Like many Gram-negative pathogens, Shigella rely on a type three secretion system (T3SS) for injection of effector proteins directly into eukaryotic host cells to initiate and sustain infection. Protein secretion through the needle-like type three secretion apparatus (T3SA) requires ATP hydrolysis by the T3SS ATPase Spa47, making it a likely target for in vivo regulation of T3SS activity and an attractive target for small molecule therapeutics against shigellosis. Here, we developed a model of an activated Spa47 homo-hexamer, identifying two distinct regions at each protomer interface that we hypothesized to provide intermolecular interactions supporting Spa47 oligomerization and enzymatic activation. Mutational analysis and a series of high-resolution crystal structures confirm the importance of these residues, as many of the engineered mutants are unable to form oligomers and efficiently hydrolyze ATP in vitro. Furthermore, in vivo evaluation of Shigella virulence phenotype uncovered a strong correlation between T3SS effector protein secretion, host cell membrane disruption, and cellular invasion by the tested mutant strains, suggesting that perturbation of the identified interfacial residues/interactions influences Spa47 activity through preventing oligomer formation, which in turn regulates Shigella virulence. The most impactful mutations are observed within the conserved Site 2 interface where the native residues support oligomerization and likely contribute to a complex hydrogen bonding network that organizes the active site and supports catalysis. The critical reliance on these conserved residues suggests that aspects of T3SS regulation may also be conserved, providing promise for the development of a cross-species therapeutic that broadly targets T3SS ATPase oligomerization and activation.

Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC).

Leaf Area index is widely used metric for the assessment of vegetation dynamics and can be used to assess the impact of regional/local climate conditions. The underlying continuity of high resolution spatio-temporal phenological processes in the presence of extensive missing values poses a number of challenges in the detection of changes at a local and regional level. The feasibility of functional data analysis methods were evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation of leaf area index (LAI) is conducted in the Columbia Watershed, as detected by NOAA AVHRR satellite imaging, and its inter-and intra-annual correlation with maximum temperature and precipitation using the ERA-Interim Reanalysis from 1996 to 2017. A functional cluster analysis model was implemented to identify regions in the Columbia Watershed that exhibit similar long-term greening trends. Across these several regions, the primary source of annual LAI variation is a trend toward seasonally earlier and higher recordings of regional average maximum LAI. Further exploratory analysis reveals that although strongly correlated to LAI, maximum temperature and precipitation do not exhibit clear longitudinal trends.

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