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Proteomics thread

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As processes and proteins contributing to AA and DA may differ, we aimed to define the proteins contributing to decelerated or accelerated aging. Therefore, as our second approach, we defined three biologically aging groups: 1) Chronological Agers (CA), with a maximum of 2 years difference between estimated age and chronological age (|ΔAge| < 2), 2) Decelerated Agers, with an underestimation in their age based on plasma proteomic profile (ΔAge < −5), and 3) Accelerated Agers, with an overestimation in their age based on proteomic profile (ΔAge >5); Figure 5A). In the Arthur dataset (Arthur et al., 2021) 46 individuals showed CA, 25 DA with a maximum underestimation of more than 11.5 years, and 28 AA with a maximum overestimate of almost 12 years. The dataset of Robbins et al. (Robbins et al., 2021) contained 255 individuals showing CA, 88 DA with a maximum underestimate of more than 16 years, and 92 AA with a maximum overestimate of almost 13 years



To explore which APs are mostly associated with disease-associated phenotypes, we linked each individual AP to AP enriched disease-associated phenotypes. Leading our top 10 proteins we found Growth/differentiation factor 15 (GDF15), a well described aging protein, and Tumor necrosis factor receptor superfamily member 1A and 1B (TNFRSF1A, TNFRSF1B), two receptors from the TNF-superfamily which are predominantly expressed by immune cells. GDF15 was associated with 91 phenotypes, which shared most phenotypes with other proteins from the top 10 (Figure 3D). Together, the top 10 shared a link to 32 phenotypes (Figure 3D), and the top 20 proteins still shared 21 phenotypes (Supplementary Figure S3). This suggests that a large group of APs plays a role across multiple shared diseases. Altogether, these results underline the link between age-associated plasma proteins and age-associated diseases and suggest the probable potential of these proteins in promoting the healthspan and reflecting health status due to their pleiotropic functioning and association to diseases.


higher levels of uric acid were associated with a higher (biologically older) ΔAge (Partial r = .267, p = .005), whereas higher levels of magnesium were associated with a lower (biologically younger) ΔAge (Partial r = −.197, p = .036). Near significant associations were found for levels of aspartate aminotransferase, a marker of liver functioning, (Partial r = −.189, p = .051) and HDL cholesterol, sometimes referred to as the good cholesterol (Partial r = −.171, p = .072).


Expression of the four proteins that overlap between DA versus CA and AA versus CA comparisons in this dataset – namely, Chondroadherin (CHAD), Complement receptor type 2 (CR2), Protein SET (SET), and TREM2 - go in opposite directions in DA compared to AA.

Since most of us are delayed agers, they are the only ones that matter here

Edited by InquilineKea
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proteomics assays for blood samples...

Best bet is an academic center:1. Duke University School of Medicine offers LC-MS/MS differential expression analysis for $459 per sample for non-Duke scientists[1].
2. The University of Connecticut Proteomics & Metabolomics Facility offers various proteomics and metabolomics services with prices ranging from $35 to $500 per sample for external academic users[5].
3. The Roy J. Carver Biotechnology Center at the University of Illinois offers LC-MS/MS analysis with prices starting at $180 per sample for external academic users[2].
4. The Nebraska Center for Biotechnology offers targeted metabolomics services with prices starting at $115 per hour for external academic users[7].Please note that these prices may vary depending on the specific services required and the institution providing the service. It is recommended to contact the facilities directly for the most accurate pricing information and to discuss your specific needs.Citations:
[1] https://medschool.duke.edu/research/research-support/service-centers/core-research-facilities/proteomics-and-metabolomics-1
[2] https://biotech.illinois.edu/proteomics/pricing
[3] https://www.uthsc.edu/research/institutional-cores/pmc/services-and-fees.php
[4] https://core.uconn.edu/resource/proteomics/
[5] https://proteomics.uconn.edu/facility-rates/
[6] https://www.ccic.ohio-state.edu/MSP-Rates
[7] https://www.biotech.cornell.edu/core-facilities-brc/price-list/30
[8] https://biotech.unl.edu/proteomics-and-metabolomics





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