Nutrition for the ageing brain: towards evidence for an optimal diet

As people age they become increasingly susceptible to chronic and extremely debilitating brain diseases. The precise cause of the neuronal degeneration underlying these disorders, and indeed normal brain ageing remains however elusive. Considering the limits of existing preventive methods, there is a desire to develop effective and safe strategies. Growing preclinical and clinical research in healthy individuals or at the early stage of cognitive decline has demonstrated the beneficial impact of nutrition on cognitive functions. The present review is the most recent in a series produced by the Nutrition and Mental Performance Task Force under the auspice of the International Life Sciences Institute Europe (ILSI Europe). The latest scientific advances specific to how dietary nutrients and non-nutrient may affect cognitive ageing are presented. Furthermore, several key points related to mechanisms contributing to brain ageing, pathological conditions affecting brain function, and brain biomarkers are also discussed. Overall, findings are inconsistent and fragmented and more research is warranted to determine the underlying mechanisms and to establish dose-response relationships for optimal brain maintenance in different population subgroups. Such approaches are likely to provide the necessary evidence to develop research portfolios that will inform about new dietary recommendations on how to prevent cognitive decline

Schematic graphs of over-represented Gene Ontology biological process terms in enriched SFG and PC reference modules.

<p><i>Seed genes</i> were obtained from the union of differentially expressed genes with most significant SNPs and primary drug targets. GO terms are represented as nodes, and the strongest GO term pairwise similarities are designated as edges in the graph. GO terms are grouped to illustrate the main metabolic signature in PC, while both metabolic and synaptic transmission functions characterize SFG.</p

Summary of statistically significant Gene Ontology biological processes functional annotation corresponding to genes in enriched reference modules.

<p>Data refers to reference modules obtained using gene expression only (expression), by integrating this information with SNPs (expression+SNPs), and by merging the mRNA expression data, SNPs and drug targets (expression+SNPs+drug targets), and by combining the mRNA expression data, SNPs, drug targets, and OMIM genes (expression +SNPs+drug targets+ OMIM). In light blue are GO terms associated to synaptic transmission and neuronal signaling, in dark green are metabolism-associated GO terms, in gray remaining relevant terms. Highlighted are the results which have been discussed in detail in the discussion section. Specific GO terms are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078919#pone.0078919.s004" target="_blank">Table S2</a>.</p

Gene lists associated to main classes of Gene Ontology biological process terms.

<p>Comparative gene lists associated to main classes of Gene Ontology biological process terms derived by integrating gene expression, SNPs and drug targets data in SFG and PC. In few cases (Fatty acid and TOR signaling) the gene list are perfectly matching, while in Insulin, Autophagy and Circadian Rhythm, they differed considerably. <i>Seed genes</i> are in bold.</p><p>PRKAA1-2, PRKAB1-2 and PRKAG1-3 are AMPK subunits, while ACACA and ACACB are ACC.</p

Simplified schematic graph of AMPK interactions with.

<p>(1) genes included in the enriched reference modules (purple), (2) differentially expressed genes (pink), (3) drug targets (light blue), and (4) SNPs (orange). Ellipses show the biological process terms associated to the genes (color as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078919#pone-0078919-g002" target="_blank">Figure 2</a>) and altered in AD; rhomboid shapes stand for histological markers of AD.</p

Schematic representation of the network analysis workflow.

<p>Significant gene expression signatures associated to AD were extracted from the GSE5281 dataset, while lists of SNPs, drug targets, and OMIM Alzheimer’s genes were obtained from public databases. These <i>seed genes</i> (in yellow and orange) inform about transcriptomic and genetic properties of AD, also providing details on drug targets in the different phases of the drug discovery process and AD associated genes in the OMIM database. Following module structure detection in the protein-protein interaction (PPI) network derived from HPRD data (see groups of nodes in the same white-background circles), we investigated the presence of reference modules where <i>seed genes</i> (obtained through the three simple lists and their union) were over-represented. We characterized the functionality of these enriched modules by testing over-represented Gene Ontology biological process terms, both considering <i>seed genes</i> (in yellow and orange) and non-<i>seed genes</i> (in light blue) that closely interact with them.</p

Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy

<div><p>Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome.</p></div

Long biomarker panel.

<p>Long biomarker panel.</p

Signature-based classification of affected vs. control subjects for each individual.

<p>(A) Heatmap of the six proteins included in the biomarker panel (<i>columns</i>) across the 70 subjects (<i>rows</i>). Control subjects: top 28 rows; affected subjects: bottom 52 rows. (B) Signatures composed of at least two proteins (<i>red boxes</i>) out of six are needed to accurately classify each subject as being a member of either the control or the affected group. (C) The heatmap of the distance matrix shows that signatures of length two are actually sufficient to correctly divide subjects into two groups. (D) A map of the subjects based on the distance matrix confirms that the two emerging groups are of homogeneous composition and points to a possible subgroup of affected individuals (<i>green</i>: control subjects, <i>red</i>: affected subjects; colors were added after the map was drawn).</p

Pathway enrichment map showing the overlap among the pathways identified by NASFinder.

<p>The nodes correspond to the pathways and the thickness of the edges connecting them is proportional to the number of shared genes (indicated on the edges).</p