Biology subjects

Johnson, T. S.

Publications and source records attributed to Johnson, T. S..

16 recordsLinked to original sources

bioRxiv2026

ARSA: an autonomous research scientist for target nomination in Alzheimer's disease and related dementias

Expanding therapeutic options for Alzheimer's disease and related dementias (ADRD) requires biologically grounded targets, yet nomination demands labor-intensive analysis and multidisciplinary evidence synthesis. To address this challenge, we present ARSA, an autonomous research scientist that transforms natural-language research interests and molecular data into prioritized, evidence-grounded target shortlists. ARSA formulates and audits hypotheses, adapts molecular analyses to observed results, and prioritizes targets using cross-cohort evidence and disease-specific knowledge, preserving the evidence and decisions underlying each nomination. Across three complementary evaluations, we show that ARSA generates hypotheses corresponding to subsequent research and identifies credible candidates within and beyond community nomination records. In structured assessment by 14 experts spanning all four technical cores of the Indiana University School of Medicine-Purdue University TREAT-AD Center, every expert assigned higher mean credibility to ARSA-retained candidates than to rejected comparators. ARSA enables systematic, transparent target exploration, opening opportunities to broaden the therapeutic mechanisms investigated in ADRD.

bioinformatics↗

bioRxiv2026

BTK inhibition enhances immunovirotherapy in glioblastoma via tertiary lymphoid structure modulation

Glioblastoma (GBM) is a highly aggressive type of glioma that is resistant to immunotherapy and is associated with poor prognosis, largely due to its immunosuppressive tumor microenvironment. Brutons tyrosine kinase (BTK) is a non-receptor kinase that not only plays an important role in oncogenic signaling, particularly in tumor growth, but also regulates the activity of tumor-infiltrating myeloid cells, including dendritic cells, macrophages, and microglia in brain tumors. High BTK expression is associated with poor survival in patients with glioma. Oncolytic herpes simplex virus type 1 (oHSV)-derived virotherapy, a novel treatment strategy, has demonstrated effectiveness against GBM; however, its efficacy is limited by the tumor microenvironment. In this study, we found that BTK is predominantly expressed in GBM-infiltrating myeloid cells. Intratumoral injection of oHSV not only promotes infiltration of myeloid cells and T cells but also activates BTK in these myeloid cells, thereby limiting oHSV infection and replication in tumor cells. Combination treatment with BTK inhibitor ibrutinib improves anti-tumor efficacy of oHSV in both human GBM12 xenograft and syngeneic murine GSC005 models. Mechanistically, BTK inhibition increases oHSV-mediated tumor cell death (cleaved caspase-3) and cytotoxic CD8 T cell infiltration, while decreasing tumor cell proliferation (Ki-67). BTK inhibition not only suppresses oHSV clearance by tumor-infiltrating microglia and macrophages but also reduces their pro-invasive effects on tumor cells. Addition of IDO inhibitor, an immune modulator, further prolongs survival in tumor-bearing mice in a syngeneic GBM model. Single-cell mRNA sequencing (scRNA-seq) analysis indicates that combination treatment modifies key signaling pathways in both tumor-infiltrating myeloid cells (macrophages and microglia) and CD8 T cells. Further analysis shows that BTK inhibition, with or without IDO inhibition, promotes the formation of tumor-infiltrating tertiary lymphoid structures (TLS) during intratumoral oHSV treatment, subsequently remodeling T cell, NKT cell, and monocyte-macrophage populations. These results indicate that BTK inhibition exerts multifaceted effects in enhancing the anti-tumor efficacy of oHSV therapy.

cancer biology↗

bioRxiv2026

Optimization and Characterization of SHIP1 Ligands for Cellular Target Engagement and Activity in Alzheimer's Disease Models

Src homology 2 domain-containing inositol 5-phosphatase 1 (SHIP1), encoded by the gene INPP5D, is a lipid phosphatase that negatively regulates immune receptor signaling in hematopoietic cells and microglia. Here, we describe a pyridyl-pyrazole-piperidine scaffold and the lead compound 3-((2-chlorobenzyl)oxy)-5-(1-(piperidin-4-yl)-1H-pyrazol-4-yl)pyridine (32), which demonstrates SHIP1 target engagement, brain exposure, and evidence of a central pharmacodynamic response in vivo. Structure-activity relationship studies, guided by biochemical and cellular assays using multiple human and murine protein constructs and cells, identified SHIP1-active ligands. A thermal shift assay using full-length SHIP1 was used to assess compounds for cellular target engagement, while studies in IL-4 conditioned THP-1 cells was used to demonstrate changes in downstream AKT signaling. Targeted lipidomics revealed changes in the overall phosphoinositide pool consistent with SHIP1 target engagement and reduction of phospho-AKT levels. In a protein-lipid overlay assay, compound 32 induced changes in the relative association of SHIP1 with multiple phosphatidylinositols on a membrane surface. In high-content cellular imaging assays, compound 32 enhanced the uptake of myelin/membrane debris and fibrillar amyloid by primary murine microglia, phenocopying a genetic model with reduced SHIP1 expression. Finally, oral administration of compound 32 resulted in brain exposure sufficient to alter gene expression and reduce IL-1{beta} levels as pharmacodynamic markers of microglial activation and neuroinflammation in an amyloidosis mouse model of Alzheimers disease. Collectively, these results define a scaffold with SHIP1 target engagement, CNS exposure, and in vivo activity, providing a foundation for the optimization of brain-penetrant SHIP1 ligands suitable for further mechanistic studies and therapeutic development for the treatment of Alzheimers disease.

pharmacology and toxicology↗

bioRxiv2025

RIPK1 regulates beta-cell fate via actions on gene expression and kinase signaling in a mouse model of beta-cell self-reactivity

Type 1 diabetes (T1D) is characterized by autoimmune destruction of pancreatic {beta}-cells, insulin insufficiency, and hyperglycemia. Receptor interacting protein kinase 1 (RIPK1) is a multifunctional regulator of cell fate with kinase and scaffolding functions, and we previously identified RIPKs as regulators of {beta}-cell cytotoxicity in vitro. Here we report that Ripk1 expression is increased in islets from aged non-obese diabetic (NOD) mice and {beta}-cells from T1D donors, suggesting that RIPK1 may drive cytokine- and autoimmune-mediated {beta}-cell demise in T1D. Using NIT-1 {beta}-cells derived from NOD mice, we observed that TNF+IFN{gamma} increase RIPK1 phosphorylation, caspase 3/7 activity, and cell death. In contrast, this cytotoxicity was blocked with small molecule RIPK1 inhibition or in Ripk1 gene-edited (Ripk1{Delta}) {beta}-cells. Co-labeling of caspase 3/7 activation and cell death in single cells revealed protection from caspase-dependent and -independent forms of death in Ripk1{Delta} cells. RNAseq uncovered differential cell death-, immune-, and identity-related gene expression, and kinome profiling identified changes in MAPK, Eph, JAK, and other kinase activity associated with protection from cell death in RIPK1 deficient {beta}-cells. Furthermore, in vitro co-culture assays and in vivo adoptive transfer experiments revealed that NIT-1 Ripk1{Delta} cells were protected from autoimmune destruction by splenocytes isolated from diabetic NOD mice. Collectively, our findings indicate that RIPK1 promotes {beta}-cell demise in response to cytokine and autoimmune stress via actions on gene expression and kinase signaling. Therapeutics targeting RIPK1 may provide novel opportunities for prevention or treatment of autoimmune diabetes.

cell biology↗

bioRxiv2025

Unique and conserved endoplasmic reticulum stress responses in neuroendocrine cells

Endocrine cells are dedicated to the production and processing of hormones, from peptides to small molecules, to regulate key physiological processes, including glucose homeostasis and metabolism. Because of this relatively high productivity, endo-crine cells must handle a variety of stresses from oxidative stress to the unfolded protein response of the endoplasmic reticulum (UPRER). While much is known about the major pathways regulating the UPRER, the roles of endocrine cell type-specific, context-dependent, and time-dependent transcriptional changes are not well explored. To identify unique and shared responses to the UPRER across a subset of endocrine cell types, we tested representative lines for {beta}-cells (insulin), -cells (glucagon), {delta}-cells (somatostatin), X/A-cells (ghrelin), L-cells (glucagon-like peptide 1 (GLP1)), and thyrotropes (thyroid hormone and thyroglobulin). We exposed each cell type to the canonical ER stressor thapsigargin for 6 and 24 h, or vehicle for 24 h and performed mRNA sequencing. Analysis of the data showed all lines responded to thapsigargin. Comparisons of differentially expressed genes between each line revealed both shared and unique transcriptional signatures. These data represent a valuable mineable set of candidate genes that may have cell type-specific functions during the UPRER and have the potential to lead to a new understanding of how different endocrine cells mitigate or succumb to ER stress.

cell biology↗

bioRxiv2025

Identify Alzheimer's disease subtypes and makers from multi-omic data of human brain and blood with a subspace merging algorithm

Identifying Alzheimers disease (AD) subtypes is essential for AD diagnosis and treatment. We integrated multiomics data from brain tissues of the ROSMAP and MSBB studies using a subspace merging algorithm and identified two AD patient clusters with notable cognitive and AD pathology differences. Analysis of differentially expressed genes (DEGs) in brain and blood samples pinpointed the LDLR gene as a potential blood biomarker linked to brain gene expression changes. Furthermore, we conducted PheWAS analysis on All of Us Projects EHR and WGS dataset for 105 eQTLs associated with the DEGs and revealed significant associations between these eQTLs and several phenotypes, shedding light on potential regulatory roles of these genes in diverse physiological processes. Our study successfully integrated multiomics data and proposes LDLR as a candidate blood biomarker for AD subtyping. The identified phenotypic signatures provide valuable insights on molecular mechanisms underlying AD heterogeneity, paving the way for personalized AD treatment.

bioinformatics↗

bioRxiv2025

Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using Deep Transfer Learning

SummaryThe examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms offers meaningful insights into specific disease processes. For existing methods, while cell types or clusters can be identified and associated with disease attributes, individual cells are unable to be associated in the same manner. MethodDiag-nostic Evidence GAuge of Single-cells and spatial transcriptomics (DEGAS), solves the above problem by employing latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells from single-cell RNA sequencing datasets. In this research, we present and evaluate DEGASs versatility in adapting to data arising from various single-cell spatially resolved transcriptomics (scSRT) platforms. DEGAS successfully identified high-risk cells and regions in liver hepatocellular carcinoma and skin cutaneous melanoma, which were validated through known markers. Additionally, DEGAS was applied to our newly generated Type II Diabetes Xenium dataset revealing high-risk cells within the tissue samples. Availability and ImplementationThe DEGAS software can be accessed at https://github.com/tsteelejohnson91/DEGAS. For the updated smoothing functions and associated codes, visit https://github.com/dchatter04/DEGAS-Spatial-Smoothing. Sources for the datasets reviewed are detailed in their respective sections. A description of some datasets, along with extra tables and figures, is provided in the Supplementary Materials file. Our newly generated Xenium data for Type II Diabetes can be found at https://doi.org/10.7303/syn68699752.

bioinformatics↗

bioRxiv2024

Target deconvolution of an insulin hypersecretion-inducer acting through VDAC1 with a distinct transcriptomic signature in beta-cells

Obesity, insulin resistance, and a host of environmental and genetic factors can drive hyperglycemia, causing {beta}-cells to compensate by increasing insulin production and secretion. In type 2 diabetes (T2D), {beta}-cells under these conditions eventually fail. Rare {beta}-cell diseases like congenital hyperinsulinism (HI) also cause inappropriate insulin secretion, and some HI patients develop diabetes. However, the mechanisms of insulin hypersecretion and how it causes {beta}-cell dysfunction are not fully understood. We previously discovered small molecules (e.g. SW016789) that cause insulin hypersecretion and lead to a loss in {beta}-cell function without cell death. Here, we uncover the protein target of SW016789 and provide the first time-course transcriptomic analysis of hypersecretory responses versus thapsigargin-mediated ER stress in {beta}-cells. In mouse MIN6 and human EndoC-{beta}H1 {beta}-cells, we identified and validated VDAC1 as a SW016789 target using photoaffinity proteomics, cellular thermal shift assays, siRNA, and small molecule inhibitors. SW016789 raises membrane potential to enhance Ca2+ influx, potentially through VDAC1. Chronically elevated intracellular Ca2+ appears to underpin the negative impacts of hypersecretion, as nifedipine protected against each small molecule hypersecretion inducer we tested. Using time- course RNAseq, we discovered that hypersecretion induced a distinct transcriptional pattern compared to ER stress. Clustering analyses led us to focus on ER-associated degradation (ERAD) as a potential mediator of the adaptive response. SW016789 reduced the abundance of ERAD substrate OS-9 and pharmacological inhibition of ERAD worsened {beta}-cell survival in response to hypersecretory stress. Changes in other ERAD components in MIN6 and EndoC-{beta}H1 at the protein level were minor with either SW016789 or thapsigargin. However, immunostaining for core ERAD components SEL1L, HRD1, and DERL3 in non-diabetic and T2D human pancreas revealed altered distributions of SEL1L/HRD1 and SEL1L/DERL3 rations in {beta}-cells of T2D islets, in alignment with altered ERAD in stressed {beta}-cells. We conclude that hypersecretory stimuli, including SW016789- mediated VDAC1 activation, cause enhanced Ca2+ influx and insulin release. Subsequent differential gene expression represents a {beta}-cell hypersecretory response signature that is reflected at the protein level for some, but not all genes. A better understanding of how {beta}-cells induce hypersecretion and the mechanisms of negative feedback on secretory rate may lead to the discovery of novel therapeutic targets for T2D and HI.

cell biology↗

bioRxiv2024

A novel induced pluripotent stem cell model of schwann cell differentiation reveals NF2-related gene regulatory networks of the extracellular matrix

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2-related schwannomatosis. We developed a novel human induced pluripotent stem cell (hiPSC) model to study Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann cells (SCs) representing distinct stages of development. To validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we identified gene co-expression modules that are dynamically regulated during hiPSC to SC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets, we identified new genes potentially associated with NF2 expression. Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

bioinformatics↗

bioRxiv2024

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet {beta}-cells are the sole source of endogenous insulin and our understanding of {beta}-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in {beta}-cell function and survival. However, it is difficult to identify which {beta}-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related {beta}-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct {beta}-cell subpopulations. A singular cluster of T2D-associated {beta}-cells was identified; however, {beta}-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected CDKN1C and DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. Both CDKN1C and DLK1 were heterogeneously expressed among {beta}-cells. CDKN1C was increased in {beta}-cells from T2D donors, in agreement with the DEGAS predictions, while DLK1 appeared depleted from T2D islets of some donors. In conclusion, DEGAS has the potential to advance our holistic understanding of the {beta}-cell transcriptomic phenotypes, including features that distinguish {beta}-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

bioinformatics↗

bioRxiv2023

Enhanced microglial dynamics and paucity of tau seeding in the amyloid plaque microenvironment contributes to cognitive resilience in Alzheimer 's disease

Asymptomatic Alzheimers disease (AsymAD) describes the status of subjects with preserved cognition but with identifiable Alzheimers disease (AD) brain pathology (i.e. A{beta}-amyloid deposits, neuritic plaques, and neurofibrillary tangles) at autopsy. In this study, we investigated the postmortem brains of a cohort of AsymAD cases to gain insight into the underlying mechanisms of resilience to AD pathology and cognitive decline. Our results showed that AsymAD cases exhibit an enrichment of core plaques and decreased filamentous plaque accumulation, as well as an increase in microglia surrounding this last type. In AsymAD cases we found less pathological tau aggregation in dystrophic neurites compared to AD and tau seeding activity comparable to healthy control subjects. We used spatial transcriptomics to further characterize the plaque niche and found autophagy, endocytosis, and phagocytosis within the top upregulated pathways in the AsymAD plaque niche, but not in AD. Furthermore, we found ARP2, an actin-based motility protein crucial to initiate the formation of new actin filaments, increased within microglia in the proximity of amyloid plaques in AsymAD. Our findings support that the amyloid-plaque microenvironment in AsymAD cases is characterized by microglia with highly efficient actin-based cell motility mechanisms and decreased tau seeding compared to AD. These two mechanisms can potentially provide protection against the toxic cascade initiated by A{beta} that preserves brain health and slows down the progression of AD pathology.

neuroscience↗

bioRxiv2021

The indoleamine 2,3 dioxygenase pathway drives intratumoral B cell maintenance.

B cells have been implicated as central regulators of immune responses in settings as diverse as mammalian pregnancy, mucosal tolerance, chronic infection states, autoimmunity, and the tumor microenvironment. Despite the established importance of B cells in these environments, the mechanisms by which B cells are maintained in these contexts remain undefined. Here, we report that IDO1 pathway inhibition with D-1-methyl-tryptophan (D-1MT) and linrodostat significantly decreases tumor infiltrating B (TIL-B) cells in a preclinical model of melanoma. Single cell RNA sequencing (scRNAseq) of murine melanoma demonstrate TIL-B cells are heterogeneous but primarily express markers consistent with an immune stimulatory phenotype. D-1MT decreases splenic B cells and bone marrow derived B cell precursors in tumor-bearing mice, suggesting that IDO1 pathway inhibition impedes B cell maturation. D-1MT decreases intratumoral myeloid derived suppressor cells (MDSCs), which are essential for maintenance of TIL-B cells. Unlike D-1MT, genetic deletion of tumor Ido1 does not impact TIL-B or MDSC numbers. In human solid tumors, intratumoral IDO1 expression consistently associates with high expression of a pan-B cell gene signature, and in patients with melanoma, scRNAseq analysis of tumor samples revealed most TIL-B cells express IDO1. Collectively, our data reveal the impact of pharmacologic IDO1 inhibition on B cells, which may have therapeutic implications for patients with solid tumors by informing the design of future oncology clinical trials.

immunology↗

bioRxiv2020

Strawberry sweetness and consumer preference are enhanced by specific volatile compounds

Breeding crops for improved flavor is challenging due to the high cost of sensory evaluation and the difficulty of connecting sensory experience to chemical composition. The main goal of this study was to identify the chemical drivers of sweetness and consumer liking for fresh strawberries (Fragaria xananassa). Fruit of 148 strawberry samples from cultivars and breeding selections were grown and harvested over seven years and were subjected to both sensory and chemical analyses. Each panel consisted of at least 100 consumers, resulting in more than 15,000 sensory data points per descriptor. Three sugars, two acids and 113 volatile compounds were quantified. Consumer liking was highly associated with sweetness intensity, texture liking, and flavor intensity, but not sourness intensity. Partial least square analyses revealed 20 volatile compounds that increased sweetness perception independently of sugars; 18 volatiles that increased liking independently of sugars; and 15 volatile compounds that had positive effects on both. Machine learning-based predictive models including sugars, acids, and volatiles explained at least 25% more variation in sweetness and liking than models accounting for sugars and acids only. Volatile compounds such as {gamma}-dodecalactone; 5-hepten-2-one, 6-methyl; and multiple medium-chain fatty acid esters may serve as targets for breeding or quality control attributes for strawberry products. A genetic association study identified two loci controlling ester production, both on linkage group 6A. Co-segregating makers in these regions can be used for increasing multiple esters simultaneously. This study demonstrates a paradigm for improvement of fruit sweetness and flavor in which consumers drive the identification of the most important chemical targets, which in turn drives the discovery of genetic targets for marker-assisted breeding.

plant biology↗

bioRxiv2020

Diagnostic Evidence GAuge of Single cells (DEGAS): A transfer learning framework to infer impressions of cellular and patient phenotypes between patients and single cells

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single cell and patient bulk tissue transcriptomic datasets from Glioblastoma Multiforme (GBM), Alzheimers Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on newly generated myeloma single cell transcriptomics led to the identification of PHF19high myeloma cells associated with progression.

bioinformatics↗

bioRxiv2019

BURMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for cell lineation and identifying bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch-Effect ReMoval Using Deep Autoencoders) -- a novel transfer-learning-based method for batch-effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

bioinformatics↗

bioRxiv2019

Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

Technical advances have enabled the identification of high-resolution cell types within tissues based on single-cell transcriptomics. However, such analyses are restricted in human brain tissue due to the limited number of brain donors. In this study, we integrate mouse and human data to predict cell-type proportions in human brain tissue, and spatially map the resulting cellular composition. By applying feature selection and linear modeling, combinations of human and mouse brain single-cell transcriptomics profiles can be integrated to \"fill in\" missing information. These combined \"in silico chimeric\" datasets are used to model the composition of nine cell types in 3,702 human brain samples in six Allen Human Brain Atlas (AHBA) donors. Cell types were spatially consistent regardless of the scRNA-Seq dataset (91% significantly correlated) or AHBA donor (p-value = 4.43E-20 by t-test) used in the model. Importantly, neuron nuclei location and neuron mRNA location were correlated only after accounting for neural connectivity (p-value = 1.26E-10), which supports the notion that gene expression is a better indicator than nuclei location of cellular localization for cells with large and irregularly shaped cell bodies, such as neurons. These results advocate for the integration of mouse and human data in models of brain tissue heterogeneity.

bioinformatics↗