Biology subjects

Webster, K.

Publications and source records attributed to Webster, K..

8 recordsLinked to original sources

bioRxiv2026

Sequence determinants of efficient exon 44 skipping in Duchenne muscular dystrophy define design principles for steric-blocking antisense oligonucleotides

Duchenne muscular dystrophy (DMD) is caused by mutations in the DMD gene that disrupt the reading frame and abolish expression of functional dystrophin protein. Antisense oligonucleotides (ASO) can restore production of partially functional dystrophins by inducing exon skipping to restore the reading frame of dystrophin transcripts. While exon skipping is an FDA approved therapeutic strategy, there are currently no approved therapies for patients amenable to exon 44 skipping (8% of DMD patients). Here, we carried out a discovery campaign to identify phosphorothioate (PS) ASOs that efficiently induce exon 44 skipping and to define key sequence and chemistry features associated with activity. A tiling and micro-tiling approach with 18mer fully PS and 2-O-methoxyethyl (2MOE) modified ASOs in patient-derived myotubes identified five exonic target regions that promote skipping. ASO activity was strongly correlated across skeletal muscle and iPSC-derived cardiomyocytes, indicating similar exon 44 splicing regulation across cell types. Optimization studies showed that for 2MOE PS ASOs, 16-20mers were generally most active, while longer ASOs often had reduced activity due in part to impaired productive uptake into cells. Swapping out 2MOE modifications at both terminal positions for locked nucleic acids (LNAs) rarely improved activity and could also reduce it. Finally, promising candidates were tested in a humanized mouse model with an exon 44 skippable deletion, where one ASO outperformed others, inducing dose-dependent exon 44 skipping and dystrophin restoration in mouse models. These findings define practical design rules for exon 44-targeted ASOs and provide a foundation for therapeutic development.

molecular biology↗

bioRxiv2025

Diverging effects of global change on future invasion risks of Agama picticauda between invaded regions: same problem, different solutions

Predicting biological invasions is challenging because multiple factors can act in contrasting directions and exert heterogeneous effects across space. Nevertheless, modelling approaches provide valuable tools to anticipate the potential spread of invasive alien species and to support mitigation strategies. With an Ecological Niche Modelling approach, we predicted the invasion risks of Peterss Rock Agama Agama picticauda, a species that is spreading globally in non-forested areas through freight transport and un-/intentional releases from the pet trade. The potential establishment of the species in new areas is of concern for multiple endemic species throughout the world. We quantified the effects of climate, anthropogenic activity and forest cover on invasion risk. We used verified records from the native and non-native range and accounted for the latest methodological recommendations. We predicted how invasion risk will vary in the future (2070) using projections from two scenarios (SSP2 and SSP5). We predict that invasion risks will vary in diverging directions, depending on the region. The risk will increase in human-populated regions and on small islands but will decrease in Florida. We recommend increasing surveillance in vehicular transportation of material especially within the Comoros and the Mascarenes archipelagos. Since many introductions are related to the pet trade in Florida, we recommend stronger legal regulations and the promotion of public awareness. Promoting tree cover may be locally beneficial to prevent establishment of A. picticauda. The effect of climate change, land use change and human activities may differ between and within both, the native and the invaded regions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/671467v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@d623cforg.highwire.dtl.DTLVardef@c91271org.highwire.dtl.DTLVardef@d9f9beorg.highwire.dtl.DTLVardef@9b4b3b_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2025

Simulating the Impacts of Climate Change on UH Manoa Lettuce (Lactuca sativa) Growth by Modifying Air Temperature, Soil Water Availability, and Atmospheric CO2 Concentration

Plant species are adapted to survive under specific ranges of temperature, water availability, and atmospheric CO2 concentration. Climate change-induced shifts in these environmental conditions have the potential to significantly affect nearly all terrestrial plants. A number of studies have explored the impacts of changing one or two of the conditions listed above, but few have examined the combined effects of all three. To study the cumulative influences of the three environmental conditions, 350 M[a]noa lettuce (Lactuca sativa) plants were grown in indoor growth chambers. Within the chambers, plants were grown under varying degrees of CO2 concentration, water availability, and temperature for 21 days. At the end of this period, the leaf mass (biomass) of each plant was cut, dried, and weighed. Percent mortality and nitrogen content were also measured. Across all combinations of temperature and water availability, elevated CO2 concentrations were associated with increased biomass production and survival rates. Survival and biomass decreased under high temperatures and both high and low water availability. The combination of environmental conditions that produced the largest amount of biomass was 750 ppm CO2, 80% of soil water capacity, and 24 {degrees}C while the treatment that produced the least amount was ambient CO2, 60% of soil water capacity, and 36 {degrees}C. Nitrogen content increased under high temperatures and water availability. These results suggest that although increased atmospheric CO2 levels have the potential to promote lettuce growth, lettuce yield is still likely to decrease in many regions due to the negative effects of high temperatures, drought, and flooding.

plant biology↗

bioRxiv2023

Logging disrupts the ecology of molecules in headwater streams

Global demand for wood products is increasing forest harvest. One understudied consequence of logging is that it accelerates mobilization of dissolved organic matter (DOM) from soils to aquatic ecosystems. Here, we tested how logging changed DOM in headwaters of hardwood-dominated catchments in northern Ontario, Canada. We apply a before-after control-impact experiment across four catchments for three years. DOM concentration in streams from logged catchments quadrupled, on average, after the first year post-harvest, but resulting changes to the molecular composition of DOM persisted for at least two-years. Ultrahigh-resolution mass spectrometry revealed that DOM within logged catchments was more energy-rich and chemically diverse than in controls, with novel highly unsaturated polyphenols, carboxylic-rich alicyclic, and nitrogen-containing formulae. The molecular composition of stream DOM measured fortnightly post-harvest was most strongly associated with DOM composition within intermediate and deeper layers of contributing soils, likely due to increased hydrological connectivity post-harvest. We estimate logging increased the total annual flux of dissolved organic carbon in streams by 6.4% of extracted wood carbon, and this carbon was more likely to be released into the atmosphere. Carbon accounting of forestry, including as a natural climate solution, must now consider the transport and fate of DOM from land into water.

ecology↗

bioRxiv2022

Early Seasonal Increases and Persistence in Relative Abundance of Potentially Toxic Cyanobacteria: Concerning Impacts of Extended Ice-Free Periods in Northern Temperate Lakes

Cyanobacteria threaten public and ecosystem health globally through the production of secondary metabolites including potent toxins, and disruption of water treatment processes. Warmer water temperatures and high nutrient availability are key characteristics associated with the occurrence of cyanobacteria. There is typically concern of cyanobacteria blooms (e.g., visible biomass accumulations) occurring in the summer season of eutrophic systems. However, in this study, the proliferation of cyanobacteria in lakes across all seasons and in absence of visual biomass indicators of bloom condition was observed in three oligotrophic lakes of the Turkey Lakes Watershed (TLW) in Ontario, located within a sugar maple dominated forest on the Canadian Shield. Almost 40 years of ice phenology data showed that rising temperatures have led to significantly longer ice-free periods and aquatic growing seasons in TLW. Warming is especially evident in the autumn, with the onset of ice-on periods commencing significantly later in the year. Cyanobacterial communities in three interconnected temperate, oligotrophic lakes were characterized over an 18-month period from July 2018 to January 2020 (across 10 synoptic sampling events) using amplicon sequencing of the V4 region of the 16S rRNA gene. During the winter, there was low abundance or occasional absence of cyanobacteria; however, a non-photosynthetic basal lineage of cyanobacteria (Melainabacteria) was present during periods of ice cover. Notably, photosynthetic populations reappeared in the water column immediately following the loss of ice-cover--they were especially abundant in lakes with surficial geology and lake morphometry that favor greater availability of fine sediment and associated nutrients. Thus, this collective analysis demonstrates that the convergence of key abiotic and biotic factors--climate forcing of hydrological and biogeochemical processes, and intrinsic landscape features--enable increases in the relative abundance of potentially toxic cyanobacteria within the temperate forest biome of Canada over increasingly longer periods of time.

ecology↗

bioRxiv2020

Automatic subtyping of individuals with Primary Progressive Aphasia

BackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians classifications. ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians. ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

neuroscience↗

bioRxiv2020

Belowground changes to community structure alter methane-cycling dynamics in Amazonia

Amazonian rainforest is undergoing increasing rates of deforestation, driven primarily by cattle pasture expansion. Forest-to-pasture conversion has been associated with changes to ecosystem processes, including substantial increases in soil methane (CH4) emission. The drivers of this change in CH4 flux are not well understood. To address this knowledge gap, we measured soil CH4 flux, environmental conditions, and belowground microbial community attributes across a land use change gradient (old growth primary forest, cattle pasture, and secondary forest regrowth) in two Amazon Basin regions. Primary forest soils exhibited CH4 uptake at modest rates, while pasture soils exhibited CH4 emission at high but variable rates. Secondary forest soils exhibited low rates of CH4 uptake, suggesting that forest regrowth following pasture abandonment could reverse the CH4 sink-to-source trend. While few environmental variables were significantly associated with CH4 flux, we identified numerous microbial community attributes in the surface soil that explained substantial variation in CH4 flux with land use change. Among the strongest predictors were the relative abundance and diversity of methanogens, which both increased in pasture relative to forests. We further identified individual taxa that were associated with CH4 fluxes and which collectively explained ~50% of flux variance. These taxa included methanogens and methanotrophs, as well as taxa that may indirectly influence CH4 flux through acetate production, iron reduction, and nitrogen transformations. Each land type had a unique subset of taxa associated with CH4 fluxes, suggesting that land use change alters CH4 cycling through shifts in microbial community composition. Taken together, our results suggest that changes in CH4 flux from agricultural conversion could be driven by microbial responses to land use change in the surface soil, with both direct and indirect effects on CH4 cycling. This demonstrates the central role of microorganisms in mediating ecosystem responses to land use change in the Amazon Basin.

ecology↗

bioRxiv2018

Nociceptor translational profiling reveals the RagA-mTORC1 network as a critical generator of neuropathic pain

Pain sensing neurons, nociceptors, are key drivers of neuropathic pain. We used translating ribosome affinity purification (TRAP) to comprehensively characterize up-and down-regulated mRNA translation in Scn10a-positive nociceptors in chemotherapy-induced neuropathic pain. We provide evidence that an underlying mechanism driving these changes in gene expression is a sustained mTORC1 activation driven by MNK1-eIF4E signaling. RagA, a GTPase controlling mTORC1 activity, is identified as a novel target of MNK1-eIF4E signaling, demonstrating a new link between these distinct signaling pathways. Neuropathic pain and RagA translation are strongly attenuated by genetic ablation of eIF4E phosphorylation, MNK1 elimination or treatment with the MNK inhibitor eFT508. We reveal a novel translational circuit for the genesis of neuropathic pain with important implications for next generation neuropathic pain therapeutics.\n\nOne Sentence SummaryCell-specific sequencing of translating mRNAs elucidates signaling pathology that can be targeted to reverse neuropathic pain

neuroscience↗