The Fatty Acid and Protein Profiles of Circulating CD81-Positive Small Extracellular Vesicles Are Associated with Disease Stage in Melanoma Patients

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Abstract

The early detection of cutaneous melanoma, a potentially lethal cancer with rising incidence, is fundamental to increasing survival and therapeutic adjustment. In stages II–IV especially, additional indications for adjuvant therapy purposes after resection and for treatment of metastatic patients are urgently needed. We investigated whether the fatty acid (FA) and protein compositions of small extracellular vesicles (sEV) derived from the plasma of stage 0–I, II and III–IV melanoma patients (n = 38) could reflect disease stage. The subpopulation of sEV expressing CD81 EV marker (CD81sEV) was captured by an ad hoc immune affinity technique from plasma depleted of large EV. Biological macromolecules were investigated by gas chromatography and mass spectrometry in CD81sEV. A higher content of FA was detectable in patients with respect to healthy donors (HD). Moreover, a higher C18:0/C18:1 ratio, as a marker of cell membrane fluidity, distinguished early (stage 0–I) from late (III–IV) stages’ CD81sEV. Proteomics detected increases in CD14, PON1, PON3 and APOA5 exclusively in stage II CD81sEV, and RAP1B was decreased in stage III–IV CD81sEV, in comparison to HD. Our results suggest that stage dependent alterations in CD81sEV’ FA and protein composition may occur early after disease onset, strengthening the potential of circulating sEV as a source of discriminatory information for early diagnosis, prediction of metastatic behavior and following up of melanoma patients.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

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The PRIDE database and related tools and resources in 2019: improving support for quantification data

Yasset Perez-Riverol, Attila Csordas, Jingwen Bai … (2018)

Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.