The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii
Open accessAbstract
BACKGROUND: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H2/CO2, and more importantly on synthesis gas (H2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. RESULTS: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. CONCLUSIONS: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures





Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“and transcriptomics analyses of C. autoethanogenum (Mock et al., 2015; Marcellin et al., 2016) and C. ljungdahlii (Nagarajan et al., 2013; Tan et al., 2013; Whitham et al., 2015).”
From Gas Fermentation—A Flexible Platform for Commercial Scale Production of Low-Carbon-Fuels and Chemicals from Waste and Renewable Feedstocks (2016) · cites this paper as Nagarajan et al., 2013
In the citing paper
“Similar models also exist for C. ljungdahlii (Nagarajan et al., 2013) and M. thermoacetica (Islam et al., 2015).”
From Gas Fermentation—A Flexible Platform for Commercial Scale Production of Low-Carbon-Fuels and Chemicals from Waste and Renewable Feedstocks (2016) · cites this paper as Nagarajan et al., 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
Acetogenesis and the Wood–Ljungdahl pathway of CO2 fixation
Ragsdale, S. W., & Pierce, E. (2008) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2008.08.012 · Cited by 1,409
Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Vassily Hatzimanikatis, A., & Palsson, B. Ø. (2007) · Molecular Systems Biology · doi:10.1038/msb4100155 · Cited by 1,408
Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol
Yim, H. H., Haselbeck, R. J., Niu, W., Pujol-Baxley, C., Burgard, A. P., Boldt, J., Khandurina, J., Trawick, J. D., Osterhout, R. E., Stephen, R., et al. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.580 · Cited by 1,114
High-throughput generation, optimization and analysis of genome-scale metabolic models
Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., & Stevens, R. L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1672 · Cited by 1,090
Cited by
Sorted by citations
Items 101–172 of 172
Kaushal, M., Chary, K. V. N., Ahlawat, S., Palabhanvi, B., Goswami, G., & Das, D. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.10.080 · Cited by 20
Engineered acetogenic bacteria as microbial cell factory for diversified biochemicals
Zhang, J., Li, Y., Xi, Z.-N., Gao, H., Zhang, Q., Liu, L., Li, F., & Ma, X. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1395540 · Cited by 20
Syngas Fermentation for Bioethanol and Bioproducts
Abubackar, H. N., Veiga, M. C., & Kennes, C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-444-64200-4.00015-3 · Cited by 19
Basics of genome-scale metabolic modeling and applications on C1-utilization
Kabimoldayev, I., Nguyen, A. D., Yang, L., Park, S., Lee, E. Y., & Kim, D. (2018) · FEMS Microbiology Letters · doi:10.1093/femsle/fny241 · Cited by 18
Schroeder, W. L., Kuil, T., van Maris, A. J. A., Olson, D. G., Lynd, L. R., & Maranas, C. D. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.04.003 · Cited by 16
Hermann, M., Teleki, A., Weitz, S., Nieß, A., Freund, A., Bengelsdorf, F. R., Dürre, P., & Takors, R. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.647853 · Cited by 16
Lemgruber, R. D. S. P., Valgepea, K., González-García, R. A., de Bakker, C. J., Palfreyman, R. W., Tappel, R. C., Köpke, M., Simpson, S. D., Nielsen, L. K., & Marcellin, E. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02549 · Cited by 16
Leonardo, P. F. D., Antonicelli, G., Agostino, V., & Re, A. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.01019-22 · Cited by 17
COxFixation to Elementary Building Blocks: Anaerobic Syngas Fermentation vs. Chemical Catalysis
Perret, L., Campos, B. L. D. O., Delgado, K. H., Zevaco, T. A., Neumann, A., & Sauer, J. (2022) · Chemie Ingenieur Technik · doi:10.1002/cite.202200153 · Cited by 15
Unveiling the potential of systems biology in biotechnology and biomedical research
Saranya, S. V., Thamanna, L., & Chellapandi, P. (2024) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-024-00286-4 · Cited by 15
Xia, M., Wang, D., Xia, Y., Shi, H., Tian, Z., Zheng, Y., & Wang, M. (2022) · Microbial Cell Factories · doi:10.1186/s12934-022-01824-2 · Cited by 14
Acetogenic production of 3-Hydroxybutyrate using a native 3-Hydroxybutyryl-CoA Dehydrogenase
Lo, J., Humphreys, J. R., Magnusson, L. R., Wachter, B., Urban, C., Hebdon, S. D., Xiong, W., Chou, K., & Maness, P. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.948369 · Cited by 14
A review of genome-scale metabolic flux modeling of anaerobiosis in biotechnology
Senger, R. S., Yen, J. Y., & Fong, S. S. (2014) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2014.08.003 · Cited by 14
Integration of proteomic data with genome‐scale metabolic models: A methodological overview
Zare, F., & Fleming, R. M. T. (2024) · Protein Science · doi:10.1002/pro.5150 · Cited by 14
Zheng, M., Xu, J., Xu, X., Tang, Z., Hou, Y., Li, X., Li, Q., Li, M., Wei, D., & Cao, Z. (2025) · International Journal of Electrochemical Science · doi:10.1016/j.ijoes.2025.101091 · Cited by 12
Caivano, A., van Winden, W. A., Dragone, G., & Mussatto, S. I. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.09.015 · Cited by 12
Serrano‐Bermúdez, L. M., Barrios, A. F. G., & Castaño, D. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209447 · Cited by 14
Klask, C.-M., Jäger, B., Casini, I., Angenent, L. T., & Molitor, B. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.887578 · Cited by 10
Ou, J., Bao, T., Erñst, P., Si, Y., Prabhu, S. D., Wu, H., Zhang, J. J., Zhou, L., Yang, S., & Liu, M. (2019) · Process Biochemistry · doi:10.1016/j.procbio.2019.10.032 · Cited by 10
Characterization of Clostridium ljungdahlii OTA1: a non-autotrophic hyper ethanol-producing strain
Whitham, J. M., Schulte, M. J., Bobay, B. G., Bruno-Bárcena, J. M., Chinn, M. S., Flickinger, M. C., Pawlak, J. J., & Grunden, A. M. (2016) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-016-7978-6 · Cited by 10
Network alignment based on multiple hypernetwork attributes
Peng, H. M., Li, S., Zhao, D., Zhong, M., Qian, C., & Wang, W. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01144-z · Cited by 9
Biotech France 2017 Conference Proceedings
van der Meer, Y. (2017) · doi:10.26799/cp-biotechfrance2017 · Cited by 8
Implementation of a Clostridium luticellarii genome-scale model for upgrading syngas fermentations
Scott, W. T., Rockx, S., Mariën, Q., Regueira, A., Candry, P., Ganigué, R., Koehorst, J. J., & Schaap, P. J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.013 · Cited by 7
Tibocha‐Bonilla, J. D., Santibáñez, R., Weng, Y., Kumar, M., & Zengler, K. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101451 · Cited by 6
Self-citationDynamic Matrix Control of a Bubble‐Column Reactor for Microbial Synthesis Gas Fermentation
Fu, Y., Chang, L., Henson, M. A., & Liu, X. G. (2017) · Chemical Engineering & Technology · doi:10.1002/ceat.201600520 · Cited by 6
Sustainable bio-based materials: opportunities and challenges
van der Meer, Y. (2017) · doi:10.26799/cp-biotechfrance2017.1 · Cited by 6
Microbial Production of Value-Added Chemicals from Pyrolysis Oil and Syngas
Neumann, A., Dörsam, S., Oswald, F., & Ochsenreither, K. (2015) · Springer eBooks · doi:10.1007/978-94-017-7475-8_4 · Cited by 8
Maps for when the living gets tough: Maneuvering through a hostile energy landscape
Mondeel, T. D. G. A., Rehman, S., Zhang, Y., Verma, M., Dürre, P., Barberis, M., & Westerhoff, H. V. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.03.002 · Cited by 6
Biosynthesis Based on One-Carbon Mixotrophy
Hong, Y., & Zeng, A. (2022) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2021_198 · Cited by 6
Systems Biology on Acetogenic Bacteria for Utilizing C1 Feedstocks
Song, Y., Bae, J., Shin, J., Jin, S., Kang, S., Lee, H., Cho, S., & Cho, B. (2022) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2021_199 · Cited by 5
Microbial degradation of herbicides in contaminated soils by following computational approaches
Dhakar, K., Eizenberg, H., Ronen, Z., Zarecki, R., & Freilich, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-89778-5.00030-1 · Cited by 5
SimDFBA: A framework for bioprocess simulation and development
Shoabjareh, A. S., Farhadi, F., & Pishvaie, M. R. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.108073 · Cited by 4
Suazo, A., Olm, I. P., Otálora, F., Martínez-Ruano, J. A., Conejeros, R., Aroca, G. E., & Sousa, D. Z. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.172982 · Cited by 3
(S)-2-Hydroxyisovalerate Production from d-Xylose with CO-Converting Clostridium ragsdalei
Schwarz, I., Rupp, M., Frank, O., Daschner, A., & Weuster‐Botz, D. (2024) · Fermentation · doi:10.3390/fermentation10110546 · Cited by 4
Li, X., & Henson, M. A. (2021) · Journal of Applied Microbiology · doi:10.1111/jam.15155 · Cited by 3
Automated high-quality reconstruction of metabolic networks from high-throughput dataPreprint
Hartleb, D., Fritzemeier, C. J., & Lercher, M. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/282251 · Cited by 3
Biomanufacturing from gaseous C1 feedstocks: A perspective on opportunities and challenges
Rachbauer, L., Awashti, D., Tejedor‐Sanz, S., Panich, J., Bansal, N., Pham, D. N., Toppari, E., Sundstrom, E. R., & Simmons, B. A. (2025) · Energy Nexus · doi:10.1016/j.nexus.2025.100601 · Cited by 3
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · BMC Microbiology · doi:10.1186/s12866-021-02357-1 · Cited by 2
Probing efficient microbial CO 2 utilisation through metabolic and process modelling
de Vries, P. J. G., Mol, V., Sonnenschein, N., Jensen, T. Ø., & Nielsen, A. T. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.14414 · Cited by 2
Wu, C., Lo, J., Urban, C., Gao, X., Humphreys, J. R., Shinde, S., Wang, X., Chou, K., Maness, P., Tsesmetzis, N., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-85001/v1 · Cited by 2
Canto-Encalada, G., Armstrong, J., Focil-Espinosa, C., Ademikanra, A. F., Peeler, I., Gebbie, W., Nunez-Garcia, J., Saldivar, A., Martinez, D., & Zúñiga, C. (2023) · Preprints.org · doi:10.20944/preprints202311.0461.v1 · Cited by 2
Harrison, E., Zheng, A., Wicks, J., Xie, K., Sargent, E. H., & Mahadevan, R. (2025) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c04176 · Cited by 1
Metabolic engineering strategies to improve microbial acetate utilization for chemical production
Liang, G., Liu, K., Gao, C., Sheng, Q., Qiao, G., Liu, J., Hu, G., Nielsen, J., & Liu, L. (2026) · Green Energy & Environment · doi:10.1016/j.gee.2026.04.006 · Cited by 1
Microbial Processes: Current Developments in Gas Fermentation
Kensy, F. (2023) · Circular economy and sustainability · doi:10.1007/978-3-031-27811-2_9 · Cited by 1
de Medeiros, E. M. (2017) · doi:10.26799/cp-biotechfrance2017.5 · Cited by 1
Clostridium autoethanogenum involving electron bifurcation 2
Mock, J., Zheng, Y., Mueller, A. P., Ly, S., Tran, L., Segovia, S., Nagaraju, S., Köpke, M., Dürre, P., Thauer, R. K., et al. (2015)
Synthetic carbon fixation for improved microbial fermentation yields
Carlson, E. D. (2024) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/8r2f-sp67
Computational inference of the transcriptional regulatory network of Candida glabrata
Xu, N., & Liu, L. (2019) · FEMS Yeast Research · doi:10.1093/femsyr/foz036
Mikrobielle Verfahren zur Umsetzung von CO2 und CO
Weuster‐Botz, D., Kensy, F., Wever, H. D., & Garcia‐Gonzalez, L. (2020) · Springer eBooks · doi:10.1007/978-3-662-60649-0_8
Syngas fermentation for production of ethanol
Istiqomah, N. A., Kresnowati, M. T. A. P., & Setiadi, T. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1143/1/012014
Model-driven design of synthetic microbial communities for the upcycling of One-Carbon feedstocks
Benito-Vaquerizo, S. (2023) · doi:10.18174/630256
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · Figshare · doi:10.6084/m9.figshare.16869705
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · Figshare · doi:10.6084/m9.figshare.16869709
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · Figshare · doi:10.6084/m9.figshare.16869707
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · Figshare · doi:10.6084/m9.figshare.16869711
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · Figshare · doi:10.6084/m9.figshare.16869703
Gao, Y., Yuan, Q., Mao, Z., Liu, H., & Ma, H. (2021) · Figshare · doi:10.6084/m9.figshare.16869701
Entangled stoichiometric objectives shape microbial catabolismPreprint
Remeijer, M., & Bruggeman, F. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.29.667445
Modeling syngas fermentation for enhancing bioethanol production: Progress and insights
Mukti, R., Istiqomah, N. A., Krista, G. M., Kresnowati, M. T. A. P., & Setiadi, T. (2025) · AIP conference proceedings · doi:10.1063/5.0269820
Benito-Vaquerizo, S., Olm, I. P., de Vroet, T., Schaap, P. J., Sousa, D. Z., dos Santos, V. A. P. M., & Diez, M. S. (2022) · Figshare · doi:10.6084/m9.figshare.20086051
Munir, S., Wan, S., Gao, X., Lai, M., Mu, Z., Wang, H., Liu, Z., Li, F., Xia, L., & Tan, Y. (2026) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-026-13714-3
Kumar, S., Goswami, G., & Das, D. (2026) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-026-00443-x
A proteomic approach for understanding the metabolic adaptations in non-small cell lung cancer
Martín-Bernabé, A. (2018) · theses.fr (ABES) · doi:10.70675/426e7b40z057az4326z8cfez7da3a5c12c7e
Emmanuel, O., Okonkwo, C. C., Akinola, S. A., Atiyeh, H. K., & Ezeji, T. C. (2026) · Bioresource Technology Reports · doi:10.1016/j.biteb.2026.102802
Bauer, J., von Kamp, A., Pflügl, S., & Klamt, S. (2026) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2026.05.007
Fang, H., Shu, Q., Li, X., Song, T., & Xie, J. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.157839
Kim, J., Cho, H., Mok, J. H., Seo, H., & Kwon, J. S. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014759
Metadata
- Authors
- Nagarajan, H., Sahin, M., Nogales, J., Latif, H., Lovley, D. R., Ebrahim, A., & Zengler, K.
- Affiliations
- University of California San Diego (US)
- University of Massachusetts Amherst (US)
- Corresponding author
- Zengler, Karsten
- Venue
- Microbial Cell Factories
- Published
- 2013-11-25
- Volume
- 12
- Issue
- 1
- Pages
- 118-118
- ISSN
- 1475-2859 (ISSN-L)
- PMC
- PMC4222884
- OpenAlex
- W2038304602
- Cited by
- 170
- References
- 52
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 10 · 2023: 9 · 2022: 23 · 2021: 11 · 2020: 13 · 2019: 13 · 2018: 19 · 2017: 19 · 2016: 11 · 2015: 13 · 2014: 13
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Microbial Cell Factories (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Science Foundation (1144086)
- U.S. Department of Energy (DEAR0000159, DEAR0000087, DESC0004917)
- Advanced Research Projects Agency - Energy (DE-AR0000159)
- Office of Science
- University of California, San Diego
- Advanced Research Projects Agency
- National Institute of General Medical Sciences (U01GM102098)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Physical Sciences › Engineering › Biomedical Engineering › Biofuel production and bioconversion
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Clostridium ljungdahlii, genome-scale metabolic models, autotrophic growth, energy conservation, CO2 fixation, metabolic engineering, biofuels
- MeSH terms
- Acetates, Clostridium, Genome, Metabolic Networks and Pathways, Biofuels, Metabolic Engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review