BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks.

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Proteomic Profiling of Samples Derived from a Murine Model Following Cryptococcus neoformans Infection.

Proteomic profiling provides in-depth information about the regulation of diverse biological processes, activation of and communication across signaling networks, and alterations to protein production, modifications, and interactions. For infectious disease research, mass spectrometry-based proteomics enables detection of host defenses against infection and mechanisms used by the pathogen to evade such responses. In this chapter, we outline protein extraction from organs, tissues, and fluids collected following intranasal inoculation of a murine model with the human fungal pathogen Cryptococcus neoformans. We describe sample preparation, followed by purification, processing on the mass spectrometer, and a robust bioinformatics analysis. The information gleaned from proteomic profiling of fungal infections supports the detection of novel biomarkers for diagnostic and prognostic purposes.

Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer's disease in a large clinical sample.

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disorder with contributions from multiple pathophysiological pathways. One of the long-recognized and important features of AD is disrupted cerebral glucose metabolism, but the underlying molecular basis remains unclear. In this study, unbiased mass spectrometry was used to survey CSF from a large clinical cohort, comparing patients who are either cognitively unimpaired (CU; n = 68), suffering from mild-cognitive impairment or dementia from AD (MCI-AD, n = 95; DEM-AD, n = 72), or other causes (MCI-other, n = 77; DEM-other, n = 23), or Normal Pressure Hydrocephalus (NPH, n = 57). The results revealed changes related to altered glucose metabolism. In particular, two glycolytic enzymes, pyruvate kinase (PKM) and aldolase A (ALDOA), were found to be upregulated in CSF from patients with AD compared to those with other neurological conditions. Increases in full-length PKM and ALDOA levels in CSF were confirmed with immunoblotting. Levels of these enzymes furthermore correlated negatively with CSF glucose in matching CSF samples. PKM levels were also found to be increased in AD in publicly available brain-tissue data. These results indicate that ALDOA and PKM may act as technically-robust potential biomarkers of glucose metabolism dysregulation in AD.

Enhancing Classification of liquid chromatography mass spectrometry data with Batch Effect Removal Neural Networks (BERNN).

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions and data acquisition techniques, significantlyimpacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of proteomics research, but current methods are not optimal for removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. Comparison of batch effect correction methods across three diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures.

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

Integrative analysis of green ash phloem transcripts and proteins during an emerald ash borer infestation.

BACKGROUND: Emerald ash borer (Agrilus planipennis; EAB) is an Asian insect species that has been invasive to North America for 20 years. During this time, the emerald ash borer has killed tens of millions of American ash (Fraxinus spp) trees. Understanding the inherent defenses of susceptible American ash trees will provide information to breed new resistant varieties of ash trees. RESULTS: We have performed RNA-seq on naturally infested green ash (F. pennsylvanica) trees at low, medium and high levels of increasing EAB infestation and proteomics on low and high levels of EAB infestation. Most significant transcript changes we detected occurred between the comparison of medium and high levels of EAB infestation, indicating that the tree is not responding to EAB until it is highly infested. Our integrative analysis of the RNA-Seq and proteomics data identified 14 proteins and 4 transcripts that contribute most to the difference between highly infested and low infested trees. CONCLUSIONS: The putative functions of these transcripts and proteins suggests roles of phenylpropanoid biosynthesis and oxidation, chitinase activity, pectinesterase activity, strigolactone signaling, and protein turnover.

DIA Proteomics and Machine Learning for the Fast Identification of Bacterial Species in Biological Samples.

Identification of bacterial species in biological samples is essential in many applications. However, the standard methods usually use a time-consuming bacterial culture (24-48 h) and sometimes lack in specificity. To overcome these limitations, we developed a new protocol, combining LC-MS/MS analysis in Data Independent Acquisition mode and machine learning algorithms, enabling the accurate identification of the bacterial species contaminating a sample in a few hours without bacterial culture. In this chapter, we describe the three steps of the protocol (spectral libraries generation, training step, identification step) to generate customized peptide signatures and use them for bacterial identification in biological samples through targeted proteomics analyses and prediction models.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers.

BACKGROUND: Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers. METHODS: Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography-tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis. RESULTS: In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064). CONCLUSION: Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

DIA proteomics data from a UPS1-spiked E.coli protein mixture processed with six software tools.

In this article, we provide a proteomic reference dataset that has been initially generated for a benchmarking of software tools for Data-Independent Acquisition (DIA) analysis. This large dataset includes 96 DIA .raw files acquired from a complex proteomic standard composed of an E.coli protein background spiked-in with 8 different concentrations of 48 human proteins (UPS1 Sigma). These 8 samples were analyzed in triplicates on an Orbitrap mass spectrometer with 4 different DIA window schemes. We also provide the spectral libraries and FASTA file used for their analysis and the software outputs of the six tools used in this study: DIA-NN, Spectronaut, ScaffoldDIA, DIA-Umpire, Skyline and OpenSWATH. This dataset also contains post-processed quantification tables where the peptides and proteins have been validated, their intensities normalized and the missing values imputed with a noise value. All the files are available on ProteomeXchange. Altogether, these files represent the most comprehensive DIA reference dataset acquired on an Orbitrap instrument ever published. It will be a very useful resource to the proteomic scientists in order to assess the performance of DIA software tools or to test their processing pipelines, to the software developers to improve their tools or develop new ones and to the students for their training on proteomics data analysis.

T cell apoptosis characterizes severe Covid-19 disease.

Severe SARS-CoV-2 infections are characterized by lymphopenia, but the mechanisms involved are still elusive. Based on our knowledge of HIV pathophysiology, we hypothesized that SARS-CoV-2 infection-mediated lymphopenia could also be related to T cell apoptosis. By comparing intensive care unit (ICU) and non-ICU COVID-19 patients with age-matched healthy donors, we found a strong positive correlation between plasma levels of soluble FasL (sFasL) and T cell surface expression of Fas/CD95 with the propensity of T cells to die and CD4 T cell counts. Plasma levels of sFasL and T cell death are correlated with CXCL10 which is part of the signature of 4 biomarkers of disease severity (ROC, 0.98). We also found that members of the Bcl-2 family had modulated in the T cells of COVID-19 patients. More importantly, we demonstrated that the pan-caspase inhibitor, Q-VD, prevents T cell death by apoptosis and enhances Th1 transcripts. Altogether, our results are compatible with a model in which T-cell apoptosis accounts for T lymphopenia in individuals with severe COVID-19. Therefore, a strategy aimed at blocking caspase activation could be beneficial for preventing immunodeficiency in COVID-19 patients.

Proteomic Analysis of Maternal Urine for the Early Detection of Preeclampsia and Fetal Growth Restriction.

BACKGROUND: To explore the use of maternal urine proteome for the identification of preeclampsia biomarkers. METHODS: Maternal urine samples from women with and without preeclampsia were used for protein discovery followed by a validation study. The targeted proteins of interest were then measured in urine samples collected at 20-24 and 30-34 weeks among nine women who developed preeclampsia, one woman with fetal growth restriction, and 20 women with uncomplicated pregnancies from a longitudinal study. Protein identification and quantification was obtained using liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS: Among the 1108 urine proteins quantified in the discovery study, 21 were upregulated in preeclampsia and selected for validation. Nineteen (90%) proteins were confirmed as upregulated in preeclampsia cases. Among them, two proteins, ceruloplasmin and serpin A7, were upregulated at 20-24 weeks and 30-34 weeks of gestation (p < 0.05) in cases of preeclampsia, and could have served to identify 60% of women who subsequently developed preeclampsia and/or fetal growth restriction at 20-24 weeks of gestation, and 78% at 30-34 weeks, for a false-positive rate of 10%. CONCLUSIONS: Proteomic profiling of maternal urine can differentiate women with and without preeclampsia. Several proteins including ceruloplasmin and serpin A7 are upregulated in maternal urine before the diagnosis of preeclampsia and potentially fetal growth restriction.

Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard.

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1-Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline, and Spectronaut) with or without the use of a DDA spectral library. On the basis of the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

Implication of EZH2 in the Pro-Proliferative and Apoptosis-Resistant Phenotype of Pulmonary Artery Smooth Muscle Cells in PAH: A Transcriptomic and Proteomic Approach.

Pulmonary arterial hypertension (PAH) is a progressive disorder characterized by a sustained elevation of pulmonary artery (PA) pressure, right ventricular failure, and premature death. Enhanced proliferation and resistance to apoptosis (as seen in cancer cells) of PA smooth muscle cells (PASMCs) is a major pathological hallmark contributing to pulmonary vascular remodeling in PAH, for which current therapies have only limited effects. Emerging evidence points toward a critical role for Enhancer of Zeste Homolog 2 (EZH2) in cancer cell proliferation and survival. However, its role in PAH remains largely unknown. The aim of this study was to determine whether EZH2 represents a new factor critically involved in the abnormal phenotype of PAH-PASMCs. We found that EZH2 is overexpressed in human lung tissues and isolated PASMCs from PAH patients compared to controls as well as in two animal models mimicking the disease. Through loss- and gain-of-function approaches, we showed that EZH2 promotes PAH-PASMC proliferation and survival. By combining quantitative transcriptomic and proteomic approaches in PAH-PASMCs subjected or not to EZH2 knockdown, we found that inhibition of EZH2 downregulates many factors involved in cell-cycle progression, including E2F targets, and contributes to maintain energy production. Notably, we found that EZH2 promotes expression of several nuclear-encoded components of the mitochondrial translation machinery and tricarboxylic acid cycle genes. Overall, this study provides evidence that, by overexpressing EZH2, PAH-PASMCs remove the physiological breaks that normally restrain their proliferation and susceptibility to apoptosis and suggests that EZH2 or downstream factors may serve as therapeutic targets to combat pulmonary vascular remodeling.

Oxidized DNA Precursors Cleanup by NUDT1 Contributes to Vascular Remodeling in Pulmonary Arterial Hypertension.

Rationale: Pulmonary arterial hypertension (PAH) is a life-threatening condition characterized by abnormally elevated pulmonary pressures and right ventricular failure. Excessive proliferation and resistance to apoptosis of pulmonary artery smooth muscle cells (PASMCs) is one of the most important drivers of vascular remodeling in PAH, for which available treatments have limited effectiveness.Objectives: To gain insights into the mechanisms leading to the development of the disease and identify new actionable targets.Methods: Protein expression profiling was conducted by two-dimensional liquid chromatography coupled to tandem mass spectrometry in isolated PASMCs from controls and patients with PAH. Multiple molecular, biochemical, and pharmacologic approaches were used to decipher the role of NUDT1 (nudrix hyrolase 1) in PAH.Measurements and Main Results: Increased expression of the detoxifying DNA enzyme NUDT1 was detected in cells and tissues from patients with PAH and animal models. In vitro, molecular or pharmacological inhibition of NUDT1 in PAH-PASMCs induced accumulation of oxidized nucleotides in the DNA, irresolvable DNA damage (comet assay), disruption of cellular bioenergetics (Seahorse), and cell death (terminal deoxynucleotidyl transferase dUTP nick end labeling assay). In two animal models with established PAH (i.e., monocrotaline and Sugen/hypoxia-treated rats), pharmacological inhibition of NUDT1 using (S)-Crizotinib significantly decreased pulmonary vascular remodeling and improved hemodynamics and cardiac function.Conclusions: Our results indicate that, by overexpressing NUDT1, PAH-PASMCs hijack persistent oxidative stress in preventing incorporation of oxidized nucleotides into DNA, thus allowing the cell to escape apoptosis and proliferate. Given that NUDT1 inhibitors are under clinical investigation for cancer, they may represent a new therapeutic option for PAH.

Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

Mass Spectrometry-based Absolute Quantification of 20S Proteasome Status for Controlled Ex-vivo Expansion of Human Adipose-derived Mesenchymal Stromal/Stem Cells.

The proteasome controls a multitude of cellular processes through protein degradation and has been identified as a therapeutic target in oncology. However, our understanding of its function and the development of specific modulators are hampered by the lack of a straightforward method to determine the overall proteasome status in biological samples. Here, we present a method to determine the absolute quantity and stoichiometry of ubiquitous and tissue-specific human 20S proteasome subtypes based on a robust, absolute SILAC-based multiplexed LC-Selected Reaction Monitoring (SRM) quantitative mass spectrometry assay with high precision, accuracy, and sensitivity. The method was initially optimized and validated by comparison with a reference ELISA assay and by analyzing the dynamics of catalytic subunits in HeLa cells following IFNγ-treatment and in range of human tissues. It was then successfully applied to reveal IFNγ- and O2-dependent variations of proteasome status during primary culture of Adipose-derived-mesenchymal Stromal/Stem Cells (ADSCs). The results show the critical importance of controlling the culture conditions during cell expansion for future therapeutic use in humans. We hypothesize that a shift from the standard proteasome to the immunoproteasome could serve as a predictor of immunosuppressive and differentiation capacities of ADSCs and, consequently, that quality control should include proteasomal quantification in addition to examining other essential cell parameters. The method presented also provides a new powerful tool to conduct more individualized protocols in cancer or inflammatory diseases where selective inhibition of the immunoproteasome has been shown to reduce side effects.

Portrait of blood-derived extracellular vesicles in patients with Parkinson's disease.

The production of extracellular vesicles (EV) is a ubiquitous feature of eukaryotic cells but pathological events can affect their formation and constituents. We sought to characterize the nature, profile and protein signature of EV in the plasma of Parkinson's disease (PD) patients and how they correlate to clinical measures of the disease. EV were initially collected from cohorts of PD (n = 60; Controls, n = 37) and Huntington's disease (HD) patients (Pre-manifest, n = 11; manifest, n = 52; Controls, n = 55) - for comparative purposes in individuals with another chronic neurodegenerative condition - and exhaustively analyzed using flow cytometry, electron microscopy and proteomics. We then collected 42 samples from an additional independent cohort of PD patients to confirm our initial results. Through a series of iterative steps, we optimized an approach for defining the EV signature in PD. We found that the number of EV derived specifically from erythrocytes segregated with UPDRS scores corresponding to different disease stages. Proteomic analysis further revealed that there is a specific signature of proteins that could reliably differentiate control subjects from mild and moderate PD patients. Taken together, we have developed/identified an EV blood-based assay that has the potential to be used as a biomarker for PD.

Vitamin C alters the amount of specific endoplasmic reticulum associated proteins involved in lipid metabolism in the liver of mice synthesizing a nonfunctional Werner syndrome (Wrn) mutant protein.

Werner syndrome (WS) is a premature aging disorder caused by mutations in a protein containing both a DNA exonuclease and DNA helicase domain. Mice lacking the helicase domain of the Wrn protein orthologue exhibit transcriptomic and metabolic alterations, some of which are reversed by vitamin C. Recent studies on these animals indicated that the mutant protein is associated with enriched endoplasmic reticulum (ER) fractions of tissues resulting in an ER stress response. In this study, we identified proteins that exhibit actual level differences in the ER enriched fraction between the liver of wild type and Wrn mutant mice using quantitative proteomic profiling with label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). Multiple Reaction Monitoring (MRM) and immunoblotting were performed to validate findings in a secondary independent cohort of wild type and Wrn mutant mice. DAVID 6.7 (NIH) was used for functional annotation analysis and indicated that the identified proteins exhibiting level changes between untreated wild type, Wrn mutant, and vitamin C treated Wrn mutant mice (ANOVA P-value < 0.05) were involved in fatty acid and steroid metabolism pathways (Bonferroni P-value = 0.0137). Finally, when we compared the transcriptomic and the proteomic data of our mouse cohorts only ~7% of the altered mRNA profiles encoding for ER gene products were consistent with their corresponding protein profiles measured by the label-free quantification methods. These results suggest that a great number of ER gene products are regulated at the post-transcriptional level in the liver of Wrn mutant mice exhibiting an ER stress response.

Ph(-) myeloproliferative neoplasm red blood cells display deregulation of IQGAP1-Rho GTPase signaling depending on CALR/JAK2 status.

Besides genetic abnormalities in MPN patients, several studies have reported alterations in protein expression that could contribute towards the clinical phenotype. However, little is known about protein modifications in Ph- MPN erythrocytes. In this context, we used a quantitative mass spectrometry proteomics approach to study the MPN erythrocyte proteome. LC-MS/MS (LTQ Orbitrap) analysis led to the identification of 51 and 86 overexpressed proteins in Polycythemia Vera and Essential Thrombocythemia respectively, compared with controls. Functional comparison using pathway analysis software showed that the Rho GTPase family signaling pathways were deregulated in MPN patients. In particular, IQGAP1 was significantly overexpressed in MPNs compared with controls. Additionally, Western-blot analysis not only confirmed IQGAP1 overexpression, but also showed that IQGAP1 levels depended on the patient's genotype. Moreover, we found that in JAK2V617F patients IQGAP1 could bind RhoA, Rac1 and Cdc42 and consequently recruit activated GTP-Rac1 and the cytoskeleton motility protein PAK1. In CALR(+) patients, IQGAP1 was not overexpressed but immunoprecipitated with RhoGDI. In JAK2V617F transduced Ba/F3 cells we confirmed JAK2 inhibitor-sensitive overexpression of IQGAP1/PAK1. Altogether, our data demonstrated alterations of IQGAP1/Rho GTPase signaling in MPN erythrocytes dependent on JAK2/CALR status, reinforcing the hypothesis that modifications in erythrocyte signaling pathways participate in Ph- MPN pathogenesis.