Lawas, L. M. | bioRxiv Science

bioRxiv2025

Impacts of projected elevated temperature on estimated chilling accumulation and bud break of rabbiteye blueberries

Climate change poses a significant threat to perennial fruit crops, including blueberries, by altering temperature-dependent developmental processes such as dormancy and bud break. This study investigates the projected impacts of mid-21st century warming on chilling accumulation and phenological development in rabbiteye blueberry (Vaccinium virgatum) cultivars Krewer and Titan. Using downscaled climate projections from 20 global climate models under the RCP 8.5 scenario, we estimated future temperature trends for a key southeastern U.S. blueberry production region. We quantified changes in chilling hours, chilling portions, and freeze event probabilities between historical (1981-2000) and projected (2041-2070) periods. Results indicate a substantial reduction in chilling accumulation--chilling hours decreased by 66.6% and chilling portions by nearly half--alongside a delayed onset and shortened duration of chilling periods. Despite warming, freezing events are still expected during critical phenological windows, potentially increasing frost damage risk. Growth chamber experiments simulating ambient and elevated temperature regimes revealed significant cultivar-specific shifts in the timing and rate of bud break and bloom stages. Logistic models developed from these data provide predictive tools for assessing phenological responses under future climates. These findings highlight the vulnerability of rabbiteye blueberry to climate-induced dormancy disruption and underscore the need for adaptive management strategies and cultivar selection to sustain productivity in a warming climate.

plant biology↗

bioRxiv2016

Incorporating the Rate of Transcriptional Change Improves Construction of Transcription Factor Regulatory Networks

Transcriptional regulatory networks (TRNs) can be developed by computational approaches that infer regulator-target gene interactions from transcriptional assays. Successful algorithms that generate predictive, accurate TRNs enable the identification of regulator-target relationships in conditions where experimentally determining regulatory interactions is a challenge. Improving the ability of TRNs to successfully predict known regulator-target relationships in model species will enhance confidence in applying these approaches to determine regulator-target interactions in non-model species where experimental validation is challenging. Many transcriptional profiling experiments are performed across multiple time points; therefore we sought to improve regulator-target predictions by adjusting how time is incorporated into the network. We created ExRANGES, which incorporates Expression in a Rate-Normalized GEne Specific manner that adjusts how expression data is provided to the network algorithm. We tested this on a two different network construction approaches and found that ExRANGES prioritizes targets differently than traditional expression and improves the ability of these networks to accurately predict known regulator targets. ExRANGES improved the ability to correctly identify targets of transcription factors in large data sets in four different model systems: mouse, human, Arabidopsis, and yeast. Finally, we examined the performance of ExRANGES on a small data set from field-grown Oryza sativa and found that it also improved the ability to identify known targets even with a limited data set.\n\nAuthor SummaryTo understand how organisms can turn a collection of genes into a physiological response, we need to understand how certain genes are turned on and off. In model organisms, the ability to identify direct targets of transcription factors via ChIP-Seq in a high-throughput manner has advanced our understanding of transcriptional regulatory networks and how organisms regulate gene expression. However, for non-model organisms, it remains a challenge to identify TF-target relationships through experimental approaches such as ChIP-Seq. Without this information, the ability to understand regulatory control is limited. Computational approaches to identify regulator-target relationships in silico from easily attainable transcriptional data offer a solution. Several approaches exist for identifying gene regulatory networks, including many that take advantage of time series data. Most of these approaches weigh the relationship between regulators and putative targets at all time points equally. However, many regulators may control a single target in response to different inputs. In our approach, we focus on the association between regulators and targets primarily at times when there is a significant change in expression. ExRANGES essentially weights the expression value of each time point by the slope change after that time point so that relationships between regulators and targets are emphasized at the time points when the transcript levels are changing. This change in input into network identification algorithms improves the ability to predict regulator-target interactions and could be applied to many different algorithms We hope this improvement will increase the ability to identify regulators of interest in non-model species.

bioinformatics↗