Proteomic profiles of left atrial volume and its influence on response to spironolactone: Findings from the HOMAGE trial and STANISLAS cohort

AIMS

High left ventricular filling pressure increases left atrial volume and causes myocardial fibrosis, which may decrease with spironolactone. We studied clinical and proteomic characteristics associated with left atrial volume indexed by body surface area (LAVi), and whether LAVi influences the response to spironolactone on biomarker expression and clinical variables.

METHODS AND RESULTS

In the HOMAGE trial, where people at risk of heart failure were randomized to spironolactone or control, we analysed 421 participants with available LAVi and 276 proteomic measurements (Olink) at baseline, month 1 and 9 (mean age 73 ± 6 years; women 26%; LAVi 32 ± 9 ml/m2). Circulating proteins associated with LAVi were also assessed in asymptomatic individuals from a population-based cohort (STANISLAS; n = 1640; mean age 49 ± 14 years; women 51%; LAVi 23 ± 7 ml/m2). In both studies, greater LAVi was significantly associated with greater left ventricular masses and volumes. In HOMAGE, after adjustment and correction for multiple testing, greater LAVi was associated with higher concentrations of matrix metallopeptidase-2 (MMP-2), insulin-like growth factor binding protein-2 (IGFBP-2) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (false discovery rates [FDR] <0.05). These associations were externally replicated in STANISLAS (all FDR <0.05). Among these biomarkers, spironolactone decreased concentrations of MMP-2 and NT-proBNP, regardless of baseline LAVi (pinteraction > 0.10). Spironolactone also significantly reduced LAVi, improved left ventricular ejection fraction, lowered E/e', blood pressure and serum procollagen type I C-terminal propeptide (PICP) concentration, a collagen synthesis marker, regardless of baseline LAVi (pinteraction > 0.10).

CONCLUSION

In individuals without heart failure, LAVi was associated with MMP-2, IGFBP-2 and NT-proBNP. Spironolactone reduced these biomarker concentrations as well as LAVi and PICP, irrespective of left atrial size.

Associations of childhood adiposity with adult intima-media thickness and inflammation: a 20-year longitudinal population-based cohort

BACKGROUND

The associations between childhood adiposity and adult increased carotid intima-media thickness (cIMT) have been well established, which might be corroborated by the association between adiposity in children and inflammation in adults. However, longitudinal data regarding biological pathways associated with childhood adiposity are lacking.

METHODS

The current study included participants from the STANISLAS cohort who had adiposity measurements at age 5-18 years [ N  = 519, mean (SD) age, 13.0 (2.9) years; 46.4% male], and who were measured with cIMT, vascular-related and metabolic-related proteins at a median follow-up of 19 ± 2 years. BMI, waist-to-height ratio and waist circumference were converted to age-specific and sex-specific z -scores.

RESULTS

A minority of children were overweight/obese (16.2% overweight-BMI z -score >1; 1.3% obesity- z -score >2). Higher BMI, waist-height ratio and waist circumference in children were significantly associated with greater adult cIMT in univariable analysis, although not after adjusting for C-reactive protein. These associations were more pronounced in those with consistently high adiposity status from childhood to middle adulthood. Participants with higher adiposity during childhood (BMI or waist-height ratio) had higher levels of insulin-like growth factor-binding protein-1, protein-2, matrix metalloproteinase-3, osteopontin, hemoglobin and C-reactive protein in adulthood. Network analysis showed that IL-6, insulin-like growth factor-1 and fibronectin were the key proteins associated with childhood adiposity.

CONCLUSION

In a population-based cohort followed for 20 years, higher BMI or waist-to-height ratio in childhood was significantly associated with greater cIMT and enhanced levels of proteins reflective of inflammation, supporting the importance of inflammation as progressive atherosclerosis in childhood adiposity.

NT-proBNP and stem cell factor plasma concentrations are independently associated with cardiovascular outcomes in end-stage renal disease hemodialysis patients

Aims

End-stage renal disease (ESRD) treated by chronic hemodialysis (HD) is associated with poor cardiovascular (CV) outcomes, with no available evidence-based therapeutics. A multiplexed proteomic approach may identify new pathophysiological pathways associated with CV outcomes, potentially actionable for precision medicine.

Methods and results

The AURORA trial was an international, multicentre, randomized, double-blind trial involving 2776 patients undergoing maintenance HD. Rosuvastatin vs. placebo had no significant effect on the composite primary endpoint of death from CV causes, nonfatal myocardial infarction or nonfatal stroke. We first compared CV risk-matched cases and controls (n = 410) to identify novel biomarkers using a multiplex proximity extension immunoassay (276 proteomic biomarkers assessed with Olink^TM). We replicated our findings in 200 unmatched cases and 200 controls. External validation was conducted from a multicentre real-life Danish cohort [Aarhus-Aalborg (AA), n = 331 patients] in which 92 Olink^TM biomarkers were assessed. In AURORA, only N-terminal pro-brain natriuretic peptide (NT-proBNP, positive association) and stem cell factor (SCF) (negative association) were found consistently associated with the trial's primary outcome across exploration and replication phases, independently from the baseline characteristics. Stem cell factor displayed a lower added predictive ability compared with NT-ProBNP. In the AA cohort, in multivariable analyses, BNP was found significantly associated with major CV events, while higher SCF was associated with less frequent CV deaths.

Conclusions

Our findings suggest that NT-proBNP and SCF may help identify ESRD patients with respectively high and low CV risk, beyond classical clinical predictors and also point at novel pathways for prevention and treatment.

Inflammation and remodeling pathways and risk of cardiovascular events in patients with ischemic heart failure and reduced ejection fraction

Patients with heart failure (HF) and coronary artery disease (CAD) have a high risk for cardiovascular (CV) events including HF hospitalization, stroke, myocardial infarction (MI) and sudden cardiac death (SCD). The present study evaluated associations of proteomic biomarkers with CV outcome in patients with CAD and HF with reduced ejection fraction (HFrEF), shortly after a worsening HF episode. We performed a case-control study within the COMMANDER HF international, double-blind, randomized placebo-controlled trial investigating the effects of the factor-Xa inhibitor rivaroxaban. Patients with the following first clinical events: HF hospitalization, SCD and the composite of MI or stroke were matched with corresponding controls for age, sex and study drug. Plasma concentrations of 276 proteins with known associations with CV and cardiometabolic mechanisms were analyzed. Results were corrected for multiple testing using false discovery rate (FDR). In 485 cases and 455 controls, 49 proteins were significantly associated with clinical events of which seven had an adjusted FDR < 0.001 (NT-proBNP, BNP, T-cell immunoglobulin and mucin domain containing 4 (TIMD4), fibroblast growth factor 23 (FGF-23), growth differentiation factor-15 (GDF-15), pulmonary surfactant-associated protein D (PSP-D) and Spondin-1 (SPON1)). No significant interactions were identified between the type of clinical event (MI/stroke, SCD or HFH) and specific biomarkers (all interaction FDR > 0.20). When adding the biomarkers significantly associated with the above outcome to a clinical model (including NT-proBNP), the C-index increase was 0.057 (0.033-0.082), p < 0.0001 and the net reclassification index was 54.9 (42.5 to 67.3), p < 0.0001. In patients with HFrEF and CAD following HF hospitalization, we found that NT-proBNP, BNP, TIMD4, FGF-23, GDF-15, PSP-D and SPON1, biomarkers broadly associated with inflammation and remodeling mechanistic pathways, were strong but indiscriminate predictors of a variety of individual CV events.

Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals

OBJECTIVES

This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes.

BACKGROUND

Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge.

METHODS

Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well.

RESULTS

Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34).

CONCLUSIONS

Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442).

Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

BACKGROUND

Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established.

METHODS

We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory.

RESULTS

Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them.

CONCLUSION

Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.

Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

The choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

Circulating plasma proteins and new-onset diabetes in a population-based study: proteomic and genomic insights from the STANISLAS cohort

OBJECTIVE

Determining the factors associated with new-onset pre-diabetes and type 2 diabetes mellitus (T2D) is important for improving the current prevention strategies and for a better understanding of the disease.

DESIGN

To study the factors (clinical, circulating protein and genetic) associated with new onset pre-diabetes and T2D in an initially healthy (without diabetes) populational familial cohort with a long follow-up (STANISLAS cohort).

METHODS

A total of 1506 participants attended both the visit 1 and visit 4, separated by ≈20 years. Over 400 proteins, GWAS and genetic associations were studied using models adjusted for potential confounders. Both prospective (V1 to V4) and cross-sectional (V4) analyses were performed.

RESULTS

People who developed pre-diabetes (n = 555) and/or T2D (n = 73) were older, had higher BMI, blood pressure, glucose, LDL cholesterol, and lower eGFR. After multivariable selection, PAPP-A (pappalysin-1) was the only circulating protein associated with the onset of both pre-diabetes and T2D with associations persisting at visit 4 (i.e. ≈20 years later). FGF-21 (fibroblast growth factor 21) was a strong prognosticator for incident T2D in the longitudinal analysis, but not in the cross-sectional analysis. The heritability of the circulating PAPP-A was estimated at 44%. In GWAS analysis, the SNP rs634737 was associated with PAPP-A both at V1 and V4. External replication also showed lower levels of PAPP-A in patients with T2D.

CONCLUSIONS

The risk of developing pre-diabetes and T2D increases with age and with features of the metabolic syndrome. Circulating PAPP-A, which has an important genetic component, was associated with both the development and presence of pre-diabetes and T2D.

Sex differences in circulating proteins in heart failure with preserved ejection fraction

BACKGROUND

Many patients with heart failure with preserved ejection fraction (HFpEF) are women. Exploring mechanisms underlying the sex differences may improve our understanding of the pathophysiology of HFpEF. Studies focusing on sex differences in circulating proteins in HFpEF patients are scarce.

METHODS

A total of 415 proteins were analyzed in 392 HFpEF patients included in The Metabolic Road to Diastolic Heart Failure: Diastolic Heart Failure study (MEDIA-DHF). Sex differences in these proteins were assessed using adjusted logistic regression analyses. The associations between candidate proteins and cardiovascular (CV) death or CV hospitalization (with sex interaction) were assessed using Cox regression models.

RESULTS

We found 9 proteins to be differentially expressed between female and male patients. Women expressed more LPL and PLIN1, which are markers of lipid metabolism; more LHB, IGFBP3, and IL1RL2 as markers of transcriptional regulation; and more Ep-CAM as marker of hemostasis. Women expressed less MMP-3, which is a marker associated with extracellular matrix organization; less NRP1, which is associated with developmental processes; and less ACE2, which is related to metabolism. Sex was not associated with the study outcomes (adj. HR 1.48, 95% CI 0.83-2.63), p = 0.18.

CONCLUSION

In chronic HFpEF, assessing sex differences in a wide range of circulating proteins led to the identification of 9 proteins that were differentially expressed between female and male patients. These findings may help further investigations into potential pathophysiological processes contributing to HFpEF.

Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes.Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression.Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12-3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism.Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

Bioprofiles and mechanistic pathways associated with Cheyne-Stokes respiration: insights from the SERVE-HF trial

INTRODUCTION

The SERVE-HF trial included patients with heart failure and reduced ejection fraction (HFrEF) with sleep-disordered breathing, randomly assigned to treatment with Adaptive-Servo Ventilation (ASV) or control. The primary outcome was the first event of death from any cause, lifesaving cardiovascular intervention, or unplanned hospitalization for worsening heart failure. A subgroup analysis of the SERVE-HF trial suggested that patients with Cheyne-Stokes respiration (CSR) < 20% (low CSR) experienced a beneficial effect from ASV, whereas in patients with CSR ≥ 20% ASV might have been harmful. Identifying the proteomic signatures and the underlying mechanistic pathways expressed in patients with CSR could help generating hypothesis for future research.

METHODS

Using a large set of circulating protein-biomarkers (n = 276, available in 749 patients; 57% of the SERVE-HF population) we sought to investigate the proteins associated with CSR and to study the underlying mechanisms that these circulating proteins might represent.

RESULTS

The mean age was 69 ± 10 years and > 90% were male. Patients with CSR < 20% (n = 139) had less apnoea-hypopnea index (AHI) events per hour and less oxygen desaturation. Patients with CSR < 20% might have experienced a beneficial effect of ASV treatment (primary outcome HR [95% CI] = 0.55 [0.34-0.88]; p = 0.012), whereas those with CSR ≥ 20% might have experienced a detrimental effect of ASV treatment (primary outcome HR [95% CI] = 1.39 [1.09-1.76]; p = 0.008); p for interaction = 0.001. Of the 276 studied biomarkers, 8 were associated with CSR (after adjustment and with a FDR1%-corrected p value). For example, higher PAR-1 and ITGB2 levels were associated with higher odds of having CSR < 20%, whereas higher LOX-1 levels were associated with higher odds of CSR ≥ 20%. Signalling, metabolic, haemostatic and immunologic pathways underlie the expression of these biomarkers.

CONCLUSION

We identified proteomic signatures that may represent underlying mechanistic pathways associated with patterns of CSR in HFrEF. These hypothesis-generating findings require further investigation towards better understanding of CSR in HFrEF.

SUMMARY OF THE FINDINGS

PAR-1 proteinase-activated receptor 1, ADM adrenomedullin, HSP-27 heat shock protein-27, ITGB2 integrin beta 2, GLO1 glyoxalase 1, ENRAGE/S100A12 S100 calcium-binding protein A12, LOX-1 lectin-like LDL receptor 1, ADAM-TS13 disintegrin and metalloproteinase with a thrombospondin type 1 motif, member13 also known as von Willebrand factor-cleaving protease.

A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants

Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations.

Meloidogyne incognita PASSE-MURAILLE (MiPM) Gene Encodes a Cell-Penetrating Protein That Interacts With the CSN5 Subunit of the COP9 Signalosome

The pathogenicity of phytonematodes relies on secreted virulence factors to rewire host cellular pathways for the benefits of the nematode. In the root-knot nematode (RKN) Meloidogyne incognita, thousands of predicted secreted proteins have been identified and are expected to interact with host proteins at different developmental stages of the parasite. Identifying the host targets will provide compelling evidence about the biological significance and molecular function of the predicted proteins. Here, we have focused on the hub protein CSN5, the fifth subunit of the pleiotropic and eukaryotic conserved COP9 signalosome (CSN), which is a regulatory component of the ubiquitin/proteasome system. We used affinity purification-mass spectrometry (AP-MS) to generate the interaction network of CSN5 in M. incognita-infected roots. We identified the complete CSN complex and other known CSN5 interaction partners in addition to unknown plant and M. incognita proteins. Among these, we described M. incognita PASSE-MURAILLE (MiPM), a small pioneer protein predicted to contain a secretory peptide that is up-regulated mostly in the J2 parasitic stage. We confirmed the CSN5-MiPM interaction, which occurs in the nucleus, by bimolecular fluorescence complementation (BiFC). Using MiPM as bait, a GST pull-down assay coupled with MS revealed some common protein partners between CSN5 and MiPM. We further showed by in silico and microscopic analyses that the recombinant purified MiPM protein enters the cells of Arabidopsis root tips in a non-infectious context. In further detail, the supercharged N-terminal tail of MiPM (NTT-MiPM) triggers an unknown host endocytosis pathway to penetrate the cell. The functional meaning of the CSN5-MiPM interaction in the M. incognita parasitism is discussed. Moreover, we propose that the cell-penetrating properties of some M. incognita secreted proteins might be a non-negligible mechanism for cell uptake, especially during the steps preceding the sedentary parasitic phase.

Discovering associations between adverse drug events using pattern structures and ontologies

BACKGROUND

Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients.

RESULTS

Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization using domain knowledge formalized in medical ontologies. Results obtained with three different settings and two different datasets show that this approach is flexible and allows extraction of association rules at various levels of generalization.

CONCLUSIONS

The chosen approach permits an expressive representation of a patient ADEs. Extracted association rules point to distinct ADEs that occur in a same group of patients, and could serve as a basis for a recommandation system. The proposed representation is flexible and can be extended to make use of additional ontologies and various patient records.

Low-Dose Alkylphenol Exposure Promotes Mammary Epithelium Alterations and Transgenerational Developmental Defects, But Does Not Enhance Tumorigenic Behavior of Breast Cancer Cells

Fetal and neonatal exposure to long-chain alkylphenols has been suspected to promote breast developmental disorders and consequently to increase breast cancer risk. However, disease predisposition from developmental exposures remains unclear. In this work, human MCF-10A mammary epithelial cells were exposed in vitro to a low dose of a realistic (4-nonylphenol + 4-tert-octylphenol) mixture. Transcriptome and cell-phenotype analyses combined to functional and signaling network modeling indicated that long-chain alkylphenols triggered enhanced proliferation, migration ability, and apoptosis resistance and shed light on the underlying molecular mechanisms which involved the human estrogen receptor alpha 36 (ERα36) variant. A male mouse-inherited transgenerational model of exposure to three environmentally relevant doses of the alkylphenol mix was set up in order to determine whether and how it would impact on mammary gland architecture. Mammary glands from F3 progeny obtained after intrabuccal chronic exposure of C57BL/6J P0 pregnant mice followed by F1-F3 male inheritance displayed an altered histology which correlated with the phenotypes observed in vitro in human mammary epithelial cells. Since cellular phenotypes are similar in vivo and in vitro and involve the unique ERα36 human variant, such consequences of alkylphenol exposure could be extrapolated from mouse model to human. However, transient alkylphenol treatments combined to ERα36 overexpression in mammary epithelial cells were not sufficient to trigger tumorigenesis in xenografted Nude mice. Therefore, it remains to be determined if low-dose alkylphenol transgenerational exposure and subsequent abnormal mammary gland development could account for an increased breast cancer susceptibility.

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease

BACKGOUND

Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination.

RESULTS

In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation.

CONCLUSIONS

This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.

Antimicrobial properties of two novel peptides derived from Theobroma cacao osmotin

The osmotin proteins of several plants display antifungal activity, which can play an important role in plant defense against diseases. Thus, this protein can be useful as a source for biotechnological strategies aiming to combat fungal diseases. In this work, we analyzed the antifungal activity of a cacao osmotin-like protein (TcOsm1) and of two osmotin-derived synthetic peptides with antimicrobial features, differing by five amino acids residues at the N-terminus. Antimicrobial tests showed that TcOsm1 expressed in Escherichia coli inhibits the growth of Moniliophthora perniciosa mycelium and Pichia pastoris X-33 in vitro. The TcOsm1-derived peptides, named Osm-pepA (H-RRLDRGGVWNLNVNPGTTGARVWARTK-NH2), located at R23-K49, and Osm-pepB (H-GGVWNLNVNPGTTGARVWARTK-NH2), located at G28-K49, inhibited growth of yeasts (Saccharomyces cerevisiae S288C and Pichia pastoris X-33) and spore germination of the phytopathogenic fungi Fusarium f. sp. glycines and Colletotrichum gossypi. Osm-pepA was more efficient than Osm-pepB for S. cerevisiae (MIC=40μM and MIC=127μM, respectively), as well as for P. pastoris (MIC=20μM and MIC=127μM, respectively). Furthermore, the peptides presented a biphasic performance, promoting S. cerevisiae growth in doses around 5μM and inhibiting it at higher doses. The structural model for these peptides showed that the five amino acids residues, RRLDR at Osm-pepA N-terminus, significantly affect the tertiary structure, indicating that this structure is important for the peptide antimicrobial potency. This is the first report of development of antimicrobial peptides from T. cacao. Taken together, the results indicate that the cacao osmotin and its derived peptides, herein studied, are good candidates for developing biotechnological tools aiming to control phytopathogenic fungi.

Integrative relational machine-learning for understanding drug side-effect profiles

Background

Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.

Results

In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site.

Conclusions

Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Extended spectrum of MBD5 mutations in neurodevelopmental disorders

Intellectual disability (ID) is a clinical sign reflecting diverse neurodevelopmental disorders that are genetically and phenotypically heterogeneous. Just recently, partial or complete deletion of methyl-CpG-binding domain 5 (MBD5) gene has been implicated as causative in the phenotype associated with 2q23.1 microdeletion syndrome. In the course of systematic whole-genome screening of individuals with unexplained ID by array-based comparative genomic hybridization, we identified de novo intragenic deletions of MBD5 in three patients leading, as previously documented, to haploinsufficiency of MBD5. In addition, we described a patient with an unreported de novo MBD5 intragenic duplication. Reverse transcriptase-PCR and sequencing analyses showed the presence of numerous aberrant transcripts leading to premature termination codon. To further elucidate the involvement of MBD5 in ID, we sequenced ten coding, five non-coding exons and an evolutionary conserved region in intron 2, in a selected cohort of 78 subjects with a phenotype reminiscent of 2q23.1 microdeletion syndrome. Besides variants most often inherited from an healthy parent, we identified for the first time a de novo nonsense mutation associated with a much more damaging phenotype. Taken together, these results extend the mutation spectrum in MBD5 gene and contribute to refine the associated phenotype of neurodevelopmental disorder.