Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation.

Within the last years, more and more noncoding RNAs (ncRNAs) became the focal point to understand cell regulatory mechanisms because one class of ncRNAs, microRNAs (miRNAs), plays an essential role in translation repression or degradation of specific mRNAs and is implicated in disease etiology. miRNAs can serve as oncomiRs (oncogenic miRNAs) and tumor suppressor miRNAs, thus, miRNA therapeutics in clinical trials have become a vital component with respect to cancer treatment. To circumvent side-effects and allow an accurate effect it is crucial to accurately predict miRNAs and their mRNA targets. Over the last two decades, different approaches for miRNA prediction as well as miRNA target prediction have been developed and improved based on sequence and structure features. Nowadays, the abundance of high-throughput sequencing data and databases of miRNAs and miRNA targets from different species allow the training, testing, and validation of predicted miRNAs and miRNA targets with machine learning methods. This book chapter focuses on the important requirements for miRNA target prediction tools using ML like common features used for miRNA-binding site prediction. Furthermore, best practices for the prediction and validation of miRNA-mRNA targets are presented and set in the context of possible applications for cancer diagnosis and therapeutics.

Genome-Wide DNA Alterations in X-Irradiated Human Gingiva Fibroblasts.

While ionizing radiation (IR) is a powerful tool in medical diagnostics, nuclear medicine, and radiology, it also is a serious threat to the integrity of genetic material. Mutagenic effects of IR to the human genome have long been the subject of research, yet still comparatively little is known about the genome-wide effects of IR exposure on the DNA-sequence level. In this study, we employed high throughput sequencing technologies to investigate IR-induced DNA alterations in human gingiva fibroblasts (HGF) that were acutely exposed to 0.5, 2, and 10 Gy of 240 kV X-radiation followed by repair times of 16 h or 7 days before whole-genome sequencing (WGS). Our analysis of the obtained WGS datasets revealed patterns of IR-induced variant (SNV and InDel) accumulation across the genome, within chromosomes as well as around the borders of topologically associating domains (TADs). Chromosome 19 consistently accumulated the highest SNVs and InDels events. Translocations showed variable patterns but with recurrent chromosomes of origin (e.g., Chr7 and Chr16). IR-induced InDels showed a relative increase in number relative to SNVs and a characteristic signature with respect to the frequency of triplet deletions in areas without repetitive or microhomology features. Overall experimental conditions and datasets the majority of SNVs per genome had no or little predicted functional impact with a maximum of 62, showing damaging potential. A dose-dependent effect of IR was surprisingly not apparent. We also observed a significant reduction in transition/transversion (Ti/Tv) ratios for IR-dependent SNVs, which could point to a contribution of the mismatch repair (MMR) system that strongly favors the repair of transitions over transversions, to the IR-induced DNA-damage response in human cells. Taken together, our results show the presence of distinguishable characteristic patterns of IR-induced DNA-alterations on a genome-wide level and implicate DNA-repair mechanisms in the formation of these signatures.

Benign uterine mass-discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study.

PURPOSE: Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score. METHODS: A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set. RESULTS: In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RRc = 2.71, CI = (1.90-3.49), p < 0.001], hypermenorrhea [RRc = 0.28, CI = (0.15-0.50), p < 0.001], dysmenorrhea [RRc = 0.22, CI = (0.10-0.51), p < 0.001], postmenstrual bleeding [RRc = 2.08, CI = (1.30-2.75), p < 0.001], suspicious sonography [RRc = 1.21, CI = (1.19-1.22), p < 0.001] and the tumor diameter (each centimeter difference: ß = 0.24, SD = 0.04, p < 0.001). pLMS achieved a mean cross-validated AUC of 0.969 (SD = 0.019) in the training set and an AUC of 0.968 in the test set. CONCLUSIONS: The presented score is based on basic clinical characteristics and allows the prediction of LMS prior to a planned surgery of a uterine mass. In case pLMS is between - 3 and + 1, we suggest subsequent diagnostics, such as endometrial biopsy, color Doppler sonography, LDH measurement, MRI and transcervical biopsy.

BDNF: mRNA expression in urine cells of patients with chronic kidney disease and its role in kidney function.

Podocyte loss and changes to the complex morphology are major causes of chronic kidney disease (CKD). As the incidence is continuously increasing over the last decades without sufficient treatment, it is important to find predicting biomarkers. Therefore, we measured urinary mRNA levels of podocyte genes NPHS1, NPHS2, PODXL and BDNF, KIM-1, CTSL by qRT-PCR of 120 CKD patients. We showed a strong correlation between BDNF and the kidney injury marker KIM-1, which were also correlated with NPHS1, suggesting podocytes as a contributing source. In human biopsies, BDNF was localized in the cell body and major processes of podocytes. In glomeruli of diabetic nephropathy patients, we found a strong BDNF signal in the remaining podocytes. An inhibition of the BDNF receptor TrkB resulted in enhanced podocyte dedifferentiation. The knockdown of the orthologue resulted in pericardial oedema formation and lowered viability of zebrafish larvae. We found an enlarged Bowman's space, dilated glomerular capillaries, podocyte loss and an impaired glomerular filtration. We demonstrated that BDNF is essential for glomerular development, morphology and function and the expression of BDNF and KIM-1 is highly correlated in urine cells of CKD patients. Therefore, BDNF mRNA in urine cells could serve as a potential CKD biomarker.

CASSIS and SMIPS: promoter-based prediction of secondary metabolite gene clusters in eukaryotic genomes.

MOTIVATION: Secondary metabolites (SM) are structurally diverse natural products of high pharmaceutical importance. Genes involved in their biosynthesis are often organized in clusters, i.e., are co-localized and co-expressed. In silico cluster prediction in eukaryotic genomes remains problematic mainly due to the high variability of the clusters' content and lack of other distinguishing sequence features. RESULTS: We present Cluster Assignment by Islands of Sites (CASSIS), a method for SM cluster prediction in eukaryotic genomes, and Secondary Metabolites by InterProScan (SMIPS), a tool for genome-wide detection of SM key enzymes ('anchor' genes): polyketide synthases, non-ribosomal peptide synthetases and dimethylallyl tryptophan synthases. Unlike other tools based on protein similarity, CASSIS exploits the idea of co-regulation of the cluster genes, which assumes the existence of common regulatory patterns in the cluster promoters. The method searches for 'islands' of enriched cluster-specific motifs in the vicinity of anchor genes. It was validated in a series of cross-validation experiments and showed high sensitivity and specificity. AVAILABILITY AND IMPLEMENTATION: CASSIS and SMIPS are freely available at https://sbi.hki-jena.de/cassis CONTACT: thomas.wolf@leibniz-hki.de or ekaterina.shelest@leibniz-hki.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The natural history of biocatalytic mechanisms.

Phylogenomic analysis of the occurrence and abundance of protein domains in proteomes has recently showed that the α/ß architecture is probably the oldest fold design. This holds important implications for the origins of biochemistry. Here we explore structure-function relationships addressing the use of chemical mechanisms by ancestral enzymes. We test the hypothesis that the oldest folds used the most mechanisms. We start by tracing biocatalytic mechanisms operating in metabolic enzymes along a phylogenetic timeline of the first appearance of homologous superfamilies of protein domain structures from CATH. A total of 335 enzyme reactions were retrieved from MACiE and were mapped over fold age. We define a mechanistic step type as one of the 51 mechanistic annotations given in MACiE, and each step of each of the 335 mechanisms was described using one or more of these annotations. We find that the first two folds, the P-loop containing nucleotide triphosphate hydrolase and the NAD(P)-binding Rossmann-like homologous superfamilies, were α/ß architectures responsible for introducing 35% (18/51) of the known mechanistic step types. We find that these two oldest structures in the phylogenomic analysis of protein domains introduced many mechanistic step types that were later combinatorially spread in catalytic history. The most common mechanistic step types included fundamental building blocks of enzyme chemistry: "Proton transfer," "Bimolecular nucleophilic addition," "Bimolecular nucleophilic substitution," and "Unimolecular elimination by the conjugate base." They were associated with the most ancestral fold structure typical of P-loop containing nucleotide triphosphate hydrolases. Over half of the mechanistic step types were introduced in the evolutionary timeline before the appearance of structures specific to diversified organisms, during a period of architectural diversification. The other half unfolded gradually after organismal diversification and during a period that spanned ∼2 billion years of evolutionary history.

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules.

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure-property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ~1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9-1.0 log S units.

PFClust: a novel parameter free clustering algorithm.

BACKGROUND: We present the algorithm PFClust (Parameter Free Clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of 'correct' cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings. RESULTS: We validate PFClust firstly with reference to a number of synthetic datasets consisting of 2D vectors, showing that its clustering performance is at least equal to that of six other leading methodologies - even though five of the other methods are told in advance how many clusters to use. We also demonstrate the ability of PFClust to classify the three dimensional structures of protein domains, using a set of folds taken from the structural bioinformatics database CATH. CONCLUSIONS: We show that PFClust is able to cluster the test datasets a little better, on average, than any of the other algorithms, and furthermore is able to do this without the need to specify any external parameters. Results on the synthetic datasets demonstrate that PFClust generates meaningful clusters, while our algorithm also shows excellent agreement with the correct assignments for a dataset extracted from the CATH part-manually curated classification of protein domain structures.

Correction: Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks.

[This corrects the article DOI: 10.1371/journal.pone.0271610.].

Neighbourhood watch: genomic epidemiology of SARS-CoV-2 variants circulating in a German federal state, Mecklenburg-Western Pomerania, in 2020-2022.

ABSTRACTGlobal and even national genome surveillance approaches do not provide the resolution necessary for rapid and accurate direct response by local public health authorities. Hence, a regional network of microbiological laboratories in collaboration with the health departments of all districts of the German federal state of Mecklenburg-Western Pomerania (M-V) was formed to investigate the regional molecular epidemiology of circulating SARS-CoV-2 lineages between 11/2020 and 03/2022. More than 4750 samples from all M-V counties were sequenced using Illumina and Nanopore technologies. Overall, 3493 (73.5%) sequences fulfilled quality criteria for time-resolved and/or spatially-resolved maximum likelihood phylogenic analyses and k-mean/ median clustering (KMC). We identified 116 different Pangolin virus lineages that can be assigned to 16 Nextstrain clades. The ten most frequently detected virus lineages belonged to B.1.1.7, AY.122, AY.43, BA.1, B.1.617.2, BA.1.1, AY.9.2, AY.4, P.1 and AY.126. Time-resolved phylogenetic analyses showed the occurrence of virus clades as determined worldwide, but with a substantial delay of one to two months. Further spatio-temporal phylogenetic analyses revealed a regional outbreak of a Gamma variant limited to western M-V counties. Finally, KMC elucidated a successive introduction of the various virus lineages into M-V, possibly triggered by vacation periods with increased (inter-) national travel activities. The COVID-19 pandemic in M-V was shaped by a combination of several SARS-CoV-2 introductions, lockdown measures, restrictive quarantine of patients and the lineage specific replication rate. Complementing global and national surveillance, regional surveillance adds value by providing a higher level of surveillance resolution tailored to local health authorities.

Is EC class predictable from reaction mechanism?

BACKGROUND: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. RESULTS: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. CONCLUSIONS: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways.The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.

Macrophages Are Polarized toward an Inflammatory Phenotype by their Aged Microenvironment in the Human Skin.

Aging of the skin is accompanied by cellular as well as tissue environmental changes, ultimately reducing the ability of the tissue to regenerate and adequately respond to external stressors. Macrophages are important gatekeepers of tissue homeostasis, and it has been reported that their number and phenotype change during aging in a site-specific manner. How aging affects human skin macrophages and what implications this has for the aging process in the tissue are still not fully understood. Using single-cell RNA-sequencing analysis, we show that there is at least a 50% increase of macrophages in human aged skin, which appear to have developed from monocytes and exhibit more proinflammatory M1-like characteristics. In contrast, the cell-intrinsic ability of aged monocytes to differentiate into M1 macrophages was reduced. Using coculture experiments with aged dermal fibroblasts, we show that it is the aged microenvironment that drives a more proinflammatory phenotype of macrophages in the skin. This proinflammatory M1-like phenotype in turn negatively influenced the expression of extracellular matrix proteins by fibroblasts, emphasizing the impact of the aged macrophages on the skin phenotype.

Stimulation of soluble guanylyl cyclase (sGC) by riociguat attenuates heart failure and pathological cardiac remodelling.

BACKGROUND AND PURPOSE: Heart failure is associated with an impaired NO-soluble guanylyl cyclase (sGC)-cGMP pathway and its augmentation is thought to be beneficial for its therapy. We hypothesized that stimulation of sGC by the sGC stimulator riociguat prevents pathological cardiac remodelling and heart failure in response to chronic pressure overload. EXPERIMENTAL APPROACH: Transverse aortic constriction or sham surgery was performed in C57BL/6N mice. After 3 weeks of transverse aortic constriction when heart failure was established, animals receive either riociguat or its vehicle for 5 additional weeks. Cardiac function was evaluated weekly by echocardiography. Eight weeks after surgery, histological analyses were performed to evaluate remodelling and the transcriptome of the left ventricles (LVs) was analysed by RNA sequencing. Cell culture experiments were used for mechanistically studies. KEY RESULTS: Transverse aortic constriction resulted in a continuous decrease of LV ejection fraction and an increase in LV mass until week 3. Five weeks of riociguat treatment resulted in an improved LV ejection fraction and a decrease in the ratio of left ventricular mass to total body weight (LVM/BW), myocardial fibrosis and myocyte cross-sectional area. RNA sequencing revealed that riociguat reduced the expression of myocardial stress and remodelling genes (e.g. Nppa, Nppb, Myh7 and collagen) and attenuated the activation of biological pathways associated with cardiac hypertrophy and heart failure. Riociguat reversed pathological stress response in cultivated myocytes and fibroblasts. CONCLUSION AND IMPLICATIONS: Stimulation of the sGC reverses transverse aortic constriction-induced heart failure and remodelling, which is associated with improved myocardial gene expression. LINKED ARTICLES: This article is part of a themed issue on cGMP Signalling in Cell Growth and Survival. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.11/issuetoc.

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks.

BACKGROUND: Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. METHOD: We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. RESULTS: Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. CONCLUSION: We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.

Riociguat attenuates the changes in left ventricular proteome and microRNA profile after experimental aortic stenosis in mice.

BACKGROUND AND PURPOSE: Development and progression of heart failure involve endothelial and myocardial dysfunction as well as a dysregulation of the NO-sGC-cGMP signalling pathway. Recently, we reported that the sGC stimulator riociguat has beneficial effects on cardiac remodelling and progression of heart failure in response to chronic pressure overload. Here, we examined if these beneficial effects of riociguat were also reflected in alterations of the myocardial proteome and microRNA profiles. EXPERIMENTAL APPROACH: Male C57BL/6N mice underwent transverse aortic constriction (TAC) and sham-operated mice served as controls. TAC and sham animals were randomised and treated with either riociguat or vehicle for 5 weeks, starting 3 weeks after surgery, when cardiac hypertrophy was established. Afterwards, we performed mass spectrometric proteome analyses and microRNA sequencing of proteins and RNAs, respectively, isolated from left ventricles (LVs). KEY RESULTS: TAC-induced changes of the LV proteome were significantly reduced by treatment with riociguat. Bioinformatics analyses revealed that riociguat improved TAC-induced cardiovascular disease-related pathways, metabolism and energy production, for example, reversed alterations in the levels of myosin heavy chain 7, cardiac phospholamban and ankyrin repeat domain-containing protein 1. Riociguat also attenuated TAC-induced changes of microRNA levels in the LV. CONCLUSION AND IMPLICATIONS: The sGC stimulator riociguat exerted beneficial effects on cardiac structure and function during pressure overload, which was accompanied by a reversal of TAC-induced changes of the cardiac proteome and microRNA profile. Our data support the potential of riociguat as a novel therapeutic agent for heart failure.

The ShGlomAssay Combines High-Throughput Drug Screening With Downstream Analyses and Reveals the Protective Role of Vitamin D3 and Calcipotriol on Podocytes.

Chronic kidney disease (CKD) is a major public health burden affecting more than 500 million people worldwide. Podocytopathies are the main cause for the majority of CKD cases due to pathogenic morphological as well as molecular biological alterations of postmitotic podocytes. Podocyte de-differentiation is associated with foot process effacement subsequently leading to proteinuria. Since currently no curative drugs are available, high throughput screening methods using a small number of animals are a promising and essential tool to identify potential drugs against CKD in the near future. Our study presents the implementation of the already established mouse GlomAssay as a semi-automated high-throughput screening method-shGlomAssay-allowing the analysis of several hundreds of FDA-verified compounds in combination with downstream pathway analysis like transcriptomic and proteomic analyses from the same samples, using a small number of animals. In an initial prescreening we have identified vitamin D3 and its analog calcipotriol to be protective on podocytes. Furthermore, by using RT-qPCR, Western blot, and RNA sequencing, we found that mRNA and protein expression of nephrin, the vitamin D receptor and specific podocyte markers were significantly up-regulated due to vitamin D3- and calcipotriol-treatment. In contrast, kidney injury markers were significantly down-regulated. Additionally, we found that vitamin D3 and calcipotriol have had neither influence on the expression of the miR-21 and miR-30a nor on miR-125a/b, a miRNA described to regulate the vitamin D receptor. In summary, we advanced the established mouse GlomAssay to a semi-automated high-throughput assay and combined it with downstream analysis techniques by using only a minimum number of animals. Hereby, we identified the vitamin D signaling pathway as podocyte protective and to be counteracting their de-differentiation.

Hormonally Induced Hepatocellular Carcinoma in Diabetic Wild Type and Carbohydrate Responsive Element Binding Protein Knockout Mice.

OBJECTIVE: In the rat, the pancreatic islet transplantation model is an established method to induce hepatocellular carcinomas (HCC), due to insulin-mediated metabolic and molecular alterations like increased glycolysis and de novo lipogenesis and the oncogenic AKT/mTOR pathway including upregulation of the transcription factor Carbohydrate-response element-binding protein (ChREBP). ChREBP could therefore represent an essential oncogenic co-factor during hormonally induced hepatocarcinogenesis. METHODS: Pancreatic islet transplantation was implemented in diabetic C57Bl/6J (wild type, WT) and ChREBP-knockout (KO) mice for 6 and 12 months. Liver tissue was examined using histology, immunohistochemistry, electron microscopy and Western blot analysis. Finally, we performed NGS-based transcriptome analysis between WT and KO liver tumor tissues. RESULTS: Three hepatocellular carcinomas were detectable after 6 and 12 months in diabetic transplanted WT mice, but only one in a KO mouse after 12 months. Pre-neoplastic clear cell foci (CCF) were also present in liver acini downstream of the islets in WT and KO mice. In KO tumors, glycolysis, de novo lipogenesis and AKT/mTOR signalling were strongly downregulated compared to WT lesions. Extrafocal liver tissue of diabetic, transplanted KO mice revealed less glycogen storage and proliferative activity than WT mice. From transcriptome analysis, we identified a set of transcripts pertaining to metabolic, oncogenic and immunogenic pathways that are differentially expressed between tumors of WT and KO mice. Of 315 metabolism-associated genes, we observed 199 genes that displayed upregulation in the tumor of WT mice, whereas 116 transcripts showed their downregulated expression in KO mice tumor. CONCLUSIONS: The pancreatic islet transplantation model is a suitable method to study hormonally induced hepatocarcinogenesis also in mice, allowing combination with gene knockout models. Our data indicate that deletion of ChREBP delays insulin-induced hepatocarcinogenesis, suggesting a combined oncogenic and lipogenic function of ChREBP along AKT/mTOR-mediated proliferation of hepatocytes and induction of hepatocellular carcinoma.

Ionizing Radiation Alters the Transition/Transversion Ratio in the Exome of Human Gingiva Fibroblasts.

Little is known about the mutational impact of ionizing radiation (IR) exposure on a genome-wide level in mammalian tissues. Recent advancements in sequencing technology have provided powerful tools to perform exome-wide analyses of genetic variation. This also opened up new avenues for studying and characterizing global genomic IR-induced effects. However, genotypes generated by next generation sequencing (NGS) studies can contain errors, which may significantly impact the power to detect signals in common and rare variant analyses. These genotyping errors are not explicitly detected by the standard Genotype Analysis ToolKit (GATK) and Variant Quality Score Recalibration (VQSR) tool and thus remain a potential source of false-positive variants in whole exome sequencing (WES) datasets. In this context, the transition-transversion ratio (Ti/Tv) is commonly used as an additional quality check. In case of IR experiments, this is problematic when Ti/Tv itself might be influenced by IR treatment. It was the aim of this study to determine a suitable threshold for variant filters for NGS datasets from irradiated cells in order to achieve high data quality using Ti/Tv, while at the same time being able to investigate radiation-specific effects on the Ti/Tv ratio for different radiation doses. By testing a variety of filter settings and comparing the obtained results with publicly available datasets, we observe that a coverage filter setting of depth (DP) 3 and genotype quality (GQ) 20 is sufficient for high quality single nucleotide variants (SNVs) calling in an analysis combining GATK and VSQR and that Ti/Tv values are a consistent and useful indicator for data quality assessment for all tested NGS platforms. Furthermore, we report a reduction in Ti/Tv in IR-induced mutations in primary human gingiva fibroblasts (HGFs), which points to an elevated proportion of transversions among IR-induced SNVs and thus might imply that mismatch repair (MMR) plays a role in the cellular damage response to IR-induced DNA lesions.

Immune system-mediated atherosclerosis caused by deficiency of long non-coding RNA MALAT1 in ApoE-/-mice.

Aims: The immune system is considered a key driver of atherosclerosis, and beyond proteins and microRNAs (miRs), long non-coding RNAs (lncRNAs) are implicated in immune control. We previously described that lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is involved in cardiac innate immunity in a myocarditis model. Here, we investigated the impact of MALAT1 deficiency upon atherosclerosis development. Methods and results: Heterozygous MALAT1-deficient ApoE-/- mice displayed massive immune system dysregulation and atherosclerosis within 2 months even when kept on normal diet. Aortic plaque area (P < 0.05) and aortic root plaque size (P < 0.001) were increased in MALAT1-deficient vs. MALAT1-wildtype ApoE-/- mice. Serum levels of interferon-γ (IFN-γ), tumour necrosis factor (TNF), and interleukin 6 (IL6) were elevated (P < 0.001) in MALAT1-deficient animals. MALAT1-deficient bone marrow-derived macrophages showed enhanced expression of TNF (P = 0.001) and inducible NO synthase (NOS2) (P = 0.002), suppressed MMP9 (P < 0.001), and impaired phagocytic activity (P < 0.001) upon lipopolysaccharide stimulation. RNA-sequencing revealed grossly altered transcriptomes of MALAT1-deficient splenocytes already at baseline, with massive induction of IFN- γ, TNF, NOS2, and granzyme B; CC and CXC chemokines and CCR8; and innate immunity genes interferon-induced protein with tetratricopeptide repeats (IFIT)1/3, interferon-induced transmembrane protein (IFITM)1/3, ISG15. Multiple miRs were up to 45-fold upregulated. Further, selective ablation of the cytosolic part of the MALAT1 system only, the enzymatically MALAT1-derived mascRNA, resulted in massive induction of TNF (P = 0.004) and IL6 (P = 0.028) in macrophages. Northern analysis of post-myocardial infarction patient vs. control peripheral blood mononuclear cells showed reduced (P = 0.005) mascRNA in the patients. CHART-enriched RNA-sequencing reads at the genomic loci of MALAT1 and neighbouring nuclear enriched abundant transcript (NEAT1) documented direct interaction between these lncRNA transcripts. Conclusion: The data suggest a molecular circuit involving the MALAT1-mascRNA system, interactions between MALAT1 and NEAT1, and key immune effector molecules, cumulatively impacting upon the development of atherosclerosis. It appears reasonable to look for therapeutic targets in this circuit and to screen for anomalies in the NEAT1-MALAT1 region in humans, too, as possible novel disease risk factors.

A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.

OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our aim is to increase the accuracy of heart rhythm estimation by the use of extreme gradient boosting trees and the development of a deep convolutional neural network for ECG segmentation. APPROACH: We trained a convolutional neural network with waveforms from PhysioNet databases to annotate QRS complexes, P waves, T waves, noise and interbeat ECG segments that characterize the essences of normal and irregular heart beats. We evaluated true positive rates, positive predictive values and mean absolute differences of our annotation based on reference annotations of the QT and MIT-BIH P-wave database. Moreover, we compared the results with standard QRS detectors and Ecgpuwave. Extreme gradient boosting trees were used to determine the heart rhythm based on hand-crafted features. More precisely, a noise estimation function was used in combination with heart rate and interval data. Furthermore we defined particular features based on ECG morphology, appearance of P waves and detection of irregular beats. We examined the feature importance and identified key features for normal sinus rhythm, atrial fibrillation, alternative rhythm and noisy recordings. The classification performance was evaluated externally using F 1 scores by applying the algorithm to the hidden test set provided by the PhysioNet/CinC Challenge 2017. MAIN RESULTS: The true positive rate of the convolutional neural network in detection of manually revised R peaks in the QT database was [Formula: see text] and the positive predictive value was [Formula: see text]. The detection of P and T waves reached a true positive rate of [Formula: see text] and [Formula: see text] respectively, given a 50 ms tolerance when comparing the reference to the test annotation set. The rhythm classification performance reached an overall F 1 score of 0.82 when applying the algorithm to the hidden test set. SIGNIFICANCE: We achieved a shared rank #9 in the post-challenge phase of the PhysioNet/CinC Challenge 2017.