Human mitochondrial heteroplasmy is an extensively investigated phenomenon in the context of medical diagnostics, forensic identification and molecular evolution. However, technical limitations of ...high-throughput sequencing hinder reliable determination of point heteroplasmies (PHPs) with minor allele frequencies (MAFs) within the noise threshold.
To investigate the PHP landscape at an MAF threshold down to 0.1%, we sequenced whole mitochondrial genomes at approximately 7.700x coverage, in multiple technical and biological replicates of longitudinal blood and buccal swab samples from 11 human donors (159 libraries in total). The results obtained by two independent sequencing platforms and bioinformatics pipelines indicate distinctive PHP patterns below and above the 1% MAF cut-off. We found a high inter-individual prevalence of low-level PHPs (MAF < 1%) at polymorphic positions of the mitochondrial DNA control region (CR), their tissue preference, and a tissue-specific minor allele linkage. We also established the position-dependent potential of minor allele expansion in PHPs, and short-term PHP instability in a mitotically active tissue. We demonstrate that the increase in sensitivity of PHP detection to minor allele frequencies below 1% within a robust experimental and analytical pipeline, provides new information with potential applicative value.
Our findings reliably show different mutational loads between tissues at sub-1% allele frequencies, which may serve as an informative medical biomarker of time-dependent, tissue-specific mutational burden, or help discriminate forensically relevant tissues in a single person, close maternal relatives or unrelated individuals of similar phylogenetic background.
Cancer is a disease potentiated by mutations in somatic cells. Cancer mutations are not distributed uniformly along the human genome. Instead, different human genomic regions vary by up to fivefold ...in the local density of cancer somatic mutations, posing a fundamental problem for statistical methods used in cancer genomics. Epigenomic organization has been proposed as a major determinant of the cancer mutational landscape. However, both somatic mutagenesis and epigenomic features are highly cell-type-specific. We investigated the distribution of mutations in multiple independent samples of diverse cancer types and compared them to cell-type-specific epigenomic features. Here we show that chromatin accessibility and modification, together with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes. The best predictors of local somatic mutation density are epigenomic features derived from the most likely cell type of origin of the corresponding malignancy. Moreover, we find that cell-of-origin chromatin features are much stronger determinants of cancer mutation profiles than chromatin features of matched cancer cell lines. Furthermore, we show that the cell type of origin of a cancer can be accurately determined based on the distribution of mutations along its genome. Thus, the DNA sequence of a cancer genome encompasses a wealth of information about the identity and epigenomic features of its cell of origin.
Biological insights can be obtained through computational integration of genomics data sets consisting of diverse types of information. The integration is often hampered by a large variety of ...existing file formats, often containing similar information, and the necessity to use complicated tools to achieve the desired results. We have built an R package, genomation, to expedite the extraction of biological information from high throughput data. The package works with a variety of genomic interval file types and enables easy summarization and annotation of high throughput data sets with given genomic annotations.
The software is currently distributed under MIT artistic license and freely available at http://bioinformatics.mdc-berlin.de/genomation, and through the Bioconductor framework.
Histones are frequently decorated with covalent modifications. These histone modifications are thought to be involved in various chromatin-dependent processes including transcription. To elucidate ...the relationship between histone modifications and transcription, we derived quantitative models to predict the expression level of genes from histone modification levels. We found that histone modification levels and gene expression are very well correlated. Moreover, we show that only a small number of histone modifications are necessary to accurately predict gene expression. We show that different sets of histone modifications are necessary to predict gene expression driven by high CpG content promoters (HCPs) or low CpG content promoters (LCPs). Quantitative models involving H3K4me3 and H3K79me1 are the most predictive of the expression levels in LCPs, whereas HCPs require H3K27ac and H4K20me1. Finally, we show that the connections between histone modifications and gene expression seem to be general, as we were able to predict gene expression levels of one cell type using a model trained on another one.
Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although ...there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.
INteractive Codon usage Analysis (INCA) provides an array of features useful in analysis of synonymous codon usage in whole genomes. In addition to computing codon frequencies and several usage ...indices, such as ‘codon bias’, effective Nc and CAI, the primary strength of INCA has numerous options for the interactive graphical display of calculated values, thus allowing visual detection of various trends in codon usage. Finally, INCA includes a specific unsupervised neural network algorithm, the self-organizing map, used for gene clustering according to the preferred utilization of codons. Availability: INCA is available for the Win32 platform and is free of charge for academic use. For details, visit the web page http://www.bioinfo-hr.org/inca or contact the author directly. Supplementary information: Software is accompanied with a user manual and a short tutorial.
Abstract
Cancer is a disease caused by errors within the multicellular system and it represents a major health issue in multicellular organisms. Although cancer research has advanced substantially, ...new approaches focusing on fundamental aspects of cancer origin and mechanisms of spreading are necessary. Comparative genomic studies have shown that most genes linked to human cancer emerged during the early evolution of Metazoa. Thus, basal animals without true tissues and organs, such as sponges (Porifera), might be an innovative model system for understanding the molecular mechanisms of proteins involved in cancer biology. One of these proteins is developmentally regulated GTP-binding protein 1 (DRG1), a GTPase stabilized by interaction with DRG family regulatory protein 1 (DFRP1). This study reveals a high evolutionary conservation of DRG1 gene/protein in metazoans. Our biochemical analysis and structural predictions show that both recombinant sponge and human DRG1 are predominantly monomers that form complexes with DFRP1 and bind non-specifically to RNA and DNA. We demonstrate the conservation of sponge and human DRG1 biological features, including intracellular localization and DRG1:DFRP1 binding, function of DRG1 in α-tubulin dynamics, and its role in cancer biology demonstrated by increased proliferation, migration and colonization in human cancer cells. These results suggest that the ancestor of all Metazoa already possessed DRG1 that is structurally and functionally similar to the human DRG1, even before the development of real tissues or tumors, indicating an important function of DRG1 in fundamental cellular pathways.
The determination of the protein's intracellular localization is essential for understanding its biological function. Protein localization studies are mainly performed on primary and secondary ...vertebrate cell lines for which most protocols have been optimized. In spite of experimental difficulties, studies on invertebrate cells, including basal Metazoa, have greatly advanced. In recent years, the interest in studying human diseases from an evolutionary perspective has significantly increased. Sponges, placed at the base of the animal tree, are simple animals without true tissues and organs but with a complex genome containing many genes whose human homologs have been implicated in human diseases, including cancer. Therefore, sponges are an innovative model for elucidating the fundamental role of the proteins involved in cancer. In this study, we overexpressed human cancer-related proteins and their sponge homologs in human cancer cells, human fibroblasts, and sponge cells. We demonstrated that human and sponge MYC proteins localize in the nucleus, the RRAS2 in the plasma membrane, the membranes of the endolysosomal vesicles, and the DRG1 in the cell's cytosol. Despite the very low transfection efficiency of sponge cells, we observed an identical localization of human proteins and their sponge homologs, indicating their similar cellular functions.
There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can ...introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity.
We compared the performance of several commonly used measures and a novel method we introduce in this paper--Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances.
We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction.
Abstract
Bacilli can form dormant, highly resistant, and metabolically inactive spores to cope with extreme environmental challenges. In this study, we examined the evolutionary age of Bacillus ...subtilis sporulation genes using the approach known as genomic phylostratigraphy. We found that B. subtilis sporulation genes cluster in several groups that emerged at distant evolutionary time-points, suggesting that the sporulation process underwent several stages of expansion. Next, we asked whether such evolutionary stratification of the genome could be used to predict involvement in sporulation of presently uncharacterized genes (y-genes). We individually inactivated a representative sample of uncharacterized genes that arose during the same evolutionary periods as the known sporulation genes and tested the resulting strains for sporulation phenotypes. Sporulation was significantly affected in 16 out of 37 (43%) tested strains. In addition to expanding the knowledge base on B. subtilis sporulation, our findings suggest that evolutionary age could be used to help with genome mining.
PDF
