Biology subjects

Jensen, J. D.

Publications and source records attributed to Jensen, J. D..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

A whole-genome scan for evidence of recent positive and balancing selection in aye-ayes (Daubentonia madagascariensis) utilizing a well-fit evolutionary baseline model

The aye-aye (Daubentonia madagascariensis) is one of the 25 most endangered primate species in the world, maintaining amongst the lowest genetic diversity of any primate measured to date. Characterizing patterns of genetic variation within aye-aye populations, and the relative influences of neutral and selective processes in shaping that variation, is thus important for future conservation efforts. In this study, we performed the first whole-genome scans for recent positive and balancing selection in the species, utilizing high-coverage population genomic data from newly sequenced individuals. We generated null thresholds for our genomic scans by creating an evolutionarily appropriate baseline model that incorporates the demographic history of this aye-aye population, and identified a small number of candidate genes. Most notably, a suite of genes involved in olfaction -- a key trait in these nocturnal primates -- were identified as experiencing long-term balancing selection. We also conducted analyses to quantify the expected statistical power to detect positive and balancing selection in this population using site frequency spectrum-based inference methods, once accounting for the potentially confounding contributions of population history, recombination and mutation rate variation, and purifying and background selection. This work, presenting the first high-quality, genome-wide polymorphism data across the functional regions of the aye-aye genome, thus provides important insights into the landscape of episodic selective forces in this highly endangered species.

evolutionary biology↗

bioRxiv2024

The landscape of structural variation in aye-ayes (Daubentonia madagascariensis)

Aye-ayes (Daubentonia madagascariensis) are one of the 25 most critically endangered primate species in the world. Endemic to Madagascar, their small and highly fragmented populations make them particularly vulnerable to both genetic disease and anthropogenic environmental changes. Over the past decade, conservation genomic efforts have largely focused on inferring and monitoring population structure based on single nucleotide variants to identify and protect critical areas of genetic diversity. However, the recent release of a highly contiguous genome assembly allows, for the first time, for the study of structural genomic variation (deletions, duplications, insertions, and inversions) which are likely to impact a substantial proportion of the species genome. Based on whole-genome, short-read sequencing data from 14 individuals, >1,000 high-confidence autosomal structural variants were detected, affecting [~]240 kb of the aye-aye genome. The majority of these variants (>85%) were deletions shorter than 200 bp, consistent with the notion that longer structural mutations are often associated with strongly deleterious fitness effects. For example, two deletions longer than 850 bp located within disease-linked genes were predicted to impose substantial fitness deficits owing to a resulting frameshift and gene fusion, respectively; whereas several other major effect variants outside of coding regions are likely to impact gene regulatory landscapes. Taken together, this first glimpse into the landscape of structural variation in aye-ayes will enable future opportunities to advance our understanding of the traits impacting the fitness of this endangered species, as well as allow for enhanced evolutionary comparisons across the full primate clade.

evolutionary biology↗

bioRxiv2024

A pedigree-based map of crossovers and non-crossovers in aye-ayes (Daubentonia madagascariensis)

Gaining a better understanding of rates and patterns of meiotic recombination is crucial for improving evolutionary genomic modelling, with applications ranging from demographic to selective inference. Although previous research has provided important insights into the landscape of crossovers in humans and other haplorrhines, our understanding of both the considerably more common outcome of recombination (i.e., non-crossovers) as well as the landscapes in more distantly-related primates (i.e., strepsirrhines) remains limited owing to difficulties associated with both the identification of non-crossover tracts as well as species sampling. Thus, in order to elucidate recombination patterns in this under-studied branch of the primate clade, we here characterize crossover and non-crossover landscapes in aye-ayes utilizing whole-genome sequencing data from six three-generation pedigrees as well as three two-generation multi-sibling families, and in so doing provide novel insights into this important evolutionary process shaping genomic diversity in one of the worlds most critically endangered primate species.

evolutionary biology↗

bioRxiv2024

Characterizing the rates and patterns of de novo germline mutations in the aye-aye (Daubentonia madagascariensis)

Given the many levels of biological variation in mutation rates observed to date in primates - spanning from species to individuals to genomic regions - future steps in our understanding of mutation rate evolution will be aided by both a greater breadth of species coverage across the primate clade, but also by a greater depth as afforded by an evaluation of multiple trios within individual species. In order to help bridge these gaps, we here present an analysis of a species representing one of the most basal splits on the primate tree (aye-ayes), combining whole-genome sequencing of seven parent-offspring trios from a three-generation pedigree with a novel computational pipeline that takes advantage of recently developed pan-genome graphs, thereby circumventing the application of (highly subjective) quality metrics that has previously been shown to result in notable differences in the detection of de novo mutations, and ultimately estimates of mutation rates. This deep sampling has enabled both a detailed picture of parental age effects as well as sex dependency in mutation rates which we here compare with previously studied primates, but has also provided unique insights into the nature of genetic variation in one of the most endangered primates on the planet.

evolutionary biology↗

bioRxiv2024

Inferring demographic and selective histories from population genomic data using a two-step approach in species with coding-sparse genomes: an application to human data

The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

genetics↗

bioRxiv2024

Engineering Memory T Cells as a platform for Long-Term Enzyme Replacement Therapy in Lysosomal Storage Disorders

Enzymopathy disorders are the result of missing or defective enzymes. Amongst these enzymopathies, mucopolysaccharidosis type I, is a rare genetic lysosomal storage disorder caused by mutations in the gene encoding alpha-L-iduronidase (IDUA), ultimately causes toxic build-up of glycosaminoglycans (GAGs). There is currently no cure and standard treatments provide insufficient relief to the skeletal structure and central nervous system (CNS). Human memory T cells (Tm) migrate throughout the bodys tissues and can persist for years, making them an attractive approach for cellular-based, systemic enzyme replacement therapy. Here, we tested genetically engineered, IDUA-expressing Tm as a cellular therapy in an immunodeficient mouse model of MPS I. Our results demonstrate that a single dose of engineered Tm leads to detectable IDUA enzyme levels in the blood for up to 22 weeks and reduced urinary GAG excretion. Furthermore, engineered Tm take up residence in nearly all tested tissues, producing IDUA and leading to metabolic correction of GAG levels in the heart, lung, liver, spleen, kidney, bone marrow, and the CNS. Our study indicates that genetically engineered Tm holds great promise as a platform for cellular-based enzyme replacement therapy for the treatment of mucopolysaccharidosis type I and potentially many other enzymopathies and protein deficiencies.

bioengineering↗

bioRxiv2024

Automated Seminal Root Angle Measurement with Corrective Annotation

Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify SNPs significantly associated with angle and length, shedding light on the genetic basis of root architecture.

plant biology↗

bioRxiv2023

The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects

Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and{delta} a{delta}i). We demonstrate that, if not properly modelled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination prior to utilizing population genomic data to quantify the effects of genetic drift (i.e., as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modelled in downstream inference. Significance StatementDespite evidence from numerous species that mutation and recombination rates vary along the genome, both rates tend to be modelled as fixed when performing population genetic inference. The impact of failing to account for this rate heterogeneity on the estimation of demographic and selective parameters has yet to be well quantified; thus, we here study this effect by comparing inference under both fixed and variable rate scenarios. Our results demonstrate that unaccounted for rate heterogeneity can increase uncertainty and lead to mis-inference in certain scenarios. We highlight the importance of utilizing mutation and recombination rate maps where possible, and of modelling the uncertainty underlying these estimates.

genetics↗

bioRxiv2023

A simulation framework for modeling the within-patient evolutionary dynamics of SARS-CoV-2

The global impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for positive selection are best performed via comparison of empirical data to simulated data wherein evolutionary factors, including mutation and recombination rates, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. While there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intra-host evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model-parameter combinations and compared them to existing empirical data. Of these, 592 models ([~]5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intra-host SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed towards strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data. Significance StatementDespite its tremendous impact on human health, a comprehensive evolutionary baseline model has yet to be developed for studying the within-host population genomics of SARS-CoV-2. Importantly, such modeling would enable improved analysis and provide insights into the key evolutionary dynamics governing SARS-CoV-2 evolution. Given this need, we have here quantified a set of plausible baseline models via large-scale simulation. The commonly shared features of these relevant models - including severe infection bottlenecks, low levels of progeny skew, and a high rate of strongly deleterious mutations - lay the foundation for sophisticated analyses of SARS-CoV-2 evolution within patients using these baseline models.

evolutionary biology↗

bioRxiv2023

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong. Teaser TextOutlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.

genomics↗

bioRxiv2023

Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history

Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection (e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome (i.e., purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.

evolutionary biology↗

bioRxiv2023

Protective effect of pre-existing natural immunity in a nonhuman primate reinfection model of congenital cytomegalovirus infection

Congenital cytomegalovirus (cCMV) is the leading infectious cause of neurologic defects in newborns with particularly severe sequelae in the setting of primary CMV infection in the first trimester of pregnancy. The majority of cCMV cases worldwide occur after non-primary infection in CMV-seropositive women; yet the extent to which pre-existing natural CMV-specific immunity protects against CMV reinfection or reactivation during pregnancy remains ill-defined. We previously reported on a novel nonhuman primate model of cCMV in rhesus macaques where 100% placental transmission and 83% fetal loss were seen in CD4+ T lymphocyte-depleted rhesus CMV (RhCMV)-seronegative dams after primary RhCMV infection. To investigate the protective effect of preconception maternal immunity, we performed reinfection studies in CD4+ T lymphocyte-depleted RhCMV-seropositive dams inoculated in late first / early second trimester gestation with RhCMV strains 180.92 (n=2), or RhCMV UCD52 and FL-RhCMV{Delta}Rh13.1/SIVgag, a wild-type-like RhCMV clone with SIVgag inserted as an immunological marker (n=3). An early transient increase in circulating monocytes followed by boosting of the pre-existing RhCMV-specific CD8+ T lymphocyte and antibody response was observed in the reinfected dams but not in control CD4+ T lymphocyte-depleted dams. Emergence of SIV Gag-specific CD8+ T lymphocyte responses in macaques inoculated with the FL-RhCMV{Delta}Rh13.1/SIVgag virus confirmed reinfection. Placental transmission was detected in only one of five reinfected dams and there were no adverse fetal sequelae. Viral whole genome, short-read, deep sequencing analysis confirmed transmission of both reinfection RhCMV strains across the placenta with [~]30% corresponding to FL-RhCMV{Delta}Rh13.1/SIVgag and [~]70% to RhCMV UCD52, consistent with the mixed human CMV infections reported in infants with cCMV. Our data showing reduced placental transmission and absence of fetal loss after non-primary as opposed to primary infection in CD4+ T lymphocyte-depleted dams indicates that preconception maternal CMV-specific CD8+ T lymphocyte and/or humoral immunity can protect against cCMV infection. Author SummaryGlobally, pregnancies in CMV-seropositive women account for the majority of cases of congenital CMV infection but the immune responses needed for protection against placental transmission in mothers with non-primary infection remains unknown. Recently, we developed a nonhuman primate model of primary rhesus CMV (RhCMV) infection in which placental transmission and fetal loss occurred in RhCMV-seronegative CD4+ T lymphocyte-depleted macaques. By conducting similar studies in RhCMV-seropositive dams, we demonstrated the protective effect of pre-existing natural CMV-specific CD8+ T lymphocytes and humoral immunity against congenital CMV after reinfection. A 5-fold reduction in congenital transmission and complete protection against fetal loss was observed in dams with pre-existing immunity compared to primary CMV in this model. Our study is the first formal demonstration in a relevant model of human congenital CMV that natural pre-existing CMV-specific maternal immunity can limit congenital CMV transmission and its sequelae. The nonhuman primate model of non-primary congenital CMV will be especially relevant to studying immune requirements of a maternal vaccine for women in high CMV seroprevalence areas at risk of repeated CMV reinfections during pregnancy.

immunology↗

bioRxiv2022

Interpreting the pervasive observation of U-shaped Site Frequency Spectra

The standard neutral model of molecular evolution has traditionally been used as the null model for population genomics. We gathered a collection of 45 genome-wide site frequency spectra from a diverse set of species, most of which display an excess of low and high frequency variants compared to the expectation of the standard neutral model, resulting in U-shaped spectra. We show that multiple merger coalescent models often provide a better fit to these observations than the standard Kingman coalescent. Hence, in many circumstances these under-utilized models may serve as the more appropriate reference for genomic analyses. We further discuss the underlying evolutionary processes that may result in the widespread U-shape of frequency spectra.

evolutionary biology↗

bioRxiv2021

Statistical inference in population genomics

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population-genetic insights out-paced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous non-adaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model-fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

evolutionary biology↗

bioRxiv2020

Revisiting the notion of deleterious sweeps

It has previously been shown that, conditional on its fixation, the time to fixation of a semi-dominant deleterious autosomal mutation in a randomly mating population is the same as that of an advantageous mutation. This result implies that deleterious mutations could generate selective sweep-like effects. Although their fixation probabilities greatly differ, the much larger input of deleterious relative to beneficial mutations suggests that this phenomenon could be important. We here examine how the fixation of mildly deleterious mutations affects levels and patterns of polymorphism at linked sites - both in the presence and absence of interference amongst deleterious mutations - and how this class of sites may contribute to divergence between-populations and species. We find that, while deleterious fixations are unlikely to represent a significant proportion of outliers in polymorphism-based genomic scans within populations, minor shifts in the frequencies of deleterious mutations can influence the proportions of private variants and the value of FST after a recent population split. As sites subject to deleterious mutations are necessarily found in functional genomic regions, interpretations in terms of recurrent positive selection may require reconsideration.

evolutionary biology↗

bioRxiv2020

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune systems highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable "phenomics" platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-{beta}- and TNF--driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

immunology↗

bioRxiv2020

Genomic prediction for malting quality traits in practical barley breeding programs

Genomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits (0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population

genetics↗

bioRxiv2020

The impact of purifying and background selection on the inference of population history: problems and prospects

Current procedures for inferring population history generally assume complete neutrality - that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects (DFE) and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the DFE as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.

evolutionary biology↗