Biomedical subjects

Jeffrey P Townsend

Publications and source records attributed to Jeffrey P Townsend.

18 recordsLinked to original sources

PubMed2026

Competing subclones and fitness diversity shape tumor evolution across cancer types.

MOTIVATION: Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. RESULTS: We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric-fitness diversity-which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

Neoplasms↗

PubMed2025

Applying multilevel selection to understand cancer evolution and progression.

Natural selection occurs at multiple levels of organization in cancer. At an organismal level, natural selection has led to the evolution of diverse tumor suppression mechanisms, while at a cellular level, it favors traits that promote cellular proliferation, survival and cancer. Natural selection also occurs at a subcellular level, among collections of cells and even among collections of organisms; selection at these levels could influence the evolution of cancer and cancer suppression mechanisms, affecting cancer risk and treatment strategies. There may also be cancer-like processes happening at different levels of organization, in which uncontrolled proliferation at lower levels may disrupt a higher level of organization. This Essay examines how selection operates across levels, highlighting how we might leverage this understanding to improve cancer research, prevention and treatment.

Animals↗

PubMed2025

Precision projections of the delay of resistance mutations in non-small cell lung cancer via suppression of APOBEC.

Genomic instability driven by stress-response-dependent mutagenesis is a key factor in cancer progression. Tyrosine kinase inhibitor therapy, a common treatment for non-small cell lung cancer, induces mutations that can facilitate the evolution of drug resistance and therapeutic failure. Here we quantified the contribution of APOBEC to mutational signatures in non-small cell lung cancer patients undergoing TKI therapy. By analyzing tumor sequence data to infer gene-specific and patient-specific trinucleotide mutation rates, we projected the potential delay of resistance obtained by suppression of APOBEC mutation. Our data-driven analysis indicates that inhibition of APOBEC activity would substantially extend therapeutic efficacy, with the degree of benefit varying based on patient-specific APOBEC mutagenesis levels. Personalized therapeutic strategies that target APOBEC offer promise for the enhancement of TKI treatment efficacy by delaying the evolution of drug resistance in lung cancer. Development of clinically safe inhibitors for use in combination with tyrosine kinase inhibitors could significantly limit tumor genetic variation and improve outcomes for non-small cell lung cancer patients.

Humans↗

PubMed2006

Eukaryotic microbes, species recognition and the geographic limits of species: examples from the kingdom Fungi.

The claim that eukaryotic micro-organisms have global geographic ranges, constituting a significant departure from the situation with macro-organisms, has been supported by studies of morphological species from protistan kingdoms. Here, we examine this claim by reviewing examples from another kingdom of eukaryotic microbes, the Fungi. We show that inferred geographic range of a fungal species depends upon the method of species recognition. While some fungal species defined by morphology show global geographic ranges, when fungal species are defined by phylogenetic species recognition they are typically shown to harbour several to many endemic species. We advance two non-exclusive reasons to explain the perceived difference between the size of geographic ranges of microscopic and macroscopic eukaryotic species when morphological methods of species recognition are used. These reasons are that microbial organisms generally have fewer morphological characters, and that the rate of morphological change will be slower for organisms with less elaborate development and fewer cells. Both of these reasons result in fewer discriminatory morphological differences between recently diverged lineages. The rate of genetic change, moreover, is similar for both large and small organisms, which helps to explain why phylogenetic species of large and small organisms show a more similar distribution of geographic ranges. As a consequence of the different rates in fungi of genetic and morphological changes, genetic isolation precedes a recognizable morphological change. The final step in speciation, reproductive isolation, also follows genetic isolation and may precede morphological change.

Classification↗

PubMed2006

Ecological and evolutionary genomics of Saccharomyces cerevisiae.

Saccharomyces cerevisiae, the budding yeast, is the most thoroughly studied eukaryote at the cellular, molecular, and genetic levels. Yet, until recently, we knew very little about its ecology or population and evolutionary genetics. In recent years, it has been recognized that S. cerevisiae occupies numerous habitats and that populations harbour important genetic variation. There is therefore an increasing interest in understanding the evolutionary forces acting on the yeast genome. Several researchers have used the tools of functional genomics to study natural isolates of this unicellular fungus. Here, we review some of these studies, and show not only that budding yeast is a prime model system to address fundamental molecular and cellular biology questions, but also that it is becoming a powerful model species for ecological and evolutionary genomics studies as well.

Ecology↗

PubMed2006

Population structure and gene evolution in Saccharomyces cerevisiae.

The fully sequenced genomes of four species within the Saccharomyces sensu stricto complex provide a wealth of information for molecular-evolutionary inference. Yet virtually nothing is known about population-genetic variation within these species, including the molecular-biological and genetic-model organism S. cerevisiae. Here we investigate the population-genetic variation and population structure of S. cerevisiae by sequencing the four loci CDC19, PHD1, FZF1 and SSU1 in 27 strains. Sequence analysis demonstrates a distinct population structure in S. cerevisiae, distinguishing strains collected from a Pennsylvanian oak forest and strains collected from vineyards, perhaps due to ecological rather than geographic factors. The low level of conflict observed between the gene trees estimated for each locus implies moderate recombination in nature. High polymorphism in the gene SSU1 provides evidence of diversifying selection on its protein product, a sulfite exporter, perhaps associated with the use of sulfur-based fungicides in vineyards. FZF1, encoding a transcription factor regulating the expression level of SSU1, displays even greater polymorphism. This, the first multilocus sequence study of population structure in natural isolates of S. cerevisiae, is the first study to demonstrate population structure within S. cerevisiae, and the first study to detect historical selection on a locus important to the natural history of wine yeast.

Cloning, Molecular↗

PubMed2005

Long-oligomer microarray profiling in Neurospora crassa reveals the transcriptional program underlying biochemical and physiological events of conidial germination.

To test the inferences of spotted microarray technology against a biochemically well-studied process, we performed transcriptional profiling of conidial germination in the filamentous fungus, Neurospora crassa. We first constructed a 70 base oligomer microarray that assays 3366 predicted genes. To estimate the relative gene expression levels and changes in gene expression during conidial germination, we analyzed a circuit design of competitive hybridizations throughout a time course using a Bayesian analysis of gene expression level. Remarkable consistency of mRNA profiles with previously published northern data was observed. Genes were hierarchically clustered into groups with respect to their expression profiles over the time course of conidial germination. A functional classification database was employed to characterize the global picture of gene expression. Consensus motif searches identified a putative regulatory component associated with genes involved in ribosomal biogenesis. Our transcriptional profiling data correlate well with biochemical and physiological processes associated with conidial germination and will facilitate functional predictions of novel genes in N.crassa and other filamentous ascomycete species. Furthermore, our dataset on conidial germination allowed comparisons to transcriptional mechanisms associated with germination processes of diverse propagules, such as teliospores of the phytopathogenic fungus Ustilago maydis and spores of the social amoeba Dictyostelium discoideum.

Animals↗

PubMed2005

Concerted evolution in the repeats of an immunomodulating cell surface protein, SOWgp, of the human pathogenic fungi Coccidioides immitis and C. posadasii.

Genome dynamics that allow pathogens to escape host immune responses are fundamental to our understanding of host-pathogen interactions. Here we present the first population-based study of the process of concerted evolution in the repetitive domain of a protein-coding gene. This gene, SOWgp, encodes the immunodominant protein in the parasitic phase of the human pathogenic fungi Coccidioides immitis and C. posadasii. We sequenced the entire gene from strains representing the geographic ranges of the two Coccidioides species. By using phylogenetic and genetic distance analyses we discovered that the repetitive part of SOWgp evolves by concerted evolution, predominantly by the mechanism of unequal crossing over. We implemented a mathematical model originally developed for multigene families to estimate the rate of homogenization and recombination of the repetitive array, and the results indicate that the pattern of concerted evolution is a result of homogenization of repeat units proceeding at a rate close to the nucleotide point mutation rate. The release of the SOWgp molecules by the pathogen during proliferation may mislead the host: we speculate that the pathogen benefits from concerted evolution of repeated domains in SOWgp by an enhanced ability to misdirect the host's immune system.

Antigens, Fungal↗

PubMed2005

Designing experiments using spotted microarrays to detect gene regulation differences within and among species.

Comparative studies of genome-wide gene expression must account for variation not only among species, but also within species. Such studies are necessarily large in scale, because they incorporate experiments on multiple individuals of multiple species in multiple developmental stages in multiple environmental conditions. If the experiments are carefully designed and performed, the data they provide are worth the effort. We describe the utility of spotted microarrays for these studies and highlight experimental design criteria that will maximize inferential and statistical power. We conclude with a discussion of experimental protocols that are designed for investigations of differential gene expression and their pitfalls.

DNA, Complementary↗

PubMed2005

Horizontal gene transfer, genome innovation and evolution.

To what extent is the tree of life the best representation of the evolutionary history of microorganisms? Recent work has shown that, among sets of prokaryotic genomes in which most homologous genes show extremely low sequence divergence, gene content can vary enormously, implying that those genes that are variably present or absent are frequently horizontally transferred. Traditionally, successful horizontal gene transfer was assumed to provide a selective advantage to either the host or the gene itself, but could horizontally transferred genes be neutral or nearly neutral? We suggest that for many prokaryotes, the boundaries between species are fuzzy, and therefore the principles of population genetics must be broadened so that they can be applied to higher taxonomic categories.

Evolution, Molecular↗

PubMed2005

A Bayesian method for analysing spotted microarray data.

In the decade since their invention, spotted microarrays have been undergoing technical advances that have increased the utility, scope and precision of their ability to measure gene expression. At the same time, more researchers are taking advantage of the fundamentally quantitative nature of these tools with refined experimental designs and sophisticated statistical analyses. These new approaches utilise the power of microarrays to estimate differences in gene expression levels, rather than just categorising genes as up- or down-regulated, and allow the comparison of expression data across multiple samples. In this review, some of the technical aspects of spotted microarrays that can affect statistical inference are highlighted, and a discussion is provided of how several methods for estimating gene expression level across multiple samples deal with these challenges. The focus is on a Bayesian analysis method, BAGEL, which is easy to implement and produces easily interpreted results.

Algorithms↗

PubMed2004

Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays.

BACKGROUND: The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge requires careful examination of the performance of a range of statistical models, as well as empirical examination of the effect of replication on the power to resolve these differences. RESULTS: New models are derived and software is developed for the analysis of microarray ratio data. These models incorporate multiplicative small error terms, and error standard deviations that are proportional to expression level. The fastest and most powerful method incorporates additive small error terms and error standard deviations proportional to expression level. Data from four studies are profiled for the degree to which they reveal statistically significant differences in gene expression. The gene expression level at which there is an empirical 50% probability of a significant call is presented as a summary statistic for the power to detect small differences in gene expression. CONCLUSIONS: Understanding the resolution of difference in gene expression that is detectable as significant is a vital component of experimental design and evaluation. These small differences in gene expression level are readily detected with a Bayesian analysis of gene expression level that has additive error terms and constrains samples to have a common error coefficient of variation. The power to detect small differences in a study may then be determined by logistic regression.

Algorithms↗

PubMed2004

Monitoring and modeling horizontal gene transfer.

Monitoring efforts have failed to identify horizontal gene transfer (HGT) events occurring from transgenic plants into bacterial communities in soil or intestinal environments. The lack of such observations is frequently cited in biosafety literature and by regulatory risk assessment. Our analysis of the sensitivity of current monitoring efforts shows that studies to date have examined potential HGT events occurring in less than 2 g of sample material, when combined. Moreover, a population genetic model predicts that rare bacterial transformants acquiring transgenes require years of growth to out-compete wild-type bacteria. Time of sampling is there-fore crucial to the useful implementation of monitoring. A population genetic approach is advocated for elucidating the necessary sample sizes and times of sampling for monitoring HGT into large bacterial populations. Major changes in current monitoring approaches are needed, including explicit consideration of the population size of exposed bacteria, the bacterial generation time, the strength of selection acting on the transgene-carrying bacteria, and the sample size necessary to verify or falsify the HGT hypotheses tested.

Ecosystem↗

PubMed2003

Multifactorial experimental design and the transitivity of ratios with spotted DNA microarrays.

BACKGROUND: Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored. RESULTS: A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design. CONCLUSIONS: In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.

Gene Expression Profiling↗

PubMed2003

Population genetic variation in genome-wide gene expression.

Evolutionary biologists seek to understand which traits display variation, are heritable, and influence differential reproduction, because such traits respond to natural selection and underlie organic evolution. Selection acts upon individual differences within a population. Whether individual differences within a natural population include variation in gene expression levels has not yet been addressed on a genome-wide scale. Here we use DNA microarray technology for measuring comparative gene expression and a refined statistical analysis for the purpose of comparing gene expression levels in natural isolates of the wine yeast Saccharomyces cerevisiae. A method for the Bayesian analysis of gene expression levels is used to compare four natural isolates of S. cerevisiae from Montalcino, Italy. Widespread variation in amino acid metabolism, sulfur assimilation and processing, and protein degradation-primarily consisting of differences in expression level smaller than a factor of 2-is demonstrated. Genetic variation in gene expression among isolates from a natural population is present on a genomic scale. It remains to be determined what role differential gene expression may play in adaptation to new or changing environments.

DNA Transposable Elements↗

PubMed2003

Horizontal acquisition of divergent chromosomal DNA in bacteria: effects of mutator phenotypes.

We examine the potential beneficial effects of the expanded access to environmental DNA offered by mutators on the adaptive potential of bacterial populations. Using parameters from published studies of recombination in E. coli, we find that the presence of mutators has the potential to greatly enhance bacterial population adaptation when compared to populations without mutators. In one specific example, for which three specific amino acid substitutions are required for adaptation to occur in a 300-amino-acid protein, we found a 3500-fold increase in the rate of adaptation. The probability of a beneficial acquisition decreased if more amino acid changes, or integration of longer DNA fragments, were required for adaptation. The model also predicts that mutators are more likely than nonmutator phenotypes to acquire genetic variability from a more diverged set of donor bacteria. Bacterial populations harboring mutators in a sequence heterogeneous environment are predicted to acquire most of their DNA conferring adaptation in the range of 13-30% divergence, whereas nonmutator phenotypes become adapted after recombining with more homogeneous sequences of 7-21% divergence. We conclude that mutators can accelerate bacterial adaptation when desired genetic variability is present within DNA fragments of up to approximately 30% divergence.

Data Interpretation, Statistical↗

PubMed2002

Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments.

BACKGROUND: Methods of microarray analysis that suit experimentalists using the technology are vital. Many methodologies discard the quantitative results inherent in cDNA microarray comparisons or cannot be flexibly applied to multifactorial experimental design. Here we present a flexible, quantitative Bayesian framework. This framework can be used to analyze normalized microarray data acquired by any replicated experimental design in which any number of treatments, genotypes, or developmental states are studied using a continuous chain of comparisons. RESULTS: We apply this method to Saccharomyces cerevisiae microarray datasets on the transcriptional response to ethanol shock, to SNF2 and SWI1 deletion in rich and minimal media, and to wild-type and zap1 expression in media with high, medium, and low levels of zinc. The method is highly robust to missing data, and yields estimates of the magnitude of expression differences and experimental error variances on a per-gene basis. It reveals genes of interest that are differentially expressed at below the twofold level, genes with high 'fold-change' that are not statistically significantly different, and genes differentially regulated in quantitatively unanticipated ways. CONCLUSIONS: Anyone with replicated normalized cDNA microarray ratio datasets can use the freely available MacOS and Windows software, which yields increased biological insight by taking advantage of replication to discern important changes in expression level both above and below a twofold threshold. Not only does the method have utility at the moment, but also, within the Bayesian framework, there will be considerable opportunity for future development.

Bayes Theorem↗

PubMed2002

Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks.

We have developed a new tool to visualize expression data on metabolic pathways and to evaluate which metabolic pathways are most affected by transcriptional changes in whole-genome expression experiments. Using the Fisher Exact Test, the method scores biochemical pathways according to the probability that as many or more genes in a pathway would be significantly altered in a given experiment by chance alone. This method has been validated on diauxic shift experiments and reproduces well known effects of carbon source on yeast metabolism. The analysis is implemented with Pathway Analyzer, one of the tools of Pathway Processor, a new statistical package for the analysis of whole-genome expression data. Results from multiple experiments can be compared, reducing the analysis from the full set of individual genes to a limited number of pathways of interest. The pathways are visualized with OpenDX, an open-source visualization software package, and the relationship between genes in the pathways can be examined in detail using Expression Mapper, the second program of the package. This program features a graphical output displaying differences in expression on metabolic charts of the biochemical pathways to which the open reading frames are assigned.

Energy Metabolism↗