Biology subjects

Cooper, M.

Publications and source records attributed to Cooper, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

A comprehensive evaluation of taxonomic classifiers in marine vertebrate eDNA studies

Environmental DNA (eDNA) metabarcoding is a widely used tool for surveying marine vertebrate biodiversity. To this end, many computational tools have been released and a plethora of bioinformatic approaches are used for eDNA-based community composition analysis. Simulation studies and careful evaluation of taxonomic classifiers are essential to establish reliable benchmarks to improve accuracy and reproducibility of eDNA-based findings. Here we present a comprehensive evaluation of nine taxonomic classifiers exploring three widely used mitochondrial markers (12S rDNA, 16S rDNA, and COI) in Australian marine vertebrates. Curated reference databases and exclusion database tests were used to simulate diverse species compositions, including three positive control and two negative control datasets. Using these simulated datasets, we were able to identify between 19% to 85% of marine vertebrate species using mitochondrial markers. We show that MMSeqs2 and Metabuli generally outperform BLAST with 10% and 11% higher F1 scores for 12S and 16S rDNA markers, respectively, and that Naive Bayes Classifiers such as Mothur outperform sequence-based classifiers except MMSeqs2 for COI markers by 11%. Database exclusion tests reveal that MMSeqs2 and BLAST are less susceptible to false positives compared to Kraken2 with default parameters. Based on these findings, we recommend that MMSeqs2 is used for taxonomic classification of marine vertebrates given its ability to improve species-level assignments while reducing the number of false positives. Our work contributes to the establishment of best practices in eDNA-based biodiversity analysis to ultimately increase the reliability of this monitoring tool in the context of marine vertebrate conservation.

ecology↗

bioRxiv2023

Crop improvement can accelerate agriculture adaptation to societal demands and climate change.

A key question today is how to harmonize future crop improvement efforts for regenerative cropping systems that can mitigate further environmental degeneration and improve societal adaptation to climate change. Here we show that the US corn-belt based maize improvement system has been adapting to the changing climate. Analyses of the longest running field experiment (1990-2021) designed to quantify yield gains (37 hybrids sold from 1930 to 1990) demonstrate that rates of genetic gain were always positive and have increased over time (from 70 to 150 kg ha-1 y-1 in 1990 and 2021, respectively). Between 1930 and 2021 for the May through October period total rainfall for the U.S. corn belt increased 113 mm, and daily temperature amplitude decreased 1.3{degrees}C. At the same time, farmers modified their farming practices, helping modern hybrids to out-perform their older counterparts by a larger degree. In contrast to the conclusions reached by other observational studies, genetic gain estimates demonstrate that maize breeding and the production system are adapting to modern scenarios. Climate change is commonly linked to fears of food insecurity, but new cropping systems capable of providing food while regenerating resources such as water, the circularization of nutrients, and reduction of greenhouse gas emissions are possible.

genetics↗

bioRxiv2023

Analyzing bacterial community compositions by GroEL-taxoproteomics

Profiling bacterial populations in mixed communities is a common task in microbiology. 16S rRNA-based sequencing is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by SDS-PAGE to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw data set obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.

microbiology↗

bioRxiv2023

Small extracellular vesicles but not microvesicles from Opisthorchis viverrini promote cell proliferation in human cholangiocytes.

Chronic infection with O. viverrini has been linked to the development of cholangiocarcinoma (CCA), which is a major public health burden in the Lower Mekong River Basin countries, including Thailand, Lao PDR, Vietnam and Cambodia. Despite its importance, the exact mechanisms by which O. viverrini promotes CCA are largely unknown. In this study, we characterized different extracellular vesicle populations released by O. viverrini (OvEVs) using proteomic and transcriptomic analyses and investigated their potential role in host-parasite interactions. While 120k OvEVs promoted cell proliferation in H69 cells at different concentrations, 15k OvEVs did not produce any effect compared to controls. The proteomic analysis of both populations showed differences in their composition that could contribute to this differential effect. Furthermore, the miRNAs present in 120k EVs were analysed and their potential interactions with human host genes was explored by computational target prediction. Different pathways involved in inflammation, immune response and apoptosis were identified as potentially targeted by the miRNAs present in this population of EVs. This is the first study showing specific roles for different EV populations in the pathogenesis of a parasitic helminth, and more importantly, an important advance towards deciphering the mechanisms used in establishment of opisthorchiasis and liver fluke infection-associated malignancy.

microbiology↗

bioRxiv2023

Intranasal VLP-RBD vaccine adjuvanted with BECC470 confers immunity against Delta SARS-CoV-2 challenge in K18-hACE2-mice

As the COVID-19 pandemic transitions to endemic, seasonal boosters are a plausible reality across the globe. We hypothesize that intranasal vaccines can provide better protection against asymptomatic infections and more transmissible variants of SARS-CoV-2. To formulate a protective intranasal vaccine, we utilized a VLP-based platform. Hepatitis B surface antigen- based virus like particles (VLP) linked with receptor binding domain (RBD) antigen were paired with the TLR4-based agonist adjuvant, BECC 470. K18-hACE2 mice were primed and boosted at four-week intervals with either VLP-RBD-BECC or mRNA-1273. Both VLP-RBD-BECC and mRNA-1273 vaccination resulted in production of RBD-specific IgA antibodies in serum. RBD- specific IgA was also detected in the nasal wash and lung supernatants and were highest in VLP-RBD-BECC vaccinated mice. Interestingly, VLP-RBD-BECC vaccinated mice showed slightly lower levels of pre-challenge IgG responses, decreased RBD-ACE2 binding inhibition, and lower neutralizing activity in vitro than mRNA-1273 vaccinated mice. Both VLP-RBD-BECC and mRNA-1273 vaccinated mice were protected against challenge with a lethal dose of Delta variant SARS-CoV-2. Both vaccines limited viral replication and viral RNA burden in the lungs of mice. CXCL10 is a biomarker of severe SARS-CoV-2 infection and we observed both vaccines limited expression of serum and lung CXCL10. Strikingly, VLP-RBD-BECC when administered intranasally, limited lung inflammation at early timepoints that mRNA-1273 vaccination did not. VLP-RBD-BECC immunization elicited antibodies that do recognize SARS-CoV-2 Omicron variant. However, VLP-RBD-BECC immunized mice were protected from Omicron challenge with low viral burden. Conversely, mRNA-1273 immunized mice had low to no detectable virus in the lungs at day 2. Together, these data suggest that VLP-based vaccines paired with BECC adjuvant can be used to induce protective mucosal and systemic responses against SARS- CoV-2.

immunology↗

bioRxiv2023

Root system growth and function response to soil temperature in maize (Zea mays L.)

Crop adaptation to the mixture of environments that defines the target population of environments is the result from a balanced resource allocation between roots, shoots and reproductive organs. Root growth places a critical role in the determination of this balance. Root growth and function responses to temperature can determine the strength of roots as sinks but also influence the crops ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize since the middle of the last century, and the genetic determinants are unknown. Low temperatures often recorded in deep soil layers limit root growth and soil exploration and may constitute a bottleneck towards increasing drought tolerance, nitrogen recovery, sequestration of carbon and productivity in maize. High throughput phenotyping (HTP) systems were developed to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here we show that there is: 1) genetic variation of root growth under low temperature and below 10{degrees}C, and 2) genotypic variation in water transport under low temperature. Using simulation, we demonstrate that the measured variation for both traits contribute to drought tolerance and explain important components of yield variation in the US corn-belt. The trait set examined herein and HTP platform developed for its characterization reveal a unique opportunity to remove a major bottleneck for crop improvement, and adaptation to climate change.

plant biology↗

bioRxiv2022

Extending the breeder's equation to take aim at the Target Population of Environments

1)A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realise these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realised trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have not given adequate attention to quantifying the structure of the TPE and the MET-TPE relationship and its potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We provide a perspective on the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.

genetics↗

bioRxiv2022

Transgene by Germplasm Interactions Can Impact Transgene Evaluation

Transgenes have been successfully commercialized for qualitatively inherited insect and herbicide resistance traits that show similar effects across genetic backgrounds. However, for quantitative traits like yield, genetic background may affect the measured transgene value. In this paper, we evaluated whether different genetic backgrounds impact the estimated value of a transgene for grain yield, ear height, and anthesis-silking interval for maize by developing isogenic pairs of lines with and without a transgene and testing them in hybrid combination with non-transgenic lines from a complementary heterotic group across eleven environments in the USA. Over all hybrid combinations, the transgene increased yield by 0.2 Mg ha-1. Across multiple non-transgenic lines of the opposing heterotic group, the transgene effect within a line pair ranged from an increase of 0.8 Mg ha-1 for the NSS4 and SS7 transgenic lines to a reduction of 0.3 Mg ha-1 for the NSS5 transgenic line when compared to their non-transgenic isoline. Transgenic hybrids were often taller than non-transgenic hybrids (P<0.05). Anthesis to silking interval was reduced by 4{square}C growing degree units overall, but no transgene x genotype interaction was detected among line pairs. Our results show the importance of testing transgene efficacy across a large sample of elite hybrid pairs to assess the genes value. By only testing in a specific hybrid background, as may be done for qualitative traits like insect resistance, transgenes could be erroneously advanced or eliminated.

plant biology↗

bioRxiv2022

Methods for Evaluating Effects of Transgenes for Quantitative Traits

Transgenes that improve quantitative traits have traditionally been evaluated in one or a few genetic backgrounds across multiple environments. However, testing across multiple genetic backgrounds can be equally important to accurately quantify the value of a transgene for breeding objectives. Creating near-isogenic lines across a wide germplasm space is costly and time consuming, which renders it impractical during early stages of testing. In this experiment, we evaluate three approaches to sample the genetic space while concurrently testing across environments. We created both transgenic and non-transgenic doubled haploid lines, F2:3 lines, and bulk F3 families to determine if all methods resulted in similar estimation of transgene value and to identify the number of yield trial plots from each method necessary to obtain a stable estimate of the transgene value. With one exception, the three methods consistently estimated a similar effect of the transgene. We suggest that bulked F3 lines topcrossed to a tester inbred is the most effective method to estimate the value of a transgene across both genetic space and environments.

plant biology↗

bioRxiv2022

Plant Breeding in the face of climate change

Climate change will have a net negative and inequitable impact on agriculture. Genetics for crop improvement ranks in the top set of technologies that can contribute to human adaptation to climate change. However, a framework for how to breed crops for climate change adaptation is lacking. Here we propose a framework to develop new genotype (G) x management (M) technologies (G x M) to adapt to climate change, and to transition from current to future G x M technologies in a way that future food security does not come at the expense of current food security. The framework integrate genomic, agronomic, and environmental (E) predictors to accomplish two critical goals: 1-predict emergent phenotypes that stems from the dynamic interplay between G, E and M, and thus enable the breeder to consider the behavior of new genetic and trait combinations in environments that plants have not been exposed or tested before, and 2-identify G x M technologies that could increase food and nutritional security while regenerating natural and production resources. We highlight the need to invest in artificial intelligence and information technologies for breeders to harness multiple sources of information to create G x M technologies to address the diverse cultural and geographically granular societal needs.

plant biology↗

bioRxiv2022

A Drosophila glial cell atlas reveals that transcriptionally defined cell types can be morphologically diverse

Morphology is a defining feature of neuronal identity. Like neurons, glia display diverse morphologies, both across and within glial classes, but are also known to be morphologically plastic. Here, we explored the relationship between glial morphology and transcriptional signature using the Drosophila central nervous system, where glia are categorized into five main classes (outer and inner surface glia, cortex glia, ensheathing glia, and astrocytes), which show within-class morphological diversity. We analysed and validated single cell RNA sequencing data of Drosophila glia in two well-characterized tissues from distinct developmental stages, containing distinct circuit types: the embryonic ventral nerve cord (motor) and the adult optic lobes (sensory). Our analysis identified a new morphologically and transcriptionally distinct surface glial population in the ventral nerve cord. However, many glial morphological categories could not be distinguished transcriptionally, and indeed, embryonic and adult astrocytes were transcriptionally analogous despite differences in developmental stage and circuit type. While we did detect extensive within-class transcriptomic diversity for optic lobe glia, this could be explained entirely by glial residence in the most superficial neuropil (lamina) and an associated enrichment for immune-related gene expression. In summary, we generated a single-cell transcriptomic atlas of glia in Drosophila, and our extensive in vivo validation revealed that glia exhibit more diversity at the morphological level than was detectable at the transcriptional level. This atlas will serve as a resource for the community to probe glial diversity and function.

neuroscience↗

bioRxiv2022

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance. Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

plant biology↗

bioRxiv2022

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: A case study of shoot branching in plants

Predictive breeding is now widely practised in crop improvement programs and has accelerated selection response (i.e., the amount of genetic gain between breeding cycles) for complex traits. However, world food production needs to increase further to meet the demands of the growing human population. The prediction of complex traits with current methods can be inconsistent across different genetic, environmental, and agronomic management contexts because the complex relationships between genomic and phenotypic variation are not well accounted for. Therefore, developing gene-to-phenotype network models for traits that integrate the knowledge of networks from systems biology, plant and crop physiology with population genomics has been proposed to close this gap in predictive modelling. Here, we develop a gene-to-phenotype network for shoot branching, a critical developmental pathway underpinning harvestable yield for many crop species, as a case study to explore the value of developing gene-to-phenotype networks to enhance understanding of selection responses. We observed that genetic canalization is an emergent property of the complex interactions among shoot branching gene-to-phenotype network components, leading to the accumulation of cryptic genetic variation, reduced selection responses, and large variation in selection trajectories across populations. As genetic canalization is expected to be pervasive in traits, such as grain yield, that result from interactions among multiple genes, traits, environments, and agronomic management practices, the need to model traits in crop improvement programs as outcomes of gene-to-phenotype networks is highlighted as an emerging opportunity to advance our understanding of selection response and the efficiency of developing resilient crops for future climates.

genetics↗

bioRxiv2021

Predicting trait phenotypes from knowledge of the topology of gene networks

In many fields there is interest in manipulating genes and gene networks to realize improved trait phenotypes. The practicality of doing so, however, requires accepted theory on the properties of gene networks that is well-tested by empirical results. The extension of quantitative genetics to include models that incorporate properties of gene networks expands the long tradition of studying epistasis resulting from gene-gene interactions. Here we consider NK models of gene networks by applying concepts from graph theory and Boolean logic theory, motivated by a desire to model the parameters that influence predictive skill for trait phenotypes under the control of gene networks; N defines the number of graph nodes, the number of genes in the network, and K defines the number of edges per node in the graph, representing the gene-gene interactions. We define and consider the attractor period of an NK network as an emergent trait phenotype for our purposes. A long-standing theoretical treatment of the dynamical properties of random Boolean networks suggests a transition from long to short attractor periods as a function of the average node degree K and the bias probability P in the applied Boolean rules. In this paper we investigate the appropriateness of this theory for predicting trait phenotypes on random and real microorganism networks through numerical simulation. We show that: (i) the transition zone between long and short attractor periods depends on the number of network nodes for random networks; (ii) networks derived from metabolic reaction data on microorganisms also show a transition from long to short attractor periods, but at higher values of the bias probability than in random networks with similar numbers of network nodes and average node degree; (iii) the distribution of phenotypes measured on microorganism networks shows more variation than random networks when the bias probability in the Boolean rules is above 0.75; and (iv) the topological structure of networks built from metabolic reaction data is not random, being best approximated, in a statistical sense, by a lognormal distribution. The implications of these results for predicting trait phenotypes where the genetic architecture of a trait is a gene network are discussed.

genetics↗

bioRxiv2021

Predicting phenotypes from genetic, environment, management, and historical data using CNNs

Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on heldout G, E, and M data (r=0.5 vs r=0.4), and performed slightly worse than standard methods when only G was held out (r=0.74 vs r=0.78). Pre-training on historical data increased accuracy by 1-36% compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance.

plant biology↗

bioRxiv2021

Modelling Human Gait using a Nonlinear Differential Equation

We introduce an innovative method for the investigation of human gait, which is based on the visualisation of the vertical component of the movement of the centre of mass during walking or running, in the space of the coordinates position, velocity, and acceleration of the centre of mass. Collected data has been numerically approximated by the best fitting curve for a non-linear model. The resulting equation for the best fitting plane or curve in this space is a differential equation of second order. The model that we suggest is a Duffing equation with coefficients that depend on the height of a walker or runner and on the angular frequency of the oscillation. Statistics about the distribution of the Duffing stiffness depending on the speed is presented. 1 Author SummaryWe study the human gait modelled by the movement of the centre of mass of the test person. This is an example of a biological process which can be considered as a periodical dynamic system. Roughly, this movement behaves in a similar way to a vibrating mass suspended on a spring, but it is more complex. The vertical component of the movement during walking or running can be visualised as an oscillogram: a graph of the position as a function of time. We suggest a visualisation of the data in 3D space, where the coordinates describe position, velocity, and acceleration. Our new visualisation method allows us to model the movement of a persons centre of mass by a nonlinear differential equation. The resulting curve for an ideal spring-mass movement, without viscosity or external force, is an ellipse in the suggested 3D space. The shape of the data curve shows at which position an additional force was applied, or the movement slowed down. Some deviations are common for all test persons and others are different. In the future we plan to investigate the reasons for these deviations, such as different running techniques or the presence of injuries.

bioengineering↗

bioRxiv2021

Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80({+/-}0.6) - 0.006({+/-}0.001) x ET; r2 = 0.59; df = 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗