Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
01 Jun 2024
01 Jun 2024
Historique:
received:
26
03
2022
accepted:
21
05
2024
medline:
2
6
2024
pubmed:
2
6
2024
entrez:
1
6
2024
Statut:
epublish
Résumé
Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.
Identifiants
pubmed: 38824157
doi: 10.1038/s41467-024-49098-z
pii: 10.1038/s41467-024-49098-z
doi:
Substances chimiques
Herbicides
0
Soil Pollutants
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4694Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 41977120
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 42177031
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 41925029
Informations de copyright
© 2024. The Author(s).
Références
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).
pubmed: 21596990
pmcid: 3303602
doi: 10.1126/science.1198719
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
pubmed: 22972295
pmcid: 3577372
doi: 10.1038/nature11550
Greenhalgh, K., Meyer, K. M., Aagaard, K. M. & Wilmes, P. The human gut microbiome in health: establishment and resilience of microbiota over a lifetime. Environ. Microbiol. 18, 2103–2116 (2016).
pubmed: 27059297
pmcid: 7387106
doi: 10.1111/1462-2920.13318
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Van Der Putten, W. H. Going back to the roots: The microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799 (2013).
pubmed: 24056930
doi: 10.1038/nrmicro3109
Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).
pubmed: 29725101
doi: 10.1038/s41477-018-0139-4
Zhao, M. et al. Integrated Meta–omics Approaches To Understand The Microbiome Of Spontaneous Fermentation Of Traditional Chinese Pu–erh Tea. mSystems 4, e00680–19 (2019).
pubmed: 31744906
pmcid: 6867877
doi: 10.1128/mSystems.00680-19
Lee, F. J., Rusch, D. B., Stewart, F. J., Mattila, H. R. & Newton, I. L. G. Saccharide breakdown and fermentation by the honey bee gut microbiome. Environ. Microbiol. 17, 796–815 (2015).
pubmed: 24905222
doi: 10.1111/1462-2920.12526
Widdig, M. et al. Effects of nitrogen and phosphorus addition on microbial community composition and element cycling in a grassland soil. Soil Biol. Biochem. 151, 1467–1477 (2020).
doi: 10.1016/j.soilbio.2020.108041
Camenzind, T., Philipp Grenz, K., Lehmann, J. & Rillig, M. C. Soil fungal mycelia have unexpectedly flexible stoichiometric C:N and C:P ratios. Ecol. Lett. 24, 208–218 (2021).
pubmed: 33169908
doi: 10.1111/ele.13632
Rabaey, K., Boon, N., Siciliano, S. D., Verhaege, M. & Verstraete, W. Biofuel cells select for microbial consortia that self–mediate electron transfer. Appl. Environ. Microbiol. 70, 5373–5382 (2004).
pubmed: 15345423
pmcid: 520914
doi: 10.1128/AEM.70.9.5373-5382.2004
Bhatia, S. K., Kim, S. H., Yoon, J. J. & Yang, Y. H. Current status and strategies for second generation biofuel production using microbial systems. Energ. Convers. Manag. 148, 1142–1156 (2017).
doi: 10.1016/j.enconman.2017.06.073
Jiang, Y., Dong, W., Xin, F. & Jiang, M. Designing synthetic microbial consortia for biofuel production. Trends Biotechnol. 38, 828–831 (2020).
pubmed: 32673585
doi: 10.1016/j.tibtech.2020.02.002
Xu, M. et al. Elevated nitrate enriches microbial functional genes for potential bioremediation of complexly contaminated sediments. ISME J. 8, 1932–1944 (2014).
pubmed: 24671084
pmcid: 4139732
doi: 10.1038/ismej.2014.42
Hu, S. et al. A synergistic consortium involved in Rac-dichlorprop degradation as revealed by DNA-stable isotope probing and metagenomics analysis. Appl. Environ. Microbiol. 87, e01562–21 (2021).
pubmed: 34524896
pmcid: 8552887
doi: 10.1128/AEM.01562-21
Cheng, M. et al. Oxygenases as powerful weapons in the microbial degradation of pesticides. Annu. Rev. Microbiol. 76, 325–348 (2022).
pubmed: 35650666
doi: 10.1146/annurev-micro-041320-091758
Wanapaisan, P. et al. Synergistic degradation of pyrene by five culturable bacteria in a mangrove sediment–derived bacterial consortium. J. Hazard. Mater. 342, 561–570 (2018).
pubmed: 28886568
doi: 10.1016/j.jhazmat.2017.08.062
Dejonghe, W. et al. Synergistic degradation of linuron by a bacterial consortium and isolation of a single linuron–degrading Variovorax strain. Appl. Environ. Microbiol. 69, 1532–1541 (2003).
pubmed: 12620840
pmcid: 150106
doi: 10.1128/AEM.69.3.1532-1541.2003
Hennessee, C. T. & Li, Q. X. Effects of polycyclic aromatic hydrocarbon mixtures on degradation, gene expression, and metabolite production in four Mycobacterium species. Appl. Environ. Microbiol. 82, 3357–3369 (2016).
pubmed: 27037123
pmcid: 4959237
doi: 10.1128/AEM.00100-16
Burmølle, M. et al. Enhanced biofilm formation and increased resistance to antimicrobial agents and bacterial invasion are caused by synergistic interactions in multispecies biofilms. Appl. Environ. Microbiol. 72, 3916–3923 (2006).
pubmed: 16751497
pmcid: 1489630
doi: 10.1128/AEM.03022-05
Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl Acad. Sci. USA. 111, E2149–E2156 (2014).
pubmed: 24778240
pmcid: 4034247
doi: 10.1073/pnas.1405641111
Roucher, A. et al. From Compartmentalization Of Bacteria Within Inorganic Macrocellular Beads To The Assembly Of Microbial Consortia. Adv. Biosyst. 2, 1700233 (2018).
doi: 10.1002/adbi.201700233
Opatovsky, I. et al. Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host–simulated environment. BMC Genomics 19, 1–14 (2018).
doi: 10.1186/s12864-018-4786-7
Xu, X. et al. Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions. ISME J. 13, 494–508 (2019).
pubmed: 30291327
doi: 10.1038/s41396-018-0288-5
Lawson, C. E. et al. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17, 725–741 (2019).
pubmed: 31548653
pmcid: 8323346
doi: 10.1038/s41579-019-0255-9
Prina, M. G., Manzolini, G., Moser, D., Nastasi, B. & Sparber, W. Classification and challenges of bottom–up energy system models—A review. Renew. Sust. Energ. Rev. 129, 109917 (2020).
doi: 10.1016/j.rser.2020.109917
Bernstein, H. C. Reconciling ecological and engineering design principles for building microbiomes. mSystems 4, e00106–e00119 (2019).
pubmed: 31138720
pmcid: 6584870
doi: 10.1128/mSystems.00106-19
Thingstad, T. F. & Våge, S. Host–virus–predator coexistence in a grey–box model with dynamic optimization of host fitness. ISME J. 13, 3102–3111 (2019).
pubmed: 31527663
pmcid: 6864060
doi: 10.1038/s41396-019-0496-7
Chang, C. Y. et al. Engineering complex communities by directed evolution. Nat. Ecol. Evol. 5, 1011–1023 (2021).
pubmed: 33986540
pmcid: 8263491
doi: 10.1038/s41559-021-01457-5
Schneijderberg, M. et al. Quantitative comparison between the rhizosphere effect of Arabidopsis thaliana and co–occurring plant species with a longer life history. ISME J. 14, 2433–2448 (2020).
pubmed: 32641729
pmcid: 7490400
doi: 10.1038/s41396-020-0695-2
Beckmann, S. et al. Long–term succession in a coal seam microbiome during in situ biostimulation of coalbed–methane generation. ISME J. 13, 632–650 (2019).
pubmed: 30323265
doi: 10.1038/s41396-018-0296-5
Zuñiga, C., Zaramela, L. & Zengler, K. Elucidation of complexity and prediction of interactions in microbial communities. Microb. Biotechnol. 10, 1500–1522 (2017).
pubmed: 28925555
pmcid: 5658597
doi: 10.1111/1751-7915.12855
Henry, C. S. et al. Microbial community metabolic modeling: a community data–driven network reconstruction. J. Cell. Physiol. 231, 2339–2345 (2016).
pubmed: 27186840
pmcid: 5132105
doi: 10.1002/jcp.25428
García–Jiménez, B., Torres–Bacete, J. & Nogales, J. Metabolic modelling approaches for describing and engineering microbial communities. Comput. Struct. Biotechnol. J. 19, 226–246 (2021).
pubmed: 33425254
doi: 10.1016/j.csbj.2020.12.003
Rocha, M. et al. Natural computation meta–heuristics for the in silico optimization of microbial strains. BMC Bioinforma. 9, 499 (2008).
doi: 10.1186/1471-2105-9-499
Chan, S. H. J., Cai, J., Wang, L., Simons–Senftle, M. N. & Maranas, C. D. Standardizing biomass reactions and ensuring complete mass balance in genome–scale metabolic models. Bioinformatics 33, 3603–3609 (2017).
pubmed: 29036557
doi: 10.1093/bioinformatics/btx453
Mundy, M., Mendes–Soares, H. & Chia, N. Mackinac: A bridge between ModelSEED and COBRApy to generate and analyze genome–scale metabolic models. Bioinformatics 33, 2416–2418 (2017).
pubmed: 28379466
pmcid: 5860119
doi: 10.1093/bioinformatics/btx185
Wei, D., Kameya, T. & Urano, K. Environmental management of pesticidal POPs in China: Past, present and future. Environ. Int. 33, 894–902 (2007).
pubmed: 17521727
doi: 10.1016/j.envint.2007.04.006
Noyes, P. D. et al. The toxicology of climate change: Environmental contaminants in a warming world. Environ. Int. 35, 971–986 (2009).
pubmed: 19375165
doi: 10.1016/j.envint.2009.02.006
Alharbi, O. M. L., Basheer, A. A., Khattab, R. A. & Ali, I. Health and environmental effects of persistent organic pollutants. J. Mol. Liq. 263, 442–453 (2018).
doi: 10.1016/j.molliq.2018.05.029
Gavrilescu, M., Demnerová, K., Aamand, J., Agathos, S. & Fava, F. Emerging pollutants in the environment: Present and future challenges in biomonitoring, ecological risks and bioremediation. N. Biotechnol. 32, 147–156 (2015).
pubmed: 24462777
doi: 10.1016/j.nbt.2014.01.001
Tesfamichael, A. A. & Kaluarachchi, J. J. A methodology to assess the risk of an existing pesticide and potential future pesticides for regulatory decision–making. Environ. Sci. Policy 9, 275–290 (2006).
doi: 10.1016/j.envsci.2005.12.004
Peterson, R. K. D. & Hulting, A. G. A comparative ecological risk assessment for herbicides used on spring wheat: the effect of glyphosate when used within a glyphosate–tolerant wheat system. Weed Sci. 52, 834–844 (2004).
doi: 10.1614/WS-03-149R
Buhl, K. J. & Faerber, N. L. Acute toxicity of selected herbicides and surfactants to larvae of the midge Chironomus riparius. Arch. Environ. Contam. Toxicol. 18, 530–536 (1989).
doi: 10.1007/BF01055019
Rosic, N., Bradbury, J., Lee, M., Baltrotsky, K. & Grace, S. The impact of pesticides on local waterways: A scoping review and method for identifying pesticides in local usage. Environ. Sci. Policy 106, 12–21 (2020).
doi: 10.1016/j.envsci.2019.12.005
Pizl, V. Interactions between earthworms and herbicides. I. Toxicity of some herbicides to earthworms in laboratory tests. Pedobiologia 32, 3–4 (1988).
doi: 10.1016/S0031-4056(23)00235-4
Golovleva, L. A., Pertsova, R. N., Kunc, F. & Vokounová, M. Decomposition of the herbicide bromoxynil in soil and in bacterial cultures. Folia Microbiol. 33, 491–499 (1988).
doi: 10.1007/BF02925776
Holtze, M. S., Sørensen, S. R., Sørensen, J. & Aamand, J. Microbial degradation of the benzonitrile herbicides dichlobenil, bromoxynil and ioxynil in soil and subsurface environments—Insights into degradation pathways, persistent metabolites and involved degrader organisms. Environ. Pollut. 154, 155–168 (2008).
pubmed: 17988770
doi: 10.1016/j.envpol.2007.09.020
Chen, K. et al. An essential esterase (BroH) for the mineralization of bromoxynil octanoate by a natural consortium of Sphingopyxis sp. strain OB-3 and Comamonas sp. strain 7D-2. J. Agric. Food Chem. 61, 11550–11559 (2013).
pubmed: 24224769
doi: 10.1021/jf4037062
Knossow, N., Siebner, H. & Bernstein, A. Isotope Fractionation (δ
pubmed: 31986047
doi: 10.1021/acs.jafc.9b07653
Achermann, S., Mansfeldt, C. B., Müller, M., Johnson, D. R. & Fenner, K. Relating Metatranscriptomic profiles to the micropollutant biotransformation potential of complex microbial communities. Environ. Sci. Technol. 54, 235–244 (2020).
pubmed: 31774283
doi: 10.1021/acs.est.9b05421
Ruan, Z. et al. Comparative genomic analysis of Pseudoxanthomonas sp. X-1, a bromoxynil octanoate degrading bacterium, and Its Related Type Strains. Curr. Microbiol. 79, 65 (2022).
pubmed: 35059857
doi: 10.1007/s00284-021-02735-y
Chen, K. et al. Molecular characterization of the enzymes involved in the degradation of a brominated aromatic herbicide. Mol. Microbiol. 89, 1121–1139 (2013).
pubmed: 23859214
doi: 10.1111/mmi.12332
Chen, K. et al. Comparative transcriptome analysis reveals the mechanism underlying 3,5–dibromo–4–hydroxybenzoate catabolism via a new oxidative decarboxylation pathway. Appl. Environ. Microbiol. 84, 1–16 (2018).
doi: 10.1128/AEM.02467-17
Li, Z. et al. A simplified synthetic community rescues Astragalus mongholicus from root rot disease by activating plant–induced systemic resistance. Microbiome 9, 217 (2021).
pubmed: 34732249
pmcid: 8567675
doi: 10.1186/s40168-021-01169-9
Debray, R. et al. Priority effects in microbiome assembly. Nat. Rev. Microbiol. 20, 109–121 (2021).
pubmed: 34453137
doi: 10.1038/s41579-021-00604-w
Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. USA. 114, E2450–E2459 (2017).
pubmed: 28275097
pmcid: 5373366
doi: 10.1073/pnas.1616148114
Taylor, B. C. et al. Consumption of fermented foods is associated with systematic differences in the gut microbiome and metabolome. mSystems 5, e00901–e00919 (2020).
pubmed: 32184365
pmcid: 7380580
Berg, G. et al. Microbiome definition re–visited: old concepts and new challenges. Microbiome 8, 103 (2020).
pubmed: 32605663
pmcid: 7329523
doi: 10.1186/s40168-020-00875-0
Javdan, B. et al. Personalized mapping of drug metabolism by the human gut microbiome. Cell 181, 1661–1679.e22 (2020).
pubmed: 32526207
pmcid: 8591631
doi: 10.1016/j.cell.2020.05.001
Kumar, V., Baweja, M., Singh, P. K. & Shukla, P. Recent developments in systems biology and metabolic engineering of plant–microbe interactions. Front. Plant Sci. 7, 1421 (2016).
pubmed: 27725824
pmcid: 5035732
doi: 10.3389/fpls.2016.01421
Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).
pubmed: 30072533
pmcid: 6405290
doi: 10.1126/science.aat1168
Bulgarelli, D., Schlaeppi, K., Spaepen, S., Van Themaat, E. V. L. & Schulze–Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).
pubmed: 23373698
doi: 10.1146/annurev-arplant-050312-120106
Maignien, L., DeForce, E. A., Chafee, M. E., Murat Eren, A. & Simmons, S. L. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. MBio 5, e00682–e00713 (2014).
pubmed: 24449749
pmcid: 3903271
doi: 10.1128/mBio.00682-13
Liu, Y., Hou, Q., Liu, W., Meng, Y. & Wang, G. Dynamic changes of bacterial community under bioremediation with Sphingobium sp. LY-6 in buprofezin-contaminated Soil. Bioprocess. Biosyst. Eng. 38, 1485–1493 (2015).
pubmed: 25832788
doi: 10.1007/s00449-015-1391-x
Wu, M. et al. Bacterial community shift and hydrocarbon transformation during bioremediation of short-term petroleum-contaminated soil. Environ. Pollut. 223, 657–664 (2017).
pubmed: 28196719
doi: 10.1016/j.envpol.2017.01.079
Liu, L. H. et al. Endophytic Phthalate-degrading Bacillus subtilis N-1-gfp colonizing in soil-crop system shifted indigenous bacterial community to remove di-n-butyl phthalate. J. Hazard. Mater. 449, 130993 (2023).
pubmed: 36812730
doi: 10.1016/j.jhazmat.2023.130993
Pacwa-Płociniczak, M., Czapla, J., Płociniczak, T. & Piotrowska-Seget, Z. The effect of bioaugmentation of petroleum-contaminated soil with Rhodococcus erythropolis strains on removal of petroleum from soil. Ecotoxicol. Environ. Saf. 169, 615–622 (2019).
pubmed: 30496993
doi: 10.1016/j.ecoenv.2018.11.081
Chen, S. et al. Soil bacterial community dynamics following bioaugmentation with Paenarthrobacter sp. W11 in atrazine-contaminated soil. Chemosphere 282, 130976 (2021).
pubmed: 34089999
doi: 10.1016/j.chemosphere.2021.130976
Dai, Y., Li, N., Zhao, Q. & Xie, S. Bioremediation using Novosphingobium strain DY4 for 2, 4-dichlorophenoxyacetic acid-contaminated soil and impact on microbial community structure. Biodegradation 26, 161–170 (2015).
pubmed: 25743701
doi: 10.1007/s10532-015-9724-7
Compant, S., Samad, A., Faist, H. & Sessitsch, A. A review on the plant microbiome: Ecology, functions, and emerging trends in microbial application. J. Adv. Res. 19, 29–37 (2019).
pubmed: 31341667
pmcid: 6630030
doi: 10.1016/j.jare.2019.03.004
Abdullaeva, Y., Ambika Manirajan, B., Honermeier, B., Schnell, S. & Cardinale, M. Domestication affects the composition, diversity, and co-occurrence of the cereal seed microbiota. J. Adv. Res. 31, 75–86 (2021).
pubmed: 34194833
doi: 10.1016/j.jare.2020.12.008
Liu, X., Chen, K., Chuang, S., Xu, X. & Jiang, J. Shift in bacterial community structure drives different atrazine–degrading efficiencies. Front. Microbiol. 10, 88 (2019).
pubmed: 30761118
pmcid: 6363660
doi: 10.3389/fmicb.2019.00088
Kost, C., Patil, K. R., Friedman, J., Garcia, S. L. & Ralser, M. Metabolic exchanges are ubiquitous in natural microbial communities. Nat. Microbiol. 8, 2244–2252 (2023).
pubmed: 37996708
doi: 10.1038/s41564-023-01511-x
LaSarre, B., McCully, A. L., Lennon, J. T. & McKinlay, J. B. Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients. ISME J. 11, 337–348 (2017).
pubmed: 27898053
doi: 10.1038/ismej.2016.141
Schäfer, M. et al. Metabolic interaction models recapitulate leaf microbiota ecology. Science 381, eadf5121 (2023).
pubmed: 37410834
doi: 10.1126/science.adf5121
Yu, J. S. L. et al. Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. Nat. Microbiol. 7, 542–555 (2022).
pubmed: 35314781
pmcid: 8975748
doi: 10.1038/s41564-022-01072-5
Ryback, B., Bortfeld-Miller, M. & Vorholt, J. A. Metabolic adaptation to vitamin auxotrophy by leaf-associated bacteria. ISME J. 16, 2712–2724 (2022).
pubmed: 35987782
pmcid: 9666465
doi: 10.1038/s41396-022-01303-x
Ge, Z. B. et al. Two-tiered mutualism improves survival and competitiveness of cross-feeding soil bacteria. ISME J. 17, 2090–2102 (2023).
pubmed: 37737252
pmcid: 10579247
doi: 10.1038/s41396-023-01519-5
Wang, X. et al. Nitrogen transfer and cross-feeding between Azotobacter chroococcum and Paracoccus aminovorans promotes pyrene degradation. ISME J. 17, 2169–2181 (2023).
pubmed: 37775536
pmcid: 10689768
doi: 10.1038/s41396-023-01522-w
Zhao, Y. et al. Inter-bacterial mutualism promoted by public goods in a system characterized by deterministic temperature variation. Nat. Commun. 14, 5394 (2023).
pubmed: 37669961
pmcid: 10480208
doi: 10.1038/s41467-023-41224-7
Lee, S. Y. & Kim, H. U. Systems strategies for developing industrial microbial strains. Nat. Biotechnol. 33, 1061–1072 (2015).
pubmed: 26448090
doi: 10.1038/nbt.3365
Takahashi, M. K. et al. A low–cost paper–based synthetic biology platform for analyzing gut microbiota and host biomarkers. Nat. Commun. 9, 3347 (2018).
pubmed: 30131493
pmcid: 6104080
doi: 10.1038/s41467-018-05864-4
St John, P. C. & Bomble, Y. J. Approaches to computational strain design in the multiomics era. Front. Microbiol. 10, 597 (2019).
doi: 10.3389/fmicb.2019.00597
Keshava, R., Mitra, R., Gope, M. L. & Gope, R. Synthetic biology: Overview and Applications. Omics Technol. Bio–Eng.: Towards Improv. Qual. Life 1, 63–93 (2018).
doi: 10.1016/B978-0-12-804659-3.00004-X
Suzuki, K. et al. In vivo genome editing via CRISPR/Cas9 mediated homology–independent targeted integration. Nature 540, 144–149 (2016).
pubmed: 27851729
pmcid: 5331785
doi: 10.1038/nature20565
Anzalone, A. V., Koblan, L. W. & Liu, D. R. Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38, 824–844 (2020).
pubmed: 32572269
doi: 10.1038/s41587-020-0561-9
Casini, A., Storch, M., Baldwin, G. S. & Ellis, T. Bricks and blueprints: Methods and standards for DNA assembly. Nat. Rev. Mol. Cell Biol. 16, 568–576 (2015).
pubmed: 26081612
doi: 10.1038/nrm4014
Liang, J., Luo, Y. & Zhao, H. Synthetic biology: Putting synthesis into biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 7–20 (2011).
pubmed: 21064036
doi: 10.1002/wsbm.104
Hughes, R. A. & Ellington, A. D. Synthetic DNA synthesis and assembly: Putting the synthetic in synthetic biology. Cold Spring Harb. Perspect. Biol. 9, a023812 (2017).
pubmed: 28049645
pmcid: 5204324
doi: 10.1101/cshperspect.a023812
Callahan, B. J. et al. DADA2: High–resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
pubmed: 27214047
pmcid: 4927377
doi: 10.1038/nmeth.3869
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
pubmed: 31341288
pmcid: 7015180
doi: 10.1038/s41587-019-0209-9
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web–based tools. Nucleic Acids Res. 41, D590–D596 (2013).
pubmed: 23193283
doi: 10.1093/nar/gks1219
Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).
pubmed: 23486247
pmcid: 3695285
doi: 10.1038/ismej.2013.16
Schloss, P. D. et al. Introducing mothur: Open–source, platform–independent, community–supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
pubmed: 19801464
pmcid: 2786419
doi: 10.1128/AEM.01541-09
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
pubmed: 21702898
pmcid: 3218848
doi: 10.1186/gb-2011-12-6-r60
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
pubmed: 25609793
doi: 10.1093/bioinformatics/btv033
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
doi: 10.1186/1471-2105-11-119
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
pubmed: 16731699
doi: 10.1093/bioinformatics/btl158
Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).
pubmed: 19497933
doi: 10.1093/bioinformatics/btp336
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
pubmed: 25402007
doi: 10.1038/nmeth.3176
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).
pubmed: 30418610
doi: 10.1093/nar/gky1085
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173
pmcid: 102409
doi: 10.1093/nar/28.1.27
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).
pubmed: 28594827
pmcid: 5481147
doi: 10.1371/journal.pcbi.1005595
Lomsadze, A. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res. 33, 6494–6506 (2005).
pubmed: 16314312
pmcid: 1298918
doi: 10.1093/nar/gki937
Chan, P. P., Lin, B. Y., Mak, A. J. & Lowe, T. M. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 49, 9077–9096 (2021).
pubmed: 34417604
pmcid: 8450103
doi: 10.1093/nar/gkab688
Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).
pubmed: 17452365
pmcid: 1888812
doi: 10.1093/nar/gkm160
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 42, D26–D31 (2014).
pubmed: 24225321
doi: 10.1093/nar/gkt1069
Henry, C. S. et al. High–throughput generation, optimization and analysis of genome–scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).
pubmed: 20802497
doi: 10.1038/nbt.1672
Vlassis, N., Pacheco, M. P. & Sauter, T. Fast reconstruction of compact context–specific metabolic network models. PLoS Comput. Biol. 10, e1003424 (2014).
pubmed: 24453953
pmcid: 3894152
doi: 10.1371/journal.pcbi.1003424
Heirendt, L. et al. Creation and analysis of biochemical constraint–based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14, 639–702 (2019).
pubmed: 30787451
pmcid: 6635304
doi: 10.1038/s41596-018-0098-2
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and Metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).
pubmed: 26585406
doi: 10.1016/j.jmb.2015.11.006
Bateman, A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
doi: 10.1093/nar/gky1049
Bateman, A. et al. UniProt: A hub for protein information. Nucleic Acids Res. 43, D204–D412 (2015).
doi: 10.1093/nar/gku989
Norsigian, C. J. et al. BiGG Models 2020: Multi–strain genome–scale models and expansion across the phylogenetic tree. Nucleic Acids Res. 48, D402–D406 (2020).
pubmed: 31696234
Chen, I. M. A. et al. IMG/M v.5.0: An integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 47, D666–D677 (2019).
pubmed: 30289528
doi: 10.1093/nar/gky901
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes–a 2019 update. Nucleic Acids Res. 48, D445–D453 (2020).
pubmed: 31586394
doi: 10.1093/nar/gkz862
Chan, S. H. J., Simons, M. N. & Maranas, C. D. SteadyCom: Predicting microbial abundances while ensuring community stability. PLoS Comput. Biol. 13, e1005539 (2017).
pubmed: 28505184
pmcid: 5448816
doi: 10.1371/journal.pcbi.1005539
Lewis, N. E. et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome–scale models. Mol. Syst. Biol. 6, 390 (2010).
pubmed: 20664636
pmcid: 2925526
doi: 10.1038/msb.2010.47
Tjaden, B. De novo assembly of bacterial transcriptomes from RNA-seq data. Genome Biol. 16, 1 (2015).
pubmed: 25583448
pmcid: 4316799
doi: 10.1186/s13059-014-0572-2
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
pubmed: 20196867
pmcid: 2864565
doi: 10.1186/gb-2010-11-3-r25
Ruan, Z. P. et al. Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling. GitHub https://doi.org/10.5072/zenodo.53095 (2023).