gene flow network with any other strains in the dataset, providing evidence that strain HSC-65S10 may have a history of ecological isolation from other strains in the dataset. The metabolic characteristics of HSC-65S10 were generally consistent with the description provided for ‘typical’ Janthinobacterium lividum [55], except that the assimilation of N-acetyl-d-glucosamine and cellobiose were described as being negative for all published strains while HSC-65S10 was positive. Strain UCD_MED1 has 97.58, 96.36, and 96.36% ANI with Janthiobacterium strains only classified to genus: NCTC8861, EB271-G4-3-1 and EB271-G4-3-2 respectively. The PopCOGenT results show that UCD_MED1 is not part of a gene flow network with any other strains in the dataset, providing evidence that strain UCD_MED1 may have a history of ecological isolation from other strains in the dataset. Whether groups such as this one should be considered separate species or subspecies is debatable. The metabolic characteristics of UCD_MED1 were generally consistent with the description provided for ‘typical’ Janthinobacterium lividum [55], except that the assimilation of N-acetyl-d-glucosamine and cellobiose were found to be negative in published strains while UCD_MED1 was positive. The three new representatives of the Janthinobacterium strains presented here could be representatives of novel species as they (1) have little gene flow with other strains represented here, (2) are phylogenetically somewhat distinct from type strains of formally described species and (3) have ANI values of <95 % with any strains that can be readily assigned to a species. However, it may be premature for proposing novel species designations here given that (1) gene flow in the genus Janthinobacterium appears to drop off precipitously even between quite closely related strains (see above), (2) ANI is an indirect and imprecise measure of species boundaries and there are other examples of ANIs of <95 % occurring within proposed species groups [23] and (3) the metabolic features of the three new strains are generally consistent with those described for typical J. lividum strains. We thus propose that without any other evidence all the Janthinobacterium strains shown in Fig. 2g be considered in the same species, namely J. lividum, and furthermore that a 90 % ANI cutoff is more appropriate for species boundaries in the genus Janthinobacerium. This thus would mean that several recently published ‘species’ be designated subspecies within this group.

During this study, we determined the taxonomic status of nine bacterial strains using phylogenomic and population ecology approaches. We found that taxonomically classifying six of these strains using relatively standard methods (a combination of analysis of 16S rRNA gene sequences, ANI and whole genome phylogeny) worked well and we suggested five of the strains be recognized as representing novel species. However, for the three strains in the genus Janthinobacterium , these standard approaches gave somewhat inconsistent and ambiguous results. Using these standard approaches we were unable to determine where species boundaries were within the group. This is not particularly surprising given that the methods being used are somewhat indirect ways to assess species boundaries and do not actually address factors such as genetic isolation between groups, which is needed to formally determine where species boundaries exist. We thus added an additional analysis using the program PopCOGenT to examine population structure within the Janthinobacterium group. PopCOGenT estimates the extent of recent genetic exchange between genomes by assessing the balance between shared identical genome regions versus total divergence of genomes. This additional analysis showed that between the genomes immediately surrounding the J. lividum type strain, low recombination rates dominate throughout the clades, suggesting that existing strains may comprise novel, unnamed species. Using a combination of standard tools as well as population genetic analysis as with PopCOGenT was useful here and will presumably be useful in future studies and will better integrate naming with species concepts for bacteria in general [56]. Experimenting with new bacterial taxonomic classification methods leads to open discussions on how taxonomists can escape the mire of subjectivity and agree on a definitive methodology for classifying bacteria to species level.

Our analysis here was made complicated by mis-classification of strains for which genome sequences are available. Overall, we suggest revision of the taxonomic status of many genome assemblies within all six of the genera evaluated in this study, especially within the genus Janthinobacterium . This highlights a major issue for microbiology in that published draft genome assemblies can sometimes be inaccurate [57, 58]. Published mis-classified assemblies are problematic because they can lead to false conclusions and mis-identified strains [59]. Misidentification can also lead to inappropriate ongoing research. We encourage more attention to be paid to the taxonomic annotations of new genomes being uploaded to public databases.

For this study we recorded classical phenotypic characteristics including cell and colony morphology, metabolic testing and antimicrobial susceptibility testing. Although not all phenotypic characteristics were determined, those that were are consistent with characteristics included previously in the descriptions of genera and species investigated. Phenotypes are observable characteristics of cells [60], and although the term ‘phenotype’ is sometimes applied more broadly, the classical phenotypic characteristics of bacteria comprise morphological, physiological and biochemical features [61]. Growth phenotypes are of particular interest because they help better understand the selective forces that may have shaped the history of a particular organism and also because they provide insights into an organism’s niche and how to work with it in the lab [60]. Many bacteria carry genes that are only expressed under certain circumstances, such as virulence factors regulated through quorum sensing and genes involved in pigment production being influenced by temperature variation [62, 63]. Although it is doubtful that individual phenotypes or small collections of phenotypes can consistently and correctly represent evolutionary relationships [64], organisms are described in phenotypic terms, and the descriptions help define a taxonomic group that may also be recognized at the phylogenetic level [65]. Even with the advent of rapid DNA sequencing, and its growing use in identifying and describing species, verification of bacterial species still requires phenotypic descriptions, whenever possible using experimental cell culture and phenotyping. Importantly, it is still not possible to accurately predict phenotypes of bacteria from complete genome sequences and thus characterizing and describing phenotypes is still a critical component of any species description. A minimal phenotypic description is not only the identity card of a taxon but also a key to its biology. Although they are accepted as necessary, differential phenotypic characters are often hard to find with a reasonable amount of effort and time [31]. This of course does not mean phenotypes are the only thing needed to describe an organism. Phenotypes are prone to convergent evolution and can differ greatly under varying conditions and even for strains that are genetically identical (e.g. due to epigenetic modifications). Thus, it is important to use both phenotypic and genetic/genomic information in characterizing taxa.

The violacein biosynthesis pathway relies on the amino acid tryptophan. The first two genes in the pathway, vioAB, are homologous with genes used for indole compounds staurosporine and rebeccamycin, which are found in some actinomycetes. VioC is homologous with a monooxygenase housekeeping gene used in various processes across a wide range of prokaryotic and eukaryotic organisms, while vioED are uniquely used for production of violacein and deoxyvioalcein. The striking violet pigment violacein is the final product of a biosynthetic pathway that also produces prodeoxyviolacein (green) from the first three genes in the pathway (vioABE), and deoxyviolacein (violet) from the first four genes (vioABEC). The end product, violacein (navy-violet), is produced when all five genes are used (vioABEDC). Deoxyviolacein is not cytotoxic, whereas violacein is, and therefore synthesizing deoxyvioalcein can be good for developing products such as biosensors and food colorants [66]. Further research should focus on predicting violacein production in cultured or uncultured bacteria by developing models and using phylogenomic methods. Investigations can elucidate how horizontal gene transfer of violacein genes may have contributed to the pigment’s distribution throughout the bacterial tree of life. Models could be useful for future research where production of this pigment can be inferred from any bacterial whole genome without the need for culturing and extraction. The construction of models that allow the user to discover new strains of violacein-producing bacteria and the methods for creating and using these models could easily be co-opted for searching for other pigments in any bacterial whole genome assemblies. In this way, the vioABCDE genes can be searched for and compared to determine if they are present in non-pigmented strains (e.g. in Janthinobacterium ) and also to determine if they were probably acquired independently through multiple horizontal gene transfers by various strains/species. The taxonomy and evolution of bacterial strains that are capable of expressing violacein, such as Janthinobacterium , are not well understood even though the biosynthesis of the compound has been clearly demonstrated in multiple genera. Future studies should focus on understanding concordance between biochemical phenotyping and metabolomic predictions. Phenotyping and genome annotation are important for understanding if the bacteria will be useful, safe and economic for future applications. This evolutionary knowledge can help provide context for future synthetic biology and genetic modification studies using violacein and non-model bacterial species such as these. The evolutionary or adaptive reasons leading to the Janthinobacterium subpopulations inferred by PopCOGenT should be further investigated. Some metabolic tests can be used to define species and biotypes within a species, and the delimitation of biotypes within a specific group has been found to generally agree with observed phylogenetic structure [67]. Future studies should use metabolic tests to observe if splitting a specific species such as Janthinobacterium lividum into biotypes based on differences in niche occupancy reflects phylogenetic subdivisions within the species. Codon usage redundancy may cause GC content to shift within members of a genus. Differences in GC content among coding sequences may be caused by mutational bias due to adaptation to different lifestyles or niches among species isolated from very different environments. The variations in metabolic capabilities and GC content between different species and lineages that have adapted to distinct niches suggest that these traits have evolved over a long period of time in response to the demands of their specific niches [67]. Using publicly available whole genome sequences, additional studies on genera with violacein-producing bacteria should also involve comparing GC contents of coding sequences as these are thought to reflect subtle differences in mutational bias as a consequence of long-term niche adaptation.

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