Biology subjects

Drake, J. M.

Publications and source records attributed to Drake, J. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Dynamics of circulating tumor cell subsets defined by PSMA and EpCAM predict survival in metastatic castration-resistant prostate cancer patients treated with 177Lu-PSMA-617

Background: 177Lu-PSMA-617 radioligand treatment represents a major advancement in the management of metastatic castration-resistant prostate cancer (mCRPC), but key questions remain about tumor-cell dynamics and evolution in patients who have received this drug. Here, we used a circulating tumor cell (CTC) capture assay to assess the prognostic capacity of CTC subset dynamics following 177Lu-PSMA-617 treatment in a cohort of mCRPC patients. Methods: Using an AI-empowered holographic imaging platform combined with in-flow protein marker analysis, we prospectively analyzed serial CTC samples from patients receiving 177Lu-PSMA-617 therapy at pre-treatment and on-treatment time points (n=39 of 100 enrolled patients). PSMA and EpCAM proteins in CTCs were determined by immunofluorescence staining. Proportions and absolute counts of CTC subsets delineated by PSMA and EpCAM proteins were assessed in PSA50 responders (n=19) and non-responders (n=20). Within-patient changes in CTC subsets were compared using Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Associations of CTC subset abundance and dynamics with overall survival were assessed using Kaplan-Meier estimates with log-rank tests and univariable Cox proportional hazards models. Results: PSMA and EpCAM stratified CTCs into subsets, some of which were associated with survival outcomes following 177Lu-PSMA-617. We observed an increase in the proportion of PSMA-/EpCAM+ CTCs following 177Lu-PSMA-617, with a larger increase occurring in PSA50 responders (6.2% to 18.7%; p=0.041) than non-responders (7.9% to 15.4%; p=0.023). This corresponded to a decrease in the proportion of PSMA+/EpCAM- CTCs, whereas the proportion of PSMA+/EpCAM+ CTCs did not decline. On-treatment counts of >5 CTCs, relative to [≤]5 CTCs, were associated with shorter overall survival in the PSMA+/EpCAM+ (HR 3.56, 95%CI 1.32-9.60, p=0.012) and PSMA+/EpCAM- (HR 7.50, 95%CI 0.96-52.99, p=0.054) subsets. Finally, on-treatment declines in PSMA+/EpCAM+ (HR 0.74, p=0.49) and PSMA+/EpCAM- (HR 0.61, p=0.215) CTC subsets were not significantly associated with overall survival relative to sustained high levels of these cells. Conclusions: In mCRPC patients receiving 177Lu-PSMA-617, dynamic changes in CTC subsets delineated by PSMA and EpCAM expression may have prognostic value, justifying additional evaluation.

cancer biology↗

bioRxiv2025

Nonlinear effects of noise on outbreaks of mosquito-borne diseases

Mosquito-borne diseases are a significant and growing public health burden globally. Predictions about the future spread and impact of mosquito-borne disease outbreaks can help inform direct control and prevention measures. However, climate change is expected to increase weather variability, which may shape the future of mosquito-borne disease outbreaks globally. In this study, we sought to determine the effects of demographic and environmental noise (stochasticity) on the duration and size of outbreaks predicted by models of mosquito-borne disease. We developed a demographically and environmentally stochastic Ross-Macdonald model to assess how noise affects the probability of an outbreak, the peak number of cases, and the duration of outbreaks at increasing levels of the basic reproduction number (R0) and environmental noise strength. Increasing environmental noise lowers the risk of endemic disease from 100% down to almost 0%, but the largest outbreaks occur at intermediate environmental noise levels. In this case, if an outbreak dies out, it ends quickly. With noise present, R0 alone is insufficient to predict definitively whether an outbreak occurs. Surprisingly, our model suggests that increasing environmental noise may reduce the risk of endemic disease and epidemics due to more frequent extreme conditions dramatically affecting mosquito populations. Author SummaryClimate change is expected to cause drastic changes in the spread of mosquito-borne disease outbreaks, both in where they occur and in their size. A key aspect of climate change is an increase in the variability of weather factors, such as rainfall and temperature, factors that also play a crucial role in mosquito survival and reproduction. We created a mathematical model to help us understand how increases in variability might affect mosquito-borne disease outbreaks in the future. Results from our modeling suggest that, depending on current levels, future increases in environmental variability could either increase or decrease the size of future outbreaks. Our work highlights the need to better understand the connections between environmental changes and mosquito biology to inform efforts to forecast and suppress mosquito outbreaks.

ecology↗

bioRxiv2025

Proteomic profiling of extracellular vesicles distinguishes prostate cancer molecular subtypes

Prostate cancer is the most common non-cutaneous cancer among men in the United States. Most prostate cancers are driven by androgen receptor (AR) signaling, but there are an increasing number of cases that lose AR and gain neuroendocrine (NE) features (AR-/NE+) or lack both (AR-/NE-). These latter subtypes are particularly aggressive and lethal. Extracellular vesicles (EVs) have shown great potential as biomarkers for noninvasive liquid biopsy assays, as EVs contain biomolecules from their cells of origin. Here, we used a shotgun proteomics approach with mass spectrometry to interrogate the global proteome of EVs isolated from prostate cancer cell lines reflecting diverse clinical subtypes, including AR-/NE+ and AR-/NE-models. We identified 3,952 EV proteins, which clustered largely by tumor subtype and provided enough proteomic coverage to derive classic gene signatures of AR or NE identity that are of high relevance for prostate cancer prognostication. EVs isolated from AR+ cells displayed high levels of proteins regulated by AR and mTOR signaling. EVs isolated from AR-/NE+ cells contained known NE markers such as SYP and CHGA, whereas EVs from AR-/NE-models were enriched in basal cell markers and proteins that regulate epithelial-to-mesenchymal transition (EMT). We integrated our cell line data with recently published EV proteomics data from 27 advanced prostate cancer patients and found 2,733 overlapping proteins including cell surface markers relevant to prostate cancer, AR activity indicators, and proteins enriched in specific subtypes (AR+, AR-/NE-, AR-/NE+). This approach is especially promising for rare cancer subtypes, such as prostate cancers that lose AR- related features and gain NE features, so as to optimize the use of these liquid biopsy samples for clinical decision making.

cancer biology↗

bioRxiv2025

SOX2 utilizes FOXA1 as a heteromeric transcriptional partner to drive proliferation in therapy-resistant prostate cancer

Treatment options and diagnostic outlook for men with advanced, therapy resistant prostate cancer (PCa) are extremely poor; this is primarily due to the common lack of durable response to androgen receptor (AR) targeted therapies and phenotypic transdifferentiation into a particularly lethal subtype known as neuroendocrine prostate cancer (NEPC). In this study, we mechanistically determine that SOX2 (a transcription factor originally repressed by AR) physically binds and acts in a concerted manner with FOXA1 (a key AR pioneering cofactor) to regulate a subset of genes which promote cell cycle progression, and lineage plasticity in AR-refractory prostate cancers. Our findings assert the SOX2/FOXA1 interaction as an important mediator of resistance to AR-targeted therapy and a driver of NEPC and lineage plasticity; their coordinated action and downstream signaling offers a potential novel therapeutic opportunity in late-stage PCa.

cancer biology↗

bioRxiv2025

Flexible Methods for Species Distribution Modeling with Small Samples

O_LISpecies distribution models (SDMs) are used for understanding where species live or could potentially live and are a key resource for ecological research and conservation decision-making. However, current SDM methods often perform poorly for rare or inadequately sampled species, which includes most species on earth as well as most of those of the greatest conservation concern. C_LIO_LIHere, we evaluate the performance of three recently developed modeling approaches specifically designed for data-deficient situations: 1) plug-and-play modeling, 2) density-ratio modeling, and 3) environmental-range modeling. We compare the performance of these methods with Maxent, a widely used method. We compare model performance across sample sizes as well as comparisons limited to only data-poor species. We also ask to what extent model cross-validation performance on training data was correlated with model performance on independent, presence-absence data. C_LIO_LIWe show that, across all species, one or more of the plug-and-play, density-ratio, or environmental-range algorithms outperformed Maxent in 72% of cases, with three of the algorithms having AUC distributions not significantly different from Maxents. For data-poor species (those with 20 or fewer occurrences), 24 of the algorithms considered had AUC distributions that were not significantly different from Maxent. However, despite these comparable AUC scores, we found that the algorithm outputs (when thresholded to predict presence vs absence) spanned a wide gradient of sensitivity vs. specificity. Specificity and prediction accuracy assessed on training data were strongly correlated with specificity and prediction accuracy assessed on independent presence-absence data, however AUC and sensitivity had weak correlations. We found that only for 16% of species was the model that performed best on the training data the best performing model when evaluated on independent, presence-absence data. Finally, we show how ensembles of models that span the sensitivity-specificity gradient can represent model disagreement in poorly sampled species and improve model predictions. C_LIO_LIThis work supports plug-and-play, density-ratio, and environmental-range modeling as useful alternatives to Maxent, particularly for data-deficient species. While our work suggests that identifying the best model for a given species is challenging, we argue that incorporating the predictions of multiple models provides a useful way forward. C_LI Data/Code for peer reviewAnonymized data and code underlying this work are publicly available at: https://anonymous.4open.science/r/Flexible_Methods_for_Small_Sample_Size_Distribution_Modeling

ecology↗

bioRxiv2024

Fast-lived Vertebrate Hosts Exhibit Higher Potential for Mosquito-borne Parasite Transmission

The emergence of mosquito-borne zoonoses has continually increased over the past decade, posing a significant global public health challenge. Ecological theory can point to the characteristics of populations that make them more likely to form reservoirs of disease. The pace of life hypothesis posits that species with more rapid reproduction and shorter lifespans are more likely to be disease reservoirs than their slower-living cousins. Mathematical modeling suggests theoretical conditions under which this hypothesis is correct for directly- and environmentally-transmitted pathogens but its applicability to mosquito-borne disease systems has yet to be examined. We parameterized a mechanistic model with host trait data to investigate the link between the position of a species on the fast-slow life history continuum and the potential for it to spread mosquitoborne diseases. We evaluated the resulting relationships for four medically important mosquito species across their thermal niches and for four pathogens characterized by infection duration and the level of susceptibility of the focal host to infection. Finally, because the fast-slow life history continuum differs across taxonomic ranks, we considered the theory in the context of two orders of vertebrate hosts, Rodentia and Primates. After parameterizing our model, we found that, near universally across all the axes of variation considered, fast-lived hosts have higher transmission potential than slow-lived hosts. There was one exception: slower-lived hosts have higher transmission potential for parasites that cause long-enduring infections to which these hosts are highly susceptible. Generally, however, these connections hinge on the often unknown links between immunological traits, population density, and pace of life. Our analyses highlight immunological traits as a key knowledge gap with strong influence over the potential for transmission. Further investigation of the pace of life hypothesis will require a better understanding of how, for mosquito-borne parasitic infections, susceptibility and infection duration vary across the members of a taxonomic order.

ecology↗

bioRxiv2024

Understanding Ecological Systems Using Knowledge Graphs: An Application to Highly Pathogenic Avian Influenza

Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of important interactions underpinning system structure, resilience, and dynamics. Knowledge graphs have been successfully applied to organize heterogeneous data systematically and to predict new linkages representing unobserved relationships in complex systems. Though not previously applied broadly in ecology, knowledge graphs have much to offer in an era of global change when system dynamics are responding to rapid changes across multiple scales simultaneously. We developed a knowledge graph to demonstrate the methods utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad animal host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include a wide range of data related to HPAI including pathogen-host associations, animal species distributions, and human population demographics, using a semantic ontology that defines relationships within the data and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying new relationships and features of HPAI ecology, underscoring the generalizable value of knowledge graphs to ecology including their utility in revealing previously known relationships between entities and generating testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

ecology↗

bioRxiv2024

Dual inhibition of ATR and DNA-PKcs radiosensitizes ATM-mutant prostate cancer

In advanced castration resistant prostate cancer (CRPC), mutations in the DNA damage response (DDR) gene ataxia telangiectasia mutated (ATM) are common. While poly(ADP-ribose) polymerase inhibitors are approved in this context, their clinical efficacy remains limited. Thus, there is a compelling need to identify alternative therapeutic avenues for ATM mutant prostate cancer patients. Here, we generated matched ATM-proficient and ATM-deficient CRPC lines to elucidate the impact of ATM loss on DDR in response to DNA damage via irradiation. Through unbiased phosphoproteomic screening, we unveiled that ATM-deficient CRPC lines maintain dependence on downstream ATM targets through activation of ATR and DNA-PKcs kinases. Dual inhibition of ATR and DNA-PKcs effectively inhibited downstream {gamma}H2AX foci formation in response to irradiation and radiosensitized ATM-deficient lines to a greater extent than either ATM-proficient controls or single drug treatment. Further, dual inhibition abrogated residual downstream ATM pathway signaling and impaired replication fork dynamics. To circumvent potential toxicity, we leveraged the RUVBL1/2 ATPase inhibitor Compound B, which leads to the degradation of both ATR and DNA-PKcs kinases. Compound B effectively radiosensitized ATM-deficient CRPC in vitro and in vivo, and impacted replication fork dynamics. Overall, dual targeting of both ATR and DNA-PKcs is necessary to block DDR in ATM-deficient CRPC, and Compound B could be utilized as a novel therapy in combination with irradiation in these patients. Graphical AbstractFigure created with BioRender.com O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/602941v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1ac5e8borg.highwire.dtl.DTLVardef@173122dorg.highwire.dtl.DTLVardef@1ae86a5org.highwire.dtl.DTLVardef@109f4f1_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1C_FLOATNO C_FIG

cancer biology↗

bioRxiv2023

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

cancer biology↗

bioRxiv2022

Adipose triglyceride lipase is regulated by CAMKK2-AMPK signaling and drives advanced prostate cancer

Lipid metabolism plays a central role in prostate cancer. To date, the major focus on prostate cancer lipid metabolism has centered on de novo lipogenesis and lipid uptake with little consideration for how cancer cells access these lipids once they are created or taken up and stored. Patient-derived phosphoproteomics identified adipose triglyceride lipase (ATGL), a previously suspected tumor suppressor, as a CAMKK2-AMPK signaling target that, conversely, promotes castration-resistant prostate cancer (CRPC) progression. Phosphorylation of ATGL increased its lipase activity, cancer cell proliferation, migration, and invasion. Shotgun lipidomics and mass spectrometry imaging demonstrated ATGLs profound regulation of lipid metabolism in vitro and in vivo, remodeling membrane composition. Inhibition of ATGL induced metabolic plasticity, causing a glycolytic shift that could be exploited therapeutically by co-targeting both metabolic pathways. Together, these data nominate ATGL and intracellular lipolysis as potential therapeutic targets for the treatment of CRPC and provide insights for future combination therapies.

cancer biology↗

bioRxiv2021

An integrative approach to mosquito dynamics reveals differences in people's everyday experiences of mosquitoes

O_LIUrban environments are heterogeneous landscapes of social and environmental features, with important consequences for human-nature entanglements, such as that of mosquito-borne disease. C_LIO_LIInvestigations into this intra-urban heterogeneity in mosquito dynamics find conflicting results, likely due to the complex socio-ecological interactions and the importance of place-based context. Integrative research, which synthesizes multiple disciplines and epistemologies, can help place ecological results into their social context to explore these place-based differences. C_LIO_LIHere, we develop an integrative approach to understanding spatial patterns of mosquito burdens in urban systems by combining entomological surveys, semi-structured interviews, and sketch maps. C_LIO_LIAlthough we found no evidence for a difference in mosquito abundance across an urban gradient, there were differences in individuals everyday experiences with mosquitoes. These differences were mediated by how individuals moved through public space and their vulnerability to hazards in these spaces. C_LIO_LIThis example of integrative research illustrates what can be gained from the inclusion of multiple epistemologies, particularly for research in socio-ecological systems. C_LI

ecology↗

bioRxiv2021

CREB5 reprograms nuclear interactions to promote resistance to androgen receptor targeting therapies

Metastatic castration resistant prostate cancers (mCRPC) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ART). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically-approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins (RIME), we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein-protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found CREB5 was associated with TGF{beta} and Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

cancer biology↗

bioRxiv2021

Predictors of zoonotic potential in helminths

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with greater than 88% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to predicting zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modeled propensity to cause zoonosis (over 70%) that have not previously been deemed to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals.

ecology↗

bioRxiv2021

Massively parallel, computationally-guided design of a pro-enzyme

Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to re-design any chosen enzyme to be similarly stimulus-responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for pro-enzyme design. As a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of the most effective designed variants shows that they are inhibited by approximately 80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro-and catalytic domains based on the design models increases the degree of inhibition to 98%, but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing pro-enzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the pro-domain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of pro-proteins with precise spatial regulation. SignificanceProteins have shown promise as therapeutics and diagnostics, but their effectiveness is limited by our inability to spatially target their activity. To overcome this limitation, we developed a computationally-guided method to design inactive "pro-enzymes" or "zymogens," which are activated through cleavage by a protease. Since proteases are differentially expressed in various tissues and disease states, including cancer, these pro-enzymes could be targeted to the desired microenvironment. We tested our method on the therapeutically-relevant protein, carboxypeptidase G2 (CPG2). We designed Pro-CPG2s that are inhibited by 80-98% and are partially to fully re-activatable following protease treatment. The developed methodology, with further refinements, could pave the way for routinely designing protease-activated protein-based therapeutics and diagnostics that act in a spatially controlled manner.

biophysics↗

bioRxiv2021

Spatial spread of white-nose syndrome in North America, 2006-2018

White-nose syndrome has caused massive mortality in multiple bat species and spread across much of North America, making it one of the most destructive wildlife diseases on record. This has also resulted in it being one of the most well-documented wildlife disease outbreaks, making it possible to look for changes in the pattern of spatial spread over time. We fit a series of spatial interaction models to the United States county-level observations of the pathogenic fungus, Pseudogymnoascus destructans, that causes white-nose syndrome. Models included the distance between caves, cave abundance, measures of winter length and winter onset, and species richness of all bats and hibernating bats only. We found that the best supported models included all of these factors, but that the particular structure and most informative covariates changed over the course of the outbreak, with winter length displacing winter onset as the most informative measure of winter conditions, and evidence for the effects total species richness and hibernation varying from year to year. We also found that weather had detectable effects on spread. While the effect sizes for cave abundance and species richness were relatively stable over the length of the outbreak, distance became less important as time went on. These findings indicate that although models produced early in the outbreak captured important and consistent aspects of the spatial spread of white-nose syndrome, there were also changes over time in the factors associated with spread, suggesting that forecasts may be improved by iterative model refinement.

ecology↗

bioRxiv2020

Assessing temperature-dependent competition between two invasive mosquito species

O_LIInvasive mosquitoes are expanding their ranges into new geographic areas and interacting with resident mosquito species. Understanding how novel interactions can affect mosquito population dynamics is necessary to predict transmission risk at invasion fronts. Mosquito life-history traits are extremely sensitive to temperature and this can lead to temperature-dependent competition between competing invasive mosquito species. C_LIO_LIWe explored temperature-dependent competition between Aedes aegypti and Anopheles stephensi, two invasive mosquito species whose distributions overlap in India, the Middle East, and North Africa. We followed mosquito cohorts raised at different intraspecific and interspecific densities across five temperatures (16{degrees}C - 32{degrees}C) to measure traits relevant for population growth and to estimate species' per capita growth rates. We then used these growth rates to derive each species competitive ability at each temperature. C_LIO_LIWe find strong evidence for asymmetric competition at all temperatures, with Ae. aegypti emerging as the dominant competitor. This was primarily due to differences in larval survival and development times across all temperatures that resulted in a higher estimated intrinsic growth rate and competitive tolerance estimate for Ae. aegypti compared to An. stephensi. C_LIO_LISynthesis and applications: The spread of An. stephensi into the African continent could lead to urban transmission of malaria, an otherwise rural disease, increasing the human population at risk and complicating malaria elimination efforts. Competition has resulted in habitat segregation of other invasive mosquito species, and our results suggest that it may play a role in determining the distribution of An. stephensi across its invasive range. C_LI

ecology↗

bioRxiv2020

Convergent evolution of p38/MAPK activation in hormone resistant prostate cancer mediates pro-survival, immune evasive, and metastatic phenotypes

Adaptation of cancer cells to targeted therapy follows ecological paradigms observed in natural populations that encounter resource depletion and changing environments, including activation of pro-survival mechanisms, migration to new locations, and escape of predation. We identified the p38 MAPK pathway as a common molecular driver of these three responses during the adaptation to hormone therapy resistance in prostate cancer. The p38 pathway is activated in therapy-resistant cells and mechanistically drives these three convergent responses through sustained AR activity, enhanced invasion and metastasis, and immune evasion. Targeting p38 signaling may represent a new therapeutic strategy to treat men with metastatic, hormone therapy-resistant prostate cancer.

cancer biology↗

bioRxiv2020

Protective Population Behavior Change in Outbreaks of Emerging Infectious Disease

During outbreaks of emerging infections, the lack of effective drugs and vaccines increases reliance on non-pharmacologic public health interventions and behavior change to limit human-to-human transmission. Interventions that increase the speed with which infected individuals remove themselves from the susceptible population are paramount, particularly isolation and hospitalization. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are zoonotic viruses that have caused significant recent outbreaks with sustained human-to-human transmission. This investigation quantified changing mean removal rates (MRR) and days from symptom onset to hospitalization (DSOH) of infected individuals from the population in seven different outbreaks of EVD, SARS, and MERS, to test for statistically significant differences in these metrics between outbreaks. We found that epidemic week and viral serial interval were correlated with the speed with which populations developed and maintained health behaviors in each outbreak.

ecology↗