Biology subjects

Pedersen, J. S.

Publications and source records attributed to Pedersen, J. S..

32 records · Page 2Linked to original sources

bioRxiv2022

Structural basis for regulation of a tripartite toxin-antitoxin system by dual phosphorylation

Many bacteria encode multiple toxin-antitoxin (TA) systems targeting separate, but closely related, cellular functions. The toxin of the E. coli hipBA system, HipA, is a kinase that inhibits translation via phosphorylation of glutamyl-tRNA synthetase. Enteropathogenic E. coli (EPEC) O127:H6 encodes the hipBA-like, tripartite TA system; hipBST, in which the HipT toxin specifically targets the tryptophanyl-tRNA synthetase, TrpS. Notably, in the tripartite system, the function as antitoxin has been taken over by the third protein, HipS, but the molecular details of how activity of HipT is inhibited remain poorly understood. Here, we show that HipBST is structurally different from E. coli HipBA and that the unique HipS protein, which is homologous to the N-terminal subdomain of HipA, inhibits the kinase through insertion of a conserved Trp residue into the active site. We also show how auto-phosphorylation at two conserved sites in the kinase toxin serve different roles and affect the ability of HipS to neutralize HipT. Finally, solution structural studies show how phosphorylation affects overall TA complex flexibility.

microbiology↗

bioRxiv2021

Sequence dependencies and mutation rates of localized mutational processes in cancer

BackgroundCancer mutations accumulate through replication errors and DNA damage coupled with incomplete repair. Individual mutational processes often show strong sequence and regional preferences. As a result, some sequence contexts mutate at much higher rates than others. Mutational hotspots, with recurrent mutations across cancer samples, represent genomic positions with elevated mutation rates, often caused by highly localized mutational processes. ResultsWe analyze the mutation rates of all 11-mer genomic sequence contexts using the PCAWG set of 2,583 pan-cancer whole genomes. We further associate individual mutations and contexts to mutational signatures and estimate their relative mutation rates. We show that hotspots generally identify highly mutable sequence contexts. Using these, we show that some mutational signatures are enriched in hotspot sequence contexts, corresponding to well-defined sequence preferences for the underlying localized mutational processes. This includes signature 17b (of unknown etiology) and signatures 62 (POLE), 7a (UV), and 72 (linked to lymphomas). In some cases, the mutation rate increases further when focusing on certain genomic regions, such as signature 62 in poised promoters, where the mutation is increased several thousand folds over the overall data set average. ConclusionWe summarize our findings in a catalog of localized mutational processes, their sequence preferences, and their estimated mutation rates.

cancer biology↗

bioRxiv2020

Structural insights on the substrate-binding proteins of the Mycobacterium tuberculosis mammalian-cell-entry (Mce) 1 and 4 complexes

Tuberculosis (Tb), caused by Mycobacterium tuberculosis (Mtb), is responsible for more than a million deaths annually. In the latent phase of infection, Mtb uses lipids as the source of carbon and energy for its survival. The lipid molecules are transported across the cell wall via multiple transport systems. One such set of widely present and less-studied transporters is the Mammalian-cell-entry (Mce) complexes. Here, we report the properties of the substrate-binding proteins (SBPs; MceA-F) of the Mce1 and Mce4 complexes from Mtb which are responsible for the import of mycolic acid/fatty acids, and cholesterol respectively. MceA-F are composed of four domains namely, transmembrane, MCE, helical and tail domains. Our studies show that MceA-F are predominantly monomeric when purified individually and do not form homohexamers unlike the reported homologs (MlaD, PqiB and LetB) from other prokaryotes. The crystal structure of MCE domain of Mtb Mce4A (MtMce4A39-140) determined at 2.9 [A] shows the formation of an unexpected domain-swapped dimer in the crystals. Further, the purification and small-angle X-ray scattering (SAXS) analysis on MtMce1A, MtMce4A and their domains suggest that the helical domain requires hydrophobic interactions with the detergent molecules for its stability. Combining all the experimental data, we propose a heterohexameric arrangement of MtMceA-F SBPs, where the soluble MCE domain of the SBPs would remain in the periplasm with the helical domain extending to the lipid layer forming a hollow channel for the transport of lipids across the membranes. The tail domain would reach the cell surface assisting in lipid recognition and binding.

biochemistry↗

bioRxiv2020

miRNA activity inferred from single cell mRNA expression

High throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n=9,679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.

bioinformatics↗

bioRxiv2020

Transcriptome-wide profiles of circular RNA and RNA binding protein interactions reveal effects on circular RNA biogenesis and cancer pathway expression

Circular RNAs (circRNAs) are stable, often highly expressed RNA transcripts with potential to modulate other regulatory RNAs. A few circRNAs have been shown to bind RNA binding proteins (RBPs), however, little is known about the prevalence and strength of these interactions in different biological contexts. Here, we comprehensively evaluate the interplay between circRNAs and RBPs in the ENCODE cell lines, HepG2 and K562, by profiling the expression of circRNAs in fractionated total RNA-sequencing samples and analyzing binding sites of 150 RBPs in large eCLIP data sets. We show that KHSRP binding sites are enriched in flanking introns of circRNAs in both HepG2 and K562 cells, and that KHSRP depletion affects circRNA biogenesis. Additionally, we show that exons forming circRNAs are generally enriched with RBP binding sites compared to non-circularizing exons. To detect individual circRNAs with regulatory potency, we computationally identify circRNAs that are highly covered by RBP binding sites and experimentally validate circRNA-RBP interactions by RNA immunoprecipitations. We characterize circCDYL, a highly expressed circRNA with clinical and functional implications in bladder cancer, which is covered with GRWD1 binding sites. We confirm that circCDYL binds GRWD1 in vivo and functionally characterizes the effect of circCDYL-GRWD1 interactions on target genes in HepG2. Furthermore, we confirm interactions between circCDYL and RBPs in bladder cancer cells and demonstrate that circCDYL depletion affects hallmarks of cancer and perturbs the expression of key cancer genes, e.g. TP53 and MYC. Finally, we show that elevated levels of highly RBP-covered circRNAs, including circCDYL, are associated with overall survival of bladder cancer patients. Our study demonstrates transcriptome-wide and cell-type-specific circRNA-RBP interactions that could play important regulatory roles in tumorigenesis.

genomics↗

bioRxiv2020

Isolation and complete genome sequencing of Pectobacterium phage Jarilo, representing a novel genus of bacteriophages within the subfamily Autographivirinae

Pectobacterium carotovorum is the causative agent of bacterial soft rot on various plant species. The use of phages for plant disease control have gained increased awareness over the past years. We here describe the isolation and characterization of Pectobacterium phage Jarilo, representing a novel genus of bacteriophages within the subfamily Autographivirinae. Jarilo possesses a double-stranded DNA genome of 40557 bp with a G+C% content of 50.08% and 50 predicted open reading frames (ORFs). Gene synteny and products seem to be somewhat conserved between Pectobacterium phage Jarilo and Enterobacteria phage T7, but limited nucleotide similarity is found between Jarilo and other phages within the subfamily Autographivirinae. We propose Pectobacterium phage Jarilo as the first member of a new genus of bacteriophages within the subfamily Autographivirinae.

microbiology↗

bioRxiv2019

NOD1 mediates non-canonical inflammasome processing of interleukin-18 in epithelial cells to Helicobacter pylori infection.

The interleukin-1 family members, IL-1{beta} and IL-18, are processed into their biologically active forms by multi-protein complexes, known as inflammasomes. Although the inflammasome pathways that mediate IL-1{beta} processing in myeloid cells have been extensively studied, those involved in IL-18 processing, particularly in non-myeloid cells, are still poorly understood. Here, we have identified the cytosolic sensor NOD1 as a key regulator of IL-18 processing in epithelial cells responding to Helicobacter pylori infection. Importantly, NOD1 processing of IL-18 occurs independently of the canonical inflammasome proteins, NLRP3 and ASC. Instead, NOD1 interacts directly with caspase-1 via homotypic binding of caspase-activation recruitment domains. We show that IL-18 is important in maintaining tissue homeostasis and protecting against pre-neoplastic changes due to gastric H. pylori infection. These findings reveal an unanticipated role for NOD1 in a new type of inflammasome that regulates epithelial cell production of bioactive IL-18 with tissue protective functions.

immunology↗

bioRxiv2018

EBADIMEX: An empirical Bayes approach to detect joint differential expression and methylation and to classify samples

1DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types.\n\nHere, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets.\n\nEBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

bioinformatics↗

bioRxiv2018

Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed \"putative passengers\") are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

genomics↗

bioRxiv2017

The whole-genome panorama of cancer drivers

The advance of personalized cancer medicine requires the accurate identification of the mutations driving each patients tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patients tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.

cancer biology↗

bioRxiv2017

Pan-cancer study of heterogeneous RNA aberrations

We present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called \"bridged\" fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.

genomics↗

bioRxiv2017

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Non-coding mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from ten cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g. TERT and CDH10) and survival (e.g. CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4,128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217 and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. This screen for significant mutation patterns followed by correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

cancer biology↗

bioRxiv2017

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

Long non-coding RNAs (lncRNAs) that drive tumorigenesis are a growing focus of cancer genomics studies. To facilitate further discovery, we have created the \"Cancer LncRNA Census\" (CLC), a manually-curated and strictly-defined compilation of lncRNAs with causative roles in cancer. CLC has two principle applications: first, as a resource for training and benchmarking de novo identification methods; and second, as a dataset for studying the fundamental properties of these genes.\n\nCLC Version 1 comprises 122 lncRNAs implicated in 29 distinct cancers. LncRNAs are included based on functional or genetic evidence for causative roles in cancer progression. All belong to the GENCODE reference annotation, to enable integration across projects and datasets. For each entry, the evidence type, biological activity (oncogene or tumour suppressor), source reference and cancer type are recorded. Supporting its usefulness, CLC genes are significantly enriched amongst de novo predicted driver genes from PCAWG. CLC genes are distinguished from other lncRNAs by a series of features consistent with biological function, including gene length, high expression and sequence conservation of both exons and promoters. We identify a trend for CLC genes to be co-localised with known protein-coding cancer genes along the human genome. Finally, by integrating data from transposon-mutagenesis functional screens, we show that mouse orthologues of CLC genes tend also to be cancer genes.\n\nThus CLC represents a valuable resource for research into long non-coding RNAs in cancer. Their evolutionary and genomic properties have implications for understanding disease mechanisms and point to conserved functions across ~80 million years of evolution.

bioinformatics↗

bioRxiv2017

A Site Specific Model And Analysis Of The Neutral Somatic Mutation Rate In Whole-Genome Cancer Data

BackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.\n\nResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.\n\nConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

bioinformatics↗