Snapshots of the PICKLE 2.0 web interface.

<p>(A) The entity identification and the interaction querying section followed by a part of the result section; (B) An example of the page that provides the original forms in which a PPI is reported by the source databases and the associated experimental evidences.</p

Data sources and entity identifier types for the reconstruction of the genetic information ontology network.

<p>The biological entity classes (gray boxes) are respectively populated with the RHCP UniProt entries and the RHCP-associated Entrez Gene IDs, Ensembl gene IDs, RefSeq and EMBL nucleotide IDs, according to UniProt. Other types of entity identifiers (all shown in white boxes) are also collected by a multitude of sources (shown in parentheses). The sources for the links between the various biological entities are shown, indicating the path of identifier normalization to the UniProt or gene level.</p

The HPRD, MintAct, BioGRID and MINT datasets involved in the unfiltered, standard and cross-checked (default) PICKLE 2.0 PPI network.

<p>RHCP stands for the reviewed human complete proteome. The primary datasets stored in PICKLE involve solely associations supported by at least one experimental evidence set with linked publication(s), with both interactors designated as human and belonging to the PICKLE RHCP genetic information ontology network. A confidence score ‘5’ suggests interactions of proteins with gene or nucleotide sequence (mRNA) entities.</p

The Fascin actin-bundling protein (FSCN1) and the GNAS complex locus protein (GNAS) interaction network in PICKLE 2.0 at the UniProt and gene levels and all three filtering modes (unfiltered, standard, cross-checked (default)).

<p>There is a clear decrease in the number of PPIs at both levels moving from the unfiltered to the cross-checked (default) filtering mode.</p

The underlying PICKLE 2.0 structure.

<p>The genetic information ontology network (left) comprises three classes of biological entities, UniProt Entry (U), Gene (G) and nucleotide sequence (mRNA) (m), linked through the genetic information flow. Primary PPIs are stored as pairings of biological entities forming a heterogeneous integrated network (right). Each stored PPI is associated with sets of evidence attributes. It should be noted that each set of evidence attributes is associated with a single or multiple publications.</p

Plasma Metabolomic Profiling Suggests Early Indications for Predisposition to Latent Insulin Resistance in Children Conceived by ICSI

<div><p>Background</p><p>There have been increasing indications about an epigenetically-based elevated predisposition of assisted reproductive technology (ART) offspring to insulin resistance, which can lead to an unfavorable cardio-metabolic profile in adult life. However, the relevant long-term systematic molecular studies are limited, especially for the IntraCytoplasmic Sperm Injection (ICSI) method, introduced in 1992. In this study, we carefully defined a group of 42 prepubertal ICSI and 42 naturally conceived (NC) children. We assessed differences in their metabolic profile based on biochemical measurements, while, for a subgroup, plasma metabolomic analysis was also performed, investigating any relevant insulin resistance indices.</p><p>Methods & Results</p><p>Auxological and biochemical parameters of 42 6.8±2.1 yrs old ICSI-conceived and 42 age-matched controls were measured. Significant differences between the groups were determined using univariate and multivariate statistics, indicating low urea and low-grade inflammation markers (YKL-40, hsCRP) and high triiodothyronine (T3) in ICSI-children compared to controls. Moreover, plasma metabolomic analysis carried out for a subgroup of 10 ICSI- and 10 NC girls using Gas Chromatography-Mass Spectrometry (GC-MS) indicated clear differences between the two groups, characterized by 36 metabolites linked to obesity, insulin resistance and metabolic syndrome. Notably, the distinction between the two girl subgroups was accentuated when both their biochemical and metabolomic measurements were employed.</p><p>Conclusions</p><p>The present study contributes a large auxological and biochemical dataset of a well-defined group of pre-pubertal ICSI-conceived subjects to the research of the ART effect to the offspring's health. Moreover, it is the first time that the relevant usefulness of metabolomics was investigated. The acquired results are consistent with early insulin resistance in ICSI-offspring, paving the way for further systematic investigations. These data support that metabolomics may unravel metabolic differences before they become clinically or biochemically evident, underlining its utility in the ART research.</p></div

The significant metabolites between the ICSI and NC girl subgroups within the inter-organ metabolic network.

<p>Positioning the significant metabolites shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094001#pone-0094001-t003" target="_blank">Table 3</a> within the reconstructed network indicates metabolic physiology differences between the ICSI and NC girl subgroups. All known metabolites detected in the plasma GC-MS metabolic profiles are shown in the area of the figure named “blood”; among these, the metabolites which were not included in the analysis after the normalization and filtering steps are shown in gray boxes. The names of the positively and negatively significant metabolites (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094001#pone-0094001-t003" target="_blank">Table 3</a>) are shown in red and green fonts, respectively, while the nonsignificant are shown in black. The plasma metabolites are connected with dashed lines with the same metabolite pool in any of the depicted tissues. The intra-tissue pools of the plasma positively and negatively significant metabolites are shown in red and green, respectively; it is noted that we cannot predict the intra-tissue metabolite concentration from its plasma concentration, but we have tried to include the most significant tissue “sources” and “sinks” that contribute to the observed plasma concentration.</p

List of positively and negatively significant metabolites in the ICSI compared to the natural conception (NC) group.

a<p>The first number in the column corresponds to the order of significance of the positively significant metabolites; the second number corresponds to the order of significance of the negatively significant metabolites.</p><p>The table shows the list of positively and negatively significant metabolites at three significance threshold cutoffs. Positively/Negatively Significant Metabolites: Metabolites the concentration of which is significantly larger in ICSI compared to the NC group; FDR (median): False Discovery Rate (median) at each significance threshold cutoff, shown in metabolite number and percentage of the total significant metabolite number (in parenthesis).</p

Biochemical, hormonal and non-conventional cardiometabolic markers in ICSI and NC girl subgroups used in metabolomics.

<p>Differences are depicted as mean±SD or median and IQR (interquartile range), according to the normality of the data.</p><p>*normal distribution, t-test for two independent samples;</p><p>**MannWhitney U test.</p><p>Abbreviations:ALP =  Alkaline Phosphatase; Apo-A1 =  apolipoprotein-A1; Apo-B =  apolipoprotein-B; hsCRP =  high-sensitivity C-reactive protein; ICSI =  Intracytoplasmic Sperm Injection; IGF1 =  Insulin like Growth Factor 1; LDL =  low-density lipoprotein; Lp(a) =  lipoprotein(a); TG =  triglycerides; YKL-40 =  Human cartilage glycoprotein 39; hsIL-6 =  high-sensitivity interleukin-6.</p

Multivariate statistical analysis of (A) metabolomic, (B) biochemical, (C) combined datasets for the girl subgroup.

<p>Partial Least Squares – Discriminant Analysis (PLS-DA) analysis indicates a fair discrimination between the ICSI and NC girl subgroups based on their (A) metabolic profiles, (B) biochemical profiles and (C) combined profiles. In these graphs, each point corresponds, respectively, to the metabolic, biochemical or combined profile of the subject, whose number is shown next to it. The axes of the graphs correspond to functions of multiple variables (i.e. metabolites and/or biochemical markers).</p