Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis.

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.

Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.

BACKGROUND: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies. METHODS: In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy. RESULTS: Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69). CONCLUSIONS: To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Forensic Proteomics for the Discovery of New post mortem Interval Biomarkers: A Preliminary Study.

Estimating the time since death (post mortem interval, PMI) represents one of the most important tasks in daily forensic casework. For decades, forensic scientists have investigated changes in post mortem body composition, focusing on different physical, chemical, or biological aspects, to discover a reliable method for estimating PMI; nevertheless, all of these attempts remain unsuccessful considering the currently available methodical spectrum characterized by great inaccuracies and limitations. However, recent promising approaches focus on the post mortem decomposition of biomolecules. In particular, significant advances have been made in research on the post mortem degradation of proteins. In the present study, we investigated early post mortem changes (during the first 24 h) in the proteome profile of the pig skeletal muscle looking for new PMI specific biomarkers. By mass spectrometry (MS)-based proteomics, we were able to identify a total of nine potential PMI biomarkers, whose quantity changed constantly and progressively over time, directly or inversely proportional to the advancement of post mortem hours. Our preliminary study underlines the importance of the proteomic approach in the search for a reliable method for PMI determination and highlights the need to characterize a large number of reliable marker proteins useful in forensic practice for PMI estimation.

2H-chromene and 7H-furo-chromene derivatives selectively inhibit tumour associated human carbonic anhydrase IX and XII isoforms.

Tumour associated carbonic anhydrases (CAs) IX and XII have been recognised as potential targets for the treatment of hypoxic tumours. Therefore, considering the high pharmacological potential of the chromene scaffold as selective ligand of the IX and XII isoforms, two libraries of compounds, namely 2H-chromene and 7H-furo-chromene derivatives, with diverse substitution patterns were designed and synthesised. The structure of the newly synthesised compounds was characterised and their inhibitory potency and selectivity towards human CA off target isoforms I, II and cancer-associated CA isoforms IX and XII were evaluated. Most of the compounds inhibit CA isoforms IX and XII with no activity against the I and II isozymes. Thus, while the potency was influenced by the substitution pattern along the chromene scaffold, the selectivity was conserved along the series, confirming the high potential of both 2H-chromene and 7H-furo-chromene scaffolds for the design of isozyme selective inhibitors.

Evaluation of Sex Differences in Preclinical Pharmacology Research: How Far Is Left to Go?

Until the last quarter of the 20th century, sex was not recognized as a variable in health research, nor was it believed to be a factor that could affect health and illness. Researchers preferred studying male models for a variety of reasons, such as simplicity, lower costs, hormone confounding effects, and fear of liability from perinatal exposure in case of pregnancy. Equitable representation is imperative for determining the safety, effectiveness, and tolerance of therapeutic agents for all consumers. Decades of female models' underrepresentation in preclinical studies has resulted in inequality in the understanding, diagnosis, and treatment of disease between the sexes. Sex bias has been highlighted as one of the contributing factors to the poor translation and replicability of preclinical research. There have been multiple calls for action, and the inclusion of sex as a biological variable is increasingly supported. However, although there has been substantial progress in the efforts to include more female models in preclinical studies, disparities today remain. In the present review, we consider the current standard practice of the preclinical research setting, why the sex bias exists, why there is the need to include female models, and what risks may arise from continuing this exclusion from experimental design.

Co-sensitization between legumes is frequently seen, but variable and not always clinically relevant.

Background: Food allergy to peanut and soybean, both legumes, is highly prevalent. The consumption of other legumes and legume protein isolates, some of which may be considered novel foods, is increasing. This may lead to an increase in sensitization and allergy and may pose a risk for legume-allergic (e.g. peanut and soybean) patients due to cross-reactivity. Objective: This study investigated the frequency of co-sensitization and co-allergy between legumes and the role of different protein families. Methods: Six legume-allergic patient groups were included: peanut (n = 30), soybean (n = 30), lupine (n = 30), green pea (n = 30), lentil (n = 17), bean (n = 9). IgE binding to total extracts, protein fractions (7S/11S globulin, 2S albumin, albumin), and 16 individual proteins from 10 legumes (black lentil, blue lupine, chickpea, faba bean, green lentil, pea, peanut, soybean, white bean, and white lupine) was measured by line blot. Results: Co-sensitization varied from 36.7% to 100%. Mono-sensitization was only found in soybean (16.7%), peanut (10%), and green pea-allergic (3.3%) patients. A high frequency of co-sensitization between the 7S/11S globulin fractions of all 10 legumes and individual 7S and 11S globulins was observed. In peanut and soybean-allergic patients, co-allergies for other legumes were uncommon (≤16,7%), while in green pea, lupine, lentil, and bean-allergic patients co-allergy for peanut (64.7%-77.8%) or soybean (50%-64.7%) was frequently seen. Conclusion: Co-sensitization between legumes was high, but generally not clinically relevant. Co-allergy to other legumes was not often seen in peanut- and soybean allergic patients. The 7S and 11S globulins were likely responsible for the observed co-sensitization.

ALDOC- and ENO2- driven glucose metabolism sustains 3D tumor spheroids growth regardless of nutrient environmental conditions: a multi-omics analysis.

BACKGROUND: Metastases are the major cause of cancer-related morbidity and mortality. By the time cancer cells detach from their primary site to eventually spread to distant sites, they need to acquire the ability to survive in non-adherent conditions and to proliferate within a new microenvironment in spite of stressing conditions that may severely constrain the metastatic process. In this study, we gained insight into the molecular mechanisms allowing cancer cells to survive and proliferate in an anchorage-independent manner, regardless of both tumor-intrinsic variables and nutrient culture conditions. METHODS: 3D spheroids derived from lung adenocarcinoma (LUAD) and breast cancer cells were cultured in either nutrient-rich or -restricted culture conditions. A multi-omics approach, including transcriptomics, proteomics, and metabolomics, was used to explore the molecular changes underlying the transition from 2 to 3D cultures. Small interfering RNA-mediated loss of function assays were used to validate the role of the identified differentially expressed genes and proteins in H460 and HCC827 LUAD as well as in MCF7 and T47D breast cancer cell lines. RESULTS: We found that the transition from 2 to 3D cultures of H460 and MCF7 cells is associated with significant changes in the expression of genes and proteins involved in metabolic reprogramming. In particular, we observed that 3D tumor spheroid growth implies the overexpression of ALDOC and ENO2 glycolytic enzymes concomitant with the enhanced consumption of glucose and fructose and the enhanced production of lactate. Transfection with siRNA against both ALDOC and ENO2 determined a significant reduction in lactate production, viability and size of 3D tumor spheroids produced by H460, HCC827, MCF7, and T47D cell lines. CONCLUSIONS: Our results show that anchorage-independent survival and growth of cancer cells are supported by changes in genes and proteins that drive glucose metabolism towards an enhanced lactate production. Notably, this finding is valid for all lung and breast cancer cell lines we have analyzed in different nutrient environmental conditions. broader Validation of this mechanism in other cancer cells of different origin will be necessary to broaden the role of ALDOC and ENO2 to other tumor types. Future in vivo studies will be necessary to assess the role of ALDOC and ENO2 in cancer metastasis.

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer.

Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942.

Comparative proteomic analysis of insulin receptor isoform A and B signaling.

The insulin receptor (IR) gene undergoes differential splicing generating two IR isoforms, IR-A and IR-B. The roles of IR-A in cancer and of IR-B in metabolic regulation are well known but the molecular mechanisms responsible for their different biological effects are poorly understood. We aimed to identify different or similar protein substrates and signaling linked to each IR isoforms. We employed mouse fibroblasts lacking IGF1R gene and expressing exclusively either IR-A or IR-B. By proteomic analysis a total of 2530 proteins were identified and quantified. Proteins and pathways mostly associated with insulin-activated IR-A were involved in cancer, stemness and interferon signaling. Instead, proteins and pathways associated with insulin-stimulated IR-B-expressing cells were mostly involved in metabolic or tumor suppressive functions. These results show that IR-A and IR-B recruit partially different multiprotein complexes in response to insulin, suggesting partially different functions of IR isoforms in physiology and in disease.

Migratory and anti-fibrotic programmes define the regenerative potential of human cardiac progenitors.

Heart regeneration is an unmet clinical need, hampered by limited renewal of adult cardiomyocytes and fibrotic scarring. Pluripotent stem cell-based strategies are emerging, but unravelling cellular dynamics of host-graft crosstalk remains elusive. Here, by combining lineage tracing and single-cell transcriptomics in injured non-human primate heart biomimics, we uncover the coordinated action modes of human progenitor-mediated muscle repair. Chemoattraction via CXCL12/CXCR4 directs cellular migration to injury sites. Activated fibroblast repulsion targets fibrosis by SLIT2/ROBO1 guidance in organizing cytoskeletal dynamics. Ultimately, differentiation and electromechanical integration lead to functional restoration of damaged heart muscle. In vivo transplantation into acutely and chronically injured porcine hearts illustrated CXCR4-dependent homing, de novo formation of heart muscle, scar-volume reduction and prevention of heart failure progression. Concurrent endothelial differentiation contributed to graft neovascularization. Our study demonstrates that inherent developmental programmes within cardiac progenitors are sequentially activated in disease, enabling the cells to sense and counteract acute and chronic injury.

The mitochondrial chaperone TRAP1 regulates F-ATP synthase channel formation.

Binding of the mitochondrial chaperone TRAP1 to client proteins shapes bioenergetic and proteostatic adaptations of cells, but the panel of TRAP1 clients is only partially defined. Here we show that TRAP1 interacts with F-ATP synthase, the protein complex that provides most cellular ATP. TRAP1 competes with the peptidyl-prolyl cis-trans isomerase cyclophilin D (CyPD) for binding to the oligomycin sensitivity-conferring protein (OSCP) subunit of F-ATP synthase, increasing its catalytic activity and counteracting the inhibitory effect of CyPD. Electrophysiological measurements indicate that TRAP1 directly inhibits a channel activity of purified F-ATP synthase endowed with the features of the permeability transition pore (PTP) and that it reverses PTP induction by CyPD, antagonizing PTP-dependent mitochondrial depolarization and cell death. Conversely, CyPD outcompetes the TRAP1 inhibitory effect on the channel. Our data identify TRAP1 as an F-ATP synthase regulator that can influence cell bioenergetics and survival and can be targeted in pathological conditions where these processes are dysregulated, such as cancer.

Intra-epithelial non-canonical Activin A signaling safeguards prostate progenitor quiescence.

The healthy prostate is a relatively quiescent tissue. Yet, prostate epithelium overgrowth is a common condition during aging, associated with urinary dysfunction and tumorigenesis. For over thirty years, TGF-ß ligands have been known to induce cytostasis in a variety of epithelia, but the intracellular pathway mediating this signal in the prostate, and its relevance for quiescence, have remained elusive. Here, using mouse prostate organoids to model epithelial progenitors, we find that intra-epithelial non-canonical Activin A signaling inhibits cell proliferation in a Smad-independent manner. Mechanistically, Activin A triggers Tak1 and p38 ΜAPK activity, leading to p16 and p21 nuclear import. Spontaneous evasion from this quiescent state occurs upon prolonged culture, due to reduced Activin A secretion, a condition associated with DNA replication stress and aneuploidy. Organoids capable to escape quiescence in vitro are also able to implant with increased frequency into immunocompetent mice. This study demonstrates that non-canonical Activin A signaling safeguards epithelial quiescence in the healthy prostate, with potential implications for the understanding of cancer initiation, and the development of therapies targeting quiescent tumor progenitors.

ETS-related gene (ERG) undermines genome stability in mouse prostate progenitors via Gsk3ß dependent Nkx3.1 degradation.

21q22.2-3 deletion is the most common copy number alteration in prostate cancer (PCa). The genomic rearrangement results in the androgen-dependent de novo expression of ETS-related gene (ERG) in prostate cancer cells, a condition promoting tumor progression to advanced stages of the disease. Interestingly, ERG expression characterizes 5-30% of tumor precursor lesions - High Grade Prostatic Intraepithelial Neoplasia (HGPIN) - where its role remains unclear. Here, by combining organoids technology with Click-chemistry coupled Mass Spectrometry, we demonstrate a prominent role of ERG in remodeling the protein secretome of prostate progenitors. Functionally, by lowering autocrine Wnt-4 signaling, ERG represses canonical Wnt pathway in prostate progenitors, and, in turn, promotes the accumulation of DNA double strand breaks via Gsk3ß-dependent degradation of the tumor suppressor Nkx3.1. On the other hand, by shaping extracellular paracrine signals, ERG strengthens the pro-oxidative transcriptional signature of inflammatory macrophages, which we demonstrate to infiltrate pre-malignant ERG positive prostate lesions. These findings highlight previously unrecognized functions of ERG in undermining adult prostate progenitor niche through cell autonomous and non-autonomous mechanisms. Overall, by supporting the survival and proliferation of prostate progenitors in the absence of growth stimuli and promoting the accumulation of DNA damage through destabilization of Nkx3.1, ERG could orchestrate the prelude to neoplastic transformation.

Mass Spectrometry-Based Glycoproteomics and Prostate Cancer.

Aberrant glycosylation has long been known to be associated with cancer, since it is involved in key mechanisms such as tumour onset, development and progression. This review will focus on protein glycosylation studies in cells, tissue, urine and serum in the context of prostate cancer. A dedicated section will cover the glycoforms of prostate specific antigen, the molecule that, despite some important limitations, is routinely tested for helping prostate cancer diagnosis. Our aim is to provide readers with an overview of mass spectrometry-based glycoproteomics of prostate cancer. From this perspective, the first part of this review will illustrate the main strategies for glycopeptide enrichment and mass spectrometric analysis. The molecular information obtained by glycoproteomic analysis performed by mass spectrometry has led to new insights into the mechanism linking aberrant glycosylation to cancer cell proliferation, migration and immunoescape.

Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis.

Filter-aided sample protocol (FASP) is widely used for proteomics sample preparation because it allows to concentrate diluted samples and it is compatible with a wide variety of detergents. Bottom-up proteomics workflows like FASP increasingly rely on LC-MS/MS methods performed in data-independent analysis (DIA) mode, a scanning method that allows deep proteome coverage and low incidence of missing values. In this report, we will provide the details of a workflow that combines a FASP protocol, a double StageTip purification step and LC-MS/MS in DIA mode for urinary proteome mapping. As a model sample, we analyzed expressed prostatic secretions (EPS)-urine, a sample collected after a digital rectal exam (DRE), which is of interest in prostate cancer biomarker discovery studies.

LC-MALDI-TOF ISD MS analysis is an effective, simple and rapid method of investigation for histones characterization: Application to EBV lymphoblastoid cell lines.

This contribution is the result of our progressive engagement to develop and to apply a top-down liquid chromatography (LC) matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) (LC-MALDI-TOF) analysis for the histone post-translational modifications (PTMs) and variants characterization, mainly in order to provide comprehensive and fast results. The histone post-translational modifications and the differential expression of the histone variants play an essential role both in the DNA packaging mechanism in chromosomes and in the regulation of gene expression in different cellular processes, also in response to molecular agents of environmental origin. This epigenetic mechanism is widely studied in different field such as cellular differentiation, development and in the understanding of mechanisms underlying diseases. The characterization of histone PTMs has traditionally performed by antibodies-based assay, but immunological methods have significant limits, and today systems that use mass spectrometry are increasingly employed. We evaluated an in-source decay (ISD) analysis for the histone investigation on human lymphoblastoid cells, and by this approach, we were able to identify and quantify several PTMs such as the di-methylation in the lysine 20 and the acetylation in the lysine 16 in H4 and the mono-methylation, di-methylation and trimethylations at K9 of the histone H3.1. Moreover, we detected and quantified in the same H2B spectrum the prevalent H2B 1C/2E type but also the minor H2B 1D, 1M and 1B/1L/1N, 1O/2F, 1J/1K variants. In this work, we show that MALDI-ISD represents an excellent methodology to obtain global information on histone PTMs and variants from cells in culture, with rapidity and simplicity of execution. Finally, this is a useful approach to get label-free relative quantitative data of histone variants and PTMs.

Cytoplasmic cleavage of IMPA1 3' UTR is necessary for maintaining axon integrity.

The 3' untranslated regions (3' UTRs) of messenger RNAs (mRNAs) are non-coding sequences involved in many aspects of mRNA metabolism, including intracellular localization and translation. Incorrect processing and delivery of mRNA cause severe developmental defects and have been implicated in many neurological disorders. Here, we use deep sequencing to show that in sympathetic neuron axons, the 3' UTRs of many transcripts undergo cleavage, generating isoforms that express the coding sequence with a short 3' UTR and stable 3' UTR-derived fragments of unknown function. Cleavage of the long 3' UTR of Inositol Monophosphatase 1 (IMPA1) mediated by a protein complex containing the endonuclease argonaute 2 (Ago2) generates a translatable isoform that is necessary for maintaining the integrity of sympathetic neuron axons. Thus, our study provides a mechanism of mRNA metabolism that simultaneously regulates local protein synthesis and generates an additional class of 3' UTR-derived RNAs.