Search for: All records

Land-use change may drive viral spillover from bats into humans, partly through dietary shifts caused by decreased availability of native foods and increased availability of cultivated foods. We experimentally manipulated diets of Jamaican fruit bats to investigate whether diet influences viral shedding. To reflect dietary changes experienced by wild bats during periods of nutritional stress, Jamaican fruit bats were fed either a standard diet or a putative suboptimal diet, which was deprived of protein (suboptimal-sugar diet) and/or supplemented with fat (suboptimal-fat diet). Upon H18N11 influenza A-virus infection, bats fed on the suboptimal-sugar diet shed the most viral RNA for the longest period, but bats fed the suboptimal-fat diet shed the least viral RNA for the shortest period. Bats on both suboptimal diets ate more food than the standard diet, suggesting nutritional changes may alter foraging behaviour. This study serves as an initial step in understanding whether and how dietary shifts may influence viral dynamics in bats, which alters the risk of spillover to humans. 

Abstract Herein, we rebut the critique of Patton et al. (2020), entitled, “No evidence that a transmissible cancer has shifted from emergence to endemism”, by Stammnitz et al. (2024). First and foremost, the authors do not conduct any phylogenetic or epidemiological analyses to rebut the inferences from the main results of the Patton et al. (2020) article, rendering the title of their rebuttal without evidence or merit. Additionally, Stammnitz et al. (2024) present a phylogenetic tree based on only 32 copy number variants (not typically used in phylogenetic analyses and evolve in a completely different way than DNA sequences) to “rebut” our tree that was inferred from 436.1 kb of sequence data and nearly two orders of magnitude more parsimony-informative sites (2520 SNPs). As such it is not surprising that their phylogeny did not have a similar branching pattern to ours, given that support for each branch of their tree was weak and the essentially formed a polytomy. That is, one could rotate their resulting tree in any direction and by nature, it would not match ours. While the authors are correct that we used suboptimal filtering of our raw whole genome sequencing data, re-analyses of the data with 30X coverage, as suggested, resulted in a mutation rate similar to that reported in Stammnitz et al. (2024). Most importantly, when we re-analyzed our data, as well as Stammnitz et al.’s own data, the results of the Patton et al. (2020) article are supported with both datasets. That is, the effective transmission rate of DFTD has transitioned over time to approach one, suggesting endemism; and, the spread of DFTD is rapid and omnidirectional despite the observed east-to-west wave of spread. Overall, Stammnitz et al. (2024) not only fail to provide evidence to contradict the findings of Patton et al. (2020), but rather help support the results with their own data. 

Abstract We present Atacama Compact Array (ACA) Band-3 observations of the protocluster SPT2349−56, an extreme system hosting >10 ultraluminous infrared galaxies (ULIRGs;L_IR ≳  10¹²L_⊙) in a 200 kpc diameter region atz =  4.3, to study its integrated molecular gas content via CO(4–3) and the long-wavelength dust continuum. The ∼30 hr integration represents one of the longest exposures yet taken on a single pointing with the ACA 7 m. The low-resolution ACA data (21 $\stackrel{\text{″}}{.}$ 0  ×  12 $\stackrel{\text{″}}{.}$ 2) reveal a 75% excess CO(4–3) flux compared to the sum of individual sources detected in higher-resolution Atacama Large Millimeter/submillimeter Array (ALMA) data (1 $\stackrel{\text{″}}{.}$ 0  ×  0 $\stackrel{\text{″}}{.}$ 8). Our work also reveals a similar result by tapering the ALMA data to 10″. In contrast, the 3.2 mm dust continuum shows little discrepancy between ACA and ALMA. A single-dish [Cii] spectrum obtained by APEX/FLASH supports the ACA CO(4–3) result, revealing a large excess in [Cii] emission relative to ALMA. The missing flux is unlikely due to undetected faint sources but instead suggests that high-resolution ALMA observations might miss extended and low-surface-brightness gas. Such emission could originate from the circumgalactic medium or the preheated protointracluster medium (proto-ICM). If this molecular gas reservoir replenishes the star formation fuel, the overall depletion timescale will exceed 400 Myr, reducing the requirement for the simultaneous ULIRG activity in SPT2349−56. Our results highlight the role of an extended gas reservoir in sustaining a high star formation rate in SPT2349−56 and potentially establishing the ICM during the transition phase to a mature cluster. 

Abstract We present Atacama Large Millimeter/submillimeter Array observations of the [CI] 492 and 806 GHz fine-structure lines in 25 dusty star-forming galaxies (DSFGs) atz= 4.3 in the core of the SPT2349–56 protocluster. The protocluster galaxies exhibit a median $L_{\left[\text{C}\mathrm{I}\right](2-1)}^{\prime }/L_{\left[\text{C}\mathrm{I}\right](1-0)}^{\prime }$ ratio of 0.94, with an interquartile range of 0.81–1.24. These ratios are markedly different to those observed in DSFGs in the field (across a comparable redshift and 850μm flux density range), where the median is 0.55, with an interquartile range of 0.50–0.76, and we show that this difference is driven by an excess of [Ci](2–1) in the protocluster galaxies for a given 850μm flux density. Assuming local thermal equilibrium, we estimate gas excitation temperatures of $T_{\mathrm{ex}}=59.1_{-6.8}^{+8.1}$ K for our protocluster sample and $T_{\mathrm{ex}}=33.9_{-2.2}^{+2.4}$ K for the field sample. Our main interpretation of this result is that the protocluster galaxies have had their cold gas driven to their cores via close-by interactions within the dense environment, leading to an overall increase in the average gas density and excitation temperature, as well as an elevated [Ci](2–1) luminosity-to-far-infrared-luminosity ratio. 

Synchronized seasonal excretion of multiple coronaviruses coincides with high rates of coinfection in immature bats. This repo contains instructions and source code for reproducing the statistical analyses in the manuscript. Repo Contents scripts: contains the source .R and .stan files to reproduce the anaysis. Each file is detailed below in the specific sections corresponding to the statistical analyses. data: contains the raw source data and model generated output. figures: contains the final output figures from the manuscript. These can be recreated with the CovOZ_Figures_Submission_Clean.R script. 1. System Requirements Hardware Requirements Our source code requires only a standard computer. Much of the Markov chain Monte Carlo code is run in parallel so a computer with ample memory and multiple cores can be advantageous. The runtimes below are generated using a macbook with the recommended specs (64 GB RAM, 8 cores at 2.7 GHz). The code will also work on linux or windows computer. Software Requirements Reproducing the statistical analyses requires a current version of R and stan. We use version 4.4.1 of R and version 2.32.2 of stan. Package dependencies and versions Users will need the following packages install the following packages to execute the code. Our versions are effective October 1, 2024 tidyverse 2.0.0 lubridate 1.9.3 stringr 1.5.1 rstan 2.32.6 cowplot 1.1.3 ggtext 0.1.2 jpeg 0.1-10 scales 1.3.0 tictoc 1.2.1 2. Installation Guide Running the analysis requires: installing R. Depending on wifi speeds, installing R usually takes a few minutes. installing stan. Depending on wifi speeds, installing stan usually takes a few minutes. installing the necessary R packages (listed above). Depending on wifi speeds, installing packages usually takes about 30 seconds per package. 3. Demo This source code is not an R package with a formal demo, but rather source code is included for the various analyses in section 4. 4. Instructions for Use 4.1 Coinfection Analysis Runs chi-squared tests on coinfections of beta 2d.iv and beta 2d.v. Generates summary statistics, test statistics, and p-values from manuscript. input files: individual_variant_covariates.csv script file: coinfection_final.R run time: approximately 1 second 4.2 Individual Level Dynamics of Infection: Dynamic Binary Regression Runs individual level dynamic binary regression models. Produces output file that can recreate figures. input files: individual_variant_covariates.csv script files: logistic_curves_final.R GP_regression.stan output files: logistic_curve_out.RData run time: approximately 66 minutes 4.3 Dynamics of Circulation at the Population Level Runs combined (individual and pooled data) dynamic models. Produces output file that can recreate figures. input files: combined_out_variant.csv script files: cluster_curves_final.R GP_withLL.stan output files: cluster_curves.csv run time: approximately 25 minutes 4.4 Manuscript Figures Combined script that uses output files created by previous scripts to recreate all figures in the manuscript. input files: model_output/cluster_curves.csv combined_out_variant.csv individual_variant_covariates.csv model_output/logistic_curve_out.RData script files: CovOZ_Figures_Submission_Clean.R output files: Figure2_final.png Figure3_final.png Figure4_A-D_final.png Figure6_AP.png Figure7.png SIFigure8.png SIFigure9.png run time: approximately 16 seconds 4.5 Model Comparison Integrated Compares LOOIC values for sets of model frameworks. input files: combined_out_variant.csv script files: Pred_Comparisons.R GP_withLL.stan output files: preds.RData run time: approximately 2 hours 4.6 Model Comparison Individual Compares LOOIC values for sets of model frameworks. input files: combined_out_variant.csv script files: logistic_curves_loo.R GP_regression.stan GP_regression_add.stan GP_regression_interact.stan output files: logistic_curve_loo_age.RData logistic_curve_loo_age_add_sex.RData logistic_curve_loo_age_interact_sex.RData run time: approximately 6:45 hours 

Bats are reservoirs of many zoonotic viruses that are fatal in humans but do not cause disease in bats. Moreover, bats generate low neutralizing antibody titers in response to experimental viral infection, although more robust antibody responses have been observed in wild-caught bats during times of food stress. Here, we compared the antibody titers and B cell receptor (BCR) diversity of Jamaican fruit bats (Artibeus jamaicensis; JFBs) and BALB/c mice generated in response to T-dependent and T-independent antigens. We then manipulated the diet of JFBs and challenged them with H18N11 influenza A-like virus or a replication incompetent Nipah virus VSV (Nipah-riVSV). Under standard housing conditions, JFBs generated a lower avidity antibody response and possessed more BCR mRNA diversity compared to BALB/c mice. However, withholding protein from JFBs improved serum neutralization in response to Nipah-riVSV and improved serum antibody titers specific to H18 but reduced BCR mRNA diversity. 

Abstract Understanding age structure in populations is foundational for ecological study, yet ascertaining the age of individuals in the wild can be problematic. Methods typically rely on the prior determination of a relationship between age and some morphological measurement specific to the species or animal group under investigation.We present a novel method to incorporate multiple measures of animal size and development to estimate age, using Bayesian parallel regression to integrate multiple regression relationships into predictions.We apply the method to Tasmanian devils, a carnivorous marsupial that is threatened by a transmissible cancer. We estimate devil age based on measures of mass, head width, canine over‐eruption and molar characteristics.Practical implication. The method provides a flexible framework with potential application to a range of species; however, the method is best suited to capture–mark–recapture style studies where repeated measures data have been collected for individuals across multiple points in time. 

Coevolution is common and frequently governs host–pathogen interaction outcomes. Phenotypes underlying these interactions often manifest as the combined products of the genomes of interacting species, yet traditional quantitative trait mapping approaches ignore these intergenomic interactions. Devil facial tumor disease (DFTD), an infectious cancer afflicting Tasmanian devils (Sarcophilus harrisii), has decimated devil populations due to universal host susceptibility and a fatality rate approaching 100%. Here, we used a recently developed joint genome-wide association study (i.e., co-GWAS) approach, 15 y of mark-recapture data, and 960 genomes to identify intergenomic signatures of coevolution between devils and DFTD. Using a traditional GWA approach, we found that both devil and DFTD genomes explained a substantial proportion of variance in how quickly susceptible devils became infected, although genomic architectures differed across devils and DFTD; the devil genome had fewer loci of large effect whereas the DFTD genome had a more polygenic architecture. Using a co-GWA approach, devil–DFTD intergenomic interactions explained ~3× more variation in how quickly susceptible devils became infected than either genome alone, and the top genotype-by-genotype interactions were significantly enriched for cancer genes and signatures of selection. A devil regulatory mutation was associated with differential expression of a candidate cancer gene and showed putative allele matching effects with two DFTD coding sequence variants. Our results highlight the need to account for intergenomic interactions when investigating host–pathogen (co)evolution and emphasize the importance of such interactions when considering devil management strategies.