Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Benson, Mark D.; Eisman, Aaron S.; Tahir, Usman A.; Katz, Daniel H.; Deng, Shuliang; Ngo, Debby; Robbins, Jeremy; Hofmann, Alissa; Shi, Xu; Zheng, Shuning; Keyes, Michelle J.; Yu, Zhi; Gao, Yan; Farrell, Laurie; Shen, Dongxiao; Chen, Zsu-Zsu; Cruz, Daniel; Sims, Mario; Correa, Adolfo; Tracy, Russell P.; Durda, Peter; Taylor, Kent D.; Liu, Yongmei; Johnson, W. Craig; Guo, Xiuqing; Yao, Jie; Chen, Yii‐Der Ida; Manichaikul, Ani; Jain, Deepti; Yang, Qiong; Bouchard, Claude; Sarzynski, Mark A.; Rich, Stephen S.; Rotter, Jerome I.; Wang, Thomas J.; Wilson, James G.; Clish, Clary B.; Sarkar, Indra Neil; Natarajan, Pradeep; Gerszten, Robert E.

doi:10.1016/j.cmet.2023.07.012

Cited by 7 publications

(3 citation statements)

References 98 publications

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“…However, analysis of interactomes for highlighted proteins according to StringDB data showed that, for a quarter of them, there are enrichments for participation in glutamic acid regulation processes. The non-randomness of our results can also be confirmed by the discovered correlations (Figure 6) for 8 (SERPINF1, FN1, CFH, PDE4D, PROS1, CFHR5, CFH, and TG) of 16 highlighted proteins with glutamic acid and/or lactic acid, identified as part of the analysis of dependencies between proteins and metabolites, manifested in human blood plasma [69]. The authors of work [69] also compared their results with several characteristics, including BMI.…”

Section: Multiomics Analysis For Bmi Predictionsupporting

confidence: 62%

“…The non-randomness of our results can also be confirmed by the discovered correlations (Figure 6) for 8 (SERPINF1, FN1, CFH, PDE4D, PROS1, CFHR5, CFH, and TG) of 16 highlighted proteins with glutamic acid and/or lactic acid, identified as part of the analysis of dependencies between proteins and metabolites, manifested in human blood plasma [69]. The authors of work [69] also compared their results with several characteristics, including BMI. As expected, the revision of the data to adjust for BMI did not change the associations.…”

Section: Multiomics Analysis For Bmi Predictionsupporting

confidence: 62%

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Multiomics Picture of Obesity in Young Adults

Kiseleva,

Pyatnitskiy,

Arzumanian

et al. 2024

Biology

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Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC–MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC–MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

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Section: Multiomics Analysis For Bmi Predictionsupporting

confidence: 62%

Section: Multiomics Analysis For Bmi Predictionsupporting

confidence: 62%

Multiomics Picture of Obesity in Young Adults

Kiseleva,

Pyatnitskiy,

Arzumanian

et al. 2024

Biology

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“…Unlike genetics, which focuses on the hereditary aspects of diseases, proteomics and metabolomics provide insights into the phenotype and progression of diseases [ 10 ]. Integrative analysis that combines proteomic and metabolomic profiling has been shown to provide novel insights into the understanding of disease mechanisms and their development [ 11 ]. These ‘omics’ technologies enable the comprehensive profiling of proteins and metabolites within biological samples, providing a snapshot of the physiological state and pathophysiological changes occurring in disease conditions [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated proteomic and metabolomic profiling reveals novel insights on the inflammation and immune response in HFpEF

Abudurexiti,

Abuduhalike,

Naman

et al. 2024

BMC Genomics

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Background The precise mechanisms leading to the development of heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. In this study, an integrative approach utilizing untargeted proteomics and metabolomics was employed to delineate the altered proteomic and metabolomic profiles in patients with HFpEF compared to healthy controls. Materials and methods Data were collected from a prospective cohort consisting of 30 HFpEF participants and 30 healthy controls, matched by gender and age. plasma samples were analyzed by multi-omics platforms. The quantification of plasma proteins and metabolites was performed using data-independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively. Additionally, Proteomic and metabolomic results were analyzed separately and integrated using correlation and pathway analysis. This was followed by the execution of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies to elucidate the biological relevance of the observed results. Results A total of 46 significantly differentially expressed proteins (DEPs) and 102 differentially expressed metabolites (DEMs) were identified. Then, GO and KEGG pathway enrichment analyses were performed by DEPs and DEMs. Integrated analysis of proteomics and metabolomics has revealed Tuberculosis and African trypanosomiasis pathways that are significantly enriched and the DEPs and DEMs enriched within them, are associated with inflammation and immune response. Conclusions Integrated proteomic and metabolomic analyses revealed distinct inflammatory and immune response pathways in HFpEF, highlighting novel therapeutic avenues.

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