Search for: All records

Abstract Uracil DNA-glycosylase (UNG) is a DNA repair enzyme that removes the highly mutagenic uracil lesion from DNA using a base flipping mechanism. Although this enzyme has evolved to remove uracil from diverse sequence contexts, UNG excision efficiency depends on DNA sequence. To provide the molecular basis for rationalizing UNG substrate preferences, we used time-resolved fluorescence spectroscopy, NMR imino proton exchange measurements, and molecular dynamics simulations to measure UNG specificity constants ( k cat / K M ) and DNA flexibilities for DNA substrates containing central AUT, TUA, AUA, and TUT motifs. Our study shows that UNG efficiency is dictated by the intrinsic deformability around the lesion, establishes a direct relationship between substrate flexibility modes and UNG efficiency, and shows that bases immediately adjacent to the uracil are allosterically coupled and have the greatest impact on substrate flexibility and UNG activity. The finding that substrate flexibility controls UNG efficiency is likely significant for other repair enzymes and has major implications for the understanding of mutation hotspot genesis, molecular evolution, and base editing.

Regional geoengineering, by reflecting sunlight over a very limited spatial domain, might be considered as a means to target specific regional impacts of climate change. One of the obvious concerns raised by such approaches is the extent to which the resulting effects would be detectable well beyond the targeted region (e.g. in neighbouring countries). A few studies have explored this question for targeted regions that are still comparatively large. We consider idealized simulations with increased ocean albedo over relatively small domains; the Gulf of Mexico (0.23% of Earth's surface) and over the Australian Great Barrier Reef (0.07%), both with negligible global radiative forcing. Applied over these very small domains, the only statistically significant non-local changes we find are some limited reduction on summer precipitation in Florida in the Gulf of Mexico case (adjacent to the targeted region). The lack of transboundary effects suggests that governance needs for such targeted interventions are quite distinct from those for more global sunlight reflection.

Abstract Low nitrogen use efficiency (NUE) is ubiquitous in agricultural systems, with mounting global scale consequences for both atmospheric aspects of climate and downstream ecosystems. Since NUE-related soil characteristics such as water holding capacity and organic matter are likely to vary at small scales (< 1 ha), understanding the influence of soil characteristics on NUE at the subfield scale (< 32 ha) could increase fertilizer NUE. Here, we quantify NUE in four conventionally managed dryland winter-wheat fields in Montana following multiple years of sub-field scale variation in experimental N fertilizer applications. To inform farmer decisions that incorporates NUE, we developed a generalizable model to predict subfield scale NUE by comparing six candidate models, using ecological and biogeochemical data gathered from open-source data repositories and from normal farm operations, including yield and protein monitoring data. While NUE varied across fields and years, efficiency was highest in areas of fields with low N availability from both fertilizer and estimated mineralization of soil organic N (SON). At low levels of applied N, distinct responses among fields suggest distinct capacities to supply non-fertilizer plant-available N, suggesting that mineralization supplies more available N in locations with higher total N, reducing efficiency for any applied rate. Comparing modelling approaches, amore »random forest regression model of NUE provided predictions with the least error relative to observed NUE. Subfield scale predictive models of NUE can help to optimize efficiency in agronomic systems, maximizing both economic net return and NUE, which provides a valuable approach for optimization of nitrogen fertilizer use.« less

Arthropod silk is vital to the evolutionary success of hundreds of thousands of species. The primary proteins in silks are often encoded by long, repetitive gene sequences. Until recently, sequencing and assembling these complex gene sequences has proven intractable given their repetitive structure. Here, using high-quality long-read sequencing, we show that there is extensive variation—both in terms of length and repeat motif order—between alleles of silk genes within individual arthropods. Further, this variation exists across two deep, independent origins of silk which diverged more than 500 Mya: the insect clade containing caddisflies and butterflies and spiders. This remarkable convergence in previously overlooked patterns of allelic variation across multiple origins of silk suggests common mechanisms for the generation and maintenance of structural protein-coding genes. Future genomic efforts to connect genotypes to phenotypes should account for such allelic variation.

Uracil is a common DNA lesion which is recognized and removed by uracil DNA-glycosylase (UDG) as a part of the base excision repair pathway. Excision proceeds by base flipping, and UDG efficiency is thought to depend on the ease of deformability of the bases neighboring the lesion. We used molecular dynamics simulations to assess the flexibility of a large library of dsDNA strands, containing all tetranucleotide motifs with U:A, U:G, T:A or C:G base pairs. Our study demonstrates that uracil damaged DNA largely follows trends in flexibility of undamaged DNA. Measured bending persistence lengths, groove widths, step parameters and base flipping propensities demonstrate that uracil increases the flexibility of DNA, and that U:G base paired strands are more flexible than U:A strands. Certain sequence contexts are more deformable than others, with a key role for the 3’ base next to uracil. Flexibilities are large when this base is an A or G, and repressed for a C or T. A 5’ T adjacent to the uracil strongly promotes flexibility, but other 5’ bases are less influential. DNA bending is correlated to step deformations and base flipping, and bending aids flipping. Our study implies that the link between substrate flexibility andmore »UDG efficiency is widely valid, helps explain why UDG prefers to bind U:G base paired strands, and suggests that the DNA bending angle of the UDG-substrate complex is optimal for base flipping.« less

Pulsar timing arrays (PTAs) are Galactic-scale gravitational wave (GW) detectors consisting of precisely timed pulsars distributed across the sky. Within the decade, PTAs are expected to detect nanohertz GWs emitted by close-separation supermassive black hole binaries (SMBHBs), thereby opening up the low-frequency end of the GW spectrum for science. Individual SMBHBs which power active galactic nuclei are also promising multi-messenger sources; they may be identified via theoretically predicted electromagnetic (EM) signatures and be followed up by PTAs for GW observations. In this work, we study the detection and parameter estimation prospects of a PTA which targets EM-selected SMBHBs. Adopting a simulated Galactic millisecond pulsar population, we envisage three different pulsar timing campaigns which observe three mock sources at different sky locations. We find that an all-sky PTA which times the best pulsars is an optimal and feasible approach to observe EM-selected SMBHBs and measure their source parameters to high precision (i.e., comparable to or better than conventional EM measurements). We discuss the implications of our findings in the context of future PTA experiments with the planned Deep Synoptic Array-2000 and the multi-messenger studies of SMBHBs such as the well-known binary candidate OJ 287.

The use of gDNAs isolated from museum specimens for high throughput sequencing, especially targeted sequencing in the context of phylogenetics, is a common practice. Yet, little understanding has been focused on comparing the quality of DNA and results of sequencing museum DNAs. Dragonflies and damselflies are ubiquitous in freshwater ecosystems and are commonly collected and preserved insects in museum collections hence their use in this study. However, the history of odonate preservation across time and museums has resulted in wide variability in the success of viable DNA extraction, necessitating an assessment of their usefulness in genetic studies. Using Anchored Hybrid Enrichment probes, we sequenced DNA from samples at 2 museums, 48 from the American Museum of Natural History (AMNH) in NYC, USA and 46 from the Naturalis Biodiversity Center (RMNH) in Leiden, Netherlands ranging from global collection localities and across a 120-year time span. We recovered at least 4 loci out of an >1,000 locus probe set for all samples, with the average capture being ~385 loci (539 loci on average when a clade of ambiguous taxa omitted). Neither specimen age nor size was a good predictor of locus capture, but recapture rates differed significantly between museums. Samples frommore »the AMNH had lower overall locus capture than the RMNH, perhaps due to differences in specimen storage over time.

« less

Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.