Pro-apoptotic and antiproliferative activity of human KCNRG, a putative tumor suppressor in 13q14 region

Deletion of 13q14.3 and a candidate gene KCNRG (potassium channel regulating gene) is the most frequent chromosomal abnormality in B-cell chronic lymphocytic leukemia and is a common finding in multiple myeloma (MM). KCNRG protein may interfere with the normal assembly of the K+ channel proteins causing the suppression of Kv currents. We aimed to examine possible role of KCNRG haploinsufficiency in chronic lymphocytic leukemia (CLL) and MM cells. We performed detailed genomic analysis of the KCNRG locus; studied effects of the stable overexpression of KCNRG isoforms in RPMI-8226, HL-60, and LnCaP cells; and evaluated relative expression of its transcripts in various human lymphomas. Three MM cell lines and 35 CLL PBL samples were screened for KCNRG mutations. KCNRG exerts growth suppressive and pro-apoptotic effects in HL-60, LnCaP, and RPMI-8226 cells. Direct sequencing of KCNRG exons revealed point mutation delT in RPMI-8226 cell line. Levels of major isoform of KCNRG mRNA are lower in DLBL lymphomas compared to normal PBL samples, while levels of its minor mRNA are decreased across the broad range of the lymphoma types. The haploinsufficiency of KCNRG might be relevant to the progression of CLL and MM at least in a subset of patients

Genotype-phenotype correlations in FSHD

Abstract Background Facial-scapular-humeral myodystrophy Landouzy-Dejerine (FSHD) is an autosomal dominant disease, the basis of its pathogenesis is ectopic expression of the transcription factor DUX4 in skeletal muscle. There are two types of the disease: FSHD1 (MIM:158900) and FSHD2 (MIM: 158901), which have different genetic causes but are phenotypically indistinguishable. In FSHD1, partial deletion of the D4Z4 repeats on the 4th chromosome affects the expression of DUX4, whereas FSHD2 is caused by the mutations in the protein regulating the methylation status of chromatin - SMCHD1. High variability of clinical picture, both intra - and inter-family indicates a large number of factors influencing clinical picture. There are key genetic, epigenetic and gender factors that influence the expressivity and penetrance of the disease. Using only one of these factors allows just a rough prediction of the course of the disease, which indicates the combined effect of all of the factors on the DUX4 expression and on the clinical picture. Results In this paper, we analyzed the impact of genetic, epigenetic and gender differences on phenotype and the possibility of using them for disease prognosis and family counselling. Conclusions Key pathogenesis factors have been identified for FSHD. However, the pronounced intra - and inter-family polymorphism of manifestations indicates a large number of modifiers of the pathological process, many of which remain unknown

The role of long non-coding RNAs in the pathogenesis of hereditary diseases

Abstract Background Thousands of long non-coding RNA (lncRNA) genes are annotated in the human genome. Recent studies showed the key role of lncRNAs in a variety of fundamental cellular processes. Dysregulation of lncRNAs can drive tumorigenesis and they are now considered to be a promising therapeutic target in cancer. However, how lncRNAs contribute to the development of hereditary diseases in human is still mostly unknown. Results This review is focused on hereditary diseases in the pathogenesis of which long non-coding RNAs play an important role. Conclusions Fundamental research in the field of molecular genetics of lncRNA is necessary for a more complete understanding of their significance. Future research will help translate this knowledge into clinical practice which will not only lead to an increase in the diagnostic rate but also in the future can help with the development of etiotropic treatments for hereditary diseases

FSHD1 Diagnosis in a Russian Population Using a qPCR-Based Approach

Facioscapulohumeral dystrophy (FSHD) is an autosomal dominant myodystrophy. Approximately 95% of cases of FSHD are caused by partial deletion of the D4Z4 macrosatellite tandem repeats on chromosome 4q35. The existing FSHD1 diagnostic methods are laborious and not widely used. Here, we present a comprehensive analysis of the currently used diagnostic methods (Southern blotting and molecular combing) against a new qPCR-based approach for FSHD1 diagnosis. We observed 93% concordance between the results obtained by the new qPCR-based approach, reference Southern blotting and molecular combing methods. Applying the qPCR-based approach in the studied population, we observed a prevalence (64.9%) of the permissive alleles in the range of 3–6 D4Z4 units for a group of patients, while in a group of carriers, the permissive alleles were mostly (84.6%) present in the range of 6–9 D4Z4 units. No prevalence of disease penetrance depending on gender was observed. The results confirmed the earlier established inverse correlation between permissive allele size and disease severity, disease penetrance. The results suggest the applicability of the qPCR-based approach for FSHD1 diagnosis and its robustness in a basic molecular genetics laboratory. To our knowledge, this is the first study of FSHD1 permissive allele distribution in a Russian population

Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules

Abstract Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word- and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, we’ve developed a Python package with a minimalistic user interface

Investigation of LINC00493/SMIM26 Gene Suggests Its Dual Functioning at mRNA and Protein Level

The amount of human long noncoding RNA (lncRNA) genes is comparable to protein-coding; however, only a small number of lncRNAs are functionally annotated. Previously, it was shown that lncRNAs can participate in many key cellular processes, including regulation of gene expression at transcriptional and post-transcriptional levels. The lncRNA genes can contain small open reading frames (sORFs), and recent studies demonstrated that some of the resulting short proteins could play an important biological role. In the present study, we investigate the widely expressed lncRNA LINC00493. We determine the structure of the LINC00493 transcript, its cell localization and influence on cell physiology. Our data demonstrate that LINC00493 has an influence on cell viability in a cell-type-specific manner. Furthermore, it was recently shown that LINC00493 has a sORF that is translated into small protein SMIM26. The results of our knockdown and overexpression experiments suggest that both LINC00493/SMIM26 transcript and protein affect cell viability, but in the opposite manner

SnS-Align: a graphic tool for alignment of distantly related proteins

Genomic sequences for many animal species are now available in the public domain. Protein similarity search in evolutionarily distant organisms by sequence comparison often turns out to be difficult. Here, we present the Structure and Sequence Alignment (SnS-Align) tool that graphically presents pairwise local alignment of sandwiched protein sequences, a hybrid of the primary protein sequence and its secondary structure. The utility of the tool is demonstrated by sample analysis of the gap junction protein superfamily of innexins/pannexins and the classic myoglobin family. SnS-Align can also be used for demarcation of the structurally conserved domains within superfamilies of paralogous genes

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies

<div><p>Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied <i>in vitro</i> models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model via the web-site: <a href="http://score.generesearch.ru/services/badmut/" target="_blank">http://score.generesearch.ru/services/badmut/</a>.</p></div