Historical HbA1c Values May Explain the Type 2 Diabetes Legacy Effect: UKPDS 88.

OBJECTIVE: Type 2 diabetes all-cause mortality (ACM) and myocardial infarction (MI) glycemic legacy effects have not been explained. We examined their relationships with prior individual HbA1c values and explored the potential impact of instituting earlier, compared with delayed, glucose-lowering therapy. RESEARCH DESIGN AND METHODS: Twenty-year ACM and MI hazard functions were estimated from diagnosis of type 2 diabetes in 3,802 UK Prospective Diabetes Study participants. Impact of HbA1c values over time was analyzed by weighting them according to their influence on downstream ACM and MI risks. RESULTS: Hazard ratios for a one percentage unit higher HbA1c for ACM were 1.08 (95% CI 1.07-1.09), 1.18 (1.15-1.21), and 1.36 (1.30-1.42) at 5, 10, and 20 years, respectively, and for MI was 1.13 (1.11-1.15) at 5 years, increasing to 1.31 (1.25-1.36) at 20 years. Imposing a one percentage unit lower HbA1c from diagnosis generated an 18.8% (95% CI 21.1-16.0) ACM risk reduction 10-15 years later, whereas delaying this reduction until 10 years after diagnosis showed a sevenfold lower 2.7% (3.1-2.3) risk reduction. Corresponding MI risk reductions were 19.7% (22.4-16.5) when lowering HbA1c at diagnosis, and threefold lower 6.5% (7.4-5.3%) when imposed 10 years later. CONCLUSIONS: The glycemic legacy effects seen in type 2 diabetes are explained largely by historical HbA1c values having a greater impact than recent values on clinical outcomes. Early detection of diabetes and intensive glucose control from the time of diagnosis is essential to maximize reduction of the long-term risk of glycemic complications.

Modelling of zero-inflation improves inference of metagenomic gene count data.

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.

Searching for neurodegeneration in multiple sclerosis at clinical onset: Diagnostic value of biomarkers.

BACKGROUND: Neurodegeneration occurs during the early stages of multiple sclerosis. It is an essential, devastating part of the pathophysiology. Tools for measuring the degree of neurodegeneration could improve diagnostics and patient characterization. OBJECTIVE: This study aimed to determine the diagnostic value of biomarkers of degeneration in patients with recent clinical onset of suspected multiple sclerosis, and to evaluate these biomarkers for characterizing disease course. METHODS: This cross-sectional study included 271 patients with clinical features of suspected multiple sclerosis onset and was the baseline of a prospective study. After diagnostic investigations, the patients were classified into the following disease groups: patients with clinically isolated syndrome (n = 4) or early relapsing remitting multiple sclerosis (early RRMS; n = 93); patients with relapsing remitting multiple sclerosis with disease durations ≥2 years (established RRMS; n = 39); patients without multiple sclerosis, but showing symptoms (symptomatic controls; n = 89); and patients diagnosed with other diseases (n = 46). In addition, we included healthy controls (n = 51) and patients with progressive multiple sclerosis (n = 23). We analyzed six biomarkers of neurodegeneration: cerebrospinal fluid neurofilament light chain levels; cerebral spinal fluid glial fibrillary acidic protein; cerebral spinal fluid tau; retinal nerve fiber layer thickness; macula volume; and the brain parenchymal fraction. RESULTS: Except for increased cerebral spinal fluid neurofilament light chain levels, median 670 ng/L (IQR 400-2110), we could not find signs of early degeneration in the early disease group with recent clinical onset. However, the intrathecal immunoglobin G production and cerebral spinal fluid neurofilament light chain levels showed diagnostic value. Moreover, elevated levels of cerebral spinal fluid glial fibrillary acidic protein, thin retinal nerve fiber layers, and low brain parenchymal fractions were associated with progressive disease, but not with the other phenotypes. Thin retinal nerve fiber layers and low brain parenchymal fractions, which indicated neurodegeneration, were associated with longer disease duration. CONCLUSIONS: In clinically suspected multiple sclerosis, intrathecal immunoglobin G production and neurofilament light chain levels had diagnostic value. Therefore, these biomarkers could be included in diagnostic work-ups for multiple sclerosis. We found that the thickness of the retinal nerve fiber layer and the brain parenchymal fraction were not different between individuals that were healthy, symptomatic, or newly diagnosed with multiple sclerosis. This finding suggested that neurodegeneration had not reached a significant magnitude in patients with a recent clinical onset of multiple sclerosis.

Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes.

Metagenomics is the study of microorganisms in environmental and clinical samples using high-throughput sequencing of random fragments of their DNA. Since metagenomics does not require any prior culturing of isolates, entire microbial communities can be studied directly in their natural state. In metagenomics, the abundance of genes is quantified by sorting and counting the DNA fragments. The resulting count data are high-dimensional and affected by high levels of technical and biological noise that make the statistical analysis challenging. In this article, we introduce an hierarchical overdispersed Poisson model to explore the variability in metagenomic data. By analyzing three comprehensive data sets, we show that the gene-specific variability varies substantially between genes and is dependent on biological function. We also assess the power of identifying differentially abundant genes and show that incorrect assumptions about the gene-specific variability can lead to unacceptable high rates of false positives. Finally, we evaluate shrinkage approaches to improve the variance estimation and show that the prior choice significantly affects the statistical power. The results presented in this study further elucidate the complex variance structure of metagenomic data and provide suggestions for accurate and reliable identification of differentially abundant genes.

Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics.

BACKGROUND: Metagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date. RESULTS: In this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies. CONCLUSIONS: Two methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.

Upper Respiratory Infections and MRI Activity in Relapsing-Remitting Multiple Sclerosis.

BACKGROUND: Although clinical reports have suggested a relationship between systemic infections and multiple sclerosis (MS) relapses, MRI evidence supporting an association is conflicting. Here we evaluated the temporal relationship between upper respiratory infections (URIs) and MRI activity in relapsing-remitting (RR) MS. METHODS: We combined individual data on URI with data on active lesions in pre-scheduled MRI examinations performed every 4 weeks for 28 weeks in 69 patients. A 4-week at-risk (AR) period started, by definition, 1 week before the onset of a URI. We recorded the relationship between the number of active lesions in each MRI with (1) the number of days of AR time in the immediately preceding 4-week period and (2) the number of days passed since the onset of a preceding URI. RESULTS: Average MRI lesions/day showed no difference between AR (0.0764) and not-AR (0.0774) periods. The number of lesions in 483 pre-scheduled MRI examinations did not correlate with the AR proportion in the prior 4-week period (rho = -0.03), and time from URI onset did not correlate with lesion number on the next MRI examination (rho = 0.003). CONCLUSION: The occurrence of a URI did not increase the risk of MRI activity evaluated in an adjacent 4-week window in RRMS.

The outcome spectrum of multiple sclerosis: disability, mortality, and a cluster of predictors from onset.

Interest in the long-term natural history of multiple sclerosis (MS) is being revived, as disability endpoints become increasingly important with the advent of highly efficacious long range but potentially harmful drugs. MS had an increasingly benign course, probably due to better assessment and changing diagnostic criteria. Incidence cohorts reduce inclusion bias, capturing both extreme benign and severe cases. We conducted a 50-year follow-up of an incidence cohort of Gothenburg residents with MS onset in 1950-1964 (n = 254; 212 with an initial relapsing-remitting course and 42 with a monophasic course, diagnostic criteria according to Poser). Patients were followed longitudinally until censoring, death, or study termination in 2012 and evaluated using Kaplan-Meier estimates and Cox regression analysis. Median time to secondary progression was 15 years. Median time to EDSS6 and EDSS7 was 26 and 48 years (n = 254), respectively. The cumulative risk of reaching EDSS6 was 50% at 55 years of age and 80% at 80 years of age (n = 212). A score based on a cluster of clinical features at onset predicted secondary progression, EDSS6, EDSS7, and EDSS10 (hazard ratio 1.6-2.3 per score unit for women, 0.99-1.49 for men). This score predicted the disease course during five decades indirectly, by predicting time to secondary progression. Age at onset predicted the course in men, with 3-6% yearly increase in the risk of reaching disability milestones. The present incidence cohort provided hard outcome data in untreated patients over several decades.

Sleep apnea-related risk of motor vehicle accidents is reduced by continuous positive airway pressure: Swedish Traffic Accident Registry data.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is associated with an increased risk of motor vehicle accidents (MVAs). The rate of MVAs in patients suspected of having OSA was determined and the effect of continuous positive airway pressure (CPAP) was investigated. DESIGN: MVA rate in patients referred for OSA was compared to the rate in the general population using data from the Swedish Traffic Accident Registry (STRADA), stratified for age and calendar year. The risk factors for MVAs, using demographic and polygraphy data, and MVA rate before and after CPAP were evaluated in the patient group. SETTING: Clinical sleep laboratory and population based control (n = 635,786). PATIENTS: There were 1,478 patients, male sex 70.4%, mean age 53.6 (12.8) y. INTERVENTIONS: CPAP. MEASUREMENTS AND RESULTS: The number of accidents (n = 74) among patients was compared with the expected number (n = 30) from a control population (STRADA). An increased MVA risk ratio of 2.45 was found among patients compared with controls (P < 0.001). Estimated excess accident risk was most prominent in the elderly patients (65-80 y, seven versus two MVAs). In patients, driving distance (km/y), EDS (Epworth Sleepiness score ≥ 16), short habitual sleep time (≤5 h/night), and use of hypnotics were associated with increased MVA risk (odds ratios 1.2, 2.1, 2.7 and 2.1, all P ≤ 0.03). CPAP use ≥ 4 h/night was associated with a reduction of MVA incidence (7.6 to 2.5 accidents/1,000 drivers/y). CONCLUSIONS: The MVA risk in this large cohort of unselected patients with OSA suggests a need for accurate tools to identify individuals at risk. Sleep apnea severity (e.g., apnea-hypopnea index) failed to identify patients at risk.

The association between HbA1c, fasting glucose, 1-hour glucose and 2-hour glucose during an oral glucose tolerance test and cardiovascular disease in individuals with elevated risk for diabetes.

OBJECTIVE: To determine the association between HbA1c, fasting plasma glucose (FPG), 1-hour (1 hPG) and 2-hour (2 hPG) glucose after an oral glucose tolerance test (OGTT) and cardiovascular disease in individuals with elevated risk for diabetes. DESIGN: We studied the relationship between baseline, updated mean and updated (last) value of HbA1c, FPG, 1 hPG and 2 hPG after an oral 75 g glucose tolerance test (OGTT) and acute CVD events in 504 individuals with impaired glucose tolerance (IGT) at baseline enrolled in the Finnish Diabetes Prevention Study. SETTING: Follow-up of clinical trial. PARTICIPANTS: 504 individuals with IGT were followed with yearly evaluations with OGTT, FPG and HbA1c. MAIN OUTCOME MEASURE: Relative risk of CVD. RESULTS: Over a median follow-up of 9.0 years 34 (6.7%) participants had a CVD event, which increased to 52 (10.3%) over a median follow-up of 13.0 years when including events that occurred among participants following a diagnosis of diabetes. Updated mean HbA1c, 1 hPG and 2 hPG, HR per 1 unit SD of 1.57 (95% CI 1.16 to 2.11), p = 0.0032, 1.51 (1.03 to 2.23), p = 0.036 and 1.60 (1.10 to 2.34), p = 0.014, respectively, but not FPG (p = 0.11), were related to CVD. In analyses of the last value prior to the CVD event the same three glycaemic measurements were associated with the CVD events, with HRs per 1 unit SD of 1.45 (1.06 to 1.98), p = 0.020, 1.55 (1.04 to 2.29), p = 0.030 and 2.19 (1.51 to 3.18), p<0.0001, respectively but only 2 hPG remained significant in pairwise comparisons. Including the follow-up period after diabetes onset updated 2 hPG (p = 0.003) but not updated mean HbA1c (p = 0.08) was related to CVD. CONCLUSIONS AND RELEVANCE: Current 2 hPG level in people with IGT is associated with increased risk of CVD. This supports its use in screening for prediabetes and monitoring glycaemic levels of people with prediabetes.

Changes in HbA1c and frequency of measuring HbA1c and adjusting glucose-lowering medications in the 10 years following diagnosis of type 2 diabetes: a population-based study in the UK.

AIMS/HYPOTHESIS: The aim of this work was to study levels of HbA1c and patterns of adjusting glucose-lowering drugs in patients with impaired glycaemic control over 10 years after diagnosis of type 2 diabetes. METHODS: We studied 4,529 individuals in The Health Improvement Network Database newly diagnosed with type 2 diabetes in the year 2000. RESULTS: From 6 months to 10 years after diagnosis, the HbA1c increased from 7.04% (53.4 mmol/mol) to 7.49% (58.3 mmol/mol) (average annual change: 0.047% [0.51 mmol/mol]). The greatest annual change occurred between 6 months and 2 years (0.21% [2.30 mmol/mol] increase per year, p < 0.001), followed by the 2-5 year time period (0.033% [0.36 mmol/mol] increase per year, p < 0.001). No significant increase in HbA1c occurred between 5 and 10 years (p = 0.20). In multivariable analyses, patients who were younger (p < 0.001), with higher BMI (p = 0.033) and who were current insulin users (p = 0.024) at diagnosis had greater increases in HbA1c between 6 months and 2 years. For individuals with HbA1c above 7.0% (53 mmol/mol) the mean time to next measurement of HbA1c was 0.53 years and increase in doses or changes to other glucose-lowering medications were performed in 26% of cases. CONCLUSIONS/INTERPRETATION: HbA1c increases by approximately 0.5% (5 mmol/mol) over 10 years after diagnosis of type 2 diabetes, with the main increase appearing in the first years after diagnosis. More frequent monitoring of HbA1c and adjustments of glucose-lowering drugs may be essential to prevent the decline.

A novel method for cross-species gene expression analysis.

BACKGROUND: Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex. RESULTS: In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified. CONCLUSIONS: The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

Transcriptional and metabolic data integration and modeling for identification of active pathways.

With the growing availability of omics data generated to describe different cells and tissues, the modeling and interpretation of such data has become increasingly important. Pathways are sets of reactions involving genes, metabolites, and proteins highlighting functional modules in the cell. Therefore, to discover activated or perturbed pathways when comparing two conditions, for example two different tissues, it is beneficial to use several types of omics data. We present a model that integrates transcriptomic and metabolomic data in order to make an informed pathway-level decision. Since metabolites can be seen as end-points of perturbations happening at the gene level, the gene expression data constitute the explanatory variables in a sparse regression model for the metabolite data. Sophisticated model selection procedures are developed to determine an appropriate model. We demonstrate that the transcript profiles can be used to informatively explain the metabolite data from cancer cell lines. Simulation studies further show that the proposed model offers a better performance in identifying active pathways than, for example, enrichment methods performed separately on the transcript and metabolite data.

Distinct cytoplasmic and nuclear functions of the stress induced protein DDIT3/CHOP/GADD153.

DDIT3, also known as GADD153 or CHOP, encodes a basic leucine zipper transcription factor of the dimer forming C/EBP family. DDIT3 is known as a key regulator of cellular stress response, but its target genes and functions are not well characterized. Here, we applied a genome wide microarray based expression analysis to identify DDIT3 target genes and functions. By analyzing cells carrying tamoxifen inducible DDIT3 expression constructs we show distinct gene expression profiles for cells with cytoplasmic and nuclear localized DDIT3. Of 175 target genes identified only 3 were regulated by DDIT3 in both cellular localizations. More than two thirds of the genes were downregulated, supporting a role for DDIT3 as a dominant negative factor that could act by either cytoplasmic or nuclear sequestration of dimer forming transcription factor partners. Functional annotation of target genes showed cell migration, proliferation and apoptosis/survival as the most affected categories. Cytoplasmic DDIT3 affected more migration associated genes, while nuclear DDIT3 regulated more cell cycle controlling genes. Cell culture experiments confirmed that cytoplasmic DDIT3 inhibited migration, while nuclear DDIT3 caused a G1 cell cycle arrest. Promoters of target genes showed no common sequence motifs, reflecting that DDIT3 forms heterodimers with several alternative transcription factors that bind to different motifs. We conclude that expression of cytoplasmic DDIT3 regulated 94 genes. Nuclear translocation of DDIT3 regulated 81 additional genes linked to functions already affected by cytoplasmic DDIT3. Characterization of DDIT3 regulated functions helps understanding its role in stress response and involvement in cancer and degenerative disorders.

Expression profiling of GIST: CD133 is associated with KIT exon 11 mutations, gastric location and poor prognosis.

In gastrointestinal stromal tumors (GISTs), KIT exon 11 deletions are associated with poor prognosis. The aim of this study was to determine the gene expression profiles of GISTs carrying KIT exon 11 deletions and to identify genes associated with poor prognosis. Expression profiling was performed on nine tumors with KIT exon 11 deletions and 7 without KIT exon 11 mutations using oligonucleotide microarrays. In addition, gene expression profiles for 35 GISTs were analyzed by meta-analysis. Expression of CD133 (prominin-1) protein was examined by tissue microarray (TMA) analysis of 204 GISTs from a population-based study in western Sweden. Survival analysis was performed on patients subjected to R0 resection (n=180) using the Cox proportional hazards model. Gene expression profiling, meta-analysis, and qPCR showed up regulation of CD133 in GISTs carrying KIT exon 11 deletions. Immunohistochemical analysis on TMA confirmed CD133 expression in 28% of all tumors. CD133 positivity was more frequent in gastric GISTs (48%) than in small intestinal GISTs (4%). CD133 positivity was also more frequent in GISTs with KIT exon 11 mutations (41%) than in tumors with mutations in KIT exon 9, platelet-derived growth factor receptor α (PDGFRA), or wild-type tumors (0-17%). Univariate survival analysis showed a significant correlation between the presence of CD133 protein and shorter overall survival (hazard ratio=2.23, p=0.027). Multivariate analysis showed that CD133 provided additional information on patient survival compared to age, sex, National Institutes of Health (NIH) risk group and mutational status. CD133 is expressed in a subset of predominantly gastric GISTs with KIT exon 11 mutations and poor prognosis.

Testing of chromosomal clumping of gene properties.

Clumping of gene properties like expression or mutant phenotypes along chromosomes is commonly detected using completely random null-models where their location is equally likely across the chromosomes. Interpretation of statistical tests based on these assumptions may be misleading if dependencies exist that are unequal between chromosomes or in different chromosomal parts. One such regional dependency is the telomeric effect, observed in several studies of Saccharomyces cerevisiae, under which e.g. essential genes are less likely to reside near the chromosomal ends. In this study we demonstrate that standard randomisation test procedures are of limited applicability in the presence of telomeric effects. Several extensions of such standard tests are here suggested for handling clumping simultaneously with regional differences in essentiality frequencies in sub-telomeric and central gene positions. Furthermore, a general non-homogeneous discrete Markov approach for combining parametrically modelled position dependent probabilities of a dichotomous property with a simple single parameter clumping is suggested. This Markov model is adapted to the observed telomeric effects and then simulations are used to demonstrate properties of the suggested modified randomisation tests. The model is also applied as a direct alternative tool for statistical analysis of the S. cerevisiae genome for clumping of phenotypes.

Genetic basis of arsenite and cadmium tolerance in Saccharomyces cerevisiae.

BACKGROUND: Arsenic and cadmium are widely distributed in nature and pose serious threats to the environment and human health. Exposure to these nonessential toxic metals may result in a variety of human diseases including cancer. However, arsenic and cadmium toxicity targets and the cellular systems contributing to tolerance acquisition are not fully known. RESULTS: To gain insight into metal action and cellular tolerance mechanisms, we carried out genome-wide screening of the Saccharomyces cerevisiae haploid and homozygous diploid deletion mutant collections and scored for reduced growth in the presence of arsenite or cadmium. Processes found to be required for tolerance to both metals included sulphur and glutathione biosynthesis, environmental sensing, mRNA synthesis and transcription, and vacuolar/endosomal transport and sorting. We also identified metal-specific defence processes. Arsenite-specific defence functions were related to cell cycle regulation, lipid and fatty acid metabolism, mitochondrial biogenesis, and the cytoskeleton whereas cadmium-specific defence functions were mainly related to sugar/carbohydrate metabolism, and metal-ion homeostasis and transport. Molecular evidence indicated that the cytoskeleton is targeted by arsenite and that phosphorylation of the Snf1p kinase is required for cadmium tolerance. CONCLUSION: This study has pin-pointed core functions that protect cells from arsenite and cadmium toxicity. It also emphasizes the existence of both common and specific defence systems. Since many of the yeast genes that confer tolerance to these agents have homologues in humans, similar biological processes may act in yeast and humans to prevent metal toxicity and carcinogenesis.

Evolutionary forces act on promoter length: identification of enriched cis-regulatory elements.

Transcription factors govern gene expression by binding to short DNA sequences called cis-regulatory elements. These sequences are typically located in promoters, which are regions of variable length upstream of the open reading frames of genes. Here, we report that promoter length and gene function are related in yeast, fungi, and plants. In particular, the promoters for stress-responsive genes are in general longer than those of other genes. Essential genes have, on the other hand, relatively short promoters. We utilize these findings in a novel method for identifying relevant cis-regulatory elements in a set of coexpressed genes. The method is shown to generate more accurate results and fewer false positives compared with other common procedures. Our results suggest that genes with complex transcriptional regulation tend to have longer promoters than genes responding to few signals. This phenomenon is present in all investigated species, indicating that evolution adjust promoter length according to gene function. Identification of cis-regulatory elements in Saccharomyces cerevisiae can be done with the web service located at http://enricher.zool.gu.se.

mRNA stability changes precede changes in steady-state mRNA amounts during hyperosmotic stress.

Under stress, cells need to optimize the activity of a wide range of gene products during the response phases: shock, adaptation, and recovery. This requires coordination of several levels of regulation, including turnover and translation efficiencies of mRNAs. Mitogen-activated protein (MAP) kinase pathways are implicated in many aspects of the environmental stress response, including initiation of transcription, translation efficiency, and mRNA turnover. In this study, we analyze mRNA turnover rates and mRNA steady-state levels at different time points following mild hyperosmotic shock in Saccharomyces cerevisiae cells. The regulation of mRNA stability is transient and affects most genes for which there is a change in transcript level. These changes precede and prepare for the changes in steady-state levels, both regarding the initial increase and the later decline of stress-induced mRNAs. The inverse is true for stress-repressed genes, which become stabilized during hyperosmotic stress in preparation of an increase as the cells recover. The MAP kinase Hog1 affects both steady-state levels and stability of stress-responsive transcripts, whereas the Hog1-activated kinase Rck2 influences steady-state levels without a major effect on stability. Regulation of mRNA stability is a wide-spread, but not universal, effect on stress-responsive transcripts during transient hyperosmotic stress. By destabilizing stress-induced mRNAs when their steady-state levels have reached a maximum, the cell prepares for the subsequent recovery phase when these transcripts are to return to normal levels. Conversely, stabilization of stress-repressed mRNAs permits their rapid accumulation in the recovery phase. Our results show that mRNA turnover is coordinated with transcriptional induction.

Evolutionary conservation of human drug targets in organisms used for environmental risk assessments.

Pharmaceuticals are typically found in very low concentrations in the aquatic environment. Accordingly, environmental effects clearly assigned to residual drugs are consistent with high affinity interactions with conserved targets in affected wildlife species rather than with a general toxic effect. Thus, evolutionarily well-conserved targets in a given species are associated with an increased risk. In this study orthologs for 1318 human drug targets were predicted in 16 species of which several are relevant for ecotoxicity testing. The conservation of different functional categories of targets was also analyzed. Zebrafish had orthologs to 86% of the drug targets while only 61% were conserved in Daphnia and 35% in green alga. The predicted presence and absence of orthologs agrees well with published experimental data on the potential for specific drug target interaction in various species. Based on the conservation of targets we propose that aquatic environmental risk assessments for human drugs should always include comprehensive studies on aquatic vertebrates. Furthermore, individual targets, especially enzymes, are well conserved suggesting that tests on evolutionarily distant organisms would be highly relevant for certain drugs. We propose that the results can guide environmental risk assessments by improving the possibilities to identify species sensitive to certain types of pharmaceuticals or to other contaminants that act through well defined mechanisms of action. Moreover, we suggest that the results can be used to interpret the relevance of existing ecotoxicity data.