extfieldsofstudy: Medicine Chemistry

s2fieldsofstudy: Biology Chemistry

text:
Molecular docking and pharmacokinetic evaluation of natural compounds as targeted inhibitors against Crz1 protein in Rhizoctonia solani

Crz1p regulates Calcineurin, a serine-threonine-specific protein phosphatase, in Rhizoctonia solani. It has attracted consideration as a novel target of antifungal therapy based on studies in numerous pathogenic fungi, including, Cryptococcus neoformans, Candida albicans and Aspergillus fumigatus. To investigate whether Calcineurin can be a useful target for the treatment of Crz1 protein in R. solani causing wet root rot in Chickpea. The work presented here reports the in-silico studies of Crz1 protein against natural compounds. This study Comprises of quantitative structure-toxicity relationship (QSTR) and quantitative structure-activity relationship (QSAR). All compounds showed high binding energy for Crz1 protein through molecular docking. Further, a pharmacokinetic study revealed that these compounds had minimal side effects. Biological activity spectrum prediction of these compounds showed potential antifungal properties by showing significant interaction with Crz1. Hence, these compounds can be used for the prevention and treatment of wet root rot in Chickpea.

©Biomedical Informatics (2019) 278 R. solani is a universal soil borne necrotroph that exacts harm on an extensive variety of economically important crops like pepper, tomato, eggplant and chickpea [11][12][13] . The fungus can damage organic substance in the soil as a saprobe and is a vital evolutionary connection between beneficial and plant disease-causing fungi [12,14]. Root rot, seedling blight, and hypocotyl rots are characteristic symptoms detected when vulnerable genotypes are implanted on high-risk fields and in conditions encouraging for disease development [15]. Unfortunately, our understanding of the mechanisms promoting infection and other aspects of hostpathogen interaction is limited, impeding the progress of resistant genotypes. The transcription factor Crz1 is a downstream effector of calcineurin and is involved in azole tolerance in C. albicans [6]; however, a Crz1 homolog in R. solani has yet to be characterized. Therefore, it is of interest to study natural compounds as targeted inhibitors against Crz1 protein in R. solani causing wet root rot in Chickpea.

Methods: Three dimensional structure prediction byI-TASSER:
Schematic diagram of the molecular docking studies is illustrated in Figure1. The sequence of 382 amino acids of Crz1 protein in R.solani was retrieved from the Swiss Prot database in FASTA format ( Figure 3A). The three-dimensional model was generated using the I-TASSER server which makes a 3D model of query sequence by multiple threading alignments and iterative structural assembly simulation [16,17]. The reasons behind the selection of this server were its availability, composite approach of modeling and performance in CASP competition. I-TASSER methodology includes general steps of threading, structural assembly, model selection, refinement, and structure-based functional annotation [18]. Then, the query sequence was threaded through the representative PDB structure library using LOMETS [19]. Then, the query sequence was threaded through the representative PDB structure library using LOMETS [20]. The quality of template alignment was checked by Z-score and the best one used for further consideration. In the next step, the continuous fragments in threading arrangements were excised to form an assembled structure model of aligned regions. The modeling accuracy of unaligned areas is generally low while threading aligned regions have high efficiency, so these template fragments keep rigid in the simulation process to the obtained high-resolution structure.The replica exchange Monte Carlo simulation technique was used for fragment assembly [21]. The simulation includes Cα/side chain correlation, H-bonds, hydrophobicity, spatial restraints from threading templates [20], and sequence-based contact predictions from SVMSEQ [22]. The conformations generated during refinement simulation process were clustered by SPICKER [23], and the average of three-dimensional coordinates of all the assembled structure was calculated to obtained cluster centroids. In the refinement process, the selected cluster centroids were again used to perform further fragment assembly simulation which helps to remove steric clashes and to refine the global topology of the cluster centroids. The PDB structures, structurally closed to the cluster centroids, were identified by TM-align [24]. The final structural models were generated by REMO [25]in which cluster centroids of second-round simulation used as input. In the last step, the function of three-dimensional models of query protein was predicted by matching the proteins of known structure and function in PDB. The functional analogs were ranked by TM-score, RMSD, sequence identity, and coverage of the structure alignment. The quality of the predicted model was determined by C-score (confidence score) which is ranged as −5 to 2. It depends on the quality of threading alignment and the convergence of structural assembly refinement simulations.

Validation of predicted model:
The Ramachandaran plot further validated the confirmation of the best model of Crz1 predicted by I-TASSER. The evidence of the expected model was calculated by analyzing the phi (Φ) and psi (Ψ) torsion angles using PROCHECK online server. The Ramachandaran plot obtained from PROCHECK describes a good quality model which has over 90% residues in the most favored region. The assessment of the model was also confirmed by MolProbity online server [26,27].

Prediction of active site of model:
The active sites of Crz1 target proteins were identified by using computed atlas of surface topography of proteins (CASTp) server (http://cast.engr.uic.edu). It defines all the possible pockets in the protein structure. It measures the area and volume of each pocket and cavity analytically, both in solvent accessible surface and molecular surface [28]. Here, we input the Crz1 target protein for predicting the ligand binding sites, and the CASTp server predicts the amino acids crucial for binding interactions for docking studies.

Preparation of Ligands and Protein Molecule:
The 3D structure of Crz1 was predicted by I-TASSER. Then, the hydrogen atoms having polar nature were added, the residue structures having lower occupancy were deleted, and the incomplete side chains were later replaced by using Auto Dock Tools (ADT) version 1.5.6 from the Scripps Research Institute. Further, to each atom having Gasteiger charges were added and the non-polar hydrogen atoms were merged to the protein structure. After that, the structures constructed were saved in PDBQT file format, for further analysis in ADT [29]. The next crucial step was the preparation of Crz1 known inhibitors, and 532 natural compounds as a Molfile achieved using the PubChem Database. Known inhibitors of Crz1 include Fludioxonil [15]. The Mol file of the ligand was then generated and converted into PDBQT file by a process like detect root, chose the torsion and set the number of torsion by using ADT [29].

Receptor Grid Formation:
Grid pre-calculates grid maps of binding energies for numerous atom types, such as hydrogen bonding oxygen, aromatic carbons, and aliphatic carbons with a macromolecule such as an RNA/ DNA, protein before docking [30]. These grid maps are then used by AutoDock 4.2 docking calculations to define the total binding energy for a ligand with a macromolecule [29]. Grid mapping calculates the crucial coordinates over the atomic protein data and assigns the coordinates of the Crz1 for docking. Also, grid mapping affords a proper surface topology for the atoms of compounds for interaction with the Crz1 active site. Grid mapping is a requirement to direct Crz1 inhibitor and 532 natural compounds to look for their region of the strong affinity of the Crz1 active site. The grid dimensions for Crz1 protein was 60× 62 × 60 grid points with spacing 0. 463 Åbetween the grid points but centered on the ligand for protein (72.94, 86.112 and 72.671 coordinates). The grid was created for the search of promising interaction and best support during the docking which represents an orientation, position, confirmation about the receptors [31].

Molecular docking:
AutoDock 4.2 with standard protocol was used to dock the Crz1 inhibitor and 532 natural compounds into the active site of Crz1 [29, 32]. The energy calculations were done using the Lamarckian genetic algorithm (LGA). The conformations with the most favorable free binding energy were selected for analyzing the interactions between the target receptor and ligands by PyMOL [29].

In silico pharmacokinetics analysis: ADMET Predictions:
Discovery studio 3.5 (Accelrys San Diego, USA) was used to generate ADMET values. The Absorption, Distribution, Metabolism, Excretion, and Toxicity commonly abbreviated as ADMET properties are considered before designing a drug as these properties play a vital role in clinical phases. Administration of these properties before drug designing leads to cost savings in drug design [33]. These studies resulted in the identification of lead natural compounds. TOPKAT administers the after-effects of drug intake. It assesses the toxicological endpoints by Quantitative Structure Toxicity Relationship (QSTR). Ames mutagen predication, Ames probability, Ames enrichment and weight of evidence are tested by toxicity profile of the compounds.

Measuring drug-likeness:
The drug-like properties of compounds were analyzed by the online tool Molinspiration server (http://www.molins piration.com). Molinspiration support for calculation of critical molecular properties is based on Lipinski Rules of five such as (molecular weight, number of hydrogen bond donors and acceptors) [34].This server also predicts bioactivity score for the most important therapeutic targets like GPCR receptors, kinase inhibitors, ion channel modulators, enzymes and nuclear receptors [35].

Biological activity spectrum (BAS):
BAS of a best-docked compound represents the complex of pharmacological effects, and the intrinsic properties of the compound depending on its structural property. The pharmacological effects, exhibited by a compound and its communication with biological entities were predicted by PASS online by uploading the SMILES string of natural compounds [36].

Results and Discussion:
Three dimensional structure prediction: The predicted model of R. solani Crz1 protein and its threedimensional coordinate files in PDB format were successfully obtained from I-TASSER. The results obtained from a server includes predicted secondary structure with a confidence score (range 0 to 9), predicted solvent accessibility, five predicted structures with C-score, top ten templates from PDB used in alignment, high ten PDB structural analogs, functional analogs protein, and binding site residues. Crz1 Model (Figure 1 I) was selected as the best-predicted model with C-score 2.25, TM-score 0.45 ± 0.14, and RMSD 12.1 ± 4.4 Å [18]. Top ten threading templates for query protein sequence were identified by LOMETS meta-server (Supplementary Table 1 -available with authors). Normalized Z-score generally estimates the threading alignment. However, a normalized Z-score >1 value reflect a particular arrangement, but in case of small alignment of the large query sequence, it does not give the significant indication of modeling accuracy. The percentage sequence identity in the threading aligned region (Iden1) and the whole chain (Iden2) considered for the first homology (Supplementary Table1). The structural alignment program, TM-align, identified 5v3jE in PDB library as the best structural analog of the top scoring model of I-TASSER with the TM-score of 0.696 (Supplementary Table 2 -available with authors).

Assessment of predicted model:
The Ramachandaran plots of the best-predicted model were obtained from PROCHECK and MolProbity servers which showed the reliability of the model. The PROCHECK Ramachandaran plot showed 89.9% residues in most favored regions and 6.6% residues in additional allowed regions, i.e., the total of 96.5% residues in allowed regions which indicates a good quality model of Crz1 (Figure 2 A). MolProbity Ramachandaran plot also showed 98.0% residues in allowed regions which again confirmed the quality of the predicted model of Crz1 (Figure 2 B).

Active site identification:
The protuberant binding site of proteins Crz1 was calculated through the CASTp server with ideal parameters (Figure 3). CASTp evaluation observed the active site amino acids, surface area (3064.847) and volume (10195.168) of Crz1. In ICL1 protein, all 86 binding pockets were categorized to find the residues about probe 1.4Å radius. The light blue color denotes the active site amino acid residues involved in the formation of binding pockets (Figure 3A &  B).

Pharmacokinetics and toxicity:
During clinical trials, most of the drugs flop due to frail pharmacokinetic properties beside with cellular toxicity. Thus, in a silicon profile of pharmacokinetic selected compounds were evaluated for putative bioavailability for Crz1 inhibitors ( Table 2). Lipophilicity (clogP), physicochemical properties, polar surface area, molecular weight (MW) and primarily aqueous solubility (logS) are linearly connected to the bioavailability and absorption of drug molecule [37,38]. All these properties are directly affecting the drug movement from its site of regulation to the blood. The CYPs (cytochrome P450) play a significant role in the metabolism of the drug and are evenly notable for nature of drugs in the body, their toxicological and pharmacological effects [39]. Here, ADMET (DS3.5) was used to get the predicted profile of pharmacokinetic molecules.  The CYP2D6 probability of all natural compounds showed the value of <0.5 that all compounds were non-inhibitor to CYP2D6 enzyme. For real drug ability, the ideal plasma protein binding (PPB) level is 0. All natural compounds except 5280863, 44256718 and 5280343showed good activity of PPB and came in greater the standard 0. PSA depends upon the confirmation and hydrogen bonding. It shows the single low-energy conformer of the molecule. For the activity of a drug, the optimum value of PSA is ≤ 140 Å [46]. The logP and hydrogen bonding value are the two critical descriptors to define the PSA of a drug molecule. All predicted compounds showed significant PSA except 5280863, 44256718 and 5280343.
With the help of the computer-aided toxicity predictor, TOPKAT the cellular toxicity of natural compounds. The carcinogenic and mutagenic effect of compounds with WOE Prediction (weight of evidence) and Ames Prediction was our primary goal. It comprises of various toxicity endpoints and models (irritation, teratogenicity, sensitization, neurotoxicity, and immunotoxicology) that are employed in the development of a drug. All the selected compounds showed Ames probability score ≤ 7 and were non-mutagen. Another toxicity predictor WOE (weight of evidence) was employed to examine the relative level of some compounds that cause cancer in humans. All compounds are predicted as noncarcinogenic except 222284. Therefore, ADMET score and TOPKAT property data of virtual natural compounds have suggested us that the selected molecules might be utilized as bioactive with some minor modification.

Drug-likeness properties:
Natural compounds were found to possess the best drug-like properties by Lipinski's rule of five shown in table 3. Interestingly all selected natural compounds bear the Mol. Wt. Range from 170 to 456 (< 500) except Cyanidin 3, five diglucoside and Quercetin. The drug molecules have low, Mol. Wt. (<500) are transported, diffused and absorbed without difficulty in comparison to large molecules. Molecular weight is one of the critical aspects in corrective drug action; if it seems to increase against the absolute limit; the compound size is known to increase correspondingly, which affects the efficiency of the drug. Some hydrogen bond donors (NH and OH) and Number of hydrogen bond acceptors (O and N atoms) are natural compounds as established in Lipinski's limit range from 1-16 & 1-11 that comes out to be less than 10 and 5 [47].  Table 4. The bioactivity score of the molecules having more than 0.00 is likely to bear significant biological activities, values -0.50 to 0.00 are presumably active moderately, and when the score comes out to be less than -0.50, it is supposed as inactive [35]. The present study results that the compounds that are investigated were experimented as biologically active and performed the physiological functions by acting together with nuclear receptor ligands, GPCR ligands, inhibit protease and other enzymes. GPCR compounds -the related cascade of signaling were further used for the development of novel functional drugs with better binding specificity profile and less harmful effects. The bioactivity score for GPCR ligand was studied to lie between >0.00 for all tested compounds except cyanidin 3, 5 diglucoside, quercetin, ellagic acid and gallic acid. The ion channel modulator has resulted in the movement of the charged particles across the cell membranes and are valuable targets, that are altered by various drugs having therapeutic value.
The score of bioactivity of ion channel having modulator activity was flanked by >0.00 and -0.50 of all ligands. The results obtained were similar for all compounds, which possess a score value of -0.50 to 0.00 except Kaemfero that was lying in >0.00. This value shows the Kaemfero can work as Kinase inhibitors. Kinase inhibitors have the property to block or modulate diseased signaling pathways [50]. All compounds were found to be in perfect bioactivity scores for the nuclear receptor ligand and inhibition of enzyme as compared to Cyanidin 3, 5 diglucoside, Quercetin, Gallic acid. Interestingly, all compounds were found to be in an excellent bioactive score against, ion channel modulator, GPCR ligand, kinase inhibitor, the protease inhibitor, enzyme inhibitory activity, and nuclear receptor ligand.

Biological activity predictions:
Using PASS online server, selected bioactive constituents were obtained to evaluate the possible biological activity. The biological activity spectrum (BAS) of a compound is known to have pharmacological effects, specific toxicities, and mechanisms of action occurring due to compounds. Because these probabilities can be calculated independently, the Pa and Pi values vary from 0 to 1, and Pa + Pi < 1. Pa belongs to the class of active whereas Pi is for stable compounds [51].PASS prediction results showed that the highest Pa value than Pi value come off for anti-inflammatory and anti-neoplastic activity and hence indicated the antifungal of selected compounds (evaluated in Table 5). However, all compounds have shown a significant Pa value as compared to Pi value. These compounds might be inhibiting fungal infection via blocking Crz1 action as evidenced by docking studies.

Conclusions:
In conclusion, we have developed PubChem database contain some compounds as possible Crz1 protein inhibitors, by extensive docking experiments validated with biological activity spectrum results. The compounds have shown substantial promising in silico results as reflected by their high binding interaction and considerable high protein-ligand stabilization energy. Analysis of the ADME and QSTR profiles of the selected compounds revealed that these compounds appear to be satisfactory drug liveliness properties. However, molecular docking and biological activity spectrum study are the one way of estimating the activity of the molecules involved. Hence, further research could prove this compound to be a probable anti-fungal drug. With our significant results, all the compounds can further be studied for structural modification, extensive and elaborated investigations to arrive at possibly novel potent agents with little therapeutic activity.


Systems biology identifies cytosolic PLA2 as a target in vascular calcification treatment

Although cardiovascular disease (CVD) is the leading cause of morbimortality worldwide, promising new drug candidates are lacking. We compared the arterial high-resolution proteome of patients with advanced versus early-stage CVD to predict, from a library of small bioactive molecules, drug candidates able to reverse this disease signature. Of the approximately 4000 identified proteins, 100 proteins were upregulated and 52 were downregulated in advanced-stage CVD. Arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor was predicted as the top drug able to reverse the advanced-stage CVD signature. Vascular cPLA2 expression was increased in patients with advanced-stage CVD. Treatment with AACOCF3 significantly reduced vascular calcification in a cholecalciferol-overload mouse model and inhibited osteoinductive signaling in vivo and in vitro in human aortic smooth muscle cells. In conclusion, using a systems biology approach, we have identified a potentially new compound that prevented typical vascular calcification in CVD in vivo. Apart from the clear effect of this approach in CVD, such strategy should also be able to generate novel drug candidates in other complex diseases. Research Article Vascular biology

Introduction
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide, accounting for about 32% of all global deaths (1,2). CVD results from multifactorial causative mechanisms and constitutes a high economic burden on society and the health care system. Typically, CVD, including coronary artery disease, stroke, or peripheral artery disease, involves endothelial dysfunction resulting from a vascular injury and is characterized by progressive arterial thickening, leading ultimately to thrombosis and vascular occlusion. This atherosclerotic process starts with early lipid deposition in the inner layers of the artery walls and progresses to late complications, such as fibrosis and calcification. While calcification of arterial intima is associated with plaque rupture and myocardial infarction, calcification of arterial media leads to vessel stiffening and increased pulse-wave velocity and reduces cardiac perfusion, with the consequence of ventricular hypertrophy and heart failure (3,4).
Although significant advancements have been made in understanding the pathophysiology of CVD (5), there is still progress to be made to unravel the detailed molecular complexity of the disease and to Although cardiovascular disease (CVD) is the leading cause of morbimortality worldwide, promising new drug candidates are lacking. We compared the arterial high-resolution proteome of patients with advanced versus early-stage CVD to predict, from a library of small bioactive molecules, drug candidates able to reverse this disease signature. Of the approximately 4000 identified proteins, 100 proteins were upregulated and 52 were downregulated in advanced-stage CVD. Arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor was predicted as the top drug able to reverse the advanced-stage CVD signature. Vascular cPLA2 expression was increased in patients with advanced-stage CVD. Treatment with AACOCF3 significantly reduced vascular calcification in a cholecalciferol-overload mouse model and inhibited osteoinductive signaling in vivo and in vitro in human aortic smooth muscle cells. In conclusion, using a systems biology approach, we have identified a potentially new compound that prevented typical vascular calcification in CVD in vivo. Apart from the clear effect of this approach in CVD, such strategy should also be able to generate novel drug candidates in other complex diseases.
transform this information into new drug candidates. The extreme complexity and multifactorial character of CVD has prompted researchers to shift from investigating already well-known candidate pathways or single molecules to unbiased omics-based studies (6). Omics allows the study of the entire "system" simultaneously, leading to a better representation of the complex molecular mechanism of CVD (7) and, thus, ultimately enabling the major aim of identifying truly novel CVD targets. This has resulted in a number of recent omics studies in the field identifying potential biomarkers of CVD (8) but not necessarily resulting in novel drugs yet. The latter could be attributed to the fact that potential targets need to be transformed into drugs, which often takes a substantial amount of time and effort, and/or that most studies employed genomic and transcriptomic strategies, which not necessarily translate to changes in proteins, the major drug targets (9).
As a response to the ever-increasing omics data and in an effort to target their optimal use for drug discovery, a number of initiatives have evolved to create a shortcut associating omics molecular signatures to drugs and small compounds. The Connectivity Map (CMap) is the most advanced example of this type of approach (10), where 564 gene expression profiles from cultured human cells treated with 164 bioactive small molecules were generated and associated via pattern-mining software. This resource was later updated to include associated signatures to 1,309 compounds (https://portals.broadinstitute.org/cmap/). Attempts have been made to use CMap in CVD but were limited to mouse models (11) or in vitro studies (12) or focused on human stem cells (13). Furthermore, none of these studies tested the therapeutic validity of the selected small CMap molecules.
To our knowledge this is the first effort to identify novel drugs in CVD using drug repurposing based on human molecular signatures. We generated a high-resolution human proteome signature of advanced-stage CVD using peripheral artery samples that allowed the identification of a potentially novel drug candidate, arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor. In vivo and in vitro investigations showed that AACOCF3 exerts a potent inhibitory action on arterial calcification.

Results
CVD patient clinical data used in vessel proteome analysis. For vessel proteome analysis of CVD vessels, common iliac, external iliac, and superficial femoral arteries from 15 patients with early-stage CVD and 17 patients with advanced-stage CVD were employed (Table 1). Advanced-stage CVD patients had clear CVD phenotypes (vascular disease, left ventricular hypertrophy, angina pectoris, intermittent claudication, myocardial infraction). These cardiovascular phenotypes were not found in early-stage CVD patients (Table 1). H&E staining ( Figure 1, A and C) and von Kossa staining ( Figure 1, B and D) confirmed intima media thickening and calcification in arteries from the advanced-stage CVD group compared with early-stage CVD patients. Advanced-stage CVD patients were older than early-stage CVD patients. The female/male ratio was also different between the groups, with almost exclusively females in the early-stage CVD group. The advanced-stage CVD group had a higher proportion of patients with hypertension, diabetes, and nicotine use. The medication use of the 2 groups was very diverse, with no clear distinguishable features between the groups (Supplemental Table 3; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.125638DS1).
Arterial protein signature of advanced-stage CVD. Proteome analysis of the arteries led to the detection of about 4000 proteins in total (2500-3500 proteins per patient, detected in at least 60% of samples). Samples were analyzed in 2 batches, and only proteins that were significantly differentially expressed in 1 batch and displayed the same expression direction in the other batch were selected. This led to the selection of 152 proteins, with 100 and 52 that were upregulated and downregulated in advanced-stage CVD, respectively (Supplemental Table 4). The change in abundance of selected proteins present in the CVD signature (SHMT1, EFEMP1, KDM5D: increase; GUCY1B2: decrease) was confirmed by Western blot analyses of the arteries (Supplemental Figure 1), supporting in general the validity of the CVD vessel proteome analysis.
AACOCF3 as a potential drug candidate for CVD treatment. We next used this 152-protein signature to identify potentially new drug candidates for CVD treatment. Using CMap (10), we screened 1309 small molecules able to reverse this CVD signature in silico. Among the molecules of the CMap database, 12 were predicted to significantly reverse the CVD signature ( Table 2). Among these 12 molecules, arachidonyltrifluoromethane, also called arachidonic acid trifluoromethyl ketone (AACOCF3), an inhibitor of cPLA2, showed the highest negative enrichment score ( Table 2). Immunohistochemistry showed that cPLA2 was present in all vascular areas in early-and late-stage CVD (Supplemental Figure 2A). Semiquantitative analysis showed that cPLA2 was significantly more abundant in the intima of advanced-compared with early-stage CVD arteries, while no significant difference was observed in the tunica media and the tunica externa (Supplemental Figure 2B). Western Blot analysis confirmed that cPLA2 expression was significantly higher (1.4-fold) in arteries from advanced-stage CVD patients compared with early-stage CVD patients ( Figure 2).
AACOCF3 protects against vascular calcification. Arterial calcifications are observed during medial calcification and late-stage atherosclerosis (4,14). Vascular smooth muscle cells (VSMCs) are important mediators for these calcific processes (4). Due to important clinical implications, we have focused our interventional approach on the effects of AACOCF3 in VSMCs. To evaluate the potential protective effects of AACOCF3 on CVD, experiments were performed in the cholecalciferol-overload (cholecalciferol is also known as vitamin D 3 ) mouse vascular calcification model without or with additional treatment with AACOCF3. Cholecalciferol overload significantly increased serum calcium, calcium × phosphorus (CaxPi) product, and FGF23 C-terminal concentrations and reduced serum phosphorus levels to similar levels in both control-and AACOCF3-treated mice (Table 3).

Table 1. Clinical parameters of the early-and advanced-stage CVD patients in whom high-resolution vessel proteome analysis was performed
Cholecalciferol overload triggered aortic calcification in mice, an effect significantly attenuated by additional treatment with AACOCF3 ( Figure 3A). In control mice, AACOCF3 did not modify aortic calcium deposition. The mRNA level of the gene encoding the most prevalent cPLA2 subtype, cPLA2α (Pla2g4a) (15), was significantly higher in aortic tissue of cholecalciferol-treated mice than in that of control mice ( Figure 3B). AACOCF3 treatment alone did not significantly affect aortic Pla2g4a mRNA expression. Western blot analysis showed that cPLA2 protein was clearly present in the aortic tissue, but no significant change was observed after vitamin D or AACOCF3 treatments (Supplemental Figure 3). These effects were paralleled by inhibition of aortic osteogenic/chondrogenic signaling by AACOCF3. As shown in Figure 4, cholecalciferol overload significantly upregulated mRNA expression of osteogenic transcription factors Msx2 and Cbfa1 and of osteogenic enzyme tissue-nonspecific alkaline phosphatase (Alpl), markers of increased osteogenic/chondrogenic transdifferentiation in the aortic tissue, effects again significantly ameliorated by AACOCF3 treatment. Thus, AACOCF3 inhibited aortic osteoinductive signaling and vascular calcification in the cholecalciferol-overload mouse model.
To investigate possible mechanisms of the protective effects of AACOCF3 on vascular calcification, further experiments were performed in primary human aortic smooth muscle cells (HAoSMCs) during high-phosphate conditions in the presence or absence of AACOCF3. As shown by alizarin red staining and quantification of calcium deposition, treatment with calcification medium increased calcification of HAoS-MCs that was significantly blunted by treatment with AACOCF3 ( Figure 5, A and B). Similarly, AACOCF3 significantly suppressed phosphate-induced MSX2, CBFA1, and ALPL mRNA expression ( Figure 5, C-E) as well as ALPL activity ( Figure 5F) in HAoSMCs. In control conditions, AACOCF3 did not significantly affect mineralization but tended to reduce CBFA1 mRNA levels (P = 0.202) and significantly downregulated MSX2 and ALPL mRNA expression in HAoSMCs ( Figure 5, A-F). Thus, AACOCF3 suppressed phosphate-induced osteogenic/chondrogenic transdifferentiation and calcification of HAoSMCs in vitro.
Moreover, phosphate triggered the release of arachidonic acid (AA) in the cell culture medium of HAoSMCs ( Figure 5G). Treatment with AACOCF3 significantly inhibited both basal and Pi-induced release of AA, the product of PLA2 activity. To confirm the involvement of cPLA2 in vascular calcification, the endogenous expression of cPLA2 in HAoSMCs was suppressed by silencing of the PLA2G4A gene using siRNA. As a result, transfection with cPLA2 siRNA significantly suppressed PLA2G4A mRNA levels in HAoSMCs, as compared with negative control-silenced HAoSMCs ( Figure 6A). Phosphate treatment upregulated PLA2G4A mRNA expression in negative control-silenced HAoSMCs ( Figure 6A). Furthermore, phosphate significantly increased MSX2, CBFA1, and ALPL mRNA expression in negative control siRNAtransfected HAoSMCs, effects significantly blunted in cPLA2 siRNA silenced HAoSMCs ( Figure 6, B-D). Taken together, the protective effects of AACOCF3 on vascular calcification may involve, at least in part, inhibition of cPLA2-mediated osteoinductive signaling.
AACOCF3 treatment of ApoE -/mice. AACOCF3 was tested in another preclinical model of CVD, ApoE -/mice known to develop atherosclerotic lesions. In our conditions, ApoE -/mice fed a high-cholesterol diet displayed dyslipidemia (Table 4, vehicle) and lipid deposition ( Figure 7A) and increased type III collagen expression ( Figure 7B) in the aortic sinus, in agreement with previous reports (16). Ten weeks of treatment with AACOCF3 did not significantly modify plasma lipids and glucose levels ( Table 4). In contrast, analysis of the aortic root showed that AACOCF3 had no influence on the total Mean represents the arithmetic mean of the connectivity scores for those signatures. n represents the number of signatures of a given compound available in the Cmap database. Enrichment indicates the degree of matching between a Query Signature (here, the CVD signature) and the reversed signature of a given compound. p (permutation p) estimates the likelihood that the enrichment would be observed by chance. Specificity provides a measure of the uniqueness of the matching between a compound and the CVD signature. The nonnull percentage measures the support for the connection between a set of compound signatures and the Query Signature based upon the behavior of the individual signature in that set. More details can be found here: https://portals.broadinstitute.org/cmap/help_topics_frames.jsp. plaque area, but it significantly inhibited lipid deposition in the plaques (-35%; Figure 7A) compared with vehicle-treated mice. In parallel, AACOCF3 significantly reduced type III collagen plaque expression (-16%; Figure 7B) but had no significant influence on total collagen accumulation, as assessed by Sirius red staining (Supplemental Figure 4). In parallel, AACOCF3 did not changed αSMA (myofibroblast marker) and significantly increased MOMA2 (macrophage marker) staining (Supplemental Figure 4). Finally, AACOCF3 did not significantly change the expression of calcification gene markers (Alpl and Cbfa1) in aortic tissue (Supplemental Figure 5). Collectively, the experiments using the ApoE -/mouse model showed that, while AACOCF3 reduces lipid accumulation, it has no effect on atherosclerotic lesions.

Discussion
In the present study, we explored the possibility of omics-based disease signature correction by small bioactive compounds to identify and validate in vivo CVD drugs. Applying this approach to the high-resolution human arterial protein signature of advanced-stage CVD, we identified among >1000 compounds the cPLA2 inhibitor AACOCF3 that reversed this CVD signature. AACOCF3 is a cell-permeant trifluoromethyl ketone analog of AA (17) that behaves as a slow-and tight-binding inhibitor of cPLA2 (18)(19)(20)(21). A review of the literature showed that cPLA2 inhibition has not been used in CVD management yet, suggesting AACOCF3 as a possible novel drug candidate for CVD treatment. Our preclinical analysis of the cholecalciferol-overload mouse model demonstrates that AACOCF3 was efficient in vivo at attenuating vascular calcification, one of the major features of CVD. To our knowledge, our work demonstrates for the first time the protective action of AACOCF3 against vascular injury. Functional analysis of the proteins present in the human CVD vessel signature highlighted enrichment of proteins associated with cholesterol and lipid metabolism. Moreover, we also detected an increase in EFEMP1, also called fibulin-3, a protein involved in the regulation of osteogenic/chondrogenic Arithmetic mean ± SEM of serum calcium, phosphorus, CaxPi product, and FGF23 C-terminal concentrations in mice receiving vehicle (CTR) or high-dosed cholecalciferol (vD), without or with additional treatment with AACOCF3 (AAC). A P < 0.001, B P < 0.01, C P < 0.05 vs. control mice. transdifferentiation of VSMCs (22). Because vascular calcification is preferentially associated with advanced-stage CVD, we focused on the mechanisms of AACOCF3 in this process. Vascular calcification is mediated mainly by osteogenic/chondrogenic reprogramming of VSMCs in response to various triggers, especially hyperphosphatemia (14,23). In liver tissue, excessive phosphate intake in rats is associated with increased AA concentrations (24). The osteogenic/chondrogenic transdifferentiated VSMCs are characterized by increased expression and activity of osteogenic and chondrogenic transcription factors as well as of osteogenic enzymes, such as tissue nonspecific ALPL, leading to active mineralization of the vascular tissue (22,(25)(26)(27). We show here that AACOCF3 treatment attenuates vascular osteogenic/chondrogenic transdifferentiation and calcification in vitro and in the cholecalciferol-overload mouse model in vivo. Moreover, vascular cPLA2 expression is increased during calcification conditions, both in vitro and in vivo. Accordingly, osteogenic/chondrogenic transdifferentiation of VSMCs during elevated phosphate levels in vitro is blunted by silencing of cPLA2, and, thus, cPLA2 knockdown mimics the protective effects of AACOCF3. These observations strongly suggest that the in vivo protective effect of AACOCF3 may result, at least partially, from its direct action on VSMCs through inhibition of cPLA2-dependent osteoinductive signaling. There exist multiple PLA2 isoforms classified as secreted (sPLA2) and cytosolic (cPLA2) that have different structures and various physiological functions. PLA2G4A, i.e., cPLA2, is a calcium-dependent cPLA2. Some secreted isoforms, such as the lipoprotein-associated PLA2 (Lp-PLA2) or sPLA2-X or sPLA2-V (28), were shown as causative in atherosclerosis, and some of their inhibitors were in clinical trials but did not reach the market (29). Expression of cPLA2 has been previously found in arteries from atherosclerotic patients, mainly localized in the intimal regions with an inflammatory infiltrate consisting of macrophages and smooth muscle cells (30). Our results confirm a localization of cPLA2 in the intima, but the clinical relevance of this observation remains to be established because immunolocalization was not assessed directly within atherosclerotic plaques. Moreover, a PLA2G4A gene variant was associated with a decreased risk of coronary artery disease, although the functional consequence of the mutation remains still elusive (31). Our results are in agreement with those observations, since we found that cPLA2 was indeed present in arteries and its expression was increased in advanced-stage CVD. So far, to our knowledge the use of a cPLA2 inhibitor as a possible drug candidate for CVD treatment has never been proposed.
A first limitation of our study is that we have deliberately compared patients with advanced vascular disease and early vascular disease. While these features were confirmed by histology, the initial screen was based on presence or absence of risk factors, and, hence, differences in age, blood pressure, and other cardiovascular risk factors between the group are present. We cannot rule out that elements of the proteomic data are driven by these risk factors rather than specifically by the vascular phenotype. However, by selecting patients with multiple risk factors and advanced disease, we believe that our findings represent a molecular signature of advanced arteriosclerosis, rather than a single risk factor. A further limitation might be that the CVD signature was established from peripheral arteries (iliac and femoral) and cannot necessarily be extrapolated to a CVD signature in other arterial locations. However, our results show that AACOCF3 treatment was efficient in the aorta of the animal models, suggesting that its protective action is not limited to peripheral arteries and that, apparently, the molecular mechanisms overlap between these different vessel compartments/segments. We are also aware that the specificity of AACOCF3 for cPLA2 is likely not exclusive. Indeed, it has been reported that AACOCF3 was able to inhibit cyclooxygenases 1 and 2 (32). However, our results show that the effect of AACOCF3 could be reproduced by cPLA2 knockdown in vitro. u.]) and ALPL activity (F, n = 4, U/mg protein) in HAoSMCs as well as arachidonic acid (AA) levels (G, n = 4; ng/mg protein) in the cell culture medium of HAoSMCs following treatment with control or with β-glycerophosphate (Pi), without or with additional treatment with 10 μM AACOCF3. *P < 0.05, **P < 0.01, ***P < 0.001 vs. control-treated HAoSMCs; † P < 0.05, † † P < 0.01, † † † P < 0.001 vs. HAoSMCs treated with Calc./Pi alone (1-way ANOVA with Games-Howell post-hoc test for B, C, and F or with Tukey-HSD post-hoc test for D, E, and G).
In conclusion, using a systems biology approach to optimally describe the molecular mechanisms of human advanced-stage CVD, we identified a potentially new compound that reversed typical vascular lesions in CVD in vivo. Apart from the clear effect of this approach in CVD, such a strategy should be able to generate novel targets in other complex diseases.

Methods
Arterial sample acquisition and CVD grading method. 18 common iliac arteries, 15 superficial femoral arteries, and 2 external iliac arteries were collected from 15 early-and 17 advanced-stage CVD patients, with the following specific distribution: early-stage CVD (11 common iliac arteries, 2 external iliac arteries, 4 superficial femoral arteries) and advanced-stage CVD (7 common iliac arteries, 11 superficial femoral arteries). All advanced-stage CVD patients had clear CVD phenotypes (vascular disease, left ventricular hypertrophy, angina pectoris, claudication intermittens, myocardial infraction). These cardiovascular phenotypes were not found early-stage CVD patients exhibits (Table 1). Following harvesting, the vascular tissues were snap frozen in liquid nitrogen and stored at -80°C until preparation for proteomics analysis. Blood and urine samples were also collected from the same patients for biochemical analysis.  , CBFA1 (C), and ALPL (D) relative mRNA expression in HAoSMCs following silencing with negative control siRNA (Neg.si) or cPLA2 siRNA (cPLA2si), without or with additional treatment with β-glycerophosphate (Pi). *P < 0.05, **P < 0.01, ***P < 0.001 vs. Neg.si-silenced HAoSMCs; † P < 0.05, † † † P < 0.001 vs. Neg.si-silenced and Pi-treated HAoSMCs (1-way ANOVA with Games-Howell post-hoc test for A or with Tukey-HSD post-hoc test for C and Steel-Dwass method for B and D).
Homogenization was performed with the bullet blender homogenizer (Next Advance) (33). Ten micrograms of each protein sample were analyzed in SDS-PAGE (5% stacking, 12% separating). Electrophoresis was stopped when samples just entered the separating gel. Gels were fixed with 30% methanol, 10% acetic acid for 30 minutes, followed by 3 washes with water (5 minutes each), and stained with Coomassie colloidal blue overnight. Each band was excised from the gel and further sliced into small pieces (1-2 mm). Gel pieces were destained with 40% acetonitrile, 50 mM NH 4 HCO 3 and then reduced with 10 mM DTE in 100 mM NH 4 HCO 3 for 20 minutes at room temperature. After reduction, samples were alkylated with 54 mM iodoacetamide in 100 mM NH 4 HCO 3 for 20 minutes at room temperature in the dark. Samples were then washed with 100 mM NH 4 H-CO 3 for 20 minutes at room temperature, followed by another wash with 40% acetonitrile, 50 mM NH 4 HCO 3 for 20 minutes at room temperature and a final wash with ultrapure water under the same conditions. Gel pieces were dried in a centrifugal vacuum concentrator and trypsinized overnight in the dark at room temperature by adding 600 ng trypsin per sample (trypsin stock solution: 10 ng/μl in 10 mM NH 4 HCO 3 , pH 8.5). Peptides  were extracted after incubation with 50 mM NH 4 HCO 3 for 15 minutes at room temperature, followed by 2 incubations with 10% formic acid, acetonitrile (1:1) for 15 minutes at room temperature. Peptides were eluted in a final volume of 600 μl and filtered with PVDF filters (Merck Millipore) before dried in a centrifugal vacuum concentrator. Dried peptides were reconstituted in mobile phase A buffer (0.1% formic acid, pH 3.5) and processed with LC-MS/MS analysis. 2.5 μg of protein digest was injected from each sample into a Dionex Ultimate 3000 RSLS nano flow system configured with a Dionex 0.1 × 20 mm 5 μm C18 nano trap column. The mobile phase was 2% ACN/0.1% formic acid with a flow rate of 5 μl/min. The analytical column was an Acclaim PepMap C18 nano column, 75 μm × 50 cm, 2 μm 100 Å, at a flow rate of 300 nl/min. The trap and nano flow column were maintained at 35°C. The samples were eluted with a gradient starting at 1% B for 5 minutes, rising to 5% B at 10 minutes, then to 25% B at 360 minutes, and 65% B at 480 minutes. Mobile phase A was 0.1% formic acid, while mobile phase B was 80% ACN/0.1% formic acid. The column was washed and reequilibrated prior to each sample injection. The eluent was ionized using a Proxeon nano spray ESI source operating in positive ion mode. For mass spectrometry analysis, an Orbitrap LTQ Velos (Thermo Finnigan) was operated in MS/MS mode, scanning from 380 to 2,000 m/z. Ionization voltage was 2.6 kV, and the capillary temperature was 275°C. The resolution of ions in MS1 was 60,000 and 7,500 for higher-energy collisional dissociation (HCD) MS2. The top 20 multiply charged ions were selected from each scan for MS/MS analysis using HCD at 35% collision energy. Dynamic exclusion was enabled with a repeat count of 1, exclusion duration of 30 seconds.
MS data processing and quantification. Raw files were analyzed with Proteome Discoverer 1.4 software package (Thermo Finnigan), using the Sequest search engine and the Uniprot human (Homo sapiens) reviewed database, downloaded on May 30, 2016. The search was performed using carbamidomethylation of cysteine as static and oxidation of methionine as dynamic modifications. Two missed cleavage sites, a precursor mass tolerance of 10 ppm and fragment mass tolerance of 0.05 Da were allowed. FDR validation was based on q values: target FDR (strict): 0.01, target FDR (relaxed): 0.05. Quantification analysis was performed at the peptide level, with the clustering approach as previously described (34), with some minor modifications. Peptides were clustered using the "window" of mass (±5 ppm) and retention time (±15 minutes). Only peptides present in 60% of the samples in at least 1 group were further processed for quantification and statistical analysis (Mann-Whitney). The differentially expressed proteins selected for further analysis were considered those with a fold change of ≥1.2 or ≤0.83 in both batches and a P value of ≤0.05 in at least 1 batch.
CMap analysis. Each upregulated and downregulated protein ID from the CVD signature was converted into gene ID and were analyzed with the bioinformatic tool CMap (build 02). CMap is a collection of genome-wide transcriptional expression signatures (6,100 treatment vs. vehicle control pairs) from cultured human cells (MCF7, PC3, and HL60) treated with 1,309 bioactive small-chemical molecules at varying concentrations and analyzed by simple pattern-matching algorithms (10).
Preparation of AACOCF3 solutions. AACOCF3 was supplied by Cayman Chemical (item 62120). For in vivo treatments, the initial solution of AACOCF3 (50 mg/5 ml ethanol) was concentrated to 250 mg/ml and 20 μl was diluted in 4 ml of a 0.5% methylcellulose (Fisher Scientific, product 10246190) in water. That solution was used to treat the animals. For in vitro treatment with AACOCF3 (Cayman Chemical), 10 mM stock solution in ethanol, was diluted to a final concentration of 10 μM in cell culture medium. Equal amounts of vehicle were used as control.
Mouse model of vascular calcification. Female C57BL/6 mice were treated with AACOCF3 (10 mg/kg) or its vehicle (methylcellulose 0.5% containing 0.5% M ethanol) via gavage and were fed a calcium-reduced diet (Altromin, C1031). Subsequently, mice were injected subcutaneously with 400,000 IU/kg BW of cholecalciferol (MilliporeSigma) or vehicle for 3 days (35,36). After 6 days of treatment, blood was collected by retroorbital puncture under isoflurane inhalation anaesthesia. The mice were immediately sacrificed by cervical dislocation, and aortic tissues were collected and snap frozen for further experiments.
Mouse model of atherosclerosis. ApoE -/mice (37, 38) (6-week-old males) (Charles River) were fed a high-cholesterol diet (1.25% cholesterol, 6% fat, and 2% sucrose [Ssniff, Germany diet, TD.96335 mod. -1.25% cholesterol]) and simultaneously treated (gavage with 200 μl; 5 days a week) with AACOCF3 (10 mg/kg) or its vehicle (methylcellulose 0.5% containing 0.5% M ethanol) for 10 weeks. The mice were sacrificed by pentobarbital injection, and the heart aortic sinus was collected and snap frozen in liquid nitrogen for further analysis. Blood was collected 1 week before sacrifice by intracaudal puncture in heparinized tubes. Ice-cold blood was centrifuged for 5 minutes at 1500 g, and upper plasma phase was collected and immediately frozen at -80°C.
Biochemical analysis. The plasma concentrations of phosphorus and calcium were measured by the QuantiChrom Phosphate assay kit and QuantiChrom Calcium assay kit, respectively (BioAssay Systems). An ELISA kit was used to determine plasma concentrations of FGF23 C-terminal (Immutopics), according to the manufacturer's instructions. Blood glucose concentration was measured using a glucometer (Roche Diagnostic). Plasma concentrations of cholesterol and triglyceride were measured by autoanalyzer (Horiba Medical).
Immunohistology. For human, histologic paraffin sections of iliac arteries samples were cut at 2 μm, mounted on charged slides, and stained with H&E and von Kossa for pathological evaluation. cPLA2 staining was performed on 1-2 μm paraffin sections of A. ilia communis samples of histopathologically classified early (n = 15) and advanced (n = 24) patients, using a 1:50 dilution of the rabbit anti-human PLA2G4A antibody (ProSci Incorporated). Heat-induced epitope retrieval was performed at pH 8. For detection, the EnVision+ System-HRP Labelled Polymer Anti-Rabbit kit (Dako) was used. Nuclei were counterstained with hematoxylin, and sections were coverslipped with gelatin. The expression levels of cPLA2 were evaluated per tissue section (vessel cross section), distinguishing between the different vessel layers (tunica intima, tunica media, and tunica externa). The abundance of cPLA2 was scored as follows: 0.5 for no expression, 1 for weak expression, 2 for moderate expression, and 3 for strong expression. Images were acquired using the AxioImager Z1 microscope (Carl Zeiss MicroImaging Inc.). All evaluations were performed in a blinded manner.
For aortic sinuses of ApoE -/mice, lipid accumulation and collagen expression were analyzed on serial cross-cryosections of the heart as previously described (39). For lipid analysis, cryosections were post-fixed with formaldehyde and stained with oil red O and counterstained with hematoxylin. For collagen, cryosections were post-fixed with methanol/acetone and exposed to a type III collagen primary antibody (Acris, BP8014) and revealed with an anti-rabbit HRP secondary antibody (DAKO, 4010). Samples were imaged with a Nikon Y-FL microscope equipped with a computer-based imaging system. Staining was expressed as the percentage of total sinus area. Reported intensities used for further statistical analysis correspond to the mean value of 5 sections per animal.
Cell culture of primary HAoSMCs. Primary HAoSMCs (Thermo Fisher Scientific) were cultured in Waymouth's MB 752/1 medium and Ham's F-12 nutrient mixture in a 1:1 ratio (Thermo Fisher Scientific) containing 10% FBS (Thermo Fisher Scientific) and 100 U/ml penicillin and 100 μg/ml streptomycin (Thermo Fisher Scientific) (40,41). HAoSMCs were used in all experiments from passages 4 to 11, and each experiment was performed in at least 2 different batches of HAoSMCs at different passages, depending on the availability of the cells. HAoSMCs were treated for 24 hours (qRT-PCR, AA release), 7 days (ALPL assay), or 11 days (calcification) with 2 mM β-glycerophosphate (MilliporeSigma) (25,36,40,42,43) or with 10 μM AACOCF3. Equal amounts of vehicle were used as control. For the mineralization analysis and alizarin red staining, treatment with 10 mM β-glycerophosphate and 1.5 mM CaCl 2 (MilliporeSigma) for 11 days was used as calcification media (44). Fresh media with agents were added every 2-3 days. HAoSMCs were transfected with 10 nM cPLA2 siRNA (Thermo Fisher Scientific, s10592) or with 10 nM negative control siRNA (Thermo Fisher Scientific, 4390843) using siPORT amine transfection agent (Thermo Fisher Scientific) according to the manufacturer's protocol. The cells were used 48 hours (qRT-PCR) after silencing, and silencing efficiency was determined by quantitative RT-PCR.
Analysis of calcification. To quantify vascular calcification, abdominal aortic tissues were incubated overnight at 37°C in 0.6 M HCl. HAoSMCs were incubated in 0.6 M HCl for 24 hours at 4°C. Calcium content in the supernatant was determined by using QuantiChrom Calcium assay kit (BioAssay Systems) according to the manufacturer's protocol. Tissues and HAoSMCs were lysed with 0.1 M NaOH/ 0.1% SDS and protein concentration was determined by the Bradford assay (Bio-Rad). The results are shown normalized


s2fieldsofstudy: Biology Medicine

text:
Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction

Epigenetics encapsulates a group of molecular mechanisms including DNA methylation, histone modification and microRNAs (miRNAs). Gestational diabetes (GDM) increases the risk of adverse perinatal outcomes and is associated with future offspring risk of obesity and type 2 diabetes. It has been hypothesised that epigenetic mechanisms mediate an effect of GDM on offspring adiposity and type 2 diabetes and this could provide a modifiable mechanism to reduce type 2 diabetes in the next generation. Evidence for this hypothesis is lacking. Epigenetic epidemiology could also contribute to reducing type 2 diabetes by identifying biomarkers that accurately predict risk of GDM and its associated future adverse outcomes. We reviewed published human studies that explored associations between any of maternal GDM, type 2 diabetes, gestational fasting or post-load glucose and any epigenetic marker (DNA methylation, histone modification or miRNA). Of the 81 relevant studies we identified, most focused on the potential role of epigenetic mechanisms in mediating intrauterine effects of GDM on offspring outcomes. Studies were small (median total number of participants 58; median number of GDM cases 27) and most did not attempt replication. The most common epigenetic measure analysed was DNA methylation. Most studies that aimed to explore epigenetic mediation examined associations of in utero exposure to GDM with offspring cord or infant blood/placenta DNA methylation. Exploration of any causal effect, or effect on downstream offspring outcomes, was lacking. There is a need for more robust methods to explore the role of epigenetic mechanisms as possible mediators of effects of exposure to GDM on future risk of obesity and type 2 diabetes. Research to identify epigenetic biomarkers to improve identification of women at risk of GDM and its associated adverse (maternal and offspring) outcomes is currently rare but could contribute to future tools for accurate risk stratification. Electronic supplementary material The online version of this article (10.1007/s00125-019-05011-8) contains peer-reviewed but unedited supplementary material including a slideset of the figures for download, which is available to authorised users.

Epigenetic epidemiology and its use in gestational diabetes research
Epigenetics encapsulates a group of molecular mechanisms, including DNA methylation, histone modification and microRNAs (miRNAs), which can influence gene expression and variation in both cellular and whole-organism phenotype. An increasing number of clinical applications are emerging that use data generated in the field of epigenetic epidemiology. These include studies increasing our understanding of mechanistic pathways culminating in adverse health outcomes across the life course [1] as well as the use of epigenetic biomarkers as informative biomarkers in diagnosis, risk prediction and prognosis [2,3].
In this review we describe the potential of epigenetic research: (1) to improve understanding of the causal paths between in utero exposure to gestational diabetes (GDM) or pregnancy hyperglycaemia and offspring adiposity and type 2 diabetes (type 2 diabetes) risk; and (2) as biomarkers for increasing the accuracy of predicting GDM risk and its associated adverse (maternal and offspring) outcomes. We then review and summarise current published human studies on the epigenetic epidemiology of GDM with a focus on these two areas of research.
Epigenetic mediation of in utero exposure to GDM on offspring health Normal pregnancy is associated with insulin resistance, particularly from the second trimester, similar to that found in type 2 diabetes [4][5][6]. These changes facilitate transport of glucose across the placenta to ensure normal fetal growth and development [4][5][6]. If maternal gestational insulin resistance becomes too pronounced then maternal GDM may be diagnosed. Traditionally, GDM has been defined as any hyperglycaemia that is first identified during pregnancy, including existing undiagnosed diabetes/hyperglycaemia. Whilst early pregnancy tests are increasingly used to identify and treat women with existing hyperglycaemia [7], this is not universal and any impact of 'GDM' on epigenetic mechanisms or adverse outcomes may be due to existing hyperglycaemia or pregnancy-induced insulin resistance. GDM is associated with adverse perinatal [6] and longerterm offspring outcomes, including higher adiposity and adverse cardiometabolic risk factors such as higher circulating glucose and insulin [6,[8][9][10]. The latter may be due to developmental overnutrition and it has been hypothesised that epigenetic dysregulation is one mechanism underlying this association. However, other mechanisms could explain these associations, including shared familial socioeconomic, lifestyle and genetic factors [11][12][13] (Fig. 1, pathways b and c).
Mediation is concerned with causal effects but it is commonly explored through conventional multivariable regression using the method suggested by Baron and Kenny more than 30 years ago [14], without exploring the assumptions specified by those authors [15]. The challenges of researching causal molecular mediation, together with suggestions for novel appropriate approaches, have recently been described [15]. The path between maternal GDM and future offspring type 2 diabetes risk could be mediated by multiple mechanisms. For epigenetic mechanisms to mediate a hypothesised developmental origins path between in utero exposure to GDM and future offspring type 2 diabetes risk (Fig. 1, pathway a), evidence for all three of the following causal effects are required: (1) effect of GDM on future offspring type 2 diabetes; (2) effect of GDM on some epigenetic mechanism in relevant tissues; and (3) effect of those epigenetic mechanisms on future offspring type 2 diabetes. Determining such causal effects requires robust replication of associations and triangulation [16] of two or more different methods with different sources of bias for assessing causality, such as Mendelian randomisation (MR), [15], parental negative control studies [17], matched within-sibship designs [10] and cross cohort comparisons [16]. Within-sibship analyses provide some evidence for a causal effect of GDM on greater offspring BMI [8,10]. In epigenetic epidemiology, a paternal negative control study found that most of the epigenome-wide associations of maternal early pregnancy BMI with cordblood DNA methylation were similar to those of paternal BMI, suggesting the maternal associations were unlikely to be causal [17]. The extent to which such methods have been used to explore epigenetic mediating mechanisms between in utero exposure to GDM and offspring outcomes is one subject of this review.
Given the systems and tissues potentially involved in this hypothesised epigenetic mediating path, any research should ideally explore epigenetic mechanisms in offspring blood (including cord blood), placenta, pancreas, liver, muscle and adipose tissue. Access to blood and placental tissue should be feasible as an increasing number of birth cohorts collect cord  Fig. 1 Summary of pathways that produce an intergenerational association between GDM and offspring type 2 diabetes (T2D). This figure is available as part of a downloadable slideset blood or infant blood from screening blood spots, as well as placental tissue [18][19][20][21][22]. However, taking fat, muscle, pancreas and liver biopsies is unlikely to be feasible and ethical except in clinical cohorts where there is a clinical need. In silico reference data, such as that available in resources like the Genotype-Tissue Expression (GTEx) project [23], may be valuable for information on differential epigenetic phenomena in these tissues but those data are likely to come from small, select and usually adult populations.
Epigenetics as biomarkers for diagnosis and risk prediction in relation to GDM Current guidelines for screening and diagnosing GDM vary between countries and institutions. Universal OGTT of all pregnant women is rare and the benefit of doing this is debated [7,24,25]. The practice of early pregnancy risk factor screening to identify those at most risk of GDM (to enable selection for a later diagnostic OGTT) does not appear effective [24,26,27]. Other early pregnancy screening approaches, such as glucose challenge tests, HbA 1c and random or fasting glucose measurements, can be useful in identifying women with undiagnosed type 2 diabetes [7] but do not seem to be useful in identifying women with GDM or predicting associated adverse outcomes [24,28]. A definitive diagnosis of GDM is made with an OGTT at around 26-28 weeks of gestation. However, emerging evidence shows that fetal growth trajectories already differ in those whose mothers are subsequently diagnosed with GDM compared with those whose mothers are not, from at least 12 weeks of gestation [29,30]. Thus, there is a need for biomarkers that are measured on samples collected in early pregnancy that accurately predict GDM and its associated adverse perinatal and later (offspring and maternal) outcomes. These could indicate which women would benefit from early interventions (lifestyle or pharmaceutical) to reduce risk, including future risk of obesity and type 2 diabetes in mothers and offspring.
Unlike the use of epigenetic measures to explore mediation, their use as biomarkers does not require them to be causally related to the outcome they are predicting [31]. Causal methods and tissue specificity are therefore not the focus in epigenetic biomarker research. What is required is to show that epigenetic biomarkers that can be readily assessed in blood or urine (which are routinely collected at antenatal visits) improve the discrimination and calibration of current risk prediction tools. It is also important that prediction tools developed in one study are validated in independent studies.   We searched PubMed for any pregnancy-related studies that explored associations between any of maternal GDM, type 2 diabetes, gestational fasting or post-load glucose and any epigenetic marker (DNA methylation, histone modification or miRNA) (Fig. 2). We did not restrict our search solely to studies of GDM because of the varying methods that were likely to have been used to diagnose GDM in different studies and because existing hyperglycaemia or pregnancy-related insulin resistance may have influenced both short-term and long-term outcomes. We included any study, whatever its aim and whether it hypothesised epigenetic variation that preceded the diabetes-related outcomes or vice versa. Cross-sectional, case-control or cohort designs and global, epigenomewide and candidate gene studies were all included. We restricted studies to those conducted in humans and written in English. We extracted key data from each study with the aim that this would provide important information on what is currently available in the published literature and how these studies might contribute to the two different potential uses of epigenetic epidemiology in GDM. We did not extract results or assess risk of bias in each study as this was considered beyond the scope of this paper. Similarly, we did not attempt to synthesise or pool results from different studies. We do, however, provide references from all studies and the data we extracted from them in electronic supplementary material (ESM) Table 2 (summarised in Fig. 3). There were two main themes of research effort identified from our literature search: (1) studies of associations of GDM with offspring and/or placenta epigenetics (n = 55 studies), which were primarily concerned with epigenetic mediation of in utero exposure to hyperglycaemia on offspring subsequent health; and (2) studies of maternal epigenetics (n = 23 studies), which were mostly concerned with the role of epigenetics in the aetiology of GDM or its progression to type 2 diabetes. Three articles spanned both of these themes, so the total numbers of studies contributing to offspring/placenta and maternal epigenetics were 58 and 26, respectively, and the denominator used when we consider study themes (rather than individual papers) was 84 (Fig. 3).
Fifty-eight of the 84 studies (69%) explored associations of GDM, glucose levels/response or pre-existing maternal diabetes in pregnancy with offspring and/or placenta epigenetics (Fig. 3a). The most commonly studied epigenetic mechanism in these studies was DNA methylation (48/58; 83%). There were ten studies of offspring miRNA and one study of histones (Fig. 3a). There were 25 studies of offspring tissues (22 in blood, one in adipose, one in adipose and muscle, one in skeletal muscle), 24 studies of placenta and nine studies of both offspring blood and placenta (Fig. 3c). In most of these studies, the hypothesis or background rationale was that epigenetic mechanisms mediate any effect of GDM/glucose traits on offspring outcomes. However, these studies mainly presented associations of GDM (or a related exposure) with offspring/placenta methylation and exploration of any causal effect or effect on downstream offspring outcomes was lacking. Five of the studies conducted mediation analysis. In one study, two-step MR provided some evidence for differential DNA methylation levels near the leptin gene (LEP) mediating the effect of maternal fasting glucose on neonatal leptin levels [32]. This study was conducted in just 485 mother-offspring pairs and both offspring DNA methylation and leptin were measured in cord blood. In a study of 835 mother-offspring pairs, evidence from structural equation modelling suggested that GDM mediated an effect of obesity on fetal-side placental DNA methylation of the LEP promotor region [33]. Three further studies using the Baron and Kenny method reported evidence that DNA methylation might mediate the following effects: (1) the effect of in utero exposure to GDM on childhood cardiometabolic traits (specifically, differential methylation around VCAM-1 [also known as VCAM1]) [34]; (2) the effect of maternal hyperglycaemia on offspring leptin levels at birth [35]; and (3) the effect of gestational type 2 diabetes on type 2 diabetes risk in offspring [36]. Of these five mediation analyses, one attempted to replicate findings in an independent study. Overall, just under half of the studies (27/58; 47%) attempted replication of findings in an independent cohort or conducted in vitro assays to support the main study findings, although at least 12 of the 58 studies (21%) noted the need for additional replication or validation (see ESM Table 2).
The second predominant theme of research effort was in identifying associations between maternal epigenetics and GDM or glucose levels/response in pregnancy (26/84 studies; 31%; Fig. 3a). Most of these studies aimed to explore the aetiology of GDM and/or the progression to type 2 diabetes. There were 20 studies of blood, two of the maternal side of the placenta, two of maternal adipose tissue, one of omentum and one of both blood and the maternal side of the placenta (Fig.  3c). Most studies of maternal epigenetics evaluated miRNA expression (15/26; 58%; Fig. 3a). Four studies explored prediction of GDM risk, using area under the receiver operating curve (AUROC) to test predictive discrimination and two of these four studies attempted to validate or replicate their findings. However, it was unclear whether the miRNAs identified from these studies were predictive of disease independently of known (clinical) predictors, or were more accurate than these known predictors, as these comparisons were not made. Fig. 3 Summary of human epigenetic studies related to GDM or hyperglycaemia in pregnancy. In (c) the size of the circles reflects the number of studies in each tissue. The numbers of studies (given within the circles) total 84 (rather than 81) because three studies contributed to both of the broad areas and are depicted twice in this figure. In (d), for case-control studies, the length of the vertical bar below the white horizontal line shows the proportion of GDM cases, relative to the total length of the bar. T1D, type 1 diabetes. This figure is available as part of a downloadable slideset  Overall, among the 26 studies that examined maternal epigenetics as causal risk factors for (or predictors of) GDM, only ten (38%) attempted validation or replication of their results or included in vitro assays to support the main study findings. At least five of the 26 studies (19%) noted that additional replication or validation of study findings were needed (see ESM Table 2).
Across both research themes, studies were split evenly between candidate-gene-and array-based approaches, with a small proportion of studies assessing global or other measures (Fig. 3b). Sample sizes were small (median n = 58, median n cases = 27) and there was no obvious pattern of association between sample size and studied epigenetic mechanism or data type (Fig. 3d).

Discussion: conclusions and future research
Our review shows that there is a substantial body of epigenetic epidemiology research in relation to GDM. Most of this articulates an interest in the possible mediation by epigenetic phenomena of a possible causal effect of maternal GDM on offspring future health including future risk of obesity and type 2 diabetes. Future research in this area should attempt to replicate findings, expand the range of causal analysis approaches applied to this question and, where possible, triangulate across these to explore whether epigenetic mechanisms that may be influenced by GDM relate to future adverse offspring outcomes.
Seeking replication and exploring causality through other methods such as negative control paternal studies and MR require large samples sizes and necessitate collaboration across studies. Thus, endeavours such as the Pregnancy And Childhood Epigenetics (PACE) consortium [37] are important for taking this research forward. Any study that has epigenome-wide data collected using the Ilumina 450K or EPIC BeadChip and any pregnancy, neonatal or childhood data can join PACE. There are no restrictions on sample size, geography or ethnicity of participants and members of the consortium can propose and lead projects (https://www.niehs.nih.gov/research/ atniehs/labs/epi/pi/genetics/pace/index.cfm). To date, the collaboration has largely looked at multivariable observational association, although one study (of maternal BMI) included a parental negative control study [17].
MR is increasingly being adopted to strengthen causal inference in epigenetic studies [38,39]. The two-step MR framework is relevant to the exploration of the causal pathways linking GDM to offspring outcomes via epigenetic mechanisms [15]. The feasibility of applying MR to address questions pertaining to the potential long-term consequences of in utero exposures, such as GDM, is improving due to the increasing availability of relevant genome-wide genetic data and the development of relevant statistical methods [40][41][42][43]. However, it may not be feasible to use MR to determine a specific intrauterine effect of exposure to maternal GDM on offspring type 2 diabetes given the overlapping pathophysiology and genetic correlates between GDM and type 2 diabetes [15]. It may also not be feasible to use MR if there are no strong genetic instruments available for a particular epigenetic mark. However, genome-wide association studies of DNA methylation have been published [44,45], with a large-scale meta-analysis underway by the Genetics of DNA Methylation Consortium (GoDMC). These efforts are generating an extensive catalogue of SNPs that tag methylation variation (meQTL) and these in turn can be used in MR and have been applied in a systematic way across many outcomes simultaneously [45].
Assessment of epigenetic measures for prediction of GDM was rare among studies identified (n = 4). The ability of epigenetic marks to integrate genetic and nongenetic factors in a biologically stable and technically reproducible way means they have high potential as biomarkers. Perhaps uniquely, epigenetic marks can 'capture' information on endogenous and exogenous exposures, including risk factors and very early consequences of disease processes, thus promising to be an effective tool in early detection and future prediction and prognosis. The use of epigenetic biomarkers in this way is established in cancer research [46], and this is an area of research that is likely to see considerably more attention in other areas in future years (including pregnancy complications, where accurate risk prediction remains poor).
In conclusion, determining whether GDM causes (via intrauterine mechanisms) increased risk of future offspring type 2 diabetes is important, because if it does there could be an intergenerational cycling or risk that would accelerate the increasing risk of type 2 diabetes and GDM. If there is a causal effect, epigenetic mechanisms could provide a potential modifiable target for intervention development to break this cycle. There is increasing optimism that epigenetic information can be used as a biomarker to predict future likelihood of adverse outcomes. As methods for identifying risk of GDM early in pregnancy (before fetal overgrowth begins) are lacking, this could be valuable for identifying women who might benefit most from moreintensive antenatal monitoring and interventions (lifestyle or pharmaceutical) to optimise fetal growth and minimise maternal hyperglycaemia. The assembled published evidence suggests that this is a fruitful avenue of research to explore. However, larger, more robust studies are required to strengthen the current evidence base.