Predicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2^nd-level, 3^rd-level, 4^th-level, and 5^th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.     

Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property

Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) “Metabolism”, (ii) “Genetic Information Processing”, (iii) “Environmental Information Processing”, (iv) “Cellular Processes”, (v) “Organismal Systems”, and (vi) “Human Diseases”. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.     

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction

Background

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism’s genome as a database restricts metabolite identification to only those compounds that the organism can produce.

Results

To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment’s MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis.

Conclusions

Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.     

Analysis of metabolic pathway using hybrid properties

Given a compounds-forming system, i.e., a system consisting of some compounds and their relationship, can it form a biologically meaningful pathway? It is a fundamental problem in systems biology. Nowadays, a lot of information on different organisms, at both genetic and metabolic levels, has been collected and stored in some specific databases. Based on these data, it is feasible to address such an essential problem. Metabolic pathway is one kind of compounds-forming systems and we analyzed them in yeast by extracting different (biological and graphic) features from each of the 13,736 compounds-forming systems, of which 136 are positive pathways, i.e., known metabolic pathway from KEGG; while 13,600 were negative. Each of these compounds-forming systems was represented by 144 features, of which 88 are graph features and 56 biological features. "Minimum Redundancy Maximum Relevance" and "Incremental Feature Selection" were utilized to analyze these features and 16 optimal features were selected as being able to predict a query compounds- forming system most successfully. It was found through Jackknife cross-validation that the overall success rate of identifying the positive pathways was 74.26%. It is anticipated that this novel approach and encouraging result may give meaningful illumination to investigate this important topic.     

Phenylpropanoid glycoside analogues: enzymatic synthesis, antioxidant activity and theoretical study of their free radical scavenger mechanism

Phenylpropanoid glycosides (PPGs) are natural compounds present in several medicinal plants that have high antioxidant power and diverse biological activities. Because of their low content in plants (less than 5% w/w), several chemical synthetic routes to produce PPGs have been developed, but their synthesis is a time consuming process and the achieved yields are often low. In this study, an alternative and efficient two-step biosynthetic route to obtain natural PPG analogues is reported for the first time. Two galactosides were initially synthesized from vanillyl alcohol and homovanillyl alcohol by a transgalactosylation reaction catalyzed by Kluyveromyces lactis β-galactosidase in saturated lactose solutions with a 30%–35% yield. To synthesize PPGs, the galactoconjugates were esterified with saturated and unsaturated hydroxycinnamic acid derivatives using Candida antarctica Lipase B (CaL-B) as a biocatalyst with 40%–60% yields. The scavenging ability of the phenolic raw materials, intermediates and PPGs was evaluated by the 2,2-diphenyl-1-picrylhydrazyl radical (DPPH•) method. It was found that the biosynthesized PPGs had higher scavenging abilities when compared to ascorbic acid, the reference compound, while their antioxidant activities were found similar to that of natural PPGs. Moreover, density functional theory (DFT) calculations were used to determine that the PPGs antioxidant mechanism proceeds through a sequential proton loss single electron transfer (SPLET). The enzymatic process reported in this study is an efficient and versatile route to obtain PPGs from different phenylpropanoid acids, sugars and phenolic alcohols.     

Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice

Background

Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses.

Methodology/Principal Findings

ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (−49%, p<0.001), CETP levels (−31%, p<0.001), CETP activity (−74%, p<0.001) and increased HDL-C (39%, p<0.05). It influenced lipidomics classes of cholesterol esters and triglycerides the most. Rimonabant induced a weight loss (−9%, p<0.05), but only a moderate improvement of lipid profiles. In vitro, SUB885C extract caused adipolysis stimulation and adipogenesis inhibition in 3T3-L1 cells.

Conclusions

SUB885C, a multi-components preparation, is able to produce anti-atherogenic changes in lipids of the ApoE*3Leiden.CETP mice, which are comparable to those obtained with compounds belonging to known drugs (e.g. rimonabant, atorvastatin, niacin). This study successfully illustrated the power of lipidomics in unraveling intervention effects and to help finding new targets or ingredients for lifestyle-related metabolic abnormality.     

Prevalence of transmitted drug resistance and impact of transmitted resistance on treatment success in the German HIV-1 Seroconverter Cohort

Background

The aim of this study is to analyse the prevalence of transmitted drug resistance, TDR, and the impact of TDR on treatment success in the German HIV-1 Seroconverter Cohort.

Methods

Genotypic resistance analysis was performed in treatment-naïve study patients whose sample was available 1,312/1,564 (83.9% October 2008). A genotypic resistance result was obtained for 1,276/1,312 (97.3%). The resistance associated mutations were identified according to the surveillance drug resistance mutations list recommended for drug-naïve patients. Treatment success was determined as viral suppression below 500 copies/ml.

Results

Prevalence of TDR was stable at a high level between 1996 and 2007 in the German HIV-1 Seroconverter Cohort (N = 158/1,276; 12.4%; CI_wilson 10.7–14.3; p _{for trend} = 0.25). NRTI resistance was predominant (7.5%) but decreased significantly over time (CI_Wilson: 6.2–9.1, p _{for trend} = 0.02). NNRTI resistance tended to increase over time (NNRTI: 3.5%; CI_Wilson: 2.6–4.6; p _{for trend}  = 0.07), whereas PI resistance remained stable (PI: 3.0%; CI_Wilson: 2.1–4.0; p _{for trend}  = 0.24). Resistance to all drug classes was frequently caused by singleton resistance mutations (NRTI 55.6%, PI 68.4%, NNRTI 99.1%). The majority of NRTI-resistant strains (79.8%) carried resistance-associated mutations selected by the thymidine analogues zidovudine and stavudine. Preferably 2NRTI/1PIr combinations were prescribed as first line regimen in patients with resistant HIV as well as in patients with susceptible strains (susceptible 45.3%; 173/382 vs. resistant 65.5%; 40/61). The majority of patients in both groups were treated successfully within the first year after ART-initiation (susceptible: 89.9%; 62/69; resistant: 7/9; 77.8%).

Conclusion

Overall prevalence of TDR remained stable at a high level but trends of resistance against drug classes differed over time. The significant decrease of NRTI-resistance in patients newly infected with HIV might be related to the introduction of novel antiretroviral drugs and a wider use of genotypic resistance analysis prior to treatment initiation.     

TSdb: A database of transporter substrates linking metabolic pathways and transporter systems on a genome scale via their shared substrates

TSdb (http://tsdb.cbi.pku.edu.cn) is the first manually curated central repository that stores formatted information on the substrates of transporters. In total, 37608 transporters with 15075 substrates from 884 organisms were curated from UniProt functional annotation. A unique feature of TSdb is that all the substrates are mapped to identifiers from the KEGG Ligand compound database. Thus, TSdb links current metabolic pathway schema with compound transporter systems via the shared compounds in the pathways. Furthermore, all the transporter substrates in TSdb are classified according to their biochemical properties, biological roles and subcellular localizations. In addition to the functional annotation of transporters, extensive compound annotation that includes inhibitor information from the KEGG Ligand and BRENDA databases has been integrated, making TSdb a useful source for the discovery of potential inhibitory mechanisms linking transporter substrates and metabolic enzymes. User-friendly web interfaces are designed for easy access, query and download of the data. Text and BLAST searches against all transporters in the database are provided. We will regularly update the substrate data with evidence from new publications.     

Impact of different economic factors on biological invasions on the global scale

Social-economic factors are considered as the key to understand processes contributing to biological invasions. However, there has been few quantified, statistical evidence on the relationship between economic development and biological invasion on a worldwide scale. Herein, using principal factor analysis, we investigated the relationship between biological invasion and economic development together with biodiversity for 91 economies throughout the world. Our result indicates that the prevalence of invasive species in the economies can be well predicted by economic factors (R² = 0.733). The impact of economic factors on the occurrence of invasive species for low, lower-middle, upper-middle and high income economies are 0%, 34.3%, 46.3% and 80.8% respectively. Greenhouse gas emissions (CO₂, Nitrous oxide, Methane and Other greenhouse gases) and also biodiversity have positive relationships with the global occurrence of invasive species in the economies on the global scale. The major social-economic factors that are correlated to biological invasions are different for various economies, and therefore the strategies for biological invasion prevention and control should be different.     

Virtual screening, identification and in vitro testing of novel inhibitors of O-acetyl-L-serine sulfhydrylase of Entamoeba histolytica

The explosive epidemicity of amoebiasis caused by the facultative gastrointestinal protozoan parasite Entamoeba histolytica is a major public health problem in developing countries. Multidrug resistance and side effects of various available antiamoebic drugs necessitate the design of novel antiamobeic agents. The cysteine biosynthetic pathway is the critical target for drug design due to its significance in the growth, survival and other cellular activities of E. histolytica. Here, we have screened 0.15 million natural compounds from the ZINC database against the active site of the EhOASS enzyme (PDB ID. 3BM5, 2PQM), whose structure we previously determined to 2.4 Å and 1.86 Å resolution. For this purpose, the incremental construction algorithm of GLIDE and the genetic algorithm of GOLD were used. We analyzed docking results for top ranking compounds using a consensus scoring function of X-Score to calculate the binding affinity and using ligplot to measure protein-ligand interactions. Fifteen compounds that possess good inhibitory activity against EhOASS active site were identified that may act as potential high affinity inhibitors. In vitro screening of a few commercially available compounds established their biological activity. The first ranked compound ZINC08931589 had a binding affinity of ∼8.05 µM and inhibited about 73% activity at 0.1 mM concentration, indicating good correlation between in silico prediction and in vitro inhibition studies. This compound is thus a good starting point for further development of strong inhibitors.     

Predicting protein phenotypes based on protein-protein interaction network

Background

Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.

Methodology/Principal Findings

Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked acording to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%.

Conclusions/Significance

The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.     

Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties

Background

With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.

Methodology/Principal Findings

Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.

Conclusions/Significance

The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.     

Systematic drug repositioning based on clinical side-effects

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.     

Retention in a NGO supported antiretroviral program in the Democratic Republic of Congo

Background

Retention of patients in ART care is a major challenge in sub-Saharan programs. Retention is also one of the key indicators to evaluate the success of ART programs.

Methods and Findings

A retrospective review of 1500 randomly selected medical charts of adult ART patients from a local non-governmental (NGO) supported ART program in the Democratic Republic of Congo (DRC). Retention was defined as any visit to the clinic in the 4 months prior to the abstraction date. Retention over time and across different sites was described. The relationship between patient characteristics and retention rates at 1 year was also examined. 1450 patients were included in the analysis. The overall retention rates were 81.4% (95% CI: 79.3–83.4), 75.2% (95% CI: 72.8–77.3), 65.0% (95% CI: 62.3–67.6) and 57.2% (95% CI: 54.0–60.3) at 6 months, 1 year, 2 years and 3 years respectively. The retention rates between sites varied between 62.1% and 90.6% at 6 months and between 55.5% and 86.2% at 1 year. During multivariable analysis weight below 50 kg (aHR: 1.33, 95%CI: 1.05–1.69), higher WHO stage at initiation (aHR: 1.22, 95%CI 0.85–1.76 for stage 3 and aHR: 2.98, 95%CI: 1.93–4.59 for stage 4), and male sex (aHR: 1.32, 95%CI: 1.05–1.65) remained as significant risk factors for attrition during the first year after ART initiation. Other independent risk factors were year of initiation (aHR: 1.73, 95%CI: 1.26–2.38 for the year 2007 and aHR: 3.06, 95%CI: 2.26–4.14 for the period 2008–2009), and site.

Conclusions

Retention is a major problem in DRC, while coverage of patients on ART is still very low. With the flattening of funding for HIV care and treatment in sub-Saharan Africa, and with decreasing funding worldwide, maximizing retention during the much needed scaling-up will even be more important.     

Coding, recording and incidence of different forms of coronary heart disease in primary care

Objectives

To evaluate the coding, recording and incidence of coronary heart disease (CHD) in primary care electronic medical records.

Methods

Data were drawn from the UK General Practice Research Database. Analyses evaluated the occurrence of 271 READ medical diagnostic codes, including categories for ‘Angina’, ‘Myocardial Infarction’, ‘Coronary Artery Bypass Grafting’ (CABG), ‘percutaneous transluminal coronary angioplasty’ (PCTA) and ‘Other Coronary Heart Disease’. Time-to-event analyses were implemented to evaluate occurrences of different groups of codes after the index date.

Results

Among 300,020 participants aged greater than 30 years there were 75,197 unique occurrences of coronary heart disease codes in 24,244 participants, with 12,495 codes for incident events and 62,702 for prevalent events. Among incident event codes, 3,607 (28.87%) were for angina, 3,262 (26.11%) were for MI, 514 (4.11%) for PCTA, 161 (1.29%) for CABG and 4,951 (39.62%) were for ‘Other CHD’. Among prevalent codes, 20,254 (32.30%) were for angina, 3,644 (5.81%) for MI, 34,542 (55.09%) for ‘Other CHD’ and 4,262 (6.80%) for CABG or PCTA. Among 3,685 participants initially diagnosed exclusively with ‘Other CHD’ codes, 17.1% were recorded with angina within 5 years, 5.6% with myocardial infarction, 6.3% with CABG and 8.6% with PCTA. From 2000 to 2010, the overall incidence of CHD declined, as did the incidence of angina, but the incidence of MI did not change. The frequency of CABG declined, while PCTA increased.

Conclusion

In primary care electronic records, a substantial proportion of coronary heart disease events are recorded with codes that do not distinguish between different clinical presentations of CHD. The results draw attention to the need to improve coding practice in primary care. The results also draw attention to the importance of code selection in research studies and the need for sensitivity analyses using different sets of codes.     

Estimation of relevant variables on high-dimensional biological patterns using iterated weighted kernel functions

Background

The analysis of complex proteomic and genomic profiles involves the identification of significant markers within a set of hundreds or even thousands of variables that represent a high-dimensional problem space. The occurrence of noise, redundancy or combinatorial interactions in the profile makes the selection of relevant variables harder.

Methodology/Principal Findings

Here we propose a method to select variables based on estimated relevance to hidden patterns. Our method combines a weighted-kernel discriminant with an iterative stochastic probability estimation algorithm to discover the relevance distribution over the set of variables. We verified the ability of our method to select predefined relevant variables in synthetic proteome-like data and then assessed its performance on biological high-dimensional problems. Experiments were run on serum proteomic datasets of infectious diseases. The resulting variable subsets achieved classification accuracies of 99% on Human African Trypanosomiasis, 91% on Tuberculosis, and 91% on Malaria serum proteomic profiles with fewer than 20% of variables selected. Our method scaled-up to dimensionalities of much higher orders of magnitude as shown with gene expression microarray datasets in which we obtained classification accuracies close to 90% with fewer than 1% of the total number of variables.

Conclusions

Our method consistently found relevant variables attaining high classification accuracies across synthetic and biological datasets. Notably, it yielded very compact subsets compared to the original number of variables, which should simplify downstream biological experimentation.     

Monitoring and prevalence rates of metabolic syndrome in military veterans with serious mental illness

Background

Cardiovascular disease is the leading cause of mortality among patients with serious mental illness (SMI) and the prevalence of metabolic syndrome—a constellation of cardiovascular risk factors—is significantly higher in these patients than in the general population. Metabolic monitoring among patients using second generation antipsychotics (SGAs)—a risk factor for metabolic syndrome—has been shown to be inadequate despite the release of several guidelines. However, patients with SMI have several factors independent of medication use that predispose them to a higher prevalence of metabolic syndrome. Our study therefore examines monitoring and prevalence of metabolic syndrome in patients with SMI, including those not using SGAs.

Methods and Findings

We retrospectively identified all patients treated at a Veterans Affairs Medical Center with diagnoses of schizophrenia, schizoaffective disorder or bipolar disorder during 2005–2006 and obtained demographic and clinical data. Incomplete monitoring of metabolic syndrome was defined as being unable to determine the status of at least one of the syndrome components. Of the 1,401 patients included (bipolar disorder: 822; schizophrenia: 222; and schizoaffective disorder: 357), 21.4% were incompletely monitored. Only 54.8% of patients who were not prescribed SGAs and did not have previous diagnoses of hypertension or hypercholesterolemia were monitored for all metabolic syndrome components compared to 92.4% of patients who had all three of these characteristics. Among patients monitored for metabolic syndrome completely, age-adjusted prevalence of the syndrome was 48.4%, with no significant difference between the three psychiatric groups.

Conclusions

Only one half of patients with SMI not using SGAs or previously diagnosed with hypertension and hypercholesterolemia were completely monitored for metabolic syndrome components compared to greater than 90% of those with these characteristics. With the high prevalence of metabolic syndrome seen in this population, there appears to be a need to intensify efforts to reduce this monitoring gap.     

Frequent and simultaneous epigenetic inactivation of TP53 pathway genes in acute lymphoblastic leukemia

Aberrant DNA methylation is one of the most frequent alterations in patients with Acute Lymphoblastic Leukemia (ALL). Using methylation bead arrays we analyzed the methylation status of 807 genes implicated in cancer in a group of ALL samples at diagnosis (n = 48). We found that 154 genes were methylated in more than 10% of ALL samples. Interestingly, the expression of 13 genes implicated in the TP53 pathway was downregulated by hypermethylation. Direct or indirect activation of TP53 pathway with 5-aza-2′-deoxycitidine, Curcumin or Nutlin-3 induced an increase in apoptosis of ALL cells. The results obtained with the initial group of 48 patients was validated retrospectively in a second cohort of 200 newly diagnosed ALL patients. Methylation of at least 1 of the 13 genes implicated in the TP53 pathway was observed in 78% of the patients, which significantly correlated with a higher relapse (p = 0.001) and mortality (p<0.001) rate being an independent prognostic factor for disease-free survival (DFS) (p = 0.006) and overall survival (OS) (p = 0.005) in the multivariate analysis. All these findings indicate that TP53 pathway is altered by epigenetic mechanisms in the majority of ALL patients and correlates with prognosis. Treatments with compounds that may reverse the epigenetic abnormalities or activate directly the p53 pathway represent a new therapeutic alternative for patients with ALL.