Investigating the native functions of [NiFe]-CODH through genomic context analysis

Genomic enzymology has been proven to help understand protein (super) families since the mid-1990s, helping to connect enzyme sequences to function through comparative genomics and neighborhood analysis (Babbitt et al., 1996; Knox and Allen, 2023). In this study, we are employing a genome neighborhood and co-occurrence analysis to help understand reactivity and functionality of the family of nickel-containing carbon monoxide dehydrogenases ([NiFe]-CODHs) and their relationship to hybrid cluster proteins (HCPs).

[NiFe]-CODHs are ancient and diverse enzymes that catalyze the interconversion between carbon monoxide (CO) and carbon dioxide (CO₂), a reaction of high interest for biotechnological applications, including CO₂ capture and conversion. Research on this enzyme spans over 60 years, and recent studies have provided important biochemical insights, such as their turnover frequency, oxygen tolerance, and catalytic mechanism (Basak et al., 2025; Can et al., 2014). These properties vary greatly between enzymes, not only between separate phylogenetic clades but also within clades, making functional prediction from sequence alone challenging. Phylogenetic analyses of available [NiFe]-CODH (hereafter referred to as CODH) sequences with different focus such as gene transfer (Techtmann et al., 2012), primary structure (Inoue et al., 2018), biome distribution (Inoue et al., 2022), and human gut microbiome (Katayama et al., 2024) have enriched our understanding of this old and diverse enzyme family. From initially small data sets of 17 sequences (Lindahl and Chang, 2001) to datasets well above 5000 sequences (this study). It has been shown that up to eight distinct phylogenetic clades (Figure 1) can be distinguished with all of them having sequence variations while preserving the overall fold, as seen with cryo-electron microscopy (Biester et al., 2024) and X-ray crystallography (Basak et al., 2025; Domnik et al., 2017; Gong et al., 2008; Jeoung et al., 2022; Jeoung and Dobbek, 2007; Wittenborn et al., 2020).

The biochemical characterization of this enzyme family is still ongoing, and it shows a wide range of turnover frequencies, as well as different degrees of O₂ tolerance. For example, looking at two CODHs from Carboxydothermus hydrogenoformans: ChCODH-II, a benchmark CODH known for its high CO oxidation activity but low O₂ tolerance, contrasts with ChCODH-IV – another enzyme from the same clade and organism – which retains 20% of activity after 1 hr of O₂ exposure but displays reduced CO oxidation capacity with an increased activation barrier and much lower K_M (Domnik et al., 2017), both belonging to clade F (Figure 1). Similarly, a less active CODH from clade E, NvCODH (formerly known as DvCODH) from Nitratidesulfovibrio vulgaris, has been reported to fully reactivate after initial inactivation by O₂ exposure (Hadj-Saïd et al., 2015). Also, the two CODHs from Thermococcus sp. AM4, TcCODH-I, and TcCODH-II belonging to clade E react slower with O₂ compared to ChCODH-II, but have overall equal O₂ sensitivity (Benvenuti et al., 2020).

Figure 1

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In addition to the previously mentioned diversity within CODH’s clades or organisms, with regard to activity and oxygen tolerance, it is known that some CODHs rely on maturases for full activation, while others do not. For example, RrCODH, from the phototroph Rhodospirillum rubrum, needs to be expressed together with three maturases (CooC, CooJ, and CooT) in order to be isolated in an active form (Kerby et al., 1997). A similar situation arises for NvCODH; however, its genomic neighborhood (Figure 2) only contains one maturase (CooC) which is required for active production (Hadj-Saïd et al., 2015). On the contrary, ChCODH-II can be heterologously expressed without co-expression of any maturases (Merrouch et al., 2018). Also, ChCODH-I needs to be co-expressed with CooC in order to reach high activity, but it can also be expressed without it, albeit with reduced activity (Inoue et al., 2014). Interestingly, much of the diversity with regard to activity, O₂ tolerance, and maturase dependence does not only occur between the different clades but also within them.

Figure 2

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Due to the homology between the CODHs in this study and the fact that active CODHs have been demonstrated from several of the clades, it is reasonable to assume that CODHs from all clades are able to interconvert CO₂/CO. However, a recent study by Dobbek and co-workers showed that C. hydrogenoformans CODH-V (ChCODH-V) from clade D was not able to perform this reaction (Jeoung et al., 2022). They showed that this enzyme has a closer similarity to the family of HCPs, due to its morphing active site, composed of iron, sulfur, and oxygen, responding with structural and stoichiometric changes upon redox shifts. A connection between HCPs and CODHs has been pointed out previously by Inoue et al., 2018, due to their close phylogenetic relationship, and was further discussed by Fujishiro and Takaoka, 2023. HCPs can be divided into three phylogenetic classes, of which class III exhibits a homodimeric structure like CODH. Generally, the two enzyme families share a similar overall fold while their active sites differ greatly, both in terms of amino acid and metallocofactor composition. Similar to ChCODH-V, HCPs do not catalyze CO₂/CO interconversion, but they do display a range of activities at low rate, such as hydroxylamine reductase, peroxidase, nitric oxide reductase, and S-nitrosylase activity. The main natural function of HCPs is debated, but it was recently established that it is most likely a nitric oxide reductase involved in nitric oxide detoxification (Hagen, 2022).

In this study, we contribute to painting a holistic phylogenetic picture of CODHs by focusing on the analysis of their genetic environment as well as harnessing the concept of synteny in which we use a semi-quantitative approach to predict characteristics of CODH, clade- and subclade-specific. We are presenting certain clade-specific trends in the operon composition in CODH. We limit our search on operon composition because the majority of our data are prokaryotic genomes, which link functional-related genes physically in an operon (Yaniv, 2011). We furthermore wanted to limit noise and false positives as much as possible; therefore, we excluded data not within our defined parameters (see Methods). Additionally, since many of the genomes included in this analysis are not completely sequenced or include multiple CODH from different clades, only including genes in close proximity to the target gene ensures that the data is not distorted. Since it is known that many organisms have multiple isoforms of CODHs coded in their genome, we analyze the co-occurrence of CODH of different clades in an organism, as well as the co-occurrences of CODH and HCP. With our findings, we want to propose a systematic approach in the analysis of new CODHs, with the focus on identifying promising CO₂ reduction catalysts, suitable for biotechnological application.

Our analysis reveals substantial diversity in the occurrence, co-occurrence, and genomic context of CODH and HCP genes, suggesting complex evolutionary and functional relationships within and across microbial lineages. The observed differences in the frequency of multiple isoforms per genome (~30% for CODH versus ~6% for HCP) indicate that CODHs are more often retained in multiple copies, potentially pointing to functional diversification among its isoforms. Similar values have been reported by Techtmann et al., 2012, where they found that a striking 43% of organisms coded for more than one CODH. On the other hand, Katayama et al., 2024, investigating only the human gut microbiome, found a number as low as 5.5%. We suspect that this number underrepresents the amount of organisms carrying multiple isoforms of CODH in the human gut, since Katayama and co-workers performed data refinements that exclude potential CODHs (such as the strict requirement for a [4Fe-4S] D-cluster, even though Inoue et al., 2018 reported on the diversity of the D-cluster earlier; ).

There are many examples of organisms coding for multiple CODH isoforms, as outlined in Figure 3; Supplementary files 10 and 11. Many of them have been known to literature for a long time (even though their CODH abundance has only been discussed sporadically), with the most famous example being C. hydrogenoformans encoding five different CODHs (Wu et al., 2005). Another interesting example is Clostridium formicoaceticum, since this organism has a total of six CODH isoforms encoded in its genome. It needs to be noted that in our analysis, this organism did not show up as an organism with six CODHs; see Figure 3A. This is due to our analysis only counting CODH stemming from the same organism when their genes are associated with the same genome assembly. We therefore rather underestimate counts of organisms with multiple CODHs, such as the aforementioned. The only CODH from C. formicoaceticum that has so far been isolated, characterized, and discussed is one of its clade E CODHs, that is associated with acetyl-CoA synthase (ACS) (Bao et al., 2019; Diekert and Thauer, 1978). Other examples from literature attempted to investigate the influence of CODH isoforms on the metabolism. Archaeglobus fulgidus contains three CODH genes, two from clade A and one from clade D. Its clade D CODH seems to have no role in the CO metabolism of this organism (Hocking et al., 2015). There also has been a report for the organism Methanosarcina acetivorans, which harbors three CODHs, all of them belonging to clade A, where only two of them are associated with ACS and are believed to be involved in the CO metabolism, the other one being a lone gene that is seemingly not involved in carbon metabolism (Matschiavelli et al., 2012). Another interesting example is Thermoanaerobacter kivui, formerly known as Acetogenium kivui (Collins et al., 1994). When TkCODH-I (clade C) is deleted from the organism, the strain loses its ability to grow on CO; however, if grown on H₂+CO₂ the overall acetate production is greatly increased (Jain et al., 2022). Similar effects have been shown for Clostridium autoethanogenum, which contains three isoforms of CODH. If its clade C CODH is deleted, the organism’s lag phase is reduced and its growth rate is greatly increased (Liew et al., 2016). The other two CODH isoforms from this organism are from clades E and D. Deletion of clade D CODH showed no immediate effect on the organism, except moderately lower overall biomass yield (Liew et al., 2016). In our analysis, we saw an increased frequency of co-occurrence of clades C and D with A, E, or F, which together with biological data may indicate a complementary role, possibly linked to redox sensing or regulatory functions. This is especially evident with the examples for clade C CODHs from T. kivui and C. autoethanogenum. For the case of clade D, which lacks catalytic activity toward CO/CO₂ interconversion (as reported by Jeoung et al., 2022, through their recombinant production of ChCODH-V) and until now has unknown influence in the metabolism that might only manifest in harsher environments, since it’s believed to be involved in stress response (Jeoung et al., 2022) (similar to HCPs [Hagen, 2022], see below). However, experimental proof for this claim is still missing.

Furthermore, clades A, E, and F rarely co-occur. Interestingly, many organisms do, however, contain multiple copies of CODHs from one of these clades, such as M. acetivorans (clade A), C. hydrogenofromans (clade F), and C. formicoaceticum (clade E). We suspect an evolutionary reason behind this, as is also outlined by others (Adam et al., 2018; Lindahl and Chang, 2001). Biochemical data indicate that CODHs from these clades possess CO/CO₂ interconversion capability, as previously mentioned. This is also in line with the genetic context of these CODHs, which is most often tuned for this CO/CO₂ interconversion chemistry (Figure 4C, see below).

The high rate of co-occurrence between CODH and HCP genes (except for clade A) suggests functional integration, a shared metabolic niche, or involvement in a coordinated response to redox stress, given that HCPs are thought to regulate nitric or oxidative stress (Hagen, 2022). The lack of co-localization for clade A CODHs might point to distinct metabolic roles or evolutionary constraints, or to the high rate of archaeal genes in clade A CODHs, even though HCP genes are known to also be found in archaea (Hagen, 2022). Interestingly, the co-occurrence between clade D CODH and HCP seems the highest for our dataset; the reason for that remains unclear. Similar to clade D CODH, empirical proof that HCP influences the activity or expression of CODH is missing.

The genomic context analysis adds another layer of functional inference, as has been done before by others with different foci (Inoue et al., 2018; Katayama et al., 2024; Matson et al., 2011; Techtmann et al., 2012). We could show again that operons containing clade A CODHs are highly conserved with one carbon pool-related genes and CooC, as they are almost exclusively found as part of the Wood-Ljungdahl pathway, which has a typical arrangement similar to Methanosarcina barkeri CODH (MbCODH, Figure 2, Supplementary file 7). Recently, another representative from this group from M. thermophila (MetCODH) has been resolved (Biester et al., 2024).

In contrast, clade B CODHs appear largely alone or associated with transport-related genes, raising the possibility of a non-canonical or even degenerated function. Its operon composition is also rather consistent, and its arrangements only vary to a small extent, as ABC transporters are either coded upstream (as for Ruminococcus flavefaciens’ CODH, RfCODH, see Figure 2) or downstream of the CODH gene. Almost all operons analyzed do not contain any maturases, except for a small cluster that branches off rather early in the tree (Supplementary file 7). This might indicate that the need for a maturase was lost due to re-purposing of the CODH. We yet await biochemical characterization of any clade B CODH.

Clade C CODHs are associated with FeS cluster proteins, regulators, and redox enzymes, pointing toward more regulatory or redox-modulatory roles, which could also be indicated in knockout studies (Liew et al., 2016). The only isolated example from this clade is TkCODH-I. Its operon exhibits a composition only partially representable for clade C CODHs, containing only one other gene coding for an FeS protein (Figure 2). Furthermore, TkCODH-I’s sequence branches off early and seems to be rather distinct (Supplementary file 5), only having one other close relative from Aceticella autotrophica (Frolov et al., 2023). Furthermore, the reported isolated CODH from Jain et al., 2022, stems from a CO-adapted strain (Weghoff and Müller, 2016), which might harbor mutations in the protein sequences that are not accessible to us at the moment. Drawn together, we conclude that right now, TkCODH-I might not be an optimal representative for clade C CODHs, and more clade C CODHs should be isolated to help us understand their biochemical properties better.

The high operonic variability and frequency of solitary coding regions in clade D might reflect either evolutionary drift or multifunctionality not restricted to operonic structure. Clade D will therefore not be discussed further.

Operons from clades E and F are more functionally complex, including components from the Wood-Ljungdahl pathway, hydrogenases, and additional redox partners, consistent with a diverse metabolic role. That being said, their operon compositions and arrangements showcased some interesting clustering. Starting with clade F, which has the highest proportion of CODHs that might be associated with hydrogenases, including the aforementioned ChCODH-I and RrCODH, both interacting with hydrogenases to produce hydrogen in vivo (Fox et al., 1996; Soboh et al., 2002), however with greatly differing operon compositions (Figure 2). ChCODH-I-like operons (see Supplementary file 7) contain their hydrogenase modules directly within the CODH operon, whereas RrCODH-like operons do not include the hydrogenase module which is coded upstream of the CODH gene, with an intergenic space of >400 bp (Fox et al., 1996). RrCODH-like operons are the only clade F operons that include two additional maturation enzymes, CooT and CooJ. Clade F also contains many ACS-associated CODHs, such as Neomoorella thermoacetica CODH, NtCODH, formerly known as Moorella thermoacetica (Gtari and Ventura, 2025). Those NtCODH-like operons all have the same arrangement. This arrangement is distinct from clade A and E ACS-associated CODH operons.

In our dataset, most of the hydrogenases and their maturation genes are associated with clade F, suggesting active hydrogen metabolism, coupling CO oxidation to H₂ production or consumption, as has been suggested earlier for a wider range of CODH clades (Inoue et al., 2018; Techtmann et al., 2012). We believe that our data grossly underestimates this relationship overall, since operon examples such as RrCODH and TcCODH-II showcase that hydrogenases associated with a CODH are not necessarily encoded in the same operon. There has also been a report of a clade A CODH from the methanogen M. thermophila (Terlesky and Ferry, 1988) being associated with a hydrogenase. However, in later studies, it was shown that the genome of M. thermophila does not contain a hydrogenase (Smith and Ingram-Smith, 2007). Investigation of the electron transport chain of its membrane could not find a hydrogen-oxidizing complex (Welte and Deppenmeier, 2011). Together with our analysis, we conclude that hydrogenase association is a trait almost exclusive to clade E and F CODHs. ChCODH-II’s operon seems to be rather uniquely constructed, as a similar operon composition only can be found for other Carboxydothermus species. A similar situation can be seen for ChCODH-IV, where its operon containing FeS- and NAD/FAD-dependent oxidoreductases is closer in similarity to some clade E CODH.

Regarding the biggest clade, clade E, its diversity is striking. NvCODH, formerly known as DvCODH (Waite et al., 2020), from the organism N. vulgaris, has a very small operon with only two genes in its close proximity, a transcriptional regulator (Zhou et al., 2012) and a maturation enzyme (CooC), see Figure 2. This is seen for a huge number of both clade E and F CODHs. The occurrence of neither CooJ nor CooT is prominent, both only appearing in two very distinct parts of clade E, all of them being associated with one carbon pool metabolism, with one exception from Clostridium pasteurianum BC1. This operon resembles the clade F RrCODH-like operon. The previously introduced archaeal CODHs, TcCODH-I and TcCODH-II from clade E, are contained in operons (Figure 2) that are rather specific and only found for a few other Thermococcus or Pyrococcus species (Benvenuti et al., 2020; Kim et al., 2015). It needs to be noted that TcCODH-I’s CooC gene is coded outside of its operon, and on the opposite strand. The CooC gene is therefore not included in our analysis. We are only aware of examples from this type of operon and don’t expect this to be a common trait of the CODH maturation machinery. However, it needs to be noted that the CooC proportion might be slightly underestimated. Interestingly, TcCODH-II, like CODH, all contain a CooT-like protein in their operon, forming the only cluster of CODH that contains only CooT-like proteins without CooJ. Within clade E, another unique genomic neighborhood from TkCODH-II must be pointed out. From experimental data, it is known that this CODH is associated with ACS (Jain et al., 2022); however, in our analysis, we did not see this ACS complex in TkCODH-II’s operon. This is due to the ACS subunit being coded further downstream of the CODH gene, not being taken into account due to our initial parameters, which showcase the limits to this study.

For HCPs, the high variability and low operon density – especially in classes I and III – point toward more modular or conditionally expressed roles, similar to clade D CODH. The clear patterning in class II operons, though based on a limited sample, may reflect specialized functions, perhaps in niche-specific oxidoreductase activities.

All codes for bioinformatic analysis presented in this paper are openly accessible at Zenodo under the following DOI's; https://doi.org/10.5281/zenodo.16736767, https://doi.org/10.5281/zenodo.16736754, https://doi.org/10.5281/zenodo.16736722, https://doi.org/10.5281/zenodo.16744414.

1. 1. Abramson J
   2. Adler J
   3. Dunger J
   4. Evans R
   5. Green T
   6. Pritzel A
   7. Ronneberger O
   8. Willmore L
   9. Ballard AJ
   10. Bambrick J
   11. Bodenstein SW
   12. Evans DA
   13. Hung C-C
   14. O’Neill M
   15. Reiman D
   16. Tunyasuvunakool K
   17. Wu Z
   18. Žemgulytė A
   19. Arvaniti E
   20. Beattie C
   21. Bertolli O
   22. Bridgland A
   23. Cherepanov A
   24. Congreve M
   25. Cowen-Rivers AI
   26. Cowie A
   27. Figurnov M
   28. Fuchs FB
   29. Gladman H
   30. Jain R
   31. Khan YA
   32. Low CMR
   33. Perlin K
   34. Potapenko A
   35. Savy P
   36. Singh S
   37. Stecula A
   38. Thillaisundaram A
   39. Tong C
   40. Yakneen S
   41. Zhong ED
   42. Zielinski M
   43. Žídek A
   44. Bapst V
   45. Kohli P
   46. Jaderberg M
   47. Hassabis D
   48. Jumper JM

   (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3

   Nature 630:493–500.

   https://doi.org/10.1038/s41586-024-07487-w

   • PubMed
   • Google Scholar
2. 1. Adam PS
   2. Borrel G
   3. Gribaldo S

   (2018) Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes

   PNAS 115:E5836–E5837.

   https://doi.org/10.1073/pnas.1716667115

   • Google Scholar
3. 1. Babbitt PC
   2. Hasson MS
   3. Wedekind JE
   4. Palmer DRJ
   5. Barrett WC
   6. Reed GH
   7. Rayment I
   8. Ringe D
   9. Kenyon GL
   10. Gerlt JA

   (1996) The enolase superfamily: a general strategy for enzyme-catalyzed abstraction of the alpha-protons of carboxylic acids

   Biochemistry 35:16489–16501.

   https://doi.org/10.1021/bi9616413

   • PubMed
   • Google Scholar
4. 1. Bao T
   2. Cheng C
   3. Xin X
   4. Wang J
   5. Wang M
   6. Yang ST

   (2019) Deciphering mixotrophic Clostridium formicoaceticum metabolism and energy conservation: genomic analysis and experimental studies

   Genomics 111:1687–1694.

   https://doi.org/10.1016/j.ygeno.2018.11.020

   • PubMed
   • Google Scholar
5. 1. Basak Y
   2. Lorent C
   3. Jeoung JH
   4. Zebger I
   5. Dobbek H

   (2025) Metalloradical-driven enzymatic CO2 reduction by a dynamic Ni–Fe cluster

   Nature Catalysis 8:794–803.

   https://doi.org/10.1038/s41929-025-01388-5

   • Google Scholar
6. 1. Benvenuti M
   2. Meneghello M
   3. Guendon C
   4. Jacq-Bailly A
   5. Jeoung JH
   6. Dobbek H
   7. Léger C
   8. Fourmond V
   9. Dementin S

   (2020) The two CO-dehydrogenases of thermococcus sp. AM4

   Biochimica et Biophysica Acta (BBA) - Bioenergetics 1861:148188.

   https://doi.org/10.1016/j.bbabio.2020.148188

   • Google Scholar
7. 1. Biester A
   2. Grahame DA
   3. Drennan CL

   (2024) Capturing a methanogenic carbon monoxide dehydrogenase/acetyl-CoA synthase complex via cryogenic electron microscopy

   PNAS 121:e2410995121.

   https://doi.org/10.1073/pnas.2410995121

   • Google Scholar
8. Software

   1. Böhm M

   (2025a) Protein-to-genome

   Zenodo.

   https://doi.org/10.5281/zenodo.16736767
9. Software

   1. Böhm M

   (2025b) Protein-per-organism

   Zenodo.

   https://doi.org/10.5281/zenodo.16736754
10. Software

    1. Böhm M

    (2025c) Protein-neighbours

    Zenodo.

    https://doi.org/10.5281/zenodo.16736722
11. Software

    1. Böhm M

    (2025d) Filter-gaps

    Zenodo.

    https://doi.org/10.5281/zenodo.16744414
12. Software

    1. Campitelli E
    2. van den Brand T
    3. olivroy

    (2025) ggnewscale: multiple fill and color scales in ggplot2, version v0.5.2

    Zenodo.

    https://doi.org/10.5281/zenodo.2543762
13. 1. Can M
    2. Armstrong FA
    3. Ragsdale SW

    (2014) Structure, function, and mechanism of the nickel metalloenzymes, CO dehydrogenase, and acetyl-CoA synthase

    Chemical Reviews 114:4149–4174.

    https://doi.org/10.1021/cr400461p

    • PubMed
    • Google Scholar
14. 1. Cantalapiedra CP
    2. Hernández-Plaza A
    3. Letunic I
    4. Bork P
    5. Huerta-Cepas J

    (2021) eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale

    Molecular Biology and Evolution 38:5825–5829.

    https://doi.org/10.1093/molbev/msab293

    • PubMed
    • Google Scholar
15. 1. Capella-Gutiérrez S
    2. Silla-Martínez JM
    3. Gabaldón T

    (2009) trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses

    Bioinformatics 25:1972–1973.

    https://doi.org/10.1093/bioinformatics/btp348

    • PubMed
    • Google Scholar
16. 1. Chamberlain SA
    2. Szöcs E

    (2013) taxize: taxonomic search and retrieval in R

    F1000Research 2:191.

    https://doi.org/10.12688/f1000research.2-191.v2

    • PubMed
    • Google Scholar
17. Software

    1. Chamberlain S
    2. Szoecs E
    3. Foster Z
    4. Arendsee Z
    5. Boettiger C
    6. Ram K
    7. Bartomeus I
    8. Baumgartner J
    9. O’Donnell J
    10. Oksanen J
    11. Tzovaras BG
    12. Marchand P
    13. Tran V
    14. Salmon M
    15. Li G
    16. Grenié M

    (2020) taxize: taxonomic information from around the web (manual), version 4c3e4a1

    Github.

    https://github.com/ropensci/taxize
18. Software

    1. Chamberlain S
    2. Arendsee Z
    3. Stirling T

    (2025) taxizedb: tools for working with “taxonomic” databases, version v0.3.1

    Zenodo.

    https://doi.org/10.5281/zenodo.1158055
19. 1. Collins MD
    2. Lawson PA
    3. Willems A
    4. Cordoba JJ
    5. Fernandez-Garayzabal J
    6. Garcia P
    7. Cai J
    8. Hippe H
    9. Farrow JA

    (1994) The phylogeny of the genus Clostridium: proposal of five new genera and eleven new species combinations

    International Journal of Systematic Bacteriology 44:812–826.

    https://doi.org/10.1099/00207713-44-4-812

    • PubMed
    • Google Scholar
20. 1. Diekert GB
    2. Thauer RK

    (1978) Carbon monoxide oxidation by Clostridium thermoaceticum and Clostridium formicoaceticum

    Journal of Bacteriology 136:597–606.

    https://doi.org/10.1128/jb.136.2.597-606.1978

    • PubMed
    • Google Scholar
21. 1. Domnik L
    2. Merrouch M
    3. Goetzl S
    4. Jeoung J-H
    5. Léger C
    6. Dementin S
    7. Fourmond V
    8. Dobbek H

    (2017) CODH-IV: a high-efficiency co-scavenging co dehydrogenase with resistance to O₂

    Angewandte Chemie 56:15466–15469.

    https://doi.org/10.1002/anie.201709261

    • PubMed
    • Google Scholar
22. 1. Fox JD
    2. He Y
    3. Shelver D
    4. Roberts GP
    5. Ludden PW

    (1996) Characterization of the region encoding the CO-induced hydrogenase of Rhodospirillum rubrum

    Journal of Bacteriology 178:6200–6208.

    https://doi.org/10.1128/jb.178.21.6200-6208.1996

    • PubMed
    • Google Scholar
23. 1. Frolov EN
    2. Elcheninov AG
    3. Gololobova AV
    4. Toshchakov SV
    5. Novikov AA
    6. Lebedinsky AV
    7. Kublanov IV

    (2023) Obligate autotrophy at the thermodynamic limit of life in a new acetogenic bacterium

    Frontiers in Microbiology 14:1185739.

    https://doi.org/10.3389/fmicb.2023.1185739

    • PubMed
    • Google Scholar
24. 1. Fujishiro T
    2. Takaoka K

    (2023) Class III hybrid cluster protein homodimeric architecture shows evolutionary relationship with Ni, Fe-carbon monoxide dehydrogenases

    Nature Communications 14:5609.

    https://doi.org/10.1038/s41467-023-41289-4

    • PubMed
    • Google Scholar
25. 1. Gong W
    2. Hao B
    3. Wei Z
    4. Ferguson DJ
    5. Tallant T
    6. Krzycki JA
    7. Chan MK

    (2008) Structure of the alpha2epsilon2 Ni-dependent CO dehydrogenase component of the Methanosarcina barkeri acetyl-CoA decarbonylase/synthase complex

    PNAS 105:9558–9563.

    https://doi.org/10.1073/pnas.0800415105

    • PubMed
    • Google Scholar
26. 1. Gtari M
    2. Ventura S

    (2025) Proposal of Neomoorella gen. nov. as a replacement name for the illegitimate prokaryotic genus name Moorella Collins et al. 1994

    International Journal of Systematic and Evolutionary Microbiology 75:006779.

    https://doi.org/10.1099/ijsem.0.006779

    • Google Scholar
27. 1. Hadj-Saïd J
    2. Pandelia M-E
    3. Léger C
    4. Fourmond V
    5. Dementin S

    (2015) The carbon monoxide dehydrogenase from desulfovibrio vulgaris

    Biochimica et Biophysica Acta 1847:1574–1583.

    https://doi.org/10.1016/j.bbabio.2015.08.002

    • PubMed
    • Google Scholar
28. 1. Hagen WR

    (2022) Structure and function of the hybrid cluster protein

    Coordination Chemistry Reviews 457:214405.

    https://doi.org/10.1016/j.ccr.2021.214405

    • Google Scholar
29. 1. Hocking WP
    2. Roalkvam I
    3. Magnussen C
    4. Stokke R
    5. Steen IH

    (2015) Assessment of the carbon monoxide metabolism of the hyperthermophilic sulfate-reducing archaeon archaeoglobus fulgidus VC-16 by comparative transcriptome analyses

    Archaea 2015:235384.

    https://doi.org/10.1155/2015/235384

    • PubMed
    • Google Scholar
30. 1. Huerta-Cepas J
    2. Serra F
    3. Bork P

    (2016) ETE 3: reconstruction, analysis, and visualization of phylogenomic data

    Molecular Biology and Evolution 33:1635–1638.

    https://doi.org/10.1093/molbev/msw046

    • PubMed
    • Google Scholar
31. 1. Huerta-Cepas J
    2. Szklarczyk D
    3. Heller D
    4. Hernández-Plaza A
    5. Forslund SK
    6. Cook H
    7. Mende DR
    8. Letunic I
    9. Rattei T
    10. Jensen LJ
    11. von Mering C
    12. Bork P

    (2019) eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses

    Nucleic Acids Research 47:D309–D314.

    https://doi.org/10.1093/nar/gky1085

    • PubMed
    • Google Scholar
32. 1. Inoue T
    2. Takao K
    3. Fukuyama Y
    4. Yoshida T
    5. Sako Y

    (2014) Over-expression of carbon monoxide dehydrogenase-I with an accessory protein co-expression: a key enzyme for carbon dioxide reduction

    Bioscience, Biotechnology, and Biochemistry 78:582–587.

    https://doi.org/10.1080/09168451.2014.890027

    • PubMed
    • Google Scholar
33. 1. Inoue M
    2. Nakamoto I
    3. Omae K
    4. Oguro T
    5. Ogata H
    6. Yoshida T
    7. Sako Y

    (2018) Structural and phylogenetic diversity of anaerobic carbon-monoxide dehydrogenases

    Frontiers in Microbiology 9:3353.

    https://doi.org/10.3389/fmicb.2018.03353

    • PubMed
    • Google Scholar
34. 1. Inoue M
    2. Omae K
    3. Nakamoto I
    4. Kamikawa R
    5. Yoshida T
    6. Sako Y

    (2022) Biome-specific distribution of Ni-containing carbon monoxide dehydrogenases

    Extremophiles 26:9.

    https://doi.org/10.1007/s00792-022-01259-y

    • PubMed
    • Google Scholar
35. 1. Jain S
    2. Katsyv A
    3. Basen M
    4. Müller V

    (2022) The monofunctional CO dehydrogenase CooS is essential for growth of Thermoanaerobacter kivui on carbon monoxide

    Extremophiles 26:4.

    https://doi.org/10.1007/s00792-021-01251-y

    • PubMed
    • Google Scholar
36. 1. Jeoung JH
    2. Dobbek H

    (2007) Carbon dioxide activation at the Ni,Fe-cluster of anaerobic carbon monoxide dehydrogenase

    Science 318:1461–1464.

    https://doi.org/10.1126/science.1148481

    • PubMed
    • Google Scholar
37. 1. Jeoung JH
    2. Fesseler J
    3. Domnik L
    4. Klemke F
    5. Sinnreich M
    6. Teutloff C
    7. Dobbek H

    (2022) A morphing [4Fe-3S-nO]-cluster within a carbon monoxide dehydrogenase scaffold

    Angewandte Chemie 61:e202117000.

    https://doi.org/10.1002/anie.202117000

    • PubMed
    • Google Scholar
38. 1. Johnson ZI
    2. Chisholm SW

    (2004) Properties of overlapping genes are conserved across microbial genomes

    Genome Research 14:2268–2272.

    https://doi.org/10.1101/gr.2433104

    • PubMed
    • Google Scholar
39. 1. Katayama YA
    2. Kamikawa R
    3. Yoshida T

    (2024) Phylogenetic diversity of putative nickel-containing carbon monoxide dehydrogenase-encoding prokaryotes in the human gut microbiome

    Microbial Genomics 10:001285.

    https://doi.org/10.1099/mgen.0.001285

    • PubMed
    • Google Scholar
40. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • PubMed
    • Google Scholar
41. 1. Kerby RL
    2. Ludden PW
    3. Roberts GP

    (1997) In vivo nickel insertion into the carbon monoxide dehydrogenase of Rhodospirillum rubrum: molecular and physiological characterization of cooCTJ

    Journal of Bacteriology 179:2259–2266.

    https://doi.org/10.1128/jb.179.7.2259-2266.1997

    • PubMed
    • Google Scholar
42. 1. Kim MS
    2. Choi AR
    3. Lee SH
    4. Jung HC
    5. Bae SS
    6. Yang TJ
    7. Jeon JH
    8. Lim JK
    9. Youn H
    10. Kim TW
    11. Lee HS
    12. Kang SG

    (2015) A novel CO-responsive transcriptional regulator and enhanced H2 production by an engineered Thermococcus onnurineus NA1 strain

    Applied and Environmental Microbiology 81:1708–1714.

    https://doi.org/10.1128/AEM.03019-14

    • PubMed
    • Google Scholar
43. 1. Knox HL
    2. Allen KN

    (2023) Expanding the viewpoint: leveraging sequence information in enzymology

    Current Opinion in Chemical Biology 72:102246.

    https://doi.org/10.1016/j.cbpa.2022.102246

    • PubMed
    • Google Scholar
44. 1. Li W
    2. Jaroszewski L
    3. Godzik A

    (2001) Clustering of highly homologous sequences to reduce the size of large protein databases

    Bioinformatics 17:282–283.

    https://doi.org/10.1093/bioinformatics/17.3.282

    • PubMed
    • Google Scholar
45. 1. Li W
    2. Jaroszewski L
    3. Godzik A

    (2002) Tolerating some redundancy significantly speeds up clustering of large protein databases

    Bioinformatics 18:77–82.

    https://doi.org/10.1093/bioinformatics/18.1.77

    • PubMed
    • Google Scholar
46. 1. Li W
    2. Godzik A

    (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

    Bioinformatics 22:1658–1659.

    https://doi.org/10.1093/bioinformatics/btl158

    • PubMed
    • Google Scholar
47. 1. Liew F
    2. Henstra AM
    3. Winzer K
    4. Köpke M
    5. Simpson SD
    6. Minton NP

    (2016) Insights into CO2 fixation pathway of clostridium autoethanogenum by targeted mutagenesis

    mBio 7:00427-16.

    https://doi.org/10.1128/mBio.00427-16

    • PubMed
    • Google Scholar
48. 1. Lindahl PA
    2. Chang B

    (2001) The evolution of acetyl-CoA synthase

    Origins of Life and Evolution of the Biosphere 31:403–434.

    https://doi.org/10.1023/a:1011809430237

    • PubMed
    • Google Scholar
49. Book

    1. Madden T

    (2013)

    The BLAST sequence analysis tool

    In: Madden T, editors. The NCBI Handbook. National Center for Biotechnology Information (US). pp. 1–15.

    • Google Scholar
50. 1. Matschiavelli N
    2. Oelgeschläger E
    3. Cocchiararo B
    4. Finke J
    5. Rother M

    (2012) Function and regulation of isoforms of carbon monoxide dehydrogenase/acetyl coenzyme A synthase in Methanosarcina acetivorans

    Journal of Bacteriology 194:5377–5387.

    https://doi.org/10.1128/JB.00881-12

    • PubMed
    • Google Scholar
51. 1. Matson EG
    2. Gora KG
    3. Leadbetter JR

    (2011) Anaerobic carbon monoxide dehydrogenase diversity in the homoacetogenic hindgut microbial communities of lower termites and the wood roach

    PLOS ONE 6:e19316.

    https://doi.org/10.1371/journal.pone.0019316

    • PubMed
    • Google Scholar
52. 1. Merrouch M
    2. Benvenuti M
    3. Lorenzi M
    4. Léger C
    5. Fourmond V
    6. Dementin S

    (2018) Maturation of the [Ni-4Fe-4S] active site of carbon monoxide dehydrogenases

    Journal of Biological Inorganic Chemistry 23:613–620.

    https://doi.org/10.1007/s00775-018-1541-0

    • PubMed
    • Google Scholar
53. 1. Minh BQ
    2. Schmidt HA
    3. Chernomor O
    4. Schrempf D
    5. Woodhams MD
    6. von Haeseler A
    7. Lanfear R

    (2020) IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era

    Molecular Biology and Evolution 37:1530–1534.

    https://doi.org/10.1093/molbev/msaa015

    • PubMed
    • Google Scholar
54. 1. O’Leary NA
    2. Cox E
    3. Holmes JB
    4. Anderson WR
    5. Falk R
    6. Hem V
    7. Tsuchiya MTN
    8. Schuler GD
    9. Zhang X
    10. Torcivia J
    11. Ketter A
    12. Breen L
    13. Cothran J
    14. Bajwa H
    15. Tinne J
    16. Meric PA
    17. Hlavina W
    18. Schneider VA

    (2024) Exploring and retrieving sequence and metadata for species across the tree of life with NCBI Datasets

    Scientific Data 11:732.

    https://doi.org/10.1038/s41597-024-03571-y

    • PubMed
    • Google Scholar
55. Software

    1. Pedersen TL

    (2025) patchwork: the composer of plots, version 1.3.2.9000

    Patchwork.

    https://patchwork.data-imaginist.com
56. 1. Price MN
    2. Dehal PS
    3. Arkin AP

    (2010) FastTree 2--approximately maximum-likelihood trees for large alignments

    PLOS ONE 5:e9490.

    https://doi.org/10.1371/journal.pone.0009490

    • PubMed
    • Google Scholar
57. Software

    1. R Development Core Team

    (2023) R: a language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
58. 1. Sayers E

    (2022)

    Entrez Programming Utilities

    A general introduction to the e-utilities, Entrez Programming Utilities, National Center for Biotechnology Information (US).

    • Google Scholar
59. 1. Shen W
    2. Le S
    3. Li Y
    4. Hu F

    (2016) SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation

    PLOS ONE 11:e0163962.

    https://doi.org/10.1371/journal.pone.0163962

    • PubMed
    • Google Scholar
60. 1. Smith KS
    2. Ingram-Smith C

    (2007) Methanosaeta, the forgotten methanogen?

    Trends in Microbiology 15:150–155.

    https://doi.org/10.1016/j.tim.2007.02.002

    • PubMed
    • Google Scholar
61. 1. Soboh B
    2. Linder D
    3. Hedderich R

    (2002) Purification and catalytic properties of a CO-oxidizing:H2-evolving enzyme complex from Carboxydothermus hydrogenoformans

    European Journal of Biochemistry 269:5712–5721.

    https://doi.org/10.1046/j.1432-1033.2002.03282.x

    • PubMed
    • Google Scholar
62. 1. Techtmann SM
    2. Lebedinsky AV
    3. Colman AS
    4. Sokolova TG
    5. Woyke T
    6. Goodwin L
    7. Robb FT

    (2012) Evidence for horizontal gene transfer of anaerobic carbon monoxide dehydrogenases

    Frontiers in Microbiology 3:132.

    https://doi.org/10.3389/fmicb.2012.00132

    • PubMed
    • Google Scholar
63. 1. Terlesky KC
    2. Ferry JG

    (1988) Ferredoxin requirement for electron transport from the carbon monoxide dehydrogenase complex to a membrane-bound hydrogenase in acetate-grown Methanosarcina thermophila

    The Journal of Biological Chemistry 263:4075–4079.

    https://doi.org/10.1016/S0021-9258(18)68892-1

    • PubMed
    • Google Scholar
64. 1. Waite DW
    2. Chuvochina M
    3. Pelikan C
    4. Parks DH
    5. Yilmaz P
    6. Wagner M
    7. Loy A
    8. Naganuma T
    9. Nakai R
    10. Whitman WB
    11. Hahn MW
    12. Kuever J
    13. Hugenholtz P

    (2020) Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities

    International Journal of Systematic and Evolutionary Microbiology 70:5972–6016.

    https://doi.org/10.1099/ijsem.0.004213

    • PubMed
    • Google Scholar
65. 1. Wang LG
    2. Lam TTY
    3. Xu S
    4. Dai Z
    5. Zhou L
    6. Feng T
    7. Guo P
    8. Dunn CW
    9. Jones BR
    10. Bradley T
    11. Zhu H
    12. Guan Y
    13. Jiang Y
    14. Yu G

    (2020) Treeio: an r package for phylogenetic tree input and output with richly annotated and associated data

    Molecular Biology and Evolution 37:599–603.

    https://doi.org/10.1093/molbev/msz240

    • PubMed
    • Google Scholar
66. 1. Weghoff MC
    2. Müller V

    (2016) CO metabolism in the thermophilic acetogen thermoanaerobacter kivui

    Applied and Environmental Microbiology 82:2312–2319.

    https://doi.org/10.1128/AEM.00122-16

    • PubMed
    • Google Scholar
67. 1. Welte C
    2. Deppenmeier U

    (2011) Membrane-bound electron transport in Methanosaeta thermophila

    Journal of Bacteriology 193:2868–2870.

    https://doi.org/10.1128/JB.00162-11

    • PubMed
    • Google Scholar
68. 1. Wickham H
    2. Averick M
    3. Bryan J
    4. Chang W
    5. McGowan L
    6. François R
    7. Grolemund G
    8. Hayes A
    9. Henry L
    10. Hester J
    11. Kuhn M
    12. Pedersen T
    13. Miller E
    14. Bache S
    15. Müller K
    16. Ooms J
    17. Robinson D
    18. Seidel D
    19. Spinu V
    20. Takahashi K
    21. Vaughan D
    22. Wilke C
    23. Woo K
    24. Yutani H

    (2019) Welcome to the tidyverse

    Journal of Open Source Software 4:1686.

    https://doi.org/10.21105/joss.01686

    • Google Scholar
69. Software

    1. Wilkins D

    (2023) gggenes: draw gene arrow maps in “ggplot2", version 0.6.0

    CRAN.

    https://wilkox.org/gggenes/
70. 1. Wittenborn EC
    2. Guendon C
    3. Merrouch M
    4. Benvenuti M
    5. Fourmond V
    6. Léger C
    7. Drennan CL
    8. Dementin S

    (2020) The solvent-exposed Fe-S D-Cluster Contributes to oxygen-resistance in Desulfovibrio vulgaris Ni-Fe carbon monoxide dehydrogenase

    ACS Catalysis 10:7328–7335.

    https://doi.org/10.1021/acscatal.0c00934

    • PubMed
    • Google Scholar
71. 1. Wu M
    2. Ren Q
    3. Durkin AS
    4. Daugherty SC
    5. Brinkac LM
    6. Dodson RJ
    7. Madupu R
    8. Sullivan SA
    9. Kolonay JF
    10. Nelson WC
    11. Tallon LJ
    12. Jones KM
    13. Ulrich LE
    14. Gonzalez JM
    15. Zhulin IB
    16. Robb FT
    17. Eisen JA

    (2005) Life in hot carbon monoxide: the complete genome sequence of carboxydothermus hydrogenoformans Z-2901

    PLOS Genetics 1:e65.

    https://doi.org/10.1371/journal.pgen.0010065

    • Google Scholar
72. 1. Xu S
    2. Dai Z
    3. Guo P
    4. Fu X
    5. Liu S
    6. Zhou L
    7. Tang W
    8. Feng T
    9. Chen M
    10. Zhan L
    11. Wu T
    12. Hu E
    13. Jiang Y
    14. Bo X
    15. Yu G

    (2021) ggtreeExtra: compact visualization of richly annotated phylogenetic data

    Molecular Biology and Evolution 38:4039–4042.

    https://doi.org/10.1093/molbev/msab166

    • PubMed
    • Google Scholar
73. 1. Yaniv M

    (2011) The 50th anniversary of the publication of the operon theory in the Journal of Molecular Biology: past, present and future

    Journal of Molecular Biology 409:1–6.

    https://doi.org/10.1016/j.jmb.2011.03.041

    • PubMed
    • Google Scholar
74. 1. Yu G
    2. Smith DK
    3. Zhu H
    4. Guan Y
    5. Lam TTY

    (2017) ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data

    Methods in Ecology and Evolution 8:28–36.

    https://doi.org/10.1111/2041-210X.12628

    • Google Scholar
75. 1. Yu G
    2. Lam TTY
    3. Zhu H
    4. Guan Y

    (2018) Two methods for mapping and visualizing associated data on phylogeny using ggtree

    Molecular Biology and Evolution 35:3041–3043.

    https://doi.org/10.1093/molbev/msy194

    • PubMed
    • Google Scholar
76. 1. Zhou A
    2. Chen YI
    3. Zane GM
    4. He Z
    5. Hemme CL
    6. Joachimiak MP
    7. Baumohl JK
    8. He Q
    9. Fields MW
    10. Arkin AP
    11. Wall JD
    12. Hazen TC
    13. Zhou J

    (2012) Functional characterization of Crp/Fnr-type global transcriptional regulators in Desulfovibrio vulgaris Hildenborough

    Applied and Environmental Microbiology 78:1168–1177.

    https://doi.org/10.1128/AEM.05666-11

    • PubMed
    • Google Scholar
77. 1. Zhu Q
    2. Mai U
    3. Pfeiffer W
    4. Janssen S
    5. Asnicar F
    6. Sanders JG
    7. Belda-Ferre P
    8. Al-Ghalith GA
    9. Kopylova E
    10. McDonald D
    11. Kosciolek T
    12. Yin JB
    13. Huang S
    14. Salam N
    15. Jiao JY
    16. Wu Z
    17. Xu ZZ
    18. Cantrell K
    19. Yang Y
    20. Sayyari E
    21. Rabiee M
    22. Morton JT
    23. Podell S
    24. Knights D
    25. Li WJ
    26. Huttenhower C
    27. Segata N
    28. Smarr L
    29. Mirarab S
    30. Knight R

    (2019) Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea

    Nature Communications 10:5477.

    https://doi.org/10.1038/s41467-019-13443-4

    • PubMed
    • Google Scholar
78. Website

    1. Zhu Q

    (2023) WoL: reference phylogeny for microbes

    Accessed April 5, 2019.

    https://biocore.github.io/wol/

Author details

1. Maximilian Böhm

   Molecular Biomimetics, Department of Chemistry – Ångström Laboratory, Uppsala University, Uppsala, Sweden

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0205-8030

2. Henrik Land

   Molecular Biomimetics, Department of Chemistry – Ångström Laboratory, Uppsala University, Uppsala, Sweden

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   henrik.land@kemi.uu.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3073-5641

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