Otolith morphogenesis during the early life stages of fish is temperature-dependent: Validation by experimental approach applied to European seabass (Dicentrarchus labrax).

Otolith shape is often used as a tool in fish stock identification. The goal of this study was to experimentally assess the influence of changing temperature and ontogenic evolution on the shape component of the European seabass (Dicentrarchus labrax) otolith during early-life stages. A total of 1079 individuals were reared in a water temperature of 16°C up to 232 days post hatch (dph). During this experiment, several specimens were transferred into tanks with a water temperature of 21°C to obtain at the end of this study four different temperature treatments, each with varying ratios between the number of days at 16 and 21°C. To evaluate the otolith morphogenesis, samples were examined at 43, 72, 86 and 100 dph. The evolution of normalized otolith shape from hatching up to 100 dph showed that there were two main successive changes. First, faster growth in the antero-posterior axis than in the dorso-ventral axis changed the circular-shaped otolith from that observed at hatching and, second, increasing the complexity relating to the area between the rostrum and the anti-rostrum. To test the effect of changing temperature, growing degree-day was used in three linear mixed-effect models. Otolith morphogenesis was positively correlated to growing degree-day, but was also dependent on temperature level. Otolith shape is influenced by environmental factors, particularly temperature, making it an efficient tool for fish stock identification.

Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness.

In quantitative proteomics, mass tag labeling techniques have been widely adopted in mass spectrometry experiments. These techniques allow peptides (short amino acid sequences) and proteins from multiple samples of a batch being detected and quantified in a single experiment, and as such greatly improve the efficiency of protein profiling. However, the batch-processing of samples also results in severe batch effects and non-ignorable missing data occurring at the batch level. Motivated by the breast cancer proteomic data from the Clinical Proteomic Tumor Analysis Consortium, in this work, we developed two tailored multivariate MIxed-effects SElection models (mvMISE) to jointly analyze multiple correlated peptides/proteins in labeled proteomics data, considering the batch effects and the non-ignorable missingness. By taking a multivariate approach, we can borrow information across multiple peptides of the same protein or multiple proteins from the same biological pathway, and thus achieve better statistical efficiency and biological interpretation. These two different models account for different correlation structures among a group of peptides or proteins. Specifically, to model multiple peptides from the same protein, we employed a factor-analytic random effects structure to characterize the high and similar correlations among peptides. To model biological dependence among multiple proteins in a functional pathway, we introduced a graphical lasso penalty on the error precision matrix, and implemented an efficient algorithm based on the alternating direction method of multipliers. Simulations demonstrated the advantages of the proposed models. Applying the proposed methods to the motivating data set, we identified phosphoproteins and biological pathways that showed different activity patterns in triple negative breast tumors versus other breast tumors. The proposed methods can also be applied to other high-dimensional multivariate analyses based on clustered data with or without non-ignorable missingness.

An approximate joint model for multiple paired longitudinal outcomes and time-to-event data.

Joint modeling of multivariate paired longitudinal data and time-to-event data presents computational challenges that supersede full likelihood estimation due to the large dimensional random effects vector needed to capture correlation due to clustering with respect to pairs, subjects, and outcomes. We propose an alternative, computationally simpler approach to estimation of complex shared parameter models where missing data is imputed based on the Posterior Predictive Distribution from a Conditional Linear Model (CLM) approximation. Existing methods for complete data are then implemented to obtain estimates of the event time model parameters. Our method is applied to examine the effects of discordant growth in anthropometric measures of longitudinal fetal growth in twin fetuses and the timing of birth. Simulation results are presented to show that our method performs relatively well with moderate measurement errors under certain CLM approximations.

Maternal investment and early thermal conditions affect performance and antipredator responses.

Exposure to increased temperatures during early development can lead to phenotypic plasticity in morphology, physiology, and behavior across a range of ectothermic animals. In addition, maternal effects are known to be important contributors to phenotypic variation in offspring. Whether the 2 factors interact to shape offspring morphology and behavior is rarely explored. This is critical because climate change is expected to impact both incubation temperature and maternal stress and resource allocation. Using a fully factorial design, and Bayesian multivariate mixed models, we explored how the manipulation of early thermal environment and yolk-quantity in eggs affected the morphology, performance, and antipredator behavior of 2 sympatric Australian skink species (Lampropholis delicata and L. guichenoti). We found that juveniles from the hot treatment were larger than those on the cold treatment in L. guichenoti but not L. delicata. Using repeated behavioral measures for individual lizards, we found an interaction between incubation temperature and maternal investment in performance, with running speed being affected in a species-specific way by the treatment. We predicted that changes in performance should influence antipredator responses. In support of this prediction, we found that maternal investment impacted antipredator behavior, with animals from the yolk-reduced and cold treatment resuming activity faster after a simulated predatory attack in L. delicata. However, the prediction was not supported in L. guichenoti. Our results highlight the importance of exploring the multifaceted role that environments play across generations to understand how different anthropogenic factors will impact wildlife in the future.