Kathryn Susan Lilley

The paper integrates mRNA and protein isoform data in human RPE-1 cells to reveal widespread translation of alternative transcripts and proteolytic processing as a major source of proteome complexity.

This study shows that the antidepressant duloxetine disrupts RNA-protein interactions in both human and bacterial cells, revealing a new mechanism of drug action.

The paper introduces SANDLE, a method for analyzing subcellular omics data that reduces false positives and analysis time while handling complex experimental designs.

The paper identifies simplifying principles governing peptide binding by the promiscuous chicken class I molecule BF2*21:01, revealing patterns that enable broad peptide repertoire while maintaining stable binding.

This study maps the spatial proteome of Plasmodium falciparum schizonts, identifying 24 cellular niches and classifying 1646 proteins, while revealing evolutionary patterns in parasite organelles.

The paper introduces a capillary-based subcellular sampling method combined with LC-MS to analyze stress granule proteomes in single cells, identifying 405 high-confidence proteins with minimal cytosolic contamination.

The paper identifies novel proteins that relocalize in adipocytes in response to insulin, highlighting their role in insulin signaling.

This paper introduces a semi-supervised Bayesian method to integrate spatial proteomics data with other omic datasets to improve protein subcellular localization inference.

This study examines the Central Dogma in Arabidopsis thaliana, showing that codon frequencies and mRNA levels are better predictors of protein expression than methylation, tRNA, or ribosome-associated transcripts.

This study creates the first complete SUMO Cell Atlas in eukaryotes, revealing how SUMO components vary spatially and are regulated during stress responses in Arabidopsis roots.

The paper shows ROS-induced IAA3/SHY2 multimerization regulates auxin responses during xerobranching and lateral root emergence in plants.

The paper uses global subcellular proteomics to identify subcellular-specific anti-ferroptotic responses in A549 cells exposed to ionizing radiation, highlighting potential mechanisms of radio-resistance.

The paper records proteomes for non-essential gene knock-outs in yeast to uncover principles of protein expression and annotate gene function.

The paper shows that parts of the TCA cycle operate in the nucleus, challenging the canonical view of metabolic compartmentalization.

The paper demonstrates improved proteomic depth using DIA-NN and FragPipe for dia-PASEF data, achieving higher sensitivity and quantification in complex samples.

This study identifies Drosophila nAChR subunits interacting with insecticidal toxins using CRISPR/Cas9 and pull-down assays, providing insights for insecticide design.

The paper evaluates the impact of prior Orbitrap software versions on low isobaric mass tag intensities and finds minimal detrimental effects on differential abundance experiments.

This paper introduces a Bayesian semi-parametric model to analyze thermal proteome profiling data, improving sensitivity and identifying new drug-protein associations.

The paper introduces BANDLE, a Bayesian method to infer differential subcellular localisation from spatial proteomics data, showing improved accuracy and novel biological insights.

This study investigates caspase cleavage of Drosophila Sgg-PD proteoforms, revealing their role in cytoskeletal and cell junction regulation and unexpected retention of cleaved N-terminal domains.

This paper introduces a semi-supervised Bayesian method for protein sub-cellular localisation and novelty detection, identifying new compartments in proteomic data.

The paper characterizes protein isoforms of the Drosophila sgg gene, revealing distinct localization, interactions, and functions for different proteoforms during development.

This paper introduces Scanning SWATH, a proteomics method that enables high-throughput quantification by using fast chromatographic gradients and improves precursor identification.

Neural-network ensembles scoring 69 peak features, plus fragment interference subtraction, boost data-independent acquisition (DIA) proteomics identifications and quantification precision, especially with short chromatographic gradients.

The paper uses a yeast model to analyze multi-omic data from OPTN overexpression, finding distinct genetic and metabolic responses to different ALS-linked genes.

The paper shows that novel ORFs can produce protein-like structures with biological functions relevant to disease, challenging the view that they are inconsequential.

This study evaluates the sub-cellular resolution of 29 spatial proteomics experiments using QSep to provide a quantitative assessment of their ability to resolve sub-cellular niches.

This paper introduces a Bayesian mixture model for spatial proteomics to probabilistically assign proteins to sub-cellular locations and quantify uncertainty in classification.

The paper analyzes transcriptional regulatory networks in yeast to identify novel features of gene regulation in protein transactions, finding that transcriptional regulation is underrepresented and controlled by few regulators.

The paper tracks the transition of mouse embryonic stem cells from ground state pluripotency to a formative phase, showing acute exit from naive status and transcriptome changes similar to early post-implantation epiblast.