Insect pollination is essential for global fruit and vegetable production, yet rising climate variability and widespread declines in bee populations threaten the reliability of this service. Flies (Diptera), despite their abundance and tolerance to different environmental conditions, remain largely overlooked as managed pollinators in agriculture. Here, we present field-based evidence from commercial farms in Australia showing that certain fly species can deliver effective and climate-resilient pollination services to high-value berry cropping systems. In replicated cage trials, European hover flies (Eristalis tenax) produced fruit comparable in weight and marketability to those pollinated by European honey bees (Apis mellifera) and Australian native stingless bees (Tetragonula carbonaria), whereas brown blow flies (Calliphora stygia) showed more variable performance. When scaled to full-sized commercial polytunnels, hover flies consistently produced market-quality blackberry fruit, matching open (mainly bee) pollination in spring and outperforming open pollination under the cooler, more variable conditions of winter. Hover fly-pollinated fruit equaled or exceeded the quality of hand-pollinated flowers and consistently outperformed self-pollinated controls. Across spring and winter floral visitation field surveys, hover flies maintained higher flower visitation than honey bees across cooler temperatures, broader humidity ranges, and early tomidday time periods, indicating greater climatic tolerance under field conditions. Together, these findings demonstrate that the hover fly, E. tenax, can provide scalable, climate-resilient pollination services and highlight the value of integrating flies into diversified pollination strategies to support resilient crop production under a changing climate.

Abstract A rapid expansion of influenza A virus (IAV) genome sequencing has transformed global surveillance but has also created major challenges for interpreting the biological significance of viral mutations, particularly amino acid replacements associated with host adaptation. Resources have been created to support mutation annotation and phylogenetic analysis, but there is a need for a tool that integrates experimentally derived phenotypic evidence with evolutionary context in a framework suitable for users without prior training in bioinformatics. Here, we present the Flu Mutation Explorer, an interactive web application that combines large-scale influenza phylogenies with a manually curated database of reported mammalian adaptation mutations, to enable the exploration and interpretation of IAV genetic variation. The underlying database comprises over 1.5 million publicly available IAV sequences and over 1000 mutations associated with mammalian adaptation. The Flu Mutation Explorer enables users to query protein sequences, visualise amino acid distributions across viral lineages, examine host-specific conservation patterns, and identify adaptation mutation with links to supporting literature. We include case studies which demonstrate the platform’s use in assessing amino acid conservation at sites of interest and in rapidly identifying candidate mammalian adaptation mutations during the ongoing H5N1 panzootic. By integrating genomic, phylogenetic, and functional information into an intuitive interface, the Flu Mutation Explorer lowers the barriers to interpreting influenza sequences for specialists and non-specialists alike.

The rising demand for plant-based proteins has intensified interest in pulse crops due to their high protein concentration. Few studies have evaluated protein and amino acid composition or variability in cultivated lentil (Lens culinaris Medik.). We evaluated protein and amino acid composition using near-infrared reflectance spectroscopy in a diversity panel grown in four site-years in Saskatchewan, Canada, followed by genome-wide association analyses with phenology-related traits as covariates. We found little correlation between protein concentration and days from sowing to flowering, region of origin, cotyledon color, or seed size. Reproductive period was correlated with protein concentration, however. We also observed variability among environments and more variability within market classes than among them. We demonstrate the potential for breeders to identify adapted germplasm and select for increased protein and amino acid concentration and quality. We were able to identify several molecular markers for use in marker-assisted breeding to select for protein concentration or quality.

COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology during acute COVID-19 is necessary to understand its diverse pathogenesis and inform more effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood markers, and lung pathology in 142 Brazilian patients hospitalized with COVID-19. We identified core clinical and peripheral blood signatures differentiating disease progression between patients who recovered from severe disease compared with those who succumbed to the disease. Signatures were heterogeneous among fatal cases yet clustered into two patient groups: "early death" (<15 days until death) and "late death" (>15 days). Progression to early death was characterized systemically and in lung histopathological samples by rapid endothelial and myeloid activation and the presence of thrombi associated with SARS-CoV-2(+) macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis, and SARS-CoV-2(+) epithelial cells in postmortem lung tissue. In late death cases, cytotoxicity, interferon, and T helper 17 (T(H)17) signatures were only detectable in the peripheral blood after 2 weeks of hospitalization. Progression to recovery was associated with higher lymphocyte counts, T(H)2 responses, and anti-inflammatory-mediated responses. By integrating antemortem longitudinal blood signatures and spatial single-cell lung signatures from postmortem lung samples, we defined clinical parameters that could be used to help predict COVID-19 outcomes.

Twenty species/isolates of cyanobacteria and green algae were isolated from cyanobacterial bloom samples in lakes associated with the upper Qu'Appelle River drainage system in southern Saskatchewan, Canada. Three amoebae species (Cochliopodium sp., Vannella sp. and Vermamoeba vermiformis) were also isolated from one of these samples, and were subjected to grazing assays to determine which species of cyanobacteria or algae could potentially serve as a food source. Amoeba grazing rates were quantified based on the diameter of the plaque after 12 days on agar plate assays, and by estimation of the amoeba population growth rate from the rate of increase of plaque area. The common cyanobacterial bloom-formers Dolichospermum sp. and Aphanizomenon flos-aquae supported high growth rates for all three amoebae, while green algae, with the exception of one green alga/amoeba combination, did not support growth of the tested amoebae. Many of the cyanobacterial and algal isolates that did not support amoebae growth were ingested, suggesting that ingestion did not determine grazing success. Overall, while the cyanobacteria Dolichospermum sp. and Aphanizomenon flos-aquae were suitable food sources for the amoebae, the other cyanobacteria were grazed in an unpredictable manner, with some species/strains grazed by some amoebae and some species not grazed at all.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to evolve throughout the coronavirus disease-19 (COVID-19) pandemic, giving rise to multiple variants of concern (VOCs) with different biological properties. As the pandemic progresses, it will be essential to test in near real time the potential of any new emerging variant to cause severe disease. BA.1 (Omicron) was shown to be attenuated compared to the previous VOCs like Delta, but it is possible that newly emerging variants may regain a virulent phenotype. Hamsters have been proven to be an exceedingly good model for SARS-CoV-2 pathogenesis. Here, we aimed to develop robust quantitative pipelines to assess the virulence of SARS-CoV-2 variants in hamsters. We used various approaches including RNAseq, RNA in situ hybridization, immunohistochemistry, and digital pathology, including software assisted whole section imaging and downstream automatic analyses enhanced by machine learning, to develop methods to assess and quantify virus-induced pulmonary lesions in an unbiased manner. Initially, we used Delta and Omicron to develop our experimental pipelines. We then assessed the virulence of recent Omicron sub-lineages including BA.5, XBB, BQ.1.18, BA.2, BA.2.75 and EG.5.1. We show that in experimentally infected hamsters, accurate quantification of alveolar epithelial hyperplasia and macrophage infiltrates represent robust markers for assessing the extent of virus-induced pulmonary pathology, and hence virus virulence. In addition, using these pipelines, we could reveal how some Omicron sub-lineages (e.g., BA.2.75 and EG.5.1) have regained virulence compared to the original BA.1. Finally, to maximise the utility of the digital pathology pipelines reported in our study, we developed an online repository containing representative whole organ histopathology sections that can be visualised at variable magnifications (https://covid-atlas.cvr.gla.ac.uk). Overall, this pipeline can provide unbiased and invaluable data for rapidly assessing newly emerging variants and their potential to cause severe disease.

Abstract Native insect flower visitors can be important contributors to crop pollination, yet little is known of their pollination abilities and the resources (habitat) they need to be supported within crop agroecosystems. Here, we compared the abundance and pollination abilities of the golden drone fly (Eristalinus punctulatus) to the European honey bee (Apis mellifera) in hybrid carrot crop fields known to produce variable seed yields in regional New South Wales, Australia. We further observed the egg‐laying behaviours of female golden drone flies at a commercial berry orchard to provide insight into the habitat needs of this species. In hybrid carrot crop fields, golden drone flies were far less abundant flower visitors than European honey bees; however, these flies deposited more carrot pollen grains on average (8.21 ± 3.04 SE) onto carrot flowers than European honey bees (3.45 ± 1.06 SE). Both insects also deposited pollen onto a similar number of carrot flowers (pollinated) per visit (about 2 out of 18). Golden drone flies were observed laying eggs within masses of discarded red raspberry plant roots and soil (root balls) at a commercial berry orchard. The natural habitat utilised by these flies, as well as their egg‐laying behaviours, were described for the first time. Our results indicate that golden drone flies are effective pollinators of hybrid carrot crop plants. The habitat that these flies utilised to lay eggs (discarded plants and water) is cheap and commonly found in crop agroecosystems. Therefore, we recommend placing this low‐cost habitat within, or nearby, crop fields as a potential management practice to support the lifecycle needs of golden drone flies and other non‐bee pollinators.

Lentil seed coats are rich in antioxidant polyphenols that are important for plant defense and have potential as valorized byproducts. Although biochemical differences among lentil seed coat colors have been previously studied, differences among seed coat patterns remain largely unexplored. This study used mass spectrometry-based untargeted metabolomics to investigate polyphenol differences among lentil seed coat patterns to search for biochemical pathways potentially responsible for seed coat pattern differences. Comparing patterned with non-patterned green lentil seed coats, 28 significantly upregulated metabolites were found in patterned seed coats; 19 of them were identified as flavones. Flavones were virtually absent in non-patterned seed coats, thereby strongly suggesting a blockage in their flavone biosynthetic pathway. Although the black pattern is not readily discernible on black seed coats, many of the same flavones found in green marbled seed coats were also found in black seed coats, indicating that black seed coats likely have a marbled pattern.

COG-UK Mutation Explorer (COG-UK-ME, https://sars2.cvr.gla.ac.uk/cog-uk/ - Last Accessed date 16/03/22) is a web resource that displays knowledge and analyses on SARS-CoV-2 virus genome mutations and variants circulating in the UK, with a focus on the observed amino acid replacements that have an antigenic role in the context of the human humoral and cellular immune response. This analysis is based on more than 2 million genome sequences (as of March 2022) for UK SARS-CoV-2 data held in the CLIMB-COVID centralised data environment. COG-UK-ME curates these data and displays analyses that are cross-referenced to experimental data collated from the primary literature. The aim is to track mutations of immunological importance that are accumulating in current variants of concern and variants of interest that could alter the neutralising activity of monoclonal antibodies (mAbs), convalescent sera and vaccines. Changes in epitopes recognised by T cells, including those where reduced T cell binding has been demonstrated, are reported. Mutations that have been shown to confer SARS-CoV-2 resistance to antiviral drugs are also included. Using visualisation tools, COG-UK-ME also allows users to identify the emergence of variants carrying mutations that could decrease the neutralising activity of both mAbs present in therapeutic cocktails, e.g., Ronapreve. COG-UK-ME tracks changes in frequency of combination of mutations and brings together curated literature on the impact of those mutations on various functional aspects of the virus and therapeutics. Given SARS-CoV-2 unpredictable nature as exemplified by yet another variant of concern, Omicron, continued surveillance of SARS-CoV-2 remains imperative to monitor virus evolution linked to the efficacy of therapeutics.

Monoclonal antibodies (mAbs) offer a treatment option for individuals with severe COVID-19 and are especially important in high-risk individuals where vaccination is not an option. Given the importance of understanding the evolution of resistance to mAbs by SARS-CoV-2, we reviewed the available in vitro neutralization data for mAbs against live variants and viral constructs containing spike mutations of interest. Unfortunately, evasion of mAb-induced protection is being reported with new SARS-CoV-2 variants. The magnitude of neutralization reduction varied greatly among mAb–variant pairs. For example, sotrovimab retained its neutralization capacity against Omicron BA.1 but showed reduced efficacy against BA.2, BA.4 and BA.5, and BA.2.12.1. At present, only bebtelovimab has been reported to retain its efficacy against all SARS-CoV-2 variants considered here. Resistance to mAb neutralization was dominated by the action of epitope single amino acid substitutions in the spike protein. Although not all observed epitope mutations result in increased mAb evasion, amino acid substitutions at non-epitope positions and combinations of mutations also contribute to evasion of neutralization. This Review highlights the implications for the rational design of viral genomic surveillance and factors to consider for the development of novel mAb therapies. In this Review, Carabelli, Robertson and colleagues explore data on the neutralization of globally circulating variants of concern by monoclonal antibodies (mAbs) and discuss how knowledge of the dynamics of viral evasion of mAbs can contribute to viral surveillance and the development of novel mAb treatments, as well as inform predictions of resistance that may arise in the future.

Amyloid-beta peptide (Aβ) is the primary component of water-insoluble extracellular plaques, one of the critical hallmarks of Alzheimer's disease (AD). Matrix metalloproteases (MMPs) are broad-spectrum proteases with diverse functions, including interactions with Aβ. Here we report single molecule measurements of MMP1 dynamics on Aβ-induced aggregates by calculating Forster Resonance Energy Transfer (FRET) between two dyes attached to the catalytic and hemopexin domains. We show that the two domains of MMP1 prefer closed conformations on Aβ-induced aggregates, in contrast to the preference for open conformations on collagen fibril, fibrin, and alpha-synuclein aggregates. We approximated the MMP1 dynamics by a two-state Poisson process and determined the kinetic rates of interconversion between the two states from histograms and correlations of FRET values. We performed molecular docking of MMP1 with Aβ using ClusPro, simulated MMP1 dynamics using different docking poses, and matched the experimental and simulated interdomain dynamics to identify an appropriate pose. We used simulations to create a two-dimensional map of correlations between every pair of MMP1 residues, which shows allosteric communications between the two MMP1 domains. We calculated a Gray Level Co-occurrence Matrix from the two-dimensional map of correlations and quantified MMP1 fluctuations by Shannon entropy. We identified the allosteric residues in the hemopexin domain by identifying residues having strong correlations with the catalytic motif residues. We identified that the residues I364, G369, P409, G410, and D418 in MMP1 have Aβ-specific allosteric correlations with the MMP1 catalytic motif by comparing residues for free and Aβ-bound MMP1. We used these Aβ-specific allosteric residues to select small molecule ligands after the virtual screening of molecules against Aβ-bound MMP1. Molecular understanding of interactions between MMP1 and Aβ-induced aggregates and identification of substrate-specific allosteric residues may enable controlling MMP1 function selectively on Aβ.

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and/or areas where new crops have been introduced, e.g., lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multi-environment dataset, phenotyped for phenology-related traits across nine locations and over three years, along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions) which has also been genotyped with an exome capture array. Genomewide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.

Synopsis The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.

Alpha-synuclein (aSyn) has implications in pathological protein aggregations in neurodegeneration. Matrix metalloproteases (MMPs) are broad-spectrum proteases and cleave aSyn, leading to aggregation. Previous reports showed that allosteric communications between the two domains of MMP1 on collagen fibril and fibrin depend on substrates, activity, and ligands. This paper reports quantification of allostery using single molecule measurements of MMP1 dynamics on aSyn-induced aggregates by calculating Forster Resonance Energy Transfer (FRET) between two dyes attached to the catalytic and hemopexin domains of MMP1. The two domains of MMP1 prefer open conformations that are inhibited by a single point mutation E219Q of MMP1 and tetracycline, an MMP inhibitor. A two-state Poisson process describes the interdomain dynamics, where the two states and kinetic rates of interconversion between them are obtained from histograms and autocorrelations of FRET values. Since a crystal structure of aSyn-bound MMP1 is unavailable, binding poses were predicted by molecular docking of MMP1 with aSyn using ClusPro. MMP1 dynamics were simulated using predicted binding poses and compared with the experimental interdomain dynamics to identify an appropriate pose. The selected aSyn-MMP1 binding pose near aSyn residue K45 was simulated and analyzed to define conformational changes at the catalytic site. Allosteric residues in aSyn-bound MMP1 exhibiting strong correlations with the catalytic motif residues were compared with allosteric residues in free MMP1, and aSyn-specific residues were identified. The allosteric residues in aSyn-bound MMP1 are K281, T283, G292, G327, L328, E329, R337, F343, G345, N346, Y348, G353, Q354, D363, Y365, S366, S367, F368, P371, R372, V374, K375, A379, F391, A394, R399, M414, F419, V426, and C466. Shannon entropy was defined to quantify MMP1 dynamics. Virtual screening was performed against a site on selected aSyn-MMP1 binding poses, which showed that lead molecules differ between free MMP1 and substrate-bound MMP1. Also, identifying aSyn-specific allosteric residues in MMP1 enabled further selection of lead molecules. In other words, virtual screening needs to take substrates into account for potential substrate-specific control of MMP1 activity in the future. Molecular understanding of interactions between MMP1 and aSyn-induced aggregates may open up the possibility of degrading aggregates by targeting MMPs.

In recent decades, the field of phenomics has lagged behind the advances in genomics, which have become increasingly high-throughput and low-cost. In comparison, manually collected phenotypes are often time-consuming, labor intensive, and more costly to obtain. The development of high-throughput phenotyping platforms (HTPP) are bridging these gaps and enabling improved spatial and temporal resolution for researchers. We used imagery from unoccupied aerial vehicles (UAV) flown over multiple site years in Saskatchewan and Italy to gather data for crop height, area and volume in a lentil diversity panel. We found high correlations for our UAV-derived traits (height & volume) with our manually collected phenotypes (height & biomass). In addition, the high-throughput nature of the UAV allowed for the collection of time-series data which enabled the modelling of growth curves for volume, height and area, which would be impractical under traditional phenotyping procedures given the large population grown in multiple environments. Principal component analysis and hierarchical clustering revealed differential growth strategies amongst our diverse lentil population across contrasting environments. Our study demonstrates the potential for HTPP to obtain data that traditionally require destructive sampling, e.g., volume as a proxy for vegetative biomass, and improve the temporal quality of phenotype data enabling researchers to take their analysis beyond single time points, e.g., model growth curves. In addition, performing our analysis on data from contrasting environments, i.e., Saskatchewan and Italy, has helped elucidate optimal adaptation with regard to growth strategies in lentils.

Although lentil has a long history of cultivation, cultivars rely on a narrow genetic base, indicating room for broadening the diversity. Two field experiments were conducted at Bardiya, Nepal, during winter 2016 and 2017, with 324 diverse lentil genotypes obtained from genebanks and breeding programs around the world. Phenological traits related to adaptation, particularly days to flower, were assessed. A photothermal model was used to predict days to flower in new environments to identify genotypes that may be suitable for additional growing regions in Nepal, allowing for the expansion of the production area. Many putatively adapted genotypes were identified for terai, mid-hill, and high-hill growing regions. The list includes large-seeded or yellow cotyledon lines, representing new market classes of lentils for Nepal.

Alpha-synuclein (aSyn) has implications in pathological protein aggregations observed in neurodegenerative disorders, including Parkinson’s and Alzheimer’s diseases. There are currently no approved prevention and cure for these diseases. In this context, matrix metalloproteases (MMPs) provide an opportunity because MMPs are broad-spectrum proteases and cleave aSyn. Previously, we showed that allosteric communications between the two domains of MMP1 on collagen fibril and fibrin depend on substrates, MMP1 activity, and ligands. However, allosteric communications in MMP1 on aSyn-induced aggregates have not been explored. Here we report quantification of allostery using single molecule measurements of MMP1 dynamics on aSyn-induced aggregates by calculating Forster Resonance Energy Transfer (FRET) between two dyes attached to the catalytic and hemopexin domains of MMP1. The two domains of MMP1 prefer open conformations, with the two domains well-separated. These open conformations are inhibited by a single point mutation E219Q of MMP1 and tetracycline, an MMP inhibitor. A two-state Poisson process describes the interdomain dynamics. The best-fit parameters for a Gaussian fit to the distributions of FRET values provide the two states. The ratio of the kinetic rates between the two states comes from the ratio of fitted areas around the two states. The decay rate of an exponential fit to the correlations between FRET values provides the sum of the kinetic rates. Since a crystal structure of aSyn-bound MMP1 is not available, we performed molecular docking of MMP1 with aSyn using ClusPro. We simulated MMP1 dynamics using different docking poses and matched the experimental and simulated interdomain dynamics to determine the most appropriate pose. We performed virtual screening against the potential ligand-binding sites on the appropriate aSyn-MMP1 binding pose and showed that lead molecules differ between free MMP1 and substrate-bound MMP1. In other words, virtual screening needs to take substrates into account for substrate-specific control of MMP1 activity. Molecular understanding of interactions between MMP1 and aSyn-induced aggregates may open up the possibility of degrading pathological aggregates in neurodegeneration by targeting MMPs. Significance We have quantified MMP1 interdomain dynamics on aSyn-induced aggregates by a two-state Poisson process. Histograms and correlations of FRET values determine the kinetic rates of interconversion between the two states. We quantify the conformational dynamics of the whole MMP1 and allosteric communications by the two-dimensional matrix of correlations between every pair of amino acids from experimentally-validated all-atom simulations. The two-dimensional correlations lead to a Gray Level Co-occurrence Matrix and a measure of Shannon entropy describing the conformational fluctuations. As such, we address the quantification of allosteric communications, a leading challenge in defining allostery. We report that the potential ligand-binding sites and lead molecules change for MMP1 upon binding alpha-synuclein and depend on the binding pose selected. This suggests that one needs to take the substrate into account while targeting MMPs.

Societal Impact Statement Lentil is a staple in many diets around the world and growing in popularity as a quick‐cooking, nutritious, plant‐based source of protein in the human diet. Lentil varieties are usually grown close to where they were bred. Future climate change scenarios will result in increased temperatures and shifts in lentil crop production areas, necessitating expanded breeding efforts. We show how we can use a daylength and temperature model to identify varieties most likely to succeed in these new environments, expand genetic diversity, and give plant breeders additional knowledge and tools to help mitigate these changes for lentil producers. Summary Lentil (Lens culinaris Medik.) is cultivated under a wide range of environmental conditions, which has led to diverse phenological adaptations and resulted in a decrease in genetic variability within breeding programs due to reluctance in using genotypes from other environments. We phenotyped 324 genotypes across nine locations over three years to assess their phenological response to the environment of major lentil production regions and to predict days from sowing to flowering (DTF) using a photothermal model. DTF was highly influenced by the environment and is sufficient to explain adaptation. We were able to predict DTF reliably in most environments using a simple photothermal model, however, in certain site‐years, results suggest there may be additional environmental factors at play. Hierarchical clustering of principal components revealed the presence of eight groups based on the responses of DTF to contrasting environments. These groups are associated with the coefficients of the photothermal model and revealed differences in temperature and photoperiod sensitivity. Future climate change scenarios will result in increased temperature and/or shifts in production areas. The ability to use the photothermal model to identify genotypes most likely to succeed in these new environments has important social impacts with respect to traditional cropping systems.

ABSTRACT Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. We interrogated the transcriptome, genome, proteome and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting the DNA damage response (DDR) and replication stress. We show that patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, co-segregates with response to platinum and PARP inhibitor therapy in vitro and in vivo . We generated a novel signature of replication stress with potential clinical utility in predicting response to ATR and WEE1 inhibitor treatment. Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR proficient PC, and post-platinum therapy. Abstract Figure STATEMENT OF SIGNIFICANCE We define therapeutic strategies that target subgroups of PC using novel signatures of DNA damage response deficiency and replication stress. This potentially offers patients with DNA repair defects therapeutic options outside standard of care platinum chemotherapy and is being tested in clinical trials on the Precision-Panc platform.