Biology subjects

Johnson, K. E.

Publications and source records attributed to Johnson, K. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Maternal diet and genetics shape the human milk metabolome

Human milk contains a diverse array of metabolites that contribute to infant nutrition, immune development, and microbial colonization. The maternal factors shaping the milk metabolome, and the relative contribution of genetics or diet vs. other factors, remain poorly understood. Here, we profiled 458 milk metabolites in 349 one-month postpartum human milk samples and integrated metabolomic data with maternal diet, clinical, transcriptomic, and genomic measurements. Maternal diet was broadly associated with milk metabolite composition, with significant correlations identified between dietary features and 323 metabolites. Coffee consumption strongly predicted milk quinic acid and 1,3-dimethyluric acid abundance, while high-fiber dietary patterns were associated with metabolites including proline-betaine and N-acetylornithine. Integration of milk transcriptomic and metabolomic data via machine learning identified biologically plausible gene-metabolite pairs, including associations between QPRT expression and quinolinic acid, and DPEP1 and cysteine-glycine dipeptide. Genome-wide association analyses identified nine study-wide significant metabolite quantitative trait loci, including novel milk-specific associations near PDE6A affecting purine metabolites and near GNE affecting free sialic acid. Comparison with plasma metabolite studies demonstrated both shared and milk-specific genetic regulation of metabolites. Finally, we found that of all tested maternal features, diet explained the largest proportion of variation in the milk metabolome. Together, these findings demonstrate that the human milk metabolome reflects both maternal exposures and mammary gland-specific biology. This work establishes a framework for understanding how genetic and environmental factors shape milk composition.

genomics↗

bioRxiv2026

A pectin-based formulation protects milk fat globule membranes in stored human milk

During household storage, expressed human milk can develop odor and flavor changes that trigger infant refusal and lead caregivers to discard their saved milk supply. We show that typical refrigeration and freezing conditions disrupt the milk fat globule membrane (MFGM), exposing milk lipids to lipases that catalyze hydrolysis and oxidation. A pectin-based formulation (PBF) maintains MFGM integrity during storage and following lipase challenge, suppressing production of glycerol, free fatty acid, and oxylipin byproducts without broadly affecting milk macronutrients, the proteome, and culturable microbial burden. Across an independent cohort of lactating individuals, lipase activity varied but tracked with maternal milk lipase gene expression, implicating endogenous lipolysis in stored-milk deterioration. In a blinded olfactory panel, PBF-treated, lipase-challenged milk smelled more like fresh milk than untreated controls. Together, these findings show that stabilizing the MFGM can protect stored human milk from lipase-driven deterioration, preserve sensory quality, and support use for infant feeding.

biochemistry↗

bioRxiv2026

Repurposing a chromosome segregation ParB-CTPase fold into an ATPase toxin for contact-dependent growth inhibition in plant and animal pathogens

Bacterial competition drives the evolution of antibacterial mechanisms, yet how new activities arise remains poorly understood. A major route to innovation is the reuse of pre-existing genetic systems, whereby conserved protein modules are repurposed in new biological contexts to generate new capabilities. Here, we show that the ParB-CTPase fold, a conserved nucleotide-binding module best known for its role in chromosome segregation, can be functionally repurposed as an antibacterial toxin. We identify ToxB, a ParB-like domain embedded within the polymorphic toxin region of contact-dependent inhibition systems and show that it functions as a potent antibacterial effector. Structural and biochemical analyses reveal that ToxB retains the core architecture of the ParB-CTPase fold but lacks DNA-binding capability and preferentially binds ATP. This shift in nucleotide specificity underpins a distinct mode of action, in which ATP binding and hydrolysis trigger rapid nucleoid compaction, chromosome segregation defects, oxidative stress, cell chaining, and ultimately cell lysis. ToxB also exhibits toxic activity in plant cells, suggesting that it targets conserved cellular processes. Together, these findings provide direct experimental evidence that the ParB-NTPase fold is biologically versatile and can be repurposed for biological roles fundamentally distinct from its ancestral function in DNA segregation.

microbiology↗

bioRxiv2023

Human Cytomegalovirus in breast milk is associated with milk composition, the infant gut microbiome, and infant growth

Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the hundreds of bioactive factors in human milk has not been tested. Here, we utilize a cohort of exclusively breastfeeding full term mother-infant pairs to test for differences in the milk transcriptome and metabolome associated with CMV, and the impact of CMV in breast milk on the infant gut microbiome and infant growth. We find upregulation of the indoleamine 2,3-dioxygenase (IDO) tryptophan-to-kynurenine metabolic pathway in CMV+ milk samples, and that CMV+ milk is associated with decreased Bifidobacterium in the infant gut. Our data indicate a complex relationship between milk CMV, milk kynurenine, and infant growth; with kynurenine positively correlated, and CMV viral load negatively correlated, with infant weight-for-length at 1 month of age. These results suggest CMV transmission, CMV-related changes in milk composition, or both may be modulators of full term infant development.

genomics↗

bioRxiv2023

Boldine modulates glial transcription and functional recovery in a murine model of contusion spinal cord injury

Membrane channels such as connexins (Cx), pannexins (Panx) and P2X7 receptors (P2X7R) are permeable to calcium ions and other small molecules such as ATP and glutamate. Release of ATP and glutamate through these channels is a key mechanism driving tissue response to traumas such as spinal cord injury (SCI). Boldine, an alkaloid isolated from the Chilean boldo tree, blocks both Cx hemichannels (HC) and Panx. To test if boldine could improve function after SCI, boldine or vehicle was administered to treat mice with a moderate severity contusion-induced SCI. Boldine led to greater spared white matter and increased locomotor function as determined by the Basso Mouse Scale and horizontal ladder rung walk tests. Boldine treatment reduced immunostaining for markers of activated microglia (Iba1) and astrocytic (GFAP) markers while increasing that for axon growth and neuroplasticity (GAP-43). Cell culture studies demonstrated that boldine blocked glial HC, specifically Cx26 and Cx30, in cultured astrocytes and blocked calcium entry through activated P2X7R. RT-qPCR studies showed that boldine treatment reduced expression of the chemokine Ccl2, cytokine IL-6 and microglial gene CD68, while increasing expression of the neurotransmission genes Snap25 and Grin2b, and Gap-43. Bulk RNA sequencing (of the spinal cord revealed that boldine modulated a large number of genes involved in neurotransmission in in spinal cord tissue just below the lesion epicenter at 14 days after SCI. Numbers of genes regulated by boldine was much lower at 28 days after injury. These results indicate that boldine treatment ameliorates injury and spares tissue to increase locomotor function.

neuroscience↗

bioRxiv2023

Human milk variation is shaped by maternal genetics and impacts the infant gut microbiome

Human milk is a complex mix of nutritional and bioactive components that provide complete nutrition for the infant. However, we lack a systematic knowledge of the factors shaping milk composition and how milk variation influences infant health. Here, we used multi-omic profiling to characterize interactions between maternal genetics, milk gene expression, milk composition, and the infant fecal microbiome in 242 exclusively breastfeeding mother-infant pairs. We identified 487 genetic loci associated with milk gene expression unique to the lactating mammary gland, including loci that impacted breast cancer risk and human milk oligosaccharide concentration. Integrative analyses uncovered connections between milk gene expression and infant gut microbiome, including an association between the expression of inflammation-related genes with IL-6 concentration in milk and the abundance of Bifidobacteria in the infant gut. Our results show how an improved understanding of the genetics and genomics of human milk connects lactation biology with maternal and infant health.

genomics↗

bioRxiv2022

Increased fatty acid metabolism and decreased glycolysis are hallmarks of metabolic reprogramming in the brain during recovery from experimental stroke

The goal of this study was to evaluate changes in metabolic homeostasis during the first 12 weeks of recovery in a distal middle cerebral artery occlusion mouse model of stroke. To achieve this goal, we compared the brain metabolomes of ipsilateral and contralateral hemispheres from aged male mice up to 12 weeks after stroke to that of age-matched naive and sham operated mice. There were 707 biochemicals detected in each sample by liquid chromatography-mass spectroscopy (LC-MS). Mitochondrial fatty acid {beta}-oxidation, indicated by acyl carnitine levels, was increased in stroked tissue at 1 day and 4 weeks following stroke. Glucose and several glycolytic intermediates were elevated in the ipsilateral hemisphere for 12 weeks compared to the aged naive controls, but pyruvate was decreased. Additionally, itaconate, a glycolysis inhibitor associated with activation of anti-inflammatory mechanisms in myeloid cells, was higher in the same comparisons. These changes correlated with reduced levels of glutamate, dopamine, and adenosine in the ipsilateral hemisphere after stroke. These results indicate that chronic metabolic differences exist between stroked and control tissue, including alterations in fatty acid metabolism and glycolysis for at least 12 weeks after stroke.

neuroscience↗

bioRxiv2022

A heterogeneous drug tolerant persister state in BRAF-mutant melanoma is characterized by ion channel dysregulation and susceptibility to ferroptosis

Drug-tolerant persisters (DTPs) represent a major obstacle to durable responses in targeted cancer therapy. DTPs are commonly described as distinct single-cell states that survive drug treatment via reversible, non-genetic mechanisms and drive tumor recurrence. Recent work demonstrates that multiple DTPs can coexist, reflecting diversity in lineage, signaling programs, or stress responses. However, each DTP is still generally viewed as a uniform cellular phenotype. Building on our prior work describing a population-level DTP termed "idling" [Paudel et al., Biophys. J. (2018) 114, 1499-1511], here we present evidence supporting a fundamentally different view: that DTPs are not single-cell states, but rather heterogeneous populations composed of multiple sub-states with distinct division and death rates that balance to produce near-zero net population growth. Using single-cell transcriptomics and lineage barcoding, we identify multiple phenotypic states within idling DTP populations, with reduced heterogeneity compared to untreated populations, and find that idling DTP cells emerge from nearly all lineages. Transcriptomic and functional analyses further reveal altered ion-channel activity in idling DTPs, which we confirm experimentally. Moreover, drug-response assays reveal increased susceptibility of idling DTPs to ferroptosis, a non-apoptotic form of regulated cell death, indicating the emergence of vulnerabilities associated with drug tolerance. Altogether, our results support a population-level view of tumor drug tolerance in which DTPs comprise stable collections of phenotypic states, shaped by treatment-defined phenotypic landscapes, which are potentially vulnerable to subsequent interventions. This perspective implies that eradicating DTPs will require a fundamental shift away from cell-type-centric strategies toward sequential treatments that progressively reduce phenotypic heterogeneity by modulating the molecular and cellular processes that establish the DTP landscape, an approach previously termed "targeted landscaping."

systems biology↗

bioRxiv2021

Diversity and selection of SARS-CoV-2 minority variants in the early New York City outbreak

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller, and use of replicate sequencing have the most significant impact on single nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false negative rates. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intrahost viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intrahost variation, viral diversity, and viral evolution. IMPORTANCEWhen viruses replicate inside a host, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus, nor strongly beneficial, can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in inclusion of false positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant calling tools. We used simulated and synthetic data to test their performance against a true set of variants, and then used these studies to inform variant identification in data from clinical SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

microbiology↗

bioRxiv2020

The industrial solvent trichloroethylene induces LRRK2 kinase activity and dopaminergic neurodegeneration in a rat model of Parkinson's disease

Gene-environment interaction is implicated in the majority of idiopathic Parkinsons disease (PD) risk, and some of the most widespread environmental contaminants are selectively toxic to dopaminergic neurons. Pesticides have long been connected to PD incidence, however, it has become increasingly apparent that other industrial byproducts likely influence neurodegeneration. For example, organic solvents, which are used in chemical, machining, and dry-cleaning industries, are of growing concern, as decades of solvent use and their effluence into the environment has contaminated much of the worlds groundwater and soil. Like some pesticides, certain organic solvents, such as the chlorinated halocarbon trichloroethylene (TCE), are mitochondrial toxicants, which are collectively implicated in the pathogenesis of dopaminergic neurodegeneration. Recently, we hypothesized a possible gene-environment interaction may occur between environmental mitochondrial toxicants and the protein kinase LRRK2, mutations of which are the most common genetic cause of familial and sporadic PD. In addition, emerging data suggests that elevated wildtype LRRK2 kinase activity also contributes to the pathogenesis of idiopathic PD. To this end, we investigated whether chronic, systemic TCE exposure (200 mg/kg) in aged rats produced wildtype LRRK2 activation and influenced predegenerative dopaminergic dysfunction. Interestingly, we found that TCE not only induced LRRK2 kinase activity in the brain, but produced a significant dopaminergic lesion in the nigrostriatal tract, elevated oxidative stress, and caused endolysosomal dysfunction and protein accumulation (-synuclein). Together, these data suggest that TCE-induced LRRK2 kinase activity contributed to the selective toxicity of dopaminergic neurons. We conclude that gene-environment interactions between certain industrial contaminants and LRRK2 likely influence PD risk.

pharmacology and toxicology↗

bioRxiv2020

Identifying non-identical-by-descent rare variants in population-scale whole genome sequencing data

The site frequency spectrum in human populations is not accurately modeled by an infinite sites model, which assumes that all mutations are unique. Despite the pervasiveness of recurrent mutations, we lack computational methods to identify these events at specific sites in population sequencing data. Rare alleles that are identical-by-descent (IBD) are expected to segregate on a long, shared haplotype background that descends from a common ancestor. However, alleles introduced by recurrent mutation or by non-crossover gene conversions are identical-by-state and will have a shorter expected shared haplotype background. We hypothesized that the expected difference in shared haplotype background length can distinguish IBD and non-IBD variants in population sequencing data without pedigree information. We implemented a Bayesian hierarchical model and used Gibbs sampling to estimate the posterior probability of IBD state for rare variants, using simulations to demonstrate that our approach accurately distinguishes rare IBD and non-IBD variants. Applying our method to whole genome sequencing data from 3,621 individuals in the UK10K consortium, we found that non-IBD variants correlated with higher local mutation rates and genomic features like replication timing. Using a heuristic to categorize non-IBD variants as gene conversions or recurrent mutations, we found that potential gene conversions had expected properties such as enriched local GC content. By identifying recurrent mutations, we can better understand the spectrum of recent mutations in human populations, a source of genetic variation driving evolution and a key factor in understanding recent demographic history.

genetics↗

bioRxiv2020

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

cancer biology↗

bioRxiv2019

Mapping the evolutionary landscape of Zika virus infection in immunocompromised mice.

The fundamental basis of how arboviruses evolve in nature and what regulates the adaptive process remain unclear. To address this problem, we established a Zika virus (ZIKV) vector-borne transmission system in immunocompromised mice that mimics evolutionary characteristics of ZIKV infection in humans. Using this system, we defined factors that influence the evolutionary landscape of ZIKV infection and show that transmission route and specific organ microenvironments impact viral diversity and defective viral genome (DVG) production. In addition, we identified in mice the emergence of ZIKV mutants previously seen in natural infections, including variants present in currently circulating strains, as well as mutations unique to the mouse infections. With these studies, we have established an insect-to-mouse transmission model to study ZIKV evolution. We also defined how organ microenvironments and infection route impact the ZIKV evolutionary landscape, providing a deeper understanding of the factors that regulate arbovirus evolution and emergence.

microbiology↗

bioRxiv2019

Assessing a causal relationship between circulating lipids and breast cancer risk via Mendelian randomization

ObjectiveTo assess a potential causal relationship between genetic variants associated with plasma lipid traits (high-density lipoprotein cholesterol, HDL; low-density lipoprotein cholesterol, LDL; triglycerides, TG) with risk for breast cancer.\n\nDesignMendelian randomization (MR) study.\n\nSetting and ParticipantsData from genome-wide association studies in up to 215,551 subjects from the Million Veterans Project were used to construct genetic instruments for plasma lipid traits. The effect of these instruments on breast cancer risk was evaluated using genetic data from the BCAC consortium based on 122,977 breast cancer cases and 105,974 controls.\n\nExposuresGenetically modified plasma levels of LDL, HDL, or TG.\n\nMain Outcomes and MeasuresOdds ratio (OR) for breast cancer risk per standard-deviation increase in HDL, LDL, or TG.\n\nResultsWe observed that a 1-SD genetically determined increase in HDL levels is associated with an increased risk for all breast cancers (HDL: OR=1.08, 95% CI=1.04-1.13, P=7.4x10-5).\n\nMultivariable MR analysis, which adjusted for the effects of LDL, TG, body mass index, and age at menarche, corroborated this observation for HDL (OR=1.06, 95% CI=1.03-1.10, P=4.9x10-4) and also a relationship between LDL and breast cancer risk (OR=1.03, 95% CI=1.01-1.07, P=0.02). We did not observe a difference in these relationships when stratified by breast tumor estrogen receptor status. We repeated this analysis using genetic variants independent of the leading association at core HDL pathway genes and found that these variants were also associated with risk for breast cancers (OR=1.11, 95% CI=1.06-1.16, P=1.5x10-6), including gene-specific associations at ABCA1, APOE-APOC1-APOC4-APOC2 and CETP. In addition, we find evidence that genetic variation at the ABO locus affects both lipid levels and breast cancer.\n\nConclusionsGenetically elevated plasma HDL levels appear to increase breast cancer risk. Future studies are required to understand the mechanism underlying this putative causal relationship, with the goal to develop potential therapeutic strategies aimed at altering the HDL-mediated effect on breast cancer risk.

genomics↗

bioRxiv2019

Expressed barcodes enable clonal characterization of chemotherapeutic responses in chronic lymphocytic leukemia

The remarkable evolutionary capacity of cancer is a major challenge to current therapeutic efforts. Fueling this evolution is its vast clonal heterogeneity and ability to adapt to diverse selective pressures. Although the genetic and transcriptional mechanisms underlying these responses have been independently evaluated, the ability to couple genetic alterations present within individual clones to their respective transcriptional or functional outputs has been lacking in the field. To this end, we developed a high-complexity expressed barcode library that integrates DNA barcoding with single-cell RNA sequencing through use of the CROP-seq sgRNA expression/capture system, and which is compatible with the COLBERT clonal isolation workflow for subsequent genomic and epigenomic characterization of specific clones of interest. We applied this approach to study chronic lymphocytic leukemia (CLL), a mature B cell malignancy notable for its genetic and transcriptomic heterogeneity and variable disease course. Here, we demonstrate the clonal composition and gene expression states of HG3, a CLL cell line harboring the common alteration del(13q), in response to front-line cytotoxic therapy of fludarabine and mafosfamide (an analog of the clinically used cyclophosphamide). Analysis of clonal abundance and clonally-resolved single-cell RNA sequencing revealed that only a small fraction of clones consistently survived therapy. These rare highly drug tolerant clones comprise 94% of the post-treatment population and share a stable, pre-existing gene expression state characterized by upregulation of CXCR4 and WNT signaling and a number of DNA damage and cell survival genes. Taken together, these data demonstrate at unprecedented resolution the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population. Further, this approach provides a template for the high-resolution study of thousands of clones and the respective gene expression states underlying their response to therapy.

cancer biology↗

bioRxiv2019

Stochastic Models for Revealing the Dynamics of the Growth of Small Tumor Populations

The widely accepted model of tumor growth assumes tumors grow exponentially at a constant rate during early tumorigenesis when populations are small. The possibility that tumors might exhibit altered slower growth dynamics or even net cell death below a critical tumor size has yet to be fully explored. Deterministic growth models are capable of describing larger populations because population variation becomes small compared with the average, but when the population being modeled is small, the inherent stochasticity of the birth and death process produces significant variation. Recent advances in high throughput data collection allow for precise and sufficiently large data sets needed to capture this variation. Therefore, we present a stochastic modeling framework to describe and test the potential for altered growth dynamics at small tumor populations.

cancer biology↗

bioRxiv2019

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations from exponential growth only at higher densities due to limited resources such as space and nutrients. However, recent pre-clinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models however, tumor growth data is limited by the lower limit of detection (i.e. a measurable lesion) and confounding variables, such as tumor microenvironment and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to consider the small number of cells in this low-density regime and use the moment approach for stochastic parameter estimation to calibrate the stochastic growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells depends on cell number. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging tumors and relapse.\n\nAuthor SummaryThe growth kinetics of cancer cells at very low cell densities are of utmost clinical importance as the ability of a small number of newly transformed or surviving cells to grow exponentially and thus, to \"take off\" underlies tumor formation and relapse after treatment. Mathematical models of stochastic tumor cell growth typically assume a stochastic birth-death process of cells impacted by limited nutrients and space when cells reach high density, resulting in the widely accepted logistic growth model. Here we present an in-depth investigation of alternate growth models adopted from ecology to describe potential deviations from a simple cell autonomous birth-death model at low cell densities. We show that our stochastic modeling framework is robust and can be used to identify the underlying structure of stochastic growth trajectories from both simulated and experimental data taken from a controlled in vitro setting in which we can capture data from the relevant low cell density regime. This work suggests that the assumption of cell autonomous proliferation via a constant exponential growth rate at low cell densities may not be appropriate for all cancer cell growth dynamics. Consideration of cooperative behavior amongst tumor cells in this regime is critical for elucidating strategies for controlling tumor cell growth.

cancer biology↗

bioRxiv2018

Signals of variation in human mutation rate at multiple levels of sequence context

Our understanding of mutation rate helps us build evolutionary models and make sense of genetic variation. Recent work indicates that the frequencies of specific mutation types have been elevated in Europe, and that many more, subtler signatures of global polymorphism variation may yet remain unidentified. Here, we present an analysis of the 1,000 Genomes Project (phase 3), suggesting additional putative signatures of mutation rate variation across populations and the extent to which they are shaped by local sequence context. First, we compiled a list of the most significantly variable polymorphism types in a cross-continental statistical test. Clustering polymorphisms together, we observed four sets of substitution types that showed similar trends of relative mutation rate across populations, and describe the patterns of these mutational clusters among continental groups. For the majority of these signatures, we found that a single flanking base pair of sequence context was sufficient to determine the majority of enrichment or depletion of a mutation type. However, local genetic context up to 2-3 base pairs away contributes additional variability, and helps to interpret a previously noted enrichment of certain polymorphism types in some East Asian groups. Building our understanding of mutation rate in this way can help us to construct more accurate evolutionary models and better understand the mechanisms that underlie genetic change.

genomics↗