Inhibition of enteric methanogenesis in dairy cows induces changes in plasma metabolome highlighting metabolic shifts and potential markers of emission.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
24 09 2020
24 09 2020
Historique:
received:
25
11
2019
accepted:
12
08
2020
entrez:
25
9
2020
pubmed:
26
9
2020
medline:
15
12
2020
Statut:
epublish
Résumé
There is scarce information on whether inhibition of rumen methanogenesis induces metabolic changes on the host ruminant. Understanding these possible changes is important for the acceptance of methane-reducing practices by producers. In this study we explored the changes in plasma profiles associated with the reduction of methane emissions. Plasma samples were collected from lactating primiparous Holstein cows fed the same diet with (Treated, n = 12) or without (Control, n = 13) an anti-methanogenic feed additive for six weeks. Daily methane emissions (CH
Identifiants
pubmed: 32973203
doi: 10.1038/s41598-020-72145-w
pii: 10.1038/s41598-020-72145-w
pmc: PMC7515923
doi:
Substances chimiques
Milk Proteins
0
Methane
OP0UW79H66
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
15591Références
Gerber, P. J. et al. Tackling climate change through livestock—A global assessment of emissions and mitigation opportunities (Food and Agriculture Organization of the United Nations (FAO), Rome, 2013).
Gidlund, H., Hetta, M., Krizsan, S. J., Lemosquet, S. & Huhtanen, P. Effects of soybean meal or canola meal on milk production and methane emissions in lactating dairy cows fed grass silage-based diets. J. Dairy Sci. 98, 8093–8106. https://doi.org/10.3168/jds.2015-9757 (2015).
doi: 10.3168/jds.2015-9757
pubmed: 26364100
Wang, M. Z. et al. Soybean oil suppresses ruminal methane production and reduces content of coenzyme F-420 in vitro fermentation. Anim. Prod. Sci. 56, 627–633. https://doi.org/10.1071/an15553 (2016).
doi: 10.1071/an15553
Lopes, J. C. et al. Effect of 3-nitrooxypropanol on methane and hydrogen emissions, methane isotopic signature, and ruminal fermentation in dairy cows. J. Dairy Sci. 99, 5335–5344. https://doi.org/10.3168/jds.2015-10832 (2016).
doi: 10.3168/jds.2015-10832
pubmed: 27085412
Jeyanathan, J., Martin, C. & Morgavi, D. P. The use of direct-fed microbials for mitigation of ruminant methane emissions: A review. Animal 8, 250–261. https://doi.org/10.1017/S1751731113002085 (2014).
doi: 10.1017/S1751731113002085
pubmed: 24274095
Pickering, N. K. et al. Animal board invited review: Genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9, 1431–1440. https://doi.org/10.1017/S1751731115000968 (2015).
doi: 10.1017/S1751731115000968
pubmed: 26055577
pmcid: 4574172
Ungerfeld, E. M. Inhibition of rumen methanogenesis and ruminant productivity: A meta-analysis. Front. Vet. Sci. 5, 113. https://doi.org/10.3389/fvets.2018.00113 (2018).
doi: 10.3389/fvets.2018.00113
pubmed: 29971241
pmcid: 6018482
Martinez-Fernandez, G. et al. Methane inhibition alters the microbial community, hydrogen flow and fermentation response in the rumen of cattle. Front. Microbiol. 7, https://doi.org/10.3389/fmicb.2016.01122 (2016).
Guyader, J. et al. Nitrate but not tea saponin feed additives decreased enteric methane emissions in nonlactating cows. J. Anim. Sci. 93, 5367–5377. https://doi.org/10.2527/jas.2015-9367 (2015).
doi: 10.2527/jas.2015-9367
pubmed: 26641056
Arbre, M. et al. Repeatability of enteric methane determinations from cattle using either the SF6 tracer technique or the GreenFeed system. Anim. Prod. Sci. 56, 238–243. https://doi.org/10.1071/an15512 (2016).
doi: 10.1071/an15512
Negussie, E. et al. Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions. J. Dairy Sci. 100, 2433–2453. https://doi.org/10.3168/jds.2016-12030 (2017).
doi: 10.3168/jds.2016-12030
pubmed: 28161178
Chilliard, Y., Martin, C., Rouel, J. & Doreau, M. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92, 5199–5211. https://doi.org/10.3168/jds.2009-2375 (2009).
doi: 10.3168/jds.2009-2375
pubmed: 19762838
Dijkstra, J. et al. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim. Feed Sci. Tech. 166–67, 590–595. https://doi.org/10.1016/j.anifeedsci.2011.04.042 (2011).
doi: 10.1016/j.anifeedsci.2011.04.042
Brade, W. & Nurnberg, K. Fatty acids in the milk: Biosynthesis and possible using as specific biomarkers. Zuchtungskunde 88, 216–232 (2016).
Vanlierde, A. et al. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98, 5740–5747. https://doi.org/10.3168/jds.2014-8436 (2015).
doi: 10.3168/jds.2014-8436
pubmed: 26026761
Basoglu, A., Sen, I., Meoni, G., Tenori, L. & Naseri, A. NMR-based plasma metabolomics at set intervals in newborn dairy calves with severe sepsis. Mediat. Inflamm. 21, https://doi.org/10.1155/2018/8016510 (2018).
Ogunade, I. et al. Biomarker of aflatoxin ingestion: (1)H NMR-based plasma metabolomics of dairy cows fed aflatoxin B1 with or without sequestering agents. Toxins 10, https://doi.org/10.3390/Toxins10120545 (2018).
Wang, Z. et al. Development of a correlative strategy to discover colorectal tumor tissue derived metabolite biomarkers in plasma using untargeted metabolomics. Anal. Chem. 91, 2401–2408. https://doi.org/10.1021/acs.analchem.8b05177 (2018).
doi: 10.1021/acs.analchem.8b05177
de Graaf, R. A., Prinsen, H., Giannini, C., Caprio, S. & Herzog, R. I. Metabolomics quantification of (1)H NMR spectra from human plasma. Metabolomics 11, 1702–1707, https://doi.org/10.1007/s11306-015-0828-1 (2015).
Artegoitia, V. M., Foote, A. P., Lewis, R. M. & Freetly, H. C. Rumen fluid metabolomics analysis associated with feed efficiency on crossbred steers. Sci. Rep. 7, 2864. https://doi.org/10.1038/s41598-017-02856-0 (2017).
doi: 10.1038/s41598-017-02856-0
pubmed: 28588266
pmcid: 5460109
Tian, H. et al. Identification of diagnostic biomarkers and metabolic pathway shifts of heat-stressed lactating dairy cows. artoJ. Proteom. 125, 17–28, https://doi.org/10.1016/j.jprot.2015.04.014 (2015).
Sun, L. W. et al. (1)H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis. J. Dairy Sci. 97, 1552–1562. https://doi.org/10.3168/jds.2013-6757 (2014).
doi: 10.3168/jds.2013-6757
pubmed: 24440255
Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267. https://doi.org/10.1126/science.1223813 (2012).
doi: 10.1126/science.1223813
pubmed: 22674330
Liu, Y. & Whitman, W. B. Metabolic, phylogenetic, and ecological diversity of the methanogenic Archaea. Ann. N. Y. Acad. Sci. 1125, 171–189. https://doi.org/10.1196/annals.1419.019 (2008).
doi: 10.1196/annals.1419.019
pubmed: 18378594
Morgavi, D. P., Forano, E., Martin, C. & Newbold, C. J. Microbial ecosystem and methanogenesis in ruminants. Animal 4, 1024–1036. https://doi.org/10.1017/S1751731110000546 (2010).
doi: 10.1017/S1751731110000546
pubmed: 22444607
Duin, E. C. et al. Mode of action uncovered for the specific reduction of methane emissions from ruminants by the small molecule 3-nitrooxypropanol. Proc. Natl. Acad. Sci. U.S.A. 113, 6172–6177. https://doi.org/10.1073/pnas.1600298113 (2016).
doi: 10.1073/pnas.1600298113
pubmed: 27140643
pmcid: 4896709
Zhou, Z., Meng, Q. & Yu, Z. Effects of methanogenic inhibitors on methane production and abundances of methanogens and cellulolytic bacteria in in vitro ruminal cultures. Appl. Environ. Microbiol. 77, 2634–2639. https://doi.org/10.1128/aem.02779-10 (2011).
doi: 10.1128/aem.02779-10
pubmed: 21357427
pmcid: 3126374
Webster, T. M. et al. Anaerobic microbial community response to methanogenic inhibitors 2-bromoethanesulfonate and propynoic acid. Microbiologyopen 5, 537–550. https://doi.org/10.1002/mbo3.349 (2016).
doi: 10.1002/mbo3.349
pubmed: 26987552
pmcid: 4985588
Bellier, S. Interprétation et valeurs usuelles des paramètres sanguins en biochimie clinique vétérinaire. RFL 43–56, 2010. https://doi.org/10.1016/s1773-035x(10)70420-2 (2010).
doi: 10.1016/s1773-035x(10)70420-2
Grissa, D. et al. Feature selection methods for early predictive biomarker discovery using untargeted metabolomic data. Front. Mol. Biosci. 3, https://doi.org/10.3389/fmolb.2016.00030 (2016).
Wold, S., Antti, H., Lindgren, F. & Öhman, J. Orthogonal signal correction of near-infrared spectra. Chemometr. Intell. Lab. Syst. 44, 175–185 (1998).
doi: 10.1016/S0169-7439(98)00109-9
Chong, J. et al. MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucl. Acids. Res. 46, W486–W494, https://doi.org/10.1093/nar/gky310 (2018).
Cottret, L. et al. MetExplore: Collaborative edition and exploration of metabolic networks. Nucl. Acids. Res. 46, W495–W502, https://doi.org/10.1093/nar/gky301 (2018).
Martin, C., Morgavi, D. P. & Doreau, M. Methane mitigation in ruminants: From microbe to the farm scale. Animal 4, 351–365. https://doi.org/10.1017/S1751731109990620 (2010).
doi: 10.1017/S1751731109990620
pubmed: 22443940
Pinares-Patiño, C. S. et al. Heritability estimates of methane emissions from sheep. Animal 7, https://doi.org/10.1017/s1751731113000864 (2013).
Li, Y. et al. Plasma metabolic profiling of dairy cows affected with clinical ketosis using LC/MS technology. Vet. Q. 34, 152–158. https://doi.org/10.1080/01652176.2014.962116 (2014).
doi: 10.1080/01652176.2014.962116
pubmed: 25299384
Wu, X. et al. Serum metabolome profiling revealed potential biomarkers for milk protein yield in dairy cows. J. Proteomics 184, 54–61. https://doi.org/10.1016/j.jprot.2018.06.005 (2018).
doi: 10.1016/j.jprot.2018.06.005
pubmed: 29913267
Wallace, R. J., Onodera, R. & Cotta, M. A. Metabolism of nitrogen-containing compounds in The Rumen Microbial Ecosystem (eds P. N. Hobson & C. S. Stewart) 283–328 (Chapman & Hall, 1997).
Ungerfeld, E. M., Rust, S. R. & Burnett, R. Increases in microbial nitrogen production and efficiency in vitro with three inhibitors of ruminal methanogenesis. Can. J. Microbiol. 53, 496–503. https://doi.org/10.1139/W07-008 (2007).
doi: 10.1139/W07-008
pubmed: 17612604
Zhang, H. Y. et al. Plasma metabolomic profiling of dairy cows affected with ketosis using gas chromatography/mass spectrometry. BMC Vet. Res. 9, 186. https://doi.org/10.1186/1746-6148-9-186 (2013).
doi: 10.1186/1746-6148-9-186
pubmed: 24070026
pmcid: 3849279
Jefferson, L. S. & Kimball, S. R. Amino acids as regulators of gene expression at the level of mRNA translation. J. Nutr. 133, 2046s–2051s (2003).
doi: 10.1093/jn/133.6.2046S
Dodd, K. M. & Tee, A. R. Leucine and mTORC1: A complex relationship. Am. J. Physiol. Endocrinol. Metab. 302, E1329-1342. https://doi.org/10.1152/ajpendo.00525.2011 (2012).
doi: 10.1152/ajpendo.00525.2011
pubmed: 22354780
Curtis, R. V., Kim, J. J. M., Doelman, J. & Cant, J. P. Maintenance of plasma branched-chain amino acid concentrations during glucose infusion directs essential amino acids to extra-mammary tissues in lactating dairy cows. J. Dairy Sci. 101, 4542–4553. https://doi.org/10.3168/jds.2017-13236 (2018).
doi: 10.3168/jds.2017-13236
pubmed: 29477518
Nichols, K. et al. Glucose supplementation stimulates peripheral branched-chain amino acid catabolism in lactating dairy cows during essential amino acid infusions. J. Dairy Sci. 99, 1145–1160. https://doi.org/10.3168/jds.2015-9912 (2016).
doi: 10.3168/jds.2015-9912
pubmed: 26627857
Dijkstra, J., Boer, H., Van Bruchem, J., Bruining, M. & Tamminga, S. Absorption of volatile fatty acids from the rumen of lactating dairy cows as influenced by volatile fatty acid concentration, pH and rumen liquid volume. Br. J. Nutr. 69, 385–396. https://doi.org/10.1079/BJN19930041 (1993).
doi: 10.1079/BJN19930041
pubmed: 8489996
Van Nevel, C. S. & Demeyer, D. I. Manipulation of rumen fermentation in The Rumen Microbial Ecosystem (ed P. N. Hobson) 387–443 (Elsevier Applied Science, 1988).
Jouany, J. P. & Morgavi, D. P. Use of “natural” products as alternatives to antibiotic feed additives in ruminant production. Animal 1, 1443–1466. https://doi.org/10.1017/S1751731107000742 (2007).
doi: 10.1017/S1751731107000742
pubmed: 22444918
Young, J. W. Gluconeogenesis in cattle: Significance and methodology. J. Dairy Sci. 60, 1–15. https://doi.org/10.3168/jds.S0022-0302(77)83821-6 (1977).
doi: 10.3168/jds.S0022-0302(77)83821-6
pubmed: 320235
Aschenbach, J. R., Kristensen, N. B., Donkin, S. S., Hammon, H. M. & Penner, G. B. Gluconeogenesis in dairy cows: The secret of making sweet milk from sour dough. IUBMB Life 62, 869–877. https://doi.org/10.1002/iub.400 (2010).
doi: 10.1002/iub.400
pubmed: 21171012
Humer, E., Khol-Parisini, A., Metzler-Zebeli, B. U., Gruber, L. & Zebeli, Q. Alterations of the lipid metabolome in dairy cows experiencing excessive lipolysis early postpartum. PLoS ONE 11, e0158633. https://doi.org/10.1371/journal.pone.0158633 (2016).
doi: 10.1371/journal.pone.0158633
pubmed: 27383746
pmcid: 4934687
Huber, K. et al. Metabotypes with properly functioning mitochondria and anti-inflammation predict extended productive life span in dairy cows. Sci. Rep. 6, 24642. https://doi.org/10.1038/srep24642 (2016).
doi: 10.1038/srep24642
pubmed: 27089826
pmcid: 4835701
Roe, D. S., Roe, C. R., Brivet, M. & Sweetman, L. Evidence for a short-chain carnitine–acylcarnitine translocase in mitochondria specifically related to the metabolism of branched-chain amino acids. Mol. Genet. Metab. 69, 69–75. https://doi.org/10.1006/mgme.1999.2950 (2000).
doi: 10.1006/mgme.1999.2950
pubmed: 10655160
Block, R. J., Stekol, J. A. & Loosli, J. K. Synthesis of sulfur amino acids from inorganic sulfate by ruminants. II. Synthesis of cystine and methionine from sodium sulfate by the goat and by the microorganisms of the rumen of the ewe. Arch. Biochem. Biophys. 33, 353–363, https://doi.org/10.1016/0003-9861(51)90123-3 (1951).
Onodera, R. Methionine and lysine metabolism in the rumen and the possible effects of their metabolites on the nutrition and physiology of ruminants. Amino Acids 5, 217–232. https://doi.org/10.1007/bf00805984 (1993).
doi: 10.1007/bf00805984
pubmed: 24190665
He, X. & Slupsky, C. M. Metabolic fingerprint of dimethyl sulfone (DMSO2) in microbial–mammalian co-metabolism. J. Proteome Res. 13, 5281–5292. https://doi.org/10.1021/pr500629t (2014).
doi: 10.1021/pr500629t
pubmed: 25245235
Martinez-Fernandez, G. et al. 3-NOP vs. halogenated compound: methane production, ruminal fermentation and microbial community response in forage fed cattle. Front. Microbiol. 9, 1582, https://doi.org/10.3389/fmicb.2018.01582 (2018).
Zinder, S. H. & Brock, T. D. Methane, carbon dioxide, and hydrogen sulfide production from the terminal methiol group of methionine by anaerobic lake sediments. Appl. Environ. Microbiol. 35, 344–352 (1978).
doi: 10.1128/AEM.35.2.344-352.1978
Hungate, R. E. Hydrogen as an intermediate in the rumen fermentation. Arch. Microbiol. 59, 158–164 (1967).
Asanuma, N., Iwamoto, M. & Hino, T. Formate metabolism by ruminal microorganisms in relation to methanogenesis. Anim. Sci. J. 69, 576–584. https://doi.org/10.2508/chikusan.69.576 (1998).
doi: 10.2508/chikusan.69.576
Sadri, H. et al. Plasma amino acids and metabolic profiling of dairy cows in response to a bolus duodenal infusion of leucine. PLoS ONE 12, e0176647. https://doi.org/10.1371/journal.pone.0176647 (2017).
doi: 10.1371/journal.pone.0176647
pubmed: 28453535
pmcid: 5409510
Triantafyllou, K., Chang, C. & Pimentel, M. Methanogens, methane and gastrointestinal motility. J. Neurogastroenterol. Motil. 20, 31–40. https://doi.org/10.5056/jnm.2014.20.1.31 (2014).
doi: 10.5056/jnm.2014.20.1.31
pubmed: 24466443
Sahakian, A. B., Jee, S.-R. & Pimentel, M. Methane and the gastrointestinal tract. Dig. Dis. Sci. 55, 2135–2143. https://doi.org/10.1007/s10620-009-1012-0 (2010).
doi: 10.1007/s10620-009-1012-0
pubmed: 19830557
Sorraing, J. M., Fioramonti, J. & Bueno, L. Effects of dopamine and serotonin on eructation rate and ruminal motility in sheep. Am. J. Vet. Res. 45, 942–947 (1984).
pubmed: 6732029
Ruckebusch, Y. Pharmacology of reticulo-ruminal motor function. J. Vet. Pharmacol. Ther. 6, 245–272. https://doi.org/10.1111/j.1365-2885.1983.tb00001.x (1983).
doi: 10.1111/j.1365-2885.1983.tb00001.x
pubmed: 6142121
Borrel, G. et al. Comparative genomics highlights the unique biology of methanomassiliicoccales, a thermoplasmatales-related seventh order of methanogenic archaea that encodes pyrrolysine. BMC Genomics 15, 679. https://doi.org/10.1186/1471-2164-15-679 (2014).
doi: 10.1186/1471-2164-15-679
pubmed: 25124552
pmcid: 4153887
Saro, C. et al. Effectiveness of interventions to modulate the rumen microbiota composition and function in pre-ruminant and ruminant lambs. Front. Microbiol. 9, https://doi.org/10.3389/fmicb.2018.01273 (2018).
Guyader, J. et al. Additive methane-mitigating effect between linseed oil and nitrate fed to cattle. J. Anim. Sci. 93, 3564–3577. https://doi.org/10.2527/jas.2014-8196 (2015).
doi: 10.2527/jas.2014-8196
pubmed: 26440025
Kaluarachchi, M. R., Boulange, C. L., Garcia-Perez, I., Lindon, J. C. & Minet, E. F. Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers. Bioanalysis 8, 2023–2043. https://doi.org/10.4155/bio-2016-0108 (2016).
doi: 10.4155/bio-2016-0108
pubmed: 27635669
Dieme, B. et al. Metabolomics study of urine in autism spectrum disorders using a multiplatform analytical methodology. J. Proteome Res. 14, 5273–5282. https://doi.org/10.1021/acs.jproteome.5b00699 (2015).
doi: 10.1021/acs.jproteome.5b00699
pubmed: 26538324
Rohart, F. et al. Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds. J. Anim. Sci. 90, 4729–4740. https://doi.org/10.2527/jas.2012-5338 (2012).
doi: 10.2527/jas.2012-5338
pubmed: 23100586
Pereira, H., Martin, J.-F., Joly, C., Sébédio, J.-L. & Pujos-Guillot, E. Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma. Metabolomics 6, 207–218 (2010).
doi: 10.1007/s11306-009-0188-9
Guitton, Y. et al. Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. Int. J. Biochem. Cell Biol. 93, 89–101, https://doi.org/10.1016/j.biocel.2017.07.002 (2017).
Tautenhahn, R., Bottcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 9, 504. https://doi.org/10.1186/1471-2105-9-504 (2008).
doi: 10.1186/1471-2105-9-504
van der Kloet, F. M., Bobeldijk, I., Verheij, E. R. & Jellema, R. H. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J. Proteome Res. 8, 5132–5141, https://doi.org/10.1021/pr900499r (2009).
Goldansaz, S. A. et al. Livestock metabolomics and the livestock metabolome: A systematic review. PLoS ONE 12, e0177675. https://doi.org/10.1371/journal.pone.0177675 (2017).
doi: 10.1371/journal.pone.0177675
pubmed: 28531195
pmcid: 5439675
Ulrich, E. L. et al. BioMagResBank. Nucl. Acids. Res. 36, D402-408. https://doi.org/10.1093/nar/gkm957 (2008).
doi: 10.1093/nar/gkm957
pubmed: 17984079
Sumner, L. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221. https://doi.org/10.1007/s11306-007-0082-2 (2007).
doi: 10.1007/s11306-007-0082-2
pubmed: 24039616
pmcid: 3772505
Kankainen, M., Gopalacharyulu, P., Holm, L. & Oresic, M. MPEA-metabolite pathway enrichment analysis. Bioinformatics 27, 1878–1879. https://doi.org/10.1093/bioinformatics/btr278 (2011).
doi: 10.1093/bioinformatics/btr278
pubmed: 21551139
Chazalviel, M. et al. MetExploreViz: Web component for interactive metabolic network visualization. Bioinformatics 34, 312–313. https://doi.org/10.1093/bioinformatics/btx588 (2017).
doi: 10.1093/bioinformatics/btx588
pmcid: 5860210
Faust, K., Dupont, P., Callut, J. & van Helden, J. Pathway discovery in metabolic networks by subgraph extraction. Bioinformatics 26, 1211–1218. https://doi.org/10.1093/bioinformatics/btq105 (2010).
doi: 10.1093/bioinformatics/btq105
pubmed: 20228128
pmcid: 2859126
Frainay, C. & Jourdan, F. Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief. Bioinform. 18, 43–56. https://doi.org/10.1093/bib/bbv115 (2017).
doi: 10.1093/bib/bbv115
pubmed: 26822099
Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
doi: 10.1101/gr.1239303
pubmed: 403769
pmcid: 403769