TbAGO1, an Argonaute protein required for RNA interference, is involved in mitosis and chromosome segregation in Trypanosoma brucei

BACKGROUND: RNA silencing processes are widespread in almost all eukaryotic organisms. They have various functions including genome protection, and the control of gene expression, development and heterochromatin formation. RNA interference (RNAi) is the post-transcriptional destruction of RNA, which is mediated by a ribonucleoprotein complex that contains, among several components, RNA helicases and Argonaute proteins. RNAi is functional in trypanosomes, protozoan parasites that separated very early from the main eukaryotic lineage and exhibit several intriguing features in terms of the control of gene expression. In this report, we investigated the functions of RNAi in Trypanosoma brucei. RESULTS: By searching through genome databases, novel Argonaute-like proteins were identified in several protozoa that belong to the kinetoplastid order, a group of organisms that diverged early from the main eukaryotic lineage. T. brucei possesses two Argonaute-like genes termed TbAGO1 and TbPWI1. Dual transient transfection assays suggest that TbAGO1, but not TbPWI1, is involved in RNAi. The entire coding region of TbAGO1 was deleted by double gene knockout. TbAGO1-/- cells turned out to be completely resistant to RNAi generated either by transfected double-stranded RNA or by expression of an inverted repeat. TbAGO1-/- cells were viable but showed a dramatically reduced growth rate. This was probably due to defects in mitosis and abnormal chromosome segregation as revealed by in situ analysis. The RNAi and growth phenotypes were complemented by the inducible expression of a GFP::TbAGO1 fusion protein that revealed the cytoplasmic location of the protein. CONCLUSIONS: The requirement of TbAGO1 for RNAi in trypanosomes demonstrates the evolutionary ancient involvement of Argonaute proteins in RNAi silencing processes. RNAi-deficient TbAGO1-/- cells showed numerous defects in chromosome segregation and mitotic spindle assembly. We propose a working hypothesis in which RNAi would be involved in heterochromatin formation at the centromere and therefore in chromosome segregation

Copy number of 8q24.3 drives HSF1 expression and patient outcome in cancer: an individual patient data meta-analysis

Background: The heat-shock transcription factor 1 (HSF1) has been linked to cell proliferation and survival in cancer and has been proposed as a biomarker for poor prognosis. Here, we assessed the role of HSF1 expression in relation to copy number alteration (CNA) and cancer prognosis. Methods: Using 10,287 cancer genomes from The Cancer Genome Atlas and Cbioportal databases, we assessed the association of HSF1 expression with CNA and cancer prognosis. CNA of 8q24.3 was categorized as diploid (reference), deletion (fewer copies), gain (+ 1 copy) and amplification (>= + 2 copies). Multivariate logistic regression modeling was used to assess 5-year survival among those with a first cancer diagnosis and complete follow-up data (N = 9568), categorized per anatomical location and histology, assessing interaction with tumor stage, and expressed as odds ratios and 95% confidence intervals. Results: We found that only 54.1% of all tumors have a normal predicted 8q24.3 copy number and that 8q24.3 located genes including HSF1 are mainly overexpressed due to increased copies number of 8q24.3 in different cancers. The tumor of patients having respectively gain (+ 1 copy) and amplification (>= + 2 copies) of 8q24.3 display a global increase of 5-year mortality (odds ratio = 1.98, 95% CI 1.22-3.21) and (OR = 2.19, 1.13-4.26) after full adjustment. For separate cancer types, tumor patients with 8q24.3 deletion showed a marked increase of 5-year mortality in uterine (OR = 4.84, [2.75-8.51]), colorectal (OR = 4.12, [1.15-14.82]), and ovarian (OR = 1.83, [1.39-2.41]) cancers; and decreased mortality in kidney cancer (OR = 0.41, [0.21-0.82]). Gain of 8q24.3 resulted in significant mortality changes in 5-year mortality for cancer of the uterus (OR = 3.67, [2.03-6.66]), lung (OR = 1.76, [1.24-2.51]), colorectal (OR = 1.75, [1.32-2.31]) cancers; and amplification for uterine (OR = 4.58, [1.43-14.65]), prostate (OR = 4.41 [3.41-5.71]), head and neck (OR = 2.68, [2.17-3.30]), and stomach (OR = 0.56, [0.36-0.87]) cancers. Conclusions: Here, we show that CNAs of 8q24.3 genes, including HSF1, are tightly linked to 8q24.3 copy number in tumor patients and can affect patient outcome. Our results indicate that the integration of 8q24.3 CNA detection may be a useful predictor for cancer prognosis

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images

Correlation of Diffusion and Metabolic Alterations in Different Clinical Forms of Multiple Sclerosis

Diffusion tensor imaging (DTI) and MR spectroscopic imaging (MRSI) provide greater sensitivity than conventional MRI to detect diffuse alterations in normal appearing white matter (NAWM) of Multiple Sclerosis (MS) patients with different clinical forms. Therefore, the goal of this study is to combine DTI and MRSI measurements to analyze the relation between diffusion and metabolic markers, T2-weighted lesion load (T2-LL) and the patients clinical status. The sensitivity and specificity of both methods were then compared in terms of MS clinical forms differentiation. MR examination was performed on 71 MS patients (27 relapsing remitting (RR), 26 secondary progressive (SP) and 18 primary progressive (PP)) and 24 control subjects. DTI and MRSI measurements were obtained from two identical regions of interest selected in left and right centrum semioval (CSO) WM. DTI metrics and metabolic contents were significantly altered in MS patients with the exception of N-acetyl-aspartate (NAA) and NAA/Choline (Cho) ratio in RR patients. Significant correlations were observed between diffusion and metabolic measures to various degrees in every MS patients group. Most DTI metrics were significantly correlated with the T2-LL while only NAA/Cr ratio was correlated in RR patients. A comparison analysis of MR methods efficiency demonstrated a better sensitivity/specificity of DTI over MRSI. Nevertheless, NAA/Cr ratio could distinguish all MS and SP patients groups from controls, while NAA/Cho ratio differentiated PP patients from controls. This study demonstrated that diffusivity changes related to microstructural alterations were correlated with metabolic changes and provided a better sensitivity to detect early changes, particularly in RR patients who are more subject to inflammatory processes. In contrast, the better specificity of metabolic ratios to detect axonal damage and demyelination may provide a better index for identification of PP patients

SWI/SNF-like chromatin remodeling factor Fun30 supports point centromere function in S. cerevisiae

Budding yeast centromeres are sequence-defined point centromeres and are, unlike in many other organisms, not embedded in heterochromatin. Here we show that Fun30, a poorly understood SWI/SNF-like chromatin remodeling factor conserved in humans, promotes point centromere function through the formation of correct chromatin architecture at centromeres. Our determination of the genome-wide binding and nucleosome positioning properties of Fun30 shows that this enzyme is consistently enriched over centromeres and that a majority of CENs show Fun30-dependent changes in flanking nucleosome position and/or CEN core micrococcal nuclease accessibility. Fun30 deletion leads to defects in histone variant Htz1 occupancy genome-wide, including at and around most centromeres. FUN30 genetically interacts with CSE4, coding for the centromere-specific variant of histone H3, and counteracts the detrimental effect of transcription through centromeres on chromosome segregation and suppresses transcriptional noise over centromere CEN3. Previous work has shown a requirement for fission yeast and mammalian homologs of Fun30 in heterochromatin assembly. As centromeres in budding yeast are not embedded in heterochromatin, our findings indicate a direct role of Fun30 in centromere chromatin by promoting correct chromatin architecture

The Schizosaccharomyces pombe JmjC-protein, Msc1, prevents H2A.Z localization in centromeric and subtelomeric chromatin domains

Eukaryotic genomes are repetitively packaged into chromatin by nucleosomes, however they are regulated by the differences between nucleosomes, which establish various chromatin states. Local chromatin cues direct the inheritance and propagation of chromatin status via self-reinforcing epigenetic mechanisms. Replication-independent histone exchange could potentially perturb chromatin status if histone exchange chaperones, such as Swr1C, loaded histone variants into wrong sites. Here we show that in Schizosaccharomyces pombe, like Saccharomyces cerevisiae, Swr1C is required for loading H2A.Z into specific sites, including the promoters of lowly expressed genes. However S. pombe Swr1C has an extra subunit, Msc1, which is a JumonjiC-domain protein of the Lid/Jarid1 family. Deletion of Msc1 did not disrupt the S. pombe Swr1C or its ability to bind and load H2A.Z into euchromatin, however H2A.Z was ectopically found in the inner centromere and in subtelomeric chromatin. Normally this subtelomeric region not only lacks H2A.Z but also shows uniformly lower levels of H3K4me2, H4K5, and K12 acetylation than euchromatin and disproportionately contains the most lowly expressed genes during vegetative growth, including many meiotic-specific genes. Genes within and adjacent to subtelomeric chromatin become overexpressed in the absence of either Msc1, Swr1, or paradoxically H2A.Z itself. We also show that H2A.Z is N-terminally acetylated before, and lysine acetylated after, loading into chromatin and that it physically associates with the Nap1 histone chaperone. However, we find a negative correlation between the genomic distributions of H2A.Z and Nap1/Hrp1/Hrp3, suggesting that the Nap1 chaperones remove H2A.Z from chromatin. These data describe H2A.Z action in S. pombe and identify a new mode of chromatin surveillance and maintenance based on negative regulation of histone variant misincorporation.publishedVersio

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests.Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features.Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms

Podbat: A Novel Genomic Tool Reveals Swr1-Independent H2A.Z Incorporation at Gene Coding Sequences through Epigenetic Meta-Analysis

Epigenetic regulation consists of a multitude of different modifications that determine active and inactive states of chromatin. Conditions such as cell differentiation or exposure to environmental stress require concerted changes in gene expression. To interpret epigenomics data, a spectrum of different interconnected datasets is needed, ranging from the genome sequence and positions of histones, together with their modifications and variants, to the transcriptional output of genomic regions. Here we present a tool, Podbat (Positioning database and analysis tool), that incorporates data from various sources and allows detailed dissection of the entire range of chromatin modifications simultaneously. Podbat can be used to analyze, visualize, store and share epigenomics data. Among other functions, Podbat allows data-driven determination of genome regions of differential protein occupancy or RNA expression using Hidden Markov Models. Comparisons between datasets are facilitated to enable the study of the comprehensive chromatin modification system simultaneously, irrespective of data-generating technique. Any organism with a sequenced genome can be accommodated. We exemplify the power of Podbat by reanalyzing all to-date published genome-wide data for the histone variant H2A.Z in fission yeast together with other histone marks and also phenotypic response data from several sources. This meta-analysis led to the unexpected finding of H2A.Z incorporation in the coding regions of genes encoding proteins involved in the regulation of meiosis and genotoxic stress responses. This incorporation was partly independent of the H2A.Z-incorporating remodeller Swr1. We verified an Swr1-independent role for H2A.Z following genotoxic stress in vivo. Podbat is open source software freely downloadable from www.podbat.org, distributed under the GNU LGPL license. User manuals, test data and instructions are available at the website, as well as a repository for third party–developed plug-in modules. Podbat requires Java version 1.6 or higher