Multi-Ontology framework of Maternal Milk for Immune Systems (MOMMIS) - Extended Abstract

Abstract

We propose a multi-ontology framework modeling the impacts of milk composition on immune health outcomes, using maternal milk as a case study. We evaluated the wide array of immunehealth promoting bioactive factors in mammalian milk and existing ontologies relevant to food, nutrition, and health. From here, we are aligning existing ontologies and developing new ontologies to fill notable gaps. MilkOligoDB, which allows for the comparison of milk oligosaccharide profiles among mammalian species and across the literature, demonstrates how the MOMMIS framework can be instantiated. MOMMIS will be useful for interdisciplinary and translational research at the intersection of food and health science disciplines.

1. Immune Health vs. Immune Disease

Neonatal immune cells, with little immunological memory, a developing immune system, and increased vulnerability to a vast array of infectious and non-infectious diseases and conditions, must simultaneously mount responses against environmental stimuli while maturing ( 1–3 ). Establishment, development, maturation, optimization and maintenance of immune system functions leads to improved disease resistance, healthspan, and longevity ( 4,5 ). Current immune system ontological structures annotate immune system components relative to their corresponding disease states and drug treatments ( 6,7 ). Ontological structures are needed, which are capable of annotating immune health system development, maturation, and improvement including both intrinsic and extrinsic factors.

2. Development of Ontologies at the Intersection of

Milk Composition and Immune Health

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Annotating milk is a worthwhile pursuit because milk plays a crucial role in the growth and development of mammals. An ontologically based maternal milk ⇔ infant health informatics framework offers the opportunity to links bodily tissues and fluids, as well as their packaging and delivery into nutritive foods with the metabolic processes and health outcomes of the consuming organism. Specifically relating to organismal immunological function, ontologically modeling maternal milk and its consumption affords the opportunity to better understand establishment, development, maturation, optimization and maintenance of immune system functions across molecular, cellular, organ, and systems levels. It will also be crucial for interdisciplinary translational research efforts, i.e. determining how an increase in oligosaccharide content in milk affects immune-mediated health outcomes or what immune health-promoting bioactive factor(s) affect the development and/or treatment of emerging viruses such as COVID-19.

2.1. Mammalian Milk Immune Components

Mammalian milk contains a variety of immune-health promoting bioactive factors (milk immune components) including but not limited to: hormones, cytokines, chemokines, lymphocytes, macrophages, neutrophils, T cells, immunoglobulins, lactoferrin, lysozyme, bioactive peptides, antibodies, stem cells, human milk oligosaccharides (HMOs), the microbiota, and microRNAs capable of the mechanisms by which they drive immune maturation ( 11,12 ). For example, HMOs play an important role in the prevention of necrotizing enterocolitis, a disease occurring in sick or premature babies ( 13 )( 14 ). Bifidobacteria infantis, a probiotic that grows selectively on specific HMOs, is currently used to treat necrotizing enterocolitis ( 15 ).

2.1.1. Existing food, nutrition, and health ontologies related to milk immune components

Annotating maternal milk components for their relationships to immune health using ontologies lays the groundwork for a comprehensive food, nutrition, and health informatics framework describing any food components and their corresponding immune health outcomes. Connecting multiple ontologies by building multi-ontology food informatics frameworks allows researchers to ask and answer more interdisciplinary questions ( 16–19 ). The Multi-Ontology framework of Maternal Milk for Immune Systems (MOMMIS) connects the following biological ontologies: FoodOn (20), Uberon (21), Compositional Dietary Nutrition Ontology (CDNO) ( 17 ), the Mass spectrometry ontology (HUPO) (22), the Mammalian phenotype ontology (23), and the Gene Ontology (GO) ( 6 ). Ontologies related to nutrition interventions and personalized nutrition experiments will also be crucial. These include but are not limited to: the Ontology of Precision Medicine and Investigation (OPMI) (24), the Ontology for Biomedical Investigations (OBI) (25), the Ontology for Nutritional Epidemiology (ONE) (26), the Ontology for Nutritional Studies (ONS) (27), the Food Biomarker Ontology (FOBI) (28), the Human Disease Ontology (DO) (29), and the Medical Action Ontology (MaXO) ( 7 ), and the Ontology of HostMicrobiome Interactions (OHMI) (30).

2.1.2. Connecting UC Milk and MilkOligoDB: Oligosaccharides as key milk immune components

UC Milk is an ontology that was developed to characterize mammalian milk components and the biological processes giving rise to their creation. The ontology describes both the production and processing of milk itself as well as the role of milk throughout the life cycle, including during three key stages: infant, pregnancy, and lactation (31). MilkOligoDB allows for the comparison of milk oligosaccharide profiles among mammalian species and across the literature. It demonstrates the considerable variation in oligosaccharide profiles both between species and within species (32). Building off of the data model for MilkOligoDB (32), we extend CheBI oligosaccharide classes to cover known classes of mammalian oligosaccharides and then instantiate these classes in the oligosaccharide class (prebiotics) to probiotics in the gut that confer immunological health benefits, including for different types of mammalian milks. Ontologies that allow us to model milk processing conditions such as pasteurization, which are especially relevant for mothers who cannot breastfeed, are being built as well (33).

3. Acknowledgements

We are grateful to the US National Science Foundation, NSF Award numbers RCN 1737573 OAC 2112606, for funding this research and the conceptualization and editing of this Special Collection, both of which were underpinned by our NSF-funded Research Coordination Network, “Developing Informational Infrastructure for Building Smart Regional Foodsheds”.

4. References

[20] Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA. Uberon, an integrative multi-species anatomy ontology. Genome Biol. 2012 Jan 31;13( 1 ):R5. [21] Mayer G, Montecchi-Palazzi L, Ovelleiro D, Jones AR, Binz PA, Deutsch EW, et al. The HUPO proteomics standards initiative- mass spectrometry controlled vocabulary. Database . 2013 Mar 12;2013:bat009. [22] Smith CL, Eppig JT. The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data. Mamm Genome. 2012 Oct;23( 9-10 ):653–68. [23] opmi: OPMI: Ontology of Precision Medicine and Investigation [Internet]. Github; [cited 2023 Aug 3]. Available from: https://github.com/OPMI/opmi [24] Bandrowski A, Brinkman R, Brochhausen M, Brush MH, Bug B, Chibucos MC, et al. The Ontology for Biomedical Investigations. PLoS One. 2016 Apr 29;11( 4 ):e0154556. [25] Yang C, Ambayo H, Baets BD, Kolsteren P, Thanintorn N, Hawwash D, et al. An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content. Nutrients [Internet]. 2019 Jun 8;11( 6 ). Available from: http://dx.doi.org/10.3390/nu11061300 [26] Vitali F, Lombardo R, Rivero D, Mattivi F, Franceschi P, Bordoni A, et al. ONS: an ontology for a standardized description of interventions and observational studies in nutrition. Genes Nutr. 2018 Apr 30;13:12. [27] Castellano-Escuder P, González-Domínguez R, Wishart DS, Andrés-Lacueva C, Sánchez-Pla A.

FOBI: an ontology to represent food intake data and associate it with metabolomic data. Database [Internet]. 2020 Jan 1;2020. Available from: http://dx.doi.org/10.1093/databa/baaa033 [28] Schriml LM, Munro JB, Schor M, Olley D, McCracken C, Felix V, et al. The Human Disease Ontology 2022 update. Nucleic Acids Res. 2022 Jan 7;50(D1):D1255–61. [29] He Y, Wang H, Zheng J, Beiting DP, Masci AM, Yu H, et al. OHMI: the ontology of host-microbiome interactions. J Biomed Semantics. 2019 Dec 30;10( 1 ):25. [30] Colet E, Lange M. uc_Milk: An Ontology for Scientifically-based Unambiguous Characterization of Mammalian Milk, their Composition and the Biological Processes Giving Rise to their Creation. In: ICBO/BioCreative [Internet]. Citeseer; 2016. Available from: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=d04a08822664808ed01fe62204ab 1b36b7c047fe [31] Durham SD, Wei Z, Lemay DG, Lange MC, Barile D. Creation of a milk oligosaccharide database, MilkOligoDB, reveals common structural motifs and extensive diversity across mammals. Sci Rep. 2023 Jun 26;13( 1 ):10345. [32] Salcedo J, Karav S, Le Parc A, Cohen JL, de Moura Bell JMLN, Sun A, et al. Application of industrial treatments to donor human milk: influence of pasteurization treatments, storage temperature, and time on human milk gangliosides. NPJ Sci Food. 2018 Mar 13;2:5.

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