Biology subjects

Johnston, L.

Publications and source records attributed to Johnston, L..

6 recordsLinked to original sources

bioRxiv2026

Ubiquitylation by the GID/CTLH complex regulates the metabolic and innate immune response of macrophages to infection by Mycobacterium tuberculosis

The GID/CTLH E3 ligase complex is implicated in several biological processes, yet its full substrate repertoire remains poorly defined. We recently identified the complex as a broad modulator of macrophage responses to Mycobacterium tuberculosis (Mtb) infection. Here, we use label-free proteomics and diGly capture analysis of Mtb-infected macrophages to define the GID/CTLH-dependent ubiquitylome. We identify thousands of dynamically altered ubiquitylation sites, with strong enrichment among proteins involved in cellular metabolism and innate immune signaling. Concurrent proteome analysis revealed extensive rewiring in GID/CTLH-deficient macrophages, with >90% of enriched pathways among increased proteins consisting of metabolic targets. Notably, inhibitory phosphatases (PTEN, INPP5D) also emerged as candidate substrates. Functional studies revealed proteasome-dependent stabilization of PTEN and INPP5D in GID/CTLH-deficient macrophages with each phosphatase individually exerting an influence on Mtb intracellular survival. Together, our study defines a GID/CTLH-dependent ubiquitylome in macrophages and identifies the complex as a central regulator of metabolism and antimicrobial immunity. Author summaryMycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), survives and replicates within macrophages, key immune cells that normally eliminate pathogens. How macrophages control their internal cellular environment in response to infection remains incompletely understood. One such important cellular control system is ubiquitylation, in which proteins are tagged with ubiquitin to determine their functional fate or target them for degradation. We recently identified the GID/CTLH E3 ligase ubiquitylation complex as a critical modulator of macrophage antimicrobial responses to Mtb. Here, we used proteomics approaches to define the proteins controlled by the GID/CTLH complex in Mtb-infected macrophages. We found that this complex ubiquitylates a broad network of proteins involved in cellular metabolism and immune signaling. When the complex is disrupted, macrophages undergo extensive metabolic reprogramming, particularly increased mitochondrial energy production, while showing reduced inflammatory signaling. Despite this dampened immune response, these cells are better able to restrict Mtb growth. We also identified the phosphatases PTEN and INPP5D as targets controlled by the GID/CTLH complex that independently influence intracellular bacterial survival. Our findings demonstrate that the GID/CTLH complex is a critical regulator of metabolism and immune function, shaping the outcomes of Mtb infection.

immunology↗

bioRxiv2025

Incorporation of macrophage immune stresses into an assay for drug tolerance in intracellular Mycobacterium tuberculosis

Development of new and improved tuberculosis (TB) chemotherapies is hampered by antibiotic resistance and drug tolerance by Mycobacterium tuberculosis (Mtb). Phenotypic drug tolerance, a phenomenon where Mtb populations can temporarily survive therapeutic antibiotic concentrations, represents a significant hurdle to TB treatment and is indeed one of the factors responsible for prolonged TB therapy. Assays that can identify compounds with improved efficacy against drug tolerant Mtb are urgently required to improve TB treatment regimens. Here, we report the development of a 96-well plate assay capable of identifying anti-Mtb drugs with activity against drug tolerant Mtb in physiologically relevant intracellular environments within macrophages. Primary murine macrophages modified either by immunological activation or specific CRISPR/Cas9 gene knockouts to generate tolerance-inducing environments were infected with an Mtb strain constitutively expressing luciferase. Following drug exposure, differences in bacterial survival were measured by bacterial outgrowth after lysis of the host macrophages. By monitoring Mtb luciferase in infected macrophages before, during and after drug treatment, we confirmed earlier observations that host immune stresses trigger induction of drug tolerance. However, while host stresses induced tolerance against some anti-TB compounds, the same host stresses were synergistic with other anti-TB drugs. Our assay provides the ability to profile the activities of anti-TB drugs on bacteria in intracellular host environments which is critical to the rational design of drug combinations that provide optimal coverage of the Mtb sub-populations in the infected host. Author summaryTB treatment is a lengthy process which at minimum takes 6-9 months in cases of drug sensitive Mtb. Long TB treatments are in part, due to drug tolerance phenotypes in the bacterial population which can be a result of immune related stresses on the bacteria in the infected host cells. We used this knowledge to develop a luciferase-based assay which can be used to screen, optimize and discriminate anti-TB drugs with enhanced activity against drug tolerant Mtb inside the infected host cells. Primary murine macrophages were modified either by cytokine activation or genetic knockout of certain host genes to mimic immune related stresses experienced by the bacteria in vivo. By infecting the modified macrophages with an Mtb strain expressing luciferase followed by drug addition, our assay was able to confirm that immune related stress induce drug tolerance to Mtb, and that the drug tolerance phenotypes are unique to certain drug classes consistent with their known mode of action. The assay provides an important addition to anti-TB drug discovery by providing a means to readily screen new drugs and drug combinations that display improved activity in tolerance inducing environments in a physiologically relevant context.

microbiology↗

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

bioRxiv2024

Genome-wide screen of Mycobacterium tuberculosis-infected macrophages identified the GID/CTLH complex as a determinant of intracellular bacterial growth.

SummaryThe eukaryotic GID/CTLH complex is a highly conserved E3 ubiquitin ligase involved in a broad range of biological processes. However, a role of this complex in host antimicrobial defenses has not been described. We exploited Mycobacterium tuberculosis (Mtb) induced cytotoxicity in macrophages in a FACS based CRISPR genetic screen to identify host determinants of intracellular Mtb growth restriction. Our screen identified 5 (GID8, YPEL5, WDR26, UBE2H, MAEA) of the 10 predicted members of the GID/CTLH complex as determinants of intracellular growth of both Mtb and Salmonella serovar Typhimurium. We show that the antimicrobial properties of the GID/CTLH complex knockdown macrophages are mediated by enhanced GABAergic signaling, activated AMPK, increased autophagic flux and resistance to cell death. Meanwhile, Mtb isolated from GID/CTLH knockdown macrophages are nutritionally starved and oxidatively stressed. Our study identifies the GID/CTLH complex activity as broadly suppressive of host antimicrobial responses against intracellular bacterial infections. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/592714v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1fc273aorg.highwire.dtl.DTLVardef@173927borg.highwire.dtl.DTLVardef@95cb02org.highwire.dtl.DTLVardef@42fcea_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.

bioinformatics↗

bioRxiv2018

Clusters of fatty acids in the serum triacylglyceride fraction associate with the disorders of type 2 diabetes

AimsOur aim was to examine longitudinal associations of triacylglyceride fatty acid (TGFA) composition with insulin sensitivity (IS) and beta-cell function.\n\nMethodsAdults at-risk for T2D (n=477) had glucose and insulin measured from a glucose challenge at 3 time points over 6 years. The outcome variables Matsuda index (ISI), HOMA2-%S, Insulinogenic Index over HOMA-IR (IGI/IR), and Insulin Secretion-Sensitivity Index-2 (ISSI-2) were computed from the glucose challenge. Gas chromatography quantified TGFA composition from the baseline. We used adjusted generalized estimating equations (GEE) models and partial least squares (PLS) regression for the analysis.\n\nResultsIn adjusted GEE models, four TGFA (14:0, 16:0, 14:1n-7, 16:1n-7 as mol%) had strong negative associations with IS while others (e.g. 18:1n-7, 18:1n-9, 20:2n-6, 20:5n-3) had strong positive associations. Few associations were seen for beta-cell function, except for 16:0, 18:1n-7, and 20:2n-6. PLS analysis indicated four TGFA (14:0, 16:0, 14:1n-7, 16:1n-7) that clustered together and strongly related with lower IS. These four TGFA also correlated highly (r>0.4) with clinically measured TG.\n\nConclusionsWe found that higher proportions of a cluster of four TGFA strongly related with lower IS as well as hypertriglyceridemia, suggesting only a few fatty acids within the TGFA composition may primarily explain lipids role in glucose dysregulation.

epidemiology↗