Telomere analysis based on high-throughput multi-omics data

Telomeres are repeated sequences at the ends of eukaryotic chromosomes that play prominent role in normal aging and disease development. They are dynamic structures that normally shorten over the lifespan of a cell, but can be elongated in cells with high proliferative capacity. Telomere elongation in stem cells is an advantageous mechanism that allows them to maintain the regenerative capacity of tissues, however, it also allows for survival of cancer cells, thus leading to development of malignancies. Numerous studies have been conducted to explore the role of telomeres in health and disease. However, the majority of these studies have focused on consequences of extreme shortening of telomeres that lead to telomere dysfunction, replicative arrest or chromosomal instability. Very few studies have addressed the regulatory roles of telomeres, and the association of genomic, transcriptomic and epigenomic characteristics of a cell with telomere length dynamics. Scarcity of such studies is partially conditioned by the low-throughput nature of experimental approaches for telomere length measurement and the fact that they do not easily integrate with currently available high-throughput data. In this thesis, we have attempted to build algorithms, in silico pipelines and software packages to utilize high-throughput –omics data for telomere biology research. First, we have developed a software package Computel, to compute telomere length from whole genome next generation sequencing data. We show that it can be used to integrate telomere length dynamics into systems biology research. Using Computel, we have studied the association of telomere length with genomic variations in a healthy human population, as well as with transcriptomic and epigenomic features of lung cancers. Another aim of our study was to develop in silico models to assess the activity of telomere maintenance machanisms (TMM) based on gene expression data. There are two main TMMs: one based on the catalytic activity of ribonucleoprotein complex telomerase, and the other based on recombination events between telomeric sequences. Which type of TMM gets activated in a cancer cell determines the aggressiveness of the tumor and the outcome of the disease. Investigation into TMM mechanisms is valuable not only for basic research, but also for applied medicine, since many anticancer therapies attempt to inhibit the TMM in cancer cells to stop their growth. Therefore, studying the activation mechanisms and regulators of TMMs is of paramount importance for understanding cancer pathomechanisms and for treatment. Many studies have addressed this topic, however many aspects of TMM activation and realization still remain elusive. Additionally, current data-mining pipelines and functional annotation approaches of phenotype-associated genes are not adapted for identification of TMMs. To overcome these limitations, we have constructed pathway networks for the two TMMs based on literature, and have developed a methodology for assessment of TMM pathway activities from gene expression data. We have described the accuracy of our TMM-based approach on a set of cancer samples with experimentally validated TMMs. We have also applied it to explore TMM activity states in lung adenocarcinoma cell lines. In summary, recent developments of high-throughput technologies allow for production of data on multiple levels of cellular organization – from genomic and transcriptiomic to epigenomic. This has allowed for rapid development of various directions in molecular and cellular biology. In contrast, telomere research, although at the heart of stem cell and cancer studies, is still conducted with low-throughput experimental approaches. Here, we have attempted to utilize the huge amount of currently accumulated multi-omics data to foster telomere research and to bring it to systems biology scale

WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother's, and, to a lesser extent, with father's TL having the strongest influence on the offspring. In this cohort, mother's, but not father's age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait.</p

Application of MATLAB in -Omics and Systems Biology

Biological data analysis has dramatically changed since the introduction of high-throughput -omics technologies, such as microarrays and next-generation sequencing. The key advantage of obtaining thousands of measurements from a single sample soon became a bottleneck limiting transformation of generated data into knowledge. It has become apparent that traditional statistical approaches are not suited to solve problems in the new reality of “big biological data.” From the other side, traditional computing languages such as C/C++ and Java, are not flexible enough to allow for quick development and testing of new algorithms, while MATLAB provides a powerful computing environment and a variety of sophisticated toolboxes for performing complex bioinformatics calculations

Telomere Maintenance Pathway Activity Analysis Enables Tissue- and Gene-Level Inferences

Telomere maintenance is one of the mechanisms ensuring indefinite divisions of cancer and stem cells. Good understanding of telomere maintenance mechanisms (TMM) is important for studying cancers and designing therapies. However, molecular factors triggering selective activation of either the telomerase dependent (TEL) or the alternative lengthening of telomeres (ALT) pathway are poorly understood. In addition, more accurate and easy-to-use methodologies are required for TMM phenotyping. In this study, we have performed literature based reconstruction of signaling pathways for the ALT and TEL TMMs. Gene expression data were used for computational assessment of TMM pathway activities and compared with experimental assays for TEL and ALT. Explicit consideration of pathway topology makes bioinformatics analysis more informative compared to computational methods based on simple summary measures of gene expression. Application to healthy human tissues showed high ALT and TEL pathway activities in testis, and identified genes and pathways that may trigger TMM activation. Our approach offers a novel option for systematic investigation of TMM activation patterns across cancers and healthy tissues for dissecting pathway-based molecular markers with diagnostic impact

Transcriptome-Guided Drug Repositioning

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies “drug target” spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs’ action for approved diseases (ulcerative colitis and Crohn’s disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn’s disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs

Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome

We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host’s response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes

PSF toolkit: an R package for pathway curation and topology-aware analysis

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit’s usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications

Atlas of mRNA translation and decay for bacteria

26 Pàg.Regulation of messenger RNA stability is pivotal for programmed gene expression in bacteria and is achieved by a myriad of molecular mechanisms. By bulk sequencing of 5' monophosphorylated mRNA decay intermediates (5'P), we show that cotranslational mRNA degradation is conserved among both Gram-positive and -negative bacteria. We demonstrate that, in species with 5'-3' exonucleases, the exoribonuclease RNase J tracks the trailing ribosome to produce an in vivo single-nucleotide toeprint of the 5' position of the ribosome. In other species lacking 5'-3' exonucleases, ribosome positioning alters endonucleolytic cleavage sites. Using our metadegradome (5'P degradome) sequencing approach, we characterize 5'P mRNA decay intermediates in 96 species including Bacillus subtilis, Escherichia coli, Synechocystis spp. and Prevotella copri and identify codon- and gene-level ribosome stalling responses to stress and drug treatment. We also apply 5'P sequencing to complex clinical and environmental microbiomes and demonstrate that metadegradome sequencing provides fast, species-specific posttranscriptional characterization of responses to drug or environmental perturbations. Finally we produce a degradome atlas for 96 species to enable analysis of mechanisms of RNA degradation in bacteria. Our work paves the way for the application of metadegradome sequencing to investigation of posttranscriptional regulation in unculturable species and complex microbial communities.Open access funding provided by Karolinska InstitutePeer reviewe

WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother’s, and, to a lesser extent, with father’s TL having the strongest influence on the offspring. In this cohort, mother’s, but not father’s age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait