Biology subjects

Stubbs, J.

Publications and source records attributed to Stubbs, J..

5 recordsLinked to original sources

bioRxiv2025

A cross model spatial and single-cell atlas reveals the conserved involvement of osteopontin in polycystic kidney disease

Polycystic kidney disease (PKD) arises from mutations in cilia-associated genes, such as PKD1 and PKD2, expressed in renal epithelial cells, leading to progressive kidney dysfunction and end- stage kidney disease (ESKD). PKD patients exhibit significant heterogeneity in disease progression, largely due to genetic and environmental modifiers. Like patients, mouse models of PKD also exhibit significant heterogeneity with regards to the gene mutated, age of disease onset, and rate of disease progression. To elucidate the cellular and molecular consequences of these variables, we constructed an integrated single-cell and spatial transcriptomics atlas across mouse models of PKD, mapping changes in cell type composition, gene expression, and intercellular signaling networks across the whole atlas and within individual models. Consistently across models, single cell RNA sequencing (scRNAseq) data revealed increased Spp1 (osteopontin) expression and signaling from PKD-enriched clusters to Ly6clo monocytes. Global deletion of Spp1 in Pkd1RC/RC mice resulted in reduced cyst severity, improved kidney function, and reduced Ly6clo monocyte numbers, suggesting that SPP1 signaling to Ly6clo monocytes promotes PKD progression. We also created a freely available, searchable website (https://bmblx.bmi.osumc.edu/scPKD/) that can be used to identify cross- and intra-model specific changes in gene expression, guiding researchers to new therapeutic targets for treating PKD.

bioinformatics↗

bioRxiv2024

Droplet microfluidics for time-resolved serial crystallography

Serial crystallography requires large numbers of microcrystals and robust strategies to rapidly apply substrates to initiate reactions in time-resolved studies. Here we report the use of droplet miniaturisation for the controlled production of uniform crystals, providing an avenue for controlled diffusion and synchronous reaction initiation. The approach was evaluated using two enzymatic systems, yielding 3-{micro}m lysozyme crystals and 2-{micro}m crystals of Pdx1, an Arabidopsis enzyme involved in vitamin B6 biosynthesis. A seeding strategy was used to overcome the improbability of Pdx1 nucleation occurring with diminishing droplet volumes. Convection within droplets was exploited for rapid crystal mixing with ligands. Mixing times of <2 milliseconds were achieved. Droplet microfluidics for crystal size engineering and rapid micromixing can be used to advance time-resolved serial crystallography.

biochemistry↗

bioRxiv2023

A redox switch allows binding of ferrous and ferric ions in the cyanobacterial iron binding protein FutA from Prochlorococcus

The marine cyanobacterium Prochlorococcus is a main contributor to global photosynthesis, whilst being limited by iron availability. Cyanobacterial genomes typically encode two different types of FutA iron binding proteins: periplasmic FutA2 ABC transporter subunits bind Fe(III), while cytosolic FutA1 binds Fe(II). Owing to their small size and their economized genome Prochlorococcus ecotypes typically possess a single futA gene. How the encoded FutA protein might bind different Fe oxidation states was previously unknown. Here we use structural biology techniques at room temperature to probe the dynamic behavior of FutA. Neutron diffraction confirmed four negatively charged tyrosinates, that together with a neutral water molecule coordinate iron in trigonal bipyramidal geometry. Positioning of the positively charged Arg103 side chain in the second coordination shell yields an overall charge-neutral Fe(III) binding state in structures determined by neutron diffraction and serial femtosecond crystallography. Conventional rotation X-ray crystallography using a home source revealed X-ray induced photoreduction of the iron center with observation of the Fe(II) binding state; here, an additional positioning of the Arg203 side chain in the second coordination shell maintained an overall charge neutral Fe(II) binding site. Dose series using serial synchrotron crystallography and an XFEL X-ray pump-probe approach capture the transition between Fe(III) and Fe(II) states, revealing how Arg203 operates as a switch to accommodate the different iron oxidation states. This switching ability of the Prochlorococcus FutA protein may reflect ecological adaptation by genome streamlining and loss of specialized FutA proteins. Significance StatementOceanic primary production by marine cyanobacteria is a main contributor to carbon and nitrogen fixation. Prochlorococcus is the most abundant photosynthetic organism on Earth, with an annual carbon fixation comparable to the net global primary production from agriculture. Its remarkable ecological success is based on the ability to thrive in low nutrient waters. To manage iron limitation, Prochlorococcus possesses the FutA protein for iron uptake and homeostasis. We reveal a molecular switch in the FutA protein that allows it to accommodate binding of iron in either the Fe(III) or Fe(II) state using structural biology techniques at room temperature and provide a plausible mechanism for iron binding promiscuity.

biophysics↗

bioRxiv2022

Allelic compatibility in plant immune receptors facilitates engineering of new effector recognition specificities.

Engineering expanded effector recognition in plant immune receptors is a promising prospect for generating new disease resistant crop varieties. However, modification of plant NLR receptors has proven challenging due to the lack of understanding of their context as part of complex immune systems. Here, we demonstrate a new avenue for NLR-mediated engineering that exploits the allelic diversity in the Pik NLR pair to allow for the generation of receptors with expanded recognition specificities, which would otherwise result in constitutive cell death. This work lays the foundation for the incorporation of new effector recognition motifs into the Pik system and advances the development of designer NLRs that can be tailored to specific secreted pathogen signatures. AbstractEngineering the plant immune system offers genetic solutions to mitigate crop diseases caused by diverse agriculturally significant pathogens and pests. Modification of intracellular plant immune receptors of the nucleotide-binding leucine rich repeat (NLRs) superfamily for expanded recognition of pathogen virulence proteins (effectors) is a promising approach for engineering novel disease resistance. However, engineering can cause NLR autoactivation, resulting in constitutive defence responses that are deleterious to the plant. This may be due to plant NLRs associating in highly complex signalling networks that co-evolve together, and changes through breeding or genetic modification can generate incompatible combinations, resulting in autoimmune phenotypes. We have previously shown how alleles of the rice NLR pair Pik have differentially co-evolved, and how sensor/helper mismatching between non-co-evolved alleles triggers constitutive activation and cell death (De la Concepcion et al., 2021b). Here, we dissect incompatibility determinants in the Pik pair and found that HMA domains integrated in Pik-1 not only evolved to bind pathogen effectors but also likely co-evolved with other NLR domains to maintain immune homeostasis. This explains why changes in integrated domains can lead to autoactivation. We then used this knowledge to facilitate engineering of new effector recognition specificities overcoming initial autoimmune penalties. We show that by mismatching alleles of the rice sensor and helper NLRs Pik-1 and Pik-2, we can enable the integration of synthetic HMA domains with novel and enhanced recognition of an effector from the rice blast fungus. Taken together, our results reveal a new strategy for engineering NLRs, which has the potential to allow an expanded set of integrations and therefore new disease resistance specificities in plants.

plant biology↗

bioRxiv2021

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that takes the cell response to single perturbations as the input and predicts the whole cell transcriptional response to the combination of inducers. We find that the HRM is able to qualitatively predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of known prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). This tool will significantly reduce the number of high-throughput sequencing experiments that need to be run to characterize the transcriptional impact of the combination of perturbations on the host.

bioinformatics↗