Distinct evolutionary lineages of Schistocephalus parasites infecting co-occurring sculpin and stickleback fishes in Alaska.

Sculpins (coastrange and slimy) and sticklebacks (ninespine and threespine) are widely distributed fishes cohabiting 2 south-central Alaskan lakes (Aleknagik and Iliamna), and all these species are parasitized by cryptic diphyllobothriidean cestodes in the genus Schistocephalus. The goal of this investigation was to test for host-specific parasitic relationships between sculpins and sticklebacks based upon morphological traits (segment counts) and sequence variation across the NADH1 gene. A total of 446 plerocercoids was examined. Large, significant differences in mean segment counts were found between cestodes in sculpin (mean = 112; standard deviation [s.d.] = 15) and stickleback (mean = 86; s.d. = 9) hosts within and between lakes. Nucleotide sequence divergence between parasites from sculpin and stickleback hosts was 20.5%, and Bayesian phylogenetic analysis recovered 2 well-supported clades of cestodes reflecting intermediate host family (i.e. sculpin, Cottidae vs stickleback, Gasterosteidae). Our findings point to the presence of a distinct lineage of cryptic Schistocephalus in sculpins from Aleknagik and Iliamna lakes that warrants further investigation to determine appropriate evolutionary and taxonomic recognition.

Clinical evaluation of a low-coverage whole-genome test for detecting homologous recombination deficiency in ovarian cancer.

BACKGROUND: The PAOLA-1/ENGOT-ov25 trial showed that maintenance olaparib plus bevacizumab increases survival of advanced ovarian cancer patients with homologous recombination deficiency (HRD). However, decentralized solutions to test for HRD in clinical routine are scarce. The goal of this study was to retrospectively validate on tumor samples from the PAOLA-1 trial, the decentralized SeqOne assay, which relies on shallow Whole Genome Sequencing (sWGS) to capture genomic instability and targeted sequencing to determine BRCA status. METHODS: The study comprised 368 patients from the PAOLA-1 trial. The SeqOne assay was compared to the Myriad MyChoice HRD test (Myriad Genetics), and results were analyzed with respect to Progression-Free Survival (PFS). RESULTS: We found a 95% concordance between the HRD status of the two tests (95% Confidence Interval (CI); 92%-97%). The Positive Percentage Agreement (PPA) of the sWGS test was 95% (95% CI; 91%-97%) like its Negative Percentage Agreement (NPA) (95% CI; 89%-98%). In patients with HRD-positive tumors treated with olaparib plus bevacizumab, the PFS Hazard Ratio (HR) was 0.38 (95% CI; 0.26-0.54) with SeqOne assay and 0.32 (95% CI; 0.22-0.45) with the Myriad assay. In patients with HRD-negative tumors, HR was 0.99 (95% CI; 0.68-1.42) and 1.05 (95% CI; 0.70-1.57) with SeqOne and Myriad assays. Among patients with BRCA-wildtype tumors, those with HRD-positive tumors, benefited from olaparib plus bevacizumab maintenance, with HR of 0.48 (95% CI: 0.29-0.79) and of 0.38 (95% CI: 0.23 to 0.63) with the SeqOne and Myriad assay. CONCLUSION: The SeqOne assay offers a clinically validated approach to detect HRD.

Complex eco-evolutionary responses of a foundational coastal marsh plant to global change.

Predicting the fate of coastal marshes requires understanding how plants respond to rapid environmental change. Environmental change can elicit shifts in trait variation attributable to phenotypic plasticity and act as selective agents to shift trait means, resulting in rapid evolution. Comparably, less is known about the potential for responses to reflect the evolution of trait plasticity. Here, we assessed the relative magnitude of eco-evolutionary responses to interacting global change factors using a multifactorial experiment. We exposed replicates of 32 Schoenoplectus americanus genotypes 'resurrected' from century-long, soil-stored seed banks to ambient or elevated CO2 , varying levels of inundation, and the presence of a competing marsh grass, across two sites with different salinities. Comparisons of responses to global change factors among age cohorts and across provenances indicated that plasticity has evolved in five of the seven traits measured. Accounting for evolutionary factors (i.e. evolution and sources of heritable variation) in statistical models explained an additional 9-31% of trait variation. Our findings indicate that evolutionary factors mediate ecological responses to environmental change. The magnitude of evolutionary change in plant traits over the last century suggests that evolution could play a role in pacing future ecosystem response to environmental change.

More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study.

Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell [Formula: see text] is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .

Selection and Admixture in a Polytypic Aposematic Frog.

AbstractPhenotypic differentiation within polytypic species is often attributed to selection, particularly when selection might be acting on a trait that serves as a signal for predator avoidance and mate choice. We evaluated this hypothesis by examining phenotypic and genotypic clines between populations of the strawberry poison frog Oophaga pumilio, a polytypic species that exhibits aposematic color pattern variation that is thought to be subject to both natural and sexual selection. Our aim was to assess the extent of admixture and to estimate the strength of selection acting on coloration across a region of Panama where monomorphic populations of distinctly colored frogs are separated by polymorphic populations containing both color variants alongside intermediately colored individuals. We detected sharp clinal transitions across the study region, which is an expected outcome of strong selection, but we also detected evidence of widespread admixture, even at sites far from the phenotypic transition zone. Additionally, genotypic and phenotypic clines were neither concordant nor coincident, and with one exception, selection coefficients estimated from cline attributes were small. These results suggest that strong selection is not required for the maintenance of phenotypic divergence within polytypic species, challenging the long-standing notion that strong selection is implicit in the evolution of warning signals.

The Food and Drug Administration's (FDA's) Drug Safety Surveillance During the COVID-19 Pandemic.

INTRODUCTION: On 4 February, 2020, the Secretary of the Department of Health and Human Services declared a public health emergency related to coronavirus disease 2019 (COVID-19), and on 27 March, 2020 declared circumstances existed to justify the authorization of the emergency use of drug and biological products (hereafter, "drugs") for COVID-19. At the outset of the pandemic with uncertainty relating to the virus, many drugs were being used to treat or prevent COVID-19, resulting in the US Food and Drug Administration's (FDA's) need to initiate heightened surveillance across these drugs. OBJECTIVE: We aimed to describe the FDA's approach to monitoring the safety of drugs to treat or prevent COVID-19 across multiple data sources and the subsequent actions taken by the FDA to protect public health. METHODS: The FDA conducted surveillance of adverse event and medication error data using the FDA Adverse Event Reporting System, biomedical literature, FDA-American College of Medical Toxicology COVID-19 Toxicology Investigators Consortium Pharmacovigilance Project Sub-registry, and the American Association of Poison Control Centers National Poison Data System. RESULTS: From 4 February, 2020, through 31 January, 2022, we identified 22,944 unique adverse event cases worldwide and 1052 unique medication error cases domestically with drugs to treat or prevent COVID-19. These were from the FDA Adverse Event Reporting System (22,219), biomedical literature (1107), FDA-American College of Medical Toxicology COVID-19 Toxicology Investigator's Consortium Sub-registry (638), and the National Poison Data System (32), resulting in the detection of several important safety issues. CONCLUSIONS: Safety surveillance using near real-time data was critical during the COVID-19 pandemic because the FDA monitored an unprecedented number of drugs to treat or prevent COVID-19. Additionally, the pandemic prompted the FDA to accelerate innovation, forging new collaborations and leveraging data sources to conduct safety surveillance to respond to the pandemic.

External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning.

BACKGROUND: An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient. METHODS: We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients. RESULTS: Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at [Formula: see text] but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature. CONCLUSIONS: For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.

Rapid evolution of a coastal marsh ecosystem engineer in response to global change.

There is increasing evidence that global change can alter ecosystems by eliciting rapid evolution of foundational plants capable of shaping vital attributes and processes. Here we describe results of a field-scale exposure experiment and multilocus assays illustrating that elevated CO2 (eCO2) and nitrogen (N) enrichment can result in rapid shifts in genetic and genotypic variation in Phragmites australis, an ecologically dominant plant that acts as an ecosystem engineer in coastal marshes worldwide. Compared to control treatments, genotypic diversity declined over three years of exposure, especially to N enrichment. The magnitude of loss also increased over time under conditions of N enrichment. Comparisons of genotype frequencies revealed that proportional abundances shifted with exposure to eCO2 and N in a manner consistent with expected responses to selection. Comparisons also revealed evidence of tradeoffs that constrained exposure responses, where any particular genotype responded favorably to one factor rather than to different factors or to combinations of factors. These findings challenge the prevailing view that plant-mediated ecosystem outcomes of global change are governed primarily by differences in species responses to shifting environmental pressures and highlight the value of accounting for organismal evolution in predictive models to improve forecasts of ecosystem responses to global change.

Microbial mediation of salinity stress response varies by plant genotype and provenance over time.

Although it is becoming widely appreciated that microbes can enhance plant tolerance to environmental stress, the nature of microbial mediation of exposure responses is not well understood. We addressed this deficit by examining whether microbial mediation of plant responses to elevated salinity is contingent on the environment and factors intrinsic to the host. We evaluated the influence of contrasting environmental conditions relative to host genotype, provenance and evolution by conducting a common-garden experiment utilizing ancestral and descendant cohorts of Schoenoplectus americanus genotypes recovered from two 100+ year coastal marsh seed banks. We compared S. americanus productivity and trait variation as well as associated endophytic microbial communities according to plant genotype, provenance, and age cohort under high and low salinity stress with and without native soil inoculation. The magnitude and direction of microbial mediation of S. americanus responses to elevated salinity varied according to individual genotype, provenance, as well as temporal shifts in genotypic variation and G × E (gene by environment) interactions. Relationships differed between plant traits and the structure of endosphere communities. Our findings indicate that plant-microbe associations and microbial mediation of plant stress are not only context-dependent but also dynamic. Our results additionally suggest that evolution can shape the fate of marsh ecosystems by altering how microbes confer plant tolerance to pressures linked to global change.

Stream and ocean hydrodynamics mediate partial migration strategies in an amphidromous Hawaiian goby.

Partial migration strategies, in which some individuals migrate but others do not, are widely observed in populations of migratory animals. Such patterns could arise via variation in migratory behaviors made by individual animals, via genetic variation in migratory predisposition, or simply by variation in migration opportunities mediated by environmental conditions. Here we use spatiotemporal variation in partial migration across populations of an amphidromous Hawaiian goby to test whether stream or ocean conditions favor completing its life cycle entirely within freshwater streams rather than undergoing an oceanic larval migration. Across 35 watersheds, microchemical analysis of otoliths revealed that most adult Awaous stamineus were freshwater residents (62% of n = 316 in 2009, 83% of n = 274 in 2011), but we found considerable variation among watersheds. We then tested the hypothesis that the prevalence of freshwater residency increases with the stability of stream flows and decreases with the availability of dispersal pathways arising from ocean hydrodynamics. We found that streams with low variation of daily discharge were home to a higher incidence of freshwater residents in each survey year. The magnitude of the shift in freshwater residency between survey years was positively associated with predicted interannual variability in the success of larval settlement in streams on each island based on passive drift in ocean currents. We built on these findings by developing a theoretical model of goby life history to further evaluate whether mediation of migration outcomes by stream and ocean hydrodynamics could be sufficient to explain the range of partial migration frequency observed across populations. The model illustrates that the proportion of larvae entering the ocean and differential survival of freshwater-resident versus ocean-going larvae are plausible mechanisms for range-wide shifts in migration strategies. Thus, we propose that hydrologic variation in both ocean and stream environments contributes to spatiotemporal variation in the prevalence of migration phenotypes in A. stamineus. Our empirical and theoretical results suggest that the capacity for partial migration could enhance the persistence of metapopulations of diadromous fish when confronted with variable ocean and stream conditions.

Accounting for variability when resurrecting dormant propagules substantiates their use in eco-evolutionary studies.

There has been a steady rise in the use of dormant propagules to study biotic responses to environmental change over time. This is particularly important for organisms that strongly mediate ecosystem processes, as changes in their traits over time can provide a unique snapshot into the structure and function of ecosystems from decades to millennia in the past. Understanding sources of bias and variation is a challenge in the field of resurrection ecology, including those that arise because often-used measurements like seed germination success are imperfect indicators of propagule viability. Using a Bayesian statistical framework, we evaluated sources of variability and tested for zero-inflation and overdispersion in data from 13 germination trials of soil-stored seeds of Schoenoplectus americanus, an ecosystem engineer in coastal salt marshes in the Chesapeake Bay. We hypothesized that these two model structures align with an ecological understanding of dormancy and revival: zero-inflation could arise due to failed germinations resulting from inviability or failed attempts to break dormancy, and overdispersion could arise by failing to measure important seed traits. A model that accounted for overdispersion, but not zero-inflation, was the best fit to our data. Tetrazolium viability tests corroborated this result: most seeds that failed to germinate did so because they were inviable, not because experimental methods failed to break their dormancy. Seed viability declined exponentially with seed age and was mediated by seed provenance and experimental conditions. Our results provide a framework for accounting for and explaining variability when estimating propagule viability from soil-stored natural archives which is a key aspect of using dormant propagules in eco-evolutionary studies.

A century-long record of plant evolution reconstructed from a coastal marsh seed bank.

Evidence is mounting that climate-driven shifts in environmental conditions can elicit organismal evolution, yet there are sparingly few long-term records that document the tempo and progression of responses, particularly for plants capable of transforming ecosystems. In this study, we "resurrected" cohorts of a foundational coastal marsh sedge (Schoenoplectus americanus) from a time-stratified seed bank to reconstruct a century-long record of heritable variation in response to salinity exposure. Common-garden experiments revealed that S. americanus exhibits heritable variation in phenotypic traits and biomass-based measures of salinity tolerance. We found that responses to salinity exposure differed among the revived cohorts, with plants from the early 20th century exhibiting greater salinity tolerance than those from the mid to late 20th century. Fluctuations in salinity tolerance could reflect stochastic variation but a congruent record of genotypic variation points to the alternative possibility that the loss and gain in functionality are driven by selection, with comparisons to historical rainfall and paleosalinity records suggesting that selective pressures vary according to shifting estuarine conditions. Because salinity tolerance in S. americanus is tightly coupled to primary productivity and other vital ecosystem attributes, these findings indicate that organismal evolution merits further consideration as a factor shaping coastal marsh responses to climate change.

Invasion and rapid adaptation of guppies (Poecilia reticulata) across the Hawaiian Archipelago.

How much does natural selection, as opposed to genetic drift, admixture, and gene flow, contribute to the evolution of invasive species following introduction to a new environment? Here we assess how evolution can shape biological invasions by examining population genomic variation in non-native guppies (Poecilia reticulata) introduced to the Hawaiian Islands approximately a century ago. By examining 18 invasive populations from four Hawaiian islands and four populations from the native range in northern South America, we reconstructed the history of introductions and evaluated population structure as well as the extent of ongoing gene flow across watersheds and among islands. Patterns of differentiation indicate that guppies have developed significant population structure, with little natural or human-mediated gene flow having occurred among populations following introduction. Demographic modeling and admixture graph analyses together suggest that guppies were initially introduced to O'ahu and Maui and then translocated to Hawai'i and Kaua'i. We detected evidence for only one introduction event from the native range, implying that any adaptive evolution in introduced populations likely utilized the genetic variation present in the founding population. Environmental association tests accounting for population structure identified loci exhibiting signatures of adaptive variation related to predators and landscape characteristics but not nutrient regimes. When paired with high estimates of effective population sizes and detectable population structure, the presence of environment-associated loci supports the role of natural selection in shaping contemporary evolution of Hawaiian guppy populations. Our findings indicate that local adaptation may engender invasion success, particularly in species with life histories that facilitate rapid evolution. Finally, evidence of low gene flow between populations suggests that removal could be an effective approach to control invasive guppies across the Hawaiian archipelago.

Intraspecific variation in landform engineering across a restored salt marsh shoreline.

Ecosystem engineers that modify landforms can be valuable tools for restoring habitat, but their use has frequently resulted in unanticipated outcomes. Departures from expectations might arise because applications discount the possibility that geomorphic processes are influenced by heritable phenotypic variation. We conducted a field-scale common garden experiment to assess whether shoreline erosion reflects intraspecific variation in the landform engineer Spartina alterniflora. Replicated plots on a shoreline denuded by the Deepwater Horizon oil spill were revegetated using plants from four genetically distinct sources: the local population, a nonlocal population, and two nursery stocks. We assessed variation in biomass, tissue nutrients, and functional traits alongside soil shear strength, surface elevation, and shoreline erosion rates over 2 years. We found that productivity, traits, nutrient content, and erosion rates varied according to plant provenance. Erosion reflected traits like root architecture more so than coarser metrics of growth. Erosion was significantly higher in plots with nonlocal plants that exhibited lower productivity, likely due to nitrogen limitation. Our results indicate that restoration practices should account for intraspecific variation in landform engineers and that in situ trials should be performed at sites slated for restoration to evaluate donor source suitability, particularly if introductions might modify local populations.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

Taste perception and lifestyle: insights from phenotype and genome data among Africans and Asians.

Taste is essential for the interaction of animals with their food and has co-evolved with diet. Humans have peopled a large range of environments and present a wide range of diets, but little is known about the diversity and evolution of human taste perception. We measured taste recognition thresholds across populations differing in lifestyles (hunter gatherers and farmers from Central Africa, nomad herders, and farmers from Central Asia). We also generated genome-wide genotype data and performed association studies and selection scans in order to link the phenotypic variation in taste sensitivity with genetic variation. We found that hunter gatherers have lower overall sensitivity as well as lower sensitivity to quinine and fructose than their farming neighbors. In parallel, there is strong population divergence in genes associated with tongue morphogenesis and genes involved in the transduction pathway of taste signals in the African populations. We find signals of recent selection in bitter taste-receptor genes for all four populations. Enrichment analysis on association scans for the various tastes confirmed already documented associations and revealed novel GO terms that are good candidates for being involved in taste perception. Our framework permitted us to gain insight into the genetic basis of taste sensitivity variation across populations and lifestyles.

Amplification of pathogenic Leptospira infection with greater abundance and co-occurrence of rodent hosts across a counter-urbanizing landscape.

Land use change can elevate disease risk by creating conditions beneficial to species that carry zoonotic pathogens. Observations of concordant global trends in increased pathogen prevalence or disease incidence and landscape change have generated concerns that urbanization could increase transmission risk of some pathogens. Yet host-pathogen relationships underlying transmission risk have not been well characterized within cities, even where contact between humans and species capable of transmitting pathogens of concern occurs. We addressed this deficit by testing the hypothesis that areas in cities experiencing greater population loss and infrastructure decline (i.e., counter-urbanization) can support a greater diversity of host species and a larger and more diverse pool of pathogens. We did so by characterizing pathogenic Leptospira infection relative to rodent host richness and abundance across a mosaic of abandonment in post-Katrina New Orleans (Louisiana, USA). We found that Leptospira infection loads were highest in areas that harboured increased rodent species richness (which ranged from one to four rodent species detected). Areas with greater host co-occurrence also harboured a greater abundance of hosts, including the host species most likely to carry high infection loads, indicating that Leptospira infection can be amplified by increases in overall and relative host abundance. Evidence of shared infection among rodent host species indicates that cross-species transmission of Leptospira probably increases infection at sites with greater host richness. Additionally, evidence that rodent co-occurrence and abundance and Leptospira infection load parallel abandonment suggests that counter-urbanization can elevate zoonotic disease risk within cities, particularly in underserved communities that are burdened with disproportionate concentrations of derelict properties.