Current Genomics - Volume 19, Issue 6, 2018

The rapid development in the last 10-15 years of microarray technologies, such as oligonucleotide array Comparative Genomic Hybridization (CGH) and Single Nucleotide Polymorphisms (SNP) genotyping array, has improved the identification of fine chromosomal structural variants, ranging in length from kilobases (kb) to megabases (Mb), as an important cause of genetic differences among healthy individuals and also as disease-susceptibility and/or disease-causing factors. Structural genomic variations due to unbalanced chromosomal rearrangements are known as Copy-Number Variants (CNVs) and these include variably sized deletions, duplications, triplications and translocations. CNVs can significantly contribute to human diseases and rearrangements in several dosagesensitive genes have been identified as an important causative mechanism in the molecular aetiology of Charcot-Marie-Tooth (CMT) disease and of several CMT-related disorders, a group of inherited neuropathies with a broad range of clinical phenotypes, inheritance patterns and causative genes. Duplications or deletions of the dosage-sensitive gene PMP22 mapped to chromosome 17p12 represent the most frequent causes of CMT type 1A and Hereditary Neuropathy with liability to Pressure Palsies (HNPP), respectively. Additionally, CNVs have been identified in patients with other CMT types (e.g., CMT1X, CMT1B, CMT4D) and different hereditary poly- (e.g., giant axonal neuropathy) and focal- (e.g., hereditary neuralgic amyotrophy) neuropathies, supporting the notion of hereditary peripheral nerve diseases as possible genomic disorders and making crucial the identification of fine chromosomal rearrangements in the molecular assessment of such patients. Notably, the application of advanced computational tools in the analysis of Next-Generation Sequencing (NGS) data has emerged in recent years as a powerful technique for identifying a genome-wide scale complex structural variants (e.g., as the ones resulted from balanced rearrangements) and also smaller pathogenic (intragenic) CNVs that often remain beyond the detection limit of most conventional genomic microarray analyses; in the context of inherited neuropathies where more than 70 disease-causing genes have been identified to date, NGS and particularly Whole-Genome Sequencing (WGS) hold the potential to reduce the number of genomic assays required per patient to reach a diagnosis, analyzing with a single test all the Single Nucleotide Variants (SNVs) and CNVs in the genes possibly implicated in this heterogeneous group of disorders.

Adult-onset neuropsychiatric diseases are one of the most challenging areas of medicine. While symptomatic treatments are available, for most of these diseases the exact pathomechanism is not known, thus, disease-modifying therapies are difficult to conceptualize and find. The two most common and best studied neuropsychiatric diseases affecting higher cortical functions in humans are schizophrenia and Alzheimer's disease; both diseases have high heritability, however, the genetic architecture is not fully elucidated. Robust Single Nucleotide Variant (SNV) studies have identified several loci with modest effect sizes. While Copy Number Variants (CNV) make an important contribution to genetic variation, CNV GWAS suffer from dependence on mainly SNP arrays with underperforming genotyping accuracy. We evaluated dynamic range of the assays for three types of CNV loci, including biallelic deletion, high copy gain, and fusion gene, to assess the depth of exploration of the contribution of CNVs to disease susceptibility. Despite the suboptimal genotyping, novel mechanisms are emerging and further large-scale studies with genotyping assays optimized for CNV detection are needed. Furthermore, the CHRFAM7A human-specific fusion gene association warrants large scale locus specific association studies in AD, schizophrenia, bipolar disorder and ADHD.

Background: Neurological disorders are a highly heterogeneous group of pathological conditions that affect both the peripheral and the central nervous system. These pathologies are characterized by a complex and multifactorial etiology involving numerous environmental agents and genetic susceptibility factors. For this reason, the investigation of their pathogenetic basis by means of traditional methodological approaches is rather arduous. High-throughput genotyping technologies, including the microarray-based comparative genomic hybridization (aCGH), are currently replacing classical detection methods, providing powerful molecular tools to identify genomic unbalanced structural rearrangements and explore their role in the pathogenesis of many complex human diseases. Methods: In this report, we comprehensively describe the design method, the procedures, validation, and implementation of an exon-centric customized aCGH (NeuroArray 1.0), tailored to detect both single and multi-exon deletions or duplications in a large set of multi- and monogenic neurological diseases. This focused platform enables a targeted measurement of structural imbalances across the human genome, targeting the clinically relevant genes at exon-level resolution. Conclusion: An increasing use of the NeuroArray platform may offer new insights in investigating potential overlapping gene signatures among neurological conditions and defining genotypephenotype relationships.

Pancreatic Ductal AdenoCarcinoma (PDAC) is one of the most lethal malignancies of all solid cancers. Precancerous lesions for PDAC include PanIN, IPMNs and MCNs. PDAC has a poor prognosis with a 5-year survival of approximately 6%. Whereas Periampulary AdenoCarcinoma (PAC) having four anatomic subtypes, pancreatic, Common Bile Duct (CBD), ampullary and duodenum shows relative better prognosis. The highest incidence of PDAC has been reported with black with respect to white population. Similarly, incidence rate of PAC also differs with different ethnic populations. Several lifestyle, environmental and occupational exposures including long-term diabetes, obesity, and smoking, have been linked to PDAC, however, for PAC the causal risk factors were poorly described. It is now clear that PDAC and PAC are a multi-stage process resulting from the accumulation of genomic alterations in the somatic DNA of normal cells as well as inherited mutations. Approximately 10% of PDAC have a familial inheritance. Germline mutations in CDKN2A, BRCA2, STK11, PALB2, PRSS1, etc., as well as certain syndromes have been well associated with predisposition to PDAC. KRAS, CDKN2A, TP53 and SMAD4 are the 4 “mountains” (high-frequency driver genes) which have been known to earliest somatic alterations for PDAC while relatively less frequent in PAC. Our understanding of the molecular carcinogenesis has improved in the last few years due to extensive research on PDAC which was not well explored in case of PAC. The genetic alterations that have been identified in PDAC and different subgroups of PAC are important implications for the development of genetic screening test, early diagnosis, and prognostic genetic markers. The present review will provide a brief overview of the incidence and prevalence of PDAC and PAC, mainly, increased risk in India, the several kinds of risk factors associated with the diseases as well as required genetic alterations for disease initiation and progression.

Aging is a complex phenomenon, where damage accumulation, increasing deregulation of biological pathways, and loss of cellular homeostasis lead to the decline of organismal functions over time. Interestingly, aging is not entirely a stochastic process and progressing at a constant rate, but it is subject to extensive regulation, in the hands of an elaborate and highly interconnected signaling network. This network can integrate a variety of aging-regulatory stimuli, i.e. fertility, nutrient availability, or diverse stresses, and relay them via signaling cascades into gene regulatory events - mostly of genes that confer stress resistance and thus help protect from damage accumulation and homeostasis loss. Transcription factors have long been perceived as the pivotal nodes in this network. Yet, it is well known that the epigenome substantially influences eukaryotic gene regulation, too. A growing body of work has recently underscored the importance of the epigenome also during aging, where it not only undergoes drastic age-dependent changes but also actively influences the aging process. In this review, we introduce the major signaling pathways that regulate age-related decline and discuss the synergy between transcriptional regulation and the epigenetic landscape.

Background: Artificial Neural Networks (ANNs) can be used to classify tumor of Hepatocellular carcinoma based on their gene expression signatures. The neural network is trained with gene expression profiles of genes that were predictive of recurrence in liver cancer, the ANNs became capable of correctly classifying all samples and distinguishing the genes most suitable for the organization. The ability of the trained ANN models in recognizing the Cancer Genes was tested as we analyzed additional samples that were not used beforehand for the training procedure, and got the correctly classified result in the validation set. Bootstrapping of training and analysis of dataset was made as external justification for more substantial result. Result: The best result achieved when the number of hidden layers was 10. The R² value with training is 0.99136, R² value obtained with testing is 0.80515, R² value obtained after validation is 0.76678 and finally, with the total number of sets the R² value is 0.93417. Performance was reported on the basis of graph plotted between Mean Squared Error (MSE) and 23 epoch. The value of gradient of the curve was 152 after 6 validation checks and 23 iterations. Conclusion: A successful attempt at developing a method for diagnostic classification of tumors from their gene-expression autographs that efficiently classify tumors and helps in decision making for providing appropriate treatment to the patients suffering from Hepatocellular carcinoma has been carried out.

Background: For the plant pathogenic phytoplasmas, as well as for several fastidious prokaryotes, axenic cultivation is extremely difficult or not possible yet; therefore, even with second generation sequencing methods, obtaining the sequence of their genomes is challenging due to host sequence contamination. Objective: With the Phytoassembly pipeline here presented, we aim to provide a method to obtain high quality genome drafts for the phytoplasmas and other uncultivable plant pathogens, by exploiting the coverage differential in the ILLUMINA sequences from the pathogen and the host, and using the sequencing of a healthy, isogenic plant as a filter. Validation: The pipeline has been benchmarked using simulated and real ILLUMINA runs from phytoplasmas whose genome is known, and it was then used to obtain high quality drafts for three new phytoplasma genomes. Conclusion: For phytoplasma infected samples containing >2-4% of pathogen DNA and an isogenic reference healthy sample, the resulting assemblies can be next to complete. The Phytoassembly source code is available on GitHub at https://github.com/cpolano/phytoassembly.

Background: Copy Number Variants (CNVs) represent a prevailing type of structural variation (deletions or duplications) in the human genome. In the last few years, several studies have demonstrated that CNVs represent significant mutations in Alzheimer's Disease (AD) hereditability. Currently, innovative high-throughput platforms and bioinformatics algorithms are spreading to screening CNVs involved in different neurological diseases. In particular, the use of custom arrays, based on libraries of probes that can detect significant genomic regions, have greatly improved the resolution of targeted regions and the identification of chromosomal aberrations. Objective: In this work, we report the use of NeuroArray, a custom CGH microarray useful to screening and further investigate the role of the recurring genomic aberrations in patients with confirmed or suspected AD. Methods: The custom oligonucleotide aCGH design includes 641 genes and 9118 exons, linked to AD. The genomic DNA was isolated from blood samples of AD affected patients. The entire protocol of custom NeuroArray included digestion, labelling and hybridization steps as a standard aCGH assay. Results: The NeuroArray analysis revealed the presence of amplifications in several genes associated with AD. In the coding regions of these genes, 14,586 probes were designed with a 348 bp median probe spacing. The majority of targeted AD genes map on chromosomes 1 and 10. A significant aspect of the NeuroArray design is that 95% of the total exon targets is covered by at least one probe, a resolution higher than CGH array platforms commercially available. Conclusion: By identifying with a high sensitivity the chromosomal abnormalities in a large panel of AD-related genes and other neurological diseases, the NeuroArray platform is a valid tool for clinical diagnosis.