Biology subjects

Saltzman, A. B.

Publications and source records attributed to Saltzman, A. B..

7 recordsLinked to original sources

bioRxiv2026

Intermolecular disulfide bond formation promotes Hsp42 higher-order assembly and shapes client selection in yeast

Cellular redox homeostasis plays a critical role in regulating protein function, including chaperone activity, through reversible oxidation of cysteine and methionine residues. Previously, we found that budding yeast cells experiencing redox imbalance due to inactivated thioredoxin reductase (trr1{Delta}) activate the heat shock response and induce hyperaccumulation of the small heat shock protein/sequestrase Hsp42 with misfolded proteins. Building on that finding, this study identified cysteine 127 (C127) within Hsp42 as a redox-active residue that becomes oxidized in trr1{Delta} cells, upon treatment with the powerful oxidant hydrogen peroxide, or by exposure to the cysteine crosslinker divinyl sulfone (DVSF). In trr1{Delta} cells, C127 oxidation promoted intermolecular disulfide bond formation and contributed to Hsp42 homo-oligomerization. We show that stable oligomerization requires both the prion-like domain (PrLD) and C127 oxidation. While Hsp42-GFP formed prominent persistent foci in trr1{Delta} cells, replacement of C127 with non-thiol reactive serine decreased foci formation. Furthermore, the C127S mutation diminished Hsp42 oligomerization and sedimentability. Immunoprecipitation coupled with mass spectrometry analysis revealed that Hsp42 in trr1{Delta} cells preferentially associated with mitochondrial precursor proteins accumulated in the cytoplasm, as well as oxidation-reduction enzymes. The observed client selectivity was altered by the C127S mutation that diversified the spectrum of Hsp42-associated proteins. Collectively, these findings identify Cys127 as a redox-active switch that regulates Hsp42 assembly, foci formation, stability, and client specificity in response to oxidative stress.

cell biology↗

bioRxiv2025

EGR3 Deletion Rescues Developmental and Epileptic Encephalopathy in Kcna1-null Mice

KCNA1 encodes the -subunit of the voltage-gated potassium channel KV1.1. Mutations in KV1.1s pore domain result in developmental and epileptic encephalopathy (DEE), where early life seizures and a culprit lesion synergistically disrupt neurodevelopmental trajectories, resulting in intellectual disability that often presents with disturbances in sleep, sociability and sensory processing. Abnormalities in the subcellular localization of Kv1.1, via mutations in/autoantibodies against LGI1 and CNTNAP2, also give rise to syndromes of epilepsy and neuropsychiatric impairment. Mice with deletions of Kcna1("-/-") display spontaneous seizures at 2-3 weeks of age and premature mortality. In this study, we applied instrumented home-cage monitoring to examine how aberrations in KCNA1 expression may result in pervasive alterations in spontaneous behavior. Compared to wildtype, Kcna1-/- mice displayed a robust multifaceted behavioral syndrome featuring marked nocturnal hyperactivity, insomnia, reduced sheltering, fragmented feeding/drinking rhythms, sensory over-responsivity and diminished wheel-running. In identical recordings, Kcna1+/- mice only displayed increased sheltering, Lgi1+/- mice displayed mild insomnia and Cntnap2-/- mice showed home-cage hypoactivity. Kcna1 loss in parvalbumin-positive interneurons (Pv-Cre) resulted in a subtle phenocopy, with mild insomnia accompanied by reduced sheltering behavior, while similar deletions in forebrain pyramidal neurons (Emx1-Cre) or dopaminergic neurons (DAT-Cre) were asymptomatic. Adult-onset conditional deletions of Kcna1 also produced only mild insomnia 6 weeks later. To survey the molecular landscape in Kcna1-/- mice, we conducted a mass spectrometry proteomic analysis of dissected hippocampal tissue (a predominant seizure onset zone and where astrogliosis is observed). This revealed significant upregulations in BDNF (brain-derived neurotrophic factor) and the immediate early transcription factor EGR3 (early growth response-3), which is necessary for the induction of BDNF following electroconvulsive seizures. Heterozygous or homozygous deletions of Egr3 in Kcna1-/- mice resulted in significant survival prolongation, a partial neurobehavioral rescue, and a significant improvement in the frequency of spontaneous seizures and spreading depolarization events. These clinical improvements were associated with an amelioration of BDNF induction, hippocampal astrogliosis and proteomic disturbances. Together, these data illustrate how an ion channel that governs excitability at millisecond scales also shapes the spatiotemporal structure of spontaneous behavior at meso- or macroscopic time scales. Our results provide a model and a set of precision endpoints to understand how ictal and interictal features of DEE may be ameliorated by inhibiting the long-term downstream transcriptional alterations imparted by early life seizures.

neuroscience↗

bioRxiv2024

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significanceIntegration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

cancer biology↗

bioRxiv2023

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

bioinformatics↗

bioRxiv2022

Kinase Inhibitor Pulldown Assay (KiP) for Clinical Proteomics

Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential, but current approaches are inadequate. For breast cancer treatment for example, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers more quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of protein yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method.

biochemistry↗

bioRxiv2021

A Network Approach to Identify Biomarkers of Differential Chemotherapy Response Using Patient-Derived Xenografts of Triple-Negative Breast Cancer

Triple negative breast cancer (TNBC) is a highly heterogeneous set of diseases that has, until recently, lacked any FDA-approved, molecularly targeted therapeutics. Thus, systemic chemotherapy regimens remain the standard of care for many. Unfortunately, even combination chemotherapy is ineffective for many TNBC patients, and side-effects can be severe or lethal. Identification of predictive biomarkers for chemotherapy response would allow for the prospective selection of responsive patients, thereby maximizing efficacy and minimizing unwanted toxicities. Here, we leverage a cohort of TNBC PDX models with responses to single-agent docetaxel or carboplatin to identify biomarkers predictive for differential response to these two drugs. To demonstrate their ability to function as a preclinical cohort, PDX were molecularly characterized using whole-exome DNA sequencing, RNAseq transcriptomics, and mass spectrometry-based total proteomics to show proteogenomic consistency with TCGA and CPTAC clinical samples. Focusing first on the transcriptome, we describe a network-based computational approach to identify candidate epithelial and stromal biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (ITGA7, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). Biomarker panels are predictive in PDX expression datasets (RNAseq and Affymetrix) for both taxane (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating both cross expression platform and cross drug class robustness. Biomarker panels were also predictive in clinical datasets with response to cisplatin or paclitaxel, thus demonstrating translational potential of PDX-based preclinical trials. This network-based approach is highly adaptable and can be used to evaluate biomarkers of response to other agents.

cancer biology↗

bioRxiv2019

Microscaled Proteogenomic Methods for Precision Oncology

Cancer proteogenomics integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics to gain insights into cancer biology and treatment efficacy. A proteogenomics approach was therefore developed for frozen core biopsies using tissue-sparing specimen processing with a \"microscaled\" proteomics workflow. For technical proof-of-principle, biopsies from ERBB2 positive breast cancers before and 48-72 hours after the first dose of neoadjuvant trastuzumab-based chemotherapy were analyzed. ERBB2 protein and phosphosite levels, as well as mTOR target phosphosites, were significantly more suppressed upon treatment in cases associated with pathological complete response, suggesting MS-based pharmacodynamics is achievable. Furthermore, integrated analyses indicated potential causes of treatment resistance including the absence of ERBB2 amplification (false-ERBB2 positive) and insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification (pseudo-ERBB2 positive). Candidate resistance features in true-ERBB2+ cases, including androgen receptor signaling, mucin expression and an inactive immune microenvironment were observed. Thus, proteogenomic analysis of needle core biopsies is feasible and clinical utility should be investigated.

cancer biology↗