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Artificial intelligence and synthetic biology approaches for human gut microbiome

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  • Achuthan, A. A., R. K. Duary, A. Madathil, H. Panwar, H. Kumar, V. K. Batish, and S. Grover. 2012. Antioxidative potential of lactobacilli isolated from the gut of Indian people. Molecular Biology Reports 39 (8):7887–97. doi: 10.1007/s11033-012-1633-9.
  • AlQuraishi, M. 2019. End-to-end differentiable learning of protein structure. Cell Systems 8 (4):292–301.e293. doi: 10.1016/j.cels.2019.03.006.
  • Arango-Argoty, G., E. Garner, A. Pruden, L. S. Heath, P. Vikesland, and L. Zhang. 2018. DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6 (1):23. doi: 10.1186/s40168-018-0401-z.
  • Arnold, J. W., J. Roach, and M. A. Azcarate-Peril. 2016. Emerging technologies for gut microbiome research. Trends in Microbiology 24 (11):887–901. doi: 10.1016/j.tim.2016.06.008.
  • Barra, M., T. Danino, and D. Garrido. 2020. Engineered probiotics for detection and treatment of inflammatory intestinal diseases. Frontiers in Bioengineering and Biotechnology 8:265–265. doi: 10.3389/fbioe.2020.00265.
  • Beal, J., A. Adler, and F. Yaman. 2016. Managing bioengineering complexity with AI techniques. Bio Systems 148:40–46. doi: 10.1016/j.biosystems.2015.08.006.
  • Beal, J., T. Lu, and R. Weiss. 2011. Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks. Plos ONE 6 (8):e22490. doi: 10.1371/journal.pone.0022490.
  • Beal, J., R. Weiss, D. Densmore, A. Adler, E. Appleton, J. Babb, S. Bhatia, N. Davidsohn, T. Haddock, J. Loyall, et al. 2012. An end-to-end workflow for engineering of biological networks from high-level specifications. ACS Synthetic Biology 1 (8):317–331. doi: 10.1021/sb300030d.
  • Beck, D., and J. A. Foster. 2014. Machine learning techniques accurately classify microbial communities by bacterial vaginosis characteristics. Plos ONE 9 (2):e87830. doi: 10.1371/journal.pone.0087830.
  • Bharti, R., and D. G. Grimm. 2019. Current challenges and best-practice protocols for microbiome analysis. Briefings in Bioinformatics 1–16. .
  • Binns, N. 2013. Probiotics, prebiotics and the gut microbiota. In ILSI Europe concise monograph series, eds. M. E. S. Glenn and R. Gibson, 40. Brussels, Belgium: ILSI Europe.
  • Bober, J. R., C. L. Beisel, and N. U. Nair. 2018. Synthetic biology approaches to engineer probiotics and members of the human microbiota for biomedical applications. Annual Review of Biomedical Engineering 20:277–300. doi: 10.1146/annurev-bioeng-062117-121019.
  • Caesar, R., V. Tremaroli, P. Kovatcheva-Datchary, P. D. Cani, and F. Bäckhed. 2015. Crosstalk between gut microbiota and dietary lipids aggravates WAT inflammation through TLR signaling. Cell Metabolism 22 (4):658–668. doi: 10.1016/j.cmet.2015.07.026.
  • Camp, J. G., C. L. Frank, C. R. Lickwar, H. Guturu, T. Rube, A. M. Wenger, J. Chen, G. Bejerano, G. E. Crawford, and J. F. Rawls. 2014. Microbiota modulate transcription in the intestinal epithelium without remodeling the accessible chromatin landscape. Genome Research 24 (9):1504–1516. doi: 10.1101/gr.165845.113.
  • Cañez, C., K. Selle, Y. J. Goh, and R. Barrangou. 2019. Outcomes and characterization of chromosomal self-targeting by native CRISPR-Cas systems in Streptococcus thermophilus. FEMS Microbiology Letters 366 (9) fnz105. doi: 10.1093/femsle/fnz105.
  • Chambers, E. S., T. Preston, G. Frost, and D. J. Morrison. 2018. Role of gut microbiota-generated short-chain fatty acids in metabolic and cardiovascular health. Current Nutrition Reports 7 (4):198–206. doi: 10.1007/s13668-018-0248-8.
  • Chen, Z., Z. Li, N. Yu, and L. Yan. 2011. Expression and secretion of a single-chain sweet protein, monellin, in Saccharomyces cerevisiae by an α-factor signal peptide. Biotechnology Letters 33 (4):721–725. doi: 10.1007/s10529-010-0479-2.
  • Chen, S., H. Zhang, H. Shi, W. Ji, J. Feng, Y. Gong, Z. Yang, and Q. Ouyang. 2012. Automated design of genetic toggle switches with predetermined bistability. ACS Synthetic Biology 1 (7):284–290. doi: 10.1021/sb300027y.
  • Chowdhury, S., S. Castro, C. Coker, T. E. Hinchliffe, N. Arpaia, and T. Danino. 2019. Programmable bacteria induce durable tumor regression and systemic antitumor immunity. Nature Medicine 25 (7):1057–1063. doi: 10.1038/s41591-019-0498-z.
  • Chu, D. M., J. Ma, A. L. Prince, K. M. Antony, M. D. Seferovic, and K. M. Aagaard. 2017. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nature Medicine 23 (3):314–326.
  • Citorik, R. J., M. Mimee, and T. K. Lu. 2014. Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases. Nature Biotechnology 32 (11):1141–1145. doi: 10.1038/nbt.3011.
  • Cloney, R. 2016. Synthetic biology: Automating genetic circuit design. Nature Reviews. Genetics 17 (6):314–315. doi: 10.1038/nrg.2016.50.
  • Cole, J. R., Q. Wang, J. A. Fish, B. Chai, D. M. McGarrell, Y. Sun, C. T. Brown, A. Porras-Alfaro, C. R. Kuske, and J. M. Tiedje. 2014. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Research 42 (Database issue):D633–D642. doi: 10.1093/nar/gkt1244.
  • Daeffler, K. N. M., J. D. Galley, R. U. Sheth, L. C. Ortiz-Velez, C. O. Bibb, N. F. Shroyer, R. A. Britton, and J. J. Tabor. 2017. Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Molecular Systems Biology 13 (4):923–923. doi: 10.15252/msb.20167416.
  • Dangi, A. K., R. Sinha, S. Dwivedi, S. K. Gupta, and P. Shukla. 2018. Cell line techniques and gene editing tools for antibody production: A review. Frontiers in Pharmacology 9:630. doi: 10.3389/fphar.2018.00630.
  • Danino, T., A. Prindle, G. A. Kwong, M. Skalak, H. Li, K. Allen, J. Hasty, and S. N. Bhatia. 2015. Programmable probiotics for detection of cancer in urine. Science Translational Medicine. 7:289ra284.
  • Dasika, M. S., and C. D. Maranas. 2008. OptCircuit: An optimization based method for computational design of genetic circuits. BMC Systems Biology 2:24. doi: 10.1186/1752-0509-2-24.
  • de Simone, C. 2019. The unregulated probiotic market. Clinical Gastroenterology and Hepatology 17 (5):809–817. doi: 10.1016/j.cgh.2018.01.018.
  • DeSantis, T. Z., P. Hugenholtz, N. Larsen, M. Rojas, E. L. Brodie, K. Keller, T. Huber, D. Dalevi, P. Hu, and G. L. Andersen. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and Environmental Microbiology 72 (7):5069–5072. doi: 10.1128/AEM.03006-05.
  • Dl, T. 1996. Short-chain fatty acids produced by intestinal bacteria. Asia Facific Journal of Clinical Nutrition 5:9–15.
  • Dou, J., and M. R. Bennett. 2018. Synthetic biology and the gut microbiome. Biotechnology Journal 13 (5):e1700159–e1700159. doi: 10.1002/biot.201700159.
  • Duan, F., and J. C. March. 2010. Engineered bacterial communication prevents Vibrio cholerae virulence in an infant mouse model. Proceedings of the National Academy of Sciences 107:11260.
  • Duary, R. K., M. A. Bhausaheb, V. K. Batish, and S. Grover. 2012. Anti-inflammatory and immunomodulatory efficacy of indigenous probiotic Lactobacillus plantarum Lp91 in colitis mouse model. Molecular Biology Reports 39 (4):4765–4775. doi: 10.1007/s11033-011-1269-1.
  • Elkrief, A., L. Derosa, L. Zitvogel, G. Kroemer, and B. Routy. 2019. The intimate relationship between gut microbiota and cancer immunotherapy. Gut Microbes 10 (3):424–428. doi: 10.1080/19490976.2018.1527167.
  • Ercolini, D., and V. Fogliano. 2018. Food design to feed the human gut microbiota. Journal of Agricultural and Food Chemistry 66 (15):3754–3758. doi: 10.1021/acs.jafc.8b00456.
  • Espinoza, J. L. 2018. Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer. Journal of Internal Medicine 284 (2):189–192.
  • Fricke, W. F., and D. A. Rasko. 2014. Bacterial genome sequencing in the clinic: Bioinformatic challenges and solutions. Nature Reviews. Genetics 15 (1):49–55. doi: 10.1038/nrg3624.
  • Goold, H. D., P. Wright, and D. Hailstones. 2018. Emerging opportunities for synthetic biology in agriculture. Genes 9 (7):341. doi: 10.3390/genes9070341.
  • Grenham, S., G. Clarke, J. F. Cryan, and T. G. Dinan. 2011. Brain-gut-microbe communication in health and disease. Frontiers in Physiology 2:94–94. doi: 10.3389/fphys.2011.00094.
  • Gupta, S. K., and P. Shukla. 2016. Advanced technologies for improved expression of recombinant proteins in bacteria: Perspectives and applications. Critical Reviews in Biotechnology 36 (6):1089–1098. doi: 10.3109/07388551.2015.1084264.
  • Han, X., A. Lee, S. Huang, J. Gao, J. R. Spence, and C. Owyang. 2019. Lactobacillus rhamnosus GG prevents epithelial barrier dysfunction induced by interferon-gamma and fecal supernatants from irritable bowel syndrome patients in human intestinal enteroids and colonoids. Gut Microbes 10 (1):59–76.
  • Hansen, M. E., R. Wangari, E. B. Hansen, I. Mijakovic, and P. R. Jensen. 2009. Engineering of Bacillus subtilis 168 for increased nisin resistance. Applied and Environmental Microbiology 75:6688.
  • Henriques, D., A. F. Villaverde, M. Rocha, J. Saez-Rodriguez, and J. R. Banga. 2017. Data-driven reverse engineering of signaling pathways using ensembles of dynamic models. PLoS Computational Biology 13 (2):e1005379. doi: 10.1371/journal.pcbi.1005379.
  • Hidalgo-Cantabrana, C., A. B. Crawley, B. Sanchez, and R. Barrangou. 2017. Characterization and Exploitation of CRISPR Loci in Bifidobacterium longum. Frontiers in Microbiology 8:1851–1851. doi: 10.3389/fmicb.2017.01851.
  • Hiscock, T. W. 2019. Adapting machine-learning algorithms to design gene circuits. BMC Bioinformatics 20 (1):214. doi: 10.1186/s12859-019-2788-3.
  • Hu, X., and I. Friedberg. 2019. Identifying core operons in metagenomic data. bioRxiv, 2019.2012.2020.885269.
  • Huttenhower, C., C. Gevers, D. Knight, R. Abubucker, S. Badger, J. H. Chinwalla, A. T. Creasy, H. H. Earl, A. M. FitzGerald, M. G. Fulton, et al. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486:207.
  • Hwang, I. Y., E. Koh, A. Wong, J. C. March, W. E. Bentley, Y. S. Lee, and M. W. Chang. 2017. Engineered probiotic Escherichia coli can eliminate and prevent Pseudomonas aeruginosa gut infection in animal models. Nature Communications 8:15028–15028. doi: 10.1038/ncomms15028.
  • Hwang, I. Y., M. H. Tan, E. Koh, C. L. Ho, C. L. Poh, and M. W. Chang. 2014. Reprogramming microbes to be pathogen-seeking killers. ACS Synthetic Biology 3 (4):228–237. doi: 10.1021/sb400077j.
  • Islam, S. U. 2016. Clinical uses of probiotics. Medicine 95 (5):e2658–e2658. doi: 10.1097/MD.0000000000002658.
  • Jiménez-Avalos, J. A., G. Arrevillaga-Boni, L. González-López, Z. Y. García-Carvajal, and M. González-Avila. 2020. Classical methods and perspectives for manipulating the human gut microbial ecosystem. Critical Reviews in Food Science and Nutrition :1–25. doi:10.1080/10408398.2020.1724075. PMC: 32114770
  • Jones, S. E., and J. Versalovic. 2009. Probiotic Lactobacillus reuteri biofilms produce antimicrobial and anti-inflammatory factors. BMC Microbiology 9:35–35. doi: 10.1186/1471-2180-9-35.
  • Kanchiswamy, C. N., M. Maffei, M. Malnoy, R. Velasco, and J.-S. Kim. 2016. Fine-Tuning Next-Generation Genome Editing Tools. Trends in Biotechnology 34 (7):562–574. doi: 10.1016/j.tibtech.2016.03.007.
  • Kearney, J. 2010. Food consumption trends and drivers. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 365 (1554):2793–2807. doi: 10.1098/rstb.2010.0149.
  • Kim, S., S. J. Kerns, M. Ziesack, L. Bry, G. K. Gerber, J. C. Way, and P. A. Silver. 2018. Quorum sensing can be repurposed to promote information transfer between bacteria in the mammalian gut. ACS Synthetic Biology 7 (9):2270–2281. doi: 10.1021/acssynbio.8b00271.
  • Knight, R., A. Vrbanac, B. C. Taylor, A. Aksenov, C. Callewaert, J. Debelius, A. Gonzalez, T. Kosciolek, L.-I. McCall, D. McDonald, et al. 2018. Best practices for analysing microbiomes. Nature Reviews. Microbiology 16 (7):410–422. doi: 10.1038/s41579-018-0029-9.
  • Knights, D., E. K. Costello, and R. Knight. 2011. Supervised classification of human microbiota. FEMS Microbiology Reviews 35 (2):343–359. doi: 10.1111/j.1574-6976.2010.00251.x.
  • Kong, W., V. S. Kapuganti, and T. Lu. 2016. A gene network engineering platform for lactic acid bacteria. Nucleic Acids Research 44 (4):e37–e37. doi: 10.1093/nar/gkv1093.
  • Krom, R. J., P. Bhargava, M. A. Lobritz, and J. J. Collins. 2015. Engineered Phagemids for Nonlytic, Targeted Antibacterial Therapies. Nano Letters 15 (7):4808–4813. doi: 10.1021/acs.nanolett.5b01943.
  • Leber, A., R. Hontecillas, V. Abedi, N. Tubau-Juni, V. Zoccoli-Rodriguez, C. Stewart, and J. Bassaganya-Riera. 2017. Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection. Artificial Intelligence in Medicine 78:1–13. doi: 10.1016/j.artmed.2017.05.003.
  • Lee, D., N. D. R. Lloyd, I. S. Pretorius, and A. R. Borneman. 2016. Heterologous production of raspberry ketone in the wine yeast Saccharomyces cerevisiae via pathway engineering and synthetic enzyme fusion. Microbial Cell Factories 15:49–49. doi: 10.1186/s12934-016-0446-2.
  • Lee, H. L., H. Shen, I. Y. Hwang, H. Ling, W. S. Yew, Y. S. Lee, and M. W. Chang. 2018. Targeted approaches for in situ gut microbiome manipulation. Genes 9 (7):351. doi: 10.3390/genes9070351.
  • Liu, L., N. Guan, J. Li, H-d Shin, G. Du, and J. Chen. 2017. Development of GRAS strains for nutraceutical production using systems and synthetic biology approaches: Advances and prospects. Critical Reviews in Biotechnology 37 (2):139–150. doi: 10.3109/07388551.2015.1121461.
  • Liu, J.-J., I. I. Kong, G.-C. Zhang, L. N. Jayakody, H. Kim, P.-F. Xia, S. Kwak, B. H. Sung, J.-H. Sohn, H. E. Walukiewicz, et al. 2016. Metabolic engineering of probiotic Saccharomyces boulardii. Applied and Environmental Microbiology 82 (8):2280–2287. doi: 10.1128/AEM.00057-16.
  • Lugagne, J.-B., S. Sosa Carrillo, M. Kirch, A. Köhler, G. Batt, and P. Hersen. 2017. Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nature Communications 8 (1):1671 doi: 10.1038/s41467-017-01498-0.
  • Ma, N., P. Guo, J. Zhang, T. He, S. W. Kim, G. Zhang, and X. Ma. 2018. Nutrients mediate intestinal bacteria–mucosal immune crosstalk. Frontiers in Immunology 9(5). doi: 10.3389/fimmu.2018.00005.
  • Ma, C., M. Han, B. Heinrich, Q. Fu, Q. Zhang, M. Sandhu, D. Agdashian, M. Terabe, J. A. Berzofsky, V. Fako, et al. 2018. Gut microbiome–mediated bile acid metabolism regulates liver cancer via NKT cells. Science 360 (6391):eaan5931. doi: 10.1126/science.aan5931.
  • Mali, P., L. Yang, K. M. Esvelt, J. Aach, M. Guell, J. E. DiCarlo, J. E. Norville, and G. M. Church. 2013. RNA-guided human genome engineering via Cas9. Science (New York, N.Y.) 339 (6121):823–826. doi: 10.1126/science.1232033.
  • Ma, N., and X. Ma. 2019. Dietary amino acids and the gut-microbiome-immune axis: Physiological metabolism and therapeutic prospects. Comprehensive Reviews in Food Science and Food Safety 18 (1):221–242. doi: 10.1111/1541-4337.12401.
  • Mamoshina, P., M. Volosnikova, I. V. Ozerov, E. Putin, E. Skibina, F. Cortese, and A. Zhavoronkov. 2018. Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification. Frontiers in Genetics 9: 242. doi: 10.3389/fgene.2018.00242.
  • Markle, J. G. M., D. N. Frank, K. Adeli, M. von Bergen, and J. S. Danska. 2014. Microbiome manipulation modifies sex-specific risk for autoimmunity. Gut Microbes 5 (4):485–493. doi: 10.4161/gmic.29795.
  • Mays, Z. J. S., and N. U. Nair. 2018. Synthetic biology in probiotic lactic acid bacteria: At the frontier of living therapeutics. Current Opinion in Biotechnology 53:224–231. doi: 10.1016/j.copbio.2018.01.028.
  • Mays, Z. J. S., and N. U. Nair. 2020. A quantitative model for oral administration of living therapeutics. bioRxiv, 2020.2004.2001.020677.
  • McDonald, D., E. Hyde, J. W. Debelius, J. T. Morton, A. Gonzalez, G. Ackermann, A. A. Aksenov, B. Behsaz, C. Brennan, Y. Chen, et al. 2018. American gut: An open platform for citizen science microbiome research. mSystems 3 (3):e00031-00018. doi: 10.1128/mSystems.00031-18.
  • Menzella, H. G., R. Reid, J. R. Carney, S. S. Chandran, S. J. Reisinger, K. G. Patel, D. A. Hopwood, and D. V. Santi. 2005. Combinatorial polyketide biosynthesis by de novo design and rearrangement of modular polyketide synthase genes. Nature Biotechnology 23 (9):1171–1176. doi: 10.1038/nbt1128.
  • Mimee, M., A. C. Tucker, C. A. Voigt, and T. K. Lu. 2015. Programming a human commensal bacterium, bacteroides thetaiotaomicron, to sense and respond to stimuli in the murine gut microbiota. Cell Systems 1 (1):62–71. doi: 10.1016/j.cels.2015.06.001.
  • Moeller, A. H., Y. Li, E. Mpoudi Ngole, S. Ahuka-Mundeke, E. V. Lonsdorf, A. E. Pusey, M. Peeters, B. H. Hahn, and H. Ochman. 2014. Rapid changes in the gut microbiome during human evolution. Proceedings of the National Academy of Sciences 111 (46):16431–16435. doi: 10.1073/pnas.1419136111.
  • Molinero, N., L. Ruiz, B. Sánchez, A. Margolles, and S. Delgado. 2019. Intestinal bacteria interplay with bile and cholesterol metabolism: Implications on host physiology. Frontiers in Physiology 10: 185. doi: 10.3389/fphys.2019.00185.
  • Mysara, M., P. Vandamme, R. Props, F.-M. Kerckhof, N. Leys, N. Boon, J. Raes, and P. Monsieurs. 2017. Reconciliation between operational taxonomic units and species boundaries. FEMS Microbiology Ecology 93 (4): fix029 doi: 10.1093/femsec/fix029.
  • Nandi, S., A. Subramanian, and R. R. Sarkar. 2017. An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features. Molecular bioSystems 13 (8):1584–1596. doi: 10.1039/c7mb00234c.
  • Naseri, G., and M. A. G. Koffas. 2020. Application of combinatorial optimization strategies in synthetic biology. Nature Communications 11 (1):2446. doi: 10.1038/s41467-020-16175-y.
  • Neish, A. S. 2009. Microbes in gastrointestinal health and disease. Gastroenterology 136 (1):65–80. doi: 10.1053/j.gastro.2008.10.080.
  • Nikolski, H. S. a M. 2016. Machine learning for metagenomics: Methods and tools. Metagenomics 1 (1): 1-19 arXiv:1510.06621v2 [q-bio.GN] doi: 10.1515/metgen-2016-0001
  • Noguchi, H., J. Park, and T. Takagi. 2006. MetaGene: Prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res 34 (19):5623–5630. doi: 10.1093/nar/gkl723.
  • Oh, J.-H., and J.-P. van Pijkeren. 2014. CRISPR-Cas9-assisted recombineering in Lactobacillus reuteri . Nucleic Acids Research 42 (17):e131–e131. doi: 10.1093/nar/gku623.
  • Paddon, C. J., P. J. Westfall, D. J. Pitera, K. Benjamin, K. Fisher, D. McPhee, M. D. Leavell, A. Tai, A. Main, D. Eng, et al. 2013. High-level semi-synthetic production of the potent antimalarial artemisinin. Nature 496 (7446):528–532. doi: 10.1038/nature12051.
  • Qu, K., F. Guo, X. Liu, Y. Lin, and Q. Zou. 2019. Application of machine learning in microbiology. Frontiers in Microbiology 10: 827. doi: 10.3389/fmicb.2019.00827.
  • Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research 41 (Database issue):D590–D596. doi: 10.1093/nar/gks1219.
  • Rahman, S. F., M. R. Olm, M. J. Morowitz, and J. F. Banfield. 2018. Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome. mSystems 3 (1): e00123-17. doi: 10.1128/mSystems.00123-17.
  • Ramachandran, G., and D. Bikard. 2019. Editing the microbiome the CRISPR way. Philosophical Transactions of the Royal Society B: Biological Sciences 374 (1772):20180103. doi: 10.1098/rstb.2018.0103.
  • Randall, A., P. Guye, S. Gupta, X. Duportet, and R. Weiss. 2011. Chapter seven - design and connection of robust genetic circuits. In Methods in enzymology , Synthetic Biology, Part A. Vol. 497, ed. C. Voigt, 159–86. Amsterdam: Academic Press.
  • Romano, K. A., E. I. Vivas, D. Amador-Noguez, and F. E. Rey. 2015. Intestinal microbiota composition modulates choline bioavailability from diet and accumulation of the proatherogenic metabolite trimethylamine-N-oxide. mBio 6:e02481–02414.
  • Ronda, C., S. P. Chen, V. Cabral, S. J. Yaung, and H. H. Wang. 2019. Metagenomic engineering of the mammalian gut microbiome in situ. Nature Methods 16 (2):167–170. doi: 10.1038/s41592-018-0301-y.
  • Rothschild, D., O. Weissbrod, E. Barkan, A. Kurilshikov, T. Korem, D. Zeevi, P. I. Costea, A. Godneva, I. N. Kalka, N. Bar, et al. 2018. Environment dominates over host genetics in shaping human gut microbiota. Nature 555 (7695):210–215. doi: 10.1038/nature25973.
  • Round, J. L., and S. K. Mazmanian. 2009. The gut microbiota shapes intestinal immune responses during health and disease. Nature Reviews. Immunology 9 (5):313–323. doi: 10.1038/nri2515.
  • Rowland, I., G. Gibson, A. Heinken, K. Scott, J. Swann, I. Thiele, and K. Tuohy. 2018. Gut microbiota functions: Metabolism of nutrients and other food components. European Journal of Nutrition 57 (1):1–24. doi: 10.1007/s00394-017-1445-8.
  • Ruotsalainen, P., R. Penttinen, S. Mattila, and M. Jalasvuori. 2019. Midbiotics: Conjugative plasmids for genetic engineering of natural gut flora. Gut Microbes 10 (6):643–11.
  • Saltepe, B., E. Ş. Kehribar, S. S. Su Yirmibeşoğlu, and U. Ö. Şafak Şeker. 2018. Cellular biosensors with engineered genetic circuits. ACS Sensors 3 (1):13–26. doi: 10.1021/acssensors.7b00728.
  • Sánchez, B., L. Ruiz, M. Gueimonde, P. Ruas-Madiedo, and A. Margolles. 2012. Toward improving technological and functional properties of probiotics in foods. Trends in Food Science & Technology 26:56–63.
  • Sander, J. D., and J. K. Joung. 2014. CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology 32 (4):347–355. doi: 10.1038/nbt.2842.
  • Santana-Gálvez, J., L. Cisneros-Zevallos, and D. A. Jacobo-Velázquez. 2019. A practical guide for designing effective nutraceutical combinations in the form of foods, beverages, and dietary supplements against chronic degenerative diseases. Trends in Food Science & Technology 88:179–193.
  • Sekirov, I., S. L. Russell, L. C. M. Antunes, and B. B. Finlay. 2010. Gut microbiota in health and disease. Physiological Reviews 90 (3):859–904. doi: 10.1152/physrev.00045.2009.
  • Singh, R. K., H.-W. Chang, D. Yan, K. M. Lee, D. Ucmak, K. Wong, M. Abrouk, B. Farahnik, M. Nakamura, T. H. Zhu, et al. 2017. Influence of diet on the gut microbiome and implications for human health. Journal of Translational Medicine 15 (1):73–73. doi: 10.1186/s12967-017-1175-y.
  • Sleight, S. C., B. A. Bartley, J. A. Lieviant, and H. M. Sauro. 2010. In-Fusion BioBrick assembly and re-engineering. Nucleic Acids Research 38 (8):2624–2636. doi: 10.1093/nar/gkq179.
  • Song, X., H. Huang, Z. Xiong, L. Ai, and S. Yang. 2017. CRISPR-Cas9 nickase-assisted genome editing in Lactobacillus casei. Applied and Environmental Microbiology 83 (22):e01259–01217. doi: 10.1128/AEM.01259-17.
  • Sonowal, R., A. Swimm, A. Sahoo, L. Luo, Y. Matsunaga, Z. Wu, J. A. Bhingarde, E. A. Ejzak, A. Ranawade, H. Qadota, et al. 2017. Indoles from commensal bacteria extend healthspan. Proceedings of the National Academy of Sciences 114 (36):E7506–E7515. doi: 10.1073/pnas.1706464114.
  • Spisni, E., M. C. Valerii, L. De Fazio, E. Cavazza, F. Borsetti, A. Sgromo, M. Candela, M. Centanni, F. Rizello, and A. Strillacci. 2015. Cyclooxygenase-2 silencing for the treatment of colitis: A combined in vivo strategy based on RNA interference and engineered Escherichia coli. Molecular Therapy : Therapy 23 (2):278–289. doi: 10.1038/mt.2014.222.
  • Spor, A., O. Koren, and R. Ley. 2011. Unravelling the effects of the environment and host genotype on the gut microbiome. Nature Reviews. Microbiology 9 (4):279–290. doi: 10.1038/nrmicro2540.
  • Statnikov, A., M. Henaff, V. Narendra, K. Konganti, Z. Li, L. Yang, Z. Pei, M. J. Blaser, C. F. Aliferis, and A. V. Alekseyenko. 2013. A comprehensive evaluation of multicategory classification methods for microbiomic data. Microbiome 1 (1):11. doi: 10.1186/2049-2618-1-11.
  • Stephens, K., and W. E. Bentley. 2020. Synthetic biology for manipulating quorum sensing in microbial consortia. Trends in Microbiology 28 (8):633–643. doi: 10.1016/j.tim.2020.03.009.
  • Suppan, S. 2014. From GMO to SMO: How synthetic biology evades regulation. Minneapolis, MN: Institute of Agriculture and Trade Policy.
  • Thomas, T., J. Gilbert, and F. Meyer. 2012. Metagenomics - a guide from sampling to data analysis. Microbial Informatics and Experimentation 2 (1):3–3. doi: 10.1186/2042-5783-2-3.
  • Thompson, J. A., R. A. Oliveira, A. Djukovic, C. Ubeda, and K. B. Xavier. 2015. Manipulation of the quorum sensing signal AI-2 affects the antibiotic-treated gut microbiota. Cell Reports 10 (11):1861–1871. doi: 10.1016/j.celrep.2015.02.049.
  • Thursby, E., and N. Juge. 2017. Introduction to the human gut microbiota. The Biochemical Journal 474 (11):1823–1836. doi: 10.1042/BCJ20160510.
  • Tofalo, R., S. Cocchi, and G. Suzzi. 2019. Polyamines and gut microbiota. Frontiers in Nutrition 6: 16. doi: 10.3389/fnut.2019.00016.
  • Treangen, T. J., and S. L. Salzberg. 2011. Repetitive DNA and next-generation sequencing: Computational challenges and solutions. Nature Reviews. Genetics 13 (1):36–46. doi: 10.1038/nrg3117.
  • Trimble, W. L., K. P. Keegan, M. D'Souza, A. Wilke, J. Wilkening, J. Gilbert, and F. Meyer. 2012. Short-read reading-frame predictors are not created equal: Sequence error causes loss of signal. BMC Bioinformatics 13:183–183. doi: 10.1186/1471-2105-13-183.
  • Tyagi, A., A. Kumar, S. V. Aparna, R. H. Mallappa, S. Grover, and V. K. Batish. 2016. Synthetic biology: Applications in the food sector. Critical Reviews in Food Science and Nutrition 56 (11):1777–1789. doi: 10.1080/10408398.2013.782534.
  • van der Helm, E., H. J. Genee, and M. O. A. Sommer. 2018. The evolving interface between synthetic biology and functional metagenomics. Nature Chemical Biology 14 (8):752–759. doi: 10.1038/s41589-018-0100-x.
  • Vashistha, R., D. Chhabra, and P. Shukla. 2018. Integrated artificial intelligence approaches for disease diagnostics. Indian Journal of Microbiology 58 (2):252–255. doi: 10.1007/s12088-018-0708-2.
  • Volk, M. J., I. Lourentzou, S. Mishra, L. T. Vo, C. Zhai, and H. Zhao. 2020. Biosystems design by machine learning. ACS Synthetic Biology 9 (7):1514–1533. doi: 10.1021/acssynbio.0c00129.
  • Voorhees, P. J., C. Cruz-Teran, J. Edelstein, and S. K. Lai. 2020. Challenges & opportunities for phage-based in situ microbiome engineering in the gut. Journal of Controlled Release : Official Journal of the Controlled Release Society 326:106–119. doi: 10.1016/j.jconrel.2020.06.016.
  • Walsh, D. I., 3rd, M. Pavan, L. Ortiz, S. Wick, J. Bobrow, N. J. Guido, S. Leinicke, D. Fu, S. Pandit, L. Qin, et al. 2019. Standardizing automated DNA assembly: Best practices, metrics, and protocols using robots. SLAS Technology 24 (3):282–290. doi: 10.1177/2472630318825335.
  • Wang, J., S. Guleria, M. A. G. Koffas, and Y. Yan. 2016. Microbial production of value-added nutraceuticals. Current Opinion in Biotechnology 37:97–104. doi: 10.1016/j.copbio.2015.11.003.
  • Wang, W., L. Lin, Y. Du, Y. Song, X. Peng, X. Chen, and C. J. Yang. 2019. Assessing the viability of transplanted gut microbiota by sequential tagging with D-amino acid-based metabolic probes. Nature Communications 10 (1):1317 doi: 10.1038/s41467-019-09267-x.
  • Wang, W.-L., S.-Y. Xu, Z.-G. Ren, L. Tao, J.-W. Jiang, and S.-S. Zheng. 2015. Application of metagenomics in the human gut microbiome. World J Gastroenterol 21 (3):803–814. doi: 10.3748/wjg.v21.i3.803.
  • Wu, H., L. Cai, D. Li, X. Wang, S. Zhao, F. Zou, and K. Zhou. 2018. Metagenomics biomarkers selected for prediction of three different diseases in Chinese population. BioMed Research International 2018:2936257–2936257. doi: 10.1155/2018/2936257.
  • Wu, Y., N. Ma, P. Song, T. He, C. Levesque, Y. Bai, A. Zhang, and X. Ma. 2019. Grape seed proanthocyanidin affects lipid metabolism via changing gut microflora and enhancing propionate production in weaned pigs. The Journal of Nutrition 149 (9):1523–1532. doi: 10.1093/jn/nxz102.
  • Yadav, M., and P. Shukla. 2020. Efficient engineered probiotics using synthetic biology approaches: A review. Biotechnology and Applied Biochemistry 67 (1):22–29. doi: 10.1002/bab.1822.
  • Yadav, R., and P. Shukla. 2017. Probiotics for human health: current progress and applications. In Recent advances in applied microbiology, ed. P. Shukla, 133–47. Singapore: Springer Singapore.
  • Yaman, F., A. Adler, and J. Beal. 2018. AI challenges in synthetic biology engineering. In AAAI Conference on Artificial Intelligence, North America.
  • Yazdani, M., B. C. Taylor, J. W. Debelius, W. Li, R. Knight, and L. Smarr. 2016. Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. In 2016 IEEE International Conference on Big Data (Big Data), 1272–1280. doi: 10.1109/BigData.2016.7840731.[]
  • Yoon, B.-J. 2009. Hidden Markov models and their applications in biological sequence analysis. Current Genomics 10 (6):402–415. doi: 10.2174/138920209789177575.
  • Yosef, I., M. Manor, R. Kiro, and U. Qimron. 2015. Temperate and lytic bacteriophages programmed to sensitize and kill antibiotic-resistant bacteria. Proceedings of the National Academy of Sciences 112 (23):7267–7272. doi: 10.1073/pnas.1500107112.
  • Yuan, S.-F., and H. S. Alper. 2019. Metabolic engineering of microbial cell factories for production of nutraceuticals. Microbial Cell Factories 18 (1):46. doi: 10.1186/s12934-019-1096-y.
  • Zeevi, D., T. Korem, N. Zmora, D. Israeli, D. Rothschild, A. Weinberger, O. Ben-Yacov, D. Lador, T. Avnit-Sagi, M. Lotan-Pompan, et al. 2015. Personalized nutrition by prediction of glycemic responses. Cell 163 (5):1079–1094. doi: 10.1016/j.cell.2015.11.001.
  • Zhou, Z., X. Chen, H. Sheng, X. Shen, X. Sun, Y. Yan, J. Wang, and Q. Yuan. 2020. Engineering probiotics as living diagnostics and therapeutics for improving human health. Microbial Cell Factories 19 (1):56 doi: 10.1186/s12934-020-01318-z.
  • Zhou, Y.-H., and P. Gallins. 2019. A review and tutorial of machine learning methods for microbiome host trait prediction. Frontiers in Genetics 10:579–579. doi: 10.3389/fgene.2019.00579.

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