Biology subjects

Chow, J.

Publications and source records attributed to Chow, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Characterization of nucleus accumbens dopamine dynamics during positive and negative operant reinforcement in rats

We previously reported, using in-vivo fiber photometry, that operant responding reinforced by access to a peer (social self-administration) is associated with phasic dopamine increases in nucleus accumbens (NAc) core following lever insertion (reward-availability cue) and gradual increases preceding lever-pressing. Here, we sought to replicate these findings and determine whether dopamine signals (1) generalize to responding for high-carbohydrate palatable food, (2) show opposite patterns during negative reinforcement (shock avoidance/escape), and (3) depend on whether reinforcers are experienced alone or together. We trained rats (n=11; 6 females) to lever-press for access to a same-sex peer (15 s/trial) and palatable food (45-mg pellet/trial), followed by shock avoidance/escape (0.18-0.26 mA). After training, we expressed the dopamine sensor GRAB-DA2m and implanted optic fibers into NAc core. We measured dopamine activity during sessions with either one- or three-reinforcers. During social self-administration, dopamine activity showed phasic increases following lever insertion and gradual increases preceding lever-pressing; responses were moderately greater during sessions with all three reinforcers. Palatable food self-administration showed a similar pattern, but responses were approximately twofold greater during single-reinforcer sessions. During shock avoidance/escape, dopamine activity showed phasic decreases at warning onset, lever insertion, and shock onset; responses were also greater during single-reinforcer sessions. Results suggest that NAc core dopamine signaling distinguishes positive from negative reinforcement and is modulated by reinforcer availability. Compared with single-reinforcer sessions, dopamine responses during food self-administration and shock avoidance/escape were reduced during sessions with all three reinforcers, whereas responses during social self-administration modestly increased. Significance statementNucleus accumbens (NAc) dopamine is critical for processing the valence of positive reinforcers, whereas its role in negative reinforcement is less well understood. Here, we extended our prior work on operant social self-administration to determine whether these findings generalize to food reinforcement, show the opposite pattern during operant negative reinforcement (shock avoidance/escape), and depend on whether reinforcers are experienced alone or together. Results suggest that NAc core dopamine activity differentiates positive and negative reinforcement and is modulated by the presence of other available reinforcers. Specifically, dopamine responses during food self-administration and shock avoidance/escape were reduced when all three reinforcers were available together compared with when each reinforcer was experienced alone, whereas responses during social self-administration showed the opposite pattern.

neuroscience↗

bioRxiv2026

Interpreting Rewards from Inverse Reinforcement Learning

Inverse reinforcement learning can recover reward functions from observed behavior, but interpreting those rewards remains a fundamental challenge for understanding intelligent behavior and decision-making. To address this challenge, we introduce a novel framework for reward interpretation that combines reward-function analysis, latent mode assignments, and short-history behavioral analysis to infer latent motivations and behavioral dynamics. As a proof-of-concept, we instantiated the framework using switching inverse reinforcement learning on a large-scale dataset of multi-agent social interactions. Our framework interpreted the learned latent modes as cautious and volatile motivational profiles, demonstrating that recovered reward functions can reveal distinct patterns of behavioral dynamics. More broadly, these findings suggest that the proposed framework provides a promising approach for reverse-engineering and interpreting latent rewards underlying intelligent behavior and decision-making.

neuroscience↗

bioRxiv2026

Structural Analysis of Prostate Cancer N-Glycans Using Graph-Based Structural Metrics

The N-linked glycans are structurally complex carbohydrate modifications that regulate protein folding, immune recognition, and cellular signaling, and their expression is extensively remodeled during cancer progression, making them promising biomarkers. In this study, prostate cancer-associated N-glycans from a range of relevant peer-reviewed studies were curated and digitized to develop a versatile computational framework that quantitatively encodes their spatial complexity across diverse biological systems. We invented two indices--the Distance & Connectivity Index (DCI) and the Position & Composition Index (PCI)--to capture the spatial information in N-glycans as layered architectures, enabling calculation of residue-level path lengths, branching structure, and compositional diversity. DCI summarizes glycan structure as both a scalar and matrix representation, while PCI does the same but also captures monosaccharide diversity, linkage heterogeneity, and cross-layer branching features. These metrics were computed with GlycoAssessor, an open-source platform that extracts information for the DCI and PCI from glycans drawn via Symbol Nomenclature for Glycans (SNFG) notation. Principal Component Analysis (PCA) was applied to evaluate whether glycans from prostate cancer tissues cluster distinctly in a disease-relevant manner. Results show that the spatial information in N-glycans: (1) increased in a multi-dimensional, non-linear manner, (2) objectively segregated structural themes, (3) could function as a potential prostate cancer biomarker that is distinct from mass-to-charge ratio and relative abundance, and (4) could objectively quantify novel subtype classifications of glycans associated with disease states and progression.

bioinformatics↗

bioRxiv2025

DINOcell: Learning Generalizable Perturbation Effects through Self-Distillation

AO_SCPLOWBSTRACTC_SCPLOWPredicting cellular responses to therapeutics is a promising approach for novel target discovery. However, state-of-the-art computational models designed to predict perturbation effects struggle to generalize and outperform simple baselines. We present DINOcell, a weakly supervised framework that adapts self-distillation to single-cell transcriptomics for predicting perturbation effects. We demonstrate that DINOcell outperforms baselines in predicting the effects of single gene perturbations. Furthermore, DINOcell accurately predicts non-additive effects of combination perturbations, indicating its capacity to model complex genetic interactions. Finally, we show that DINOcell learns representations that capture biological signals and is a promising, generalizable approach for in silico perturbation modeling, providing a valuable tool for accelerating therapeutic target discovery.

bioinformatics↗

bioRxiv2025

Effect of morphine dependence and withdrawal on operant social interaction in male and female rats

Opioid addiction is linked to decreased social connections. In preclinical models, non-contingent experimenter-administered morphine both decreases unconditioned social interaction and place preference for social reward. We tested if these effects generalize to an operant rat model of social self - administration, in which rats work volitionally for access to a peer. Based on the literature, we also tested if a kappa opioid receptor (KOR) antagonist (LY2456302) and serotonin and dopamine reuptake inhibitors (fluoxetine and GBR12909), would independently reverse the effect of morphine exposure on social self-administration. We trained rats (n = 66; 32 females) to lever-press for 15-s access to a peer (fixed ratio 1 reinforcement schedule, 45 min, every other day). In Experiments 1-3, we assessed social self-administration during opioid dependence ([~]16 h post-injection), and after early (2-to-6 days) and protracted (21-28 days) withdrawal with different morphine exposure regimens (0-to-80 mg/kg, s.c., twice daily; 0-to-80 mg/kg, once daily; or 0-to-40 mg/kg, every-other-day). In Experiment 4, we tested the effects of LY2456302, fluoxetine, and GBR12909 on social self-administration during morphine exposure (every-other-day, 0-to-30 mg/kg). As in our previous studies, social interaction functioned as an operant reinforcer. Operant social interaction was decreased during morphine exposure (dependence state) but not during early or protracted withdrawal. None of the tested compounds (LY2456302: 5, 10 mg/kg, s.c.; fluoxetine: 1, 3 mg/kg, i.p.; GBR12909: 3, 10 mg/kg, i.p.) reversed this effect. Opioid dependence, but not withdrawal, decreased operant social interaction in male and female rats. This effect appears independent of KOR, serotonin, or dopamine signaling. HighlightsO_LIOpioid dependence decreased operant social interaction C_LIO_LIEarly and protracted withdrawal had no effect on operant social interaction C_LIO_LIPharmacological manipulations of Dyn, DA, and 5-HT did not restore social behavior C_LI

animal behavior and cognition↗

bioRxiv2025

Comprehensive characterization of human color discrimination thresholds

Color discrimination thresholds--the smallest detectable color differences--provide a benchmark for models of color vision, enable quantitative evaluation of eye diseases, and inform the design of display technologies. Despite their importance, a comprehensive characterization of these thresholds has long been considered intractable due to the psychophysical curse of dimensionality. Here, we address this challenge using a novel semi-parametric Wishart Process Psychophysical Model (WPPM), which leverages the feature that the internal noise limiting color discrimination varies smoothly across stimulus space. The model was fit to data collected with a non-parametric adaptive trial-placement procedure, enabling efficient stimulus selection. Together, through the combination of adaptive trial placement and post hoc WPPM fitting, we achieved a comprehensive characterization of color discrimination in the isoluminant plane with only [~]6,000 trials per participant (N = 8). Once fit, the WPPM allows readouts of discrimination performance for any stimulus pair. We validated these readouts against 25 probe psychometric functions, measured with an additional 6,000 trials per participant held out from model fitting. In conclusion, our study provides a foundational dataset for color vision, and our approach generalizes beyond color to any domain in which the internal noise limiting performance varies smoothly across stimulus space, offering a powerful and efficient method for comprehensively characterizing various perceptual discrimination thresholds.

neuroscience↗

bioRxiv2025

Predicting continuous outcomes: Some new tests of associative approaches to contingency learning.

Associative learning models have traditionally simplified contingency learning by relying on binary classification of cues and outcomes, such as administering a medical treatment (or not) and observing whether the patient recovered (or not). While successful in capturing fundamental learning phenomena across human and animal studies, these models are not capable of representing variability in human experiences that are common in many real-world contexts. Indeed, where variation in outcome magnitude exists (e.g., severity of illness in a medical scenario), this class of models, at best, approximate the outcome mean with no ability to represent the underlying distribution of values. In this paper, we introduce one approach to incorporating a distributed architecture into a prediction error learning model that tracks the contingency between cues and dimensional outcomes. Our Distributed Model allows associative links to form between the cue and outcome nodes that provide distributed representation depending on the magnitude of the outcome, thus enabling learning that extends beyond approximating the mean. Comparing the Distributed Model against a Simple Delta Model across four contingency learning experiments, we found that the Distributed Model provides significantly better fit to empirical data in virtually all participants. These findings suggest human learners rely on a means of encoding outcomes that preserves the continuous nature of experienced events, advancing our understanding of causal inference in complex environments. Author SummaryWhen we learn about cause and effect in everyday life--such as whether a medicine helps recovery from illness--we experience outcomes that vary in degree rather than simply happening or not happening. Traditional models of how humans and animals learn have largely focused on these all-or-nothing scenarios, essentially tracking the average value when outcomes are dimensional. We developed a model that extends on simple error-correction models to represent how people learn about relationships between cues and outcomes that can take on a range of values. Instead of just tracking the average, our Distributed Model captures the full spectrum of possible outcomes and their frequencies. We tested this model against a conventional single point-estimate approach across four experiments and found that our Distributed Model better matched how people make predictions in nearly every case. Our findings suggest that a relatively simple adjustment to conventional prediction-error learning algorithms that allows representation of outcome magnitudes provide a powerful way to capture the information that we preserve when we learn about variable outcomes. This has important implications for understanding how people make predictions and decisions in real-world situations where outcomes naturally vary, from medical treatments to environmental changes.

animal behavior and cognition↗

bioRxiv2025

Dual targeting of transferrin receptor and CD98hc enhances brain exposure of large molecules

Targeting proteins highly expressed at the blood-brain barrier, including transferrin receptor (TfR) and CD98hc, is a transformative approach enabling more effective brain delivery of biotherapeutics for treatment of neurological diseases. TfR-mediated delivery promotes rapid, high brain uptake, while CD98hc-mediated delivery is slower with more prolonged exposure. Here, we engineer a huIgG Fc domain to bind both TfR and CD98hc to create a dual transport vehicle (TV) platform that drives distinct brain delivery properties. Dual TVs achieve significantly higher brain concentrations than TVs targeting either TfR or CD98hc alone. Modulation of TfR and CD98hc affinities shifts dual TV brain exposure kinetics and biodistribution. Stronger TfR affinity drives faster brain uptake and clearance, while stronger CD98hc affinity yields higher, more sustained concentrations, likely due to CD98hc affinity-dependent reduction in TfR-mediated neuronal internalization. This dual targeting strategy leverages the complementary properties of TfR and CD98hc-mediated brain exposure to increase optionality for brain delivery of biotherapeutics.

neuroscience↗

bioRxiv2025

Differential expression of transposable elements and upregulation of nucleic acid sensing pathways in pterygium

PurposeThe mechanisms that lead to pterygium pathogenesis are poorly understood. Ultraviolet (UV) exposure is a leading environmental risk factor. We propose that UV triggers the de-repression of transposable elements (TEs), including endogenous retroviruses (ERVs), and subsequently activate double-stranded RNA and DNA sensors such as RIG-I and cGAS. MethodsIn the present study, we analyzed publicly available RNA sequencing data from pterygium and healthy conjunctiva specimens. We used Ingenuity Pathway Analysis (IPA) to examine the relative expression of nucleic acid sensing pathways and related processes. We also used Telescope to identify TEs. We then repeated this analysis using RNA collected from cultured human conjunctival fibroblasts exposed over multiple days to a range of UV intensities. ResultsWe found that pathways involved in cellular stress, nucleic acid sensing, and innate immune signaling were upregulated in both cell lines exposed to increased amounts of UV energy as well as pterygium. We also observed differential expression of TEs. ConclusionsTaken together, our findings suggest a mechanistic link between environmental factors and pterygium. Understanding the molecular pathways activated by UV exposure may aid in developing therapeutic strategies.

genomics↗

bioRxiv2024

Transferrin receptor-mediated transport at the blood-brain barrier is elevated in early development but maintained across adult aging

Transferrin receptor (TfR)-mediated transcytosis across the blood-brain barrier (BBB) is a promising strategy to improve delivery of biologics to the central nervous system (CNS). However, it remains unclear whether age and aging-related diseases impact TfR expression and/or BBB transport capacity. Here, we used the TfR-targeted antibody transport vehicle (ATVTfR) to enhance CNS delivery in healthy mice and in the 5xFAD mouse model of Alzheimers disease (AD). Healthy neonates exhibited the highest vascular TfR expression and ATVTfR brain exposure, whereas BBB transport capacity remained stable across adulthood. Additionally, neither TfR expression nor ATVTfR brain uptake changed significantly in 5xFAD mice. Further, vascular TfR expression in AD patient brains was similar to age-matched controls, suggesting that TfR transport may be conserved for AD in humans. The elevated TfR-mediated brain delivery observed in early mouse development suggests the potential of added efficacy in utilizing TfR platforms for the treatment of early childhood diseases. Preservation of ATVTfR transport in adult mice across healthy aging and in an AD model supports continued application of TfR platforms in age-related diseases.

neuroscience↗

bioRxiv2024

Rare and novel RELA variants are common in systemic autoimmunity

ObjectivePhenotypic diversity of autoimmune diseases presents an ongoing diagnostic and drug development challenge for clinicians and scientists. Recent discovery of mutations in RELA (encoding RELA) in patients with different diagnoses has highlighted that different pathogenic molecular mechanisms are at play and may explain the observed phenotypic diversity. We identified seven novel/rare RELA variants in patients with autoimmune diseases and examine the functional consequences on immune signalling. MethodsWild type and mutant RELA proteins were ectopically expressed in HEK293 cells. Western blot and NF-{kappa}B/IFN{beta} luciferase reporter assays were used to determine RELA expression and transcriptional activity, respectively. In patients (n=3), B and T cell populations were examined via flow cytometry and NF-{kappa}B and interferon stimulated genes in PBMCs were assessed via qPCR following toll-like receptor activation. ResultsRELAI250V, RELAR295H and RELAE3* displayed a loss in NF-{kappa}B transcriptional activity. Comparative to RELAWT, RELAI250V protein expression was reduced. Two variants, RELAI250V and RELAR295H, induced hyperactivation of the IFN{beta} promoter. An elevated IFN gene signature was not detected in patient PBMCs following toll-like receptor activation, however the patient heterozygous for I250V had elevated IFN{beta} transcripts at baseline and after TLR7/8 activation. A reduction in transitional, unswitched memory and memory B cell and cTfh (CCR6-CXCR3-) T cell subsets was shared by the patient group. ConclusionWe expand upon the clinical syndromes linked to RELA dysfunction and uncover rare and novel variants that have distinct functional effects on gene transcription downstream of NF-{kappa}B and IFN{beta} promoter elements. These findings reinforce an important role for RELA in a range of autoimmune and autoinflammatory diseases.

immunology↗

bioRxiv2024

Novel high-content and open-source image analysis tools for profiling mitochondrial morphology in neurological cell models

Mitochondria undergo dynamic morphological changes depending on cellular cues, stress, genetic factors, or disease. The structural complexity and disease-relevance of mitochondria have stimulated efforts to generate image analysis tools for describing mitochondrial morphology for therapeutic development. Using high-content analysis, we measured multiple morphological parameters and employed unbiased feature clustering to identify the most robust pair of texture metrics that described mitochondrial state. Here, we introduce a novel image analysis pipeline to enable rapid and accurate profiling of mitochondrial morphology in various cell types and pharmacological perturbations. We applied a high-content adapted implementation of our tool, MitoProfilerHC, to quantify mitochondrial morphology changes in i) a mammalian cell dose response study and ii) compartment-specific drug effects in primary neurons. Next, we expanded the usability of our pipeline by using napari, a Python-powered image analysis tool, to build an open-source version of MitoProfiler and validated its performance and applicability. In conclusion, we introduce MitoProfiler as both a high-content-based and an open-source method to accurately quantify mitochondrial morphology in cells, which we anticipate to greatly facilitate mechanistic discoveries in mitochondrial biology and disease.

cell biology↗

bioRxiv2024

Engineering anti-amyloid antibodies with transferrin receptor targeting improves safety and brain biodistribution

Although the first generation of immunotherapies for Alzheimers disease (AD) are now clinically approved, amyloid-related imaging abnormalities (ARIA) remain a major safety problem for this class of drugs. Here, we report an antibody transport vehicle (ATV) targeting the transferrin receptor (TfR) for brain delivery of amyloid beta (A{beta}) antibodies that significantly reduced ARIA-like lesions and improved plaque target engagement in a mouse model of amyloid deposition. Asymmetrical Fc mutations (ATVcisLALA) allowed the molecule to selectively retain effector function only when bound to A{beta} while mitigating TfR-related hematology liabilities. Mice treated with ATVcisLALA:A{beta} exhibited broad brain parenchymal antibody distribution; in contrast, anti-A{beta} IgG was highly enriched at arterial perivascular spaces where vascular A{beta} localizes and likely plays a role in induction of ARIA. Importantly, ATVcisLALA: A{beta} almost completely eliminated ARIA-like lesions and vascular inflammation associated with anti-A{beta} treatment. Taken together, ATVcisLALA has the potential to significantly improve both safety and efficacy of A{beta} immunotherapy through enhanced biodistribution mediated by transport across the blood-brain barrier.

neuroscience↗

bioRxiv2024

Fc-engineered large molecules targeting blood-brain barrier transferrin receptor and CD98hc have distinct central nervous system and peripheral biodistribution compared to standard antibodies

The blood-brain barrier (BBB) poses a significant challenge drug delivery to the brain. BBB-crossing molecules are emerging as a new class of therapeutics with significant potential for central nervous system (CNS) indications. In particular, transferrin receptor (TfR)- and CD98 heavy chain (CD98hc)-targeting molecules have been demonstrated to cross the BBB for enhanced brain delivery. Previously, we reported TfR and CD98hc antibody transport vehicles (ATVTfR and ATVCD98hc) that utilize these BBB receptors to improve CNS drug delivery1,2. Here, we provide a comprehensive and unbiased biodistribution characterization of ATVTfR and ATVCD98hc compared to a standard IgG at a multiscale level, ranging from whole-body to brain region- and cell type-targeting specificity. Mouse whole-body tissue clearing revealed distinct organ localization for each molecule. In the CNS, ATVTfR and ATVCD98hc not only achieves enhanced brain delivery but importantly, much broader parenchymal distribution in contrast to the severely limited distribution observed with a standard antibody that was not able to be improved even at very high dose levels. Using cell sorting and single-cell RNA sequencing of mouse brain, we revealed that standard IgG predominantly localizes to perivascular and leptomeningeal cells and reaches the CNS by entering the CSF, rather than crossing the BBB. In contrast, ATVTfR and ATVCD98hc enables broad parenchymal cell-specific distribution via transcytosis through brain endothelial cells (BECs) along the neurovasculature. Finally, we extended the translational relevance of our findings by revealing enhanced and broad brain and spinal cord biodistribution of ATVTfR compared to standard IgG in cynomolgus monkey. Taken together, this multiscale analysis reveals in-depth biodistribution differences between ATVTfR, ATVCD98hc, and standard IgG. These results may better inform platform selection for specific therapeutic targets of interest, optimally matching platforms to desired CNS target engagement, peripheral organ exposures, and predict or potentially reduce off-target effects.

neuroscience↗

bioRxiv2024

Innate Immune Activation and Mitochondrial ROS Invoke Persistent Cardiac Conduction System Dysfunction after COVID-19

BackgroundCardiac risk rises during acute SARS-CoV-2 infection and in long COVID syndrome in humans, but the mechanisms behind COVID-19-linked arrhythmias are unknown. This study explores the acute and long term effects of SARS-CoV-2 on the cardiac conduction system (CCS) in a hamster model of COVID-19. MethodsRadiotelemetry in conscious animals was used to non-invasively record electrocardiograms and subpleural pressures after intranasal SARS-CoV-2 infection. Cardiac cytokines, interferon-stimulated gene expression, and macrophage infiltration of the CCS, were assessed at 4 days and 4 weeks post-infection. A double-stranded RNA mimetic, polyinosinic:polycytidylic acid (PIC), was used in vivo and in vitro to activate viral pattern recognition receptors in the absence of SARS-CoV-2 infection. ResultsCOVID-19 induced pronounced tachypnea and severe cardiac conduction system (CCS) dysfunction, spanning from bradycardia to persistent atrioventricular block, although no viral protein expression was detected in the heart. Arrhythmias developed rapidly, partially reversed, and then redeveloped after the pulmonary infection was resolved, indicating persistent CCS injury. Increased cardiac cytokines, interferon-stimulated gene expression, and macrophage remodeling in the CCS accompanied the electrophysiological abnormalities. Interestingly, the arrhythmia phenotype was reproduced by cardiac injection of PIC in the absence of virus, indicating that innate immune activation was sufficient to drive the response. PIC also strongly induced cytokine secretion and robust interferon signaling in hearts, human iPSC-derived cardiomyocytes (hiPSC-CMs), and engineered heart tissues, accompanied by alterations in electrical and Ca2+ handling properties. Importantly, the pulmonary and cardiac effects of COVID-19 were blunted by in vivo inhibition of JAK/STAT signaling or by a mitochondrially-targeted antioxidant. ConclusionsThe findings indicate that long term dysfunction and immune cell remodeling of the CCS is induced by COVID-19, arising indirectly from oxidative stress and excessive activation of cardiac innate immune responses during infection, with implications for long COVID Syndrome.

pathology↗

bioRxiv2023

Identification of critical cell-types using genetic modules: A case study of neurodevelopmental disorders

Identifying the critical cell-types impacted by various diseases is crucial for understanding disease mechanisms and developing targeted therapeutics. Recent advances in disease genetic module discovery and single-cell technologies provide a unique opportunity to study critical cell-types based on functional pathways and modules. Disease genetic modules are defined as sets of genes with correlated expression that are part of the same biological pathways and are disrupted in the disease. Critical cell-types for a biological function are defined as clusters of similar cells most "active" or "involved" in that biological function. In this paper, we provide a formal problem definition for the critical cell discovery problem using the recently introduced local correlation concept, and show that the proposed problem is intractable in theory. We propose a novel method, MoToCC (Module To Critical Cell-types), to find sets of similar cells with local correlated gene expression activity for input modules. We evaluated MoToCC on four neurodevelopmental disorder modules using single-cell expression data from the developing human cortex. Finally, we demonstrate that the objective value returned by MoToCC for the tested modules is an acceptable approximation to the optimal solution. Overall, our work provides a valuable tool for studying critical cell-types and their role in disease mechanisms, which could lead to the development of more effective targeted therapeutics. The MoToCC package is available at https://github.com/jchow32/MoToCC

bioinformatics↗

bioRxiv2023

Targeting Transferrin Receptor to Transport Antisense Oligonucleotides Across the Blood-Brain Barrier

Antisense oligonucleotides (ASO) are promising therapies for neurological disorders, though they are unable to cross the blood-brain barrier (BBB) and must be delivered directly to the central nervous system (CNS). Here, we use a human transferrin receptor (TfR)-binding molecule to transport ASO across the BBB in mice and non-human primates, termed oligonucleotide transport vehicle (OTV). Systemically delivered OTV drives significant, cumulative, and sustained knockdown of the ASO target across multiple CNS regions and all major cell types. Further, systemic OTV delivery enables more uniform ASO biodistribution and knockdown compared to two other clinically relevant ASO delivery routes: a standard, high affinity TfR antibody, or direct ASO delivery to the CSF. Together, our data support systemically delivered OTV as a potential therapeutic platform for neurological disorders. One-Sentence SummarySystemically dosed OTV delivered via TfR1 targeting shows widespread and cumulative target knockdown in the mouse and NHP CNS.

neuroscience↗

bioRxiv2022

The first archaeal PET-degrading enzyme belongs to the feruloyl-esterase family

Polyethylene terephthalate (PET) is a commodity polymer known to globally contaminate marine and terrestrial environments. Today, around 40 bacterial and fungal PET-active enzymes (PETases) are known, originating from four bacterial and two fungal phyla. In contrast, no archaeal enzyme has been identified to degrade PET. Here we report on the structural and biochemical characterization of PET46, an archaeal promiscuous feruloyl esterase exhibiting degradation activitiy on PET, bis-, and mono-(2-hydroxyethyl) terephthalate (BHET and MHET). The enzyme, found by a sequence-based metagenome search, was derived from a non-cultivated, deep-sea Candidatus Bathyarchaeota archaeon. Biochemical characterization demonstrated that PET46 is a promiscuous, heat-adapted hydrolase. Its crystal structure was solved at a resolution of 1.71 [A]. It shares the core alpha/beta-hydrolase fold with bacterial PETases, but contains a unique lid common in feruloyl esterases, which is involved in substrate binding. Thus, our study significantly widens the currently known diversity of PET-hydrolyzing enzymes, by demonstrating PET depolymerization by a lignin-degrading esterase.

biochemistry↗