Biology subjects

Thompson, E. G.

Publications and source records attributed to Thompson, E. G..

4 recordsLinked to original sources

bioRxiv2026

High-throughput discovery of arginine-depleted peptides enables effective antisense delivery for Duchenne muscular dystrophy

Phosphorodiamidate morpholino oligomers (PMOs) are approved exon-skipping antisense therapeutics for Duchenne muscular dystrophy (DMD), but their clinical utility is limited by poor uptake in muscle tissue, necessitating frequent high-dose administration. Cell-penetrating peptides (CPPs) can enhance intracellular delivery of PMOs, yet conventional arginine-rich CPPs often cause dose-limiting toxicity, including renal damage, which hinders their clinical translation. To address this challenge, we developed a high-throughput, charge-based chromatographic enrichment platform capable of screening over 15,000 synthetic peptides, including sequences with noncanonical (abiotic) amino acids. This approach enabled de novo discovery of arginine-depleted CPPs with improved delivery profiles. Four lead candidates demonstrated efficient nuclear PMO delivery with ~10-fold lower in vitro toxicity compared to standard CPPs such as penetratin. The top-performing peptide, CXP1, showed robust splice-switching activity and favorable tolerability in both cellular and animal models. In dystrophic mdx mice, CXP1-PMO conjugates achieved greater exon skipping compared to PMOs conjugated to R6G at equivalent doses. Tissue levels of CXP1-PMO correlated with exon-skipping efficacy, establishing a clear pharmacokinetic-pharmacodynamic relationship. These findings highlight a mechanistically novel and translationally relevant discovery strategy, demonstrating the potential of high-throughput platforms to generate more effective CPP-based delivery vehicles for antisense therapeutics in DMD and related neuromuscular disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=44 SRC="FIGDIR/small/730741v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@84b7f2org.highwire.dtl.DTLVardef@1487850org.highwire.dtl.DTLVardef@d2e263org.highwire.dtl.DTLVardef@10ce371_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

A robust evaluation of TDP-43, poly GP, cellular pathology, and behavior in a AAV-C9ORF72 (G4C2)66 mouse model

The G4C2 hexanucleotide repeat expansion in C9ORF72 is the major genetic cause of both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) (C9-ALS/FTD). Despite considerable efforts, the development of mouse models of C9-ALS/FTD useful for therapeutic development has proven challenging due to the intricate interplay of genetic and molecular factors underlying this neurodegenerative disorder, in addition to species differences. This study presents a robust investigation of the cellular pathophysiology and behavioral outcomes in a previously described AAV mouse model of C9-ALS expressing 66 G4C2 hexanucleotide repeats. Despite displaying key molecular ALS pathological markers including RNA foci, dipeptide repeat (DPR) protein aggregation, p62 positive stress granule formation as well as mild gliosis, the AAV-(G4C2)66 mouse model in this study exhibits negligible neuronal loss, no motor deficits, and functionally unimpaired TAR DNA-binding protein-43 (TDP-43). While our findings indicate and support that this is a robust and pharmacologically tractable model for investigating the molecular mechanisms and cellular consequences of (G4C2) repeat driven DPR pathology, it is not suitable for investigating the development of disease associated neurodegeneration, TDP-43 dysfunction, gliosis, and motor performance. Our findings underscore the complexity of ALS pathogenesis involving genetic mutations and protein dysregulation and highlight the need for more comprehensive model systems that reliably replicate the multifaceted cellular and behavioral aspects of C9-ALS.

neuroscience↗

bioRxiv2022

Astrocyte MCT1 expression does not contribute to the axonal degenerative phenotype observed with ubiquitous MCT1 depletion.

We recently reported that loss of oligodendrocyte metabolic support through the lactate and pyruvate transporter Monocarboxylate Transporter 1 (MCT1) is well tolerated into adulthood. Only with advanced aging did we observe axonal degeneration and hypomyelination due to loss of MCT1 from oligodendroglia lineage cells. MCT1 is also expressed by other glial subtypes, such as astrocytes and endothelial cells where it has been suggested to be essential for learning and memory tasks. However, the importance of MCT1 in these cell types for long-term axonal metabolic support is still unknown. We therefore addressed whether conditional loss of MCT1 from either of these cell types would lead to widespread axonal degeneration with aging. Using a conditional null approach, similar to what was used for oligodendrocyte MCT1 depletion, we observed that conditional knockout of MCT1 from either astrocytes or endothelial cells did not cause neuronal injury. On the other hand, inducible ubiquitous depletion of MCT1 causes late-onset axonal degeneration, comparable with what was observed in our previous study using the oligodendrocyte lineage MCT1 null mice. In summary, we conclude that unlike oligodendrocyte MCT1, astrocyte MCT1 is not an essential driver of astrocyte mediated axonal energy homeostasis with aging.

neuroscience↗

bioRxiv2018

Multinomial modelling of TB/HIV co-infection yields a robust predictive signature and generates hypotheses about the HIV+TB+ disease state.

BackgroundCurrent diagnostics are inadequate to meet the challenges presented by coinfection with Mycobacterium tuberculosis (Mtb) and HIV, the leading cause of death for HIV-infected individuals. Improved characterisation of Mtb/HIV coinfection as a distinct disease state may lead to better identification and treatment of affected individuals.\n\nMethodsFour previously published TB and HIV co-infection related datasets were used to train and validate multinomial machine learning classifiers that simultaneously predict TB and HIV status. Classifier predictive performance was measured using leave-one-out cross validation on the training set and blind predictive performance on multiple test sets using area under the ROC curve (AUC) as the performance metric. Linear modelling of signature gene expression was applied to systematically classify genes as TB-only, HIV-only or combined TB/HIV.\n\nResultsThe optimal signature discovered was a single 10-gene random forest multinomial signature that robustly discriminates active tuberculosis (TB) from other non-TB disease states with improved performance compared with previously published signatures (AUC: 0. 87), and specifically discriminates active TB/HIV co-infection from all other conditions (AUC: 0.88). Signature genes exhibited a variety of transcriptional patterns including both TB-only and HIV-only response genes and genes with expression patterns driven by interactions between HIV and TB infection states, including the CD8+ T-cell receptor LAG3 and the apoptosis-related gene CERKL.\n\nConclusionsBy explicitly including distinct disease states within the machine learning analysis framework, we developed a compact and highly diagnostic signature that simultaneously discriminates multiple disease states associated with Mtb/HIV co-infection. Examination of the expression patterns of signature genes suggests mechanisms underlying the unique inflammatory conditions associated with active TB in the presence of HIV. In particular, we observed that disregulation of CD8+ effector T-cell and NK-cell associated genes may be an important feature of Mtb/HIV co-infection.

systems biology↗