Current Practice in Untargeted Human Milk Metabolomics

Isabel Ten-Doménech, Victoria Ramos-Garcia, José David Piñeiro-Ramos, María Gormaz, Anna Parra-Llorca, Máximo Vento, Julia Kuligowski, Guillermo Quintás, Isabel Ten-Doménech, Victoria Ramos-Garcia, José David Piñeiro-Ramos, María Gormaz, Anna Parra-Llorca, Máximo Vento, Julia Kuligowski, Guillermo Quintás

Abstract

Human milk (HM) is considered the gold standard for infant nutrition. HM contains macro- and micronutrients, as well as a range of bioactive compounds (hormones, growth factors, cell debris, etc.). The analysis of the complex and dynamic composition of HM has been a permanent challenge for researchers. The use of novel, cutting-edge techniques involving different metabolomics platforms has permitted to expand knowledge on the variable composition of HM. This review aims to present the state-of-the-art in untargeted metabolomic studies of HM, with emphasis on sampling, extraction and analysis steps. Workflows available from the literature have been critically revised and compared, including a comprehensive assessment of the achievable metabolome coverage. Based on the scientific evidence available, recommendations for future untargeted HM metabolomics studies are included.

Keywords: capillary electrophoresis – mass spectrometry (CE-MS); extraction; gas chromatography–mass spectrometry (GC-MS); human milk; liquid chromatography–mass spectrometry (LC-MS); metabolome; nuclear magnetic resonance (NMR); sampling.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of literature selection and review process. Search “criterion 1”: term (“human milk” OR “breast milk”), AND “metabolom*”, AND “infant”; only articles. Search “criterion 2”: term (“human milk” OR “breast milk”), AND “metabolom*”, AND (“GC” OR “LC” OR “NMR” OR “CE”); only articles. Web of Science database was employed for literature search.
Figure 2
Figure 2
Reporting frequency of factors relevant to the human milk (HM) sampling process: Maternal-infant-related factors (blue bars), time-related factors (green bars), and HM collection-related factors (orange bars). Note: BMI = body mass index.
Figure 3
Figure 3
Sample preparation approaches employed in human milk (HM) metabolomics.
Figure 4
Figure 4
Venn diagram of metabolites reported in human milk (HM) according to the technique in [73]. Note: GC-MS, gas chromatography—mass spectrometry; LC-MS, liquid chromatography—mass spectrometry; NMR, nuclear magnetic resonance.
Figure 5
Figure 5
Distribution of metabolite classes annotated and/or identified in HM according to technique. Note: GC-MS, gas chromatography—mass spectrometry; LC-MS, liquid chromatography—mass spectrometry; NMR, nuclear magnetic resonance.

References

    1. World Health Organization Breastfeeding. [(accessed on 2 July 2019)]; Available online:
    1. Geddes D., Perrella S. Breastfeeding and human lactation. Nutrients. 2019;11:802–806. doi: 10.3390/nu11040802.
    1. Owen C.G., Martin R.M., Whincup P.H., Davey Smith G., Cook D.G. Does breastfeeding influence risk of type 2 diabetes in later life? A quantitative analysis of published evidence. Am. J. Clin. Nutr. 2006;84:1043–1054. doi: 10.1093/ajcn/84.5.1043.
    1. Der G., Batty G.D., Deary I.J. Effect of breast feeding on intelligence in children: Prospective study, sibling pairs analysis, and meta-analysis. Br. Med. J. 2006;333:945–948. doi: 10.1136/bmj.38978.699583.55.
    1. Rozé J.C., Darmaun D., Boquien C.Y., Flamant C., Picaud J.C., Savagner C., Claris O., Lapillonne A., Mitanchez D., Branger B., et al. The apparent breastfeeding paradox in very preterm infants: Relationship between breast feeding, early weight gain and neurodevelopment based on results from two cohorts, EPIPAGE and LIFT. BMJ Open. 2012;2:1–9. doi: 10.1136/bmjopen-2012-000834.
    1. Horta B.L., Loret De Mola C., Victora C.G. Breastfeeding and intelligence: A systematic review and meta-analysis. Acta Paediatr. Int. J. Paediatr. 2015;104:14–19. doi: 10.1111/apa.13139.
    1. Lönnerdal B. Bioactive proteins in human milk: Mechanisms of action. J. Pediatr. 2010;156:S26–S30. doi: 10.1016/j.jpeds.2009.11.017.
    1. Musilova S., Rada V., Vlkova E., Bunesova V. Beneficial effects of human milk oligosaccharides on gut microbiota. Benef. Microbes. 2014;5:273–283. doi: 10.3920/BM2013.0080.
    1. Ballard O., Morrow A.L. Human milk composition: Nutrients and bioactive factors. Pediatr. Clin. N. Am. 2013;60:49–74. doi: 10.1016/j.pcl.2012.10.002.
    1. Alsaweed M., Hartmann P.E., Geddes D.T., Kakulas F. MicroRNAs in Breastmilk and the Lactating Breast: Potential Immunoprotectors and Developmental Regulators for the Infant and the Mother. Int. J. Environ. Res. Public Health. 2015;12:13981–14020. doi: 10.3390/ijerph121113981.
    1. Van den Berg M., Kypke K., Kotz A., Tritscher A., Lee S.Y., Magulova K., Fiedler H., Malisch R. WHO/UNEP global surveys of PCDDs, PCDFs, PCBs and DDTs in human milk and benefit–risk evaluation of breastfeeding. Arch. Toxicol. 2017;91:83–96. doi: 10.1007/s00204-016-1802-z.
    1. Garwolińska D., Namieśnik J., Kot-Wasik A., Hewelt-Belka W. State of the art in sample preparation for human breast milk metabolomics—Merits and limitations. TrAC Trends Anal. Chem. 2019;114:1–10. doi: 10.1016/j.trac.2019.02.014.
    1. Marincola F.C., Noto A., Caboni P., Reali A., Barberini L., Lussu M., Murgia F., Santoru M.L., Atzori L., Fanos V. A metabolomic study of preterm human and formula milk by high resolution NMR and GC/MS analysis: Preliminary results. J. Matern.-Fetal Neonatal Med. 2012;25:62–67. doi: 10.3109/14767058.2012.715436.
    1. Smilowitz J.T., Sullivan A.O.Õ., Barile D., German J.B., Lo B. The human milk metabolome reveals diverse oligosaccharide profiles. J. Nutr. 2013;143:1709–1718. doi: 10.3945/jn.113.178772.
    1. Li K., Jiang J., Xiao H., Wu K., Qi C., Sund J., Li D. Changes in metabolites profile of breast milk over lactation stages and their relationship with dietary intake in Chinese: HPLC-QTOFMS based metabolomic analysis. Food Funct. 2018;9:5189–5197. doi: 10.1039/C8FO01005F.
    1. Longini M., Tataranno M.L., Proietti F., Tortoriello M., Belvisi E., Vivi A., Tassini M., Perrone S., Buonocore G. A metabolomic study of preterm and term human and formula milk by proton MRS analysis: Preliminary results. J. Matern.-Fetal Neonatal Med. 2014;7058:27–33. doi: 10.3109/14767058.2014.955958.
    1. Murgia A., Scano P., Contu M., Ibba I., Altea M., Demuru M., Porcu A., Caboni P. Characterization of donkey milk and metabolite profile comparison with human milk and formula milk. LWT. 2016;74:427–433. doi: 10.1016/j.lwt.2016.07.070.
    1. Qian L., Zhao A., Zhang Y., Chen T., Zeisel S.H., Jia W., Cai W. Metabolomic approaches to explore chemical diversity of human breast-milk, formula milk and bovine milk. Int. J. Mol. Sci. 2016;17:2128–2143. doi: 10.3390/ijms17122128.
    1. Scano P., Murgia A., Demuru M., Consonni R., Caboni P. Metabolite profiles of formula milk compared to breast milk. Food Res. Int. 2016;87:76–82. doi: 10.1016/j.foodres.2016.06.024.
    1. Lopes T.I.B., Cañedo M.C., Oliveira F.M.P., Ancantara G.B. Toward precision nutrition: Commercial infant formulas and human milk compared for stereospecific distribution of fatty acids using metabolomics. Omics J. Integr. Biol. 2018;22:484–492. doi: 10.1089/omi.2018.0064.
    1. O’Sullivan A., He X., McNiven E.M.S., Hinde K., Haggarty N.W., Lönnerdal B., Slupsky C.M. Metabolomic phenotyping validates the infant rhesus monkey as a model of human infant metabolism. J. Pediatr. Gastroenterol. Nutr. 2013;56:355–363. doi: 10.1097/MPG.0b013e31827e1f07.
    1. Spevacek A.R., Smilowitz J.T., Chin E.L., Underwood M.A., German J.B., Slupsky C.M. Infant maturity at birth reveals minor differences in the maternal milk metabolome in the first month of lactation. J. Nutr. 2015;145:1698–1708. doi: 10.3945/jn.115.210252.
    1. Sundekilde U.K., Downey E., Mahony J.A.O., Shea C.O., Ryan C.A., Kelly A.L., Bertram H.C. The effect of gestational and lactational age on the human milk metabolome. Nutrients. 2016;8:304–318. doi: 10.3390/nu8050304.
    1. Alexandre-Gouabau M.-C., Moyon T., David-Sochard A., Fenaille F., Cholet S., Royer A.-L., Guitton Y., Billard H., Darmaun D., Rozé J.-C., et al. Comprehensive preterm breast milk metabotype associated with optimal infant early growth pattern. Nutrients. 2019;11:528–553. doi: 10.3390/nu11030528.
    1. Alexandre-Gouabau M.C., Moyon T., Cariou V., Antignac J.P., Qannari E.M., Croyal M., Soumah M., Guitton Y., David-Sochard A., Billard H., et al. Breast milk lipidome is associated with early growth trajectory in preterm infants. Nutrients. 2018;10:164–192. doi: 10.3390/nu10020164.
    1. Dessì A., Briana D., Corbu S., Gavrili S., Marincola F.C., Georgantzi S., Pintus R., Fanos V., Malamitsi-Puchner A. Metabolomics of breast milk: The importance of phenotypes. Metabolites. 2018;8:79–88. doi: 10.3390/metabo8040079.
    1. Villaseñor A., Garcia-Perez I., Garcia A., Posma J.M., Fernández-Lópes M., Nicholas A.J., Modi N., Holmes E., Barbas C. Breast milk metabolome characterization in a single-phase extraction, multiplatform analytical approach. Anal. Chem. 2014;86:8245–8252. doi: 10.1021/ac501853d.
    1. Andreas N.J., Hyde M.J., Gomez-romero M., Lopez-Gonzalvez M.A., Villaseñor A., Wijeyesekera A., Barbas C., Modi N., Holmes E., Garcia-Perez I. Multiplatform characterization of dynamic changes in breast milk during lactation. Electrophoresis. 2015;36:2269–2285. doi: 10.1002/elps.201500011.
    1. Wu J., Domellöf M., Zivkovic A.M., Larsson G., Öhman A., Nording M.L. NMR-based metabolite profiling of human milk: A pilot study of methods for investigating compositional changes during lactation. Biochem. Biophys. Res. Commun. 2016;469:626–632. doi: 10.1016/j.bbrc.2015.11.114.
    1. Isganaitis E., Venditti S., Matthews T.J., Lerin C., Demerath E.W., Fields D.A. Maternal obesity and the human milk metabolome: Associations with infant body composition and postnatal weight gain. Am. J. Clin. Nutr. 2019;110:111–120. doi: 10.1093/ajcn/nqy334.
    1. Dangat K., Upadhyay D., Kilari A., Sharma U., Kemse N., Mehendale S., Lalwani S., Wagh G., Joshi S., Jagannathan N.R. Altered breast milk components in preeclampsia; An in-vitro proton NMR spectroscopy study. Clin. Chim. Acta. 2016;463:75–83. doi: 10.1016/j.cca.2016.10.015.
    1. Praticò G., Capuani G., Tomassini A., Baldassarre E., Delfini M., Miccheli A. Exploring human breast milk composition by NMR-based metabolomics. Nat. Prod. Res. 2013;28:95–101. doi: 10.1080/14786419.2013.843180.
    1. Gay M.C.L., Koleva P.T., Slupsky C.M., Toit E., Eggesbo M., Johnson C.C., Wegienka G., Shimojo N. Worldwide variation in human milk metabolome: Indicators of breast physiology and maternal lifestyle? Nutrients. 2018;10:1151–1162. doi: 10.3390/nu10091151.
    1. Gómez-Gallego C., Morales J.M., Monleón D., du Toit E., Kumar H., Linderborg K.M., Zhang Y., Yang B., Isolauri E., Salminen S., et al. Human breast milk NMR metabolomic profile across specific geographical locations and its association with the milk microbiota. Nutrients. 2018;10:1355–1375. doi: 10.3390/nu10101355.
    1. Hewelt-Belka W., Garwolińska D., Belka M., Bączek T., Namieśnik J., Kot-Wasik A. A new dilution-enrichment sample preparation strategy for expanded metabolome monitoring of human breast milk that overcomes the simultaneous presence of low- and high-abundance lipid species. Food Chem. 2019;288:154–161. doi: 10.1016/j.foodchem.2019.03.001.
    1. Urbaniak C., Mcmillan A., Angelini M., Gloor G.B., Sumarah M., Burton J.P., Reid G. Effect of chemotherapy on the microbiota and metabolome of human milk, a case report. Microbiome. 2014;2:24–35. doi: 10.1186/2049-2618-2-24.
    1. Demmelmair H., Koletzko B. Variation of metabolite and hormone contents in human milk. Clin. Perinatol. 2017;44:151–164. doi: 10.1016/j.clp.2016.11.007.
    1. Marincola F.C., Dessì A., Corbu S., Reali A., Fanos V. Clinical impact of human breast milk metabolomics. Clin. Chim. Acta. 2015;451:103–106. doi: 10.1016/j.cca.2015.02.021.
    1. Slupsky C.M. Metabolomics in human milk research. Nestle Nutr. Inst. Workshop Ser. 2019;90:179–190.
    1. Bardanzellu F., Fanos V., Reali A. “Omics” in human colostrum and mature milk: Looking to old data with new eyes. Nutrients. 2017;9:843–867. doi: 10.3390/nu9080843.
    1. George A.D., Gay M.C.L., Trengove R.D., Geddes D.T. Human milk lipidomics: Current techniques and methodologies. Nutrients. 2018;10:1169–1179. doi: 10.3390/nu10091169.
    1. Lönnerdal B., Erdmann P., Thakkar S.K., Sauser J., Destaillats F. Longitudinal evolution of true protein, amino acids and bioactive proteins in breast milk: A developmental perspective. J. Nutr. Biochem. 2017;41:1–11. doi: 10.1016/j.jnutbio.2016.06.001.
    1. Gidrewicz D.A., Fenton T.R. A systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr. 2014;14:216–230. doi: 10.1186/1471-2431-14-216.
    1. Hampel D., Shahab-Ferdows S., Islam M.M., Peerson J.M., Allen L.H. Vitamin concentrations in human milk vary with time within feed, circadian rhythm, and single-dose supplementation. J. Nutr. 2017;147:603–611. doi: 10.3945/jn.116.242941.
    1. Mung D., Li L. Development of chemical isotope labeling LC-MS for milk metabolomics: Comprehensive and quantitative profiling of the amine/phenol submetabolome. Anal. Chem. 2017;89:4435–4443. doi: 10.1021/acs.analchem.6b03737.
    1. Mung D., Li L. Applying quantitative metabolomics based on chemical isotope labeling LC-MS for detecting potential milk adulterant in human milk. Anal. Chim. Acta. 2018;1001:78–85. doi: 10.1016/j.aca.2017.11.019.
    1. Garcia C., Duan R.D., Brévaut-Malaty V., Gire C., Millet V., Simeoni U., Bernard M., Armand M. Bioactive compounds in human milk and intestinal health and maturity in preterm newborn: An overview. Cell. Mol. Biol. 2013;59:108–131.
    1. Garwolińska D., Namieśnik J., Kot-Wasik A., Hewelt-Belka W. Chemistry of human breast milk—A comprehensive review of the composition and role of milk metabolites in child development. J. Agric. Food Chem. 2018;66:11881–11896. doi: 10.1021/acs.jafc.8b04031.
    1. Totten S.M., Zivkovic A.M., Wu S., Ngyuen U., Freeman S.L., Ruhaak L.R., Darboe M.K., German J.B., Prentice A.M., Lebrilla C.B. Comprehensive profiles of human milk oligosaccharides yield highly sensitive and specific markers for determining secretor status in lactating mothers. J. Proteome Res. 2012;11:6124–6133. doi: 10.1021/pr300769g.
    1. Lubetzky R., Littner Y., Mimouni F.B., Dollberg S., Mandel D., Lubetzky R., Lubetzky R., Littner Y., Mimouni F.B., Dollberg S., et al. Circadian variations in fat content of expressed breast milk from mothers of preterm infants. J. Am. Coll. Nutr. 2006;25:151–154. doi: 10.1080/07315724.2006.10719526.
    1. Saarela T., Kokkonen J., Koivisto M. Macronutrient and energy contents of human milk fractions during the first six months of lactation. Acta Paediatr. 2005;94:1176–1181. doi: 10.1111/j.1651-2227.2005.tb02070.x.
    1. Jensen R.G., Lammi-Keefe C.J., Koletzko B. Representative sampling of human milk and the extraction of fat for analysis of environmental lipophilic contaminants. Toxicol. Environ. Chem. 1997;62:229–247. doi: 10.1080/02772249709358510.
    1. Bertino E., Peila C., Cresi F., Maggiora E., Sottemano S., Gazzolo D., Arslanoglu S., Coscia A. Donor human milk: Effects of storage and heat treatment on oxidative stress markers. Front. Pediatr. 2018;6:1–5. doi: 10.3389/fped.2018.00253.
    1. Peila C., Moro G.E., Bertino E., Cavallarin L., Giribaldi M., Giuliani F., Cresi F., Coscia A. The effect of holder pasteurization on nutrients and biologically-active components in donor human milk: A review. Nutrients. 2016;8:447. doi: 10.3390/nu8080477.
    1. García-Lara N.R., Vieco D.E., De La Cruz-Bértolo J., Lora-Pablos D., Velasco N.U., Pallás-Alonso C.R. Effect of holder pasteurization and frozen storage on macronutrients and energy content of breast milk. J. Pediatr. Gastroenterol. Nutr. 2013;57:377–382. doi: 10.1097/MPG.0b013e31829d4f82.
    1. Billard H., Simon L., Desnots E., Sochard A., Boscher C., Riaublanc A., Alexandre-Gouabau M.C., Boquien C.Y. Calibration adjustment of the mid-infrared analyzer for an accurate determination of the macronutrient composition of human milk. J. Hum. Lact. 2016;32:NP19–NP27. doi: 10.1177/0890334415588513.
    1. Folch J., Lees M., Sloane Stantley G.H. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 1957;266:497–509.
    1. Bligh E.G., Dyer W.J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 1959;37:911–917. doi: 10.1139/o59-099.
    1. Broadhurst D., Goodacre R., Reinke S.N., Kuligowski J., Wilson I.D., Lewis M.R., Dunn W.B. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018;14:72–89. doi: 10.1007/s11306-018-1367-3.
    1. Sumner L.W., Samuel T., Noble R., Gmbh S.D., Barrett D., Beale M.H., Hardy N. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI) Metabolomics. 2007;3:211–221. doi: 10.1007/s11306-007-0082-2.
    1. Vinaixa M., Schymanski E.L., Neumann S., Navarro M., Salek R.M., Yanes O. Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects. TrAC Trends Anal. Chem. 2016;78:23–35. doi: 10.1016/j.trac.2015.09.005.
    1. Fiehn O., Barupal D.K., Kind T. Extending biochemical databases by metabolomic surveys. J. Biol. Chem. 2011;286:23637–23643. doi: 10.1074/jbc.R110.173617.
    1. Wishart D.S., Feunang Y.D., Marcu A., Guo A.C., Liang K., Vázquez-Fresno R., Sajed T., Johnson D., Li C., Karu N., et al. HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Res. 2018;46:D608–D617. doi: 10.1093/nar/gkx1089.
    1. Smith C.A., O’Maille G., Want E.J., Qin C., Trauger S.A., Brandon T.R., Custodio D.E., Abagyan R., Siuzdak G. METLIN: A metabolite mass spectral database. Ther. Drug Monit. 2005;27:747–751. doi: 10.1097/01.ftd.0000179845.53213.39.
    1. Kind T., Wohlgemuth G., Lee D.Y., Lu Y., Palazoglu M., Shahbaz S., Fiehn O. FiehnLib—Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 2009;81:10038–10048. doi: 10.1021/ac9019522.
    1. Cardiff University. Babraham Institute. University of California, S.D LIPID MAPS Lipidomics Gateway. [(accessed on 8 November 2019)]; Available online:
    1. Foroutan A., Guo A.C., Vazquez-fresno R., Lipfert M., Zhang L., Zheng J., Badran H., Budinski Z., Mandal R., Ametaj B.N., et al. Chemical composition of commercial cow’s milk. J. Agric. Food Chem. 2019;67:4897–4914. doi: 10.1021/acs.jafc.9b00204.
    1. Milk Composition Database. [(accessed on 5 November 2019)]; Available online:
    1. KEGG PATHWAY Database. [(accessed on 8 November 2019)]; Available online: .
    1. Li L., Li R., Zhou J., Zuniga A., Stanislaus A.E., Wu Y., Huan T., Zheng J., Shi Y., Wishart D.S., et al. MyCompoundID: Using an evidence-based metabolome library for metabolite identification. Anal. Chem. 2013;85:3401–3408. doi: 10.1021/ac400099b.
    1. Gil de la Fuente A., Godzien J., Saugar S., Garcia-Carmona R., Badran H., Wishart D.S., Barbas C., Otero A. CEU Mass Mediator 3.0: A Metabolite Annotation Tool. J. Proteome Res. 2019;18:797–802. doi: 10.1021/acs.jproteome.8b00720.
    1. CEU Mass Mediator. [(accessed on 5 November 2019)]; Available online:
    1. Oliveros J.C. Venny. An Interactive Tool for Comparing Lists with Venn’s Diagrams. [(accessed on 4 November 2019)]; Available online: .

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