YENİ NESİL DİZİLEME TEKNİĞİ KULLANILARAK KARACİĞER KANSERİNİN SİSTEM BİYOLOJİSİ ANALİZİ

The underlying mechanism for the development of Hepatocellular Carcinoma (HCC) is highly complex due to tissue heterogeneity. Although the traditional approaches mainly focus on a single gene or locus, understanding the variations in the signaling pathways of cancerogenic cells during hepatocarcinogenesis may help to develop novel strategies for treatment and drug development to prevent cancer progression in the patients. This thesis study primarily focuses on unveiling the transcriptome sequencing of differentially expressed genes in HCC, which mainly concentrate on known disease signaling pathways. For this purpose, RNA-seq data of two HCC cell lines were targeted by three different kinase inhibitors and two of their combinations with Sorafenib. The functional pathways enriched with differentially expressed genes were identified by solving a graph problem called as Prize Collecting Steiner Tree (PCST) on human interactome generating inhibitor specific networks. As a result of this study, we found that combinatory treatment of Sorafenib with PIK-75 to HCC cell lines Huh7 and Mahlavu stimulates apoptosis, while TGX-221 with Sorafenib strikingly promotes cell growth antagonizing cellular death, especially for Mahlavu cell line. The states of transcriptomes for different kinase inhibitors were visualized using Cytoscape and molecular interactions were scanned deeply to understand synergistic or antagonistic effects of these kinase inhibitory treatments. Hence, this study provides comprehensive pathways analysis for differential kinase inhibitor reactions of HCC. Using these data, novel HCC drug targets were identified which may lead to more cost-effective and diverse treatment options available for the treatment of liver cancer.Hepatosellüler Kanser (HCC) gelişimi altında yatan mekanizma, kanser dokularının heterojenliği nedeniyle oldukça karmaşıktır. Geleneksel yaklaşımlar temelde tek gen veya lokusa odaklansa da hepatokarsinogenez sırasında kanserojen hücrelerin sinyal yollarındaki varyasyonları anlamak, hastalardaki kanserin ilerlemesini önlemek adına tedavi ve ilaç keşfi için yeni stratejiler geliştirmeye yardımcı olabilir. Bu doktora tez çalışmasının amacı, HCC içerisinde değişken eksprese edilen genlerin başlıca bilinen hastalık sinyal yolaklarında yoğunlaşarak transkriptom dizilemesi yoluyla ortaya çıkarılmasıdır. Bu amaçla, iki çeşit HCC hücre hattı, üç farklı kinaz inhibitörünün tekli veya Sorafenib ile kombinasyonları olacak şekilde hedeflenerek RNA dizilemesi elde edildi. Price Collecting Steiner Tree (PCST) algoritmasının insan transkriptom ağı üzerinde, diferansiyel kontrol edilen genlerle çözümü bize bu genlerle yoğunlaştırılmış fonksiyonel yolaklar sunmuştur. Bu tez çalışması sonucunda, Sorafenib ve PIK-75 inhibitörlerinin birlikte kullanılması ile hem Huh7 hem de Mahlavu hücre hatlarında apoptozu uyardığını buna karşın TGX-221 inhibitörü ile Sorafenib’in birlikte kullanılmasının çarpıcı bir şekilde hücre büyümesini desteklediğini bulduk. Bu kombinasyonun özellikle Mahlavu hücre hattında Sorafenib ile oluşan hücresel ölümü antagonize ettiği gösterilmiştir. Farklı kinaz inhibitörleri ile elde edilen diferansiyel gen ekspresyon statüleri, Cytoscape aracı kullanılarak görselleştirilmiş ve bu kinaz inhibitörlerinin olası sinerjistik ve antagonistik etkilerini anlamak adına yolaklar içerisindeki moleküler etkileşimler derinlemesine analiz edilerek karşılaştırmalar yapılmıştır. Dolayısı ile, bu çalışma HCC hücrelerinin kinaz inhibitörleri karşısında oluşturduğu diferansiyel ekspresyonları incelemek adına kapsamlı bir yaklaşım sunmaktadır. Bu veriler ışığında, karaciğer kanser tedavisi için daha uygun maliyetli seçenekleri çeşitlendirebilecek yeni ilaç hedefleri belirlenmiştir.Ph.D. - Doctoral Progra

Sekansa dayalı bir miRNA kümeleme yöntemi tasarımı; bitki mantar miRNAları analizleri ve konaktaki hedef genleri.

MicroRNAs are small non-coding RNA molecules which contain 21-25 nucleotides, and function in post transcriptional regulation by inhibiting the translation of mRNA targets. miRNAs typically affect gene regulation by forming composite feed forward circuits (cFFCs) which also comprise a transcription factor (TF) and a target gene. By analyzing these cFFCs, the contribution of miRNAs in altering TF networks can be revealed. These contributions could either be the de-escalation of the target gene repertoire or to increase the redundancy through cFFC formation. To conduct the analysis, the connections between genes, miRNAs, and TFs are obtained using two datasets one of which is obtained from human myeloid leukemia cell line. These two datasets are also different from each other in terms of the numbers of TFs and miRNAs that are included in the networks and the significance of the predicted connections. The first dataset which contains connectivity information of a normal cell involves 83 TFs, 564 miRNAs and 5169 genes which construct 124,740 and 34,298 human-mouse conserved TF and miRNA regulatory connections, respectively. The second dataset which contains 137 miRNAs, 274 TFs and 6749 genes which are compiled from the FANTOM 4 database from which the total number of human-mouse conserved regulatory connections is identified as 6631 for miRNAs and 60969 for TFs. Then, in order to reveal the significance on a statistical level, the randomization tests are applied to the connectivity matrix. Obtaining the significance of miRNA-based cFFCs lead us to conclusions about the effect of miRNAs in fine-tuning gene regulatory networks and the evolutionary role of miRNAs in the cell regulation.M.S. - Master of Scienc

Mutualist kök simbiyozu olan endofitik arbüsküler mikorhiza P. indica nın mikro-RNAlarının ilk kez saptanması

TÜBİTAK KBAG Proje01.07.201

Participatory design meetings : genetic information included personal health record application

Human genome research has had an important impact on healthcare since it was first announced to be completed in 2003. As the clinical use of genetic and genomic testing increases, it is leading personalized medicine approaches. The effective use of genetic and genomic data in clinic requires careful management of the data, especially addressing the security, privacy and confidentiality issues. Personalize health record (PHR) systems on online and mobile platforms have the potential to cover these concerns as it will require participation of individuals to the management of their own health data. In order to determine the essential design components of a PHR, which can manage genetic and genomic data along with other medical records and healthcare data, we have applied participatory design strategy. PD involves stakeholders, end-users and the team into the design process in order to help ensure that the end-product meets the needs of users

Enfeksiyon Sonrası Patojene Ait Transkriptom ve Anlatım Düzeyi Analizi

TÜBİTAK TBAG Proje01.06.201

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants

PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants