GnRH Deficient Patients With Congenital Hypogonadotropic Hypogonadism: Novel Genetic Findings in ANOS1, RNF216, WDR11, FGFR1, CHD7, and POLR3A Genes in a Case Series and Review of the Literature.

Background: Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic disease caused by Gonadotropin-Releasing Hormone (GnRH) deficiency. So far a limited number of variants in several genes have been associated with the pathogenesis of the disease. In this original research and review manuscript the retrospective analysis of known variants in ANOS1 (KAL1), RNF216, WDR11, FGFR1, CHD7, and POLR3A genes is described, along with novel variants identified in patients with CHH by the present study. Methods: Seven GnRH deficient unrelated Cypriot patients underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified novel variants were initially examined by in silico computational algorithms and structural analysis of their predicted pathogenicity at the protein level was confirmed. Results: In four non-related GnRH males, a novel X-linked pathogenic variant in ANOS1 gene, two novel autosomal dominant (AD) probably pathogenic variants in WDR11 and FGFR1 genes and one rare AD probably pathogenic variant in CHD7 gene were identified. A rare autosomal recessive (AR) variant in the SRA1 gene was identified in homozygosity in a female patient, whilst two other male patients were also, respectively, found to carry novel or previously reported rare pathogenic variants in more than one genes; FGFR1/POLR3A and SRA1/RNF216. Conclusion: This report embraces the description of novel and previously reported rare pathogenic variants in a series of genes known to be implicated in the biological development of CHH. Notably, patients with CHH can harbor pathogenic rare variants in more than one gene which raises the hypothesis of locus-locus interactions providing evidence for digenic inheritance. The identification of such aberrations by NGS can be very informative for the management and future planning of these patients.

Neurophysiological and Genetic Findings in Patients With Juvenile Myoclonic Epilepsy.

OBJECTIVE: Transcranial magnetic stimulation (TMS), a non-invasive procedure, stimulates the cortex evaluating the central motor pathways. The response is called motor evoked potential (MEP). Polyphasia results when the response crosses the baseline more than twice (zero crossing). Recent research shows MEP polyphasia in patients with generalized genetic epilepsy (GGE) and their first-degree relatives compared with controls. Juvenile Myoclonic Epilepsy (JME), a GGE type, is not well studied regarding polyphasia. In our study, we assessed polyphasia appearance probability with TMS in JME patients, their healthy first-degree relatives and controls. Two genetic approaches were applied to uncover genetic association with polyphasia. METHODS: 20 JME patients, 23 first-degree relatives and 30 controls underwent TMS, obtaining 10-15 MEPs per participant. We evaluated MEP mean number of phases, proportion of MEP trials displaying polyphasia for each subject and variability between groups. Participants underwent whole exome sequencing (WES) via trio-based analysis and two-case scenario. Extensive bioinformatics analysis was applied. RESULTS: We identified increased polyphasia in patients (85%) and relatives (70%) compared to controls (47%) and significantly higher mean number of zero crossings (i.e., occurrence of phases) (patients 1.49, relatives 1.46, controls 1.22; p < 0.05). Trio-based analysis revealed a candidate polymorphism, p.Glu270del,in SYT14 (Synaptotagmin 14), in JME patients and their relatives presenting polyphasia. Sanger sequencing analysis in remaining participants showed no significant association. In two-case scenario, a machine learning approach was applied in variants identified from odds ratio analysis and risk prediction scores were obtained for polyphasia. The results revealed 61 variants of which none was associated with polyphasia. Risk prediction scores indeed showed lower probability for non-polyphasic subjects on having polyphasia and higher probability for polyphasic subjects on having polyphasia. CONCLUSION: Polyphasia was present in JME patients and relatives in contrast to controls. Although no known clinical symptoms are linked to polyphasia this neurophysiological phenomenon is likely due to common cerebral electrophysiological abnormality. We did not discover direct association between genetic variants obtained and polyphasia. It is likely these genetic traits alone cannot provoke polyphasia, however, this predisposition combined with disturbed brain-electrical activity and tendency to generate seizures may increase the risk of developing polyphasia, mainly in patients and relatives.

Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches.

BACKGROUND: Pathogenesis and aetiology of systemic sclerosis (SSc) are currently unclear, thus rendering disease prognosis, diagnosis and treatment challenging. The aim of this study was to use paired skin biopsy samples from affected and unaffected areas of the same patient, in order to compare the proteomes and identify biomarkers and pathways which are associated with SSc pathogenesis. METHODS: Biopsies were obtained from affected and unaffected skin areas of SSc patients. Samples were cryo-pulverised and proteins were extracted and analysed using mass spectrometry (MS) discovery analysis. Differentially expressed proteins were revealed after analysis with the Progenesis QIp software. Pathway analysis was performed using the Enrichr Web server. Using specific criteria, fifteen proteins were selected for further validation with targeted-MS analysis. RESULTS: Proteomic analysis led to the identification and quantification of approximately 2000 non-redundant proteins. Statistical analysis showed that 169 of these proteins were significantly differentially expressed in affected versus unaffected tissues. Pathway analyses showed that these proteins are involved in multiple pathways that are associated with autoimmune diseases (AIDs) and fibrosis. Fifteen of these proteins were further investigated using targeted-MS approaches, and five of them were confirmed to be significantly differentially expressed in SSc affected versus unaffected skin biopsies. CONCLUSION: Using MS-based proteomics analysis of human skin biopsies from patients with SSc, we identified a number of proteins and pathways that might be involved in SSc progression and pathogenesis. Fifteen of these proteins were further validated, and results suggest that five of them may serve as potential biomarkers for SSc.

DCL-suppressed Nicotiana benthamiana plants: valuable tools in research and biotechnology.

RNA silencing is a universal mechanism involved in development, epigenetic modifications and responses to biotic and abiotic stresses. The major components of this mechanism are Dicer-like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) proteins. Understanding the role of each component is of great scientific and agronomic importance. Plants, including Nicotiana benthamiana, an important plant model, usually possess four DCL proteins, each of which has a specific role, namely being responsible for the production of an exclusive small RNA population. Here, we used RNA interference (RNAi) technology to target DCL proteins and produced single and combinatorial mutants for DCL. We analysed the phenotype for each DCL knockdown plant, together with the small RNA profile, by next-generation sequencing (NGS). We also investigated transgene expression, as well as viral infections, and were able to show that DCL suppression results in distinct developmental defects, changes in small RNA populations, increases in transgene expression and, finally, higher susceptibility in certain RNA viruses. Therefore, these plants are excellent tools for the following: (i) to study the role of DCL enzymes; (ii) to overexpress proteins of interest; and (iii) to understand the complex relationship between the plant silencing mechanism and biotic or abiotic stresses.

Positioning Europe for the EPITRANSCRIPTOMICS challenge.

The genetic alphabet consists of the four letters: C, A, G, and T in DNA and C,A,G, and U in RNA. Triplets of these four letters jointly encode 20 different amino acids out of which proteins of all organisms are built. This system is universal and is found in all kingdoms of life. However, bases in DNA and RNA can be chemically modified. In DNA, around 10 different modifications are known, and those have been studied intensively over the past 20 years. Scientific studies on DNA modifications and proteins that recognize them gave rise to the large field of epigenetic and epigenomic research. The outcome of this intense research field is the discovery that development, ageing, and stem-cell dependent regeneration but also several diseases including cancer are largely controlled by the epigenetic state of cells. Consequently, this research has already led to the first FDA approved drugs that exploit the gained knowledge to combat disease. In recent years, the ~150 modifications found in RNA have come to the focus of intense research. Here we provide a perspective on necessary and expected developments in the fast expanding area of RNA modifications, termed epitranscriptomics.

Impaired clearance of apoptotic cells leads to HMGB1 release in the bone marrow of patients with myelodysplastic syndromes and induces TLR4-mediated cytokine production.

Excessive pro-inflammatory cytokine production in the bone marrow has been associated with the pathogenesis of myelodysplastic syndromes. We herein investigated the involvement of toll-like receptors and their endogenous ligands in the induction/maintenance of the inflammatory process in the marrow of patients with myelodysplastic syndromes. We evaluated the expression of toll-like receptors in marrow monocytes of patients (n=27) and healthy controls (n=25) by flow-cytometry and also assessed the activation of the respective signaling using a real-time polymerase chain reaction-based array. We measured the high mobility group box-1 protein, a toll-like receptor-4 ligand, in marrow plasma and long-term bone marrow culture supernatants by an enzyme-linked immunosorbent assay and we performed cross-over experiments using marrow plasma from patients and controls in the presence/absence of a toll-like receptor-4 inhibitor to evaluate the pro-inflammatory cytokine production by chemiluminescence. We assessed the apoptotic cell clearance capacity of patients' macrophages using a fluorescence microscopy-based assay. We found over-expression of toll-like receptor-4 in patients' marrow monocytes compared to that in controls; this over-expression was associated with up-modulation of 53 genes related to the respective signaling. Incubation of patients' monocytes with autologous, but not with normal, marrow plasma resulted in over-production of pro-inflammatory cytokines, an effect that was abrogated by the toll-like receptor-4 inhibitor suggesting that the pro-inflammatory cytokine production in myelodysplastic syndromes is largely mediated through toll-like receptor-4. The levels of high mobility group box-1 protein were increased in patients' marrow plasma and culture supernatants compared to the levels in controls. Patients' macrophages displayed an impaired capacity to engulf apoptotic cells and this defect was associated with excessive release of high mobility group box-1 protein by dying cells. A primary apoptotic cell clearance defect of marrow macrophages in myelodysplastic syndromes may contribute to the induction/maintenance of the inflammatory process through aberrant release of molecules inducing toll-like receptor-4 such as high mobility group box-1 protein.

A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2.

Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about the strengths or weaknesses of each particular algorithm, the fact of the matter is that they fall substantially short of capturing the full detail of physical, temporal and spatial requirements of miRNA::target-mRNA interactions. Here, we introduce a novel miRNA target prediction tool called Targetprofiler that utilizes a probabilistic learning algorithm in the form of a hidden Markov model trained on experimentally verified miRNA targets. Using a large scale protein downregulation data set we validate our method and compare its performance to existing tools. We find that Targetprofiler exhibits greater correlation between computational predictions and protein downregulation and predicts experimentally verified miRNA targets more accurately than three other tools. Concurrently, we use primer extension to identify the mature sequence of a novel miRNA gene recently identified within a cancer associated genomic region and use Targetprofiler to predict its potential targets. Experimental verification of the ability of this small RNA molecule to regulate the expression of CCND2, a gene with documented oncogenic activity, confirms its functional role as a miRNA. These findings highlight the competitive advantage of our tool and its efficacy in extracting biologically significant results.

Finding cancer-associated miRNAs: methods and tools.

Changes in the structure and/or the expression of protein coding genes were thought to be the major cause of cancer for many decades. The recent discovery of non-coding RNA (ncRNA) transcripts (i.e., microRNAs) suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) have been under investigation due to their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Due to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the features of miRNA biogenesis, have resulted in the prediction of novel miRNA genes. Computational approaches provide clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and cheaper. In combination with large scale, high throughput methods, such as deep sequencing, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This review focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution towards unraveling the role of miRNAs in cancer.

Computational identification of miRNAs involved in cancer.

Changes in the structure and/or the expression of protein-coding genes were thought to be the major cause of cancer for many decades. However, the recent discovery of non-coding RNA (ncRNA) transcripts suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) are key players of the family of ncRNAs and they have been under extensive investigation because of their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Owing to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the characteristic features of miRNA biogenesis, have resulted in the prediction of multiple novel miRNA genes. Computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and significantly cheaper. Moreover, in combination with large-scale, high-throughput methods, such as deep sequencing and tilling arrays, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This chapter focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution toward unraveling the role of miRNAs in cancer.

Prediction of novel microRNA genes in cancer-associated genomic regions--a combined computational and experimental approach.

The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.

MicroRNAs and cancer-the search begins!

For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer.

Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.

Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified.