Subnational economic complexity analysis: case-study of the Kaliningrad region

Currently, the economic complexity (EC) theory is of considerable relevance. Developed and rapidly developing countries invest heavily in research and development to increase their products complexity as it brings an economy’s competitiveness and revenues to a higher level. The article presents the main results of the 2017-2019 EC analysis of a Russian exclave, the Kaliningrad region, whose trade and production specialization have changed dramatically. The study relies on the data of the Atlas of Economic Complexity, the Federal Customs Service of Russia, the Kaliningrad Regional Customs. It applies the author’s method for “cleaning” the data. The key feature of the study is the incorporation of the regional data into the global trade statistics. The analysis reveals general trends towards an increase in capabilities in low complexity products. The paper emphasizes that the regional government needs to pursue an active sectoral policy aimed at increasing the economic complexity

Proteomic Signature of Extracellular Vesicles for Lung Cancer Recognition

The proteins of extracellular vesicles (EVs) that originate from tumors reflect the producer cells’ proteomes and can be detected in biological fluids. Thus, EVs provide proteomic signatures that are of great interest for screening and predictive cancer diagnostics. By applying targeted mass spectrometry with stable isotope-labeled peptide standards, we assessed the levels of 28 EV-associated proteins, including the conventional exosome markers CD9, CD63, CD81, CD82, and HSPA8, in vesicles derived from the lung cancer cell lines NCI-H23 and A549. Furthermore, we evaluated the detectability of these proteins and their abundance in plasma samples from 34 lung cancer patients and 23 healthy volunteers. The abundance of TLN1, TUBA4A, HSPA8, ITGB3, TSG101, and PACSIN2 in the plasma of lung cancer patients was measured using targeted mass spectrometry and compared to that in plasma from healthy volunteers. The most diagnostically potent markers were TLN1 (AUC, 0.95), TUBA4A (AUC, 0.91), and HSPA8 (AUC, 0.88). The obtained EV proteomic signature allowed us to distinguish between the lung adenocarcinoma and squamous cell carcinoma histological types. The proteomic cargo of the extracellular vesicles represents a promising source of potential biomarkers

200+Protein Concentrations in Healthy Human Blood Plasma: Targeted Quantitative SRM SIS Screening of Chromosomes 18, 13, Y, and the Mitochondrial Chromosome Encoded Proteome

Mitochondria are undeniably the cell powerhouse, directly affecting cell survival and fate. Growing evidence suggest that mitochondrial protein repertoire affects metabolic activity and plays an important role in determining cell proliferation/differentiation or quiescence shift. Consequently, the bioenergetic status of a cell is associated with the quality and abundance of the mitochondrial populations and proteomes. Mitochondrial morphology changes in the development of different cellular functions associated with metabolic switches. It is therefore reasonable to speculate that different cell lines do contain different mitochondrialassociated proteins, and the investigation of these pools may well represent a source for mining missing proteins (MPs). A very effective approach to increase the number of IDs through mass spectrometry consists of reducing the complexity of the biological samples by fractionation. The present study aims at investigating the mitochondrial proteome of five phenotypically different cell lines, possibly expressing some of the MPs, through an enrichment 12fractionation approach at the organelle and protein level. We demonstrate a substantial increase in the proteome coverage, which, in turn, increases the likelihood of detecting low abundant proteins, often falling in the category of MPs, and resulting, for the present study, in the identification of METTL12, FAM163A, and RGS13. All MS data have been deposited to the MassIVE data repository (https://massive.ucsd. edu) with the data set identifier MSV000082409 and PXD010446