More than 50% of mammalian genomes consist of retrotransposon sequences. Silencing of retrotransposons by heterochromatin is essential to ensure genomic stability and transcriptional integrity. Here, ...we identified a short sequence element in intracisternal A particle (IAP) retrotransposons that is sufficient to trigger heterochromatin formation. We used this sequence in a genome‐wide shRNA screen and identified the chromatin remodeler Atrx as a novel regulator of IAP silencing. Atrx binds to IAP elements and is necessary for efficient heterochromatin formation. In addition, Atrx facilitates a robust and largely inaccessible heterochromatin structure as Atrx knockout cells display increased chromatin accessibility at retrotransposons and non‐repetitive heterochromatic loci. In summary, we demonstrate a direct role of Atrx in the establishment and robust maintenance of heterochromatin.
Synopsis
Atrx contributes to retrotransposon silencing in ES cells by promoting inaccessible heterochromatin formation. The data also suggest that retrotransposon silencing requires the histone chaperone Daxx but is independent of histone H3.3.
We identify a small heterochromatin initiation site in IAP‐Ez retrotransposons.
Atrx assists in Setdb1‐/Trim28‐dependent heterochromatin establishment and maintenance.
A locus‐specific chromatin accessibility assay reveals that Atrx‐deficient heterochromatin is more accessible and vulnerable to challenges.
Atrx contributes to retrotransposon silencing in ES cells by promoting inaccessible heterochromatin formation. The data also suggest that retrotransposon silencing requires the histone chaperone Daxx but is independent of histone H3.3.
For decades, cold-adapted, temperature-sensitive (ca/ts) strains of influenza A virus have been used as live attenuated vaccines. Due to their great public health importance it is crucial to ...understand the molecular mechanism(s) of cold adaptation and temperature sensitivity that are currently unknown. For instance, secondary RNA structures play important roles in influenza biology. Thus, we hypothesized that a relatively minor change in temperature (32-39°C) can lead to perturbations in influenza RNA structures and, that these structural perturbations may be different for mRNAs of the wild type (wt) and ca/ts strains. To test this hypothesis, we developed a novel in silico method that enables assessing whether two related RNA molecules would undergo (dis)similar structural perturbations upon temperature change. The proposed method allows identifying those areas within an RNA chain where dissimilarities of RNA secondary structures at two different temperatures are particularly pronounced, without knowing particular RNA shapes at either temperature. We identified such areas in the NS2, PA, PB2 and NP mRNAs. However, these areas are not identical for the wt and ca/ts mutants. Differences in temperature-induced structural changes of wt and ca/ts mRNA structures may constitute a yet unappreciated molecular mechanism of the cold adaptation/temperature sensitivity phenomena.
Abstract
Summary
Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To ...address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization.
Availability and implementation
The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.
Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle ...illness trajectories, and investigate genetic associations.
To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement.
This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019.
A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables.
Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean SD partial η2 = 0.014 0.003) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort.
Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups.
Psychiatric illnesses such as bipolar disorder, schizophrenia and schizoaffective disorder are severe, disabling disorders associated with decreased Quality Of Life (QOL) and functioning. ...Stigmatization, co-morbidities, adverse effects of medications and chronic symptoms due to treatment resistance are factors responsible for this association. In this study, we aim to characterize patients with good and poor outcomes according to QOL and functioning scores. Using cluster analysis, we sought to identify longitudinal trajectories and investigate whether levels of QOL and functioning are associated with polygenic risk scores. Determining clusters of patients at higher risk of poorer outcomes is critical to provide early and effective interventions.
Longitudinal data was used from the Clinical Research Group 241 and PsyCourse studies in Germany. Participants were phenotyped using a comprehensive battery which included data on socio-demographics, history of illness, symptomatology, QOL and functioning. Data was collected at four equidistant time points over 18 months. The Infinium Psycharray from Illumina was used to genotype patients. The QOL and functioning domain was represented by 28 longitudinally measures items (WHOQOL-BREF; GAF; work status). Dimension reduction of the 28 items by means of Factor Analysis for Mixed Data (FAMD) was applied. This allowed for the computation of abstract data dimensions which were used for calculation of longitudinal trajectories. These trajectories are a representation of the overall status of patients and both the overall level as well as the longitudinal change of this status were used as inputs for k-mean clustering of longitudinal data. This, in turn, resulted in the identification of three distinct subpopulations of patients. A multinomial regression of cluster membership on schizophrenia Polygenic Risk Scores (PRS) (11 p-value thresholds) was performed.
Individual trajectories across time on nine dimensions were used for cluster analysis. Together, these dimensions explained 27.13% of variance in the data. These dimensions were mainly driven by QOL items. In a sample of 254 patients, three clusters were observed; cluster A (63%) consisted of participants with the highest average scores for functioning, cluster B (33%) and cluster C (4%) consisting of participants with lower functioning over the course of the longitudinal study. A significantly higher proportion of individuals in cluster B were hospitalized at baseline in comparison to cluster A. Furthermore, individuals in cluster A had a significantly higher GAF score at baseline than those individuals in cluster B. No significant differences were seen for age, center, diagnosis, sex, work status, or duration of illness between the clusters. A nominally significant effect of SZ-PRS on cluster membership was observed from the threshold 0.2 on, however this did not survive FDR correction. In terms of cluster membership, the highest polygenic risk loading was observed for cluster B and the lowest for cluster C.
Phenotypic data provides insight to target sufferers of severe mental illness with worse outcomes. Further work is needed to improve methods to deal with missing data and increase sample size. Associations with other biological data will be explored (e.g. microRNA; methylation data; proteomics; imaging data).
Abstract
Copy number aberrations (CNAs) are known to strongly affect oncogenes and tumour suppressor genes. Given the critical role CNAs play in cancer research, it is essential to accurately ...identify CNAs from tumour genomes. One particular challenge in finding CNAs is the effect of confounding variables. To address this issue, we assessed how commonly used CNA identification algorithms perform on SNP 6.0 genotyping data in the presence of confounding variables. We simulated realistic synthetic data with varying levels of three confounding variables—the tumour purity, the length of a copy number region and the CNA burden (the percentage of CNAs present in a profiled genome)—and evaluated the performance of OncoSNP, ASCAT, GenoCNA, GISTIC and CGHcall. Furthermore, we implemented and assessed CGHcall*, an adjusted version of CGHcall accounting for high CNA burden. Our analysis on synthetic data indicates that tumour purity and the CNA burden strongly influence the performance of all the algorithms. No algorithm can correctly find lost and gained genomic regions across all tumour purities. The length of CNA regions influenced the performance of ASCAT, CGHcall and GISTIC. OncoSNP, GenoCNA and CGHcall* showed little sensitivity. Overall, CGHcall* and OncoSNP showed reasonable performance, particularly in samples with high tumour purity. Our analysis on the HapMap data revealed a good overlap between CGHcall, CGHcall* and GenoCNA results and experimentally validated data. Our exploratory analysis on the TCGA HNSCC data revealed plausible results of CGHcall, CGHcall* and GISTIC in consensus HNSCC CNA regions. Code is available at https://github.com/adspit/PASCAL.
Abstract
Background
Separation of individuals into schizophrenia and bipolar diagnoses has long been questioned, with some suggesting that the classification impairs the understanding of etiology, ...the accuracy of prognoses, and treatment selection. In this study, we employed unbiased statistical techniques to identify subgroups of individuals with chronic illness using a large array of variables commonly evaluated at the bedside. We then validated the resulting groups by investigating age of onset, schizophrenia polygenic risk scores (PRS), and functional outcomes at a 1-year follow-up period. Our hypothesis was that transdiagnostic subgroups would be stratified based on illness onset whereby individuals with earlier onset would have higher genetic risk loading and poorer functional outcomes.
Methods
Participants were selected from a longitudinal, naturalistic, multi-site project (PsyCourse) designed to investigate psychiatric illness course and outcomes. A total of 329 participants (age(SD)=45.7(12.6); 54% female; years of illness duration(SD) = 13.7(10.3)) with a DSM-IV diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder were assessed from 17 centers at baseline and 1-year follow-up periods. A clinical battery measuring sociodemographic, illness history, symptoms, cognition, and personality questionnaires (199 variables) was used to subgroup individuals. A non-negative factor analytic consensus clustering MATLAB toolbox was created based on previous methodological work in oncology. PRS were generated using widely used strategies, and differences between resulting subgroups were investigated with MANCOVA controlling for ancestry effects. Differences in functional outcomes were investigated with repeated measures ANOVA.
Results
A 4-subgroup solution was robustly defined as the optimal solution using resampling techniques and cluster validity indices. Diagnoses were mixed in two subgroups, but predominantly bipolar or schizophrenia in the other two. All subgroups had equal illness durations (p>0.05), but the age of onset showed a decreasing trend with the earliest age being linked to two subgroups: a mixed bipolar-schizophrenia group with intermediate levels of general functioning and in a schizophrenia group with low levels of functioning (p<0.001). PRS scores were significantly increased in the early-onset, mixed bipolar-schizophrenia subgroup (p=0.007, uncorrected) and in the schizophrenia group (p=0.025, uncorrected). Prognoses differed between the four groups (p=0.003), with the greatest increases in functional outcomes in a late-onset mixed diagnostic subgroup (p=0.006) and in the schizophrenia group (p=0.002).
Discussion
Four subgroups were detected and our hypothesis was supported by a relationship between earlier illness onset and higher schizophrenia genetic risk loading. While one of the subgroups with an earlier onset mostly consisted of individuals with schizophrenia, the other subgroup was diagnostically mixed. Our results tentatively suggest that transdiagnostic clustering may identify subgroups that could be effectively used to understand etiology and prognoses. Future research will investigate the possibility of differential treatment effects in these subgroups.
Biological research and clinical management in psychiatry face two major impediments: the high degree of overlap in psychopathology between diagnoses and the inherent heterogeneity with regard to ...severity. Here, we aim to stratify cases into homogeneous transdiagnostic subgroups using psychometric information with the ultimate aim of identifying individuals with higher risk for severe illness.
397 participants of the PsyCourse study with schizophrenia- or bipolar-spectrum diagnoses were prospectively phenotyped over 18 months. Factor analysis of mixed data of different rating scales and subsequent longitudinal clustering were used to cluster disease trajectories.
Five clusters of longitudinal trajectories were identified in the psychopathologic dimensions. Clusters differed significantly with regard to Global Assessment of Functioning, disease course, and—in some cases—diagnosis while there were no significant differences regarding sex, age at baseline or onset, duration of illness, or polygenic burden for schizophrenia.
Longitudinal clustering may aid in identifying transdiagnostic homogeneous subgroups of individuals with severe psychiatric disease.
Inferring catalysis in biological systems Kondofersky, Ivan; Theis, Fabian J; Fuchs, Christiane
IET systems biology,
12/2016, Letnik:
10, Številka:
6
Journal Article
Recenzirano
Odprti dostop
In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal ...experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple-step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non-informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.
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