Biology subjects

Miller, J. P.

Publications and source records attributed to Miller, J. P..

10 recordsLinked to original sources

bioRxiv2026

IL1β/IL1R1/IRAK4 Drives Inflammatory Ovarian Cancer Seeding at the inflamed sites and Is Reversed by an IRAK4 inhibitor UR241-2

Inflammation-driven tumor implantation, such as port-site metastasis (PSM) following laparoscopic gynecologic surgery and peritoneal seeding during post-surgical recurrence, represents an aggressive clinical problem that remains poorly understood and lacks targeted therapies. To address this, we developed a non-surgical Mesothelium-Inflammation/Injury-Metastasis (MIM) model and investigated the role of the IL-1{beta}/IL1R1/MYD88/IRAK1/4 axis and NLRP3 in epithelial ovarian cancer (EOC) seeding at inflamed or injured sites. This model created by a needle injury recapitulates inflammation-driven peritoneal seeding and mimics PSM and inflammation associated dissemination in peritoneum during recurrence. Seeding was dependent on Il1r1 but not Nlrp3, despite its role in regulating IL-1{beta} production, as Il1ra-/- and Nlrp3-/- mice phenocopied wild-type C57BL/6 mice. Given the limited antitumor efficacy of IL-1{beta}-targeting agents such as Anakinra and Canakinumab, we focused on IRAK4 as a therapeutic target. IRAK4 knockdown significantly prolonged survival, reduced tumor cell adhesion, downregulated E-cadherin and Wnt4, and induced S-phase/mitotic arrest. This led to the development of UR241-2, a small-molecule IRAK4 inhibitor, which was validated through molecular simulations, hotspot analysis, nanoBRET, global kinome profiling, and NF-{kappa}{beta} reporter assays. UR241-2 inhibited NF-{kappa}{beta} nuclear translocation and blocked IL-1{beta}-induced IRAK4 phosphorylation. UR241-2 exhibited favorable drug-like properties, including absence of CYP or hERG inhibition, and acceptable CaCo-2 permeability, plasma protein binding, microsomal stability, and pharmacokinetics. In vivo, UR241-2 reduced SKOV3 xenograft growth, suppressed mesothelial seeding, and increased MHC-II macrophages and activated neutrophils in syngeneic high-grade epithelial ovarian HGS3 tumors. RNA-seq revealed enrichment of neutrophil activation signatures and suppression of extracellular matrix (ECM) gene programs. Together, these findings establish a role for the IL-1{beta}/IL1R1/IRAK4 axis in inflammation-driven PSM and peritoneal seeding and ECM regulation in EOC, and demonstrate that IRAK4 inhibition activates antitumor immune responses, providing a therapeutic strategy to block metastatic seeding and improve tumor control.

cancer biology↗

bioRxiv2025

Effects of a novel proteasome inhibitor, UR238 on the tumor immune microenvironment and growth in epithelial ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a deadly gynecologic malignancy, often diagnosed at an advanced stage and most patients will experience recurrence and resistance to platinum-based chemotherapy. While there are few targeted therapies available for HGSOC, there are no effective immunotherapies available to treat this disease. The ubiquitin-proteasome system (UPS) maintains cellular protein homeostasis by degrading misfolded or damaged proteins. In epithelial ovarian cancer (EOC) elevated expression of proteasome subunit PSMB4 correlates with epithelial ovarian cancer growth and poor prognosis. A proteasome inhibitor has yet to be approved for EOC treatment despite evidence of activity in phase-1/2 clinical trials. Limitations of past generation proteosome inhibitors include sub-optimal solid tumor/tissue penetration due to boronic acid functionality, and poor solubility. Here, we show that a novel proteasome inhibitor, UR238, suppresses viability of several EOC cell lines and reduces tumor burden in both murine xenograft and rat syngeneic models. In a rat EOC model, UR238 treatment reduced tumor burden, shifted the predominantly suppressive immune microenvironment to an inflammatory phenotype with reduced suppressive macrophages, and decreased PD-L1 expression on myeloid cells. Additionally, in a mouse model, UR238 also induced robust immune changes in HGSOC bearing mice without a corresponding reduction in tumor burden. Our data highlights the promise of UR238 for the treatment of ovarian cancers as a single-agent or in combination with immunotherapies, where modulating the immune composition of these tumors will be important in improving outcomes for ovarian cancer patients.

immunology↗

bioRxiv2025

OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine

Real-world biomedical scientific discovery operates as an iterative lifecycle integrating four core pillars: the targeted identification and analysis of question-specific omics datasets; the context-aware interpretation of molecular data using rich biomedical prior knowledge database; comprehensive literature reviews; and the subjective creativity and expert intuition of human scientists. Together, these pillars drive robust evidence synthesis and novel hypothesis generation. The recently reported AI agents support automated omics analysis and literature review, which typically require users to predefine and curate disease-specific datasets, which is a process that remains challenging and time-consuming. In this study, we present OmniCellAgent, a novel multi-agent AI framework built on large-scale single-cell RNA sequencing (scRNA-seq) datasets, and can autonomously retrieve and analyze disease and control-related scRNAseq datasets of diverse cell types across tissues and conditions. Moreover, it incorporates a biomedical prior knowledge and literature review agents, and disease domain-specific expert agents to systematically annotate omic data-derived targets. By aggregating evidence across agents, the framework generates structured analytical reports and potential scientific hypotheses. We evaluated OmniCellAgent across multiple disease settings, demonstrating its ability to identify relevant datasets, generate omic data analysis results, and produce structured reports and scientific hypotheses. Our findings demonstrate that multi-agent AI systems lower the technical barriers to omics-driven research, thereby accelerating scientific discovery and hypothesis generation in biomedical research and precision medicine. The source code is publicly available at: https://github.com/FuhaiLiAiLab/OmniCellAgent.

bioinformatics↗

bioRxiv2025

IL-1β/IRAK4 Axis Promotes Ovarian Tumor Development at the Mesothelium Injury Sites

Epithelial ovarian cancer (EOC) cells seed at mesothelial inflammation or injury sites. Lack of animal models recapitulating tumor cells seeding at inflamed sites in EOC hinders mechanistic studies and therapy developments. Here, we developed a non-surgical MIM (Mesothelium Inflammation/Injury Metastasis) model that recapitulates tumor cell seeding at inflamed sites. This model captures temporal changes in tumor immune microenvironment and tumor growth allowing for deeper mechanistic and preclinical therapeutic studies of EOC in-vivo. We show here that HGS-3 high-grade murine serous EOC cells seed at needle-induced injury sites in mesothelium/peritoneal wall, forming tumors both internally and protruding outward. Using MIM model, we found that deletion of IL1R1 in mice reduced EOC cell seeding at mesothelium injury/inflamed site in WT but not IL1ra-deficient mice. Treatment of MiM mice with a novel IRAK4 inhibitor we recently developed (UR241-2) revealed an essential role for IRAK4 signaling downstream IL-1{beta}/IL-1R1 in fostering an anti-tumor inflammatory environment, and reduced tumor burden. We conclude that IRAK4 inhibitors can be more effective than IL-1/IL-1R1 targeting agents to control metastasis and peritoneal tumors, an unmet medical need in EOC recurrence. Downregulation of extracellular matrix (ECM), upregulation of neutrophil activation genes, reduced cell adhesion and migration exhibit how UR241-2 corrects ECM and immune disorders in EOC, making it less conducive to metastasis and tumorigenesis.

cancer biology↗

bioRxiv2025

Evolution of chronic lymphocytic leukemia after allogeneic stem cell transplant

Allogeneic stem cell transplant (alloSCT) for patients with relapsed/refractory chronic lymphocytic leukemia (CLL) can result in cure in some patients. Analogous to chemotherapy and targeted therapy, we hypothesized that allogeneic cellular immunotherapies, including alloSCT and donor lymphocyte infusion (DLI), would impact malignant evolution through the application of selective immunologic pressure with reciprocal changes in the T cell compartment. We tested a cohort of 24 patients treated with HLA-matched alloSCT +/- DLI, two mediators of the graft versus leukemia (GVL) effect. Comparison of pre-alloSCT samples revealed that a key difference between responders (n=13) and non-responders (n=11) is the cellularity of leukemic cells. We further mapped mutational trajectories of tumor cells by whole exome sequencing (WES) of sort-purified CLL in 11 post-transplant relapsed patients and found evidence of subclonal leukemic evolution in 8/11 patients after nonmyeloablative human leukocyte antigen (HLA)-matched alloSCT. Different patterns of CLL evolution were observed, and these changes included putative CLL drivers in every case. To investigate the presence of immune-related variants in patients, we collected 19 T cell co-culture CRISPR datasets and identified the top positive and negative regulators of cancer cells response to T-cell-dependent killing. We found that most mutations linked to T-cell killing emerged after allo-SCT treatment, suggesting that selective pressures from the GVL effect may drive the evolution of these mutations. Together, these data identify cellular homogeneity as a key biomarker for susceptibility to GVL-driven immunity in CLL and illustrate how the leukemic cells further evolve to evade immunosurveillance. SIGNIFICANCEThe impact of allogeneic stem cell transplant (alloSCT) on subclonal leukemia evolution remains poorly understood. By performing whole exome sequencing (WES) on pre- and post-alloSCT patient samples, we reveal different patterns of CLL evolution and potential immune-related driver genes influencing CLL relapse and refractory disease post-alloSCT.

cancer biology↗

bioRxiv2024

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica, an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica

bioinformatics↗

bioRxiv2023

Tessellation Of Artificial Touch Via Microstimulation Of Human Somatosensory Cortex

When we interact with objects, we rely on signals from the hand that convey information about the object and our interaction with it. A basic feature of these interactions, the locations of contacts between the hand and object, is often only available via the sense of touch. Information about locations of contact between a brain-controlled bionic hand and an object can be signaled via intracortical microstimulation (ICMS) of somatosensory cortex (S1), which evokes touch sensations that are localized to a specific patch of skin. To provide intuitive location information, tactile sensors on the robotic hand drive ICMS through electrodes that evoke sensations at skin locations matching sensor locations. This approach requires that ICMS-evoked sensations be focal, stable, and distributed over the hand. To systematically investigate the localization of ICMS-evoked sensations, we analyzed the projected fields (PFs) of ICMS-evoked sensations - their location and spatial extent - from reports obtained over multiple years from three participants implanted with microelectrode arrays in S1. First, we found that PFs vary widely in their size across electrodes, are highly stable within electrode, are distributed over large swaths of each participants hand, and increase in size as the amplitude or frequency of ICMS increases. Second, while PF locations match the locations of the receptive fields (RFs) of the neurons near the stimulating electrode, PFs tend to be subsumed by the corresponding RFs. Third, multi-channel stimulation gives rise to a PF that reflects the conjunction of the PFs of the component channels. By stimulating through electrodes with largely overlapping PFs, then, we can evoke a sensation that is experienced primarily at the intersection of the component PFs. To assess the functional consequence of this phenomenon, we implemented multi-channel ICMS-based feedback in a bionic hand and demonstrated that the resulting sensations are more localizable than are those evoked via single-channel ICMS.

neuroscience↗

bioRxiv2022

Microparticle-delivered Cxcl9 delays the relapse of Braf inhibitor-treated melanoma

Patients with BRAF-mutant melanoma show significant responses to combined BRAF and MEK inhibition, but most relapse within 2 years. A major reservoir for drug resistance is minimal residual disease (MRD), comprised of drug-tolerant tumor cells laying in a dormant state. Towards exploiting potential therapeutic vulnerabilities of MRD, we established a genetically engineered mouse model of BrafV600E-driven melanoma MRD wherein genetic BrafV600E extinction leads to strong but incomplete tumor regression. Transcriptional time-course analysis after BrafV600E extinction revealed that after an initial surge of immune activation, tumors later became immunologically "cold" after MRD establishment. Computational analysis identified candidate T-cell recruiting chemokines that may be central players in the process, being strongly upregulated initially and steeply decreasing as the immune response faded. Therefore, we hypothesized that sustaining the chemokine signaling could impair MRD maintenance through increased recruitment of effector T-cells. We show that intratumoral administration of recombinant Cxcl9, either naked or loaded in microparticles, significantly impaired MRD relapse in BRAF-inhibited tumors, including several complete responses after microparticle-delivered rCxcl9 combined with BRAF and MEK-inhibition. Our experiments constitute a proof of concept that chemokine-based microparticle delivery systems are a potential strategy to forestall tumor relapse and thus improve the clinical success of frontline treatment methods.

cancer biology↗

bioRxiv2020

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: 1) Force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); 2) Grasp type affected force representation within multi-unit neural features and offline force classification accuracy; and 3) Grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type. Significance StatementIntracortical brain-computer interfaces (iBCIs) have emerged as a promising technology to potentially restore hand grasping and object interaction in people with tetraplegia. This study is among the first to quantify the degree to which hand grasp affects force-related - or kinetic - neural activity and decoding performance in individuals with tetraplegia. The study results enhance our overall understanding of how the brain encodes kinetic parameters across varying kinematic behaviors -- and in particular, the degree to which these parameters have independent versus interacting neural representations. Such investigations are a critical first step to incorporating force control into human-operated iBCI systems, which would move the technology towards restoring more functional and naturalistic tasks.

neuroscience↗

bioRxiv2018

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Speaking is a sensorimotor behavior whose neural basis is difficult to study at the resolution of single neurons due to the scarcity of human intracortical measurements and the lack of animal models. We recorded from electrode arrays in the hand knob area of motor cortex in people with tetraplegia. Neurons in this area, which have not previously been implicated in speech, modulated during speaking and during non-speaking movement of the tongue, lips, and jaw. This challenges whether the conventional model of a motor homunculus division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential utility of intracortical recordings for brain-computer interfaces (BCIs) to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a large initial condition-invariant signal, followed by rotatory dynamics. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.

neuroscience↗