Isolation of Extracellular Vesicles Using Titanium Dioxide Microspheres

Extracellular vesicles (EVs) are bilayer membrane particles released from several cell types to the extracellular environment. EVs have a crucial role in cell-cell communication, involving different biological processes in health and diseases. Due to the potential of biomarkers for several diseases as diagnostic and therapeutic tools, it is relevant to understand the biology of the EVs and their content. One of the current challenges involving EVs is regarding the purification method, which is a critical step for EV's functional and characterization studies. Ultracentrifugation is the most used method for EV isolation, where the nanoparticles are separated in sequential centrifugation to isolate the EVs based on their size. However, for viscous biofluids such as plasma, there is a co-isolation of the most abundant proteins, which can impair the EV's protein identification due to the low abundance of these proteins and signal suppression by the most abundant plasma proteins. Emerging techniques have gained attention in recent years. Titanium dioxide (TiO2) is one of the most promising techniques due to its property for selective isolation based on the interaction with phospholipids in the EV membrane. Using a small amount of TiO2 beads and a low volume of plasma, it is possible to isolate EVs with reduced plasma protein co-isolation. This study describes a comprehensive workflow for the isolation and characterization of plasma extracellular vesicles (EVs) using mass spectrometry-based proteomics techniques. The aim of this chapter is describe the EV isolation using TiO2 beads enrichment and high-throughput mass spectrometry techniques to efficiently identify the protein composition of EVs in a fast and straightforward manner.

Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses

Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.

Deciphering the Functional Status of Breast Cancers through the Analysis of Their Extracellular Vesicles

Breast cancer (BC) accounts for the highest incidence of tumor-related mortality among women worldwide, justifying the growing search for molecular tools for the early diagnosis and follow-up of BC patients under treatment. Circulating extracellular vesicles (EVs) are membranous nanocompartments produced by all human cells, including tumor cells. Since minimally invasive methods collect EVs, which represent reservoirs of signals for cell communication, these particles have attracted the interest of many researchers aiming to improve BC screening and treatment. Here, we analyzed the cargoes of BC-derived EVs, both proteins and nucleic acids, which yielded a comprehensive list of potential markers divided into four distinct categories, namely, (i) modulation of aggressiveness and growth; (ii) preparation of the pre-metastatic niche; (iii) epithelial-to-mesenchymal transition; and (iv) drug resistance phenotype, further classified according to their specificity and sensitivity as vesicular BC biomarkers. We discuss the therapeutic potential of and barriers to the clinical implementation of EV-based tests, including the heterogeneity of EVs and the available technologies for analyzing their content, to present a consistent, reproducible, and affordable set of markers for further evaluation.

Complement System Activation Is a Plasma Biomarker Signature during Malaria in Pregnancy

Malaria in pregnancy (MiP) is a public health problem in malaria-endemic areas, contributing to detrimental outcomes for both mother and fetus. Primigravida and second-time mothers are most affected by severe anemia complications and babies with low birth weight compared to multigravida women. Infected erythrocytes (IE) reach the placenta, activating the immune response by placental monocyte infiltration and inflammation. However, specific markers of MiP result in poor outcomes, such as low birth weight, and intrauterine growth restriction for babies and maternal anemia in women infected with Plasmodium falciparum are limited. In this study, we identified the plasma proteome signature of a mouse model infected with Plasmodium berghei ANKA and pregnant women infected with Plasmodium falciparum infection using quantitative mass spectrometry-based proteomics. A total of 279 and 249 proteins were quantified in murine and human plasma samples, of which 28% and 30% were regulated proteins, respectively. Most of the regulated proteins in both organisms are involved in complement system activation during malaria in pregnancy. CBA anaphylatoxin assay confirmed the complement system activation by the increase in C3a and C4a anaphylatoxins in the infected plasma compared to non-infected plasma. Moreover, correlation analysis showed the association between complement system activation and reduced head circumference in newborns from Pf-infected mothers. The data obtained in this study highlight the correlation between the complement system and immune and newborn outcomes resulting from malaria in pregnancy.

Protein Arginylation Is Regulated during SARS-CoV-2 Infection

BACKGROUND

In 2019, the world witnessed the onset of an unprecedented pandemic. By February 2022, the infection by SARS-CoV-2 has already been responsible for the death of more than 5 million people worldwide. Recently, we and other groups discovered that SARS-CoV-2 infection induces ER stress and activation of the unfolded protein response (UPR) pathway. Degradation of misfolded/unfolded proteins is an essential element of proteostasis and occurs mainly in lysosomes or proteasomes. The N-terminal arginylation of proteins is characterized as an inducer of ubiquitination and proteasomal degradation by the N-degron pathway.

RESULTS

The role of protein arginylation during SARS-CoV-2 infection was elucidated. Protein arginylation was studied in Vero CCL-81, macrophage-like THP1, and Calu-3 cells infected at different times. A reanalysis of in vivo and in vitro public omics data combined with immunoblotting was performed to measure levels of arginyl-tRNA-protein transferase (ATE1) and its substrates. Dysregulation of the N-degron pathway was specifically identified during coronavirus infections compared to other respiratory viruses. We demonstrated that during SARS-CoV-2 infection, there is an increase in ATE1 expression in Calu-3 and Vero CCL-81 cells. On the other hand, infected macrophages showed no enzyme regulation. ATE1 and protein arginylation was variant-dependent, as shown using P1 and P2 viral variants and HEK 293T cells transfection with the spike protein and receptor-binding domains (RBD). In addition, we report that ATE1 inhibitors, tannic acid and merbromine (MER) reduce viral load. This finding was confirmed in ATE1-silenced cells.

CONCLUSIONS

We demonstrate that ATE1 is increased during SARS-CoV-2 infection and its inhibition has potential therapeutic value.

MALDI-TOF mass spectrometry of saliva samples as a prognostic tool for COVID-19

Background

Methods

Results

Conclusion

The thermal proteome stability profile of Trypanosoma cruzi in epimastigote and trypomastigote life stages

Trypanosoma cruzi is a flagellate protozoa being the etiological agent of Chagas disease, a neglected tropical disease, which still poses a public health problem worldwide. The intricate molecular changes during T. cruzi-host interaction have been explored using different largescale omics techniques. However, protein stability is largely unknown. Thermal proteome profiling (TPP) methodology has the potential to characterize proteome-wide stability highlighting key proteins during T. cruzi infection and life stage transition from the invertebrate to the mammalian host. In the present work, T. cruzi epimastigotes and trypomastigotes cell lysates were subjected to TPP workflow and analyzed by quantitative large-scale mass spectrometry-based proteomics to fit a melting profile for each protein. A total of 2884 proteins were identified and associated to 1741 melting curves being 1370 in trypomastigotes (Tm_AVG 53.53 °C) and 1279 in epimastigotes (Tm_AVG 50.89 °C). A total of 453 proteins were identified with statistically different melting profiles between the two life stages. Proteins associated to pathogenesis and intracellular transport had regulated melting temperatures. Membrane and glycosylated proteins had a higher average Tm in trypomastigotes compared to epimastigotes. This study represents the first large-scale comparison of parasite protein stability between life stages. SIGNIFICANCE: Trypanosoma cruzi, a unicellular flagellate parasite, is the etiological agent of Chagas disease, endemic in South America and affecting more that 7 million people worldwide. There is an intense research to identify novel chemotherapeutic and diagnostic targets of Chagas disease. Proteomic approaches have helped in elucidating the quantitative proteome and PTMs changes of T. cruzi during life cycle transition and upon different biotic and abiotic stimuli. However, a comprehensive knowledge of the protein-protein interaction and protein conformation is still missing. In order to fill this gap, this manuscript elucidates the T. cruzi Y strain proteome-wide thermal stability map in the epimastigote and trypomastigote life stages. Comparison between life stages showed a higher average melting temperature stability for trypomastigotes than epimastigotes indicating a host temperature adaptation. Both presented a selective thermal stability shift for cellular compartments, molecular functions and biological processes based on the T. cruzi life stage. Membrane and glycosylated proteins presented a higher thermal stability in trypomastigotes when compared to the epimastigotes.

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.