Biology subjects

Hey, J.

Publications and source records attributed to Hey, J..

17 recordsLinked to original sources

bioRxiv2026

Correcting for Global Synonymous Selection Improves the Accuracy of Episodic Positive Selection Inference

The ratio of nonsynonymous to synonymous substitution rates ({omega}) constitutes a fundamental parameter for inferring adaptive protein evolution, predicated upon the assumption that synonymous substitutions are selectively inert. This premise, however, is increasingly untenable given evidence of selection acting on synonymous substitutions, driven by various biological processes such as translational efficiency and mRNA stability. In this study, we demonstrate that unmodelled synonymous selection introduces substantial bias into{omega} estimation, resulting in elevated false positive rates in tests for positive selection. To rectify this, we present BUSTED+S+MSS, a statistical framework incorporating Multiclass Synonymous Substitution (MSS) models into BUSTED, a method for detecting episodic selection. By partitioning synonymous codons into empirically derived rate classes, this approach accounts for global synonymous constraints. Application to five diverse clades--Drosophila, Caenorhabditis, Enterobacteria, Saccharomyces, and Primates--reveals that the inclusion of MSS components consistently improves model fit and reduces the proportion of genes inferred to be under positive selection. In Enterobacteria, genes retaining significance under the corrected model exhibit weaker constraint on synonymous substitutions (dSs), consistent with the hypothesis that unmodelled purifying selection drives spurious signals of adaptation. Furthermore, an information-theoretic analysis indicates that whilst site-specific variation (SRV) provides the primary correction, global synonymous rate variation (MSS) contributes a distinct second-order correction. In highly divergent alignments, these signals act in concert to improve model fit. The BUSTED+S+MSS framework, especially when coupled with an "error-sink" to absorb alignment artifacts, thus offers a computationally feasible means to disentangle adaptive nonsynonymous substitution from the confounding effects of synonymous constraint.

bioinformatics↗

bioRxiv2026

Estimating the evolutionary fitness of specific synonymous codon changes

Synonymous mutations do not alter proteins but undergo natural selection in many species. For Drosophila melanogaster, reports on selection strength vary from undetectable to surprisingly strong. Here we apply a new method to estimate the population selection coefficient (2 Ns) for all 134 ordered pairs of synonymous codon changes. The method uses ratios of site frequency spectra (SFS) for codon changes to neutral changes, and does not depend on divergence data, or codon frequencies, and is relatively insensitive to demographic history. Results indicate that natural selection on synonymous codons is weak, with |2 Ns|<2.07 for all pairs of codons and |2 Ns|<1 for 64% of codon changes. Despite being derived solely from polymorphism data, codon fitness estimates are strongly correlated with observed codon frequencies. A selection-mutation-drift model based on our 2 Ns estimates accurately predicts codon usage, while a model based on mutation alone fails. Codon fitnesses correlate strongly with a measure of codon frequency covariation among genes, and codons with large frequency differences between high- and low-expression genes have high fitness values. Finally, we detect clear signs that selection favors codon changes that stabilize mRNA secondary structure. By avoiding divergence data, which have multiple sources of additional variance, and focusing solely on allele frequencies, a clear picture emerges of selection on codon usage. The convergence of multiple independent lines of evidence (codon frequencies, expression-dependent usage, covariation patterns, and mRNA structure) validates this polymorphism-based approach and provides a coherent framework for understanding selection on synonymous site evolution. Significance statementSynonymous mutations do not change proteins yet affect gene expression in multiple ways. Here we provide the first fitness estimates for each type of synonymous codon change. Using only allele frequencies--without divergence data, codon counts, or assumptions about preferred codons--we estimate the fitness of all 134 single-step synonymous codon changes in Drosophila melanogaster. Results reveal weak but non-negligible selection, with codon fitness values accurately predicting codon usage patterns, including expression-dependent biases, codon covariation across genes, and mRNA secondary structure effects. This work offers the first comprehensive fitness estimates for individual synonymous mutations, providing a direct population-level view of how subtle genetic changes shape molecular evolution and establishing a general strategy for quantifying weak selection using polymorphism data alone.

evolutionary biology↗

bioRxiv2024

A new comparative framework for estimating selection on synonymous substitutions.

Selection on synonymous codon usage is a well known and widespread phenomenon, yet existing models often do not account for it or its effect on synonymous substitution rates. In this article, we develop and expand the capabilities of Multiclass Synonymous Substitution (MSS) models, which account for such selection by partitioning synonymous substitutions into two or more classes and estimating a relative substitution rate for each class, while accounting for important confounders like mutation bias. We identify extensive heterogeneity among relative synonymous substitution rates in an empirical dataset of [~]12,000 gene alignments from twelve Drosophila species. We validate model performance using data simulated under a forward population genetic simulation, demonstrating that MSS models are robust to model misspecification. MSS rates are significantly correlated with other covariates of selection on codon usage (population-level polymorphism data and tRNA abundance data), suggesting that models can detect weak signatures of selection on codon usage. With the MSS model, we can now study selection on synonymous substitutions in diverse taxa, independent of any a priori assumptions about the forces driving that selection.

bioinformatics↗

bioRxiv2024

Isolating selective from non-selective forces using site frequency ratios

A new method is introduced for estimating the distribution of mutation fitness effects using site frequency spectra. Unlike previous methods, which make assumptions about non-selective factors, or that try to incorporate such factors into the underlying model, this new method mostly avoids non-selective effects by working with the ratios of counts of selected sites to neutral sites. An expression for the likelihood of a set of selected/neutral ratios is found by treating the ratio of two Poisson random variables as the ratio of two gaussian random variables. This approach also avoids the need to estimate the relative mutation rates of selected and neutral sites. Simulations over a wide range of demographic models, with linked selection effects show that the new SF-Ratios method performs well for statistical tests of selection, and it performs well for estimating the distribution of selection effects. Applications to two populations of Drosophila melanogaster reveal clear but very weak selection on synonymous sites. For nonsynonymous sites, selection was estimated to be far weaker than previous estimates for Drosophila populations.

evolutionary biology↗

bioRxiv2024

Allele age estimators designed for whole genome datasets show only a modest decrease in accuracy when applied to whole exome datasets.

Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in the number of genomes, larger numbers of rare variants are being discovered, leading to important initiatives in identifying the functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population. However, allele age estimators such as those implemented in the programs Relate, Genealogical Estimator of Variant Age (GEVA), and Runtc, were developed based on the assumption that datasets include the entire genome. We examined the performance of each of these estimators on simulated exome data under a neutral constant population size model, as well as under population expansion and background selection models. We found that each provides usable estimates of allele age from whole-exome datasets. Relate performs the best amongst all three estimators with Pearson coefficients of 0.83 and 0.73 (with respect to true simulated values, for neutral constant and expansion population model, respectively) with a 12 percent and 20 percent decrease in correlation between whole genome and whole exome estimations. Of the three estimators, Relate is best able to parallelize to yield quick results with little resources, however, Relate is currently only able to scale to thousands of samples making it unable to match the hundreds of thousands of samples being currently released. While more work is needed to expand the capabilities of current methods of estimating allele age, these methods show a modest decrease in performance in the estimation of the age of mutations. Article SummaryIncreasing availability of whole exome sequencing yields large numbers of rare variants that have direct impact on disease phenotypes. Many methods of identifying the functional impact of mutations exist including the estimation of the time a mutation entered a population. Popular methods of estimating this time assume whole genome data in the estimate of the allele age based on haplotypes. We simulated genome and exome data under a constant and expansion population demography model and found that there is a decrease in performance in all three methods on exome data of 15-30% depending on the method. Testing the robustness of the best performing method, Relate, further simulations introducing background selection and varying the sample size were also undertaken with similar results.

genomics↗

bioRxiv2023

Oncogenic RAS-Pathway Activation Drives Oncofetal Reprogramming and Creates Therapeutic Vulnerabilities in Juvenile Myelomonocytic Leukemia

Aberrant fetal gene expression facilitates tumor-specific cellular plasticity by hijacking molecular programs of embryogenesis1. Persistent fetal gene signatures in childhood malignancies are typically explained by their prenatal origins2-6. In contrast, reactivation of fetal gene expression is considered a consequence of oncofetal reprogramming (OFR) in adult malignancies and is associated with aggressive disease7-10. To date, OFR has not been described in the context of childhood malignancies. Here, we performed a comprehensive multi-layered molecular characterization of juvenile myelomonocytic leukemia (JMML) and identified OFR as a hallmark of aggressive JMML. We observed that hematopoietic stem cells (HSCs) aberrantly express mixed developmental programs in JMML. Expression of fetal gene signatures combined with a postnatal epigenetic landscape suggested OFR, which was validated in a JMML mouse model, demonstrating that postnatal activation of RAS signaling is sufficient to induce fetal gene signatures. Integrative analysis identified the fetal HSC maturation marker CD52 as a novel therapeutic target for aggressive JMML. Anti-CD52 treatment depleted human JMML HSCs and disrupted disease propagation in vivo. In summary, this study implicates OFR, defined as postnatal acquisition of fetal transcription signatures, in the pathobiology of a childhood malignancy. We provide evidence for the direct involvement of oncogenic RAS signaling in OFR. Finally, we demonstrate how OFR can be leveraged for the development of novel treatment strategies. Highlights{blacksquare} Epigenomic and transcriptomic landscape of juvenile myelomonocytic leukemia (JMML) in the context of hematopoietic development. {blacksquare}The presence of fetal transcription signatures in childhood malignancies is not indicative of a developmental maturation block. {blacksquare}High-risk JMML is characterized by oncofetal reprogramming of postnatal hematopoietic stem cells (HSCs). {blacksquare}RAS-pathway mutations induce fetal-like gene expression signatures in murine postnatal HSCs. {blacksquare}The fetal maturation marker CD52 is a novel therapeutic target in high-risk JMML.

cancer biology↗

bioRxiv2023

Epigenetic deregulation of IFN and WNT pathways in AT2 cells impairs alveolar regeneration (in COPD)

Chronic lung diseases, including chronic obstructive pulmonary disease (COPD), affect over 500 million people and are a leading cause of death worldwide. A common feature of both chronic and acute lung diseases is altered respiratory barrier integrity and impaired lung regeneration. We hypothesized that alveolar type 2 (AT2) cells, as alveolar epithelial progenitors, will carry molecular alterations that compromise alveolar regeneration in COPD. Sorted AT2 cells from ex-smokers with and without COPD at different disease stages were subjected to RNA sequencing and whole-genome bisulfite sequencing to generate unbiased transcriptome and DNA methylation maps of alveolar progenitors in the lung. Our analysis revealed genome-wide epigenetic changes in AT2 cells during COPD that were associated with global gene expression changes. Integrative data analysis uncovered a strong anti-correlation between gene expression and promoter methylation, suggesting that dysregulation of COPD-associated pathways in AT2 cells may be regulated by DNA methylation. Interferon (IFN) signaling was the top-upregulated pathway associated with the concomitant loss of promoter DNA methylation. Epigenetic regulation of the IFN pathway was validated in both global and targeted DNA demethylation assays in A549 cells. Notably, targeted DNA demethylation of IRF9 triggered upregulation of IFN signaling, mimicking the effects observed in COPD AT2 cells in the profiling data. Our findings suggest that COPD-triggered epigenetic alterations in AT2 cells may impair internal regeneration programs in human lung parenchyma.

cell biology↗

bioRxiv2023

Analyses of allele age and impact reveal human beneficial alleles are older than neutral controls

A classic population genetic prediction is that alleles experiencing directional selection should swiftly traverse allele frequency space, leaving detectable reductions in genetic variation in linked regions. However, despite this expectation, identifying clear footprints of beneficial allele passage has proven to be surprisingly challenging. We addressed the basic premise underlying this expectation by estimating the ages of large numbers of beneficial and deleterious alleles in a human population genomic data set. Deleterious alleles were found to be young, on average, given their allele frequency. However, beneficial alleles were older on average than non-coding, non-regulatory alleles of the same frequency. This finding is not consistent with directional selection and instead indicates some type of balancing selection. Among derived beneficial alleles, those fixed in the population show higher local recombination rates than those still segregating, consistent with a model in which new beneficial alleles experience an initial period of balancing selection due to linkage disequilibrium with deleterious recessive alleles. Alleles that ultimately fix following a period of balancing selection will leave a modest soft sweep impact on the local variation, consistent with the overall paucity of species-wide hard sweeps in human genomes. Impact StatementAnalyses of allele age and evolutionary impact reveal that beneficial alleles in a human population are often older than neutral controls, suggesting a large role for balancing selection in adaptation.

evolutionary biology↗

bioRxiv2023

Altered enhancer-promoter interaction leads to MNX1 expression in pediatric acute myeloid leukemia with t(7;12)(q36;p13)

Acute myeloid leukemia (AML) with the t(7;12)(q36;p13) translocation occurs only in very young children and has a poor clinical outcome. The expected oncofusion between breakpoint partners (MNX1 and ETV6) has only been reported in a subset of cases. However, a universal feature is the strong transcript and protein expression of MNX1, a homeobox transcription factor that is normally not expressed in hematopoietic cells. Here, we map the translocation breakpoints on chromosomes 7 and 12 in affected patients to a region proximal to MNX1 and either introns 1 or 2 of ETV6. The frequency of MNX1 overexpression in pediatric AML (n=1556, own and published data) is 2.4% and occurs predominantly in t(7;12)(q36;p13) AML. Chromatin interaction assays in a t(7;12)(q36;p13) iPSC cell line model unravel an enhancer-hijacking event that explains MNX1 overexpression in hematopoietic cells. Our data suggest that enhancer-hijacking is a more common and overlooked mechanism for structural rearrangement-mediated gene activation in AML. Key pointsO_LIExpression analysis of over 1500 pediatric AML samples demonstrates MNX1 expression as a universal feature of t(7;12)(q36;p13) AML as well as in rare cases without t(7;12)(q36;p13) C_LIO_LIMNX1 is activated by an enhancer-hijacking event in t(7;12)(q36;p13) AML and not, as previously postulated, by the creation of a MNX1::ETV6 oncofusion gene. C_LI

cancer biology↗

bioRxiv2022

Mnx1 Induces Leukemia Transformation Through Altering Histone Methylation in a Model of Pediatric t(7;12) Acute Myeloid Leukemia.

Certain subtypes of acute myeloid leukemia (AML) in children have inferior outcome. One of these has a translocation t(7;12)(q36;p13) leading to a MNX1::ETV6 fusion along with high expression of MNX1. Here we identified the transforming event in this AML and possible ways of treatment. Only MNX1 was able to induce AML in mice, with similar gene expression and pathway enrichment to t(7;12) AML patient data. Importantly, this leukemia was only induced in immune incompetent mice using fetal but not adult hematopoietic stem and progenitor cells. The restriction in transforming capacity to cells from fetal liver is in alignment with t(7;12)(q36;p13) AML being mostly seen in infants. Expression of MNX1 led to increased histone 3 lysine 4 mono-, di- and trimethylation, reduction in H3K27me3, accompanied with changes in genome-wide chromatin accessibility and genome expression, likely mediated through MNX1 interaction with the methionine cycle and methyltransferases. MNX1 expression increased DNA damage, depletion of the Lin-/Sca1+/c-Kit+ population and skewing toward the myeloid lineage. These effects, together with leukemia development, was prevented by pretreatment with the S-adenosylmethionine analog Sinefungin. In conclusion, we have shown the importance of MNX1 in development of AML with t(7;12), supporting a rationale for targeting MNX1 and downstream pathways.

cancer biology↗

bioRxiv2022

High-resolution epigenetic profiling identifies novel regulators of COPD in human lung fibroblasts

Patients with chronic obstructive pulmonary disease (COPD) are still waiting for curative treatments. Considering the environmental cause of COPD (e.g., cigarette smoke) and disease phenotypes, including stem-cell senescence and impaired differentiation, we hypothesized that COPD will be associated with altered epigenetic signaling in lung cells. We generated genome-wide DNA methylation maps at single CpG resolution of primary human lung fibroblasts (HLFs) isolated from distal parenchyma of ex-smoker controls and COPD patients, with both mild and severe disease. The epigenetic landscape is markedly changed in lung fibroblasts across COPD stages, with DNA methylation changes occurring predominantly in regulatory regions, including promoters and enhancers. RNA sequencing of matched fibroblasts demonstrated dysregulation of genes involved in proliferation, DNA repair, and extracellular matrix organization. Notably, we identified epigenetic and transcriptional dysregulation already in mild COPD patients, providing unique insights into early disease. Integration of profiling data identified 110 candidate regulators of disease phenotypes, including epigenetic factors. Using phenotypic screens, we verified the regulator capacity of multiple candidates and linked them to repair processes in the human lung. Our study provides first integrative high-resolution epigenetic and transcriptomic maps of human lung fibroblasts across stages of COPD. We reveal novel transcriptomic and epigenetic signatures associated with COPD onset and progression and identify new candidate regulators involved in the pathogenesis of chronic respiratory diseases. The presence of various epigenetic factors among the candidates demonstrates that epigenetic regulation in COPD is an exciting research field that holds promise for novel therapeutic avenues for patients.

cell biology↗

bioRxiv2021

Diverse routes of Club cell evolution in lung adenocarcinoma

The high plasticity of lung epithelial cells, has for many years, confounded the correct identification of the cell-of-origin of lung adenocarcinoma (LUAD), one of the deadliest malignancies worldwide. Here, we address the cell-of-origin of LUAD, by employing lineage-tracing mouse models combined with a CRISPR/Cas9 system to induce an oncogenic Eml4-Alk rearrangement in virtually all epithelial cell types of the lung. We find that Club cells give rise to lung tumours with a higher frequency than AT2 cells. Based on whole genome methylome, we identified that tumours retain an epigenetic memory derived from their originating cell type but also develop a tumour-specific pattern regardless of their origin. Single-cell transcriptomic analyses identified two trajectories of Club cell evolution which are similar to the ones used during lung regeneration, providing a link between lung regeneration and cancer initiation. On both routes, tumours lose their Club cell identity and gain an AT2- like phenotype. Together, this study highlights the role of Club cells in LUAD initiation and unveils key mechanisms conferring LUAD heterogeneity.

cancer biology↗

bioRxiv2020

AmpliconDesign - An interactive web server for the design of high-throughput targeted DNA methylation assays

Targeted analysis of DNA methylation patterns based on bisulfite-treated genomic DNA (BT-DNA) is considered as a gold-standard for epigenetic biomarker development. Existing software tools facilitate primer design, primer quality control or visualization of primer localization. However, high-throughput design of primers for BT-DNA amplification is hampered by limits in throughput and functionality of existing tools, requiring users to repeatedly perform specific tasks manually. Consequently, the design of PCR primers for BT-DNA remains a tedious and time-consuming process. To bridge this gap, we developed AmpliconDesign, a webserver providing a scalable and user-friendly platform for the design and analysis of targeted DNA methylation studies based on BT-DNA, e.g. deep amplicon bisulfite sequencing (ampBS-seq), EpiTYPER MassArray, or pyrosequencing. Core functionality of the web server includes high-throughput primer design and binding site validation based on in silico bisulfite-converted DNA sequences, prediction of fragmentation patterns for EpiTYPER MassArray, an interactive quality control as well as a streamlined analysis workflow for ampBS-seq. Availability and ImplementationThe AmpliconDesign webserver is freely available online at: https://amplicondesign.dkfz.de/. AmpliconDesign has been implemented using the R Shiny framework (Chang et al., 2018). The source code is publicly available under the GNU General Public License v3.0 (https://github.com/MaxSchoenung/AmpliconDesign). ContactDaniel B. Lipka (d.lipka@dkfz.de) & Maximilian Schonung (m.schoenung@dkfz.de)

bioinformatics↗

bioRxiv2019

The Pop-gen Pipeline Platform: A Software Platform for Facilitating Population Genomic Analyses

Here we present the Pop-Gen Pipeline Platform (PPP), a software platform with the goal of reducing the computational expertise required for conducting population genomic analyses. The PPP was designed as a collection of scripts that facilitate common population genomic workflows in a consistent and standardized Python environment. Functions were developed to encompass entire workflows, including: input preparation, file format conversion, various population genomic analyses, output generation, and visualization. By facilitating entire workflows, the PPP offers several benefits to prospective end users - it reduces the need of redundant in-house software and scripts that would require development time and may be error-prone, or incorrect. The platform has also been developed with reproducibility and extensibility of analyses in mind. The PPP is an open-source package that is available for download and use at https://ppp.readthedocs.io/en/latest/PPP_pages/install.html

bioinformatics↗

bioRxiv2019

Model-based Assessments of Differential Introgression and Linked Natural Selection During Divergence and Speciation

Demographic changes such as fluctuating population size and differential introgression can mask the effects of natural selection, and affect rates of genome evolution, local adaptation, reproductive isolation, and eventual speciation. Besides identifying differentially introgressing genes (and genomic regions) that are \"labeled\" to be retroactively causal to adaptive evolution and speciation, there is significant impetus to understand, and perhaps estimate the underlying demography that affects current genomic diversity. Using model-based likelihood methods to directly estimate, and decouple the effects of differential intogression and demography across genomic loci offers an ideal solution to detect differential introgression, and population demography and build hypotheses around its underlying evolutionary processes. We describe a computationally efficient parallelized implementation of mixture-model based isolation with migration (IM) analyses to assign loci to classes based on their shared coalescent histories (population sizes, or migration rates). We apply this method to several genomic data sets (great apes - chimpanzees and bonobos, Anopheles mosquitoes, threespine sticklebacks, Mullerian mimics of Heliconius butterflies, mice, European rabbits, and fruitflies), that have been previously characterized (perhaps erroneously) using genome-wide scans of differentiation. We show that we cannot reject a model of differential introgression, or linked selection across a majority of species analyzed, with two species showing the combined effects of differential introgression and linked natural selection across multiple, non-independent genomic loci.

evolutionary biology↗

bioRxiv2018

Age distributions of rare lineages reveal recent demographic history and selection

The age of an allele of a given frequency offers insight into both its function and origin, and the distribution of ages of alleles in a population conveys significant information about its history. The rarer the allele the more likely it is to reveal functional biological insight and the more recent the historical revelation. By measuring the length of the haplotype shared between an individual carrying a rare variant and its closest relative not carrying the variant we are able to approximate the age of the variant and can apply this method even when only a single copy of a variant has been sampled in a population. Applying this technique to rare variants in a large population sample from the United Kingdom, we identify historical migration from West Africa approximately 400 years ago, evidence of direct selection against novel protein-altering rare variants in individual biological pathways, continued negative frequency dependent selection on protein-altering variants in olfactory transducers and the innate immune system, and map the impact of background selection on the most recent portions of the sample genealogy.

genomics↗

bioRxiv2017

Recent African gene flow responsible for excess of old rare genetic variation in Great Britain

Population genomic studies can reveal the allele frequencies at millions of SNPs, with the numbers of observed low frequency SNPs increasing as more genomes are sequenced. Rare alleles tend to be younger than common alleles and are especially useful for studying demographic history, selection and heritability1,2. However, allele frequency can be a poor proxy for allele age, as genetic drift and natural selection can lead to alleles that are both rare and old. In order to allow joint assessments of allele frequency and allele age, a new estimator of allele age was developed that can be applied to variants of the lowest observed frequencies (singletons). By examining the geographic and age distribution of very rare variants in a large genomic sample from the UK3, we identify new evidence of gene flow from Africa into the ancestors of the modern UK population. A substantial proportion of variants with observed frequencies as low as 1.4 x 10-4 are orders of magnitude older than can be explained without African gene flow and are found at much higher frequencies within modern African populations. We estimate that African populations contributed approximately 1.2% of the UK gene pool and did so approximately 400 years ago. These findings are relevant both to our understanding of human history and to the nature of rare variation segregating within populations: a variant that is rare because it is a recent mutation in the direct ancestor of the population will have had a very different evolutionary history than an ancient one that has persisted at high frequencies in a diverged population and only recently arrived through migration.

genomics↗