Paul Piehowski

This study profiles multi-omics data in Picochlorum celeri under CO2 limitation, revealing a CO2-dependent switch in Rubisco subunits and extensive post-translational modifications.

ChatGEM enables interactive simulation of genome-scale metabolic models via natural language, improving performance and reducing execution time for complex tasks.

This study investigates how circadian rhythms affect macrophage responses to SARS-CoV-2 spike proteins, finding that timing of exposure leads to distinct immunometabolic states.

The paper develops a computationally efficient, library-free DIA workflow for metaproteomics, improving throughput and reducing reliance on spectral libraries.

This study integrates multi-omics data from MPNSTs to identify drug targets based on chromosome 8q status, suggesting PLK1 and EGFR inhibitors could be effective in different subtypes.

This study identifies stem-monocytic AML as a distinct subtype resistant to venetoclax and shows that PU.1 inhibition synergizes with BCL2 blockade to enhance therapeutic response.

spammR is an R package for analyzing and integrating spatial multi-omic data, focusing on mass-spectrometry datasets and enabling cross-type integration within tissue samples.

Laser-microdissected islets of 3 multiple-autoantibody-positive stage 1 type 1 diabetes donors versus matched controls show 202 altered proteins: immune and glycolytic up, translation and ER-stress proteins down.

This paper introduces a method for label-free single voxel proteomics using wcSOP, enabling spatial resolution of protein expression in tissue samples.

This study examines how endurance exercise training affects rat subcutaneous white adipose tissue, revealing sex-specific molecular responses and differences in adiposity and metabolism.

The paper develops a spatial proteomics method to map the human pancreatic islet microenvironment and identifies spatial gradients in RNA processing proteins.

This study profiles multi-omic changes in multiple tissues of rats during endurance exercise training, identifying molecular adaptations and sex-specific responses across time.

The paper predicts ex vivo drug sensitivity in AML patients using proteomic and phosphoproteomic signatures, showing better accuracy than mutation or mRNA-based signatures.