Leishmaniinae: Evolutionary inferences based on protein expression profiles (PhyloQuant) congruent with phylogenetic relationships among Leishmania, Endotrypanum, Porcisia, Zelonia, Crithidia, and Leptomonas.

Evolutionary relationships among parasites of the subfamily Leishmaniinae, which comprises pathogen agents of leishmaniasis, were inferred based on differential protein expression profiles from mass spectrometry-based quantitative data using the PhyloQuant method. Evolutionary distances following identification and quantification of protein and peptide abundances using Proteome Discoverer and MaxQuant software were estimated for 11 species from six Leishmaniinae genera. Results clustered all dixenous species of the genus Leishmania, subgenera L. (Leishmania), L. (Viannia), and L. (Mundinia), sister to the dixenous species of genera Endotrypanum and Porcisia. Placed basal to the assemblage formed by all these parasites were the species of genera Zelonia, Crithidia, and Leptomonas, so far described as monoxenous of insects although eventually reported from humans. Inferences based on protein expression profiles were congruent with currently established phylogeny using DNA sequences. Our results reinforce PhyloQuant as a valuable approach to infer evolutionary relationships within Leishmaniinae, which is comprised of very tightly related trypanosomatids that are just beginning to be phylogenetically unraveled. In addition to evolutionary history, mapping of species-specific protein expression is paramount to understand differences in infection processes, tissue tropisms, potential to jump from insects to vertebrates including humans, and targets for species-specific diagnostic and drug development.

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.

Mass Spectrometry-Based Characterization of Protein Aggregates in Tissues and Biofluids.

Protein aggregation is a common mechanism in multiple neurodegenerative and heart diseases and the accumulation of proteins in aggregates is toxic to cells, causing injury and death. The degree of protein aggregation directly correlates with the severity of the disease. Misfolded proteins present thermodynamic barriers that culminate in the loss of structure and function and the exposure of hydrophobic residues. The exposure of hydrophobic residues is the driving force behind protein aggregation, as it reduces surface free energy and increases the propensity for the formation of large insoluble aggregates. Exploring the protein content of aggregates is fundamental to understanding their formation mechanism and pathophysiological effects. We demonstrate here a method for isolating aggregated protein content in human plasma and mouse brain samples. The samples were characterized by mass spectrometry analysis, transmission electron microscopy, and western blotting. We report the identification of proteins associated with neurodegenerative diseases in the isolated pellets. The western blotting analyses of the isolated pellet showed the positivity for CD89 and CD63, consolidated markers of exosomes, confirming the presence of exosomes within the pellet but not in the supernatant in human plasma. Notably, the concomitant isolation of exosomes together with the protein aggregates was feasible starting from 200 µL of human plasma. Moreover, the presented methodology separated albumin from the aggregated pellet, allowing identification of larger diversity of proteins through mass spectrometry analysis.

A computational pipeline elucidating functions of conserved hypothetical Trypanosoma cruzi proteins based on public proteomic data.

The proteome is complex, dynamic, and functionally diverse. Functional proteomics aims to characterize the functions of proteins in biological systems. However, there is a delay in annotating the function of proteins, even in model organisms. This gap is even greater in other organisms, including Trypanosoma cruzi, the causative agent of the parasitic, systemic, and sometimes fatal disease called Chagas disease. About 99.8% of Trypanosoma cruzi proteome is not manually annotated (unreviewed), among which>25% are conserved hypothetical proteins (CHPs), calling attention to the knowledge gap on the protein content of this organism. CHPs are conserved proteins among different species of various evolutionary lineages; however, they lack functional validation. This study describes a bioinformatics pipeline applied to public proteomic data to infer possible biological functions of conserved hypothetical Trypanosoma cruzi proteins. Here, the adopted strategy consisted of collecting differentially expressed proteins between the epimastigote and metacyclic trypomastigotes stages of Trypanosoma cruzi; followed by the functional characterization of these CHPs applying a manifold learning technique for dimension reduction and 3D structure homology analysis (Spalog). We found a panel of 25 and 26 upregulated proteins in the epimastigote and metacyclic trypomastigote stages, respectively; among these, 18 CHPs (8 in the epimastigote stage and 10 in the metacyclic stage) were characterized. The data generated corroborate the literature and complement the functional analyses of differentially regulated proteins at each stage, as they attribute potential functions to CHPs, which are frequently identified in Trypanosoma cruzi proteomics studies. However, it is important to point out that experimental validation is required to deepen our understanding of the CHPs.

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.

Virulence Profiles of Wild-Type, P.1 and Delta SARS-CoV-2 Variants in K18-hACE2 Transgenic Mice.

Since December 2019, the world has been experiencing the COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we now face the emergence of several variants. We aimed to assess the differences between the wild-type (Wt) (Wuhan) strain and the P.1 (Gamma) and Delta variants using infected K18-hACE2 mice. The clinical manifestations, behavior, virus load, pulmonary capacity, and histopathological alterations were analyzed. The P.1-infected mice showed weight loss and more severe clinical manifestations of COVID-19 than the Wt and Delta-infected mice. The respiratory capacity was reduced in the P.1-infected mice compared to the other groups. Pulmonary histological findings demonstrated that a more aggressive disease was generated by the P.1 and Delta variants compared to the Wt strain of the virus. The quantification of the SARS-CoV-2 viral copies varied greatly among the infected mice although it was higher in P.1-infected mice on the day of death. Our data revealed that K18-hACE2 mice infected with the P.1 variant develop a more severe infectious disease than those infected with the other variants, despite the significant heterogeneity among the mice.

Three-Dimensional Bioprinting of an In Vitro Lung Model.

In December 2019, COVID-19 emerged in China, and in January 2020, the World Health Organization declared a state of international emergency. Within this context, there is a significant search for new drugs to fight the disease and a need for in vitro models for preclinical drug tests. This study aims to develop a 3D lung model. For the execution, Wharton's jelly mesenchymal stem cells (WJ-MSC) were isolated and characterized through flow cytometry and trilineage differentiation. For pulmonary differentiation, the cells were seeded in plates coated with natural functional biopolymer matrix as membrane until spheroid formation, and then the spheroids were cultured with differentiation inductors. The differentiated cells were characterized using immunocytochemistry and RT-PCR, confirming the presence of alveolar type I and II, ciliated, and goblet cells. Then, 3D bioprinting was performed with a sodium alginate and gelatin bioink in an extrusion-based 3D printer. The 3D structure was analyzed, confirming cell viability with a live/dead assay and the expression of lung markers with immunocytochemistry. The results showed that the differentiation of WJ-MSC into lung cells was successful, as well as the bioprinting of these cells in a 3D structure, a promising alternative for in vitro drug testing.

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.

Aberrant Protein Glycosylation in Brain Cancers, with Emphasis on Glioblastoma.

Aberrant glycosylation has been associated with several processes of tumorigenesis from cell signaling, migration and invasion, to immune regulation and metastasis formation. The biosynthesis of glycoconjugates is regulated through concerted and finely tuned enzymatic reactions. This includes the levels and activity of glycosyltransferases and glycosidases, nucleotide sugar metabolism, substrate availability, epigenetic condition, and cellular functional state. Glioblastoma (GBM) is the most aggressive brain tumor, frequently occurring in adults with overall survival not surpassing 17 months after diagnosis. GBM has been classified by the World Health Organization (WHO) as a grade 4 astrocytoma and stratified into G-CIMP, proneural, classical, and mesenchymal subtypes. Several biomolecular features associated with GBM aggressiveness have been elucidated; however, more studies are needed to elucidate the role of glycosylation in GBM pathology, looking at their potential as cancer targets. Here, we focus on the alteration of genes involved in protein N- and O-linked glycosylation in GBM. Specifically, the mRNA levels of glycogenes were analyzed using astrocytoma-TCGA-RNAseq datasets from public repositories. A total of 68 genes were differentially regulated in the most aggressive, mesenchymal subtype of GBM compared to the proneural and classical subtypes, and the expression of these genes was compared to normal brain tissues. Among them, we focused on 38 genes coding for proteins that belong to: 1) asparagine glycosylation (ALG); 2) glycosyltransferases (B3T, B4T); 3) fucosyltransferase (FUT); 4) acetylgalactosaminyltransferases (GALNT); 5) hexosaminidase (HEX); 6) mannosidase (MAN); 7) acetylglucosaminyltransferase (MGAT); 8) sialidase or neuraminidase (NEU); 9) solute carrier 35 family (SLC); and 10) sialyltransferase (ST). The differential expression of some genes was already reported in several solid tumors; however, several of them were found to be dysregulated in GBM for the first time. These data represent an important starting point to perform further orthogonal and functional validations to pinpoint the role of these glycogenes in GBM as diagnostic and therapeutic targets.

Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry.

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.

Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles.

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

Glycoprotein molecular dynamics analysis: SARS-CoV-2 spike glycoprotein case study.

Molecular Dynamics (MD) is a method used to calculate the movement of atoms and molecules broadly applied to several aspects of science. It involves computational simulation, which makes it, at first glance, not easily accessible. The rise of several automated tools to perform molecular simulations has allowed researchers to navigate through the various steps of MD. This enables to elucidate structural properties of proteins that could not be analyzed otherwise, such as the impact of glycosylation. Glycosylation dictates the physicochemical and biological properties of a protein modulating its solubility, stability, resistance to proteolysis, interaction partners, enzymatic activity, binding and recognition. Given the high conformational and compositional diversity of the glycan chains, assessing their influence on the protein structure is challenging using conventional analytical techniques. In this manuscript, we present a step-by-step workflow to build and perform MD analysis of glycoproteins focusing on the SPIKE glycoprotein of SARS-CoV-2 to appraise the impact of glycans in structure stabilization and antibody occlusion.

Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 in Wuhan, China, and has proven to be highly pathogenic, making it a global public health threat. The immediate need to understand the mechanisms and impact of the virus made omics techniques stand out, as they can offer a holistic and comprehensive view of thousands of molecules in a single experiment. Mastering bioinformatics tools to process, analyze, integrate, and interpret omics data is a powerful knowledge to enrich results. We present a robust and open access computational pipeline for extracting information from quantitative proteomics and transcriptomics public data. We present the entire pipeline from raw data to differentially expressed genes. We explore processes and pathways related to mapped transcripts and proteins. A pipeline is presented to integrate and compare proteomics and transcriptomics data using also packages available in the Bioconductor and providing the codes used. Cholesterol metabolism, immune system activity, ECM, and proteasomal degradation pathways increased in infected patients. Leukocyte activation profile was overrepresented in both proteomics and transcriptomics data. Finally, we found a panel of proteins and transcripts regulated in the same direction in the lung transcriptome and plasma proteome that distinguish healthy and infected individuals. This panel of markers was confirmed in another cohort of patients, thus validating the robustness and functionality of the tools presented.

Global RNAseq of ocular cells reveals gene dysregulation in both asymptomatic and with Congenital Zika Syndrome infants exposed prenatally to Zika virus.

In 2015, Brazil reported an outbreak identified as Zika virus (ZIKV) infection associated with congenital abnormalities. To date, a total of 86 countries and territories have described evidence of Zika infection and recently the appearance of the African ZIKV lineage in Brazil highlights the risk of a new epidemic. The spectrum of ZIKV infection-induced alterations at both cellular and molecular levels is not completely elucidated. Here, we present for the first time the gene expression responses associated with prenatal ZIKV infection from ocular cells. We applied a recently developed non-invasive method (impression cytology) which use eye cells as a model for ZIKV studies. The ocular profiling revealed significant differences between exposed and control groups, as well as a different pattern in ocular transcripts from Congenital Zika Syndrome (CZS) compared to ZIKV-exposed but asymptomatic infants. Our data showed pathways related to mismatch repair, cancer, and PI3K/AKT/mTOR signaling and genes probably causative or protective in the modulation of ZIKV infection. Ocular cells revealed the effects of ZIKV infection on primordial neuronal cell genes, evidenced by changes in genes associated with embryonic cells. The changes in gene expression support an association with the gestational period of the infection and provide evidence for the resulting clinical and ophthalmological pathologies. Additionally, the findings of cell death- and cancer-associated deregulated genes raise concerns about the early onset of other potential pathologies including the need for tumor surveillance. Our results thus provide direct evidence that infants exposed prenatally to the Zika virus, not only with CZS but also without clinical signs (asymptomatic) express cellular and molecular changes with potential clinical implications.

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

BACKGROUND: The SARS-CoV-2 infections are still imposing a great public health challenge despite the recent developments in vaccines and therapy. Searching for diagnostic and prognostic methods that are fast, low-cost and accurate are essential for disease control and patient recovery. The MALDI-TOF mass spectrometry technique is rapid, low cost and accurate when compared to other MS methods, thus its use is already reported in the literature for various applications, including microorganism identification, diagnosis and prognosis of diseases. METHODS: Here we developed a prognostic method for COVID-19 using the proteomic profile of saliva samples submitted to MALDI-TOF and machine learning algorithms to train models for COVID-19 severity assessment. RESULTS: We achieved an accuracy of 88.5%, specificity of 85% and sensitivity of 91.5% for classification between mild/moderate and severe conditions. When we tested the model performance in an independent dataset, we achieved an accuracy, sensitivity and specificity of 67.18, 52.17 and 75.60% respectively. CONCLUSION: Saliva is already reported to have high inter-sample variation; however, our results demonstrates that this approach has the potential to be a prognostic method for COVID-19. Additionally, the technology used is already available in several clinics, facilitating the implementation of the method. Further investigation using a larger dataset is necessary to consolidate the technique.

The impact of Zika virus exposure on the placental proteomic profile.

Zika virus (ZIKV) infection has caused severe unexpected clinical outcomes in neonates and adults during the recent outbreak in Latin America, particularly in Brazil. Congenital malformations associated with ZIKV have been frequently reported; nevertheless, the mechanism of vertical transmission and the involvement of placental cells remains unclear. In this study, we applied quantitative proteomics analysis in a floating explant model of chorionic villi of human placental tissues incubated with ZIKV and with ZIKV pre-adsorbed with anti-ZIKV envelope protein. Proteomic data are available via ProteomeXchange with identifier PXD025764. Altered levels of proteins were involved in cell proliferation, apoptosis, inflammatory processes, and the integrin-cytoskeleton complex. Antibody-opsonized ZIKV particles differentially modulated the pattern of protein expression in placental cells; this phenomenon may play a pivotal role in determining the course of infection and the role of mixed infections. The expression of specific proteins was also evaluated by immunoperoxidase assays. These data fill gaps in our understanding of early events after ZIKV placental exposure and help identify infection control targets.

HDL proteome remodeling associates with COVID-19 severity.

BACKGROUND: Besides the well-accepted role in lipid metabolism, high-density lipoprotein (HDL) also seems to participate in host immune response against infectious diseases. OBJECTIVE: We used a quantitative proteomic approach to test the hypothesis that alterations in HDL proteome associate with severity of Coronavirus disease 2019 (COVID-19). METHODS: Based on clinical criteria, subjects (n=41) diagnosed with COVID-19 were divided into two groups: a group of subjects presenting mild symptoms and a second group displaying severe symptoms and requiring hospitalization. Using a proteomic approach, we quantified the levels of 29 proteins in HDL particles derived from these subjects. RESULTS: We showed that the levels of serum amyloid A 1 and 2 (SAA1 and SAA2, respectively), pulmonary surfactant-associated protein B (SFTPB), apolipoprotein F (APOF), and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) were increased by more than 50% in hospitalized patients, independently of sex, HDL-C or triglycerides when comparing with subjects presenting only mild symptoms. Altered HDL proteins were able to classify COVID-19 subjects according to the severity of the disease (error rate 4.9%). Moreover, apolipoprotein M (APOM) in HDL was inversely associated with odds of death due to COVID-19 complications (odds ratio [OR] per 1-SD increase in APOM was 0.27, with 95% confidence interval [CI] of 0.07 to 0.72, P=0.007). CONCLUSION: Our results point to a profound inflammatory remodeling of HDL proteome tracking with severity of COVID-19 infection. They also raise the possibility that HDL particles could play an important role in infectious diseases.

Extracellular Matrix Proteome Remodeling in Human Glioblastoma and Medulloblastoma.

Medulloblastomas (MBs) and glioblastomas (GBMs) are high-incidence central nervous system tumors. Different origin sites and changes in the tissue microenvironment have been associated with the onset and progression. Here, we describe differences between the extracellular matrix (ECM) signatures of these tumors. We compared the proteomic profiles of MB and GBM decellularized tumor samples between each other and their normal decellularized brain site counterparts. Our analysis revealed that 19, 28, and 11 ECM proteins were differentially expressed in MBs, GBMs, and in both MBs and GBMs, respectively. Next, we validated key findings by using a protein tissue array with 53 MB and 55 GBM cases and evaluated the clinical relevance of the identified differentially expressed proteins through their analysis on publicly available datasets, 763 MB samples from the GSE50161 and GSE85217 studies, and 115 GBM samples from RNAseq-TCGA. We report a shift toward a denser fibrillary ECM as well as a clear alteration in the glycoprotein signature, which influences the tumor pathophysiology. MS data have been submitted to the PRIDE repository, project accession: PXD023350.

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 (TmAVG 53.53 °C) and 1279 in epimastigotes (TmAVG 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.