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Article

Comparative Genomic Analysis of Extracellular Electron Transfer in Bacteria

by
Daniel Liu
1,
Jimmy Kuo
2,3 and
Chorng-Horng Lin
1,*
1
Department of Biomedical Sciences, Da-Yeh University, 168 University Road, Dacun, Changhua 51591, Taiwan
2
Department of Planning and Research, National Museum of Marine Biology and Aquarium, Pingtung 94450, Taiwan
3
Graduate Institute of Marine Biology, National Dong Hwa University, Pingtung 94450, Taiwan
*
Author to whom correspondence should be addressed.
Processes 2024, 12(12), 2636; https://doi.org/10.3390/pr12122636
Submission received: 1 October 2024 / Revised: 14 November 2024 / Accepted: 18 November 2024 / Published: 22 November 2024
(This article belongs to the Special Issue Computational Biology Approaches to Genome and Protein Analyzes)
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Abstract

:
Certain bacteria can transfer extracellular electrons and are applied in microbial fuel cells (MFCs). In this study, we compared the extracellular electron transfer characteristics of 85 genomes from nine genera, namely Blautia, Bradyrhizobium, Desulfuromonas, Dialister, Geobacter, Geothrix, Shewanella, Sphingomonas, and Phascolarctobacterium, using the bioinformatic tools Prokka 1.14.6, Roary 3.13.0, Panaroo 1.3.4, PEPPAN 1.0.6, and Twilight. The unweighted pair-group method with arithmetic mean (UPGMA) clustering of genes related to extracellular electron transfer revealed a good genus-level structure. The relative abundance and hierarchical clustering analyses performed in this study suggest that the bacteria Desulfuromonas, Geobacter, Geothrix, and Shewanella have more extracellular electron transfer genes and cluster together. Further functional differences among the genomes showed that 66 genes in these bacteria were significantly higher in abundance than in the other five bacteria (p < 0.01) based on PEPPAN followed by a Twilight analysis. Our work provides new potential insights into extracellular electron transfer in microorganisms.

1. Introduction

Exoelectrogenic bacteria or electricigens, such as Shewanella oneidensis, Geobacter metallireducens, and Geobacter sulfurreducens, are microorganisms that can transfer electrons outside the cell; these bacteria use cytochromes as electron transfer proteins or oxidoreductases to catalyze the reduction of reactants, which is why microbial fuel cell devices generate electricity [1,2]. A possible electron transfer pathway in an anode is for electrogenic bacteria to provide electrons to the anode through direct contact with nanowires or biofilms (metal-reducing pathways). The indirect path provides electrons to the anode through the redox reaction of soluble electron mediator substances. In addition, in devices using mixed bacteria in microbial fuel cells, electron transfer may originate from the bacterial population utilizing existing chemical nutrients through specific metabolic pathways or through intercellular messaging, such as anaerobic methane oxidation. The oxidation of methane by archaea promotes sulfate reduction by sulfate-reducing bacteria and subsequent electron transfer [1,2,3,4,5].
Currently, research on genes related to electricity production has focused mainly on Shewanella oneidensis, Geobacter metallireducens, and Geobacter sulfurreducens. Shewanella oneidensis can reduce Fe(III), Mn(III), and Mn(IV) metal ions in minerals. CymA (inner membrane tetraheme cytochrome c, quinol oxidase) on the cell membrane oxidizes hydroquinone, and electrons are transferred to MtrA (metal-reducing), MtrB, and MtrC in the outer membrane via the periplasmic proteins FCC3 (tetraheme flavocytochrome c3) and STC (small tetraheme cytochrome). Finally, on the bacterial surface, the MtrC and OmcA (outer membrane multiheme c-type cytochrome) linkages transfer electrons to metal ions. In addition, studies have shown that the PilD (prepilin leader peptidase) protein on the bacterial surface contributes to electron transfer [2,3,5,6,7]. In the study of electron transfer in Geobacter sulfurreducens, benzene is oxidized by ImcH (inner membrane multiheme c-type cytochrome, quinol oxidase) and CbcL (quinol oxidase) on the cell membrane, and electrons are transferred to the outer membrane proteins OmbB (porin-like outer membrane protein), OmbC, OmaB (periplasmic c-type cytochrome), OmaC, OmcB, and OmcC via PpcA (periplasmic c-type cytochrome) and PpcD in the periplasm [2,3,5,6,7]. The FliC (a flagellar filament structural protein), PilA (pilin protein), PilM-Q (type IV pili intracellular biogenesis system), and PilT (homohexameric ATPase) proteins on the bacterial surface also contribute to electron transfer for Geobacter metallireducens and Geobacter sulfurreducens [2,3,5,6,7]. Therefore, these direct electron transfer mechanisms can be divided into (1) nanowire and (2) metal-reducing pathways, in which PilD, FliC, PilA, PilM-Q, and PilT can be attributed to the nanowire machinery, and CymA, MtrA, MtrB, MtrC, FCC3, STC, OmcA, ImcH, CbcL, OmaB, OmaC, OmcB, OmcC, PpcA, and PpcD can be attributed to the metal-reducing machinery.
In addition to bacteria Shewanella oneidensis, Geobacter metallireducens, and Geobacter sulfurreducens, several bacteria, such as Acidithiobacillus ferrooxidans, Bacillus subtilis, Clostridium butyricum, Desulfuromonas acetoxidans, Escherichia coli, Geothrix fermentans, Pseudomonas aeruginosa, Rhodobacter sphaeroides, Rhodopseudomonas palustris, Thermincola potens, etc., have been shown to be exoelectrogenic. The extracellular electron transfer pathway for Rhodopseudomonas palustris occurs through phototrophic iron oxidation. For the Fe(III)-reducing bacterium Thermincola potens, multiheme c-type cytochromes are involved in electron transfer to Fe(III) oxides. For the Fe(II)-oxidizing bacterium Acidithiobacillus ferrooxidans, the c-type cytochromes and CoxAB complex (cytoplasmic membrane protein complex) work to couple the oxidation of Fe(II) and the reduction of O2 [2]. However, the extracellular electron transfer pathways for other exoelectrogens are still unknown, and the current application of comparative genome analysis in the study of extracellular electron transfer mechanisms is still in its preliminary stage.
In this study, we collected 85 genome sequences of nine bacterial genera, Blautia, Bradyrhizobium, Desulfuromonas, Dialister, Geobacter, Geothrix, Shewanella, Sphingomonas, and Phascolarctobacterium, from the National Center for Biotechnology Information (NCBI) database. We selected the bacteria Desulfuromonas, Geobacter, Geothrix, and Shewanella because they are potential exoelectrogens [1,3,8]. We selected the bacteria Blautia, Bradyrhizobium, Dialister, Sphingomonas, and Phascolarctobacterium because they are present in the anode-associated soil of sediment MFCs, and a Spearman correlation analysis showed that Geobacter was positively correlated with Dialister and negatively correlated with Blautia, Bradyrhizobium, Sphingomonas, and Phascolarctobacterium [9]. The role of these bacteria in sediment MFCs is unclear, and there is no research in the literature explicitly considering them as exoelectrogenic bacteria. We used the programs Prokka, Roary, Panaroo, and PEPPAN to perform pangenome analyses. Based on a comparison of gene presence/absence, genes related to extracellular electron transport were extracted for subsequent analysis and comparison, and the Twilight program was then further applied to cluster genes based on genus level. The aim of this study was to analyze and compare the extracellular electron transfer in bacteria using pangenome analysis tools.

2. Materials and Methods

2.1. Strains in This Study

We downloaded the genome sequences from NCBI with the ncbi-genome-download command, and the parameters were --section refseq, --genus “Genus” bacteria, --format all, and --assembly levels complete. Except for the Geothrix genus, the assembly levels were filtered as “all” because there were no hits at the complete assembly level. Nine genera and a total of 85 genomes were selected for analysis (Table S1).

2.2. Average Nucleotide Identity (ANI) Analysis

Pyani 0.2.12 was employed for the whole-genome classification of microbes [10]. The command used was average_nucleotide_identity.py, and the method used was ANIb, which uses BLASTN+ to align 1020 nucleotide fragments of the input sequences [11]. The percentage identity among the genomes was calculated, and an ANI above 95% between two genomes indicated that they were the same species.

2.3. Genome Annotation and Pangenome Analyses

We used Prokka for genome annotation [12]. The .gff annotation files were further used for pangenome analyses via Roary [13], Panaroo [14], and PEPPAN [15]. The command and parameters are roary -f roary -e -n -g 300000 -r -p 6 *.gff (Roary), panaroo -i *.gff -o results --clean-mode strict --remove-invalid-genes -t 6 (Panaroo), and PEPPAN -p PEPPAN -t 6 --min_cds 90 *.gff and PEPPAN_parser -g PEPPAN.PEPPAN.gff -s PEPPAN_out -t -c -a 95 (PEPPAN).

2.4. Retrieval of Extracellular Electron Transfer Genes

The previously mentioned genes related to extracellular electron transfer were retrieved from the gene_presence_absence matrix of Roary and Panaroo, and only FliC, PilY (type IV pili), PilT, Pilus, Pilin, cytochrome c-type, Imc, Omcs, and outer membrane porin genes could be retrieved. The binary data were further analyzed using the DGGEstat 1.0 software tool (Eric van Hannen, The Netherlands Institute for Ecological Research, Wageningen, The Netherlands) with the calculated distance matrix according to the Dice coefficient (Sorenson, Nei, and Li) and the unweighted pair-group mean arithmetic method (UPGMA) cluster analysis. The bootstrap values (1000 trials) over 50 are shown for nodes, and the results of the cluster analysis were plotted in iTOL (Interactive Tree of Life) in unrooted mode [16].
The retrieved binary data of electron transfer genes in different genera were further analyzed via the Twilight program based on genus grouping with the command classify_genes.R to obtain the frequency of each gene in each genus. Hierarchical clustering was generated in R version 4.3.1 with the scale, dist, and hclust functions in the Euclidean and complete distance methods [17].

2.5. Functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analyses

The gene_presence_absence matrix derived from Roary, Panaroo, and PEPPAN was further analyzed via the Twilight program based on genus grouping with the command classify_genes.R [18]. Significant genes were identified using the statistical analysis of metagenomic profiles (STAMP 2.1.3) software package with Welch’s t-test between the E and non-E groups (p < 0.35 for Roary and Panaroo, p < 0.01 for PEPPAN) [19]. E represents the bacterial group capable of extracellular electron transfer, which contained Desulfuromonas, Geobacter, Geothrix, and Shewanella. Non-E represents the bacterial group not capable of extracellular electron transfer and included Blautia, Bradyrhizobium, Dialister, Sphingomonas, and Phascolarctobacterium. The genes from the PEPPAN analysis were obtained through a BLASTX (search protein databases using a translated nucleotide query) search of the NCBI. The genes were subjected to KEGG pathway mapping into metabolism, genetic information processing, environmental information processing, and cellular process pathways [20].

3. Results

3.1. Strategy

In this work, we used the Roary, Panaroo, and PEPPAN tools for pangenome analyses to obtain binary lists of gene_presence_absence. (1) We then retrieved genes related to extracellular electron transfer derived from Roary and Panaroo but not from PEPPAN, because the gene names in the binary table of PEPPAN are code names. We then performed a UPGMA analysis to cluster each genome based on extracellular electron transfer characteristics. (2) We further analyzed the extracellular electron transfer characteristics between genera via the Twilight tool to investigate which genera could be exoelectrogenic bacteria. (3) We used the Twilight tool to classify genes based on the genus, followed by a STAMP-based statistical analysis of two groups, E and non-E, to investigate the same genes in bacteria Desulfuromonas, Geobacter, Geothrix, and Shewanella, which are different from the bacteria Blautia, Bradyrhizobium, Dialister, Sphingomonas, and Phascolarctobacterium (Figure 1).

3.2. The Genes Related to Extracellular Electron Transfer May Cluster Well at the Genus Level

To compare the whole-genome sequence similarities of the 85 genomes, we used the pyani software tools to analyze ANI scores with percentage identity. The dendrogram showed that most bacteria could be clustered together according to the same genus, except for Geobacter, whereby seven Geobacter sulfurreducens strains represented the most common group and the other five Geobacter strains the second most common. The latter were closely related to the bacteria Desulfuromonas (Figure 2), with a likely explanation being that both genera belong to the same Thermodesulfobacteriota phylum. It is possible to improve the cluster by increasing the genome number for each genus; however, this causes a burden on memory.
We then retrieved genes related to extracellular electron transfer from both the gene_presence_absence binary tables of Roary and Panaroo, and 260 and 136 genes were retrieved, respectively. The extracellular electron transfer profiles in each genome were clustered via a UPGMA analysis, as shown in Figure 3. Nine and ten Geobacter strains were clustered together via Roary and Panaroo, respectively, suggesting that Geobacter strains contain close extracellular electron transfer characteristics. This analysis clustered well for the bacteria Bradyrhizobium, Geobacter, Geothrix, Shewanella, and Phascolarctobacterium, suggesting that the extracellular electron transfer profiles of these bacteria have genus patterns.

3.3. Bacteria with Extracellular Electron Transfer Ability Have a Greater Abundance of Related Genes and Cluster Together

Figure 4 shows the average relative abundance of the genes related to extracellular electron transfer among bacteria. We used the Twilight tool to investigate the characteristics of extracellular electron transfer at the genus level. The previous binary tables of 260 (Roary, Figure 4A) and 136 (Panaroo, Figure 4B) genes retrieved were further analyzed using the Twilight tool to obtain frequency data, and according to direct electron transfer mechanisms, we separated the genes into three groups: (1) genes involved in nanowires, (2) genes involved in metal-reducing pathways, and (3) extracellular electron transfer genes (genes involved in nanowires plus genes involved in metal-reducing pathways). All the strains had nanowire genes but with different percentages. In contrast, genes related to metal-reducing pathways were found in six bacteria. In addition to the known exoelectrogens (Desulfuromonas, Geobacter, Geothrix, and Shewanella), Bradyrhizobium and Sphingomonas also had metal-reducing genes. Bradyrhizobium and Sphingomonas were presumed to be non-exoelectrogens. For extracellular electron transfer genes, the exoelectrogens showed higher relative percentages than the percentages in non-exoelectrogens (Figure 4), except in Desulfuromonas (Figure 4B), and the results thus showed the tendency for exoelectrogens to have more extracellular electron transfer genes than non-exoelectrogens. However, the average abundance originated from binary data and was calculated using the Twilight program, and the statistical analysis showed no significance. Furthermore, we used hierarchical clustering to group bacteria (Figure 4), and interestingly, the Roary and Panaroo methods both showed that Bradyrhizobium and Sphingomonas were clustered together and separated from Desulfuromonas, Geobacter, Geothrix, and Shewanella. These results ideally clustered the known exoelectrogenic bacteria together, suggesting that the gene profiles related to extracellular electron transfer could be applied for the investigation of potential exoelectrogenic bacteria.

3.4. Functional and Metabolic Pathways

To compare the functional differences among the genomes, we again used the Twilight tool to classify genes based on genus distribution from the gene_presence_absence binary tables of Roary, Panaroo, and PEPPAN. We then used the STAMP software package to perform a statistical Welch’s t-test between the E and non-E groups (p < 0.35 for Roary and Panaroo, p < 0.01 for PEPPAN); the results are shown in Figure 5. Figure 5A,B show that the heatmaps of genes from the Roary and Panaroo pangenome analyses were not clustered well, and little gene information was provided; for example, DNA adenine methylase and the tyrosine recombinase XerC were more abundant in the E group. According to the results of the PEPPAN pangenome analysis, 66 genes exhibited a higher abundance in the E group (p < 0.01). Among these genes, 36.4%, 30.3%, 21.2%, and 7.6% were associated with metabolism, genetic information processing, environmental information processing, and cellular process pathways, respectively. Three genes were not classified (Figure 5C). We found three redox proteins, cytochrome ubiquinol oxidase subunit I, 4Fe-4S dicluster domain-containing protein, and (2Fe-2S)-binding protein, that could be related to extracellular electron transfer in the bacteria Desulfuromonas, Geobacter, Geothrix, and Shewanella; however, no evidence has been provided. Although hierarchical clustering analysis based on extracellular electron transfer patterns could cluster well to the bacteria Desulfuromonas, Geobacter, Geothrix, and Shewanella (Figure 4), none of the genes retrieved as extracellular electron transfer genes could be found in the heatmap of genes by PEPPAN (Figure 5C). A possible explanation is that although these bacteria have similar extracellular electron transport genes, the presence of the genes was not significantly abundant. In addition, the origin of these extracellular electron transport genes was convergent evolution. We found that the Roary and Panaroo pangenome tools were not good for different genus analyses since both tools were designed for the pangenome analysis of a given species. Interestingly, PEPPAN was originally used for the pangenome analysis of a given genus and performed well in this study on the different genera involved, as shown in Figure 5C and in the phylogenetic tree based on the gene_presence_absence tables (Figure S1). However, the results of these functional analyses only reflect the number of annotated genes within the genome, and actual differences in expression levels still need to be confirmed through transcriptome analysis. More intriguingly, the presence of these genes may suggest that these bacteria share similar physiological and metabolic functions.

4. Discussion

We used the pangenome tools Roary, Panaroo, and PEPPAN to compare the extracellular electron transfer characteristics of the genomes. All of these methods generated the gene_presence_absence binary tables. Because the binary tables of Roary and Panaroo provide gene names, we were able to retrieve the genes related to extracellular electron transfer, except for PEPPAN, for which only the code number was provided. The UPGMA clustering of genes related to extracellular electron transfer showed an improved genus cluster (Figure 3) compared to the tree generated based on whole gene_presence_absence tables (Figure S1). The results suggested that the bacteria of each genus exhibit a genus-unique pattern of extracellular electron transfer. The capability of extracellular electron transfer could be confirmed after gene classification by the Twilight program (Figure 4), in which Desulfuromonas, Geobacter, Geothrix, and Shewanella had a greater average abundance of extracellular electron transfer genes and were clustered together. Although from the Panaroo tool, Desulfuromonas was less common than Bradyrhizobium and Sphingomonas, the hierarchical cluster analysis grouped Desulfuromonas with the other three exoelectrogens (Figure 4B), the same as the result from Roary analysis (Figure 4A). Recently, a study on the Desulfuromonas sp. AOP6 strain showed that the genome contains genes encoding c-type cytochromes and type IV pili [21,22]. The Desulfuromonas versatilis NIT-T3 strain has 76 putative c-type cytochromes, including 6 unique c-type cytochromes [23]. Research has strongly suggested that Desulfuromonas species use similar extracellular electron transfer pathways. Geothrix fermentans secrete redox-active compounds to utilize electron acceptors [24]; therefore, electron shuttling is an electron transfer mechanism. However, genes related to the nanowire and metal-reducing machinery could still be detected in Geothrix (Figure 4). Genome comparison should be helpful in addressing this uncertainty. Bradyrhizobium, a nitrogen-fixing bacterium, can form symbiotic nodules on legumes. Nevertheless, Bradyrhizobium is not considered an exoelectrogen, and Bradyrhizobium has fixABCX genes (nitrogen fixation genes), which are electron transfer flavoprotein genes responsible for electron bifurcation characterized by the coupling of exergonic and endergonic redox reactions to simultaneously generate (or utilize) low- and high-potential electrons [25,26]. Bradyrhizobium performs denitrification reactions involving NO2 or NO3- reduction [27,28]. Sphingomonas is also not an exoelectrogen; it is a Gram-stain-negative aerobic bacterium with a remarkable ability to breakdown refractory contaminants; an outer membrane containing glycosphingolipids but lacking lipopolysaccharide; and a cell envelope comprising a cell membrane with proteins, respiratory quinones, and phospholipids [29]. The bacteria Blautia, Dialister, and Phascolarctobacterium had no metal-reducing genes retrieved but had nanowire structures (Figure 4); again, these bacteria are not exoelectrogenic, and interestingly, they are gut bacteria [30,31,32].
In this study, we used the Roary, Panaroo, and PEPPAN pangenome tools. Roary and Panaroo are both suitable for population-level comparisons, and Panaroo identifies more core genes and fewer accessory genomes than Roary by correcting errors from annotation, fragmented assemblies, and contamination [13,14]. We found better clustering for Bradyrhizobium, Geobacter, Geothrix, and Shewanella with Panaroo, as shown in Figure 3, and for the different retrieved gene numbers (260 and 136) between the two tools. The PEPPAN pangenome tool extends the genome information from the population level to encompass an entire genus by correcting for errors from inconsistent gene and pseudogene annotations and the presence of paralogous genes [15]. The phylogenetic tree generated based on gene_presence_absence showed good genus clustering compared to the trees of Roary and Panaroo (Figure S1). Although we were not able to retrieve the genes associated with extracellular electron transfer from the binary table of PEPPAN, because of the code names, we further applied the Twilight tool to classify genes according to the assumption that Desulfuromonas, Geobacter, Geothrix, and Shewanella were the exoelectrogens shown in Figure 5C. Unfortunately, only cytochrome ubiquinol oxidase subunit I, 4Fe-4S dicluster domain-containing protein, and (2Fe-2S)-binding protein might be related to electron transfer [33], and further analysis is required to clarify this.
Diverse bacteria have been established as exoelectrogenic bacteria, including Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria (Thermodesulfobacteriota), Acidobacteria, and Firmicutes. In this study, four genera were considered exoelectrogenic bacteria: Desulfuromonas (Thermodesulfobacteriota), Geobacter (Thermodesulfobacteriota), Geothrix (Acidobacteria), and Shewanella (Gammaproteobacteria) [1,2,3,5,8]. There are several electroactive extremophiles, cable bacteria, and ammonia-oxidizing exoelectrogens considered novel exoelectrogens [8]. A promising facultative anaerobic electrogenic strain, Klebsiella sp. SQ-1, was isolated from sludge in a biotechnology plant [34]. Adding solid iron (III) oxide to the anode would regulate extracellular electron transfer in the anode biofilm, and a sequence analysis showed that the dominant bacterial genus was Pelobacter [35]. Further, paleomarine sediments of iron-rich marlstone appeared to be the best source of electrogenic bacterial genera, Geovibrio and Geobacter [36]. To promote the growth of extracellular electrogenic bacteria on the anode and reduce the increase in resistance caused by biofilm thickening, a microbial fuel cell with an anode modified by macroporous carbon foam modified with nitrogen-doped carbon nanowires was designed, and the results showed an improvement in power density [37]. Graphite anodes modified with polyaniline can support robust biofilm growth, in which bacteria Pseudomonas, Clostridium, Enterococcus, and Bifidobacterium showed a relatively high abundance [38]. In addition, a new type of microbial flow fuel cell transfers electrons from floating Shewanella to electrodes through flowing redox mediators [39] to reduce thick biofilm for better power output. Interestingly, the biofilm promotion factor protein is related to biofilm formation and current density [40], showing that this gene is also related to the extracellular electron transfer ability of bacteria.
This study might provide a novel approach to investigating potential exoelectrogens in comparative genomes; however, some important issues need to be raised: (1) There might be additional mechanisms other than nanowire or metal-reducing pathways. (2) This study does not include the indirect electron transfer mechanism by an electron mediator. (3) Bacteria from the same genus might not have similar characteristics, such as exocellular electron transfer. In conclusion, our work provides pangenome tools and gene retrieval information that could be applied to investigate exocellular electron transfer in microorganisms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr12122636/s1, Figure S1: Phylogeny of 85 genomes based on the gene_presence_absence matrix of (A) PEPPAN, (B) Roary, and (C) Panaroo; Table S1: Strains used in this study.

Author Contributions

D.L., J.K. and C.-H.L. designed and performed experiments and analyzed data. C.-H.L. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Original data are available upon request.

Acknowledgments

The authors would like to thank Chieh-Ni Wei for her correction of English writing.

Conflicts of Interest

The authors have no competing interests to declare that are relevant to the content of this article.

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Figure 1. A workflow for data collection, pangenome, and post-processing analysis. Genomes were downloaded from NCBI with the ncbi-genome-download command and filtered using refseq, all formats, and complete assembly levels. For the Geothrix genus, the assembly levels filtered were “all”. Nine genera were selected for analysis, and the number in the bracket indicates the number of genomes (85 genomes in total). P represents phylum. The ANI was calculated using the pyani program. Protein-coding genes were annotated with the Prokka program followed by a pangenome analysis with the Roary, Panaroo, and PEPPAN programs. The gene_presence_absence matrices generated by Roary and Panaroo were further applied for genes related to the extracellular electron transfer analysis. The Twilight program was then employed for a post-processing analysis based on the trait of extracellular electron transfer followed by a STAMP statistical analysis.
Figure 1. A workflow for data collection, pangenome, and post-processing analysis. Genomes were downloaded from NCBI with the ncbi-genome-download command and filtered using refseq, all formats, and complete assembly levels. For the Geothrix genus, the assembly levels filtered were “all”. Nine genera were selected for analysis, and the number in the bracket indicates the number of genomes (85 genomes in total). P represents phylum. The ANI was calculated using the pyani program. Protein-coding genes were annotated with the Prokka program followed by a pangenome analysis with the Roary, Panaroo, and PEPPAN programs. The gene_presence_absence matrices generated by Roary and Panaroo were further applied for genes related to the extracellular electron transfer analysis. The Twilight program was then employed for a post-processing analysis based on the trait of extracellular electron transfer followed by a STAMP statistical analysis.
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Figure 2. A dendrogram of the average nucleotide identity (ANI) using percentage identity among genomes. The ANI was calculated using the pyani program with the ANIb command after blastn alignment. An ANI above 95% between two genomes indicated that they are the same species. Each bacterial genus is marked with a different color for visualization.
Figure 2. A dendrogram of the average nucleotide identity (ANI) using percentage identity among genomes. The ANI was calculated using the pyani program with the ANIb command after blastn alignment. An ANI above 95% between two genomes indicated that they are the same species. Each bacterial genus is marked with a different color for visualization.
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Figure 3. The unweighted pair-group mean arithmetic method (UPGMA) clustering of genes related to extracellular electron transfer profiles according to the Dice coefficient. Genes related to extracellular electron transfer were retrieved from the gene_presence_absence matrix of (A) Roary and (B) Panaroo. The binary data were further analyzed using the DGGEstat 1.0 software tool (Eric van Hannen, The Netherlands Institute for Ecological Research, Wageningen, The Netherlands). The bootstrap values (1000 trials) over 50 are shown for nodes. The results of cluster analysis were plotted in iTOL (Interactive Tree of Life) in unrooted mode. Each bacterial genus is marked with a different color for visualization.
Figure 3. The unweighted pair-group mean arithmetic method (UPGMA) clustering of genes related to extracellular electron transfer profiles according to the Dice coefficient. Genes related to extracellular electron transfer were retrieved from the gene_presence_absence matrix of (A) Roary and (B) Panaroo. The binary data were further analyzed using the DGGEstat 1.0 software tool (Eric van Hannen, The Netherlands Institute for Ecological Research, Wageningen, The Netherlands). The bootstrap values (1000 trials) over 50 are shown for nodes. The results of cluster analysis were plotted in iTOL (Interactive Tree of Life) in unrooted mode. Each bacterial genus is marked with a different color for visualization.
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Figure 4. The relative abundance and the hierarchical cluster analysis of genes related to extracellular electron transfer among bacteria. Genes related to extracellular electron transfer were retrieved from the gene_presence_absence matrix of (A) Roary and (B) Panaroo and further analyzed using the classify_genes. R command of the Twilight program based on genus grouping. The y-axis represents the average percentage. Hierarchical clustering was generated in R version 4.3.1 with the scale, dist, and hclust functions in the Euclidean and complete distance methods.
Figure 4. The relative abundance and the hierarchical cluster analysis of genes related to extracellular electron transfer among bacteria. Genes related to extracellular electron transfer were retrieved from the gene_presence_absence matrix of (A) Roary and (B) Panaroo and further analyzed using the classify_genes. R command of the Twilight program based on genus grouping. The y-axis represents the average percentage. Hierarchical clustering was generated in R version 4.3.1 with the scale, dist, and hclust functions in the Euclidean and complete distance methods.
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Figure 5. A heatmap of the genes of (A) Roary, (B) Panaroo, and (C) PEPPAN. The gene_presence_absence matrix from pangenome analyses was further analyzed using the classify_genes. R command of the Twilight program based on genus grouping. Significant genes were identified using the STAMP software package with Welch’s t-test between the E and non-E groups (p < 0.35 for Roary and Panaroo, p < 0.01 for PEPPAN). E represents the bacterial group capable of extracellular electron transfer, and non-E represents the bacterial group not capable of extracellular electron transfer. The abundance scale bar represents the percentage with a blue–white scale. The genes from PEPPAN analysis were obtained through a Blastx (search protein databases using a translated nucleotide query) search of NCBI. The genes were referred to the KEGG pathway maps into metabolism, genetic information processing, environmental information processing, and cellular process pathways with different color codes. Blank denotes that the protein is not classified.
Figure 5. A heatmap of the genes of (A) Roary, (B) Panaroo, and (C) PEPPAN. The gene_presence_absence matrix from pangenome analyses was further analyzed using the classify_genes. R command of the Twilight program based on genus grouping. Significant genes were identified using the STAMP software package with Welch’s t-test between the E and non-E groups (p < 0.35 for Roary and Panaroo, p < 0.01 for PEPPAN). E represents the bacterial group capable of extracellular electron transfer, and non-E represents the bacterial group not capable of extracellular electron transfer. The abundance scale bar represents the percentage with a blue–white scale. The genes from PEPPAN analysis were obtained through a Blastx (search protein databases using a translated nucleotide query) search of NCBI. The genes were referred to the KEGG pathway maps into metabolism, genetic information processing, environmental information processing, and cellular process pathways with different color codes. Blank denotes that the protein is not classified.
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Liu, D.; Kuo, J.; Lin, C.-H. Comparative Genomic Analysis of Extracellular Electron Transfer in Bacteria. Processes 2024, 12, 2636. https://doi.org/10.3390/pr12122636

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Liu D, Kuo J, Lin C-H. Comparative Genomic Analysis of Extracellular Electron Transfer in Bacteria. Processes. 2024; 12(12):2636. https://doi.org/10.3390/pr12122636

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Liu, Daniel, Jimmy Kuo, and Chorng-Horng Lin. 2024. "Comparative Genomic Analysis of Extracellular Electron Transfer in Bacteria" Processes 12, no. 12: 2636. https://doi.org/10.3390/pr12122636

APA Style

Liu, D., Kuo, J., & Lin, C.-H. (2024). Comparative Genomic Analysis of Extracellular Electron Transfer in Bacteria. Processes, 12(12), 2636. https://doi.org/10.3390/pr12122636

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