During aging, neuronal organelles filled with neuromelanin (a dark-brown pigment) and lipid bodies accumulate in the brain, particularly in the substantia nigra, a region targeted in Parkinson’s disease. We have investigated protein and lipid systems involved in the formation of these organelles and in the synthesis of the neuromelanin of human substantia nigra. Membrane and matrix proteins characteristic of lysosomes were found in neuromelanin-containing organelles at a lower number than in typical lysosomes, indicating a reduced enzymatic activity and likely impaired capacity for lysosomal and autophagosomal fusion. The presence of proteins involved in lipid transport may explain the accumulation of lipid bodies in the organelle and the lipid component in neuromelanin structure. The major lipids observed in lipid bodies of the organelle are dolichols with lower amounts of other lipids. Proteins of aggregation and degradation pathways were present, suggesting a role for accumulation by this organelle when the ubiquitin-proteasome system is inadequate. The presence of proteins associated with aging and storage diseases may reflect impaired autophagic degradation or impaired function of lysosomal enzymes. The identification of typical autophagy proteins and double membranes demonstrates the organelle’s autophagic nature and indicates that it has engulfed neuromelanin precursors from the cytosol. Based on these data, it appears that the neuromelanin-containing organelle has a very slow turnover during the life of a neuron and represents an intracellular compartment of final destination for numerous molecules not degraded by other systems.

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Systemic light chain (AL) amyloidosis is a life-threatening disease caused by aggregation and deposition of monoclonal immunoglobulin light chains (LC) in target organs. Severity of heart involvement is the most important factor determining prognosis. Here, we report the 4.0 Å resolution cryo-electron microscopy (cryo-EM) map and structural model of amyloid fibrils extracted from the heart of an AL patient affected by severe amyloid cardiomyopathy. The fibrils are composed of one asymmetric protofilament, showing typical 4.9 Å stacking and parallel cross-β architecture. Two distinct polypeptide stretches belonging to the LC variable domain (Vl) could be modelled in the density (total of 77 residues), stressing the role of the Vl domain in fibril assembly and LC aggregation. Despite high levels of Vl sequence variability, residues stabilising the observed fibril core are conserved through several Vl domains, highlighting structural motifs that may be common to misfolded LCs. Our data shed first light on the architecture of life-threatening LC amyloid deposits, and provide a rationale for correlating LC amino acid sequences and fibril structures.

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Accumulating evidence shows that Mesenchymal Stem/Stromal Cells (MSCs) exert their therapeutic effects by the release of secretome, made of both soluble proteins and nano/microstructured extracellular vesicles (EVs). In this work, for the first time, we proved by a proteomic investigation that adipose-derived (AD)-MSC-secretome contains alpha-1-antitrypsin (AAT), the main elastase inhibitor in the lung, 72 other proteins involved in protease/antiprotease balance, and 46 proteins involved in the response to bacteria. By secretome fractionation, we proved that AAT is present both in the soluble fraction of secretome and aggregated and/or adsorbed on the surface of EVs, that can act as natural carriers promoting AAT in vivo stability and activity. To modulate secretome composition, AD-MSCs were cultured in different stimulating conditions, such as serum starvation or chemicals (IL-1β and/or dexamethasone) and the expression of the gene encoding for AAT was increased. By testing in vitro the anti-elastase activity of MSC-secretome, a dose-dependent effect was observed; chemical stimulation of AD-MSCs did not increase their secretome anti-elastase activity. Finally, MSC-secretome showed anti-bacterial activity on Gram-negative bacteria, especially for Klebsiella pneumoniae. These preliminary results, in addition to the already demonstrated immunomodulation, pave the way for the use of MSC-secretome in the treatment of AAT-deficiency lung diseases.

ObjectiveTo develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform.MethodsPlasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort.ResultsDistinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations.ConclusionsImmune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.

The lacrimal film has attracted increasing interest in the last decades as a potential source of biomarkers of physiopathological states, due to its accessibility, moderate complexity, and responsiveness to ocular and systemic diseases. High‐performance liquid chromatography‐mass spectrometry (LC‐MS) has led to effective approaches to tear proteomics, despite the intrinsic limitations in sample amounts. This review focuses on the recent progress in strategy and technology, with an emphasis on the potential for personalized medicine. After an introduction on lacrimal‐film composition, examples of applications to biomarker discovery are discussed, comparing approaches based on pooled‐sample and single‐tear analysis. Then, the most critical steps of the experimental pipeline, that is, tear collection, sample fractionation, and LC‐MS implementation, are discussed with reference to proteome‐coverage optimization. Advantages and challenges of the alternative procedures are highlighted. Despite the still limited number of studies, tear quantitative proteomics, including single‐tear investigation, could offer unique contributions to the identification of low‐invasiveness, sustained‐accessibility biomarkers, and to the development of personalized approaches to therapy and diagnosis.

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