Complementation of bacteriophage lambda integrase mutants: evidence for an intersubunit active site.

Site-specific recombination of bacteriophage lambda starts with the formation of higher-order protein--DNA complexes, called 'intasomes', and is followed by a series of steps, including the initial DNA cleavage, top-strand exchange, branch migration and bottom-strand exchange, to produce recombinant products. One of the intasomes formed during excisive recombination (the attL complex) is composed of the phage-encoded integrase (Int), integration host factor (IHF) and one of the recombination substrates, attL DNA. Int is the catalytic recombinase and has two different DNA binding domains. When IHF is present, Int binds to two types of sites in attL DNA, the three arm-type sites (P'123) and the core-type sites (B and C') where the reciprocal strand exchange takes place. The Tyr342 residue of Int serves as a nucleophile during strand cleavage and covalently attaches to the DNA through a phosphotyrosyl bond. In vitro complementation assays have been performed for strand cleavage using attL suicide substrates and mutant proteins containing amino acid substitutions at residues conserved in the integrase family of recombinases. We demonstrate that at least two Int monomers are required to form the catalytically-competent species that performs cleavage at the B site. It is likely that the active site is formed by two Int monomers.     

Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050–2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype–environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.     

Three research priorities for just and sustainable urban systems: Now is the time to refocus

Now is the time to refocus efforts in urban research and design. A changing climate and extreme weather events are presenting unique challenges to urban systems around the world. These challenges illuminate the social barriers that accompany disruptive events such as resource inequities and injustices. In this perspective, we provide three research priorities for just and sustainable urban systems that help to address these matters. The three research priorities are: (1) social equity and justice, (2) circularity, and (3) digital twins. Conceptual context and future research directions are provided for each. For social equity and justice, the future directions are mandatory equity analysis and inclusionary practices, understanding and reconciling historical injustices, and intentional integration with diverse community stakeholders. For circularity applications, they are better metrics for integration, more robust evaluation frameworks, and dynamic modeling at multiple spatial and temporal scales. Future directions for digital twins include developing principles to reduce complexity, integrating model and system components, and reducing barriers to data access. These research priorities are core to meeting several of the United Nations Sustainable Development Goals (i.e., 1—No Poverty, 8—Decent Work and Economic Growth, 10—Reduced Inequalities, and 11—Sustainable Cities and Communities). Useful social and technical matters are discussed throughout, where we highlight the importance of prioritizing localized research efforts, provide guidance for community-engaged research and co-development practices, and explain how these priorities interact to align with the evolving field of industrial ecology.     

Regeneration of transgenic tamarillo plants

Media were developed to regenerate shoots from leaf pieces of tamarillo (Cyphomandra betacea (Cav.) Sendtner). Shoots were derived via organogenesis and could be easily rooted and transferred to the growth chamber. Transgenic tamarillo plants were produced using the binary vector pKIWI110 in the avirulent Agrobacterium strain LBA4404. All transgenic plants were kanamycin resistant and some plants expressed the D-glucuronidase (gusA) reporter gene and were chlorsulfuron resistant. Molecular evidence for transformation was obtained using PCR (polymerase chain reaction) and Southern hybridization. Inheritance of the transgenic phenotypes was demonstrated in seedling progeny.     

Induction of programmed cell death in a dorsal root ganglia × neuroblastoma cell line

Growth factor-dependent neurons die when they are deproved of their specific growth factor. This “programmed” cell death (PCD) requires macromolecular synthesis and is distinct from necrotic cell death. To investigate the mechanisms involved in neuronal PCD, we have studied the sequence of events that occur when a neuronal cell line (F-11: Mouse neuroblastoma X rat dorsal root ganglia) is deprived of serum in a manner analogous to growth factor deprivation from neurons. Protein synthesis was inhibited within the first 8 h of serum deprivation, while DNA cleavage into nucleosome ladders was prominent by 24 h. The DNA cleavage could be inhibited by cycloheximide, consistent with a requirement for protein synthesis. In contrast, mitochondrial function was not compromised by serum deprivation. Rather, the cells appeared to be metabolically activated after serum removal as shown by an increased reduction of MTT by mitochondrial dehydrogenases and an increase in cellular autofluorescence, which is thought to be due to elevated levels of NADH and flavoproteins. Assessment of cell viability by propidium iodide staining showed no indication of cell death within 24 h. After 48 h of serum deprivation, cells decreased in size and increased propidium iodide uptake. Thus, serum deprivation activates PCD in F-11 cells and may be a useful model to study the intracellular events responsible for PCD. © 1993 John Wiley & Sons, Inc.     

Development of DNA methylation-based epigenetic age predictors in loblolly pine (Pinus taeda)

Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age-related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH-methylation contexts in the loblolly pine tree (Pinus taeda) and construct epigenetic clocks capable of predicting ages in this species within 6% of its maximum lifespan. Although patterns of CHH-methylation showed little association with age, both CpG and CHG-methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age-associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age-associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.     

snpR: User friendly population genomics for SNP data sets with categorical metadata

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.     

A differential proteomics study of cerebrospinal fluid from individuals with Niemann-Pick disease,Type C1

Niemann-Pick, type C1 (NPC1) is a fatal, neurodegenerative disease, which belongs to the family of lysosomal diseases. In NPC1, endo/lysosomal accumulation of unesterified cholesterol and sphingolipids arise from improper intracellular trafficking resulting in multi-organ dysfunction. With the proximity between the brain and cerebrospinal fluid (CSF), performing differential proteomics provides a means to shed light to changes occurring in the brain. In this study, CSF samples obtained from NPC1 individuals and unaffected controls were used for protein biomarker identification. A subset of these individuals with NPC1 are being treated with miglustat, a glycosphingolipid synthesis inhibitor. Of the 300 identified proteins, 71 proteins were altered in individuals with NPC1 compared to controls including cathepsin D, and members of the complement family. Included are a report of 10 potential markers for monitoring therapeutic treatment. We observed that pro-neuropeptide Y (NPY) was significantly increased in NPC1 individuals relative to healthy controls; however, individuals treated with miglustat displayed levels comparable to healthy controls. In further investigation, NPY levels in a NPC1 mouse model corroborated our findings. We posit that NPY could be a potential therapeutic target for NPC1 due to its multiple roles in the central nervous system such as attenuating neuroinflammation and reducing excitotoxicity.