Normalizasyon Yöntemlerinin RNA- Seq Verileri Üzerinde Çıkarılan Gen Birlikte İfade Edilme Ağlarının Performansına Etkisi

Protein sentezi sürecinde meydana gelen farklılaşmaların metabolik hastalıklar, kanser gibi kompleks hastalıklara neden olduğu farklı çalışmalarda belirtilmiştir. Protein sentezindeki değişimlerin anlaşılması için proteinleri oluşturan genlerin belirlenmesi ve bu genlerin diğer genlerle ilişkilerin ortaya çıkarılması gerekmektedir. Yeni nesil dizileme teknikleriyle hastalıklara neden olan moleküler düzeyde ilişkilerin doğruluklu olarak belirlenmesi kolaylaşmıştır. Gen birlikte ifade edilme (GBİE) ağları düzenleyen-düzenleyici ilişkisi içermeden benzer biyolojik süreçlere katılan genler arasındaki ilişkileri araştırmacılara göstermektedir. Çalışmamızda RNA-Seq verileri kullanılarak prostat kanseriyle ilişkili GBİE ağları elde edilmiştir. RNA- Seq verileri farklı nükleotit uzunluğundaki genlerden ve farklı sayıda okumalar içeren örneklerden oluştuğu için normalizasyon teknikleri moleküler ilişki çıkarımında önem taşımaktadır. Çalışmamızda gen birlikte ifade edilme ağları ham veri ve farklı iki normalizasyon yaklaşımı olan M- Değerinin Kırpılmış Ortalaması (MDKO), Göreceli Log İfadesi (GLİ) hesaplamalarıyla ayrı ayrı oluşturulmuş veriler üzerinde çıkartılarak örtüşme analizi ve topolojik performans değerlendirilmesi yapılmıştır. Örtüşme analizine göre normalize edilmiş RNA- Seq verileri kullanarak elde edilmiş gen birlikte ifade edilme ağlarının ham verilere göre daha fazla literatürde bulunan ilişkileri tahmin ettiği gözlemlenmiştir. İki normalizasyon yöntemiyle elde edilen GBİE'lere ait örtüşme analizi performans metrikleri değerleri ise birbirlerine yakın çıkmıştır. Topolojik değerlendirme sonuçlara göre normalize edilmiş veriler üzerinde elde edilen GBİE ağlarının ölçeksiz ağ tanımına daha yakın olduğu gözlemlenmiştir. Çalışmamızda aynı zamanda ham ve normalize edilmiş veriler üzerinde GBİE ağ çıkarım algoritmaları olan C3NET, ARACNE ve WGCNA yaklaşımlarının performansları da karşılaştırılmıştır

Optimizing transcriptomics to study the evolutionary effect of FOXP2

The field of genomics was established with the sequencing of the human genome, a pivotal achievement that has allowed us to address various questions in biology from a unique perspective. One question in particular, that of the evolution of human speech, has gripped philosophers, evolutionary biologists, and now genomicists. However, little is known of the genetic basis that allowed humans to evolve the ability to speak. Of the few genes implicated in human speech, one of the most studied is FOXP2, which encodes for the transcription factor Forkhead box protein P2 (FOXP2). FOXP2 is essential for proper speech development and two mutations in the human lineage are believed to have contributed to the evolution of human speech. To address the effect of FOXP2 and investigate its evolutionary contribution to human speech, one can utilize the power of genomics, more specifically gene expression analysis via ribonucleic acid sequencing (RNA-seq). To this end, I first contributed in developing mcSCRB-seq, a highly sensitive, powerful, and efficient single cell RNA-seq (scRNA-seq) protocol. Previously having emerged as a central method for studying cellular heterogeneity and identifying cellular processes, scRNA-seq was a powerful genomic tool but lacked the sensitivity and cost-efficiency of more established protocols. By systematically evaluating each step of the process, I helped find that the addition of polyethylene glycol increased sensitivity by enhancing the cDNA synthesis reaction. This, along with other optimizations resulted in developing a sensitive and flexible protocol that is cost-efficient and ideal in many research settings. A primary motivation driving the extensive optimizations surrounding single cell transcriptomics has been the generation of cellular atlases, which aim to identify and characterize all of the cells in an organism. As such efforts are carried out in a variety of research groups using a number of different RNA-seq protocols, I contributed in an effort to benchmark and standardize scRNA-seq methods. This not only identified methods which may be ideal for the purpose of cell atlas creation, but also highlighted optimizations that could be integrated into existing protocols. Using mcSCRB-seq as a foundation as well as the findings from the scRNA-seq benchmarking, I helped develop prime-seq, a sensitive, robust, and most importantly, affordable bulk RNA-seq protocol. Bulk RNA-seq was frequently overlooked during the efforts to optimize and establish single-cell techniques, even though the method is still extensively used in analyzing gene expression. Introducing early barcoding and reducing library generation costs kept prime-seq cost-efficient, but basing it off of single-cell methods ensured that it would be a sensitive and powerful technique. I helped verify this by benchmarking it against TruSeq generated data and then helped test the robustness by generating prime-seq libraries from over seventeen species. These optimizations resulted in a final protocol that is well suited for investigating gene expression in comprehensive and high-throughput studies. Finally, I utilized prime-seq in order to develop a comprehensive gene expression atlas to study the function of FOXP2 and its role in speech evolution. I used previously generated mouse models: a knockout model containing one non-functional Foxp2 allele and a humanized model, which has a variant Foxp2 allele with two human-specific mutations. To study the effect globally across the mouse, I helped harvest eighteen tissues which were previously identified to express FOXP2. By then comparing the mouse models to wild-type mice, I helped highlight the importance of FOXP2 within lung development and the importance of the human variant allele in the brain. Both mcSCRB-seq and prime-seq have already been used and published in numerous studies to address a variety of biological and biomedical questions. Additionally, my work on FOXP2 not only provides a thorough expression atlas, but also provides a detailed and cost-efficient plan for undertaking a similar study on other genes of interest. Lastly, the studies on FOXP2 done within this work, lay the foundation for future studies investigating the role of FOXP2 in modulating learning behavior, and thereby affecting human speech

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs. CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0165-z) contains supplementary material, which is available to authorized users

Integrative approaches to high-throughput data in lymphoid leukemias (on transcriptomes, the whole-genome mutational landscape, flow cytometry and gene copy-number alterations)

Within this thesis I developed a new approach for the analysis and integration of heterogeneous leukemic data sets applicable to any high-throughput analysis including basic research. All layers are stored in a semantic graph which facilitates modifications by just adding edges (relationships/attributes) and nodes (values/results) as well as calculating biological consensus and clinical correlation. The front-end is accessible through a GUI (graphical user interface) on a Java-based Semantic Web server. I used this framework to describe the genomic landscape of T-PLL (T-cell prolymphocytic leukemia), which is a rare (~0.6/million) mature T-cell malignancy with aggressive clinical course, notorious treatment resistance, and generally low overall survival. We have conducted gene expression and copy-number profiling as well as NGS (next-generation sequencing) analyses on a cohort comprising 94 T-PLL cases. TCL1A (T-cell leukemia/lymphoma 1A) overexpression and ATM (Ataxia Telangiectasia Mutated) impairment represent central hallmarks of T-PLL, predictive for patient survival, T-cell function and proper DNA damage responses. We identified new chromosomal lesions, including a gain of AGO2 (Argonaute 2, RISC Catalytic Component; 57.14% of cases), which is decisive for the chromosome 8q lesion. While we found significant enrichments of truncating mutations in ATM mut/no del (p=0.01365), as well as FAT (FAT Atypical Cadherin) domain mutations in ATM mut/del (p=0.01156), JAK3 (Janus Kinase 3) mut/ATM del cases may represent another tumor lineage. Using whole-transcriptome sequencing, we identified novel structural variants affecting chromosome 14 that lead to the expression of a TCL1A-TCR (T-cell receptor) fusion transcript and a likely degradated TCL1A protein. Two clustering approaches of normal T-cell subsets vs. leukemia gene expression profiles, as well as immunophenotyping-based agglomerative clustering and TCR repertoire reconstruction further revealed a restricted, memory-like T-cell phenotype. This is to date the most comprehensive, multi-level, integrative study on T-PLL and it led to an evolutionary disease model and a histone deacetylase-inhibiting / double strand break-inducing treatment that performs better than the current standard of chemoimmunotherapy in preclinical testing

Recent Developments in Cancer Systems Biology

This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book

34th Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2019): part 2

Document type: Articl