Biology subjects

Cooper, M.

Publications and source records attributed to Cooper, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Genome-wide dissection of tillering responsiveness to neighbour proximity in sorghum

Tillering plasticity is a key adaptive trait in sorghum influencing resource use efficiency via a plants ability to adjust branching to neighbour density. Neighbour detection through red:far-red (R:FR) light sensing regulates this plasticity. While molecular pathways regulating tiller outgrowth are partly known, the genetic architecture underlying density-responsive tillering has not been resolved in any grass species. A sorghum diversity panel (n = 895) was evaluated over two growing seasons (2023 and 2024) with plant spacing ranging from 5 to 60 cm. A linear mixed model incorporating neighbour distance and tiller counts estimated genotype-specific response. GWAS was conducted on isolated plants (no neighbours within 60 cm) and on estimated responsiveness to neighbours. GWAS identified 52 baseline tillering QTLs and 50 for spacing responsiveness, with 10 overlapping, suggesting shared genetic control. Comparison with 41 R:FR pathway candidate genes revealed enrichment in responsiveness QTLs (5/50, 10%) versus baseline (0/52, 0%) (Fishers exact test, P = 0.025). Our model identified 40 unique density-responsive tillering QTL regions. Reducing genotype response to neighbour absence could be a selection target to develop water-efficient sorghum varieties where controlled architecture may be more valuable than natural plasticity.

plant biology↗

bioRxiv2026

Ectopic expression of ARGOS8 mediates silk elongation rate response to water deficit in maize (Zea mays L.)

Water deficit is ubiquitous in maize (Zea mays L.) cropping systems worldwide. Ethylene insensitivity in maize has been implicated in improving kernel set and yield under drought, and ARGOS genes modulate ethylene signal transduction by reducing ethylene sensitivity. Given the strong water sensitivity of silk elongation, ARGOS8 overexpression is expected to alter silk growth responses to drought. Experiments were conducted under controlled and field conditions to test the effect of ARGOS8 gene overexpression on silk growth under water deficit. Silk lengths and water use were continuously monitored, and silk elongation rate (SER) response to the fraction of transpirable soil water (FTSW) was evaluated. Silk emergence dynamics were measured in the field by daily counting the silks under contrasting water regimes. ARGOS8 transgenics maintained SER at lower FTSW than controls; however, responses varied among hybrids. Higher SER under water stress translated into a faster silk exertion rate in ARGOS8 transgenics than controls (45 vs. 25 silks d-1), leading to a greater number of exerted silks at three days post-silking (424 vs. 377, [~]90% vs. 84% of total silks, p < 0.01). Together, these results help clarify the mechanism underlying the ectopically expressed ARGOS8 effect on maize yield improvement under water stress. HighlightARGOS8 transgenic expression sustains silk elongation rates and increases silk emergence under water deficit, improving the reproductive performance of maize in water-limited environments.

plant biology↗

bioRxiv2026

A pre-rRNA positive feedback loop drives malignant ribosome biogenesis

Altered nucleoli are a well-established hallmark of cancer1, but how oncogenic signalling remodels the nucleolus remains poorly understood. Here we used an inducible mouse model of pancreatic ductal adenocarcinoma (PDAC)2 to generate spatially resolved proteomic and phosphoproteomic maps of the nucleolus upon RAS oncogene activation. We identify a phosphorylation programme initiated by translocation of the Casein Kinase 2 (CK2) holoenzyme to the nucleolus. This programme amplifies rRNA synthesis and malignant ribosome biogenesis by phosphorylating factors that control RNA polymerase I transcription and early ribosomal RNA (rRNA) processing. Preventing the nucleolar activity of CK2 inhibits oncogene-induced rRNA production, while constitutive nucleolar trapping of CK2 is sufficient to activate rRNA synthesis in the absence of RAS oncogene. Mechanistically, CK2 accumulation in the nucleolus is mediated by direct binding to the 3 External Transcribed Sequence (3ETS) of nascent precursor rRNA, creating an RNA-dependent self-amplifying feedback loop. Nucleolar CK2 accumulation is conserved across diverse human cancers, and its disruption synergises with inhibition of oncogenic RAS signalling to suppress anchorage-independent growth and tumourigenesis. Our study reveals 3ETS as a CK2 signalling scaffold that amplifies oncogenic ribosome biogenesis, and defines a druggable nucleolar vulnerability that can be exploited by targeting this process.

cancer biology↗

bioRxiv2026

Predicting Lodging Severity in Sorghum Breeding Trials Using UAV-Based Photogrammetrically Derived Height Data

Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)-derived metrics provide a potential high-throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot-level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV-derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest root mean square error (RMSE=16-18%), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.

plant biology↗

bioRxiv2026

Additive Channels in Curved Fitness Landscapes

Additive models of inheritance predict the short-term response to selection remarkably well, even when the underlying biology involves widespread dominance and gene-gene interaction. We argue that this success reflects a property of how fitness varies with the additive genetic component of a trait, not a property of the molecular architecture beneath. We make this quantitative through an additivity index, [A]g, that measures the fraction of local log-fitness variance captured by a linear approximation, with the remainder attributable to local curvature of the fitness surface. Under Gaussian/quadratic assumptions, [A]g equals the squared correlation between the linear approximation and local log-fitness, so 1- [A]g is the fraction of local log-fitness variance not captured by a purely linear predictor. We call regions of breeding-value space where [A]g is high additive channels and develop a coupled selection-inheritance framework that identifies when populations enter, persist in, and leave them. The framework predicts that stable, well-adapted populations can be those for which additive prediction is least informative for directional-response prediction. Article summaryGene interactions are common, yet additive genetic models often predict short-term evolution. We propose that additivity is a local property of where populations sit in breeding-value space on a curved fitness landscape, not a global property of the biology beneath. We define an index, [A]g, that compares slope variance to curvature variance and identifies populations in additive channels for which linear prediction performs well. The framework predicts that elite breeding pools enter such channels under sustained selection, while natural populations near a fitness optimum may leave them because the directional signal collapses.

evolutionary biology↗

bioRxiv2026

Improved Ensemble Performance by Weight Optimisation for the Genomic Prediction of Maize Flowering Time Traits

Ensembles of multiple genomic prediction models have demonstrated improved prediction performance over the individual models contributing to the ensemble. The outperformance of ensemble models is expected from the Diversity Prediction Theorem, which states that for ensembles constructed with diverse prediction models, the ensemble prediction error becomes lower than the mean prediction error of the individual models. While a naive ensemble-average model provides baseline performance improvement by aggregating all individual prediction models with equal weights, optimising weights for each individual model could further enhance ensemble prediction performance. The weights can be optimised based on their level of informativeness regarding prediction error and diversity. Here, we evaluated weighted ensemble-average models with three possible weight optimisation approaches (linear transformation, Nelder-Mead and Bayesian) using flowering time and tillering traits from two maize nested associated mapping (NAM) datasets; TeoNAM and MaizeNAM. The three proposed weighted ensemble-average approaches improved prediction performance in several of the prediction scenarios investigated. In particular, the weighted ensemble models enhanced prediction performance when the adjusted weights differed substantially from the equal weights used by the naive ensemble models. For performance comparisons among the weighted ensembles, there was no clear superiority among the proposed approaches in both prediction accuracy and error across the prediction scenarios. Weight optimisation for ensembles warrants further investigation to explore the opportunities to improve their prediction performance; for example, integration of a weighted ensemble with a simultaneous hyperparameter tuning process may offer a promising direction for further research.

bioinformatics↗

bioRxiv2026

Clustering Compresses Attractors in Watts-Strogatz Threshold Boolean Networks

AbstractDoes higher clustering shorten attractor periods? We examine whether the global clustering coefficient C, a direct measure of triangle density, predicts attractor lengths in synchronous, signed-threshold Boolean networks on Watts-Strogatz (WS) graphs. We generate 330 directed, signed WS networks spanning sizes N = 10-100 and mean degrees [Formula], simulate dynamics from M = 100 random initial states per graph with exact cycle detection, and summarise each graph by the average log attractor period (equivalently, the geometric mean period). Our primary analysis relates this log-period summary to C while adjusting for N, [Formula], the mean directed shortest path (MSP), and including nonlinear size-degree and clustering-degree interactions. Main findingHigher clustering robustly shortens attractor periods: a 0.10 increase in C (C [isin] [0, 1]) corresponds to an {approx} 13-15% lower expected geometric mean period, and moving from C = 0.000 to C = 0.460 yields an {approx} 50% reduction, holding other properties fixed. The effect persists when the linear C term is replaced by a nonlinear function of C, and it replicates in held-out graph instances (graphs not used to fit the model). Shorter periods are not explained by an increase in fixed points under the strict comparator (>); rather, higher triangle density shifts mass from long cycles to medium-length cycles. SignificanceIn threshold-like logic, settling speed and oscillatory stability are central to computation and control. Our results provide a direct, quantitative link between triangle density and these long-run behaviours, showing that C acts as a structural lever on temporal complexity. All figures and tables are reproducible from the accompanying code, data, and analysis scripts.

bioinformatics↗

bioRxiv2025

Ensembles of Graph Neural Networks Supervised by Genotype-to-Phenotype Structures Improved Genomic Prediction Performance

Accurate selection of favourable crop genotypes has motivated the exploration of diverse prediction algorithms for crop breeding applications. One genomic prediction method that has not been fully explored is graph attention networks (GAT). By directly analysing graphical data with the attention mechanism, GAT can incorporate the genotype-to-phenotype (G2P) structure to regularise predictions. As one potential G2P structure, a gene network can be inferred from interpretable machine learning models to effectively learn key features of prediction patterns, potentially improving prediction performance. Here, we investigated whether incorporating such data-driven prior knowledge into GAT improved prediction performance compared to GAT models representing a continuum of G2P structures, ranging from infinitesimal to fully connected. Applying the Diversity Prediction Theorem, we also combined these diverse G2P structures into an ensemble of GAT genomic prediction models to integrate complementary strengths of multiple models. The results for flowering time traits in two maize nested association mapping datasets showed a lack of consistent performance improvement in the data-driven prior knowledge GAT model. However, consistent outperformance was observed for the ensemble of GAT models. Improved predictions from the ensemble model may be driven by its ability to capture a more complete representation of the inferred gene network through the integration of information from diverse G2P structures. The observed results using the GAT methodology provided the foundation for potential performance improvement using GAT by integrating biological prior knowledge derived from omics data and empirically verified gene interactions in future research, thereby potentially enhancing the GAT ensemble performance.

genomics↗

bioRxiv2025

Back to the future 2: the implications of germplasm structure on thebalance between short and long-term genetic gain in a changingtarget population of environments

Plant breeding operates within a highly complex genetic landscape determined by gene effects emerging through biological networks and their interactions with the environment. This environment is not constant but subject to short-term fluctuations and long-term shifts. This significantly complicates the task of plant breeders in finding a balance between adapting their germplasm for the short- and long-term. Here we build on previous work of us that investigated the implications of genetic complexity on breeding program design, by adding an environmental dimension in the form of the E(NK) model to the simulation framework. We found that the addition of environmental interactivity and change creates greater uncertainty associated with pursuing any specific selection trajectory, as compared to a static environment. This advantages preserving genetic variability and genetic landscape exploration over quickly exposing additive variation by constraining genetic space around a particular and temporary local optimum. Nonetheless, we found that also in a dynamically changing environment, a structure in which several breeding programs explore genetic space while maintaining constant germplasm exchange, finds the best balance between short and long-term objectives, as opposed to isolated programs or one large undifferentiated program, which exclusively emphasize short respectively long-term objectives. We furthermore highlight the difficulty of exchanging germplasm to restore genetic variability with non-stationary and germplasm context dependent genetic effects. In summary we found that also with addition of environmental complexity and change, the structural features that characterized breeding operations hitherto and allowed them to navigating biological complexity apply. Namely the necessity to constraining genetic space in order for heritable additive variation to emerge. We end by arguing that optimal breeding program design depends on the level of genetic and environmental complexity, which should be appropriately reflected when modeling the long-term behavior of selection programs and the implications of specific interventions into these.

genetics↗

bioRxiv2025

Ensemble-based genomic prediction for maize flowering time reveals novel insights into trait genetic architecture and improves prediction for breeding applications

While various genomic prediction models have been evaluated for their potential to accelerate genetic gain for multiple traits, no individual genomic prediction model has outperformed all others across all applications. As an alternative approach, ensembles of multiple individual genomic prediction models can be applied to utilise the complementary strengths of individual prediction models and offset the prediction errors of each. We used the EasiGP (Ensemble AnalySis with Interpretable Genomic Prediction) pipeline to investigate the performance of an ensemble approach, targeting flowering-time traits measured in two maize nested association mapping datasets. For both datasets, the ensemble-based prediction approach achieved higher prediction accuracy and lower prediction error across the flowering-time traits compared to each individual model. Multiple genomic regions known to contain key flowering-time related genes were repeatedly included as features across individual genomic prediction models, indicating the models successfully captured SNPs as features that are associated with genomic regions known to contain flowering-time genes. Although repeatability was high for some genomic regions, estimated marker effects varied across many genomic regions, suggesting that the models might also have captured different aspects of the genetic variation underlying the traits. The ensemble combination of the diverse views likely contributed to the improvement of prediction performance by the ensemble-based approach over the individual prediction models. Ensemble-based prediction can be applied to overcome limitations observed in the continuous exploration for the best individual genomic prediction models that can consistently achieve the highest prediction performance, thereby potentially contributing to improved prediction accuracy for applications in crop breeding. Article summaryThis study targets researchers interested in the performance of genomic prediction models. To demonstrate potential advantages of an ensemble of diverse individual genomic prediction models, we investigated the prediction of key flowering-time traits (days to anthesis and anthesis to silking interval) in two maize datasets. The ensemble approach consistently improved the prediction performance. The improvement was attributed to the offset of prediction errors by combining multiple different dimensions of trait genetic variation. Ensembles can lead to higher selection accuracy of desirable individuals for applications in crop breeding.

bioinformatics↗

bioRxiv2025

Sequential exposure to anoxic/oxic conditions leads to biotransformation and detoxification of sitagliptin in urban hyporheic zones

Pharmaceuticals are increasingly recognized as contaminants of concern in aquatic environments. Sitagliptin, an antidiabetic drug that carries a trifluoromethyl group, which is a precursor of the persistent trifluoroacetic acid, is excreted largely unmetabolized and inefficiently removed in wastewater treatment plants, leading to its widespread detection in surface waters. The hyporheic zone -- a region between surface water and groundwater -- serves as a natural bioreactor with high microbial activity and diverse redox conditions, offering the potential for sitagliptin attenuation. This study explored the biotransformation of sitagliptin in hyporheic sediments under varying redox conditions through batch experiments and field observations. Furthermore, we showed that batch experiments can complement field observations to capture both mechanistic insights and their environmental relevance. Batch experiments revealed amide hydrolysis and N-acetylation of sitagliptin under anoxic conditions, with subsequent deamination and oxidation of transformation products under oxic conditions. Metagenome-resolved metaproteomics suggested Pseudomonas asiatica as a key player in the oxic transformation. Field analysis of pore water samples identified up to 6.47 {micro}g L-{superscript 1} sitagliptin and ten transformation products with concentrations of up to 4.82 {micro}g L-{superscript 1}. Amide hydrolysis products were the most abundant transformation products and preferentially formed under anoxic conditions. All investigated transformation products exhibited lower cytotoxicity and oxidative stress response than sitagliptin in in vitro bioassays, highlighting the detoxification potential of the hyporheic zone. By identifying conditions that promote sitagliptin transformation and characterizing its transformation products toxicologically, our work provides parameters for enhanced sitagliptin removal in aquatic environments and improved risk assessment of fluorinated trace organic contaminants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/657625v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@b43cf4org.highwire.dtl.DTLVardef@10dd64eorg.highwire.dtl.DTLVardef@170ca9corg.highwire.dtl.DTLVardef@5e433_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Circos Plots for Genome Level Interpretation of Genomic Prediction Models

Ensemble of multiple genomic prediction models have grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyse the predictive behaviour at the genome level are lacking. Here, we develop a computational tool called Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP) that uses circos plots to visualise how different genomic prediction models quantify contributions of marker effects to trait phenotypes. As a demonstration of EasiGP, multiple genomic prediction models, spanning conventional statistical and machine learning algorithms, were used to infer the genetic architecture of days to anthesis (DTA) in a maize mapping population. The results indicate that genomic prediction models can capture different views of trait genetic architecture, even when their overall profiles of prediction accuracy are similar. Combinations of diverse views of the genetic architecture for the DTA trait in the TeoNAM study might explain the improved prediction performance achieved by ensembles, aligned with the implication of the Diversity Prediction Theorem. In addition to identifying well-known genomic regions contributing to the genetic architecture of DTA in maize, the ensemble of genomic prediction models highlighted several new genomic regions that have not been previously reported for DTA. Finally, different views of trait genetic architecture were observed across sub-populations, highlighting challenges for between-population genomic prediction. A deeper understanding of genomic prediction models with enhanced interpretability using EasiGP can reveal several critical findings at the genome level from the inferred genetic architecture, providing insights into the improvement of genomic prediction for crop breeding programs. Plain Language SummaryWhile an ensemble of genomic prediction models has been applied in crop breeding, the prediction mechanism has not been well-investigated due to the lack of a computational tool to interpret the predictive behaviour. It is critical to investigate prediction models at the genome level to understand how each model quantifies genomic marker effects contributing to the trait genetic architecture. Hence, we developed a computational tool, the Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP), to investigate the genome features and predictive behaviours of the ensemble. Here, we demonstrate the utility of EasiGP using a maize breeding dataset. EasiGP visualised the genetic architecture from diverse interpretable genomic prediction models and identified several well-known key maize genes. EasiGP also revealed several potential new genomic regions for further investigation. EasiGP helps us investigate the trait genetic architecture that can be utilised to benefit crop breeding. Core ideasO_LIA new computational tool, EasiGP, was created to interpret multiple genomic prediction models at the genomic level C_LIO_LIEasiGP visualises the inferred trait genetic architecture from multiple genomic prediction models with circos plots C_LIO_LIAs a case study, EasiGP highlighted several well-known genes regulating the target trait, days to anthesis C_LIO_LIEasiGP can facilitate the discovery of novel genome regions underlying target traits for further investigation C_LI

bioinformatics↗

bioRxiv2025

Calcium-permeable AMPAR in hippocampal parvalbumin-expressing interneurons protect against memory interference

Parvalbumin-expressing (PV+) interneurons exert exquisite control over spike output, and plasticity in these inhibitory circuits may be important for maintaining network stability in learning and memory. PV+ interneuron recruitment is primarily mediated by GluA2-lacking Ca2+-permeable AMPA receptors (CP-AMPAR), which support anti-Hebbian plasticity. However, the functional significance of CP-AMPAR-mediated plasticity remains unknown. Using a viral approach to artificially express the GluA2 subunit in hippocampal PV+ interneurons, we replaced CP-AMPAR with GluA2-containing receptors, and in doing so reduced synaptically-evoked Ca2+ transients and anti-Hebbian plasticity. Transfection of hippocampal PV+ interneurons with GluA2 resulted in delay-dependent spatial working memory deficits which increased across trials per session, and impaired reversal learning in the Morris water maze but not initial acquisition. Our data suggest that loss of CP-AMPAR-mediated plasticity in these cells leads to proactive interference, revealing a significant role for dynamic recruitment of PV+ interneurons in the segregation of memories and accurate memory retrieval. HighlightsO_LIViral expression of GluA2 in PV+ interneurons alters synaptic AMPA receptor profile C_LIO_LIGluA2 overexpression in PV+ cells reduces synaptic Ca2+ transients and plasticity C_LIO_LIUpregulating GluA2 in PV+ cells causes delay-dependent deficits in working memory C_LIO_LIAcquisition of reference memories is preserved, but reversal learning is impaired C_LI

neuroscience↗

bioRxiv2025

Envirotyping Facilitates Understanding of Genotype x Environment Interactions and Highlights the Potential of Stay-green Traits in Wheat

Better understanding genotype by environment interaction (GxE) can help breeding for better adapted varieties. Envirotyping for environmental water status was applied to assist interpretation of GxE interactions for wheat yield in multi-environment trials conducted in drought-prone Australian environments. Genotypes from a multi-reference parent nested association mapping (MR-NAM) population were tested in 10 trials across the Australian wheatbelt. Genotype yield and phenology were measured in all trials, while traits associated with the stay-green phenotype were assessed for a subset of 5 trials. Envirotyping was conducted by characterizing water stress experienced by genotypes at each trial using crop modelling. Envirotyping facilitated the understanding of GxE interactions by explaining 75, 67, and 66% of the genotypic variance for yield in severe water-limited (ET3), mild terminal water-stress (ET2), and water-sufficient (ET1) environments, respectively. Yield and stay-green were negatively correlated with flowering time in most trials. However, when focusing on genotypes flowering at similar times within a trial, no significant correlation was found between yield and flowering. Importantly stay-green traits remained significantly correlated with yield. Stay-green traits such as delayed onset of senescence and slower senescence rate benefited yield by 0.2 to 1.1 t ha-1 across environments, highlighting the breeding potential for stay-green traits in both water-sufficient and water-limited environments. Hence, sustaining green leaf area during grain filling helped to enhance yield. Envirotyping to better understand GxE interactions for yield, coupled with screening for traits exhibiting superior adaptive mechanisms, are powerful assets in assisting plant breeders to select more effectively drought adapted genotypes. HighlightsGenotype x environment interaction for yield could be reduced with envirotyping. Envirotyping enabled a 75% gain in genotypic variance in severe drought conditions. Envirotyping clarified breeding potential of stay-green traits across environments. Stay-green has potential to enhance wheat yield across diverse environments.

physiology↗

bioRxiv2025

Germline sequence variation within the ribosomal DNA is associated with human complex traits

The ribosome is one of the core macromolecules in the cell. The ribosomal RNAs (rRNA), which are essential components of the ribosome, are coded by the multi-copy ribosomal DNA (rDNA). Despite its highly conserved function, the rDNA displays substantial variation within all species analysed to date. This variation comprises both inter-individual differences in total copy number (CN) as well as inter- and intragenomic sequence variation in the form of single nucleotide variants (SNV) and insertions/deletions (INDELs) across rDNA copies. Whether germline variation of rDNA sequence associates with phenotypic traits in humans is, to date, unknown. Here, using the UK Biobank whole genome sequencing data, we first derive a high confidence list of rDNA-associated SNVs and INDELs that we validate in multiple ways. Using this list, we show that specific rDNA variants associate with several human traits. In particular, traits associated with body size appear enriched in variants within the Expansion Segment 15L region in the 28S rRNA. The strength of these associations does not diminish when accounting for the total rDNA CN of each individual. Our work represents the first large-scale association analysis of human traits with germline sequence variation in the rDNA, a source of human complex trait-relevant genetic variation that has thus far been largely ignored.

genetics↗

bioRxiv2024

Improvements in Prediction Performance of Ensemble Approaches for Genomic Prediction in Crop Breeding

The improvement of selection accuracy of genomic prediction is a key factor in accelerating genetic gain for crop breeding. Traditionally, efforts have focused on developing superior individual genomic prediction models. However, this approach has limitations due to the absence of a consistently "best" individual genomic prediction model, as suggested by the No Free Lunch Theorem. The No Free Lunch Theorem states that the performance of an individual prediction model is expected to be equivalent to the others when averaged across all prediction scenarios. To address this, we explored an alternative method: combining multiple genomic prediction models into an ensemble. The investigation of ensembles of prediction models is motivated by the Diversity Prediction Theorem, which indicates the prediction error of the many-model ensemble should be less than the average error of the individual models due to the diversity of predictions among the individual models. To investigate the implications of the No Free Lunch and Diversity Prediction Theorems, we developed a naive ensemble-average model, which equally weights the predicted phenotypes of individual models. We evaluated this model using two traits influencing crop yield--days to anthesis and tiller number per plant--in the Teosinte Nested Association Mapping dataset. The results show that the ensemble approach increased prediction accuracies and reduced prediction errors over individual genomic prediction models. The advantage of the ensemble was derived from the diverse predictions among the individual models, suggesting the ensemble captures a more comprehensive view of the genomic architecture of these complex traits. These results are in accordance with the expectations of the Diversity Prediction Theorem and suggest that ensemble approaches can enhance genomic prediction performance and accelerate genetic gain in crop breeding programs. Article summaryThis research targets selective breeding industries and researchers developing genomic prediction models to accelerate genetic gain in breeding programs. We applied the concept of an ensemble, combining multiple individual genomic prediction models, to predict key traits (days to anthesis and tiller number per plant) in a crop breeding dataset. Here, we show that an ensemble approach increased prediction accuracies and reduced prediction errors over individual genomic prediction models. These results indicate the potential for ensembles of multiple, diverse genomic prediction models to accelerate genetic gain in breeding programs by increasing the accuracy of selection decisions.

bioinformatics↗

bioRxiv2024

Diet outperforms microbial transplant to drive microbiome recovery post-antibiotics

High-fat, low-fiber Western-style diets (WD) induce microbiome dysbiosis characterized by reduced taxonomic diversity and metabolic breadth1,2, which in turn increases risk for a wide array of metabolic3-5, immune6 and systemic pathologies. Recent work has established that WD can impair microbiome resilience to acute perturbations like antibiotic treatment7,8, although we know little about the mechanism of impairment and the specific host consequences of prolonged post-antibiotic dysbiosis. Here, we characterize the trajectory by which the gut microbiome recovers its taxonomic and functional profile after antibiotic treatment in mice on regular chow (RC) and WD, and find that only mice on RC undergo a rapid successional process of recovery. Metabolic modeling indicates that RC diet promotes the development of syntrophic cross- feeding interactions, while on WD, a dominant taxon monopolizes readily available resources without releasing syntrophic byproducts. Intervention experiments reveal that an appropriate dietary resource environment is both necessary and sufficient for rapid and robust microbiome recovery, whereas microbial transplant is neither. Furthermore, prolonged post-antibiotic dysbiosis in mice on WD renders them susceptible to infection by the intestinal pathogen Salmonella enterica serovar Typhimurium. Our data challenge widespread enthusiasm for fecal microbiota transplant (FMT) as a strategy to address dysbiosis and demonstrate that specific dietary interventions are, at minimum, an essential prerequisite for effective FMT, and may afford a safer, more natural, and less invasive alternative to FMT.

microbiology↗

bioRxiv2024

Structural basis for allosteric regulation of human phosphofructokinase-1

Phosphofructokinase-1 (PFK1) catalyzes the rate-limiting step of glycolysis, committing glucose to conversion into cellular energy. PFK1 is highly regulated to respond to the changing energy needs of the cell. In bacteria, the structural basis of PFK1 regulation is a textbook example of allostery; molecular signals of low and high cellular energy promote transition between an active R-state and inactive T-state conformation, respectively. Little is known, however, about the structural basis for regulation of eukaryotic PFK1. Here, we determine structures of the human liver isoform of PFK1 (PFKL) in the R- and T-state by cryoEM, providing insight into eukaryotic PFK1 allosteric regulatory mechanisms. The T-state structure reveals conformational differences between the bacterial and eukaryotic enzyme, the mechanisms of allosteric inhibition by ATP binding at multiple sites, and an autoinhibitory role of the C-terminus in stabilizing the T-state. We also determine structures of PFKL filaments that define the mechanism of higher-order assembly and demonstrate that these structures are necessary for higher-order assembly of PFKL in cells.

biochemistry↗