Biallelic PI4KA Mutations Disrupt B-Cell Metabolism and Cause B-Cell Lymphopenia and Hypogammaglobulinemia.

PURPOSE: PI4KA-related disorder is a highly clinically variable condition characterized by neurological (limb spasticity, developmental delay, intellectual disability, seizures, ataxia, nystagmus) and gastrointestinal (inflammatory bowel disease and multiple intestinal atresia) manifestations. Although features consistent with immunodeficiency (autoimmunity/autoinflammation and recurrent infections) have been reported in a subset of patients, the burden of B-cell deficiency and hypogammaglobulinemia has not been extensively investigated. We sought to describe the clinical presentation and manifestations of patients with PI4KA-related disorder and to investigate the metabolic consequences of biallelic PI4KA variants in B cells. METHODS: Clinical data from patients with PI4KA variants were obtained. Multi-omics analyses combining transcriptome, proteome, lipidome and metabolome analyses in conjunction with functional assays were performed in EBV-transformed B cells. RESULTS: Clinical and laboratory data of 13 patients were collected. Recurrent infections (7/13), autoimmune/autoinflammatory manifestations (5/13), B-cell deficiency (8/13) and hypogammaglobulinemia (8/13) were frequently observed. Patients' B cells frequently showed increased transitional and decreased switched memory B-cell subsets. Pathway analyses based on differentially expressed transcripts and proteins confirmed the central role of PI4KA in B cell differentiation with altered B-cell receptor (BCR) complex and signalling. By altering lipids production and tricarboxylic acid cycle regulation, and causing increased endoplasmic reticulum stress, biallelic PI4KA mutations disrupt B cell metabolism inducing mitochondrial dysfunction. As a result, B cells show hyperactive PI3K/mTOR pathway, increased autophagy and deranged cytoskeleton organization. CONCLUSION: By altering lipid metabolism and TCA cycle, impairing mitochondrial activity, hyperactivating mTOR pathway and increasing autophagy, PI4KA-related disorder causes a syndromic inborn error of immunity presenting with B-cell deficiency and hypogammaglobulinemia.

Therapy-Induced Senescence: Novel Approaches for Markers Identification.

Therapy-induced senescence (TIS) represents a major cellular response to anticancer treatments. Both malignant and non-malignant cells in the tumor microenvironment undergo TIS and may be harmful for cancer patients since TIS cells develop a senescence-associated secretory phenotype (SASP) that can sustain tumor growth. The SASP also modulates anti-tumor immunity, although the immune populations involved and the final results appear to be context-dependent. In addition, senescent cancer cells are able to evade senescence growth arrest and to resume proliferation, likely contributing to relapse. So, research data suggest that TIS induction negatively affects therapy outcomes in cancer patients. In line with this, new interventions aimed at the removal of senescent cells or the reprogramming of their SASP, called senotherapy, have become attractive therapeutic options. To date, the lack of reliable, cost-effective, and easy-to-use TIS biomarkers hinders the application of recent anti-senescence therapeutic approaches in the clinic. Hence, the identification of biomarkers for the detection of TIS tumor cells and TIS non-neoplastic cells is a high priority in cancer research. In this review article, we describe the current knowledge about TIS, outline critical gaps in our knowledge, and address recent advances and novel approaches for the discovery of TIS biomarkers.

Feasibility of MALDI-MSI-Based Proteomics Using Bouin-Fixed Pathology Samples: Untapping the Goldmine of Nephropathology Archives.

The application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts. A large proportion of the features (270, 68.9%) was detected in both BFPE and FFPE renal samples, demonstrating only limited variability in signal intensity (10.22-10.06%). Samples processed with either fixative were able to discriminate the principal parenchyma regions along with diverse renal substructures, such as glomeruli, tubules, and vessels. This was observed when performing an additional "stress test", showing comparable results in both BFPE and FFPE samples when the distribution of several amyloid fingerprint proteins was mapped. These results suggest the utility of BFPE tissue specimens in MSI-based nephropathology research, further widening their application in this field.

The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations.

Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology.

INTRODUCTION: Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions. AREAS COVERED: This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions. EXPERT COMMENTARY: Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Cytological Cytospin Preparation for the Spatial Proteomics Analysis of Thyroid Nodules Using MALDI-MSI.

The application of innovative spatial omics approaches in the context of cytological specimens may open new frontiers for their diagnostic assessment. In particular, spatial proteomics using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) represents one of the most promising avenues, owing to its capability to map the distribution of hundreds of proteins within a complex cytological background in a multiplexed and relatively high-throughput manner. This approach may be particularly beneficial in the heterogeneous context of thyroid tumors where certain cells may not present clear-cut malignant morphology upon fine-needle aspiration biopsy, highlighting the necessity for additional molecular tools which are able to improve their diagnostic performance.This chapter aims to provide a detailed overview of a cytospin-based preparation workflow that has been optimized to facilitate the reliable spatial proteomics analysis of cytological thyroid specimens using MALDI-MSI, indicating the key aspects which should be considered when handling such samples.

Towards the Definition of the Molecular Hallmarks of Idiopathic Membranous Nephropathy in Serum Proteome: A DIA-PASEF Approach.

Idiopathic membranous nephropathy (IMN) is a pathologically defined disorder of the glomerulus, primarily responsible for nephrotic syndromes (NS) in nondiabetic adults. The underlying molecular mechanisms are still not completely clarified. To explore possible molecular and functional signatures, an optimised mass spectrometry (MS) method based on next-generation data-independent acquisition combined with ion-mobility was applied to serum of patients affected by IMN (n = 15) or by other glomerulopathies (PN) (n = 15). The statistical comparison highlighted a panel of 57 de-regulated proteins with a significant increase in lipoprotein-related proteins (APOC1, APOB, APOA1, APOL1 and LCAT) and a substantial quantitative alteration of key serpins (including A4, D1, A7, A6, F2, F1 and 1) possibly associated with IMN or NS and podocyte stress. A critical dysregulation in metabolisms of lipids (e.g., VLDL assembly and clearance) likely to be related to known hyperlipidemia in IMN, along with involvement of non-classical complement pathways and a putative enrolment of ficolin-2 in sustaining the activation of the lectin-mediated complement system have been pinpointed. Moreover, mannose receptor CD206 (MRC1-down in IMN) and biotinidase (BTD-up in IMN) are able alone to accurately distinguish IMN vs. PN. To conclude, our work provides key proteomic insights into the IMN complexity, opening the way to an efficient stratification of MN patients.

Proteomic Fingerprint of Lung Fibrosis Progression and Response to Therapy in Bleomycin-Induced Mouse Model.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by the aberrant accumulation of extracellular matrix in the lungs. nintedanib is one of the two FDA-approved drugs for IPF treatment; however, the exact pathophysiological mechanisms of fibrosis progression and response to therapy are still poorly understood. In this work, the molecular fingerprint of fibrosis progression and response to nintedanib treatment have been investigated by mass spectrometry-based bottom-up proteomics in paraffin-embedded lung tissues from bleomycin-induced (BLM) pulmonary fibrosis mice. Our proteomics results unveiled that (i) samples clustered depending on the tissue fibrotic grade (mild, moderate, and severe) and not on the time course after BLM treatment; (ii) the dysregulation of different pathways involved in fibrosis progression such as the complement coagulation cascades, advanced glycation end products (AGEs) and their receptors (RAGEs) signaling, the extracellular matrix-receptor interaction, the regulation of actin cytoskeleton, and ribosomes; (iii) Coronin 1A (Coro1a) as the protein with the highest correlation when evaluating the progression of fibrosis, with an increased expression from mild to severe fibrosis; and (iv) a total of 10 differentially expressed proteins (padj-value ≤ 0.05 and Fold change ≤-1.5 or ≥1.5), whose abundance varied in the base of the severity of fibrosis (mild and moderate), were modulated by the antifibrotic treatment with nintedanib, reverting their trend. Notably, nintedanib significantly restored lactate dehydrogenase B (Ldhb) expression but not lactate dehydrogenase A (Ldha). Notwithstanding the need for further investigations to validate the roles of both Coro1a and Ldhb, our findings provide an extensive proteomic characterization with a strong relationship with histomorphometric measurements. These results unveil some biological processes in pulmonary fibrosis and drug-mediated fibrosis therapy.

Spatial resolution of renal amyloid deposits through MALDI-MSI: a combined digital and molecular approach to monoclonal gammopathies.

AIMS: Identification and characterisation of monoclonal gammopathies of renal significance (MGRS) is critical for therapeutic purposes. Amyloidosis represents one of the most common forms of MGRS, and renal biopsy remains the gold standard for their classification, although mass spectrometry has shown greater sensitivity in this area. METHODS: In the present study, a new in situ proteomic technique, matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), is investigated as an alternative to conventional laser capture microdissection MS for the characterisation of amyloids. MALDI-MSI was performed on 16 cases (3 lambda light chain amyloidosis (AL), 3 AL kappa, 3 serum amyloid A amyloidosis (SAA), 2 lambda light chain deposition disease (LCDD), 2 challenging amyloid cases and 3 controls). Analysis began with regions of interest labelled by the pathologist, and then automatic segmentation was performed. RESULTS: MALDI-MSI correctly identified and typed cases with known amyloid type (AL kappa, AL lambda and SAA). A 'restricted fingerprint' for amyloid detection composed of apolipoprotein E, serum amyloid protein and apolipoprotein A1 showed the best automatic segmentation performance (area under the curve >0.7). CONCLUSIONS: MALDI-MSI correctly assigned minimal/challenging cases of amyloidosis to the correct type (AL lambda) and identified lambda light chains in LCDD cases, highlighting the promising role of MALDI-MSI for amyloid typing.