Proteomics for Cerebrospinal Fluid Biomarker Identification in Parkinsons Disease: Methods and Critical Aspects

Parkinson's disease (PD), similar with other neurodegenerative disorders, would benefit from the identification of early biomarkers for differential diagnosis and prognosis to address prompt clinical treatments. Together with hypothesis driven approaches, PD has been investigated by high-throughput differential proteomic analysis of cerebrospinal fluid (CSF) protein content. The principal methodologies and techniques utilized in the proteomics field for PD biomarker discovery from CSF are presented in this mini review. The positive aspects and challenges in proteome-based biomarker research are also discussed

Gender, Contraceptives and Individual Metabolic Predisposition Shape a Healthy Plasma Lipidome

Lipidomics of human blood plasma is an emerging biomarker discovery approach that compares lipid profiles under pathological and physiologically normal conditions, but how a healthy lipidome varies within the population is poorly understood. By quantifying 281 molecular species from 27 major lipid classes in the plasma of 71 healthy young Caucasians whose 35 clinical blood test and anthropometric indices matched the medical norm, we provided a comprehensive, expandable and clinically relevant resource of reference molar concentrations of individual lipids. We established that gender is a major lipidomic factor, whose impact is strongly enhanced by hormonal contraceptives and mediated by sex hormone-binding globulin. In lipidomics epidemiological studies should avoid mixed-gender cohorts and females taking hormonal contraceptives should be considered as a separate sub-cohort. Within a gender-restricted cohort lipidomics revealed a compositional signature that indicates the predisposition towards an early development of metabolic syndrome in ca. 25% of healthy male individuals suggesting a healthy plasma lipidome as resource for early biomarker discovery

Sparse Similarity and Network Navigability for Markov Clustering Enhancement

Markov clustering (MCL) is an effective unsupervised pattern recognition algorithm for data clustering in high-dimensional feature space that simulates stochastic flows on a network of sample similarities to detect the structural organization of clusters in the data. However, it presents two main drawbacks: (1) its community detection performance in complex networks has been demonstrating results far from the state-of-the-art methods such as Infomap and Louvain, and (2) it has never been generalized to deal with data nonlinearity. In this work both aspects, although closely related, are taken as separated issues and addressed as such. Regarding the community detection, field under the network science ceiling, the crucial issue is to convert the unweighted network topology into a ‘smart enough’ pre-weighted connectivity that adequately steers the stochastic flow procedure behind Markov clustering. Here a conceptual innovation is introduced and discussed focusing on how to leverage network latent geometry notions in order to design similarity measures for pre-weighting the adjacency matrix used in Markov clustering community detection. The results demonstrate that the proposed strategy improves Markov clustering significantly, to the extent that it is often close to the performance of current state-of-the-art methods for community detection. These findings emerge considering both synthetic ‘realistic’ networks (with known ground-truth communities) and real networks (with community metadata), even when the real network connectivity is corrupted by noise artificially induced by missing or spurious links. Regarding the nonlinearity aspect, the development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Minimum Curvilinearity (MC) is a principle that approximates nonlinear sample distances in the high-dimensional feature space by curvilinear distances, which are computed as transversal paths over their minimum spanning tree, and then stored in a kernel. Here, a nonlinear MCL algorithm termed MC-MCL is proposed, which is the first nonlinear kernel extension of MCL and exploits Minimum Curvilinearity to enhance the performance of MCL in real and synthetic high-dimensional data with underlying nonlinear patterns. Furthermore, improvements in the design of the so-called MC-kernel by applying base modifications to better approximate the data hidden geometry have been evaluated with positive outcomes. Thus, different nonlinear MCL versions are compared with baseline and state-of-art clustering methods, including DBSCAN, K-means, affinity propagation, density peaks, and deep-clustering. As result, the design of a suitable nonlinear kernel provides a valuable framework to estimate nonlinear distances when its kernel is applied in combination with MCL. Indeed, nonlinear-MCL variants overcome classical MCL and even state-of-art clustering algorithms in different nonlinear datasets. This dissertation discusses the enhancements and the generalized understanding of how network geometry plays a fundamental role in designing algorithms based on network navigability

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities