Biology subjects

Pedersen, M. L.

Publications and source records attributed to Pedersen, M. L..

4 recordsLinked to original sources

bioRxiv2026

Glutamate transporter xCT is important for cGAS-dependent interferon responses to DNA and to HSV-1

Metabolic reprogramming is a key component of antiviral immunity, yet how metabolite transport regulates innate immune signaling remains incompletely understood. Here, we show that infection with herpes simplex virus 1 (HSV-1) and stimulation with cytosolic DNA induce the cellular export of glutamate via the xCT (SLC7A11) transporter and that inhibition of xCT reduces cellular resistance to viral replication. Mechanistically, xCT inhibition impaired cGAS-STING signaling by reducing DNA-induced cGAMP production, thereby diminishing type I interferon (IFN/{beta}) responses and downstream induction of interferon-stimulated genes. Interestingly, modulating intracellular glutamate levels through inhibition of other glutamate pathways, e.g., glutaminolysis or glutamate import, also affected cellular IFN responses, suggesting that glutamate is a central control knob for DNA sensing. Finally, we demonstrate that HSV-1 suppresses xCT expression via a mechanism dependent on the immediate early viral protein ICP27, thereby promoting viral replication by limiting cGAS-dependent IFN induction. Together, these findings identify xCT-dependent glutamate transport as a critical metabolic regulator of cGAS-STING-mediated antiviral immunity.

immunology↗

bioRxiv2022

Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM

Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision making intended to capture processes jointly giving rise to reaction time distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of sequential sampling models. In addition, recent work has motivated the combination of SSMs with reinforcement learning (RL) models, which had historically been considered in separate literatures. Here we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user extensible) wide variety of SSMs, and how they can be combined with RL models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.

bioinformatics↗

bioRxiv2022

Nanobody-mediated Complement Activation to Kill HIV-infected Cells

The complement system which is part of the innate immune response against invading pathogens, represents a powerful mechanism for killing of infected cells. Utilizing direct complement recruitment for complement-mediated elimination of HIV-1-infected cells is underexplored. We developed a novel therapeutic modality to direct complement activity to the surface of HIV-1-infected cells. This bispecific complement engager (BiCE) is comprised of a nanobody recruiting the complement-initiating protein C1q, and single-chain variable fragments of broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope (Env) protein. Here, we show that two anti-HIV BiCEs targeting the V3 loop and the CD4 binding site, respectively, increase C3 deposition and mediate complement-dependent cytotoxicity (CDC) of HIV-1 Env expressing Raji cells. Furthermore, anti-HIV BiCEs trigger complement activation on primary CD4 T cells infected with laboratory-adapted HIV-1 strain and facilitates elimination of HIV-1-infected cells over time. In summary, we present a novel approach to direct complement deposition to the surface of HIV-1-infected cells leading to complement-mediated killing of these cells.

immunology↗

bioRxiv2019

Dissecting the cognitive phenotype of post-stroke fatigue using drift diffusion modeling of sustained attention

Post-stroke fatigue (PSF) is a prevalent symptom among stroke patients. Its symptom burden is pervasive, persistent and associated with poor rehabilitation outcomes, though its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. In contrast to conventional neuropsychological assessment, computational modeling of behavioral data allows for a dissection of specific cognitive processes associated with group or individual differences in fatigue. With the aim to zero in on the cognitive phenotype of PSF, we fitted a hierarchical drift diffusion model (hDDM) to response time data from Attention Network Test (ANT) obtained from 53 chronic stroke patients. The computational model accurately reconstructed the individual level response time distributions in the different ANT conditions, and hDDM regressions identified an interaction between trial number and fatigue symptoms on non-decision time, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained attentional effort. These novel results demonstrate the significance of considering the sustained nature of cognitive effort when defining the cognitive phenotype of post-stroke fatigue, and suggest that the use of computational approaches offers a further characterization of the specific processes underlying observed behavioral differences.

neuroscience↗