Biology subjects

Kivioja, T.

Publications and source records attributed to Kivioja, T..

9 recordsLinked to original sources

bioRxiv2024

Crossover model of Lep-Rec reveals higher heritability of recombination

Meiotic recombination, a process that reshuffles DNA between parental chromosomes, is almost universal in multicellular organisms. Recombination accelerates the response to selection by breaking the linkage and creating new allelic combinations that can affect the fitness of the progeny. This motivates us to characterise recombination rate variation and to take it into account in evolutionary models and studies. Linkage mapping utilises recombination to obtain recombination distances for genetic markers. With (whole) genome sequencing data, very dense linkage maps can be produced, locating crossovers precisely in the genome. This enables direct and accurate calculation of recombination distances, correction of possible errors in the genome and maps, and studying the relation between recombination and physical base-pair distances. This is now a relevant problem, as high-quality genomes are emerging for many species, and available dense linkage map data would supplement these genomes. Here we present a novel software Lep-Rec to compute the local re-combination rate, i.e. the percentage of crossovers per individual per megabase (cM/Mb) along the genome. Moreover, it can also estimate the underlying, only partly observed, tetrad crossover distribution for each chromosome, while modelling crossover and chromatid interference. Together with Lep-MAP3 and Lep-Anchor, Lep-Rec forms a complete toolbox for studying recombination and crossovers: Lep-MAP3 can robustly construct linkage maps for large number of markers and individuals, while Lep-Anchor can anchor, validate and correct genome assemblies using linkage maps, and together these software provide consistent and complete physical and linkage maps for further analysis with Lep-Rec. Lep-Rec is available from http://sourceforge.net/projects/lep-anchor. Finally, we demonstrate the performance of Lep-Rec using real and simulated data: It outperforms and simplifies currently available tools and its estimated crossover distribution can improve association analysis and heritability estimates of recombination.

bioinformatics↗

bioRxiv2024

Sex-specific fish recombination landscapes link recombination and karyotype evolution

Meiotic recombination is an ubiquitous feature of sexual reproduction across eukaryotes. While recombination has been widely studied both theoretically and experimentally, the causes of its variation across species are still poorly understood. Composing a coherent view across species has been difficult because of the differences in recombination map generation and reporting of the results. Thus, fundamental questions like why recombination rates differ between sexes (heterochiasmy) in many but not all species remain unanswered. Here we present the first collection of recombination maps that allows quantitative comparisons across a diverse set of species. We generated sex-specific high-density linkage maps for 40 fish species using the same computational pipeline. Comparing the maps revealed that the higher genome-wide recombination rate in females compared to males was linked to the karyotype of the species. The difference between the sexes in the positioning of the crossovers was also highly variable and unrelated to the difference in their total number. Especially in males, CpG content of the sequence was a strong indicator of the broad scale distribution of crossovers between and within chromosomes. More generally, the collection of recombination landscapes can serve as a link between the theoretical and experimental work on recombination.

genomics↗

bioRxiv2022

Lineage-specific oncogenes drive growth of major forms of human cancer using common downstream mechanisms

Mutations in hundreds of genes have been associated with formation of human cancer, with different oncogenic lesions prevalent in different cancer types. Yet, the malignant phenotype is simple, characterized by unrestricted growth of cells that invade neighboring healthy tissue and in many cases metastasize to distant organs. One possible hypothesis explaining this dichotomy is that the cancer genes regulate a common set of target genes, which then function as master regulators of essential cancer phenotypes, such as growth, invasion and metastasis. To identify mechanisms that drive the most fundamental feature shared by all tumors - unrestricted cell proliferation - we used a multiomic approach to identify common transcriptional and posttranslational targets of major oncogenic pathways active in different cancer types, and combined this analysis with known regulators of the cell cycle. We identified translation and ribosome biogenesis as common targets of both transcriptional and posttranslational oncogenic pathways. By combining proteomic analysis of clinical samples with functional studies of cell cultures, we also establish NOLC1 as a key node whose convergent regulation both at transcriptional and posttranslational level is critical for tumor cell proliferation. Our results indicate that lineage-specific oncogenic pathways commonly regulate the same set of targets important for growth control, revealing novel key downstream nodes that could be targeted for cancer therapy or chemoprevention.

cancer biology↗

bioRxiv2021

Competitive precision genome editing (CGE) assay for functional analysis at single base resolution

MYC is an oncogenic transcription factor that controls major pathways promoting cell growth and proliferation. MYC has been implicated in the regulation of large number of genes, but the exact target genes responsible for its proliferative effects are still not known. Here, we use a novel competitive genome editing (CGE) assay for studying the functional consequence of precise mutations of MYC binding sites on cell proliferation. The CGE method is based on precision genome editing, where a CRISPR/Cas9-induced DNA break is repaired using a template that either reconstitutes the original feature or introduces an altered sequence. Both types of repair templates harbor sequence tags that allow direct comparison between cells that carry original and mutant features and generate a large number of replicate cultures. The CGE method overcomes the limitations of CRISPR/Cas9-technology in analyzing the effect of genotype on phenotype, namely the difficulty of cutting DNA exactly at the intended site, and the decreased cell proliferation caused by the DNA cuts themselves. Importantly, it provides a powerful method for studying subtle effects elicited by mutation of individual transcription factor binding sites. We show here that E-box mutations at several MYC target gene promoters resulted in reduced cellular fitness, demonstrating a direct correlation between MYC-regulated cellular processes and MYC binding and identifying important transcriptional targets responsible for its functions.

genomics↗

bioRxiv2021

Sequence determinants of human gene regulatory elements

DNA determines where and when genes are expressed, but the full set of sequence determinants that control gene expression is not known. To obtain a global and unbiased view of the relative importance of different sequence determinants in gene expression, we measured transcriptional activity of DNA sequences that are in aggregate [~]100 times longer than the human genome in three different cell types. We show that enhancers can be classified to three main types: classical enhancers1, closed chromatin enhancers and chromatin-dependent enhancers, which act via different mechanisms and differ in motif content. Transcription factors (TFs) act generally in an additive manner with weak grammar, with classical enhancers increasing expression from promoters by a mechanism that does not involve specific TF-TF interactions. Few TFs are strongly active in a cell, with most activities similar between cell types. Chromatin-dependent enhancers are enriched in forkhead motifs, whereas classical enhancers contain motifs for TFs with strong transactivator domains such as ETS and bZIP; these motifs are also found at transcription start site (TSS)-proximal positions. However, some TFs, such as NRF1 only activate transcription when placed close to the TSS, and others such as YY1 display positional preference with respect to the TSS. TFs can thus be classified into four non-exclusive subtypes based on their transcriptional activity: chromatin opening, enhancing, promoting and TSS determining factors - consistent with the view that the binding motif is the only atomic unit of gene expression.

genomics↗

bioRxiv2021

PlotMI: visualization of pairwise interactions and positional preferences learned by a deep learning model from sequence data

Deep learning models have recently gained success in various tasks related to understanding information coded in biological sequences. Although offering state-of-the art predictive performance, the predictions made by deep learning models can be difficult to understand. In virtually all biological research, the understanding of how a predictive model works is as, or even more important as the raw predictive performance. Thus interpretation of deep learning models is an emerging hot topic especially in context of biological research. Here we describe PlotMI, a mutual information based model interpretation tool that can intuitively visualize positional preferences and pairwise dependencies learned by any machine learning model trained on sequence data such as DNA, RNA or amino acid sequence. PlotMI can also be used to compare dependencies present in training data to the dependencies learned by the model and to compare dependencies learned by different types of models that are trained to perform the same task. PlotMI is freely available at https://github.com/hartonen/plotMI.

bioinformatics↗

bioRxiv2018

Binding specificities of human RNA binding proteins towards structured and linear RNA sequences

Sequence specific RNA-binding proteins (RBPs) control many important processes affecting gene expression. They regulate RNA metabolism at multiple levels, by affecting splicing of nascent transcripts, RNA folding, base modification, transport, localization, translation and stability. Despite their central role in most aspects of RNA metabolism and function, most RBP binding specificities remain unknown or incompletely defined. To address this, we have assembled a genome-scale collection of RBPs and their RNA binding domains (RBDs), and assessed their specificities using high throughput RNA-SELEX (HTR-SELEX). Approximately 70% of RBPs for which we obtained a motif bound to short linear sequences, whereas ~30% preferred structured motifs folding into stem-loops. We also found that many RBPs can bind to multiple distinctly different motifs. Analysis of the matches of the motifs in human genomic sequences suggested novel roles for many RBPs. We found that three cytoplasmic proteins, ZC3H12A, ZC3H12B and ZC3H12C bound to motifs resembling the splice donor sequence, suggesting that these proteins are involved in degradation of cytoplasmic viral and/or unspliced transcripts. Surprisingly, structural analysis revealed that the RNA motif was not bound by the conventional C3H1 RNA-binding domain of ZC3H12B. Instead, the RNA motif was bound by the ZC3H12Bs PilT N-terminus (PIN) RNase domain, revealing a potential mechanism by which unconventional RNA binding domains containing active sites or molecule-binding pockets could interact with short, structured RNA molecules. Our collection containing 145 high resolution binding specificity models for 86 RBPs is the largest systematic resource for the analysis of human RBPs, and will greatly facilitate future analysis of the various biological roles of this important class of proteins.

biochemistry↗

bioRxiv2017

A small core set of transcription factors bind strongly to DNA in different cell types

It is well established that transcription factors (TFs) play crucial roles in determining cell identity, and that a large fraction of all TFs are expressed in most cell types. In order to globally characterize activities of TFs in cells, we have developed a novel massively parallel protein activity assay, Active TF Identification (ATI) that measures DNA-binding activity of all TFs from any species or tissue type. In contrast to previous studies based on mRNA expression or protein abundance, we found that a set of TFs binding to only around ten distinct motifs display strong DNA-binding activity in any given cell or tissue type. Mass spectrometric identification of TFs revealed that within these highly active TFs, there were both housekeeping TFs, which were universally found in all cell types, and specific TFs, which were highly enriched in known factors that determine the fate of the analyzed tissue or cell type. The importance of a small subset of TFs for determining the overall accessible chromatin landscape of a cell suggests that gene regulatory logic may be simpler than what has previously been appreciated.

cell biology↗

bioRxiv2017

CRISPR/Cas9 screening using unique molecular identifiers

Loss of function screening by CRISPR/Cas9 gene knockout with pooled, lentiviral guide libraries is a widely applicable method for systematic identification of genes contributing to diverse cellular phenotypes. Here, random sequence labels (RSLs) are incorporated into the guide library, which act as unique molecular identifiers (UMIs) to allow massively parallel lineage tracing and lineage dropout screening. RSLs greatly improve the reproducibility of results by increasing both the precision and the accuracy of screens. They reduce the number of cells and sequencing reads needed to reach a set statistical power, or allow a more robust screen using the same number of cells.

genomics↗