An omics perspective on drug target discovery platforms.

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery.

SUMMARY: Estimating efficacy of gene-target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene-disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug-target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)-disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes. AVAILABILITY AND IMPLEMENTATION: ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA. CONTACT: vittorio.fortino@uef.fi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients.

BACKGROUND: Genetic testing in hypertrophic cardiomyopathy (HCM) is a published guideline-based recommendation. The diagnostic yield of genetic testing and corresponding HCM-associated genes have been largely documented by single center studies and carefully selected patient cohorts. Our goal was to evaluate the diagnostic yield of genetic testing in a heterogeneous cohort of patients with a clinical suspicion of HCM, referred for genetic testing from multiple centers around the world. METHODS: A retrospective review of patients with a suspected clinical diagnosis of HCM referred for genetic testing at Blueprint Genetics was undertaken. The analysis included syndromic, myopathic and metabolic etiologies. Genetic test results and variant classifications were extracted from the database. Variants classified as pathogenic (P) or likely pathogenic (LP) were considered diagnostic. RESULTS: A total of 1376 samples were analyzed. Three hundred and sixty-nine tests were diagnostic (26.8%); 373 P or LP variants were identified. Only one copy number variant was identified. The majority of diagnostic variants involved genes encoding the sarcomere (85.0%) followed by 4.3% of diagnostic variants identified in the RASopathy genes. Two percent of diagnostic variants were in genes associated with a cardiomyopathy other than HCM or an inherited arrhythmia. Clinical variables that increased the likelihood of identifying a diagnostic variant included: an earlier age at diagnosis (p < 0.0001), a higher maximum wall thickness (MWT) (p < 0.0001), a positive family history (p < 0.0001), the absence of hypertension (p = 0.0002), and the presence of an implantable cardioverter-defibrillator (ICD) (p = 0.0004). CONCLUSION: The diagnostic yield of genetic testing in this heterogeneous cohort of patients with a clinical suspicion of HCM is lower than what has been reported in well-characterized patient cohorts. We report the highest yield of diagnostic variants in the RASopathy genes identified in a laboratory cohort of HCM patients to date. The spectrum of genes implicated in this unselected cohort highlights the importance of pre-and post-test counseling when offering genetic testing to the broad HCM population.

Putative metabolites involved in the beneficial effects of wholegrain cereal: Nontargeted metabolite profiling approach.

BACKGROUND AND AIMS: Wholegrain cereals have been implicated in the reduction of lifestyle-related chronic diseases risk including cardiovascular diseases and type 2 diabetes. Molecular mechanisms responsible for the beneficial health effects are not entirely understood. The aims of this study were 1) to identify new potential plasma biomarker candidate metabolites of wholegrain cereal foods intake and 2) to examine whether some putative metabolites associated with wholegrain foods intake may play a role in the improvement of cardiometabolic risk factors. METHODS AND RESULTS: Analysis have been conducted in 54 individuals with metabolic syndrome of both genders, age 40-65 years, randomly assigned to 2 dietary interventions lasting 12-week: 1) wholegrain enriched diet (n = 28), and 2) refined-wheat cereals diet (control diet) (n = 26). Nontargeted metabolite profiling analysis was performed on fasting plasma samples collected at baseline and at the end of the experimental diets. Our data show that, at the end of the intervention, a higher intake of wholegrain (tertile 3) was significantly associated with a marked increase in several lipid compounds, as PC (20:4/16:1), LPC (20:4), LPC (22:6), LPC (18:3), LPC (22:5), and a phenolic compound (P < .05 for all). In the wholegrain group, higher concentrations of these metabolites (tertile 3 vs tertile 1 of each metabolite) were significantly associated with lower postprandial insulin and triglyceride responses (P < .05) by 29% and 37%, respectively. CONCLUSION: These observations suggest a possible role of lipid and polyphenol metabolites in the postprandial metabolic benefits of wholegrains in subjects at high risk of cardiovascular disease. In addition, they provide insight into the role of these metabolites as potential candidate biomarkers of wholegrain foods. The study was registered on ClinicalTrials.gov (identifier: NCT00945854).

Susceptibility to Cardiac Arrhythmias and Sympathetic Nerve Growth in VEGF-B Overexpressing Myocardium.

VEGF-B gene therapy is a promising proangiogenic treatment for ischemic heart disease, but, unexpectedly, we found that high doses of VEGF-B promote ventricular arrhythmias (VAs). VEGF-B knockout, alpha myosin heavy-chain promoter (αMHC)-VEGF-B transgenic mice, and pigs transduced intramyocardially with adenoviral (Ad)VEGF- B186 were studied. Immunostaining showed a 2-fold increase in the number of nerves per field (76 vs. 39 in controls, p < 0.001) and an abnormal nerve distribution in the hypertrophic hearts of 11- to 20-month-old αMHC-VEGF-B mice. AdVEGF-B186 gene transfer (GT) led to local sprouting of nerve endings in pig myocardium (141 vs. 78 nerves per field in controls, p < 0.05). During dobutamine stress, 60% of the αMHC-VEGF-B hypertrophic mice had arrhythmias as compared to 7% in controls, and 20% of the AdVEGF-B186-transduced pigs and 100% of the combination of AdVEGF-B186- and AdsVEGFR-1-transduced pigs displayed VAs and even ventricular fibrillation. AdVEGF-B186 GT significantly increased the risk of sudden cardiac death in pigs when compared to any other GT with different VEGFs (hazard ratio, 500.5; 95% confidence interval [CI] 46.4-5,396.7; p < 0.0001). In gene expression analysis, VEGF-B induced the upregulation of Nr4a2, ATF6, and MANF in cardiomyocytes, molecules previously linked to nerve growth and differentiation. Thus, high AdVEGF-B186 overexpression induced nerve growth in the adult heart via a VEGFR-1 signaling-independent mechanism, leading to an increased risk of VA and sudden cardiac death.

Varanto: variant enrichment analysis and annotation.

SUMMARY: Genome-wide association studies (GWAS) aim to identify associations of genetic variations such as single-nucleotide polymorphisms (SNPs) to a specific trait or a disease. Identifying common themes such as pathways, biological processes and diseases associations is needed to further explore and interpret these results. Varanto is a novel web tool for annotating, visualizing and analyzing human genetic variations using diverse data sources. Varanto can be used to query a set of input variations, retrieve their associated variation and gene level annotations, perform annotation enrichment analysis and visualize the results. AVAILABILITY AND IMPLEMENTATION: Varanto web app is developed with R and implemented as Shiny app with PostgreSQL database and is freely available at http://bioinformatics.uef.fi/varanto. Source code for the tool is available at https://github.com/oqe/varanto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Contribution of Staffing to Medication Administration Errors: A Text Mining Analysis of Incident Report Data.

PURPOSE: (a) To describe trigger terms that can be used to identify reports of inadequate staffing contributing to medication administration errors, (b) to identify such reports, (c) to compare the degree of harm within incidents with and without those triggers, and (d) to examine the association between the most commonly reported inadequate staffing trigger terms and the incidence of omission errors and "no harm" terms. DESIGN AND SETTING: This was a retrospective study using descriptive statistical analysis, text mining, and manual analysis of free text descriptions of medication administration-related incident reports (N = 72,390) reported to the National Reporting and Learning System for England and Wales in 2016. METHODS: Analysis included identifying terms indicating inadequate staffing (manual analysis), followed by text parsing, filtering, and concept linking (SAS Text Miner tool). IBM SPSS was used to describe the data, compare degree of harm for incidents with and without triggers, and to compare incidence of "omission errors" and "no harm" among the inadequate staffing trigger terms. FINDINGS: The most effective trigger terms for identifying inadequate staffing were "short staffing" (n = 81), "workload" (n = 80), and "extremely busy" (n = 51). There was significant variation in omission errors across inadequate staffing trigger terms (Fisher's exact test = 44.11, p < .001), with those related to "workload" most likely to accompany a report of an omission, followed by terms that mention "staffing" and being "busy." Prevalence of "no harm" did not vary statistically between the trigger terms (Fisher's exact test = 11.45, p = 0.49), but the triggers "workload," "staffing level," "busy night," and "busy unit" identified incidents with lower levels of "no harm" than for incidents overall. CONCLUSIONS: Inadequate staffing levels, workload, and working in haste may increase the risk for omissions and other types of error, as well as for patient harm. CLINICAL RELEVANCE: This work lays the groundwork for creating automated text-analytical systems that could analyze incident reports in real time and flag or monitor staffing levels and related medication administration errors.

Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.

BACKGROUND: LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis. RESULTS: Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin. CONCLUSION: Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

A multiomic approach to characterize the temporal sequence in Alzheimer's disease-related pathology.

No single-omic approach completely elucidates the multitude of alterations taking place in Alzheimer's disease (AD). Here, we coupled transcriptomic and phosphoproteomic approaches to determine the temporal sequence of changes in mRNA, protein, and phosphopeptide expression levels from human temporal cortical samples, with varying degree of AD-related pathology. This approach highlighted fluctuation in synaptic and mitochondrial function as the earliest pathological events in brain samples with AD-related pathology. Subsequently, increased expression of inflammation and extracellular matrix-associated gene products was observed. Interaction network assembly for the associated gene products, emphasized the complex interplay between these processes and the role of addressing post-translational modifications in the identification of key regulators. Additionally, we evaluate the use of decision trees and random forests in identifying potential biomarkers differentiating individuals with different degree of AD-related pathology. This multiomic and temporal sequence-based approach provides a better understanding of the sequence of events leading to AD.

Digitally supported program for type 2 diabetes risk identification and risk reduction in real-world setting: protocol for the StopDia model and randomized controlled trial.

BACKGROUND: The StopDia study is based on the convincing scientific evidence that type 2 diabetes (T2D) and its comorbidities can be prevented by a healthy lifestyle. The need for additional research is based on the fact that the attempts to translate scientific evidence into actions in the real-world health care have not led to permanent and cost-effective models to prevent T2D. The specific aims of the StopDia study following the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework are to 1) improve the Reach of individuals at increased risk, 2) evaluate the Effectiveness and cost-effectiveness of the digital lifestyle intervention and the digital and face-to-face group lifestyle intervention in comparison to routine care in a randomized controlled trial (RCT), and 3) evaluate the Adoption and Implementation of the StopDia model by the participants and the health care organizations at society level. Finally, we will address the Maintenance of the lifestyle changes at participant level and that of the program at organisatory level after the RCT. METHODS: The StopDia study is carried out in the primary health care system as part of the routine actions of three provinces in Finland, including Northern Savo, Southern Carelia, and Päijät-Häme. We estimate that one fifth of adults aged 18-70 years living in these areas are at increased risk of T2D. We recruit the participants using the StopDia Digital Screening Tool, including questions from the Finnish Diabetes Risk Score (FINDRISC). About 3000 individuals at increased risk of T2D (FINDRISC ≥12 or a history of gestational diabetes, impaired fasting glucose, or impaired glucose tolerance) participate in the one-year randomized controlled trial. We monitor lifestyle factors using the StopDia Digital Questionnaire and metabolism using laboratory tests performed as part of routine actions in the health care system. DISCUSSION: Sustainable and scalable models are needed to reach and identify individuals at increased risk of T2D and to deliver personalized and effective lifestyle interventions. With the StopDia study we aim to answer these challenges in a scientific project that is fully digitally integrated into the routine health care. TRIAL REGISTRATION: ClinicalTials.gov . Identifier: NCT03156478 . Date of registration 17.5.2017.

Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports.

BACKGROUND: Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining. METHOD: Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas. RESULTS: The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions. CONCLUSIONS: Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.

In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury-Lessons Learned.

We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro (p < 0.05) and (d) target engagement of Nrf2 target genes (p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore (p < 0.05). Weight loss (p < 0.05) and prolonged upregulation of plasma cytokines (p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

Text Mining Method for Studying Medication Administration Incidents and Nurse-Staffing Contributing Factors: A Pilot Study.

Incident reporting systems are being implemented globally, thus increasing the profile and prevalence of incidents, but the analysis of free-text descriptions remains largely hidden. The aims of the study were to explore the extent to which incident reports recorded staffing issues as contributors to medication administration incidents. Incident reports related to medication administration (N = 1012) were collected from two hospitals in Finland between January 1, 2013, and December 31, 2014. The SAS Enterprise Miner 13.2 and its Text Miner tool were used to excavate terms and descriptors and to uncover themes and concepts in the free-text descriptions of incidents with (n = 194) and without (n = 818) nurse staffing-related contributing factors. Text mining included (1) text parsing, (2) text filtering, and (3) modeling text clusters and text topics. The term "rush/hurry" was the sixth most common term used in incidents where nurse-staffing was identified as a contributing factor. Nurse-staffing factors, however, were not pronounced in clusters or in text topics of either data set. Text mining offers the opportunity to analyze large free-text mass and holds promise for providing insight into the antecedents of medication administration incidents.

SEPT8 modulates ß-amyloidogenic processing of APP by affecting the sorting and accumulation of BACE1.

Dysfunction and loss of synapses are early pathogenic events in Alzheimer's disease. A central step in the generation of toxic amyloid-ß (Aß) peptides is the cleavage of amyloid precursor protein (APP) by ß-site APP-cleaving enzyme (BACE1). Here, we have elucidated whether downregulation of septin (SEPT) protein family members, which are implicated in synaptic plasticity and vesicular trafficking, affects APP processing and Aß generation. SEPT8 was found to reduce soluble APPß and Aß levels in neuronal cells through a post-translational mechanism leading to decreased levels of BACE1 protein. In the human temporal cortex, we identified alterations in the expression of specific SEPT8 transcript variants in a manner that correlated with Alzheimer's-disease-related neurofibrillary pathology. These changes were associated with altered ß-secretase activity. We also discovered that the overexpression of a specific Alzheimer's-disease-associated SEPT8 transcript variant increased the levels of BACE1 and Aß peptides in neuronal cells. These changes were related to an increased half-life of BACE1 and the localization of BACE1 in recycling endosomes. These data suggest that SEPT8 modulates ß-amyloidogenic processing of APP through a mechanism affecting the intracellular sorting and accumulation of BACE1.

Decreased plasma ß-amyloid in the Alzheimer's disease APP A673T variant carriers.

We investigated the association of Alzheimer's disease (AD)-related rare variants APP A673T and ABCA7 rs200538373-C with the levels of ß-amyloid (Aß) and parameters of metabolic and cardiovascular health in a population-based cohort of healthy middle-aged and elderly men. Carriers of protective APP A673T variant had, on average, 28% lower levels of Aß40 and Aß42 in plasma as compared to the controls and the carriers of ABCA7 rs200538373-C. This is the first report to show decreased Aß levels in plasma in APP A673T carriers and thus provides evidence that lower Aß levels throughout life may be protective against AD. Ann Neurol 2017;82:128-132.

Kuopio birth cohort - design of a Finnish joint research effort for identification of environmental and lifestyle risk factors for the wellbeing of the mother and the newborn child.

BACKGROUND: A Finnish joint research effort Kuopio Birth Cohort (KuBiCo) seeks to evaluate the effects of genetics, epigenetics and different risk factors (medication, nutrition, lifestyle factors and environmental aspects) during pregnancy on the somatic and psychological health status of the mother and the child. METHODS: KuBiCo will ultimately include information on 10,000 mother-child pairs who have given their informed consent to participate in this cohort. Identification of foetal health risk factors that can potentially later manifest as disease requires a repository of relevant biological samples and a flexible open up-to-date data handling system to register, store and analyse biological, clinical and questionnaire-based data. KuBiCo includes coded questionnaire-based maternal background data gathered before, during and after the pregnancy and bio-banking of maternal and foetal samples that will be stored in deep freezers. Data from the questionnaires and biological samples will be collected into one electronic database. KuBiCo consists of several work packages which are complementary to each other: Maternal, foetal and placental metabolism and omics; Paediatrics; Mental wellbeing; Prenatal period and delivery; Analgesics and anaesthetics during peripartum period; Environmental effects; Nutrition; and Research ethics. DISCUSSION: This report describes the set-up of the KuBiCo and descriptive analysis from 3532 parturients on response frequencies and feedback to KuBiCo questionnaires gathered from June 2012 to April 2016. Additionally, we describe basic demographic data of the participants (n = 1172). Based on the comparison of demographic data between official national statistics and our descriptive analysis, KuBiCo represents a cross-section of Finnish pregnant women.

Screening approach for identifying candidate drugs and drug-drug interactions related to hip fracture risk in persons with Alzheimer disease.

PURPOSE: To assess whether a "drugome-wide" screen with case-crossover design is a feasible approach for identifying candidate drugs and drug-drug interactions. METHODS: All community-dwelling residents of Finland who received a clinically verified Alzheimer disease diagnosis in 2005 to 2011 and experienced incident hip fracture (HF) afterwards (N = 4851). Three scenarios were used to test the sensitivity of this approach (1) hazard period 0 to 30 and control period 31 to 61 days before HF, (2) hazard period 0 to 30 and control period 336 to 366 days before HF, and (3) hazard period 0 to 14 and control period 16 to 30 days before HF. RESULTS: Nine, 44, and 5 drugs were associated with increased HF risk and 8, 23, and 4 with decreased risk in scenarios 1, 2, and 3, respectively. Six drugs were identified with scenario 1 only and 54 and 1 with scenarios 2 and 3, respectively. Only six drugs (metoprolol, simvastatin, trimethoprim, codeine combinations, fentanyl, and paracetamol) were associated with HF in all scenarios, four with 1 and 2 (cefalexin, buprenorphine, olanzapine, and memantine), and one with 1 and 3 (enalapril) or 2 and 3 (ciprofloxacin). The direction of associations was the same in all/both scenarios. The interaction results were equally versatile, with hydroxocobalamin*oxazepam being the only interaction observed in all scenarios. CONCLUSIONS: Case-crossover analysis is a potential approach for identifying candidate drugs and drug-drug interactions associated with adverse events as it implicitly controls for fixed confounders. The results are highly dependent on applied hazard and control periods, but the choice of periods can help in targeting the analyses to different phases of drug use.

Relationship between ubiquilin-1 and BACE1 in human Alzheimer's disease and APdE9 transgenic mouse brain and cell-based models.

Accumulation of ß-amyloid (Aß) and phosphorylated tau in the brain are central events underlying Alzheimer's disease (AD) pathogenesis. Aß is generated from amyloid precursor protein (APP) by ß-site APP-cleaving enzyme 1 (BACE1) and γ-secretase-mediated cleavages. Ubiquilin-1, a ubiquitin-like protein, genetically associates with AD and affects APP trafficking, processing and degradation. Here, we have investigated ubiquilin-1 expression in human brain in relation to AD-related neurofibrillary pathology and the effects of ubiquilin-1 overexpression on BACE1, tau, neuroinflammation, and neuronal viability in vitro in co-cultures of mouse embryonic primary cortical neurons and microglial cells under acute neuroinflammation as well as neuronal cell lines, and in vivo in the brain of APdE9 transgenic mice at the early phase of the development of Aß pathology. Ubiquilin-1 expression was decreased in human temporal cortex in relation to the early stages of AD-related neurofibrillary pathology (Braak stages 0-II vs. III-IV). There was a trend towards a positive correlation between ubiquilin-1 and BACE1 protein levels. Consistent with this, ubiquilin-1 overexpression in the neuron-microglia co-cultures with or without the induction of neuroinflammation resulted in a significant increase in endogenously expressed BACE1 levels. Sustained ubiquilin-1 overexpression in the brain of APdE9 mice resulted in a moderate, but insignificant increase in endogenous BACE1 levels and activity, coinciding with increased levels of soluble Aß40 and Aß42. BACE1 levels were also significantly increased in neuronal cells co-overexpressing ubiquilin-1 and BACE1. Ubiquilin-1 overexpression led to the stabilization of BACE1 protein levels, potentially through a mechanism involving decreased degradation in the lysosomal compartment. Ubiquilin-1 overexpression did not significantly affect the neuroinflammation response, but decreased neuronal viability in the neuron-microglia co-cultures under neuroinflammation. Taken together, these results suggest that ubiquilin-1 may mechanistically participate in AD molecular pathogenesis by affecting BACE1 and thereby APP processing and Aß accumulation.

Associations of TM6SF2 167K allele with liver enzymes and lipid profile in children: the PANIC Study.

BACKGROUND: The 167K allele in the TM6SF2 gene has been suggested to protect against cardiovascular disease at the cost of developing nonalcoholic fatty liver disease in adults. METHODS: We performed a cross-sectional study in a population sample of 462 Caucasian children aged 6-9 y, genotyped the polymorphism using HumanCoreExome BeadChip, and assessed several cardiometabolic risk factors. RESULTS: The 51 (11%) carriers of the 167K allele had higher plasma alanine aminotransferase (ALT) (20.8 vs. 18.4 U/l, P = 0.011) but lower plasma triglycerides (0.54 vs. 0.61 mmol/l, P = 0.024), total cholesterol (4.08 vs. 4.30 mmol/l, P = 0.016), and low-density lipoprotein (LDL) cholesterol (2.22 vs. 2.38 mmol/l, P = 0.012) than the 411 noncarriers. In factor analysis, the first factor was heavily loaded by plasma ALT (factor loading 0.63), triglycerides (-0.82), LDL cholesterol (-0.71), and waist circumference (0.61) in the carriers but not in the noncarriers. CONCLUSIONS: The carriers of the 167K allele have higher plasma ALT but lower plasma triglycerides and total and LDL cholesterol than the noncarriers already in childhood.

The effects of a 2-year individualized and family-based lifestyle intervention on physical activity, sedentary behavior and diet in children.

OBJECTIVE: To investigate the effects of a long-term, individualized and family-based lifestyle intervention on physical activity, sedentary behavior and diet quality in children. METHODS: We carried out a 2-year intervention study in a population sample of 506 children aged 6-8years in Finland in 2007-2012. We allocated the participants at baseline in the intervention and control group. We assessed physical activity and sedentary behavior by questionnaires and diet by food records. RESULTS: Total physical activity (+9min/d in intervention group vs. -5min/d in control group, p=0.001 for time*group interaction), unsupervised physical activity (+7min/d vs. -9min/d, p<0.001) and organized sports (+8min/d vs. +3min/d, p=0.001) increased in the intervention group but not in the control group. Using computer and playing video games increased less in the intervention group than in the control group (+9min/d vs. +19min/d, p=0.003). Consumption of vegetables (+12g/d vs. -12g/d, p=0.001), high-fat vegetable-oil based margarine (+10g/d vs. +3g/d, p<0.001) and low-fat milk (+69g/d vs. +11g/d, p=0.042) and intake of dietary fiber (+1.3g/d vs. +0.2g/d, p=0.023), vitamin C (+4.5mg/d vs. -7.2mg/d, p=0.042) and vitamin E (+1.4mg/d vs. +0.5mg/d, p=0.002) increased in the intervention group but not in the control group. Consumption of butter-based spreads increased in the control group but not in the intervention group (+2g/d vs. -1g/d, p=0.002). CONCLUSIONS: Individualized and family-based lifestyle intervention increased physical activity, attenuated increase in sedentary behavior and enhanced diet quality in children. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01803776.