Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).
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).
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).
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).
Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).
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).
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).
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).
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).
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).
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).
Jiang, Y., Dong, W., Xin, F. & Jiang, M. Designing synthetic microbial consortia for biofuel production. Trends Biotechnol. 38, 828–831 (2020).
Xu, M. et al. Elevated nitrate enriches microbial functional genes for potential bioremediation of complexly contaminated sediments. ISME J. 8, 1932–1944 (2014).
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).
Cheng, M. et al. Oxygenases as powerful weapons in the microbial degradation of pesticides. Annu. Rev. Microbiol. 76, 325–348 (2022).
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).
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).
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).
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).
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).
Roucher, A. et al. From Compartmentalization Of Bacteria Within Inorganic Macrocellular Beads To The Assembly Of Microbial Consortia. Adv. Biosyst. 2, 1700233 (2018).
Opatovsky, I. et al. Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host–simulated environment. BMC Genomics 19, 1–14 (2018).
Xu, X. et al. Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions. ISME J. 13, 494–508 (2019).
Lawson, C. E. et al. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17, 725–741 (2019).
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).
Bernstein, H. C. Reconciling ecological and engineering design principles for building microbiomes. mSystems 4, e00106–e00119 (2019).
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).
Chang, C. Y. et al. Engineering complex communities by directed evolution. Nat. Ecol. Evol. 5, 1011–1023 (2021).
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).
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).
Zuñiga, C., Zaramela, L. & Zengler, K. Elucidation of complexity and prediction of interactions in microbial communities. Microb. Biotechnol. 10, 1500–1522 (2017).
Henry, C. S. et al. Microbial community metabolic modeling: a community data–driven network reconstruction. J. Cell. Physiol. 231, 2339–2345 (2016).
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).
Rocha, M. et al. Natural computation meta–heuristics for the in silico optimization of microbial strains. BMC Bioinforma. 9, 499 (2008).
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).
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).
Wei, D., Kameya, T. & Urano, K. Environmental management of pesticidal POPs in China: Past, present and future. Environ. Int. 33, 894–902 (2007).
Noyes, P. D. et al. The toxicology of climate change: Environmental contaminants in a warming world. Environ. Int. 35, 971–986 (2009).
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).
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).
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).
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).
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).
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).
Pizl, V. Interactions between earthworms and herbicides. I. Toxicity of some herbicides to earthworms in laboratory tests. Pedobiologia 32, 3–4 (1988).
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).
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).
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).
Knossow, N., Siebner, H. & Bernstein, A. Isotope Fractionation (δ13C, δ15N) in the microbial degradation of bromoxynil by aerobic and anaerobic soil enrichment cultures. J. Agric. Food Chem. 68, 1546–1554 (2020).
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).
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).
Chen, K. et al. Molecular characterization of the enzymes involved in the degradation of a brominated aromatic herbicide. Mol. Microbiol. 89, 1121–1139 (2013).
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).
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).
Debray, R. et al. Priority effects in microbiome assembly. Nat. Rev. Microbiol. 20, 109–121 (2021).
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).
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).
Berg, G. et al. Microbiome definition re–visited: old concepts and new challenges. Microbiome 8, 103 (2020).
Javdan, B. et al. Personalized mapping of drug metabolism by the human gut microbiome. Cell 181, 1661–1679.e22 (2020).
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).
Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).
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).
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).
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).
Wu, M. et al. Bacterial community shift and hydrocarbon transformation during bioremediation of short-term petroleum-contaminated soil. Environ. Pollut. 223, 657–664 (2017).
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).
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).
Chen, S. et al. Soil bacterial community dynamics following bioaugmentation with Paenarthrobacter sp. W11 in atrazine-contaminated soil. Chemosphere 282, 130976 (2021).
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).
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).
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).
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).
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).
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).
Schäfer, M. et al. Metabolic interaction models recapitulate leaf microbiota ecology. Science 381, eadf5121 (2023).
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).
Ryback, B., Bortfeld-Miller, M. & Vorholt, J. A. Metabolic adaptation to vitamin auxotrophy by leaf-associated bacteria. ISME J. 16, 2712–2724 (2022).
Ge, Z. B. et al. Two-tiered mutualism improves survival and competitiveness of cross-feeding soil bacteria. ISME J. 17, 2090–2102 (2023).
Wang, X. et al. Nitrogen transfer and cross-feeding between Azotobacter chroococcum and Paracoccus aminovorans promotes pyrene degradation. ISME J. 17, 2169–2181 (2023).
Zhao, Y. et al. Inter-bacterial mutualism promoted by public goods in a system characterized by deterministic temperature variation. Nat. Commun. 14, 5394 (2023).
Lee, S. Y. & Kim, H. U. Systems strategies for developing industrial microbial strains. Nat. Biotechnol. 33, 1061–1072 (2015).
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).
St John, P. C. & Bomble, Y. J. Approaches to computational strain design in the multiomics era. Front. Microbiol. 10, 597 (2019).
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).
Suzuki, K. et al. In vivo genome editing via CRISPR/Cas9 mediated homology–independent targeted integration. Nature 540, 144–149 (2016).
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).
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).
Liang, J., Luo, Y. & Zhao, H. Synthetic biology: Putting synthesis into biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 7–20 (2011).
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).
Callahan, B. J. et al. DADA2: High–resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
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).
Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).
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).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
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).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
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).
Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
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).
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
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).
Lomsadze, A. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res. 33, 6494–6506 (2005).
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).
Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 42, D26–D31 (2014).
Henry, C. S. et al. High–throughput generation, optimization and analysis of genome–scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).
Vlassis, N., Pacheco, M. P. & Sauter, T. Fast reconstruction of compact context–specific metabolic network models. PLoS Comput. Biol. 10, e1003424 (2014).
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).
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).
Bateman, A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
Bateman, A. et al. UniProt: A hub for protein information. Nucleic Acids Res. 43, D204–D412 (2015).
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).
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).
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes–a 2019 update. Nucleic Acids Res. 48, D445–D453 (2020).
Chan, S. H. J., Simons, M. N. & Maranas, C. D. SteadyCom: Predicting microbial abundances while ensuring community stability. PLoS Comput. Biol. 13, e1005539 (2017).
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).
Tjaden, B. De novo assembly of bacterial transcriptomes from RNA-seq data. Genome Biol. 16, 1 (2015).
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
Ruan, Z. P. et al. Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling. GitHub https://doi.org/10.5072/zenodo.53095 (2023).
- SEO Powered Content & PR Distribution. Get Amplified Today.
- PlatoData.Network Vertical Generative Ai. Empower Yourself. Access Here.
- PlatoAiStream. Web3 Intelligence. Knowledge Amplified. Access Here.
- PlatoESG. Carbon, CleanTech, Energy, Environment, Solar, Waste Management. Access Here.
- PlatoHealth. Biotech and Clinical Trials Intelligence. Access Here.
- Source: https://www.nature.com/articles/s41467-024-49098-z