Biology subjects

Drucker, B.

Publications and source records attributed to Drucker, B..

4 recordsLinked to original sources

bioRxiv2026

Detection of Melanoma Using Deep Serum Proteome Profiling and Machine Learning

Early detection determines melanoma outcomes, yet current screening relies on visual inspection that misses molecular changes preceding clinical diagnosis. To identify serum protein signatures of melanoma development, we leveraged the Department of Defense Serum Repository, analyzing 390 longitudinal serum samples from 73 melanoma cases and matched controls across four timepoints (4 and 2 years prior, diagnosis, and 2 years post-diagnosis) using data-independent acquisition mass spectrometry with Seer Proteograph nanoparticle enrichment. We quantified 3,364 proteins and applied machine-learning strategies combining cross-sectional case-control comparisons with longitudinal tracking of within-individual changes. Cross-sectional analysis at diagnosis achieved AUC 0.823 (95% CI: 0.723-0.923), identifying eight consensus features selected in >50% of cross-validation folds: PRSS1, GSK3B, FAM20C, CES1, CCL14, EPHA10, LMAN2, and ITIH1. These signatures, spanning proteases, immune activation, and extracellular matrix remodeling, demonstrate proof-of-concept for serum-based melanoma detection and provide candidate biomarkers for validation.

Cancer Biology↗

bioRxiv2026

Post-translational modifications in the brain are critical contributors to Alzheimers disease neuropathology and cognitive decline

Post-translational modifications (PTMs) in APP and MAPT contribute to plaques and tangles in Alzheimers disease (AD). Yet broader proteome-wide PTMs in the AD brain are relatively unexplored. Therefore, this study highlights associations between PTMs, quantified by mass spectrometry in prefrontal cortex tissue, and Alzheimers disease neuropathology and cognition. Leveraging PTMs quantified from prefrontal cortices in 101 Rush Memory and Aging Project participants. We assessed associations with post-mortem amyloid-{beta} and tau burden, global cognition, and cognitive decline. First, APP and MAPT PTM associations were assessed on these outcomes given their known relevance in AD, followed by assessment of protein-wide effects of PTMs. Then, kinase enrichment analysis was performed on each outcome to assess which kinases might contribute to the results. We observed a novel association of APP-K687 acetylation, a known mutation hotspot driving pathology, with amyloid-{beta} load ({beta}=0.44, P=3.9e-8), while confirming known MAPT PTMs with tangle burden. Further, we identified 20+ novel PTMs associations with AD neuropathology, including ENO2-K256 ubiquitination ({beta}=0.353, P=1.13e-6), PSMD13-K31 ubiquitination ({beta}=0.568, P=1.34e-6), and PLXND1-K1826 ubiquitination ({beta}=0.577, P=7.08e-8) for tangle burden and SYP-K23 ubiquitination ({beta}=1.50, P=4.7e-8), TMEFF2-C80 cysteine oxidation ({beta}=1.64, P=1.1e-8), and STX1B-T121 phosphorylation ({beta}=0.898, P=3.3e-7) for amyloid-{beta} load. Further, kinase enrichment analyses highlight the complexity of disease-related proteome changes with some kinases like CDK5 showing expected over-enrichment (amyloid z=3.44, P=3.0e-4; tau z=4.98, P=3.3e-7) but others like PKC family kinases showing divergent enrichment between amyloid (z=8.98-11.55, P<1.0e-18) and tau (z=-2.83--3.88, P<0.006). This study provides an atlas of brain PTMs within crucial proteins like MAPT and APP and at the proteome-wide level, that impact AD neuropathology and clinical presentation. Further, we explored what kinases might be driving phosphorylation results, emphasizing the complex proteome changes which impact AD. In sum, these results highlight robust post-translational alterations in the AD brain and provide novel targets for future mechanistic studies.

neuroscience↗

bioRxiv2022

Structure and dynamics that specialize neurons for high-frequency coincidence detection in the barn owl nucleus laminaris

A principal cue for sound source localization is the difference in arrival times of sounds at an animals two ears (interaural time difference, ITD). Neurons that process ITDs are specialized to compare the timing of inputs with submillisecond precision. In the barn owl, ITD processing begins in the nucleus laminaris (NL) region of the auditory brainstem. Remarkably, NL neurons are sensitive to ITDs in high-frequency sounds (kilohertz-range). This contrasts with ITD-based sound localization in analogous regions in mammals where ITD-sensitivity is typically restricted to lower-frequency sounds. Guided by previous experiments and modeling studies of tone-evoked responses of NL neurons, we propose NL neurons achieve high-frequency ITD sensitivity if they respond selectively to the small-amplitude, high-frequency fluctuations in their inputs, and remain relatively non-responsive to mean input level. We use a biophysically-based model to study the effects of soma-axon coupling on dynamics and function in NL neurons. First, we show that electrical separation of the soma from the axon region in the neuron enhances high-frequency ITD sensitivity. This soma-axon coupling configuration promotes linear subthreshold dynamics and rapid spike initiation, making the model more responsive to input fluctuations, rather than mean input level. Second, we provide new evidence for the essential role of phasic dynamics for high-frequency neural coincidence detection. Transforming our model to the phasic firing mode further tunes the model to respond selectively to the fluctuating inputs that carry ITD information. Similar structural and dynamical mechanisms specialize mammalian auditory brainstem neurons for ITD-sensitivity, thus our work identifies common principles of ITD-processing and neural coincidence detection across species and for sounds at widely-different frequencies. Author summaryDifferences in the arrival times of sounds at the two ears are essential for creating a sense of auditory space. For many animals, the utility of these interaural time-differences for sound source localization is thought to be restricted to relatively low-frequency sounds, due to limits of temporal precision in the auditory pathway. Barn owls, remarkably, use temporal processing to localize high-frequency (kilohertz-scale) sounds. This capability is critical for their activities as nocturnal predators. Building on insights from previous experimental and modeling studies, we propose that these neurons encode time differences in high-frequency sounds because they respond selectively to input fluctuations, and are relatively non-responsive to input mean. We use a biophysically-based computational model to show that electrical separation between a neurons input region (soma) and spike-generating region (axon) improves sensitivity to input fluctuations. This structural configuration produces linear integration of subthreshold inputs and rapid spike initiation, two dynamical features that improve time-difference sensitivity to high-frequency sound-evoked inputs. Neural coincidence detection in the neuron model is further enhanced if it operates in a phasic firing mode. Taken together, we provide new insights into the dynamical and structural mechanisms that support high-frequency sound localization by coincidence detector neurons.

neuroscience↗

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↗