Biology subjects

Simpson, G. L.

Publications and source records attributed to Simpson, G. L..

6 recordsLinked to original sources

bioRxiv2026

A widespread bacterial toxin reveals a deeply divergent DNase family neutralized by diverse immunity proteins.

Bacteria compete for space and resources by delivering toxin proteins into neighboring cells. Many toxins remain uncharacterized because they lack similarity to proteins of known function. Here we identify Rehh as a widespread antibacterial toxin associated with multiple bacterial secretion systems and show that it is used in interbacterial competition. Purified recombinant Rehh functions as a manganese-dependent DNase, consistent with our in-vivo findings that Rehh damages target cell DNA, inducing the SOS response and the accumulation of double-strand breaks. The crystal structure of Rehh in complex with a cognate immunity protein reveals a positively charged histidine-rich metal-binding pocket embedded within a protein scaffold that bears no significant similarity to previously characterized nucleases. However, local structural analysis demonstrates that this pocket preserves the catalytic geometry characteristic of the HNH/His-Me-finger superfamily, revealing Rehh to be a deeply divergent HNH nuclease whose ancestry has been obscured through extensive structural remodeling. The cognate immunity protein neutralizes the toxin via an acidic surface that occludes the catalytic pocket, consistent with mimicry of the electrostatic properties of the DNA phosphate backbone. Comparative genomics further shows that Rehh homologs are associated with multiple unrelated immunity families, suggesting that distinct immunity domains repeatedly converge on a common strategy for inhibition. Together, these findings demonstrate how conflict-driven diversification can obscure enzyme ancestry while preserving catalytic function and reveal a possible case of convergent evolution among immunity proteins that independently exploit the same toxin biochemical vulnerability.

microbiology↗

bioRxiv2026

Chemoproteomics discovery of a CNS-penetrant covalent inhibitor of PIKfyve

PIKfyve is a lipid kinase involved in regulating protein clearance mechanisms and is a promising target for the treatment of neurodegenerative diseases. Here, we present the discovery and optimization of a CNS-penetrant covalent PIKfyve inhibitor, DUN058, which achieves sustained target occupancy in vivo. Covalent screening hits, identified from chemoproteomics experiments performed in live cells, were rapidly optimized to deliver a brain-penetrant covalent inhibitor of PIKfyve. This covalency centered approach employed a suite of mass spectrometry, biochemical and in vivo assays to optimize compound potency, selectivity, and CNS permeability. The target nucleophile, cysteine 1970, is on a flexible loop that appears distal from the kinase active site, highlighting the power of chemoproteomics screening to identify novel nucleophilic amino acids for covalent modification. DUN058 achieves efficient covalency at the target cysteine, as well as highly selective covalent and reversible selectivity profiles. Covalent PIKfyve inhibition results in modulation of downstream pathway activity, including activation of the transcription factor TFEB, upregulation of protein clearance pathways, and increased GPNMB transcription and secretion of exosome markers. When dosed in vivo, DUN058 achieves sustained target occupancy in the brains of mice long after systemic compound clearance, holding promise for achieving a sustained duration of action in the CNS at low doses, without prolonged effects in the periphery. Taken together, the development of DUN058 is a demonstration of chemoproteomics-based discovery for a high value CNS target, providing an orally bioavailable and covalent PIKfyve inhibitor.

biochemistry↗

bioRxiv2023

Response diversity in the context of multifarious environmental change

1.Response diversity represents the inter- and intraspecific trait variation in organismal responses to the environment. Assemblages composed of organisms displaying large variation in their response to the environment (that is, having high response diversity) are expected to have higher temporal stability of aggregate community and ecosystem properties such as ecosystem functioning (i.e., an insurance effect). Yet, response diversity is not commonly measured in empirical studies, and when it is measured, this is done in different ways. Moreover, most proposed measures of response diversity concern situations with only one driver of environmental change. Thus far, no specific approach exists to measure response diversity in the context of multiple simultaneously changing (multifarious) environmental drivers. Here, we propose a new method to empirically quantify response diversity in the context of multifarious environmental change. First, we illustrate this method using simulated data. Next, we reveal the role of the direction of environmental change in shaping response diversity when multiple drivers of environmental change fluctuate over time. We show that, when the direction of the environmental change is unknown (that is, there is no information or a priori expectation about how an environmental condition has changed or will change in future), we can quantify the potential response diversity for a given community under any possible future environmental change scenario. That is, we can estimate the potential response capacity of a system under a range of extreme or realistic environmental changes, capturing its complete insurance capacity, with utility for predicting future responses to even multifarious environmental change. Finally, we investigate the drivers of response diversity in a multifarious environmental change context, showing how response diversity depends on: 1) the diversity of species responses to each environmental variable considered, 2) the relative effect of each environmental variable on species performance, 3) the correlation between the diversity in species responses to different environmental variables, and 4) the mean temporal value of the environmental variable. In doing so, we take an important step towards understanding the insurance capacity of ecological communities exposed to multifarious environmental change.

ecology↗

bioRxiv2019

Habitat specificity is not universal proxy for sensitivity to habitat conversion among rodents on the Canadian Prairies

Converting habitat for agricultural production threatens biodiversity loss worldwide and has significant implications for human well-being. Debates on how to conserve biodiversity as the demand for agriculture products rises is being informed by studies using habitat specificity as a proxy for sensitivity to land modification, assuming all species respond to habitat loss and fragmentation relative to their affinity towards the habitat type being converted. Here, we test this assumption among rodent assemblages on the Canadian Prairies, hypothesizing negative responses among grassland obligates and neutral or positive responses among habitat generalists to landscape change along a gradient of increasing agricultural intensity. We found complex, sometimes contradictory responses among rodent species, which depended on the magnitude of habitat loss that had occurred and did not always reflect each species relative affinity for grassland habitat. Our results suggest future studies should avoid assuming a single habitat type appropriately characterizes resource limitation among all species, and instead carefully consider which dimension of the ecological niche defines specificity for each species. Our results indicate habitat specificity is not always a reliable proxy for sensitivity to land modification, with significant implications for biodiversity conservation when used to inform agriculture and land use policies.

ecology↗

bioRxiv2018

Established rodent community delays recovery of dominant competitor following experimental disturbance

Human activities alter processes that control local biodiversity, causing changes in the abundance and identity of species in many ecosystems. However, restoring biodiversity to a previous state is rarely as simple as reintroducing lost species or restoring processes to their pre-disturbance state. Theory suggests that established species can impede shifts in species composition via a variety of mechanisms, including direct interference (e.g. territoriality), preempting resources, or habitat alteration. Here we use a long-term experimental manipulation of a desert rodent community to examine differences in the recolonization dynamics of a dominant competitor (kangaroo rats of the genus Dipodomys) when patches were already occupied by an existing rodent community relative to when patches were empty. Recovery of kangaroo rat populations was slow on plots with an established community of other rodent species, taking approximately two years. In contrast, recovery of kangaroo rat populations was rapid on empty plots with no established residents (approximately 3 months). We found little evidence that the delay in kangaroo rat colonization was due to direct interference from competitors, or could be explained by differences in habitat, implicating resource preemption by the established community as the most likely mechanism. These results demonstrate that the presence of an established alternate community inhibits recolonization by new species, even those that should be dominant in the community. This has important implications for understanding how biodiversity may change in the future, and what processes may slow or prevent this change.\n\nSignificance statementEcological communities are changing due to human activities altering the processes governing local biodiversity. However restoring these processes often fails to restore the previous biodiversity state, implying that additional mechanisms contribute to community dynamics. Here we use an experimental manipulation of a desert rodent community--in which dominant competitors (kangaroo rats) were removed and then reintroduced years later--to show that the presence of previously-established species alters the dynamics of the dominant competitors recovery. Kangaroo rat populations took two years to recover on patches where inferior competitors were already established, compared to three months on uninhabited patches. This suggests that priority effects and initial conditions are critical to consider when predicting community response to disturbance, or in ecological restoration projects.

ecology↗

bioRxiv2018

Modelling palaeoecological time series using generalized additive models

In the absence of annual laminations, time series generated from lake sediments or other similar stratigraphic sequences are irregularly spaced in time, which complicates formal analysis using classical statistical time series models. In lieu, statistical analyses of trends in palaeoen-vironmental time series, if done at all, have typically used simpler linear regressions or (non-) parametric correlations with little regard for the violation of assumptions that almost surely occurs due to temporal dependencies in the data or that correlations do not provide estimates of the magnitude of change, just whether or not there is a linear or monotonic trend. Alternative approaches have used LO_SCPLOWOESSC_SCPLOW-estimated trends to justify data interpretations or test hypotheses as to the causal factors without considering the inherent subjectivity of the choice of parameters used to achieve the LO_SCPLOWOESSC_SCPLOW fit (e.g. span width, degree of polynomial). Generalized additive models (GAMs) are statistical models that can be used to estimate trends as smooth functions of time. Unlike LO_SCPLOWOESSC_SCPLOW, GAMs use automatic smoothness selection methods to objectively determine the complexity of the fitted trend, and as formal statistical models, GAMs, allow for potentially complex, non-linear trends, a proper accounting of model uncertainty, and the identification of periods of significant temporal change. Here, I present a consistent and modern approach to the estimation of trends in palaeoenvironmental time series using GAMs, illustrating features of the methodology with two example time series of contrasting complexity; a 150-year bulk organic matter{delta} 15N time series from Small Water, UK, and a 3000-year alkenone record from Braya-S{phi}, Greenland. I discuss the underlying mechanics of GAMs that allow them to learn the shape of the trend from the data themselves and how simultaneous confidence intervals and the first derivatives of the trend are used to properly account for model uncertainty and identify periods of change. It is hoped that by using GAMs greater attention is paid to the statistical estimation of trends in palaeoenvironmental time series leading to more a robust and reproducible palaeoscience.

ecology↗