Biology subjects

Hoffman, H.

Publications and source records attributed to Hoffman, H..

5 recordsLinked to original sources

bioRxiv2025

The Pdgfd-Pdgfrb axis orchestrates tumor-nerve crosstalk in pancreatic cancer

Nerves are an integral component of the tumor microenvironment, contributing to cancer progression, metastasis, morbidity, and mortality. In pancreatic ductal adenocarcinoma (PDAC), worse clinical outcomes are associated with perineural invasion (PNI), a process by which cancer cells surround and invade nerves. Here, we employed whole-transcriptome and single-cell spatial transcriptomics to identify candidate tumor-nerve interactions that promote PNI. We discovered that Pdgfd signaling promotes key features of nerve invasion. Mechanistically, Pdgfd stimulated cancer cell invasiveness, neurite outgrowth, and direct physical engagement with glia. Pharmacological blockade of this axis reduced each of these processes in vitro as well as PNI in vivo. Thus, Pdgfd-Pdgfrb signaling mediates PNI by coordinating multifaceted cancer-neuron-glia interactions and represents a promising therapeutic strategy aimed at disrupting harmful cancer-nerve crosstalk.

cancer biology↗

bioRxiv2025

Distinct malignant cell states and myeloid glutamate signaling associated with aggressive pancreatic neuroendocrine tumors

Pancreatic neuroendocrine tumors (PNET) are rare malignancies of the endocrine pancreas with diverse clinical outcomes. While some PNETs are indolent, others are aggressive and metastasize quickly. However, clinically-relevant molecular stratification for PNET to predict outcomes and guide therapeutic decision-making is limited. Thus, there is an urgent need to understand the molecular heterogeneity of PNETs to refine prognostication and discover novel therapeutic vulnerabilities. We performed single-nucleus RNA sequencing on resected primary and metastatic PNETs (n = 20), including two PNETs with neoadjuvant treatment. We inferred gene expression programs (GEPs) of malignant and non-malignant cells and investigated associations with clinical outcomes. Next, we inferred interactions in the tumor microenvironment (TME) and performed transwell assays for functional validation. Finally, we explored genomic and transcriptomic evolution in a unique case study of an untreated primary PNET with two asynchronous hepatic metastases. A malignant GEP enriched for neural/synaptic signaling genes was associated with worse overall survival, broad chromosomal loss of heterozygosity, and alternative lengthening of telomeres. Another malignant GEP enriched for VEGF signaling increased throughout metastatic progression in our case study. We found that macrophage-derived glutamate drives polarization towards an immunosuppressive phenotype and activates the MAPK/ERK pathway in malignant cells to increase migratory capacity. This study provides a detailed single-nucleus transcriptomic classification of malignant, stromal, and immune cell types and states in PNETs, their interactions in the TME, and associations with clinical outcomes. The refined molecular taxonomy of PNET may guide the development of more efficacious biomarkers and therapeutic strategies.

cancer biology↗

bioRxiv2023

Asymmetric cortical projections to striatal direct and indirect pathways distinctly control actions

The striatal direct and indirect pathways constitute the core for basal ganglia function in action control. Although both striatal D1- and D2-spiny projection neurons (SPNs) receive excitatory inputs from the cerebral cortex, whether or not they share inputs from the same cortical neurons, and how pathway-specific corticostriatal projections control behavior remain largely unknown. Here using a G-deleted rabies system in mice, we found that more than two-thirds of excitatory inputs to D2-SPNs also target D1-SPNs, while only one-third do so vice versa. Optogenetic stimulation of striatal D1- vs. D2-SPN-projecting cortical neurons differently regulate locomotion, reinforcement learning and sequence behavior, implying the functional dichotomy of pathway-specific corticostriatal subcircuits. These results reveal the partially segregated yet asymmetrically overlapping cortical projections on striatal D1- vs. D2-SPNs, and that the pathway-specific corticostriatal subcircuits distinctly control behavior. It has important implications in a wide range of neurological and psychiatric diseases affecting cortico-basal ganglia circuitry. In BriefKlug, Yan et al. employed a modified rabies system in combination with slice physiology, optogenetics and behavioral tests to reveal that pathway-specific corticostriatal subcircuits distinctly control actions. HighlightsO_LIOne-third of the excitatory inputs to D1-SPNs project to D2-SPNs, while two-third of the excitatory inputs to D2-SPNs also target D1-SPNs C_LIO_LIActivation of D1-SPN projecting cortical neurons triggers behavioral effects in line with postsynaptic striatal direct pathway activation C_LIO_LIActivation of D2-SPN projecting cortical neurons causes behavioral effects similar with co-activation of both direct and indirect pathways C_LIO_LICorticostriatal subcircuits control actions in a brain-region and pathway-specific manner C_LI

neuroscience↗

bioRxiv2020

Prenatal Methadone Exposure Disrupts Behavioral Development and Alters Motor Neuron Intrinsic Properties and Local Circuitry

Despite the rising prevalence of methadone treatment in pregnant women with opioid use disorder, the effects of methadone on neurobehavioral development remain unclear. We developed a translational mouse model of prenatal methadone exposure (PME) that resembles the typical pattern of opioid use by pregnant women who first use oxycodone then switch to methadone maintenance pharmacotherapy, and subsequently become pregnant while maintained on methadone. We investigated the effects of PME on physical development, sensorimotor behavior, and motor neuron properties using a multidisciplinary approach of physical, biochemical, and behavioral assessments along with brain slice electrophysiology and in vivo magnetic resonance imaging. PME produced substantial impairments in offspring physical growth, activity in an open field, and sensorimotor milestone acquisition which were associated with alterations in motor neuron functioning and connectivity. The present study adds to the limited work examining PME by providing a comprehensive, translationally relevant characterization of how PME disrupts offspring development.

neuroscience↗

bioRxiv2018

Predicting the effects of epidural stimulation to improve hand function in patients with spinal cord injury: An active learning-based solution using dynamic sample weighting

In patients with chronic spinal cord injury (SCI), few therapies are available to improve neurological function. Neuromodulation of the spinal cord with epidural stimulation (EDS) has shown promise enabling the voluntary activation of motor pools caudal to the level of the injury. EDS is performed with multiple electrode arrays in which several stimulation variables such as the frequency, amplitude, and location of the stimulation significantly affect the type and amplitude of motor responses. This paper presents a novel technique to predict the final functionality of a patient with SCI after cervical EDS within a deep learning framework. Additionally, we suggest a committee-based active learning method to reduce the number of clinical experiments required to optimize EDS stimulation variables by exploring the stimulation configuration space more efficiently. We also developed a novel method to dynamically weight the results of different experiments using neural networks to create an optimal estimate of the quantity of interest. The essence of our approach was to use machine learning methods to predict the hand contraction force in a patient with chronic SCI based on different EDS parameters. The accuracy of the prediction of stimulation outcomes was evaluated based on three measurements: mean absolute error, standard deviation, and correlation coefficient. The results show that the proposed method can be used to reliably predict the outcome of cervical EDS on maximum voluntary contraction force of the hand with a prediction error of approximately 15%. This model could allow scientists to establish stimulation parameters more efficiently for SCI patients to produce enhanced motor responses in this novel application.\n\nAuthor SummarySpinal cord injury (SCI) can lead to permanent sensorimotor deficits that have a major impact on quality of life. In patients with a motor complete injury, there is no therapy available to reliably improve motor function. Recently, neuromodulation of the spinal cord with epidural stimulation (EDS) has allowed patients with motor-complete SCI regain voluntary movement below the level of injury in the cervical and thoracic spine. EDS is performed using multi-electrode arrays placed in the dorsal epidural space spanning several spinal segments. There are numerous stimulation parameters that can be modified to produce different effects on motor function. Previously, defining these parameters was based on observation and empiric testing, which are time-consuming and inefficient processes. There is a need for an automated method to predict motor and sensory function based on a given combination of EDS settings. We developed a novel method to predict the gripping function of a patient with SCI undergoing cervical EDS based on a set of stimulation parameters within a deep learning framework. We also addressed a limiting factor in machine learning methods in EDS, which is a general lack of training measurements for the learning model. We proposed a novel active learning method to minimize the number of training measurements required. The model for predicting responses to EDS could be used by scientists and clinicians to efficiently determine a set of stimulation parameters that produce a desired effect on motor function.

neuroscience↗