Volume 13, Issue 4 e1433

ORIGINAL ARTICLE

Open Access

Vitamin B₁₂ as a source of variability in isotope effects for chloroform biotransformation by Dehalobacter

Elizabeth Phillips,

Elizabeth Phillips

orcid.org/0000-0002-3412-5480

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada

Contribution: Conceptualization, Investigation, Writing - review & editing

Search for more papers by this author

Katherine Picott,

Katherine Picott

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Conceptualization, Visualization, Investigation, Writing - review & editing

Search for more papers by this author

Steffen Kümmel,

Steffen Kümmel

Department of Technical Biogeochemistry, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Contribution: Investigation, Writing - review & editing

Search for more papers by this author

Olivia Bulka,

Olivia Bulka

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Writing - review & editing, Investigation

Search for more papers by this author

Elizabeth Edwards,

Elizabeth Edwards

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Conceptualization, Writing - review & editing, Supervision

Search for more papers by this author

Po-Hsiang Wang,

Po-Hsiang Wang

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Investigation, Writing - review & editing

Search for more papers by this author

Matthias Gehre,

Matthias Gehre

Department of Technical Biogeochemistry, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Contribution: Investigation

Search for more papers by this author

Ivonne Nijenhuis,

Ivonne Nijenhuis

Department of Technical Biogeochemistry, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Contribution: Investigation, Writing - review & editing

Search for more papers by this author

Barbara S. Lollar,

Corresponding Author

Barbara S. Lollar

[email protected]

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada

Correspondence Barbara S. Lollar, Department of Earth Sciences, University of Toronto, 22 Russell St., Toronto, ON M5S 3B1, Canada.

Email: [email protected]

Contribution: Investigation, Conceptualization, Supervision, Writing - review & editing

Search for more papers by this author

Elizabeth Phillips,

Elizabeth Phillips

orcid.org/0000-0002-3412-5480

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada

Contribution: Conceptualization, Investigation, Writing - review & editing

Search for more papers by this author

Katherine Picott,

Katherine Picott

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Conceptualization, Visualization, Investigation, Writing - review & editing

Search for more papers by this author

Steffen Kümmel,

Steffen Kümmel

Department of Technical Biogeochemistry, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Contribution: Investigation, Writing - review & editing

Search for more papers by this author

Olivia Bulka,

Olivia Bulka

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Writing - review & editing, Investigation

Search for more papers by this author

Elizabeth Edwards,

Elizabeth Edwards

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Conceptualization, Writing - review & editing, Supervision

Search for more papers by this author

Po-Hsiang Wang,

Po-Hsiang Wang

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

Contribution: Investigation, Writing - review & editing

Search for more papers by this author

Matthias Gehre,

Matthias Gehre

Department of Technical Biogeochemistry, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Contribution: Investigation

Search for more papers by this author

Ivonne Nijenhuis,

Ivonne Nijenhuis

Department of Technical Biogeochemistry, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Contribution: Investigation, Writing - review & editing

Search for more papers by this author

Barbara S. Lollar,

Corresponding Author

Barbara S. Lollar

[email protected]

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada

Correspondence Barbara S. Lollar, Department of Earth Sciences, University of Toronto, 22 Russell St., Toronto, ON M5S 3B1, Canada.

Email: [email protected]

Contribution: Investigation, Conceptualization, Supervision, Writing - review & editing

Search for more papers by this author

https://doi.org/10.1002/mbo3.1433

Citations: 3

Share a link

Email
Wechat
Bluesky

Graphical Abstract

The presence of vitamin B₁₂ has a significant effect on carbon isotope effects, consistent with an isotope masking effect. We interpret this in the context of predicted enzyme structures.

Abstract

Carbon and chlorine isotope effects for biotransformation of chloroform by different microbes show significant variability. Reductive dehalogenases (RDase) enzymes contain different cobamides, affecting substrate preferences, growth yields, and dechlorination rates and extent. We investigate the role of cobamide type on carbon and chlorine isotopic signals observed during reductive dechlorination of chloroform by the RDase CfrA. Microcosm experiments with two subcultures of a Dehalobacter-containing culture expressing CfrA—one with exogenous cobamide (Vitamin B₁₂, B12⁺) and one without (to drive native cobamide production)—resulted in a markedly smaller carbon isotope enrichment factor (ε_{C, bulk}) for B12⁻ (−22.1 ± 1.9‰) compared to B12⁺ (−26.8 ± 3.2‰). Both cultures exhibited significant chlorine isotope fractionation, and although a lower ε_{Cl, bulk} was observed for B12⁻ (−6.17 ± 0.72‰) compared to B12⁺ (−6.86 ± 0.77‰) cultures, these values are not statistically different. Importantly, dual-isotope plots produced identical slopes of Λ_Cl/C (Λ_{Cl/C, B12+} = 3.41 ± 0.15, Λ_{Cl/C, B12}− = 3.39 ± 0.15), suggesting the same reaction mechanism is involved in both experiments, independent of the lower cobamide bases. A nonisotopically fractionating masking effect may explain the smaller fractionations observed for the B12⁻ containing culture.

1 INTRODUCTION

Chloroform (CF, trichloromethane) has historically been used as an anesthetic and a precursor for refrigerants and fluoropolymers (Holbrook, 2000). CF is a groundwater contaminant found at ∼25% of the United States Environmental Protection Agency priority sites, (USEPA, 2015) a concern due to its carcinogenic effects (IARC, 1999) and toxicity to the central nervous system, liver, and kidneys (Agency for Toxic Substances and Disease Registry, 1997). Contaminant concentrations can decrease via physical processes (e.g., volatilization, dilution, diffusion) and transformation processes, with the latter preferred for site remediation, as transformation removes CF mass and can produce less harmful end-products (Bulka, Webb, et al., 2023; Justicia-Leon et al., 2014; Lee et al., 2012; Wang et al., 2022).

Compound-specific isotope analysis (CSIA) is a powerful tool to identify contaminant transformation in the field and estimate remediation rates and extents based on differences in activation energies between bonds containing exclusively light isotopes of an element (^LE) and those containing a heavy isotope (^HE) (Hunkeler et al., 2008). This varying activation energy causes slight differences in reaction rate constants for molecules containing exclusively light (^Lk) isotopes versus one or more heavy (^Hk) isotopes of an element in a reactive position (primary isotope effect) or its vicinity (secondary isotope effect)—that is, the kinetic isotope effect (KIE).

Over the last decade, CSIA has been used to probe transformation mechanisms and kinetics (reviewed elsewhere [Ojeda et al., 2020]; Elsner, 2010). Briefly, the intrinsic KIE magnitude is determined by the transition state structure, which is influenced by the order and manner of bond breakage and formation in the transformation step(s). However, biotransformation processes are comprised of multiple elementary reaction steps, including nontransformation steps such as mass transfer (Bosma et al., 1997) or substrate–enzyme complex formation (Michaelis & Menten, 1913). If a nontransformation step preceding the transformation step is rate-limiting, the observed KIE may be suppressed, (Jencks, 1969) producing a lower apparent KIE (AKIE), known as a “masking effect.” Identifying masking effects and conditions that influence them are thus important in applying and interpreting CSIA (Ehrl et al., 2018; Nijenhuis et al., 2005; Thullner et al., 2013). Since masking typically affects both elements equally, the ratios of dual-element isotope effects are insensitive to masking. Hence dual-isotope plots, where isotopic compositions of two elements within a reactive bond are plotted against each other, can overcome masking effects. These plots generally reflect a linear relationship, with a regression slope (lambda, Λ) that yields more direct insight into reaction mechanisms (Ojeda et al., 2020). However, several exceptions (Gafni et al., 2020; Renpenning et al., 2015) have been reported, where Λ varies for the same transformation mechanism due to additional rate-limiting steps that cause isotope fractionation.

Previous investigations of CF biotransformation have shown large and reproducible carbon isotope effects during biodegradation by organohalide-respiring enrichment culture ACT-3 CF with ethanol and lactate as electron donors (ACT-3/EL) (Chan et al., 2012; Heckel et al., 2019; Phillips et al., 2022). A description of ACT-3 is included in the Supporting Information S1. ACT-3 CF/EL grown with vitamin B₁₂ has similar dual carbon and chlorine isotope effects (ε_C = −27.91 ± 1.66, ε_Cl = −4.00 ± 0.20, Λ = 6.64 ± 0.14) to abiotic degradation of CF by vitamin B₁₂ (ε_C = −26.04 ± 0.91, ε_Cl = −4.20 ± 0.26, Λ = 6.46 ± 0.20) (Heckel et al., 2019) suggesting a common reaction mechanism. However, a large range of ε_C (−1.52 to −27.91‰) and ε_Cl (+2.52 to −6.86‰) have been observed in other CF biotransformation studies, producing different Λ_C/Cl (6.64 to −1.2) (Chan et al., 2012; Heckel et al., 2019; Lee et al., 2015; Phillips et al., 2022; Soder-Walz et al., 2022). The cause of variability in observed carbon and chlorine isotope effects for CF biotransformation remains unresolved to date, though isotope effects appear to be enzyme-specific.

Reductive dehalogenases (RDases) are involved in organohalide respiratory processes and utilize a cobamide cofactor to reduce halogenated substrates (Jugder et al., 2015, 2016; Leys et al., 2013). Cobamides contain a tetrapyrrole ring, a ribose, a centrally chelated cobalt ion, and two axial ligands—an upper and a lower—and are differentiated primarily by the lower ligand/base structure. The three main lower ligand classes are benzimidazoles, phenolics, and purines. Cobalamin (vitamin B₁₂) is a cobamide with 5′,6′-dimethylbenzimidazole as a lower base. ACT-3 contains Dehalobacter sp. CF which possesses the complete cobamide biosynthesis pathway, experimentally verified by Wang et al. (2017), although the native cobamide structure remains unknown and does not correspond to any known cobamides. Different cobamide lower bases in RDases have been shown to affect substrate preferences, growth yields, and dechlorination rates and extent (Keller et al., 2014, 2018; Yan et al. 2012, 2013, 2016), yet how the lower bases exert these controls remains elusive. To date, most studies investigating how different cobamides affect RDases have focused on chloroethene dehalogenating RDases.

This study applies CSIA to investigate the impact of vitamin B₁₂ on isotope effects produced during the biotransformation of CF. These results are considered in the context of isotope masking effects, predicted protein–cobamide interactions using structural models, and site remediation significance. Differences in the microbial community in cultures grown with and without vitamin B₁₂ were assessed, and a metagenomic analysis was performed to investigate cobamide synthesis pathways in the B₁₂−free culture. These findings inform how environmental conditions such as cobamide limitation should be considered when using CSIA to evaluate in situ degradation.

2 EXPERIMENTAL PROCEDURES

2.1 Cultures and growth conditions

Details about the parent culture (ACT-3) are provided in Supporting Information S1. B12⁺ experiments are from Phillips et al. (2022) where cultures and experimental setup are described. ACT-3 contains strain CF that dechlorinates CF to dichloromethane (DCM) linked to growth using CfrA (Grostern & Edwards, 2006). A subculture of ACT-3, CF sub (B₁₂⁻), was maintained on CF for 4 years in mineral medium (modified from Edwards and Grbić-Galić [1994] without vitamin B₁₂). Two subcultures were scaled up to 2 L with simultaneously increasing CF concentrations to 1 mM: one from ACT-3 CF/EL parent culture in medium containing cobalamin (B₁₂⁺) (Phillips et al., 2022) and the second from CF sub B₁₂⁻, maintained medium without the addition of vitamin B₁₂ (B₁₂⁻). Cell density was not measured, but cultures were visibly cloudy and consistently degrading 1 mM of CF within 1 week.

2.2 Analytical methods

Detailed analytical methods and calculations are presented in Supporting Information S1. Briefly, CF and DCM concentrations were quantified using a Varian 3400 gas chromatograph (GC) with a flame ionization detector (FID). Three-point calibration curves (concentrations 1.2, 0.6, 0.12 mM, n = 3 for each standard) for CF and DCM were prepared and checked daily. Uncertainty was quantified using daily standard reproducibility (n = 9), which was within typical GC/FID uncertainty, ±5%. δ¹³C was measured on a Finnigan MAT 252 isotope–ratio mass spectrometer interfaced with a Hewlett-Packard 6890 GC and combustion oven. Isotopically characterized laboratory working standards of CF (δ¹³C = −49.8 ± 0.1‰) and DCM (δ¹³C = −39.6 ± 0.3‰) were injected daily to ensure accuracy. The total δ¹³C uncertainty is ±0.5‰ incorporating both accuracy and reproducibility (Sherwood Lollar et al., 2007). δ³⁷Cl was measured on a Neptune MC-ICPMS (Thermo Fisher Scientific) interfaced with a Thermo Scientific Trace 1310 GC after the method of Renpenning et al. (2018) Three offline-characterized in-house standards were used to normalize sample measurements on the standard mean ocean chloride (SMOC) scale and ensure accuracy. The maximum reproducibility (1σ) observed for sample and control measurements was within ±0.3‰.

CSIA measures stable isotope ratios of an element (R = ^HE/^LE), typically expressed in δ notation (Equation 1):

{{\text{\unicode{x003B4}}}}^{{\rm{H}}}E(\text{\unicode{x02030}})={(R}_{\text{sample}}/{R}_{\text{standard}})-1,

()

where R_standard is the isotopic ratio of an international reference standard for the element of interest (V-PDB for carbon, SMOC for chlorine [Hunkeler et al., 2008]). Isotopic enrichment factors were calculated using the Rayleigh model (Equation 2):

1000\times \mathrm{ln}(R/{R}_{{\rm{o}}})={\varepsilon }_{{\rm{E}}}\times \mathrm{ln}(f),

()

where in contaminant hydrogeology R_o is the initial isotopic composition of the parent contaminant, f is the fraction of the original contaminant remaining, and ε_E is an isotopic enrichment factor, the magnitude of isotopic fractionation for a given reaction.

2.3 Community composition

To determine the microbial community composition of ACT-3 CF/B12⁺ and ACT- CF/B12⁻, DNA samples were taken from each culture and extracted using the Kingfisher Duo Prime MagMax microbiome kit (Thermo Fisher Scientific). Amplicon sequencing of the 16S ribosomal RNA gene V6–V8 region was performed by Genome Quebec as previously described and run through a previously established QIIME2 pipeline (Bolyen et al., 2019; Bulka, Webb, et al., 2023).

2.4 Metagenomic sequencing, assembly, and cobalamin synthesis search

The ACT-3 CF culture metagenome was sequenced and assembled as previously described, (Bulka, Picott, et al., 2023) and a detailed description is pending. Briefly, 300 mL of the ACT-3 CF culture was sampled and pelleted twice in November 2020 for DNA extraction as described above. DNA was sequenced by Genome Quebec using both Illumina MiSeq and PacBio Sequel II technologies. Illumina reads were trimmed using Trimmomatic and FastQC (Andrews et al., 2023; Bolger et al., 2014). Long and short reads were assembled using hybridSPAdes (Antipov et al., 2016). Additional quality control, mapping and binning were performed using Anvi'o Snakemake (Eren et al., 2015, 2021; Shaiber et al., 2020). The ACT-3 CF metagenome was deposited publicly under BioProject PRJNA80805, at accession JAVKYP000000000. Contigs were annotated using MetaErg, (Dong & Strous, 2019) and annotations were searched for hidden Markov models representing anaerobic cobalamin biosynthesis genes (Supporting Information S1: Table S3) (Lu et al., 2020).

2.5 Protein models

Protein models were produced for CfrA from Dehalobacter sp. CF (protein accession: AFV05253, UniProt ID: K4LFB7) (Tang et al., 2016). CfrA models were obtained using the AlphaFill online server, (Hekkelman et al., 2023) which takes AlphaFold (Jumper et al., 2021) models from a UniProt database and transplants ligands into the model based on homologous crystal structures. CfrA models with two [4Fe-4S] clusters and either cobalamin or norpseudo-B₁₂ transplanted were downloaded from AlphaFill (Hekkelman et al., 2023). The YASARA (Krieger et al., 2009) energy minimization server was used for model optimization and water addition. The TAT signal peptide sequence was removed from the structure by predicting the cleavage sites using SignalP 6.0, (Teufel et al., 2022) and trimming the sequence to match the cut-site suggested for the homologous enzyme, TmrA (Jugder et al., 2017). The PceA model is visualized as the monomer of structure 4UR0 in the Protein Data Bank (Bommer et al., 2014). Solvent accessibility was assessed using access channels predicted using the CAVER 3.0 plugin in PyMOL v2.3.4, only major channels accessing the cobamide binding site were kept. Polar contacts were assessed, and images were produced using PyMOL v2.3.4.

3 RESULTS

3.1 Carbon and chlorine isotope effects in B12⁺ and B12⁻

No significant change in δ¹³C (Supporting Information S1: Figure S1), δ³⁷Cl (Supporting Information S1: Figure S2), or mass (Supporting Information S1: Figures S3 and S4) was observed for sterile controls. CF was transformed at similar rates within experimental replicates; to f = ∼0.2 within 6 days for B12⁺ (Supporting Information S1: Figure S3), and to ∼0.15 within 7 days for B12⁻ (Supporting Information S1: Figure S4). Biotransformation of CF produced significant carbon and chlorine isotope fractionation in both B12⁺and B12⁻, with enrichment in ¹³C (Δδ¹³C) of up to 45.1‰ and 34.6‰ and Δδ³⁷Cl of up to 13.4‰ and 11.2‰ in B12⁺ and B12⁻, respectively. Equation (2) was used to calculate ε_{C, bulk} and ε_{Cl, bulk} from the data in Figure 1a,b. Data from reaction progress f < 0.2 were not included in ε_bulk calculations as higher uncertainty in these data can significantly impact calculated ε_bulk (Bigeleisen & Allen, 1951; Mundle et al., 2013). Correlation coefficients (R² values) were ≥0.95, consistent with observations for biotransformation-associated isotope experiments in the literature. Based on recent recommendations, (Ojeda et al., 2019, 2021) the York method (York, 1966, 1969) was used for linear regression of the data in Figure 1c,d to calculate Λ_C/Cl. The associated mean square of weighted deviates and p-values (Figure 1c,d) indicate the model is appropriate for the data. Statistical tests (z tests) were used to compare regression slopes (Ojeda et al., 2019). Figure 1 shows Rayleigh and dual-isotope plots for B12⁺ and B12⁻. B12⁻ produced a statistically different ε_C (−22.1 ± 1.9‰) versus B12⁺ (ε_C = −26.8 ± 3.2‰), with p < 0.05 (Table 1). The difference in ε_C between B12⁺ and B12⁻ is particularly significant considering the consistency of the value for B12⁺ ε_C (−26.8 ± 3.2‰) with earlier studies of CF biotransformation by ACT-3 (Chan et al., 2012; Heckel et al., 2019) (Supporting Information S1: Table S1). Further, the corresponding B12⁺ AKIE_C (1.0275 ± 0.0034) is consistent with the theoretical KIE_C for C–Cl bond cleavage, 1.03, calculated using theoretical semiclassical Streitwieser Limits assuming 50% bond cleavage in the transition state (Huskey, 1991). Both ε_C and ε_Cl are lower for B12⁻, although ε_Cl values are not statistically different based on hypothesis tests (Table 1). However, a lower p-value (0.158) is produced for statistical tests of ε_Cl, particularly when compared to the p-value for Λ_C/Cl (0.872; Table 1), indicating a lower probability that the ε_Cl values represent the same underlying “true” value. The absence of exogenous vitamin B₁₂ in B12⁻ may indeed cause ε_Cl suppression that is unresolvable relative to the δ³⁷Cl uncertainty, that is, the true underlying ε_Cl values are different, yet high uncertainty in the regression slopes (95% confidence interval) does not allow us to statistically differentiate them at the designated confidence level (α = 0.05). Importantly, differences in ε_C and ε_Cl do not result in statistically different Λ_C/Cl values between B12⁺ and B12⁻ (Figure 1c,d and Table 1).

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Rayleigh plots (a, b) for the B12− (blue) and B12+ (red) subcultures (from Phillips et al. [2022]) for carbon (a) and chlorine (b) and dual-isotope plots (c, d) for B12− (c) and B12+ (d). Shading represents 95% confidence interval of the slope (see text for details). The mean square of weighted deviations and the associated p-value are shown for dual-isotope plots, regressed using York regression, to assess model fit (detailed discussion provided in Ojeda et al. [2021]).

Table 1. Comparison of ε_C and ε_Cl, calculated AKIE values and Λ_C/Cl values between the B12⁺ and B12⁻ subcultures.

	B12− Expt	B12+ Expt	p Values	Statistically different (α = 0.05)
ε_C	−22.1 ± 1.9‰	−26.8 ± 3.2‰	8.21E−03	Yes
AKIE_C	1.0226 ± 0.0020	1.0275 ± 0.0034	8.21E−03	Yes
ε_Cl	−6.17 ± 0.72‰	−6.86 ± 0.77‰	0.158	No
AKIE_Cl	1.0189 ± 0.0022	1.0210 ± 0.0011	0.158	No
Λ_C/Cl (York)	3.41 ± 0.15	3.39 ± 0.15	0.872	No

Note: Error for AKIE is propagated through Equation (3) using standard error propagation. All uncertainty is reported to two significant figures and the measured or calculated value is reported to the same digit as the uncertainty. Statistically different regression slopes (determined by p-values from Z tests) are indicated by green shading while regression slopes that are not statistically different are indicated by orange shading)
Abbreviation: AKIE, apparent kinetic isotope effect.

3.2 Community composition in B12⁻ versus B12⁺

Dehalobacter is the most abundant bacterial genus in ACT-3 CF, comprising ∼75% of bacterial reads in both subcultures (Supporting Information S1: Figure S5). All ASVs are summarized in Supporting Information S1: Table S4. Notable differences between the two culture conditions include an increased abundance of an Acinetobacter sp. and a Desulfovibrio sp. in the B₁₂−free condition, as well as an archaeal shift from 100% Methanosphaerula to 63% Methanosaeta (Supporting Information S1: Figure S5). Interestingly, Methanosaeta are acetoclastic methanogens, while Methanosphaerula is strictly hydrogenotrophic, suggesting an increased production of acetate by the bacterial species in the B₁₂−free culture (Cadillo-Quiroz et al., 2009; Patel & Sprott, 1990). Desulfovibrio is known to produce acetate and exist in syntrophic relationships with methanogens (Stolyar et al., 2007). Acinetobacter also uses acetate as a carbon source and may be consuming this byproduct from Desulfovibrio (Pirog & Kuz'minskaya, 2003).

Additionally, both Acinetobacter and Desulfovibrio have been previously shown to produce cobamides. Desulfovibrio vulgaris, for example, has been shown to possess all necessary genes for cobalamin synthesis (Hildenborough et al., 2008). Additionally, some Acinetobacter strains encode the necessary genes for nucleotide loop assembly and can convert cobinamide precursors into different cobamides, including cobalamin (Villa & Escalante-Semerena, 2022). A closer metagenomic look at ACT-3 CF was performed to provide more insight into the role of these microorganisms in B12⁻.

3.3 Cobamide pathways identified via metagenomic sequencing

Partial cobamide biosynthesis pathways were found in 102 genera and 59 families in the ACT-3 CF culture. All hits and their predicted taxonomies are shown in the attached file (Supporting Information S1: Table S3). Taxa with more than 10 different biosynthetic genes, including Dehalobacter and Desulfovibrio, are summarized in Table 2. Other potential cobalamin producers in ACT-3 CF are Sporomusa and UBA5314 of the Syntrophomonadaceae family. Acinetobacter was not detected in the metagenome, and metagenome sequencing of B12⁻ should be performed for a more thorough analysis of this subculture.

Table 2. Summary of key genera encoding the cobamide biosynthetic pathway in the ACT-3 CF metagenome.

Step code	HMM	g_Sporomusa_C	g_Dehalobacter	g_Desulfovibrio_F	f__Syntrophomonadaceae; g_UBA5314	c_Peptococcia; g_UBA5318	f__Syntrophomonadaceae; g_UBA4844	f__Anaerovoracaceae; g_UBA7709	g_Methanosphaerula	g_Propionicimonas	g_Sedimentibacter
CbiD	PF01888	1	1	1	2	3	1	4	1		3
CbiG	PF01890	1	1	1	1	3		1	1
CbiK/X	PF01903	2	1	1	2		1	2	1	4	1
CbiC	PF02570	1	1	1	1	2			1	1	1
CbiJ	PF02571	1	1		1	1	1	3			2
CbiK/X	PF06180	5		2	2		1	4			1
CbiD	TIGR00312	1	1	1	1	2	1	1
CbiA	TIGR00379	1	1	1	1	2	1		2	1
CbiJ	TIGR00715	1	1		1	1	1	1
CbiF	TIGR01465	1	1	1	1	1	1
CbiH	TIGR01466	1	1	1	1	2	1	1	1		1
CbiL	TIGR01467	1	1	1	2	2		2
CobA	TIGR01469	1	1	1					1	1
CbiE	TIGR02467	1	1	1		2	1	1		1	1
CbiT	TIGR02469	1	1	1	1	3	1	1		1
CobA	PF01923	1						1		2	1
CobU	PF02283	1	1	1	2			1		1
CobS	PF02654	2	1	1	1			2	1	1
CobD	PF03186	1	1	1	1	1	1	1	1		1
CobQ	TIGR00313	1	1	1	1	1		2	1	1
CobS	TIGR00317	1	1		1				1
AcbPsyn	TIGR00380	1	1		1	1	1		1		1
CobA	TIGR00636	1								1
CobA	TIGR00708	1						1		1
ThrP_dc	TIGR01140	1	1	1
CobT	TIGR03160	1	1	1	1			1		1
CobC/Z	TIGR03161
CobC/Z	TIGR03162				1

Note: Gene copies are highlighted in green, and optional steps are marked with gray. Genera derived from uncultivated bacterial and archaeal (UBA) taxa are also labeled with their best-named rank. The steps are described in Supporting Information S1: Table S2. For all hits, see Supporting Information S1: Table S3.
Abbreviations: CF, chloroform; HMM, hidden Markov models.

4 DISCUSSION

4.1 Isotope masking effects in B12⁻

ε_C Values are statistically different for CF biotransformation by B12⁺ versus B12⁻ (Table 1). While hypothesis tests do not support statistically different ε_Cl values at a confidence level of 0.05, this result is partly due to the uncertainty in ε_Cl which is >∼±0.7‰ (Table 1). The absolute differences between ε_C (4.7‰) and ε_Cl (0.69‰) for B12⁺ and B12⁻ are considerably larger than differences observed for chlorinated ethene biotransformation with different cobamide types, where Δε were <0.3‰ for both carbon and chlorine (see discussion below). Overall, the carbon and chlorine isotope results suggest possible masking of isotope effects in B12⁻. The available data however is insufficient to identify the rate-limiting step causing masking. Both subcultures were derived from the same parent culture (ACT-3 CF) maintained under whole-cell conditions. Thus, common rate-limiting steps that may lead to masking, such as diffusion through the cell membrane, (Nijenhuis et al., 2005; Renpenning et al., 2015) are unlikely to cause masking in B12⁻ relative to B12⁺. Although evolutionary changes in the cell membrane between the two cultures cannot be ruled out, this is unlikely since there was no evolutionary pressure on the organisms to evolve different mechanisms for diffusion through the cell membrane. Commitment to catalysis (Cleland et al., 1976; Northrop, 1981), where enzyme–substrate binding is the rate-limiting step, is a possible cause of masking in B12⁻. In such cases, masking is proportional to the ratio of the rate constant for transformation (k₂) and the enzyme–substrate dissociation step (k₋₁), also known as the commitment factor (C) (Cleland et al., 1976; Northrop, 1981) (Equation 3).

\text{AKIE}\,=\frac{\text{KIE}+C}{1+C},\text{where}\hspace{0.17em}C=\text{commitment factor}={k}_{2}/{k}_{-1}.

()

Because the difference in growth conditions between the two subcultures is the presence of vitamin B₁₂, a masking effect caused by a difference in k₂/k₋₁ is a reasonable mechanism to explain the observations. The difference in k₂/k₋₁ may arise from altered enzyme–substrate dissociation (i.e., changes in k₋₁) due to differences in the protein structure with different cobamides (discussed in more detail in the context of the CfrA structural prediction below). Other mechanisms that could explain the results include considerations of crystallization structures. Those available for RDases (Bommer et al., 2014; Payne et al., 2015) show the cobamide in base-off coordination (i.e., the lower base and the centrally chelated cobalt are not coordinated). Crystal structures do not account for enzyme dynamics, and the lower base could play a role in conformational changes during catalysis, in which case, dissociation of the lower ligand from the cobalt ion, causing a masking effect, cannot be ruled out. Any one of these three scenarios could control the reaction kinetics of the enzyme–substrate binding step, and any would be consistent with a commitment to catalysis acting as the underlying control on the differences in observed isotope effects between these two cultures.

Consistent Λ_C/Cl values suggest a common underlying reaction mechanism in both subcultures and that any masking in B12⁻ compared to B12⁺ (Figure 1a,b) is due to a nonfractionating additional rate-limiting step. Based on dual carbon/chlorine isotope results from Heckel et al. (2019) this reaction likely proceeds via bimolecular nucleophilic substitution (S_N2). Λ_Cl/C values reported here (3.41 ± 0.15 for B12⁻ and 3.39 ± 0.16 for B12⁺) and in previous studies for reductive dechlorination of CF (Λ_Cl/C ∼ 6–8, for a discussion on this variation see Supporting Information S1: Section 3) are significantly different from other engineered remediation strategies such as persulfate oxidation (17 ± 2) and alkaline hydrolysis (13.0 ± 0.8) (Ojeda et al., 2020). Thus, dual-isotope analysis can reliably differentiate CF reductive dechlorination from other transformation pathways, regardless of whether vitamin B₁₂ is present or not.

Despite the likely presence of masking affecting the observed carbon isotope effects for B12⁻ relative to B12⁺ and the importance of this finding with respect to interpreting the details of reaction mechanisms, the effect of these different ε_C values on the calculation of the extent of degradation is minor. For example, using both values, we can estimate the fraction of contaminant remaining (using Equation [3] with propagated error using standard error propagation and a δ¹³C_o = −49.9‰ (δ¹³C_o in the B12⁻ experiment)). At the first time step where δ¹³C = −49.1‰, the corresponding calculated f (in %) using ε_C = −22.1 ± 1.9‰ (B12⁻) is between 93% and 100%, and using ε_C = −26.8 ± 3.2‰ (B12⁺) the calculated range is 94%–100%. At the other end of the spectrum, a similar calculation can be applied for the most enriched values observed in the B12⁺ experiment, specifically δ¹³C = −3.7‰. In this case, the calculated estimates for B12⁻ and B12⁺ range from 9% to 14% and 13%–21%, respectively. Within uncertainty (based on the calculation error propagated through Equation [3] using standard error propagation), these ranges overlap, indicating that either ε_C value can provide a reasonable basis to calculate the extent of biodegradation and from that, to derive CSIA-based biodegradation rates consistent with the best practice EPA guidelines (Jugder et al., 2015).

4.2 Effects of cobamide

The structure and microbial origin of the cobamide in CfrA in B12⁻ is unknown as the native Dehalobacter strain CF cobamide has eluded characterization (Wang et al., 2017) and due to many cobamide biosynthesis genes across multiple taxonomies encoded by the ACT-3 CF metagenome (Table 2). It may be produced by Dehalobacter strain CF itself or scavenged from other organisms in the culture, such as Desulfovibrio or Acinetobacter (Supporting Information S1: Figure S5). However, the different ε_C values observed suggest that differences in enzyme activity occur related to the presence/absence of exogenous vitamin B₁₂. A previous study using PceA grown in Sulfurospirillum multivorans with two different norcobamides (“nor” indicates a lack of a methyl linking moiety compared to cobamides), nor-B12 versus S. multivorans native cobamide, norpseudo-B12, revealed no differences in ε_C, ε_Cl, or Λ with different norcobamides in PceA (Renpenning et al., 2014). A separate study (Buchner et al., 2022) also showed no variability in carbon, chlorine, or dual-isotope effects in PceA with different supplied cobalt species. Keller et al. (2018) used nuclear magnetic resonance (NMR) spectroscopy for a structural analysis of norcobamides with crystal structures of PceA, showing water accessibility of the cobamide lower base. These authors proposed substitutions of the lower base are accommodated by changes in water structure rather than changes in the base position or reorientation of the surrounding side chains (Keller et al., 2018). The PceA crystal structure (Bommer et al., 2014) (Figure 2a) shows water accessibility of the lower base, interpreted by overlapping solvent-accessible channels (mesh shading in Figure 2a) with the lower base of the cobamide, in agreement with Keller et al. (2018) Figure 2b shows interactions between the lower base (circled in red) with water molecules (red spheres).

Importantly, in this study, the CfrA structural prediction (Figure 2c) shows more restricted solvent-accessible channels compared to the PceA structure. More highly confined channels within the CfrA binding pocket could restrict solvent access to the lower base in CfrA, suggesting changes in the lower base of CfrA are accommodated by a change in protein structure rather than water structure. Using the AlphaFill (Hekkelman et al., 2023) and YASARA (Krieger et al., 2009) energy minimization servers, cofactor and water molecule locations and interactions can be combined with predicted protein structures from AlphaFold (Jumper et al., 2021). Figure 2d shows the predicted CfrA structure with vitamin B₁₂. Although a water molecule is present, there are no predicted polar contacts between the water molecule and the lower base of vitamin B₁₂, in agreement with this hypothesis. When CfrA is docked with norpseudo-B12 using AlphaFill (Figure 2e), polar contacts are predicted between the cobamide and with water and protein residues. Though the native cobamide in CfrA is unknown, this docking prediction suggests a varying mechanism to accommodate different cobamides in CfrA, involving protein residues or water. A change in polar contacts involving protein residues could result in changes in protein structure with different cobamides. Changes in protein structure could plausibly alter substrate binding to enzymes when different cobamides are incorporated (e.g., here—more efficient binding in CfrA with vitamin B₁₂ compared to native cobamide). If structural changes indeed result in more efficient binding with vitamin B₁₂ versus the unknown cobamide, this would explain the lower isotope effects observed for B12⁻ versus B12⁺. These findings suggest a basis for the different findings from Renpenning et al.'s (2014) for tetrachloroethene (PCE), compared to those observed here for CF. Additional experimental validation of this hypothesis and predicted CfrA model through determining a CfrA crystal structure will be important for future work.

These results demonstrate that the underlying causes controlling variation in AKIEs should be investigated when studying different cultures in laboratory experiments. Using the Rayleigh equation to identify and evaluate degradation rates and extents can be improved with accurate estimates of ε_E and Λ. Most importantly, the results here demonstrate that a detailed understanding of controls on ε_E and Λ in transformation reactions, even at the level of lower base substitutions, can be gleaned from integrating CSIA in culture-based experiments with protein structural models. Further, this work reinforces the power of a dual-isotope approach for evaluating masking effects and providing a basis for robust identification of transformation pathways even if masking effects occur. Along with other recent studies on the effects of active site residues, (Gafni et al., 2020) this study highlights the importance of detailed integration of the structures and activity of proteins with information on reaction efficiency provided by naturally occurring compound-specific isotope effects as a dual-pronged approach to understanding the details of contaminant transformation relevant to both experimental and field-based studies.

AUTHOR CONTRIBUTIONS

Elizabeth Phillips: Conceptualization; investigation; writing—review and editing. Katherine Picott: Conceptualization; visualization; investigation; writing—review and editing. Steffen Kümmel: Investigation; writing—review and editing. Olivia Bulka: Writing—review and editing; investigation. Elizabeth Edwards: Conceptualization; writing—review and editing; supervision. Po-Hsiang Wang: Investigation; writing—review and editing. Matthias Gehre: Investigation. Ivonne Nijenhuis: Investigation; writing—review and editing. Barbara Sherwood Lollar: Investigation; conceptualization; supervision; writing—review and editing.

ACKNOWLEDGMENTS

The authors would like to thank Axel Horst of the Helmholtz Centre of Environmental Research and Tetyana Gilevska from the University of Toronto for their help with stable isotope analysis. The authors are thankful for the use of the analytical facilities of the Centre for Chemical Microscopy (ProVIS) at the Helmholtz Centre for Environmental Research—UFZ supported by European Regional Development Funds (EFRE—Europe funds Saxony) and the Helmholtz Association. Elizabeth Phillips was supported by Schmidt Science Fellows, in partnership with the Rhodes Trust. The authors are also grateful for the use of the analytical facilities of the Laboratories for Stable Isotopes (LSI) of the Helmholtz Centre for Environmental Research. Funding for this study was provided by the Canada Research Chair and NSERC Discovery Grants to Barbara S. Lollar with additional funds from Agriscience Canada and the CIFAR Earth4D program.

CONFLICT OF INTEREST STATEMENT

None declared.

ETHICS STATEMENT

None required.

Open Research

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in the NCBI repository at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA80805.

Supporting Information

REFERENCES

Agency for Toxic Substances and Disease Registry. (1997). Toxicological profile for chloroform. https://doi.org/10.1201/9781420061888_ch59
10.1201/9781420061888_ch59
Google Scholar
Andrews, S., Krueger, F., Seconds-Pichon, A., Biggins, F., & Wingett, S. F. Q. C. (2023). A quality control tool for high throughput sequence data.
Google Scholar
Antipov, D., Korobeynikov, A., McLean, J. S., & Pevzner, P. A. (2016). HybridSPAdes: An algorithm for hybrid assembly of short and long reads. Bioinformatics, 32(7), 1009–1015. https://doi.org/10.1093/bioinformatics/btv688
10.1093/bioinformatics/btv688
CAS PubMed Web of Science® Google Scholar
Bigeleisen, J., & Allen, T. L. (1951). Fractionation of the carbon isotopes in decarboxylation reactions. IV. The relative rates of decomposition of 1-C12 and 1-C13 trichloracetate ions. The Journal of Chemical Physics, 19, 760–764.
10.1063/1.1748348
CAS Web of Science® Google Scholar
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for illumina sequence data. Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170
10.1093/bioinformatics/btu170
CAS PubMed Web of Science® Google Scholar
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9
10.1038/s41587-019-0209-9
CAS PubMed Web of Science® Google Scholar
Bommer, M., Kunze, C., Fesseler, J., Schubert, T., Diekert, G., & Dobbek, H. (2014). Structural basis for organohalide respiration. Science, 346(6208), 455–458. https://doi.org/10.1126/science.1258118
10.1126/science.1258118
CAS PubMed Web of Science® Google Scholar
Bosma, T. N. P., Middeldorp, P. J. M., Schraa, G., & Zehnder, A. J. B. (1997). Mass transfer limitation of biotransformation: Quantifying bioavailability. Environmental Science & Technology, 31(1), 248–252. https://doi.org/10.1021/es960383u
10.1021/es960383u
CAS Web of Science® Google Scholar
Buchner, D., Martin, P. R., Scheckenbach, J., Behrens, S., & Haderlein, S. B. (2022). Effects of bacterial growth conditions on carbon and chlorine isotope fractionation associated with TCE biotransformation. ACS ES&T Water, 2(12), 2510–2518. https://doi.org/10.1021/acsestwater.2c00326
10.1021/acsestwater.2c00326
CAS Google Scholar
Bulka, O., Picott, K., Mahadevan, R., & Edwards, E. A. (2023). Identification of functional genes in a chloroform and dichloromethane-degrading microbial culture. bioRxiv, 11, 566631. https://doi.org/10.1101/2023.11.10.566631
10.1101/2023.11.10.566631
Google Scholar
Bulka, O., Webb, J., Dworatzek, S., Mahadevan, R., & Edwards, E. A. (2023). A multifunctional Dehalobacter? Tandem chloroform and dichloromethane degradation in a mixed microbial culture. Environmental Science and Technology, 57(48), 19912–19920. https://doi.org/10.1021/acs.est.3c06686
10.1021/acs.est.3c06686
CAS PubMed Google Scholar
Bulka, O. E. E. A. (Submitted). Metagenome from ACT-3/CF: An anaerobic chloroform-degrading microbial community. Microbiology Resource Announcements.
Google Scholar
Cadillo-Quiroz, H., Yavitt, J. B., & Zinder, S. H. (2009). Methanosphaerula palustris Gen. Nov., Sp. Nov., a hydrogenotrophic methanogen isolated from a minerotrophic fen peatland. International Journal of Systematic and Evolutionary Microbiology, 59(5), 928–935. https://doi.org/10.1099/ijs.0.006890-0
10.1099/ijs.0.006890-0
CAS PubMed Google Scholar
Chan, C. C. H., Mundle, S. O. C., Eckert, T., Liang, X., Tang, S., Lacrampe-Couloume, G., Edwards, E. A., & Sherwood Lollar, B. (2012). Large carbon isotope fractionation during biodegradation of chloroform by Dehalobacter cultures. Environmental Science & Technology, 46(18), 10154–10160. https://doi.org/10.1021/es3010317
10.1021/es3010317
CAS PubMed Google Scholar
Cleland, W. W., Northrop, D. B., & O'Leary, M. H. (1976). Isotope effects in enzyme-catalyzed reactions. Paper presented at Proceedings of the Sixth Annual Harry Steenbock Symposium. University Park Press.
Google Scholar
Dong, X., & Strous, M. (2019). An integrated pipeline for annotation and visualization of metagenomic contigs. Frontiers in Genetics, 10, 999. https://doi.org/10.3389/fgene.2019.00999
10.3389/fgene.2019.00999
CAS PubMed Web of Science® Google Scholar
Edwards, E. A., & Grbić-Galić, D. (1994). Anaerobic degradation of toluene and O-xylene by a methanogenic consortium. Applied and Environmental Microbiology, 60(1), 313–322.
10.1128/aem.60.1.313-322.1994
CAS PubMed Web of Science® Google Scholar
Ehrl, B. N., Gharasoo, M., & Elsner, M. (2018). Isotope fractionation pinpoints membrane permeability as a barrier to atrazine biodegradation in Gram-negative Polaromonas sp. Nea-C. Environmental Science and Technology, 52(1), 1–8. https://doi.org/10.1021/acs.est.7b06599
10.1021/acs.est.7b06599
PubMed Google Scholar
Elsner, M. (2010). Stable isotope fractionation to investigate natural transformation mechanisms of organic contaminants: Principles, prospects and limitations. Journal of Environmental Monitoring, 12, 2005–2031.
10.1039/c0em00277a
CAS PubMed Web of Science® Google Scholar
Eren, A. M., Esen, Ö. C., Quince, C., Vineis, J. H., Morrison, H. G., Sogin, M. L., & Delmont, T. O. (2015). Anvi'o: An advanced analysis and visualization platform for 'omics data. PeerJ, 3, e1319. https://doi.org/10.7717/peerj.1319
10.7717/peerj.1319
PubMed Web of Science® Google Scholar
Eren, A. M., Kiefl, E., Shaiber, A., Veseli, I., Miller, S. E., Schechter, M. S., Fink, I., Pan, J. N., Yousef, M., Fogarty, E. C., Trigodet, F., Watson, A. R., Esen, Ö. C., Moore, R. M., Clayssen, Q., Lee, M. D., Kivenson, V., Graham, E. D., Merrill, B. D., … Willis, A. D. (2021). Community-led, integrated, reproducible multi-omics with Anvi'o. Nature Microbiology, 6(1), 3–6. https://doi.org/10.1038/s41564-020-00834-3
10.1038/s41564-020-00834-3
CAS PubMed Web of Science® Google Scholar
Gafni, A., Gelman, F., Ronen, Z., & Bernstein, A. (2020). Variable carbon and chlorine isotope fractionation in TCE co-metabolic oxidation. Chemosphere, 242, 125130. https://doi.org/10.1016/j.chemosphere.2019.125130
10.1016/j.chemosphere.2019.125130
CAS PubMed Google Scholar
Grostern, A., & Edwards, E. A. (2006). A 1,1,1-trichloroethane-degrading anaerobic mixed microbial culture enhances biotransformation of mixtures of chlorinated ethenes and ethanes. Applied and Environmental Microbiology, 72(12), 7849–7856. https://doi.org/10.1128/AEM.01269-06
10.1128/AEM.01269-06
CAS PubMed Google Scholar
Heckel, B., Phillips, E., Edwards, E., Sherwood Lollar, B., Elsner, M., Manefield, M. J., & Lee, M. (2019). Reductive dehalogenation of trichloromethane by two different Dehalobacter restrictus strains reveal opposing dual element isotope effects. Environmental Science & Technology, 53(5), 2332–2343. https://doi.org/10.1021/acs.est.8b03717
10.1021/acs.est.8b03717
CAS PubMed Google Scholar
Hekkelman, M. L., de Vries, I., Joosten, R. P., & Perrakis, A. (2023). AlphaFill: Enriching AlphaFold models with ligands and cofactors. Nature Methods, 20(2), 205–213. https://doi.org/10.1038/s41592-022-01685-y
10.1038/s41592-022-01685-y
CAS PubMed Web of Science® Google Scholar
Holbrook, M. T. (2000). Chloroform. In K. Othmer (Ed.), Kirk-Othmer encyclopedia of chemical technology. Wiley. https://doi.org/10.1002/0471238961.0308121508151202.a01
10.1002/0471238961.0308121508151202.a01
Google Scholar
Hunkeler, D., Meckenstock, R. U., Sherwood Lollar, B., Schmidt, T. C., & Wilson, J. T. (2008). A guide for assessing biodegradation and source identification of organic ground water contaminants using compound specific isotope analysis (CSIA). USEPA Publication.
Google Scholar
Huskey, W. P. (1991). Enzyme mechanism from isotope effects. CRC Press.
Google Scholar
IARC. (1999). Some chemicals that cause tumours of the kidney or urinary bladder in rodents and some other substances.
Google Scholar
Jencks, W. P. (1969). Catalysis in chemistry and enzymology. McGraw-Hill.
Google Scholar
Jugder, B. E., Bohl, S., Lebhar, H., Healey, R. D., Manefield, M., Marquis, C. P., & Lee, M. (2017). A bacterial chloroform reductive dehalogenase: Purification and biochemical characterization. Microbial Biotechnology, 10(6), 1640–1648. https://doi.org/10.1111/1751-7915.12745
10.1111/1751-7915.12745
CAS PubMed Web of Science® Google Scholar
Jugder, B. E., Ertan, H., Bohl, S., Lee, M., Marquis, C. P., & Manefield, M. (2016). Organohalide respiring bacteria and reductive dehalogenases: Key tools in organohalide bioremediation. Frontiers in Microbiology, 7, 1–12. https://doi.org/10.3389/fmicb.2016.00249
10.3389/fmicb.2016.00249
PubMed Google Scholar
Jugder, B. E., Ertan, H., Lee, M., Manefield, M., & Marquis, C. P. (2015). Reductive dehalogenases come of age in biological destruction of organohalides. Trends in Biotechnology, 33(10), 595–610. https://doi.org/10.1016/j.tibtech.2015.07.004
10.1016/j.tibtech.2015.07.004
CAS PubMed Google Scholar
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
10.1038/s41586-021-03819-2
CAS PubMed Web of Science® Google Scholar
Justicia-Leon, S. D., Higgins, S., Mack, E. E., Griffiths, D. R., Tang, S., Edwards, E. A., & Löffler, F. E. (2014). Bioaugmentation with distinct Dehalobacter strains achieves chloroform detoxification in microcosms. Environmental Science & Technology, 48(3), 1851–1858. https://doi.org/10.1021/es403582f
10.1021/es403582f
CAS PubMed Google Scholar
Keller, S., Kunze, C., Bommer, M., Paetz, C., Menezes, R. C., Svatoš, A., Dobbek, H., & Schubert, T. (2018). Selective utilization of benzimidazolyl-norcobamides as cofactors by the tetrachloroethene reductive dehalogenase of Sulfurospirillum multivorans. Journal of Bacteriology, 200, 1–14.
10.1128/JB.00584-17
CAS Google Scholar
Keller, S., Ruetz, M., Kunze, C., Kräutler, B., Diekert, G., & Schubert, T. (2014). Exogenous 5,6-dimethylbenzimidazole caused production of a non-functional tetrachloroethene reductive dehalogenase in Sulfurospirillum multivorans. Environmental Microbiology, 16(11), 3361–3369. https://doi.org/10.1111/1462-2920.12268
10.1111/1462-2920.12268
CAS PubMed Web of Science® Google Scholar
Krieger, E., Joo, K., Lee, J., Lee, J., Raman, S., Thompson, J., Tyka, M., Baker, D., & Karplus, K. (2009). Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins: Structure, Function, and Bioinformatics, 77, 114–122. https://doi.org/10.1002/prot.22570
10.1002/prot.22570
CAS PubMed Web of Science® Google Scholar
Lee, M., Low, A., Zemb, O., Koenig, J., Michaelsen, A., & Manefield, M. (2012). Complete chloroform dechlorination by organochlorine respiration and fermentation. Environmental Microbiology, 14(4), 883–894. https://doi.org/10.1111/j.1462-2920.2011.02656.x
10.1111/j.1462-2920.2011.02656.x
CAS PubMed Web of Science® Google Scholar
Lee, M., Wells, E., Wong, Y. K., Koenig, J., Adrian, L., Richnow, H. H., & Manefield, M. (2015). Relative contributions of Dehalobacter and zerovalent iron in the degradation of chlorinated methanes. Environmental Science & Technology, 49(7), 4481–4489. https://doi.org/10.1021/es5052364
10.1021/es5052364
CAS PubMed Google Scholar
Leys, D., Adrian, L., & Smidt, H. (2013). Organohalide respiration: Microbes breathing chlorinated molecules. Philosophical Transactions of the Royal Society, B: Biological Sciences, 368(1616), 20120316. https://doi.org/10.1098/rstb.2012.0316
10.1098/rstb.2012.0316
PubMed Google Scholar
Lobo, S. A. L., Brindley, A. A., Romão, C. V., Leech, H. K., Warren, M. J., & Saraiva, L. M. (2008). Two distinct roles for two functional cobaltochelatases (CbiK) in Desulfovibrio vulgaris Hildenborough. Biochemistry, 47(21), 5851–5857. https://doi.org/10.1021/bi800342c
10.1021/bi800342c
CAS PubMed Google Scholar
Lu, X., Heal, K. R., Ingalls, A. E., Doxey, A. C., & Neufeld, J. D. (2020). Metagenomic and chemical characterization of soil cobalamin production. The ISME Journal, 14(1), 53–66. https://doi.org/10.1038/s41396-019-0502-0
10.1038/s41396-019-0502-0
CAS PubMed Google Scholar
Michaelis, L., & Menten, M. L. (1913). Die Kinetik Der Invertinwirkung. Biochemische Zeitschrift, 49, 333–369. https://doi.org/10.1021/bi201284u
10.1021/bi201284u
CAS Web of Science® Google Scholar
Mundle, S. O. C., Vandersteen, A. A., Lacrampe-Couloume, G., Kluger, R., & Sherwood Lollar, B. (2013). Pressure-monitored headspace analysis combined with compound-specific isotope analysis to measure isotope fractionation in gas-producing reactions. Rapid Communications in Mass Spectrometry, 27(15), 1778–1784. https://doi.org/10.1002/rcm.6625
10.1002/rcm.6625
CAS PubMed Web of Science® Google Scholar
Nijenhuis, I., Andert, J., Beck, K., Kästner, M., Diekert, G., & Richnow, H. H. (2005). Stable isotope fractionation of tetrachloroethene during reductive dechlorination by Sulfurospirillum multivorans and Desulfitobacterium sp. strain PCE-S and abiotic reactions with cyanocobalamin. Applied and Environmental Microbiology, 71(7), 3413–3419. https://doi.org/10.1128/AEM.71.7.3413-3419.2005
10.1128/AEM.71.7.3413-3419.2005
CAS PubMed Web of Science® Google Scholar
Northrop, D. B. (1981). The expression of isotope effects on enzyme-catalyzed reactions. Annual Review of Biochemistry, 50(1), 103–131. https://doi.org/10.1146/annurev.bi.50.070181.000535
10.1146/annurev.bi.50.070181.000535
CAS PubMed Google Scholar
Ojeda, A. S., Phillips, E., Mancini, S. A., & Lollar, B. S. (2019). Sources of uncertainty in biotransformation mechanistic interpretations and remediation studies using CSIA. Analytical Chemistry, 91, 9147–9153.
10.1021/acs.analchem.9b01756
CAS PubMed Google Scholar
Ojeda, A. S., Phillips, E., & Sherwood Lollar, B. (2020). Multi-element (C, H, Cl, Br) stable isotope fractionation as a tool to investigate transformation processes for halogenated hydrocarbons. Environmental Science: Processes & Impacts, 22, 567–582. https://doi.org/10.1039/c9em00498j
10.1039/C9EM00498J
CAS PubMed Google Scholar
Ojeda, A. S., Zheng, J., Phillips, E., & Sherwood Lollar, B. (2021). Implications of regression bias for multi-element isotope analysis for environmental remediation. Talanta, 226, 122113.
10.1016/j.talanta.2021.122113
CAS PubMed Google Scholar
Patel, G. B., & Sprott, G. D. (1990). Methanosaeta concilii gen. nov., sp. nov. (“Methanothrix concilii”) and Methanosaeta thermoacetophila nom. rev., comb. nov. International Journal of Systematic Bacteriology, 40(1), 79–82. https://doi.org/10.1099/00207713-40-1-79
10.1099/00207713-40-1-79
Web of Science® Google Scholar
Payne, K. A. P., Quezada, C. P., Fisher, K., Dunstan, M. S., Collins, F. A., Sjuts, H., Levy, C., Hay, S., Rigby, S. E. J., & Leys, D. (2015). Reductive dehalogenase structure suggests a mechanism for B₁₂-dependent dehalogenation. Nature, 517(7535), 513–516. https://doi.org/10.1038/nature13901
10.1038/nature13901
CAS PubMed Web of Science® Google Scholar
Phillips, E., Bulka, O., Picott, K., Kümmel, S., Edwards, E. A., Nijenhuis, I., Gehre, M., Dworatzek, S., Webb, J., & Sherwood Lollar, B. (2022). Investigation of active site amino acid influence on carbon and chlorine isotope fractionation during reductive dechlorination. FEMS Microbiology Ecology, 98(8), fiac072. https://doi.org/10.1093/femsec/fiac072
10.1093/femsec/fiac072
CAS PubMed Google Scholar
Pirog, T. P., & Kuz'minskaya, Y. V. (2003). Regulation of acetate metabolism in a strain of Acinetobacter sp. growing on ethanol. Applied Biochemistry and Microbiology, 39, 158–165.
10.1023/A:1022533828803
CAS Web of Science® Google Scholar
Renpenning, J., Horst, A., Schmidt, M., & Gehre, M. (2018). Online isotope analysis of ³⁷Cl/³⁵Cl universally applied for semi-volatile organic compounds using GC-MC-ICPMS. Journal of Analytical Atomic Spectrometry, 33(2), 314–321. https://doi.org/10.1039/c7ja00404d
10.1039/C7JA00404D
CAS Web of Science® Google Scholar
Renpenning, J., Keller, S., Cretnik, S., Shouakar-Stash, O., Elsner, M., Schubert, T., & Nijenhuis, I. (2014). Combined C and Cl isotope effects indicate differences between corrinoids and enzyme (Sulfurospirillum multivorans PceA) in reductive dehalogenation of tetrachloroethene, but not trichloroethene. Environmental Science & Technology, 48(20), 11837–11845. https://doi.org/10.1021/es503306g
10.1021/es503306g
CAS PubMed Web of Science® Google Scholar
Renpenning, J., Rapp, I., & Nijenhuis, I. (2015). Substrate hydrophobicity and cell composition influence the extent of rate limitation and masking of isotope fractionation during microbial reductive dehalogenation of chlorinated ethenes. Environmental Science & Technology, 49, 4293–4301. https://doi.org/10.1021/es506108j
10.1021/es506108j
CAS PubMed Web of Science® Google Scholar
Shaiber, A., Willis, A. D., Delmont, T. O., Roux, S., Chen, L.-X., Schmid, A. C., Yousef, M., Watson, A. R., Lolans, K., Esen, Ö. C., Lee, S. T. M., Downey, N., Morrison, H. G., Dewhirst, F. E., Mark Welch, J. L., & Eren, A. M. (2020). Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biology, 21(1), 292. https://doi.org/10.1186/s13059-020-02195-w
10.1186/s13059-020-02195-w
PubMed Web of Science® Google Scholar
Sherwood Lollar, B., Hirschorn, S. K., Chartrand, M. M. G., & Lacrampe-Couloume, G. (2007). An approach for assessing total instrumental uncertainty in compound-specific carbon isotope analysis: Implications for environmental remediation studies. Analytical Chemistry, 79(9), 3469–3475. https://doi.org/10.1021/ac062299v
10.1021/ac062299v
CAS PubMed Web of Science® Google Scholar
Soder-Walz, J. M., Torrentó, C., Algora, C., Wasmund, K., Cortés, P., Soler, A., Vicent, T., Rosell, M., & Marco-Urrea, E. (2022). Trichloromethane dechlorination by a novel Dehalobacter sp. strain 8M reveals a third contrasting C and Cl isotope fractionation pattern within this genus. Science of the Total Environment, 813, 152659. https://doi.org/10.1016/j.scitotenv.2021.152659
10.1016/j.scitotenv.2021.152659
CAS PubMed Web of Science® Google Scholar
Stolyar, S., Van Dien, S., Hillesland, K. L., Pinel, N., Lie, T. J., Leigh, J. A., & Stahl, D. A. (2007). Metabolic modeling of a mutualistic microbial community. Molecular Systems Biology, 3, 92. https://doi.org/10.1038/msb4100131
10.1038/msb4100131
PubMed Web of Science® Google Scholar
Tang, S., Wang, P. H., Higgins, S. A., Löffler, F. E., & Edwards, E. A. (2016). Sister Dehalobacter genomes reveal specialization in organohalide respiration and recent strain differentiation likely driven by chlorinated substrates. Frontiers in Microbiology, 7, 100. https://doi.org/10.3389/fmicb.2016.00100
10.3389/fmicb.2016.00100
PubMed Web of Science® Google Scholar
Teufel, F., Almagro Armenteros, J. J., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von Heijne, G., & Nielsen, H. (2022). SignalP 6.0 predicts all five types of signal peptides using protein language models. Nature Biotechnology, 40(7), 1023–1025. https://doi.org/10.1038/s41587-021-01156-3
10.1038/s41587-021-01156-3
CAS PubMed Web of Science® Google Scholar
Thullner, M., Fischer, A., Richnow, H. H., & Wick, L. Y. (2013). Influence of mass transfer on stable isotope fractionation. Applied Microbiology and Biotechnology, 97, 441–452. https://doi.org/10.1007/s00253-012-4537-7
10.1007/s00253-012-4537-7
CAS PubMed Web of Science® Google Scholar
USEPA. (2015). Assessment of the potential impacts of hydraulic fracturing for oil and gas on drinking water resources.
Google Scholar
Villa, E. A., & Escalante-Semerena, J. C. (2022). Acinetobacter baumannii catabolizes ethanolamine in the absence of a metabolosome and converts cobinamide into adenosylated cobamides. mBio, 13(4), e0179322. https://doi.org/10.1128/mbio.01793-22
10.1128/mbio.01793-22
PubMed Google Scholar
Wang, H., Yu, R., Webb, J., Dollar, P., & Freedman, D. L. (2022). Anaerobic biodegradation of chloroform and dichloromethane with a Dehalobacter enrichment culture. Applied and Environmental Microbiology, 88, 1–16.
10.1128/aem.01970-21
Google Scholar
Wang, P. H., Tang, S., Nemr, K., Flick, R., Yan, J., Mahadevan, R., Yakunin, A. F., Löffler, F. E., & Edwards, E. A. (2017). Refined experimental annotation reveals conserved corrinoid autotrophy in chloroform-respiring Dehalobacter isolates. The ISME Journal, 11(3), 626–640. https://doi.org/10.1038/ismej.2016.158
10.1038/ismej.2016.158
PubMed Google Scholar
Yan, J., Im, J., Yang, Y., & Löffler, F. E. (2013). Guided cobalamin biosynthesis supports dehalococcoides mccartyi reductive dechlorination activity. Philosophical Transactions of the Royal Society, B: Biological Sciences, 368(1616), 20120320. https://doi.org/10.1098/rstb.2012.0320
10.1098/rstb.2012.0320
PubMed Google Scholar
Yan, J., Ritalahti, K. M., Wagner, D. D., & Löffler, F. E. (2012). Unexpected specificity of interspecies cobamide transfer from Geobacter spp. to organohalide-respiring Dehalococcoides Mccartyi strains. Applied and Environmental Microbiology, 78(18), 6630–6636. https://doi.org/10.1128/AEM.01535-12
10.1128/AEM.01535-12
CAS PubMed Google Scholar
Yan, J., Şimşir, B., Farmer, A. T., Bi, M., Yang, Y., Campagna, S. R., & Löffler, F. E. (2016). The corrinoid cofactor of reductive dehalogenases affects dechlorination rates and extents in organohalide-respiring dehalococcoides mccartyi. The ISME Journal, 10(5), 1092–1101. https://doi.org/10.1038/ismej.2015.197
10.1038/ismej.2015.197
CAS PubMed Web of Science® Google Scholar
York, D. (1966). Leastsquares fitting of a straight line. Canadian Journal of Physics, 44, 1079–1086.
10.1139/p66-090
Web of Science® Google Scholar
York, D. (1969). Least squares fitting of a straight line with correlated errors. Earth and Planetary Science Letters, 5(2), 320–324.
CAS Google Scholar

Citing Literature

Volume13, Issue4

August 2024

e1433

Filename	Description
mbo31433-sup-0001-B12_TableS-3.xlsx44.1 KB	Supporting information.
mbo31433-sup-0002-240624_ASV-table.xlsx25.4 KB	Supporting information.
mbo31433-sup-0003-B12_Isotope_Experiment_Data.xlsx18.5 KB	Supporting information.
mbo31433-sup-0004-Clean_REVISIONS_B12SUPP_JUNE2024_MO_all_coauthors.docx1.4 MB	Supporting information.
mbo31433-sup-0005-Figure_S-4_ASV-table.xlsx25.4 KB	Supporting information.

Vitamin B₁₂ as a source of variability in isotope effects for chloroform biotransformation by Dehalobacter

Graphical Abstract

Abstract

1 INTRODUCTION