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Label-free quantitative proteomics in serum reveals candidate biomarkers associated with low bone mineral density in Mexican postmenopausal women

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Abstract

Postmenopausal osteoporosis is a public health problem leading to an increased risk of fractures, negatively impacting women’s health. The absence of sensitive and specific biomarkers for early detection of osteoporosis represents a substantial challenge for improving patient management. Herein, we aimed to identify potential candidate proteins associated with low bone mineral density (BMD) in postmenopausal women from the Mexican population. Serum samples from postmenopausal women (40 with normal BMD, 40 with osteopenia (OS), and 20 with osteoporosis (OP)) were analyzed by label-free LC–MS/MS quantitative proteomics. Proteome profiling revealed significant differences between the OS and OP groups compared to individuals with normal BMD. A quantitative comparison of proteins between groups indicated 454 differentially expressed proteins (DEPs). Compared to normal BMD, 14 and 214 DEPs were found in OS and OP groups, respectively, while 226 DEPs were identified between OS and OP groups. The protein–protein interaction and enrichment analysis of DEPs were closely linked to the bone mineral content, skeletal morphology, and immune response activation. Based on their role in bone metabolism, a panel of 12 candidate biomarkers was selected, of which 1 DEP (RYR1) was found upregulated in the OS and OP groups, 8 DEPs (APOA1, SHBG, FETB, MASP1, PTK2B, KNG1, GSN, and B2M) were upregulated in OP and 3 DEPs (APOA2, RYR3, and HBD) were downregulated in OS or OP. The proteomic analysis described here may help discover new and potentially non-invasive biomarkers for the early diagnosis of osteoporosis in postmenopausal women.

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Acknowledgements

The authors thankfully acknowledge the technical support provided by Sergio A Román-González PhD and Monserrat Rojano-Vilchis, for the depletion of the most abundant proteins, performed in the Proteomics Core Unit at the INMEGEN, Mexico City, Mexico. Quantitative mass spectrometry-based proteomics was performed in Genomics, Proteomics and Metabolomics Core Facility (UGPM), LaNSE, CINVESTAV-IPN. We want to thank to ChemE. Nataly Ramos Buendía for their support during sample preparation and LC-MS analysis. The authors also wish to thank the staff of the Epidemiological Research Unit and Services of Health-IMSS, Cuernavaca, Morelos.

Funding

This work was funded by the Consejo Nacional de Ciencia y Tecnología (Grant Ciencia de Frontera CF2019 – 102962), and received partial funding from Instituto Nacional de Medicina Genómica (314–07/2017/I and 266–17/2016/I). A.B.-C. is supported by a Postdoctoral Fellowship from the Consejo Nacional de Humanidades, Ciencia y Tecnología (CONAHCYT-Estancia Posdoctoral de Incidencia Inicial 2022 with CVU 508876).

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Author notes

  1. Diana I. Aparicio-Bautista and Adriana Becerra-Cervera contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Laboratorio de Estructura de Proteínas, Instituto Nacional de Medicina Genómica (INMEGEN), 14610, Ciudad de Mexico, Mexico

    Diana I. Aparicio-Bautista & Juan P. Reyes-Grajeda

  2. Laboratorio de Genómica del Metabolismo Óseo, Instituto Nacional de Medicina Genómica (INMEGEN), 14610, Ciudad de Mexico, Mexico

    Adriana Becerra-Cervera & Rafael Velázquez-Cruz

  3. Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT), 03940, Ciudad de Mexico, Mexico

    Adriana Becerra-Cervera

  4. Centro de Investigación en Políticas, Población y Salud, Facultad de Medicina, Universidad Nacional Autónoma de México, 04510, Ciudad de México, Mexico

    Berenice Rivera-Paredez & Jorge Salmerón

  5. Departamento de Supercómputo, Instituto Nacional de Medicina Genómica (INMEGEN), 14610, Ciudad de Mexico, Mexico

    Israel Aguilar-Ordoñez

  6. Unidad de Genómica, Proteómica y Metabolómica (UGPM), LaNSE, Cinvestav-IPN, 07360, Ciudad de Mexico, Mexico

    Emmanuel Ríos-Castro

  7. Departamento de Medicina Genómica, Instituto Nacional de Rehabilitación, 14389, Ciudad de México, Mexico

    Alberto Hidalgo-Bravo

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  1. Diana I. Aparicio-Bautista
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Contributions

D.I.A.-B., A.B.-C., A.H.-B., and R.V.-C. contributed to conception and design of the study. J.S. and B.R.-P. collected and reviewed the data of the study. E.R. performed the label-free quantitative proteomics and provided raw proteomic data. J.P.R.-G. provision of study materials. D.I.A.-B., A.B.-C., I.A., E.R., and B.R.-P. performed the bioinformatic and statistical analysis. D.I.A.-B., A.B.-C., A.H.-B., and R.V.-C. drafted and revised the manuscript. All the authors have reviewed the manuscript and approved the submitted version.

Corresponding authors

Correspondence to Alberto Hidalgo-Bravo or Rafael Velázquez-Cruz.

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The authors declare no competing interests.

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Supplementary Information

Below is the link to the electronic supplementary material.

11357_2023_977_MOESM1_ESM.png

Supplementary file1 (PNG 2069 KB) Quality control metrics for all proteomics experiments. (A) The Histogram represents 762,978 detected peptides in all injections (60), 90.37% fall into an error maximum of ±10 ppm (strong orange). Exhibiting that the mass spectrometer sustained the exact mass calibration as expected. (B) Pie chart representing the types of peptides detected in all analysis; most of them (88.3%), called PepFrag1 and VarMod, represent peptides that have the highest reliability because they satisfied a theoretical created model that considers complete enzymatic digestion, a high number of ions products matched, a good correlation to the sum of product ions intensity between the intensity of precursor ions, and also, 14 physicochemical attributes that contribute to the peptide score; in the case of VarMod, they represent peptides with variable modification including PTMs. 3.1% are considered PepFrag2, which represent peptides with less reliability because they were identified with less restrictions than PepFrag1 on product ion intensity, it means that those peptides were identified in a second round during the database search, 5.8% represent missed cleavage peptides, which demonstrates the digestion efficiency performed for the commercial kit PreOmics®, 2.1% represent fragmented peptides at the ion source, and finally, only 0.7% of all peptides presented neutral loss of H2O, NH3 or H3PO4. These percentages show that the enzymatic digestion was done efficiently and that the parameters on the ion source as well as the alignment of the capillary tip at the source were done correctly. (C) As we expected, most high-quality peptides are concentrated at a maximum of ±10 ppm throughout the analyzed m/z range (black), and the highest proportion of peptides comprise a m/z range of 500-1000, which is normal due to the complexity of an enzymatically digested sample. (D) Chart showing the log10 abundance from peptide ions. These peptide ions comprising a little bit more of six orders of magnitude (expressed as base 10 logarithm), demonstrating a good sensibility of the mass spectrometer. Data are binned and shown as a proportion. Identified peptide ions are shown in yellow.

11357_2023_977_MOESM2_ESM.png

Supplementary file2 (PNG 864 KB) Three-dimensional representation of proteome data between groups. Partial least squares-discriminant analysis (A-C) and the unsupervised principal component analysis (PCA) (D-F) were generated from global proteome data. PCA represents DEPs data when FC > 1.5 (G-I). Blue dots represent an individual injection of Normal group samples, orange dots the OS group, and the purple dots the OP group; each sample pool per group (n=4) was injected by triplicate.

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Supplementary file3 (PNG 33 KB) Number of DEPs between each group. Graph bars indicate the proteins over-and down- expressed with a FC >2 or <2 and FC >1.5 or <1.5.

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Supplementary file4 (PNG 966 KB) Results of each candidate biomarker evaluation between groups. (A-C) ROC curve in OP and N. (D-F) ROC curve in OS and N. (G-I) ROC curve in OP and OS. AUC and 95% CI values were expressed as percentages. All ROC curves were generated using the protein abundance adjusted for age.

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Supplementary file10 (XLSX 22 KB)

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Aparicio-Bautista, D.I., Becerra-Cervera, A., Rivera-Paredez, B. et al. Label-free quantitative proteomics in serum reveals candidate biomarkers associated with low bone mineral density in Mexican postmenopausal women. GeroScience 46, 2177–2195 (2024). https://doi.org/10.1007/s11357-023-00977-1

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  • DOI: https://doi.org/10.1007/s11357-023-00977-1

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