Biology subjects

Hoang, K.

Publications and source records attributed to Hoang, K..

6 recordsLinked to original sources

bioRxiv2024

Quantitative genetics of photosynthetic trait variation in maize

Natural genetic variation in photosynthesis-related traits can aid both in identifying genes involved in regulating photosynthetic processes and developing crops with improved productivity and photosynthetic efficiency. However, rapidly fluctuating environmental parameters create challenges for measuring photosynthetic parameters in large populations under field conditions. We measured chlorophyll fluorescence and absorbance-based photosynthetic traits in a maize diversity panel in the field using an experimental design that allowed us to estimate and control multiple confounding factors. Controlling the impact of day of measurement and light intensity as well as patterns of two-dimensional spatial variation in the field substantially increased heritability with the heritability of 7 out of 14 traits measured exceeding 0.4. We were able to identify high confidence GWAS signals associated with variation in four spatially corrected traits (the quantum yield of photosystem II, non-photochemical quenching, redox state of QA, and relative chlorophyll content). Insertion alleles for Arabidopsis orthologs of three candidate genes exhibited phenotypes consistent with our GWAS results. Collectively these results illustrate the potential of applying best practices from quantitative genetics research to address outstanding questions in plant physiology and understand the mechanisms underlying natural variation in photosynthesis. Highlights[bullet] Controlling spatial and environmental confounding factors increased heritability of photosynthetic traits. [bullet]GWAS identified high confidence signals associated with variation in relative chlorophyll, {Phi}PSII, {Phi}NPQ, and qL. [bullet]Insertion alleles of the Arabidopsis orthologs of maize candidate genes exhibited photosynthesis related phenotypes consistent with the GWAS results.

plant biology↗

bioRxiv2024

Activation of the endoplasmic reticulum stress regulator IRE1α compromises pulmonary host defenses

The endoplasmic reticulum (ER) stress sensor inositol-requiring enzyme 1- (IRE1) is associated with lung infections where innate immune cells are drivers for progression and resolution of inflammation. Yet, the role of IRE1 in pulmonary innate immune host defense during acute respiratory infection remains unexplored. Here, we found that activation of IRE1 in infected lungs compromises immunity against methicillin-resistant Staphylococcus aureus (MRSA)-induced primary and secondary pneumonia. Moreover, activation of IRE1 in MRSA-infected lungs and alveolar macrophages (AMs) leads to exacerbated production of inflammatory mediators followed by cell death. Ablation of myeloid IRE1 or global IRE1 inhibition confers protection against MRSA-induced pneumonia with improves survival, bacterial clearance, cytokine reduction, and lung injury. In addition, loss of myeloid IRE1 protects mice against MRSA-induced secondary to influenza pneumonia by promoting AM survival. Thus, activation of IRE1 is detrimental to pneumonia and therefore, it shows potential as a target to control excessive unresolved lung inflammation.

immunology↗

bioRxiv2024

Within-host competition sparks pathogen molecular evolution and perpetual microbiota dysbiosis.

Pathogens newly invading a host must compete with resident microbiota. This within-host microbial warfare could lead to more severe disease outcomes or constrain the evolution of virulence. Using experimental evolution of a widespread pathogen (Staphylococcus aureus) and a native microbiota community in C. elegans nematode hosts, we show that a competitively superior pathogen displaced microbiota and reduced species richness, whilst maintaining virulence across generations. Conversely, pathogen populations and microbiota passaged separately caused more host harm relative to their respective ancestral controls. We find the evolved increase in virulence exhibited by pathogen populations passaged independently (compared to ancestral controls) was partly mediated by enhanced expression of the global virulence regulator agr and increased biofilm formation. Whole genome sequencing revealed shifts in the mode of selection from directional (on pathogens evolving alone) to fluctuating (on pathogens evolving with a host microbiota), with competitive interactions driving early diversification among pathogen populations. Metagenome sequencing of the evolved microbiota shows that evolution in infected hosts caused a significant reduction in community stability, along with restrictions on the co- existence of some species based on nutrient competition. Our study reveals how microbial competition during emerging infection determines the patterns and processes of evolution with major consequences for host health.

evolutionary biology↗

bioRxiv2024

Host and antibiotic jointly select for greater virulence in Staphylococcus aureus

Widespread antibiotic usage has resulted in the rapid evolution of drug-resistant bacterial pathogens and poses significant threats to public health. Resolving how pathogens respond to antibiotics under different contexts is critical for understanding disease emergence and evolution going forward. The impact of antibiotics has been demonstrated most directly through in vitro pathogen passaging experiments. Independent from antibiotic selection, interactions with hosts have also altered the evolutionary trajectories and fitness landscapes of pathogens, shaping infectious disease outcomes. However, it is unclear how interactions between hosts and antibiotics impact the evolution of pathogen virulence. Here, we evolved and re-sequenced Staphylococcus aureus, a major bacterial pathogen, varying exposure to host and antibiotics to tease apart the contributions of these selective pressures on pathogen adaptation. After 12 passages, S. aureus evolving in Caenorhabditis elegans nematodes exposed to a sub-minimum inhibitory concentration of antibiotic (oxacillin) became highly virulent, regardless of whether the ancestral pathogen was methicillin-resistant (MRSA) or methicillin-sensitive (MSSA). Host and antibiotic exposure selected for reduced drug susceptibility in MSSA lineages while increasing MRSA total growth outside hosts. We identified mutations in genes involved in complex regulatory networks linking virulence and metabolism, including codY, agr, and gdpP, suggesting that rapid adaptation to infect hosts may have pleiotropic effects. In particular, MSSA populations under selection from host and antibiotic accumulated mutations in the global regulator gene codY, which controls biofilm formation in S. aureus. These populations had indeed evolved more robust biofilms--a trait linked to both virulence and antibiotic resistance--suggesting evolution of one trait can confer multiple adaptive benefits. Mutations that arose in these genes were also enriched in clinical isolates associated with systemic infections in humans. Despite evolving in similar environments, MRSA and MSSA populations--differing only in the presence of an intact accessory gene (mecA)--proceeded on divergent evolutionary paths, with MSSA populations exhibiting more similarities across replicate populations. Our results underscore the importance of considering the host context as a critical driver of pathogen traits like virulence and antibiotic resistance.

evolutionary biology↗

bioRxiv2024

High-Throughput Phenotyping of Seed Quality Traits Using Imaging and Deep Learning in Dry Pea

Seed traits, such as seed color and seed size, directly impact seed quality, affecting the marketability and value of dry peas [1]. Assessing seed quality is integral to a plant breeding programs to ensure optimal seed standards. This research introduced a phenotyping tool to assess seed quality traits specifically tailored for pulse crops, which integrates image processing with cutting-edge deep learning models. The proposed method is designed for automation, seamlessly processing a sequence of images while minimizing human intervention. The pipeline standardized red-green-blue (RGB) images captured from a color light box and used deep learning models to segment and detect seed features. Our method extracted up to 86 distinct seed characteristics, ranging from basic size metrics to intricate texture details and color nuances. Compared to traditional methods, our pipeline demonstrated a 95 percent similarity in seed quality assessment and increased time efficiency (from 2 weeks to 30 minutes for processing time). Specifically, we observed an improvement in the accuracy of seed trait identification by simply using an RGB value instead of a categorical, non-standard description, which allowed for an increase in the range of detectable seed quality characteristics. By integrating conventional image processing techniques with foundational deep learning models, this approach emerges as a pivotal instrument in pulse breeding programs, guaranteeing the maintenance of superior seed quality standards.

plant biology↗

bioRxiv2022

Super-resolution stimulated Raman Scattering microscopy with A-PoD

Unlike traditionally-mapped Raman imaging, stimulated Raman scattering (SRS) imaging achieved the capability of imaging metabolic dynamics and a greatly improved signal-noise-ratio. However, its spatial resolution is still limited by the numerical aperture or scattering cross-section. To achieve super-resolved SRS imaging, we developed a new deconvolution algorithm - Adam optimization-based Pointillism Deconvolution (A-PoD) - for SRS imaging, and demonstrated a spatial resolution of 52 nm on polystyrene beads. By changing the genetic algorithm to A-PoD, the image deconvolution process was shortened by more than 3 orders of magnitude, from a few hours to a few seconds. By applying A-PoD to spatially correlated multi-photon fluorescence (MPF) imaging and deuterium oxide (D2O)-probed SRS (DO-SRS) imaging data from diverse samples, we compared nanoscopic distributions of proteins and lipids in cells and subcellular organelles. We successfully differentiated newly synthesized lipids in lipid droplets using A-PoD coupled with DO-SRS. The A-PoD-enhanced DO-SRS imaging method was also applied to reveal the metabolic change in brain samples from Drosophila on different diets. This new approach allows us to quantitatively measure the nanoscopic co-localization of biomolecules and metabolic dynamics in organelles. We expect that the A-PoD algorithm will have a wide range of applications, from nano-scale measurements of biomolecules to processing astronomical images.

bioengineering↗