Lee, Dong-Yup

Lindemann, Stephen R.

Song, Hyun-Seob

English

PubMed

DigitalCommons@University of Nebraska

University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Biological Systems Engineering: Papers and Publications Biological Systems Engineering 2021 Editorial: Predictive Modeling of Human Microbiota and Their Role in Health and Disease Hyun-Seob Song Stephen R. Lindemann Dong-Yup Lee Follow this and additional works at: https://digitalcommons.unl.edu/biosysengfacpub  Part of the Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, and the Other Civil and Environmental Engineering Commons This Article is brought to you for free and open access by the Biological Systems Engineering at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Biological Systems Engineering: Papers and Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. EDITORIALpublished: 30 November 2021doi: 10.3389/fmicb.2021.782871Frontiers in Microbiology | www.frontiersin.org 1 November 2021 | Volume 12 | Article 782871Edited by:George Tsiamis,University of Patras, GreeceReviewed by:Neil Surana,Duke University, United StatesSamuel C. Forster,Wellcome Sanger Institute (WT),United Kingdom*Correspondence:Hyun-Seob Songhsong5@unl.eduSpecialty section:This article was submitted toSystems Microbiology,a section of the journalFrontiers in MicrobiologyReceived: 24 September 2021Accepted: 11 November 2021Published: 30 November 2021Citation:Song H-S, Lindemann SR andLee D-Y (2021) Editorial: PredictiveModeling of Human Microbiota andTheir Role in Health and Disease.Front. Microbiol. 12:782871.doi: 10.3389/fmicb.2021.782871Editorial: Predictive Modeling ofHuman Microbiota and Their Role inHealth and DiseaseHyun-Seob Song 1,2*, Stephen R. Lindemann 3,4 and Dong-Yup Lee 51Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States, 2Department ofFood Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, NE, United States,3Department of Food Science, Whistler Center for Carbohydrate Research, Purdue University, West Lafayette, IN,United States, 4Department of Nutrition Science, Purdue University, West Lafayette, IN, United States, 5 School of ChemicalEngineering, Sungkyunkwan University, Suwon, South KoreaKeywords: microbial biomarkers, machine learning, microbial association networks, species and gene profiling,precision nutritionEditorial on the Research TopicPredictive Modeling of HumanMicrobiota and Their Role in Health and DiseaseThe human microbiota—communities of microorganisms living in or on humans as their host—are deeply involved in various biological processes and functions in our body and play an importantrole in maintaining physiological and mental health. The criticality of human microbiota is oftenindicated by the fact that bacterial cells (>100 trillion cells) in our body outnumber human cells(Sender et al., 2016) and carry ∼150 times more genes than the entire human genome (Ursellet al., 2014)—thus called the second genome (Grice and Segre, 2012). Unlike the first genome,which remains largely invariable after being inherited from parents, the second genome’s contentdynamically changes under a variety of conditions affected by diets, drugs, stress, injuries, andmyriad lifestyle and environmental factors. Hence, understanding of how microbiota respond tothose perturbations and how the outcomes influence human health is important for developingmicrobiome-mediated strategies to improve it.Mathematical modeling of human microbiota and their interactions with host cells undervarious environmental conditions is indispensable in this regard. The effectiveness of mathematicalmodels has been demonstrated through various applications, including inferring microbe-microbe,microbe-host, and microbe-host-diet interactions (Song et al., 2014; Li et al., 2019; Chowdhuryand Fong, 2020), creating new insights into the role of human microbiota in health and diseasestate (Kumar et al., 2019), and proposing new engineering strategies for intervention (Sheth et al.,2016; Kessell et al., 2020). In addition to process-based models, data-driven modeling is also beingpopularly used (Marcos-Zambrano et al., 2021) and the combination of these complementaryapproaches is expected to significantly increase the scope of prediction (Kessell et al., 2020).Despite promising progress over the past decade, we still lack a complete understanding of therelationships between microbiota (composition and function) and the host (health and disease)under perturbed conditions as well as in homeostasis, and consequently have a limited capability ofpredicting their interplays. Toward addressing those challenges and promoting new opportunities,this Research Topic collects nine research articles and one review that present the state-of-the-artmodeling approaches in various areas of human microbiome research. Below we summarize thecontributions from lead computational biologists under the following several categories: discoveryofmicrobial biomarkers and signatures of diseases, quantitative assessment of the impact of bacteriaon human health and their association with disease, improved profiling of species and genes inSong et al. Editorial: Predictive Modeling of Human Microbiotathe microbiome, and incorporation of microbiome datawith other clinical features for improved precision nutritionand medicine.Human microbiota show compositional changes acrossconditions, including physiological states of the host in healthand disease. Particular enrichments of microbes, if observed,can therefore be used as a biomarker of specific diseases. Thosesignatures could further be considered potential diagnostic andtherapeutic targets. Several contributions in the Research Topicaddress this issue. To predict the risk for developing colorectalmetachronous adenoma (MA) after surgical resection, Liu etal. developed a random forest (RF) model using the relativeabundance of the gut microbial populations with or withoutthe clinical risk factors. This work is based on the hypothesisthat the composition of the gut microbiota before surgerywas associated with the risk of developing MA and, therefore,could serve as a potential biomarker for MA. The resulting RFmodel identified Escherichia-Shigella and Acinetobacter as keymicrobiome biomarkers, although the accuracy of predictionwas improved when linked with other clinical risk factors suchas synchronous adenoma and body mass index. Kort et al.examined how the hypothesis on the use of gut microbiomes asa biomarker can be extended to identify the association of gutmicrobiota with language development of young children. Usingdata from rural 3-year-olds in Uganda, they developed regressionmodels by accounting for all possible combinations of three orfour species. This comprehensive survey of regression modelsof all subsets of species led to the identification of Coprococcuseutactus, an anaerobic butyrate-producing gut bacterium, asa major predictor of language development in children. Inthe study of non-small cell lung cancer patients treated withdifferent cycles of osimertinib therapy, Cong et al. identifiedthe shifts in microbial biomarkers between post- and pre-therapy. Through the analysis of intestinal microbial ecologicalnetworks constructed by random matrix theory methods, theyalso found the structure of microbial interaction networksbecame complicated by including more compact modules inresponse to osimertinib therapy. A knowledgebase system ofthe human colorectal cancer (CRC) microbiome constructed byZhou et al. integrates complementary data and information toimprove the predictive power of models in biomarker prediction.The web-based platform allows for systematic inquiry andcomparison across different models or databases to identifymicrobial biomarkers through statistical analysis. The importantgoal of this platform is to facilitate diagnosis of CRC, identifykey factors for clinical transformation, and contribute to thedevelopment of cost-effective screening strategies.Beyond discovering microbiome biomarkers and signaturesof disease, another central research challenge is to decipher directlinkages between microorganisms and specific disease types,e.g., through a data-driven network analysis or mechanisticmodeling. Along this line, Lei and Wang proposed a newmethod that enables integrating two similarity-based networksof microorganisms and diseases through the known microbe-disease associations. The resulting integrative network ofmicrobes and diseases may potentially create a new mechanisticunderstanding of microbe-disease associations that arepreviously unknown. Compared to existing approaches,the proposed method showed effectiveness in predictingmicrobe-disease association, as demonstrated through casestudies of asthma, chronic obstructive pulmonary disease, andinflammatory bowel disease. A more mechanistic predictionof the bacterial impact on risk factors that may cause diseasewas made by Bourgin et al. In order to evaluate the impactof the microbial activity vs. host on the cholesterol cycle,they developed a whole-body human model of cholesterolmetabolism by incorporating bacterial conversion of bile saltsand cholesterol into the existing models that focus on hostmetabolism. Comprehensive simulations using the modelshowed that cholesterol conversion to bile salts is the main fluxof cholesterol cycle, indicating that bacterial metabolism likelydrives cholesterol regulation.Further, maintenance of the community structure andfunction of human microbiomes by regulating the ecologicalbalance of microbial populations is key, as disturbances arelinked with negative outcomes on human physiological andpsychological health. Maintaining a desirable ecological balanceof populations in the human microbiome is important becausealterations in their composition and function (i.e., dysbiosis) arelinked with detrimental physiological and psychological impacts,and result in a wide array of disease conditions. The dynamicmodel developed by Dedrick et al. can serve as a useful toolto understand bacterial coexistence and stability. The authorsconstructed an in silico model of nasal microbiota composed ofup to 20 isolates to predict how the community compositionresponds to the variation of pH fluctuations in amplitude orfrequency. The simulation results showed no significant impactof temporal pH fluctuations on the species coexistence andcomposition. The numerical model also suggested cooperativeinteractions among member species that have low niche overlapas a potential mechanism for the observed robustness ofnasal microbiota.Development of predictive human microbiome models isfacilitated by advanced gene sequencing technologies, includingamplicon sequencing for bacterial composition profiling andshotgun sequencing for metagenomic analyses. As pointedout by Gwak and Rho, it is challenging to perform accuratetaxonomic assignment at species level because the 16S rRNAsequences among species in the same genus are highlyhomologous or even identical. To improve the resolution, theyreannotated inconsistent or mislabeled taxa in three major16S rRNA databases and determined species-level taxonomyusing a k-nearest neighbor algorithm and the consensus modelsconstructed for each species. In the case studies using salivaryand gut microbiome data, the proposed method successfullyidentified the variation in bacterial composition across differentgroups based on improved species-level profiling. Ma arguedthat metagenomic gene abundance data can be analyzed ina similar fashion to operational taxonomic unit analysis byviewing microbiomes as a community of genes, rather thanspecies. Using Taylor’s power law, this work analyzed the impactof obesity, inflammatory bowel disease, and diabetes on thehuman microbiota, highlighting the importance of a soundunderstanding of metagenomic heterogeneity for the successFrontiers in Microbiology | www.frontiersin.org 2 November 2021 | Volume 12 | Article 782871Song et al. Editorial: Predictive Modeling of Human Microbiotaof personalized and precision medicine to treat the humanmicrobiome-associated diseases.Finally, one of the major goals in the human microbiomeresearch is to fundamentally elucidate the complex interactionsamong diet, gut microbiome, and human health so that we havean improved capability of monitoring wellness states, treatingdiseases, designing food products, and administering healthinterventions. Eetemadi et al. provide a review on this issue bydiscussing a critical role of predictive computational models,particularly machine learning and artificial intelligence. Theyshared the state of dietary recommendation systems (RSs) andhighlight the transition from population-wide to microbiome-aware RSs to provide users with personalized guidelines. Theyalso discussed the details about three complementary approachesfor realizing microbiome-aware RS, including knowledge-based,content-based, and collaborative filtering RSs.The overarching goal of computational modeling of humanmicrobiomes is to enhance our ability to predict theirdynamics, association with human disease, and control pointsthat can be used to shape microbiome composition andfunctions toward improved human health. Some of thesecontrol points may target the organisms themselves, othersmay shape their environments to accomplish these ends.The work contained in this Research Topic uses multipletechniques to provide increased insight into environment-human microbiome-health connections and to substantiallyadvance the field of predictive human microbiome modeling.AUTHOR CONTRIBUTIONSAll authors listed have made a substantial, direct, and intellectualcontribution to the work and approved it for publication.FUNDINGD-YL received funding from the SMC-SKKU FutureConvergence Research Program grant and the NationalResearch Foundation of Korea (NRF) grant funded by the Koreagovernment (MSIT) (No. 2020R1A2C2007192).ACKNOWLEDGMENTSWe thank all the authors and reviewers for their contributions tothis Research Topic.REFERENCESChowdhury, S., and Fong, S. S. (2020). Computational modeling of thehuman microbiome. Microorganisms 8:197. doi: 10.3390/microorganisms8020197Grice, E. A., and Segre, J. A. (2012). The human microbiome: oursecond genome. Annu. Rev. Genomics Hum. Genet. 13, 151–170.doi: 10.1146/annurev-genom-090711-163814Kessell, A. K., Mccullough, H. C., Auchtung, J. M., Bernstein, H.C., and Song, H. S. (2020). Predictive interactome modeling forprecision microbiome engineering. Curr. Opin. Chem. Eng. 30, 77–85.doi: 10.1016/j.coche.2020.08.003Kumar, M., Ji, B. Y., Zengler, K., and Nielsen, J. (2019). Modellingapproaches for studying the microbiome. Nat. Microbiol. 4, 1253–1267.doi: 10.1038/s41564-019-0491-9Li, H., Wang, Y. Q., Jiang, J. W., Zhao, H. C., Feng, X., Zhao, B. H.,et al. (2019). A novel human microbe-disease association prediction methodbased on the bidirectional weighted network. Front. Microbiol. 10:676.doi: 10.3389/fmicb.2019.00676Marcos-Zambrano, L. J., Karaduzovic-Hadziabdic, K., Turukalo, T. L., Przymus,P., Trajkovik, V., Aasmets, O., et al. (2021). Applications of machine learningin human microbiome studies: a review on feature selection, biomarkeridentification, disease prediction and treatment. Front. Microbiol. 12:634511.doi: 10.3389/fmicb.2021.634511Sender, R., Fuchs, S., and Milo, R. (2016). Revised estimatesfor the number of human and bacteria cells in thebody. PLoS Biol. 14:e1002533. doi: 10.1371/journal.pbio.1002533Sheth, R. U., Cabral, V., Chen, S. P., and Wang, H. H. (2016). Manipulatingbacterial communities by in situ microbiome engineering. Trends Genet. 32,189–200. doi: 10.1016/j.tig.2016.01.005Song, H.-S., Cannon, W. R., Beliaev, A. S., and Konopka, A. (2014). Mathematicalmodeling of microbial community dynamics: a methodological review.Processes 2, 711–752. doi: 10.3390/pr2040711Ursell, L. K., Haiser, H. J., Van Treuren, W., Garg, N., Reddivari, L., Vanamala, J.,et al. (2014). The intestinal metabolome: an intersection between microbiotaand host. Gastroenterology 146, 1470–1476. doi: 10.1053/j.gastro.2014.03.001Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2021 Song, Lindemann and Lee. This is an open-access articledistributed under the terms of the Creative Commons Attribution License (CC BY).The use, distribution or reproduction in other forums is permitted, provided theoriginal author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Microbiology | www.frontiersin.org 3 November 2021 | Volume 12 | Article 782871

Editorial: Predictive Modeling of
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Editorial: Predictive Modeling of Human Microbiota and Their Role in Health and Disease

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