Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.

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Revisiting biomarker discovery by plasma proteomics.

Summary Proteins in the circulatory system mirror an individual's physiology. In daily clinical practice, protein levels are generally determined using single-protein immunoassays. High-throughput, quantitative analysis using mass-spectrometry-based proteomics of blood, plasma, and serum would be advantageous but is challenging because of the high dynamic range of protein abundances. Here, we introduce a rapid and robust "plasma proteome profiling" pipeline. This single-run shotgun proteomic workflow does not require protein depletion and enables quantitative analysis of hundreds of plasma proteomes from 1 μl single finger pricks with 20 min gradients. The apolipoprotein family, inflammatory markers such as C-reactive protein, gender-related proteins, and >40 FDA-approved biomarkers are reproducibly quantified (CV

https://pure.mpg.de/pubman/item/item_2352959_3/component/file_2352964/1-s2.0-S2405471216300722-main.pdf

Plasma Proteome Profiling to Assess Human Health and Disease.

Traditionally viewed as an autodigestive pathway, autophagy also facilitates cellular secretion; however, the mechanisms underlying these processes remain unclear. Here, we demonstrate that components of the autophagy machinery specify secretion within extracellular vesicles (EVs). Using a proximity-dependent biotinylation proteomics strategy, we identify 200 putative targets of LC3-dependent secretion. This secretome consists of a highly interconnected network enriched in RNA-binding proteins (RBPs) and EV cargoes. Proteomic and RNA profiling of EVs identifies diverse RBPs and small non-coding RNAs requiring the LC3-conjugation machinery for packaging and secretion. Focusing on two RBPs, heterogeneous nuclear ribonucleoprotein K (HNRNPK) and scaffold-attachment factor B (SAFB), we demonstrate that these proteins interact with LC3 and are secreted within EVs enriched with lipidated LC3. Furthermore, their secretion requires the LC3-conjugation machinery, neutral sphingomyelinase 2 (nSMase2) and LC3-dependent recruitment of factor associated with nSMase2 activity (FAN). Hence, the LC3-conjugation pathway controls EV cargo loading and secretion.

/pdf/the-lc3-conjugation-machinery-specifies-the-loading-of-rna-2973angaex.pdf

The LC3-conjugation machinery specifies the loading of RNA-binding proteins into extracellular vesicles.

Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Commentary

Markers are needed to facilitate early detection of pancreatic ductal adenocarcinoma (PDAC), which is often diagnosed too late for effective therapy. Starting with a PDAC cell reprogramming model that recapitulated the progression of human PDAC, we identified secreted proteins and tested a subset as potential markers of PDAC. We optimized an enzyme-linked immunosorbent assay (ELISA) using plasma samples from patients with various stages of PDAC, from individuals with benign pancreatic disease, and from healthy controls. A phase 1 discovery study (n = 20), a phase 2a validation study (n = 189), and a second phase 2b validation study (n = 537) revealed that concentrations of plasma thrombospondin-2 (THBS2) discriminated among all stages of PDAC consistently. The receiver operating characteristic (ROC) c-statistic was 0.76 in the phase 1 study, 0.84 in the phase 2a study, and 0.87 in the phase 2b study. The plasma concentration of THBS2 was able to discriminate resectable stage I cancer as readily as stage III/IV PDAC tumors. THBS2 plasma concentrations combined with those for CA19-9, a previously identified PDAC marker, yielded a c-statistic of 0.96 in the phase 2a study and 0.97 in the phase 2b study. THBS2 data improved the ability of CA19-9 to distinguish PDAC from pancreatitis. With a specificity of 98%, the combination of THBS2 and CA19-9 yielded a sensitivity of 87% for PDAC in the phase 2b study. A THBS2 and CA19-9 blood marker panel measured with a conventional ELISA may improve the detection of patients at high risk for PDAC.

Detection of early pancreatic ductal adenocarcinoma with thrombospondin-2 and CA19-9 blood markers

Plasma Proteome Database (PPD; http://www.plasmaproteomedatabase.org/) was initially described in the year 2005 as a part of Human Proteome Organization's (HUPO's) pilot initiative on Human Plasma Proteome Project. Since then, improvements in proteomic technologies and increased throughput have led to identification of a large number of novel plasma proteins. To keep up with this increase in data, we have significantly enriched the proteomic information in PPD. This database currently contains information on 10,546 proteins detected in serum/plasma of which 3784 have been reported in two or more studies. The latest version of the database also incorporates mass spectrometry-derived data including experimentally verified proteotypic peptides used for multiple reaction monitoring assays. Other novel features include published plasma/serum concentrations for 1278 proteins along with a separate category of plasma-derived extracellular vesicle proteins. As plasma proteins have become a major thrust in the field of biomarkers, we have enabled a batch-based query designated Plasma Proteome Explorer, which will permit the users in screening a list of proteins or peptides against known plasma proteins to assess novelty of their data set. We believe that PPD will facilitate both clinical and basic research by serving as a comprehensive reference of plasma proteins in humans and accelerate biomarker discovery and translation efforts.

Plasma Proteome Database as a resource for proteomics research: 2014 update

Aim: To evaluate the use of potentially inappropriate medicines in elderly inpatients in a tertiary care teaching hospital. Materials and Methods: Retrospective analysis was performed for cases of elderly patients admitted between January 2010 and December 2010. Data on age, gender, diagnosis, duration of hospital stay, treatment, and outcome were collected. Prescriptions were assessed for the use of potentially inappropriate medications in geriatric patients by using American Geriatric Society Beer's criteria (2012) and PRISCUS list (2010). Results: A total of 676 geriatric patients (52.12% females) were admitted in the medicine ward. The average age of geriatric patients was 72.69 years. According to Beer's criteria, at least one inappropriate medicine was prescribed in 590 (87.3%) patients. Metoclopramide (54.3%), alprazolam (9%), diazepam (8%), digoxin > 0.125 mg/day (5%), and diclofenac (3.7%) were the commonly used inappropriate medications. Use of nonsteroidal anti-inflammatory drugs (NSAIDs) in heart and renal failure patients was the commonly identified drug-disease interaction. According to PRISCUS list, at least one inappropriate medication was prescribed in 210 (31.06%) patients. Conclusion: Use of inappropriate medicines is highly prevalent in elderly patients.

Utilization of potentially inappropriate medications in elderly patients in a tertiary care teaching hospital in India.

Objective: To evaluate drug utilization pattern in terms of defined daily dose along with pharmacoeconomic analysis in geriatric patients admitted in medical ward of a tertiary care hospital. Materials and Methods: Retrospective medical record analysis was performed for indoor cases of the geriatric patients (age ≥65 years) admitted in medicine ward from January 2010 to December 2010 were analyzed for demographics, indications for admission, various systems involved, duration of hospital stay, various drugs prescribed, and adverse drug reaction. The drugs were categorized by anatomical therapeutic classification and defined daily dose was calculated. The World Health Organization prescribing indicators were assessed. Cost of the drugs was calculated to assess the economic burden. Results: Cardiovascular diseases were the common cause for admission. Antiplatelet drugs-B01AC (93%), H 2 blockers-A02BA (77.22%), antiemetics-A03FA (67.6%), vasodilators-C01D (55%), and hypolipidemic drugs-C10AA (52%) were commonly utilized groups. Average number of drugs per patient was 9.37 (95% CI: 9.09-9.64). Average number of antimicrobials prescribed per patient was 0.91 (95% CI: 0.82-0.99). Cefotaxime was the commonly prescribed antimicrobial drug. Average cost of treatment was ₹540.5 (95% CI: ₹458.0-623.0). Patients shared 45% of the economic burden for prescribed medicines. The average economic burden for drugs was significantly higher in expired than survived patients (₹749.49 vs. 457.59). Conclusion: Polypharmacy and irrational use of medicines are common problems in geriatric prescription. Prescription guidelines should be formatted for them.

Binit N Jhaveri

Papers

Plasma Proteome Database as a resource for proteomics research: 2014 update

Utilization of potentially inappropriate medications in elderly patients in a tertiary care teaching hospital in India.

Drug utilization pattern and pharmacoeconomic analysis in geriatric medical in-patients of a tertiary care hospital of India