Composition of active bacterial communities and presence of opportunistic pathogens in disinfected and non-disinfected drinking water distribution systems in Finland.

Many factors, including microbiome structure and activity in the drinking water distribution system (DWDS), affect the colonization potential of opportunistic pathogens. The present study aims to describe the dynamics of active bacterial communities in DWDS and identify the factors that shape the community structures and activity in the selected DWDSs. Large-volume drinking water and hot water, biofilm, and water meter deposit samples were collected from five DWDSs. Total nucleic acids were extracted, and RNA was further purified and transcribed into its cDNA from a total of 181 water and biofilm samples originating from the DWDS of two surface water supplies (disinfected with UV and chlorine), two artificially recharged groundwater supplies (non-disinfected), and a groundwater supply (disinfected with UV and chlorine). In chlorinated DWDSs, concentrations of <0.02-0.97 mg/l free chlorine were measured. Bacterial communities in the RNA and DNA fractions were analysed using Illumina MiSeq sequencing with primer pair 341F-785R targeted to the 16S rRNA gene. The sequence libraries were analysed using QIIME pipeline, Program R, and MicrobiomeAnalyst. Not all bacterial cells were active based on their 16S rRNA content, and species richness was lower in the RNA fraction (Chao1 mean value 490) than in the DNA fraction (710). Species richness was higher in the two DWDSs distributing non-disinfected artificial groundwater (Chao1 mean values of 990 and 1 000) as compared to the two disinfected DWDSs using surface water (Chao1 mean values 190 and 460) and disinfected DWDS using ground water as source water (170). The difference in community structures between non-disinfected and disinfected water was clear in the beta-diversity analysis. Distance from the waterworks also affected the beta diversity of community structures, especially in disinfected distribution systems. The two most abundant bacteria in the active part of the community (RNA) and total bacterial community (DNA) belonged to the classes Alphaproteobacteria (RNA 28 %, DNA 44 %) and Gammaproteobacteria (RNA 32 %, DNA 30 %). The third most abundant and active bacteria class was Vampirovibrionia (RNA 15 %), whereas in the total community it was Paceibacteria (DNA 11 %). Class Nitrospiria was more abundant and active in both cold and hot water in DWDS that used chloramine disinfection compared to non-chlorinated or chlorine-using DWDSs. Thirty-eight operational taxonomic units (OTU) of Legionella, 30 of Mycobacterium, and 10 of Pseudomonas were detected among the sequences. The (RT)-qPCR confirmed the presence of opportunistic pathogens in the DWDSs studied as Legionella spp. was detected in 85 % (mean value 4.5 × 104 gene copies/100 ml), Mycobacterium spp. in 95 % (mean value 8.3 × 106 gene copies/100 ml), and Pseudomonas spp. in 78 % (mean value 1.6 × 105 gene copies/100 ml) of the water and biofilm samples. Sampling point inside the system (distance from the waterworks and cold/hot system) affected the active bacterial community composition. Chloramine as a chlorination method resulted in a recognizable community composition, with high abundance of bacteria that benefit from the excess presence of nitrogen. The results presented here confirm that each DWDS is unique and that opportunistic pathogens are present even in conditions when water quality is considered excellent.

Diagnostic yield of genetic testing in a multinational heterogeneous cohort of 2088 DCM patients.

Background: Familial dilated cardiomyopathy (DCM) causes heart failure and may lead to heart transplantation. DCM is typically a monogenic disorder with autosomal dominant inheritance. Currently disease-causing variants have been reported in over 60 genes that encode proteins in sarcomeres, nuclear lamina, desmosomes, cytoskeleton, and mitochondria. Over half of the patients undergoing comprehensive genetic testing are left without a molecular diagnosis even when patient selection follows strict DCM criteria. Methods and results: This study was a retrospective review of patients referred for genetic testing at Blueprint Genetics due to suspected inherited DCM. Next generation sequencing panels included 23-316 genes associated with cardiomyopathies and other monogenic cardiac diseases. Variants were considered diagnostic if classified as pathogenic (P) or likely pathogenic (LP). Of the 2,088 patients 514 (24.6%) obtained a molecular diagnosis; 534 LP/P variants were observed across 45 genes, 2.7% (14/514) had two diagnostic variants in dominant genes. Nine copy number variants were identified: two multigene and seven intragenic. Diagnostic variants were observed most often in TTN (45.3%), DSP (6.7%), LMNA (6.7%), and MYH7 (5.2%). Clinical characteristics independently associated with molecular diagnosis were: a lower age at diagnosis, family history of DCM, paroxysmal atrial fibrillation, absence of left bundle branch block, and the presence of an implantable cardioverter-defibrillator. Conclusions: Panel testing provides good diagnostic yield in patients with clinically suspected DCM. Causative variants were identified in 45 genes. In minority, two diagnostic variants were observed in dominant genes. Our results support the use of genetic panels in clinical settings in DCM patients with suspected genetic etiology.

GRINL1A Complex Transcription Unit Containing GCOM1, MYZAP, and POLR2M Genes Associates with Fully Penetrant Recessive Dilated Cardiomyopathy.

Background: Familial dilated cardiomyopathy (DCM) is a monogenic disorder typically inherited in an autosomal dominant pattern. We have identified two Finnish families with familial cardiomyopathy that is not explained by a variant in any previously known cardiomyopathy gene. We describe the cardiac phenotype related to homozygous truncating GCOM1 variants. Methods and Results: This study included two probands and their relatives. All the participants are of Finnish ethnicity. Whole-exome sequencing was used to test the probands; bi-directional Sanger sequencing was used to identify the GCOM1 variants in probands' family members. Clinical evaluation was performed, medical records and death certificates were obtained. Immunohistochemical analysis of myocardial samples was conducted. A homozygous GCOM1 variant was identified altogether in six individuals, all considered to be affected. None of the nine heterozygous family members fulfilled any cardiomyopathy criteria. Heart failure was the leading clinical feature, and the patients may have had a tendency for atrial arrhythmias. Conclusions: This study demonstrates the significance of GCOM1 variants as a cause of human cardiomyopathy and highlights the importance of searching for new candidate genes when targeted gene panels do not yield a positive outcome.

Diverse and active archaea communities occur in non-disinfected drinking water systems-Less activity revealed in disinfected and hot water systems.

The knowledge about the members of active archaea communities in DWDS is limited. The current understanding is based on high-throughput 16S ribosomal RNA gene (DNA-based) amplicon sequencing that reveals the diversity of active, dormant, and dead members of the prokaryote (bacteria, archaea) communities. The sequencing primers optimized for bacteria community analysis may underestimate the share of the archaea community. This study characterized archaea communities at five full-scale drinking water distribution systems (DWDS), representing a variety of drinking water production units (A-E); A&B use artificially recharged non-disinfected groundwater (ARG), the other DWDS's supplied water disinfected by using ultraviolet (UV) light and chlorine compounds, C&D were surface waterworks and E was a ground waterworks. For the first time for archaea community analyses, this study employed the archaea-specific high-throughput sequencing primers for 16S ribosomal RNA (rRNA) as a target (reverse-transcribed cDNA; an RNA-based approach) in addition to the previously used 16S rRNA gene target (rDNA; a DNA-based approach) to reveal the active fraction of the archaea present in DWDS. The archaea community structure in varying environmental conditions in the water and biofilm of the five DWDSs were investigated by taking into consideration the system properties (cold or hot water system) and water age (distance from the treatment plants) in samples from each season of one year. The RNA-based archaea amplicon reads were obtained mostly from cold water samples from DWDSs (A-B) distributing water without disinfection where the DNA-based and RNA-based analysis created separate clusters in a weighted beta-diversity analysis. The season and location in DWDS A further affected the diversity of these archaea communities as was seen by different clusters in beta-diversity plots. The recovery of archaea reads was not adequate for analysis in any of the disinfected samples in DWDSs C-E or non-disinfected hot water in DWDSs A-B when utilizing RNA-based template. The metabolically active archaea community of DWDSs thus seemed to be effectively controlled by disinfection of water and in the hot water systems by the temperature. All biofilms regardless of DWDS showed lower species richness values (mainly Nitrososphaeria class) than non-disinfected water from DWDSs A-B where several archaea classes occurred (e.g. Woesearchaeia, Nitrososphaeria, Micrarchaeia, Methanomicrobia, Iairchaeia, Bathyarchaeia) indicating only part of the archaea members were able to survive in biofilms. Thus, Archaea has been shown as a significant part of normal DWDS biota, and their role especially in non-disinfected DWDS may be more important than previously considered.

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.

Biallelic loss-of-function in NRAP is a cause of recessive dilated cardiomyopathy.

BACKGROUND: Familial dilated cardiomyopathy (DCM) is typically a monogenic disorder with dominant inheritance. Although over 40 genes have been linked to DCM, more than half of the patients undergoing comprehensive genetic testing are left without molecular diagnosis. Recently, biallelic protein-truncating variants (PTVs) in the nebulin-related anchoring protein gene (NRAP) were identified in a few patients with sporadic DCM. METHODS AND RESULTS: We determined the frequency of rare NRAP variants in a cohort of DCM patients and control patients to further evaluate role of this gene in cardiomyopathies. A retrospective analysis of our internal variant database consisting of 31,639 individuals who underwent genetic testing (either panel or direct exome sequencing) was performed. The DCM group included 577 patients with either a confirmed or suspected DCM diagnosis. A control cohort of 31,062 individuals, including 25,912 individuals with non-cardiac (control group) and 5,150 with non-DCM cardiac indications (Non-DCM cardiac group). Biallelic (n = 6) or two (n = 5) NRAP variants (two PTVs or PTV+missense) were identified in 11 unrelated probands with DCM (1.9%) but none of the controls. None of the 11 probands had an alternative molecular diagnosis. Family member testing supports co-segregation. Biallelic or potentially biallelic NRAP variants were enriched in DCM vs. controls (OR 1052, p<0.0001). Based on the frequency of NRAP PTVs in the gnomAD reference population, and predicting full penetrance, biallelic NRAP variants could explain 0.25%-2.46% of all DCM cases. CONCLUSION: Loss-of-function in NRAP is a cause for autosomal recessive dilated cardiomyopathy, supporting its inclusion in comprehensive genetic testing.

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).

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.

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.

"notame": Workflow for Non-Targeted LC-MS Metabolic Profiling.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

Publisher Correction: Prioritizing target-disease associations with novel safety and efficacy scoring methods.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

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.

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.

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.

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.

Active eukaryotes in drinking water distribution systems of ground and surface waterworks.

BACKGROUND: Eukaryotes are ubiquitous in natural environments such as soil and freshwater. Little is known of their presence in drinking water distribution systems (DWDSs) or of the environmental conditions that affect their activity and survival. METHODS: Eukaryotes were characterized by Illumina high-throughput sequencing targeting 18S rRNA gene (DNA) that estimates the total community and the 18S rRNA gene transcript (RNA) that is more representative of the active part of the community. DWDS cold water (N = 124), hot water (N = 40), and biofilm (N = 16) samples were collected from four cities in Finland. The sampled DWDSs were from two waterworks A-B with non-disinfected, recharged groundwater as source water and from three waterworks utilizing chlorinated water (two DWDSs of surface waterworks C-D and one of ground waterworks E). In each DWDS, samples were collected from three locations during four seasons of 1 year. RESULTS: A beta-diversity analysis revealed that the main driver shaping the eukaryotic communities was the DWDS (A-E) (R = 0.73, P < 0.001, ANOSIM). The kingdoms Chloroplastida (green plants and algae), Metazoa (animals: rotifers, nematodes), Fungi (e.g., Cryptomycota), Alveolata (ciliates, dinoflagellates), and Stramenopiles (algae Ochrophyta) were well represented and active-judging based on the rRNA gene transcripts-depending on the surrounding conditions. The unchlorinated cold water of systems (A-B) contained a higher estimated total number of taxa (Chao1, average 380-480) than chlorinated cold water in systems C-E (Chao1 ≤ 210). Within each DWDS, unique eukaryotic communities were identified at different locations as was the case also for cold water, hot water, and biofilms. A season did not have a consistent impact on the eukaryotic community among DWDSs. CONCLUSIONS: This study comprehensively characterized the eukaryotic community members within the DWDS of well-maintained ground and surface waterworks providing good quality water. The study gives an indication that each DWDS houses a unique eukaryotic community, mainly dependent on the raw water source and water treatment processes in place at the corresponding waterworks. In particular, disinfection as well as hot water temperature seemed to represent a strong selection pressure that controlled the number of active eukaryotic species.

Prioritizing target-disease associations with novel safety and efficacy scoring methods.

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

Healthy Nordic Diet Modulates the Expression of Genes Related to Mitochondrial Function and Immune Response in Peripheral Blood Mononuclear Cells from Subjects with Metabolic Syndrome-A SYSDIET Sub-Study.

SCOPE: To explore the effect of a healthy Nordic diet on the global transcriptome profile in peripheral blood mononuclear cells (PBMCs) of subjects with metabolic syndrome. METHODS AND RESULTS: Subjects with metabolic syndrome undergo a 18/24 week randomized intervention study comparing an isocaloric healthy Nordic diet with an average habitual Nordic diet served as control (SYSDIET study). Altogether, 68 participants are included. PBMCs are obtained before and after intervention and total RNA is subjected to global transcriptome analysis. 1302 probe sets are differentially expressed between the diet groups (p-value < 0.05). Twenty-five of these are significantly regulated (FDR q-value < 0.25) and are mainly involved in mitochondrial function, cell growth, and cell adhesion. The list of 1302 regulated probe sets is subjected to functional analyses. Pathways and processes involved in the mitochondrial electron transport chain, immune response, and cell cycle are downregulated in the healthy Nordic diet group. In addition, gene transcripts with common motifs for 42 transcription factors, including NFR1, NFR2, and NF-κB, are downregulated in the healthy Nordic diet group. CONCLUSION: These results suggest that benefits of a healthy diet may be mediated by improved mitochondrial function and reduced inflammation.

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.