Field Study and Correlative Studies of Factor IX Variant FIX-R338L in Participants Treated with Fidanacogene Elaparvovec.

BACKGROUND: Fidanacogene elaparvovec, an adeno-associated virus-based gene therapy vector expressing the high-activity factor IX (FIX) variant FIX-R338L, is in development for hemophilia B. One-stage clotting (OS) assays and chromogenic substrate (CS) assays are commonly used to measure FIX-R338L variant activity. Data from ongoing trials suggest FIX activity varies between different OS and CS assays. MATERIAL AND METHODS: To better understand FIX-R338L activity in clinical samples, an international multisite field study was conducted across a central laboratory and 18 local laboratories, using standard protocols, reagents, and instrumentation, with individual participant samples from a phase 1/2a study of fidanacogene elaparvovec. RESULTS: Unlike the wild-type FIX control, FIX-R338L activity was higher with the OS silica-based assay versus OS ellagic acid-based and CS assays. Variation in FIX activity was greater at the lowest activity levels. Activated FIX (FIXa) in plasma could result in higher OS assay activity or increased thrombin generation, which could overestimate FIX activity. However, FIXa was not detected in the participant samples, indicating that it was not contributing to the OS assay differences. Since individuals on gene therapy may receive exogenous replacement FIX products, replacement products were spiked into patient plasma samples to target a therapeutic concentration. Exogenous FIX was additive to endogenous FIX-R338L, with no interference from FIX-R338L. CONCLUSION: These results demonstrate FIX-R338L activity can be measured with OS and CS assays in clinical laboratories and provide insight into assay variability when measuring FIX with endogenously produced FIX-R338L. The findings may help establish best practices for measuring FIX-R338L activity (Clinicaltrials.gov identifier: NCT02484092).

The Occurrence of Karenia species in mid-Atlantic coastal waters: Data from the Delmarva Peninsula, USA.

A bloom of Karenia papilionacea that occurred along the Delaware coast in late summer of 2007 was the first Karenia bloom reported on the Delmarva Peninsula (Delaware, Maryland, and Virginia, USA). Limited spatial and temporal monitoring conducted by state agencies and citizen science groups since 2007 have documented that several Karenia species are an annual component of the coastal phytoplankton community along the Delmarva Peninsula, often present at background to low concentrations (100 to 10,000 cells L-1). Blooms of Karenia (> 105 cells L-1) occurred in 2010, 2016, 2018, and 2019 in different areas along the Delmarva Peninsula coast. In late summer and early autumn of 2017, the lower Chesapeake Bay experienced a K. papilionacea bloom, the first recorded in Bay waters. Blooms typically occurred summer into autumn but were not monospecific; rather, they were dominated by either K. mikimotoi or K. papilionacea, with K. selliformis, K. brevis-like cells, and an undescribed Karenia species also present. Cell concentrations during these mid-Atlantic Karenia spp. blooms equalled concentrations reported for other Karenia blooms. However, the negative impacts to environmental and human health often associated with Karenia red tides were not observed. The data compiled here report on the presence of multiple Karenia species in coastal waters of the Delmarva Peninsula detected through routine monitoring and opportunistic sampling conducted between 2007 and 2022, as well as findings from research cruises undertaken in 2018 and 2019. These data should be used as a baseline for future phytoplankton community analyses supporting coastal HAB monitoring programs.

A large-effect fitness trade-off across environments is explained by a single mutation affecting cold acclimation.

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Cannabinoids function in defense against chewing herbivores in Cannabis sativa L.

In the decades since the first cannabinoids were identified by scientists, research has focused almost exclusively on the function and capacity of cannabinoids as medicines and intoxicants for humans and other vertebrates. Very little is known about the adaptive value of cannabinoid production, though several hypotheses have been proposed including protection from ultraviolet radiation, pathogens, and herbivores. To test the prediction that genotypes with greater concentrations of cannabinoids will have reduced herbivory, a segregating F2 population of Cannabis sativa was leveraged to conduct lab- and field-based bioassays investigating the function of cannabinoids in mediating interactions with chewing herbivores. In the field, foliar cannabinoid concentration was inversely correlated with chewing herbivore damage. On detached leaves, Trichoplusia ni larvae consumed less leaf area and grew less when feeding on leaves with greater concentrations of cannabinoids. Scanning electron and light microscopy were used to characterize variation in glandular trichome morphology. Cannabinoid-free genotypes had trichomes that appeared collapsed. To isolate cannabinoids from confounding factors, artificial insect diet was amended with cannabinoids in a range of physiologically relevant concentrations. Larvae grew less and had lower rates of survival as cannabinoid concentration increased. These results support the hypothesis that cannabinoids function in defense against chewing herbivores.

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Ecological genetics of local adaptation in Arabidopsis: An 8-year field experiment.

There is considerable evidence for local adaptation in nature, yet important questions remain regarding its genetic basis. How many loci are involved? What are their effect sizes? What is the relative importance of conditional neutrality versus genetic trade-offs? Here we address these questions in the self-pollinating, annual plant Arabidopsis thaliana. We used 400 recombinant inbred lines (RILs) derived from two locally adapted populations in Italy and Sweden, grew the RILs and parents at the parental locations, and mapped quantitative trait loci (QTL) for mean fitness (fruits/seedling planted). We previously published results from the first 3 years of the study, and here add five additional years, providing a unique opportunity to assess how temporal variation in selection might affect QTL detection and classification. We found 10 adaptive and one maladaptive QTL in Italy, and six adaptive and four maladaptive QTL in Sweden. The discovery of maladaptive QTL at both sites suggests that even locally adapted populations are not always at their genotypic optimum. Mean effect sizes for adaptive QTL, 0.97 and 0.55 fruits in Italy and Sweden, respectively, were large relative to the mean fitness of the RILs (approximately 8 fruits/seedling planted at both sites). Both genetic trade-offs (four cases) and conditional neutrality (seven cases) contribute to local adaptation in this system. The 8-year dataset provided greater power to detect QTL and to estimate their locations compared to our previous 3-year study, identifying one new genetic trade-off and resolving one genetic trade-off into two conditionally adaptive QTL.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

Climate change challenges, plant science solutions.

Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.

Genome-wide polymorphism and genic selection in feral and domesticated lineages of Cannabis sativa.

A comprehensive understanding of the degree to which genomic variation is maintained by selection versus drift and gene flow is lacking in many important species such as Cannabis sativa (C. sativa), one of the oldest known crops to be cultivated by humans worldwide. We generated whole genome resequencing data across diverse samples of feralized (escaped domesticated lineages) and domesticated lineages of C. sativa. We performed analyses to examine population structure, and genome wide scans for FST, balancing selection, and positive selection. Our analyses identified evidence for sub-population structure and further support the Asian origin hypothesis of this species. Feral plants sourced from the U.S. exhibited broad regions on chromosomes 4 and 10 with high F̄ST which may indicate chromosomal inversions maintained at high frequency in this sub-population. Both our balancing and positive selection analyses identified loci that may reflect differential selection for traits favored by natural selection and artificial selection in feral versus domesticated sub-populations. In the U.S. feral sub-population, we found six loci related to stress response under balancing selection and one gene involved in disease resistance under positive selection, suggesting local adaptation to new climates and biotic interactions. In the marijuana sub-population, we identified the gene SMALLER TRICHOMES WITH VARIABLE BRANCHES 2 to be under positive selection which suggests artificial selection for increased tetrahydrocannabinol yield. Overall, the data generated, and results obtained from our study help to form a better understanding of the evolutionary history in C. sativa.

Systemic Relapse in a Young Adult Patient with Primary CNS Diffuse Large B-Cell Lymphoma.

Primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL) is a rare form of non-Hodgkin's lymphoma, characterized by an aggressive disease course. While CNS relapse is common, systemic relapse is rare with no consensus on optimal treatment. This paper presents an unusual case of advanced PCNS-DLBCL with systemic relapse, including adrenal gland involvement. A review of the existing literature and a discussion on the management of systemic relapse in PCNS-DLBCL is also provided.

Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes.

High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.

Role of antibody-based therapy in indolent non-Hodgkin's lymphoma.

Monoclonal antibodies (mAb) for indolent non-Hodgkin's lymphoma (iNHL) including follicular and marginal zone lymphomas was a key therapeutic development that changed the natural history of these diseases. Rituximab, a chimeric anti-CD20 mAb, was the first immunotherapy ever used in cancer, and a current cornerstone of lymphoma therapies. Since, we saw development of humanized antibodies, next generations anti-CD20, mAbs targeting other markers on tumor cells (CD19 and CD22), its microenvironment (PD-1, CD47), antibody drug conjugates and bispecific T cell engagers. Given their activity, safety and specificity, mAbs are well poised to remain an essential therapeutic tool for iNHL and other malignancies.

Quantitative trait loci controlling agronomic and biochemical traits in Cannabis sativa.

Understanding the genetic basis of complex traits is a fundamental goal of evolutionary genetics. Yet, the genetics controlling complex traits in many important species such as hemp (Cannabis sativa) remain poorly investigated. Because hemp's change in legal status with the 2014 and 2018 U.S. Federal Farm Bills, interest in the genetics controlling its numerous agriculturally important traits has steadily increased. To better understand the genetics of agriculturally important traits in hemp, we developed an F2 population by crossing two phenotypically distinct hemp cultivars (Carmagnola and USO31). Using whole-genome sequencing, we mapped quantitative trait loci (QTL) associated with variation in numerous agronomic and biochemical traits. A total of 69 loci associated with agronomic (34) and biochemical (35) trait variation were identified. We found that most QTL co-localized, suggesting that the phenotypic distinctions between Carmagnola and USO31 are largely controlled by a small number of loci. We identified TINY and olivetol synthase as candidate genes underlying co-localized QTL clusters for agronomic and biochemical traits, respectively. We functionally validated the olivetol synthase candidate by expressing the alleles in yeast. Gas chromatography-mass spectrometry assays of extracts from these yeast colonies suggest that the USO31 olivetol synthase is functionally less active and potentially explains why USO31 produces lower cannabinoids compared to Carmagnola. Overall, our results help modernize the genomic understanding of complex traits in hemp.

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops.

Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

The population genomics of adaptive loss of function.

Discoveries of adaptive gene knockouts and widespread losses of complete genes have in recent years led to a major rethink of the early view that loss-of-function alleles are almost always deleterious. Today, surveys of population genomic diversity are revealing extensive loss-of-function and gene content variation, yet the adaptive significance of much of this variation remains unknown. Here we examine the evolutionary dynamics of adaptive loss of function through the lens of population genomics and consider the challenges and opportunities of studying adaptive loss-of-function alleles using population genetics models. We discuss how the theoretically expected existence of allelic heterogeneity, defined as multiple functionally analogous mutations at the same locus, has proven consistent with empirical evidence and why this impedes both the detection of selection and causal relationships with phenotypes. We then review technical progress towards new functionally explicit population genomic tools and genotype-phenotype methods to overcome these limitations. More broadly, we discuss how the challenges of studying adaptive loss of function highlight the value of classifying genomic variation in a way consistent with the functional concept of an allele from classical population genetics.

Investigating genetic relationship of Brassica juncea with B. nigra via virtual allopolyploidy and hexaploidy strategy.

Brassica juncea is an important economic crop of the world; however, the narrow genetic base of this crop has tremendously decreased its crop productivity. As an ancestral species of B. juncea, B. nigra is of great importance in widening the genetic diversity of B. juncea. In the present study, 42 SSR markers were employed to screen the genetic diversity among 83 B. nigra, 16 B. juncea, and other Brassica accessions. The molecular characteristics of 498 virtual B. juncea lines were deduced based on the bands of B. nigra and B. rapa via a virtual allopolyploid strategy, and then compared with natural B. juncea accessions. It was found that B. nigra had rich genetic diversity and could be classified into four subgroups, of which subgroup B-III and subgroup B-IV exhibited the closest and the most distant genetic relationship with B. juncea, respectively. To verify this, a hexaploidy strategy was applied to generated synthetic B. juncea from 20 B. nigra accessions, resulting in 45 new-type B. juncea genotypes. The genetic analyses detected that synthetic B. juncea derived from B. nigra in subgroup B-III was close to natural B. juncea, while B. juncea synthesized with B. nigra from subgroup B-IV exhibited wide genetic diversity and was most distant with current B. juncea. This study revealed a great potential of B. nigra in widening genetic diversity of B. juncea particularly using B. nigra in subgroup B-IV, and is helpful in better understanding of the genetic relationship between B. nigra and B. juncea. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-020-01197-7.

Development and application of 'systems thinking' principles for quality improvement.

INTRODUCTION: 'Systems thinking' is often recommended in healthcare to support quality and safety activities but a shared understanding of this concept and purposeful guidance on its application are limited. Healthcare systems have been described as complex where human adaptation to localised circumstances is often necessary to achieve success. Principles for managing and improving system safety developed by the European Organisation for the Safety of Air Navigation (EUROCONTROL; a European intergovernmental air navigation organisation) incorporate a 'Safety-II systems approach' to promote understanding of how safety may be achieved in complex work systems. We aimed to adapt and contextualise the core principles of this systems approach and demonstrate the application in a healthcare setting. METHODS: The original EUROCONTROL principles were adapted using consensus-building methods with front-line staff and national safety leaders. RESULTS: Six interrelated principles for healthcare were agreed. The foundation concept acknowledges that 'most healthcare problems and solutions belong to the system'. Principle 1 outlines the need to seek multiple perspectives to understand system safety. Principle 2 prompts us to consider the influence of prevailing work conditions-demand, capacity, resources and constraints. Principle 3 stresses the importance of analysing interactions and work flow within the system. Principle 4 encourages us to attempt to understand why professional decisions made sense at the time and principle 5 prompts us to explore everyday work including the adjustments made to achieve success in changing system conditions.A case study is used to demonstrate the application in an analysis of a system and in the subsequent improvement intervention design. CONCLUSIONS: Application of the adapted principles underpins, and is characteristic of, a holistic systems approach and may aid care team and organisational system understanding and improvement.