Editorial to the special issue “lipidomics and neurodegenerative diseases”

The contribution of dysregulation of lipid signaling and metabolism to neurodegenerative diseases including Alzheimer’s and Parkinson’s is the focus of this special issue. Here, the matter of three reviews and one research article is summarized

Synthesis and matrix properties of α-cyano-5-phenyl-2,4-pentadienic acid (CPPA) for intact proteins analysis by matrix-assisted laser desorption/ionization mass spectrometry

The effectiveness of a synthesized matrix, α-cyano-5-phenyl-2,4-pentadienic acid (CPPA), for protein analysis by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) in complex samples such as foodstuff and bacterial extracts, is demonstrated. Ultraviolet (UV) absorption along with laser desorption/ionization mass spectrometry (LDI-MS) experiments were systematically conducted in positive ion mode under standard Nd:YLF laser excitation with the aim of characterizing the matrix in terms of wavelength absorption and proton affinity. Besides, the results for standard proteins revealed that CPPA significantly enhanced the protein signals, reduced the spot-to-spot variability and increased the spot homogeneity. The CPPA matrix was successful employed to investigate intact microorganisms, milk and seed extracts for protein profiling. Compared to conventional matrices such as sinapinic acid (SA), α-cyano-4-hydroxycinnamic acid (CHCA) and 4-chloro-α-cyanocinnamic acid (CClCA), CPPA exhibited better signal-to-noise (S/N) ratios and a uniform response for most examined proteins occurring in milk, hazelnut and in intact bacterial cells of E. coli. These findings not only provide a reactive proton transfer MALDI matrix with excellent reproducibility and sensitivity, but also contribute to extending the battery of useful matrices for intact protein analysis

Lipidomics of the edible brown alga wakame (Undaria pinnatifida) by liquid chromatography coupled to electrospray ionization and tandem mass spectrometry

The lipidome of a brown seaweed commonly known as wakame (Undaria pinnatifida), which is grown and consumed around the world, including Western countries, as a healthy nutraceutical food or supplement, was here extensively examined. The study was focused on the characterization of phospholipids (PL) and glycolipids (GL) by liquid chromatography (LC), either hydrophilic interaction LC (HILIC) or reversed-phase LC (RPLC), coupled to electrospray ionization (ESI) and mass spectrometry (MS), operated both in high and in low-resolution mode. Through the acquisition of single (MS) and tandem (MS/MS) mass spectra more than 200 PL and GL of U. pinnatifida extracts were characterized in terms of lipid class, fatty acyl (FA) chain composition (length and number of unsaturations), and regiochemistry, namely 16 SQDG, 6 SQMG, 12 DGDG, 5 DGMG, 29 PG, 8 LPG, 19 PI, 14 PA, 19 PE, 8 PE, 38 PC, and 27 LPC. The FA (C16:0) was the most abundant saturated acyl chain, whereas the monounsaturated C18:1 and the polyunsaturated C18:2 and C20:4 chains were the prevailing ones. Odd-numbered acyl chains, i.e., C15:0, C17:0, C19:0, and C19:1, were also recognized. While SQDG exhibited the longest and most unsaturated acyl chains, C18:1, C18:2, and C18:3, in the sn-1 position of glycerol, they were preferentially located in the sn-2 position in the case of PL. The developed analytical approach might pave the way to extend lipidomic investigations also for other edible marine algae, thus emphasizing their potential role as a source of bioactive lipids

Bioactive Secoiridoids in Italian Extra-Virgin Olive Oils: Impact of Olive Plant Cultivars, Cultivation Regions and Processing

In the last two decades, phenolic compounds occurring in olive oils known as secoiridoids have attracted a great interest for their bioactivity. Four major olive oil secoiridoids, i.e., oleuropein and ligstroside aglycones, oleacin and oleocanthal, were previously characterized in our laboratory using reversed-phase liquid chromatography with electrospray ionization-Fourier transform-mass spectrometry (RPLC-ESI-FTMS). The same analytical approach, followed by multivariate statistical analysis (i.e., Principal Component Analysis), was applied here to a set of 60 Italian extra-virgin olive oils (EVOO). The aim was to assess the secoiridoid contents as a function of olive cultivars, place of cultivation (i.e., different Italian regions) and olive oil processing, in particular two- vs. three-phase horizontal centrifugation. As expected, higher secoiridoid contents were generally found in olive oils produced by two-phase horizontal centrifugation. Moreover, some region/cultivar-related trends were evidenced, as oleuropein and ligstroside aglycones prevailed in olive oils produced in Apulia (Southern Italy), whereas the contents of oleacin and oleocanthal were relatively higher in EVOO produced in Central Italy (Tuscany, Lazio and Umbria). A lower content of all the four secoiridoids was generally found in EVOO produced in Sicily (Southern Italy) due to the intrinsic low abundance of these bioactive compounds in cultivars typical of that region

Multi-technique characterization of pictorial organic binders on XV century polychrome sculptures by combining microand non-invasive sampling approaches

A stony sculptural composition of the Nativity Scene is preserved in Altamura’s Cathedral (Apulia, Italy). This commonly called Apulian “presepe”, attributed to an unknown stonemason, is composed of polychrome carbonate white stone sculptures. While earlier stratigraphic tests have unveiled a complex superimposition of painting layers—meaning that several editions of the sculptures succeeded from the 16th to 20th century—a chemical investigation intended to identify the organic binding media used in painting layers was undertaken. Drawing on current literature, two strategies were exploited: a non-invasive in situ digestion analysis and an approach based on microremoval of painting film followed by the Bligh and Dyer extraction protocol. Both peptide and lipid mixtures were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry (MALDIMS) and reversed-phase liquid chromatography coupled to mass spectrometry by electrospray ionization (RPLC-ESI-MS). Attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) examinations were also performed on micro-samples of painting films before lipids and proteins extraction. While human keratins were found to be common contaminants of the artwork’s surfaces, traces of animal collagen, siccative oils, and egg white proteins were evidenced in different sampling zones of the sculptures, thus suggesting the use of non-homogeneous painting techniques in the colored layers

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data

The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma: A Systematic Review

BACKGROUND: Numerous studies were performed to illuminate mechanisms of tumorigenesis and metastases from gene expression profiles of Head and Neck Squamous Cell Carcinoma (HNSCC). The objective of this review is to conduct a network-based meta-analysis to identify the underlying biological signatures of the HNSCC transcriptome. METHODS AND FINDINGS: We included 63 HNSCC transcriptomic studies into three specific categories of comparisons: Pre, premalignant lesions v.s. normal; TvN, primary tumors v.s. normal; and Meta, metastatic or invasive v.s. primary tumors. Reported genes extracted from the literature were systematically analyzed. Participation of differential gene activities across three progressive stages deciphered the evolving nature of HNSCC. In total, 1442 genes were verified, i.e. reported at least twice, with ECM1, EMP1, CXCL10 and POSTN shown to be highly reported across all three stages. Knowledge-based networks of the HNSCC transcriptome were constructed, demonstrating integrin signaling and antigen presentation pathways as highly enriched. Notably, functional estimates derived from topological characteristics of integrin signaling networks identified such important genes as ITGA3 and ITGA5, which were supported by findings of invasiveness in vitro. Moreover, we computed genome-wide probabilities of reporting differential gene activities for the Pre, TvN, and Meta stages, respectively. Results highlighted chromosomal regions of 6p21, 19p13 and 19q13, where genomic alterations were shown to be correlated with the nodal status of HNSCC. CONCLUSIONS: By means of a systems-biology approach via network-based meta-analyses, we provided a deeper insight into the evolving nature of the HNSCC transcriptome. Enriched canonical signaling pathways, hot-spots of transcriptional profiles across the genome, as well as topologically significant genes derived from network analyses were highlighted for each of the three progressive stages, Pre, TvN, and Meta, respectively

In Silico Simulation of Corticosteroids Effect on an NFkB- Dependent Physicochemical Model of Systemic Inflammation

During the onset of an inflammatory response signaling pathways are activated for "translating" extracellular signals into intracellular responses converging to the activation of nuclear factor (NF)-kB, a central transcription factor in driving the inflammatory response. An inadequate control of its transcriptional activity is associated with the culmination of a hyper-inflammatory response making it a desired therapeutic target. Predicated upon the nature of the response, a systems level analysis might provide rational leads for the development of strategies that promote the resolution of the response.A physicochemical host response model is proposed to integrate biological information in the form of kinetic rules and signaling cascades with pharmacokinetic models of drug action for the modulation of the response. The unifying hypothesis is that the response is triggered by the activation of the NFkB signaling module and corticosteroids serve as a template for assessing anti-inflammatory strategies. The proposed in silico model is evaluated through its ability to predict and modulate uncontrolled responses. The pre-exposure of the system to hypercortisolemia, i.e. 6 hr before or simultaneously with the infectious challenge "reprograms" the dynamics of the host towards a balanced inflammatory response. However, if such an intervention occurs long before the inflammatory insult a symptomatic effect is observed instead of a protective relief while a steroid infusion after inducing inflammation requires much higher drug doses.We propose a reversed engineered inflammation model that seeks to describe how the system responds to a multitude of external signals. Timing of intervention and dosage regimes appears to be key determinants for the protective or symptomatic effect of exogenous corticosteroids. Such results lie in qualitative agreement with in vivo human studies exposed both to LPS and corticosteroids under various time intervals thus improving our understanding of how interacting modules generate a behavior