Integrated proteomic and metabolomic modules identified as biomarkers of mortality in the Atherosclerosis Risk in Communities study and the African American Study of Kidney Disease and Hypertension

Association of Integrated Proteomic and Metabolomic Modules with Risk of Kidney Disease Progression

Key Points

Integrated analysis of proteome and metabolome identifies modules associated with CKD progression and kidney failure. Ephrin transmembrane proteins and podocyte-expressed CRIM1 and NPNT emerged as central components and warrant experimental and clinical investigation.

Background

Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes.

Methods

We present an integrated analysis of 4091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort study (N =1708; average follow-up for kidney failure, 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method, and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors, including the eGFR and urine protein–creatinine ratio. Associations were identified in a discovery sample (random two thirds, n =1139) and then evaluated in a replication sample (one third, n =569).

Results

We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components. Modules and principal component loadings were projected onto the replication sample, which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane-related terms were identified as overrepresented in these modules. Transmembrane–ephrin receptor activity displayed the largest odds (odds ratio=13.2, P value = 5.5×10−5). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure (P value = 2.6×10−5).

Conclusions

This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.

Serum multi-omics analysis in hindlimb unloading mice model: Insights into systemic molecular changes and potential diagnostic and therapeutic biomarkers

Microgravity, in space travel and prolonged bed rest conditions, induces cardiovascular deconditioning along with skeletal muscle mass loss and weakness. The findings of microgravity research may also aid in the understanding and treatment of human health conditions on Earth such as muscle atrophy, and cardiovascular diseases. Due to the paucity of biomarkers and the unknown underlying mechanisms of cardiovascular and skeletal muscle deconditioning in these environments, there are insufficient diagnostic and preventative measures. In this study, we employed hindlimb unloading (HU) mouse model, which mimics astronauts in space and bedridden patients, to first evaluate cardiovascular and skeletal muscle function, followed by proteomics and metabolomics LC-MS/MS-based analysis using serum samples. Three weeks of unloading caused changes in the function of the cardiovascular system in c57/Bl6 mice, as seen by a decrease in mean arterial pressure and heart weight. Unloading for three weeks also changed skeletal muscle function, causing a loss in grip strength in HU mice and atrophy of skeletal muscle indicated by a reduction in muscle mass. These modifications were partially reversed by a two-week recovery period of reloading condition, emphasizing the significance of the recovery process. Proteomics analysis revealed 12 dysregulated proteins among the groups, such as phospholipid transfer protein, Carbonic anhydrase 3, Parvalbumin alpha, Major urinary protein 20 (Mup20), Thrombospondin-1, and Apolipoprotein C-IV. On the other hand, metabolomics analysis showed altered metabolites among the groups such as inosine, hypoxanthine, xanthosine, sphinganine, l-valine, 3,4-Dihydroxyphenylglycol, and l-Glutamic acid. The joint data analysis revealed that HU conditions mainly impacted pathways such as ABC transporters, complement and coagulation cascades, nitrogen metabolism, and purine metabolism. Overall, our results indicate that microgravity environment induces significant alterations in the function, proteins, and metabolites of these mice. These observations suggest the potential utilization of these proteins and metabolites as novel biomarkers for assessing and mitigating cardiovascular and skeletal muscle deconditioning associated with such conditions.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

Identification and validation of hub genes involved in foam cell formation and atherosclerosis development via bioinformatics

Background

Foam cells play crucial roles in all phases of atherosclerosis. However, until now, the specific mechanisms by which these foam cells contribute to atherosclerosis remain unclear. We aimed to identify novel foam cell biomarkers and interventional targets for atherosclerosis, characterizing their potential mechanisms in the progression of atherosclerosis.

Methods

Microarray data of atherosclerosis and foam cells were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expression genes (DEGs) were screened using the "LIMMA" package in R software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were both carried out. Hub genes were found in Cytoscape after a protein-protein interaction (PPI) enrichment analysis was carried out. Validation of important genes in the GSE41571 dataset, cellular assays, and tissue samples.

Results

A total of 407 DEGs in atherosclerosis and 219 DEGs in foam cells were identified, and the DEGs in atherosclerosis were mainly involved in cell proliferation and differentiation. CSF1R and PLAUR were identified as common hub genes and validated in GSE41571. In addition, we also found that the expression of CSF1R and PLAUR gradually increased with the accumulation of lipids and disease progression in cell and tissue experiments.

Conclusion

CSF1R and PLAUR are key hub genes of foam cells and may play an important role in the biological process of atherosclerosis. These results advance our understanding of the mechanism behind atherosclerosis and potential therapeutic targets for future development.

Circulating Proteins and Mortality in CKD: A Proteomics Study of the AASK and ARIC Cohorts

Rationale & Objective

Proteomics could provide pathophysiologic insight into the increased risk of mortality in patients with chronic kidney disease (CKD). This study aimed to investigate associations between the circulating proteome and all-cause mortality among patients with CKD.

Study Design

Observational cohort study.

Setting & Participants

Primary analysis in 703 participants in the African American Study of Kidney Disease and Hypertension (AASK) and validation in 1,628 participants with CKD in the Atherosclerosis Risk in Communities (ARIC) study who attended visit 5.

Exposure

Circulating proteins.

Outcome

All-cause mortality.

Analytical Approach

Among AASK participants, we evaluated the associations of 6,790 circulating proteins with all-cause mortality using multivariable Cox proportional hazards models. Proteins with significant associations were further studied in ARIC Visit 5 participants with CKD.

Results

In the AASK cohort, the mean age was 54.5 years, 271 (38.5%) were women, and the mean measured glomerular filtration rate (GFR) was 46 mL/min/1.73 m2. The median follow-up was 9.6 years, and 7 distinct proteins were associated with all-cause mortality at the Bonferroni-level threshold (P < 0.05 of the 6,790) after adjustment for demographics and clinical factors, including baseline measured estimated GFR and proteinuria. In the ARIC visit 5 cohort, the mean age was 77.2 years, 903 (55.5%) were women, the mean estimated GFR was 54 mL/min/1.73 m2 and median follow-up was 6.9 years. Of the 7 proteins found in AASK, 3 (β2-microglobulin, spondin-1, and N-terminal pro-brain natriuretic peptide) were available in the ARIC data, with all 3 significantly associated with death in ARIC.

Limitations

Possibility of unmeasured confounding. Cause of death was not known.

Conclusions

Using large-scale proteomic analysis, proteins were reproducibly associated with mortality in 2 cohorts of participants with CKD.

Plain-Language Summary

Patients with chronic kidney disease (CKD) have a high risk of premature death, with various pathophysiological processes contributing to this increased risk of mortality. This observational cohort study aimed to investigate the associations between circulating proteins and all-cause mortality in patients with CKD using large-scale proteomic analysis. The study analyzed data from the African American Study of Kidney Disease and Hypertension (AASK) study and validated the findings in the Atherosclerosis Risk in Communities (ARIC) Study. A total of 6,790 circulating proteins were evaluated in AASK, and 7 proteins were significantly associated with all-cause mortality. Three of these proteins (β2-microglobulin, spondin-1, and N-terminal pro-brain natriuretic peptide (BNP)) were also measured in ARIC and were significantly associated with death. Additional studies assessing biomarkers associated with mortality among patients with CKD are needed to evaluate their use in clinical practice.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.