Significance Statement: Although gene expression changes have been characterized in human diabetic kidney disease (DKD), unbiased tissue proteomics information for this condition is lacking. The authors conducted an unbiased aptamer-based proteomic analysis of samples from patients with DKD and healthy controls, identifying proteins with levels that associate with kidney function (eGFR) or fibrosis, after adjusting for key covariates. Overall, tissue gene expression only modestly correlated with tissue protein levels. Kidney protein and RNA levels of matrix metalloproteinase 7 (MMP7) strongly correlated with fibrosis and with eGFR. Single-cell RNA sequencing indicated that kidney tubule cells are an important source of MMP7. Furthermore, plasma MMP7 levels predicted future kidney function decline. These findings identify kidney tissue MMP7 as a biomarker of fibrosis and blood MMP7 as a biomarker for future kidney function decline. Background: Diabetic kidney disease (DKD) is responsible for close to half of all ESKD cases. Although unbiased gene expression changes have been extensively characterized in human kidney tissue samples, unbiased protein-level information is not available. Methods: We collected human kidney samples from 23 individuals with DKD and ten healthy controls, gathered associated clinical and demographics information, and implemented histologic analysis. We performed unbiased proteomics using the SomaScan platform and quantified the level of 1305 proteins and analyzed gene expression levels by bulk RNA and single-cell RNA sequencing (scRNA-seq). We validated protein levels in a separate cohort of kidney tissue samples as well as in 11,030 blood samples. Results: Globally, human kidney transcript and protein levels showed only modest correlation. Our analysis identified 14 proteins with kidney tissue levels that correlated with eGFR and found that the levels of 152 proteins correlated with interstitial fibrosis. Of the identified proteins, matrix metalloprotease 7 (MMP7) showed the strongest association with both fibrosis and eGFR. The correlation between tissue MMP7 protein expression and kidney function was validated in external datasets. The levels of MMP7 RNA correlated with fibrosis in the primary and validation datasets. Findings from scRNA-seq pointed to proximal tubules, connecting tubules, and principal cells as likely cellular sources of increased tissue MMP7 expression. Furthermore, plasma MMP7 levels correlated not only with kidney function but also associated with prospective kidney function decline. Conclusions: Our findings, which underscore the value of human kidney tissue proteomics analysis, identify kidney tissue MMP7 as a diagnostic marker of kidney fibrosis and blood MMP7 as a biomarker for future kidney function decline.

Global proteomics profiling identified FGF20, TNFSF12, and ANGPT1 as protective against progression of renal function decline in those with diabetes.

Background: Reliable, noninvasive tools to diagnose at-risk metabolic dysfunction–associated steatohepatitis (MASH) are urgently needed to improve management. We developed a risk stratification score incorporating proteomics-derived serum markers with clinical variables to identify high-risk patients with MASH (NAFLD activity score >4 and fibrosis score >2). Methods: In this 3-phase proteomic study of biopsy-proven metabolic dysfunction–associated steatotic fatty liver disease, we first developed a multi-protein predictor for discriminating NAFLD activity score >4 based on SOMAscan proteomics quantifying 1305 serum proteins from 57 US patients. Four key predictor proteins were verified by ELISA in the expanded US cohort (N = 168) and enhanced by adding clinical variables to create the 9-feature MASH Dx score, which predicted MASH and also high-risk MASH (F2+). The MASH Dx score was validated in 2 independent, external cohorts from Germany (N = 139) and Brazil (N = 177). Results: The discovery phase identified a 6-protein classifier that achieved an AUC of 0.93 for identifying MASH. Significant elevation of 4 proteins (THBS2, GDF15, SELE, and IGFBP7) was verified by ELISA in the expanded discovery and independently in the 2 external cohorts. MASH Dx score incorporated these proteins with established MASH risk factors (age, body mass index, ALT, diabetes, and hypertension) to achieve good discrimination between MASH and metabolic dysfunction–associated steatotic fatty liver disease without MASH (AUC: 0.87—discovery; 0.83—pooled external validation cohorts), with similar performance when evaluating high-risk MASH F2-4 (vs. MASH F0-1 and metabolic dysfunction–associated steatotic fatty liver disease without MASH). Conclusions: The MASH Dx score offers the first reliable noninvasive approach combining novel, biologically plausible ELISA-based fibrosis markers and clinical parameters to detect high-risk MASH in patient cohorts from the United States, Brazil, and Europe. Export

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood–brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We have identified protein and miRNA signatures in these EVs that can lead to the interaction of EVs with astrocytes and, as such, have the potential to serve as targets for development of diagnostics and therapeutics for early detection and therapeutic intervention in breast cancer brain metastasis.

Background: The incidence and mortality of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) is higher in African Americans (AA) than other racial/ethnic groups in the U.S., but the reasons for this disparity are unknown. There is an urgent need for the discovery of novel molecular signatures for HCV disease progression to understand the underlying biological basis for this cancer rate disparity to improve the clinical outcome.Methods: We performed differential proteomics with isobaric labeling tags for relative and absolute quantitation (iTRAQ) and MS/MS analysis to identify proteins differentially expressed in cirrhotic (CIR) and HCC as compared to normal tissues of Caucasian American (CA) patients. The raw data were analyzed using the ProteinPilot v3.0. Searches were performed against all known sequences populating the Swiss-Prot, Refseq, and TrEMBL databases. Quality control analyses were accomplished using pairwise correlation plots, boxplots, principal component analysis, and unsupervised hierarchical clustering. Supervised analysis was carried out to identify differentially expressed proteins. Candidates were validated in independent cohorts of CA and AA tissues by qRT-PCR or Western blotting.Results: A total of 238 unique proteins were identified. Of those, around 15% were differentially expressed between normal, CIR & HCC groups. Target validation demonstrates racially distinct alteration in the expression of certain proteins. For example, the mRNA expression levels of transferrin (TF) were 2 and18-fold higher in CIR and HCC in AA as compared to CA. Similarly; the expression of Apolipoprotein A1 (APOA1) was 7-fold higher in HCC of AA. This increase was mirrored in the protein expression levels. Interestingly, the level of hepatocyte nuclear factor4α (HNF4α) protein was down regulated in AA, whereas repression of transcription is seen more in CA compared to AA. These data suggest that racial disparities in HCC could be a consequence of differential dysregulation of HNF4α transcriptional activity.Conclusion: This study identifies novel molecular signatures in HCV-induced HCC using iTRAQ-based tissue proteomics. The proteins identified will further enhance a molecular explanation to the biochemical mechanism(s) that may play a role in HCC racial disparities.

The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy—whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.