Biology subjects

Piehowski, P.

Publications and source records attributed to Piehowski, P..

9 recordsLinked to original sources

bioRxiv2026

Multiomics dissection of the CO2-dependent fast growth of Picochlorum celeri

With a sub-3-micrometer cell and a 27-Mbp diploid genome, the small but mighty Picochlorum celeri doubles within three hours and exceeds 30 g m-2 d-1 outdoors with CO2 supplied. In climate-simulating photobioreactors, productivity declined from 39 g m-2 d-1 under 2.25% CO2 to near zero in air and resumed within hours of CO2 restoration with photosystem II photochemistry largely retained. We profiled the transcriptome, proteome, phosphoproteome, ubiquitinome, acetylome, metabolome and lipidome, including the first site-resolved green-algal ubiquitinome. In air, the Rubisco large subunit tripled to 7.6% of protein while its two small-subunit isoforms, 70% identical, exchanged near-reciprocally, a novel CO2-dependent switch that we propose retunes Rubisco to low CO2. Photorespiratory enzymes increased in abundance whereas proteins involved in external nitrogen assimilation decreased, consistent with increased reliance on internal nitrogen recycling as carbon fixation became limiting. Transcript and protein responses were nearly uncoupled, with cytosolic ribosomal proteins declining despite increased transcripts and plastid ribosomal proteins spared. Phosphorylation was extensively remodeled, as were the lysine modifications ubiquitination, acetylation and CO2-dependent carbamylation, where a CO2-competition assay identified 86 bicarbonate-protected lysines, including RbcL K252. CO2 limitation therefore imposes a reversible growth arrest that reduces cytosolic translational investment while preserving photosynthetic capacity for rapid recovery.

plant biology↗

bioRxiv2026

ChatGEM: An Agentic Architecture Enabling Interactive Simulation of Genome-Scale Metabolic Models

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular phenotypes and guiding microbial strain engineering, yet broad adoption remains challenging due to the computational expertise required. To overcome that, we present ChatGEM, an agentic platform that enables interactive GEM simulation through natural language. Built on the multi-agent ADEPT framework, ChatGEM integrates COBRApy within a retrieval-augmented generation (RAG) architecture that coordinates code generation and execution through specialized agents. Benchmarking across three tasks of increasing complexity showed that RAG-enabled code generation improved the mean overall performance score from 2.63 to 4.20 while reducing the execution time significantly starting from routine to complex tasks. Application of ChatGEM using an enzyme-constrained GEM (ecGEM) for four engineered Pseudomonas putida KT2440 strains identified the constitutive strain as the optimal chassis for succinate overproduction using a succinate leakage index - a prediction observed experimentally. Therefore, ChatGEM democratizes metabolic modeling by enabling researchers without computational expertise to perform sophisticated GEM-based analyses through natural language, and, hence, accelerating scientific discovery.

systems biology↗

bioRxiv2026

Multi-omics integration of malignant peripheral nerve sheath tumors identifies potential targets based on chromosome 8q status

BackgroundChromosome 8q (chr8q) copy number gain is associated with high-grade transformation in malignant peripheral nerve sheath tumors (MPNST), an aggressive soft tissue tumor with poor outcomes in the high-risk and metastatic settings. Although chr8q gain is associated with inferior overall survival in patients with MPNST, standard of care therapies do not currently consider stratification by genomic features, including chr8q status. MethodsWe employed a proteogenomic approach to characterize proteomic and transcriptional programs associated with chr8q and nominate drug targets for potential treatment stratification based on chr8q status. We leveraged our growing library of fully characterized MPNST patient-derived xenografts (PDX) and collected LC-MS/MS global and phospho-proteomics measurements for six of these samples. We then integrated these data with transcriptomics and copy number data to identify molecular changes that are correlated with chr8q copy number. We nominated pathways, transcription factors, and kinases that were differentially active in chr8q gain samples and posited that these samples would respond differently to drugs compared to chr8q wildtype samples. We then tested this hypothesis in vitro. ResultsOur results suggest that the chr8q gene MYC may be a key driver of downstream effects that can be targetable with inhibitors of PLK1. Conversely, EGFR inhibition may be more effective in MYC-diploid MPNSTs than those with MYC gain. These results nominate candidate pathways and drug classes to target tumor heterogeneity in MPNST through the proteogenomic integration and drug sensitivity prediction in distinct tumor subpopulations. ConclusionsWe show that integration of multiomics data can identify specific drug therapies to selectively target tumor cells based on chr8q copy number. This not only provides novel avenues for drug nomination going forward but also may be important for stratifying treatment and mitigating resistance in heterogeneous tumors.

cancer biology↗

bioRxiv2025

spammR: an R package designed for analysis and integration of spatial multi-omic measurements

SummarySpatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance profiles for multiple different omics types and associated microscopy imaging data, as well as their analysis in a spatial context. However, most analytical tools are designed for spatial transcriptomics platforms and are challenging to use in other contexts such as mass spectrometry-based measurements or metagenomics. To this end we present spammR (spatial analysis of multi-omics measurements in R), an R package that enables end-to-end analysis with a specific focus on mass-spectrometry derived spatial omics datasets with the goal of integration across multiple data types (e.g. sequencing, metabolites, proteins) within the same tissue sample. Availability and implementationspammR is implemented in R. The package is currently installable from GitHub (https://github.com/PNNL-CompBio/spammR).

bioinformatics↗

bioRxiv2025

Multi-Omic Profiling Identifies Conserved Metabolic Pathways Critical for SARS-CoV-2 Variants Infection

AO_SCPLOWBSTRACTC_SCPLOWThe rapid evolution of SARS-CoV-2 has led to the emergence of numerous variants with enhanced transmissibility and immune evasion. Despite widespread vaccination, infections persist, and the mechanisms by which SARS-CoV-2 reprograms host metabolism remain incompletely understood. Here, we investigated whether virus-induced lipid remodeling is conserved across variants and whether changes in lipid abundance correlate with alterations in lipid biosynthetic enzymes. Using global untargeted lipidomics and quantitative proteomics, we analyzed A549-ACE2 cells infected with the Delta (B.1.617.2) or Omicron (B.1.1.529) variants and compared them to cells infected with the ancestral WA1 strain. In parallel, we conducted quantitative proteomics to assess virus-induced changes in the host proteome. Our results reveal that SARS-CoV-2 drives a remarkably consistent pattern of metabolic rewiring at both the lipidomic and proteomic levels across all three variants. We mapped changes in the expression of host metabolic enzymes and compared these to corresponding shifts in lipid abundance. This integrative analysis identified key host proteins involved in virus-mediated lipid remodeling, including fatty acid synthase (FASN), lysosomal acid lipase (LIPA), and ORM1-like protein 2 (ORMDL2). Together, these findings highlight conserved metabolic dependencies of SARS-CoV-2 variants and underscore host lipid metabolism as a potential target for broad-spectrum antiviral strategies.

systems biology↗

bioRxiv2024

Increased Inflammation as well as Decreased Endoplasmic Reticulum Stress and Translation Differentiate Pancreatic Islets of Pre-symptomatic Stage 1 Type 1 Diabetes and Non-diabetic Cases

Aims/hypothesisProgression to type 1 diabetes (T1D) is associated with genetic factors, the presence of autoantibodies, and a decline in {beta} cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting {beta}-cells prior to the onset of T1D. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet autoantibody-positive organ donors with pre-symptomatic stage 1 T1D (HbA1c [≤] 6). We aimed to identify pathways in islets that are indicative of {beta}-cell dysfunction. MethodsMultiple islet sections were collected through laser microdissection of frozen pancreatic tissues of organ donors positive for islet autoantibodies (AAb+, n=5), compared to age/sex-matched nondiabetic controls (ND, n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to mass spectrometry-based proteomics and analyzed with label-free quantification followed by pathway and functional annotations. ResultsAnalyses resulted in [~]4,500 proteins identified with low false discovery rate (FDR) <1%, with 2,165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on the three multiple AAb+ cases (mAAb+) with high genetic risk and their three matched controls for a final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different between mAAb+ cases versus ND. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including proteomics dataset from in vitro proinflammatory cytokine-treated human islets and single cell RNA-seq data sets from AAb+ cases. Conclusion/interpretationIn-situ human islet proteome alterations at the stage 1 of AAb+ T1D centered around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation / HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on {beta} cell changes during T1D progression and pathogenesis.

pathology↗

bioRxiv2022

Proteome mapping of the human pancreatic islet microenvironment reveals endocrine-exocrine signaling sphere of influence

The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller sample number and amount of tissue measu red, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of tissues involved while maintaining spatial information and the degree of the islets sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be measured. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.

systems biology↗

bioRxiv2022

Proteomic Dissection of a Giant Cell

Many individual proteins have been identified as having defined positions relative to cell polarity axes, raising the question of what fraction of all proteins may have polarized localizations. We took advantage of the giant ciliate Stentor coeruleus to quantify the extent of polarized localization proteome-wide. This trumpet-shaped unicellular organism shows a clear morphological anterior-posterior axis defined by a circular array of cilia known as a membranellar band at one end, and a holdfast at the other end. Because individual Stentor cells are over a millimeter in length, we were able to cut the cells into three pieces along the anterior-posterior axis, followed by proteomic analysis of proteins enriched in each piece. We find that approximately 30% of all detected proteins show a polarized location relative to the anterior-posterior cell axis. Proteins with polarized enrichment include centrin-like proteins, calcium-regulated kinases, orthologs of SFI1 and GAS2, and proteases. At the organelle level, nuclear and mitochondrial proteins are enriched in the anterior half of the cell body, but not in the membranellar band itself, while ribosome related proteins are apparently uniformly distributed. RNAi of signaling proteins enriched in the membranellar band, which is the anterior-most structure in the cell, revealed a protein phosphatase 2 subunit b ortholog required for closure of the membranellar band into the ring shape characteristic of Stentor. These results suggest that a large fraction of the Stentor proteome has a polarized localization, and provide a protein-level framework for future analysis of pattern formation and regeneration in Stentor as well as defining a general strategy for subcellular spatial proteomics based on physical dissection of cells.

cell biology↗

bioRxiv2021

Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.

bioinformatics↗