Biology subjects

Liu, S. T.

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

3 recordsLinked to original sources

bioRxiv2025

SARS-CoV-2 Infects Peripheral Sensory Neurons and Promotes Axonal Degeneration via TRPV1 Activation

Common neurological symptoms of COVID-19, such as anosmia, headaches, and cognitive dysfunction, depend on interactions between the peripheral and central nervous systems. However, the molecular mechanisms by which SARS-CoV-2 affects the peripheral nervous system remain poorly understood, with ongoing debate about whether sensory neurons can be directly infected by the virus. In this study, human iPSC-derived sensory neurons were exposed to the SARS-CoV-2 BA.5 variant, a mutant virus, or viral S1 proteins. Under control conditions, sensory neurons exhibited low expression of ACE2. However, exposure to BA.5 or S1 proteins significantly upregulated ACE2 expression in peripherin-positive sensory neurons. Virological analysis confirmed that SARS-CoV-2 directly infects TRPV1-expressing sensory neurons, including olfactory neurons. Moreover, exposure to the live virus or S1 proteins induced TRPV1 upregulation and translocation from the nucleus to the cytosol, resulting in axonal destruction. Single-nucleus transcriptomic analysis revealed that viral exposure enhanced cAMP signaling, virus receptor and transmembrane transporter activities, and inflammatory regulation of TRP channels, which collectively contributed to synaptic and axonal damage. Importantly, treatment with a TRPV1 antagonist demonstrated neuroprotective effects. These findings underscore the need for further research into the interaction between SARS-CoV-2 and TRPV1, as well as its downstream signaling pathways, to develop therapeutic strategies for preventing sensory neuron loss during viral infections. HighlightsO_LIiPSC technology was employed to generate peripheral sensory neurons from human induced pluripotent stem cells (iPSCs), providing a valuable platform for studying the impact of SARS-CoV-2 on peripheral sensory neurons. C_LIO_LIOur findings demonstrated that the SARS-CoV-2 Omicron BA.5 variant exerted both direct and indirect effects on peripheral sensory neurons. Virus exposure upregulated the angiotensin-converting enzyme 2 (ACE2) receptor in peripherin-positive neurons. Additionally, exposure to the virus or its S1 spike protein increased transient receptor potential vanilloid 1 (TRPV1) expression and trafficking, leading to axonal degeneration. C_LIO_LISingle-nucleus RNA sequencing revealed that BA.5 exposure enhanced cAMP signaling pathway, virus receptor and transmembrane transporter activities, and inflammatory regulation of TRP channels that led to the significantly damage on synapses and axon guidance. C_LIO_LIThe TRPV1 antagonist capsazepine inhibited TRPV1 activation and mitigated axonal damage, offering neuroprotective effects for sensory neurons exposed to SARS-CoV-2. C_LI

cell biology↗

bioRxiv2022

Adaptive mechanisms facilitate robust performance in noise and in reverberation in an auditory categorization model

For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or both adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.

neuroscience↗

bioRxiv2018

Optimal features for auditory categorization

Humans and vocal animals use vocalizations (human speech or animal calls) to communicate with members of their species. A necessary function of auditory perception is to generalize across the high variability inherent in the production of these sounds and classify them into perceptually distinct categories ( words or call types). Here, we demonstrate using an information-theoretic approach that production-invariant classification of calls can be achieved by detecting mid-level acoustic features. Starting from randomly chosen marmoset call features, we used a greedy search algorithm to determine the most informative and least redundant set of features necessary for call classification. Call classification at >95% accuracy could be accomplished using only 10 - 20 features per call type. Most importantly, predictions of the tuning properties of putative neurons selective for such features accurately matched some previously observed responses of superficial layer neurons in primary auditory cortex. Such a feature-based approach succeeded in categorizing calls of other species such as guinea pigs and macaque monkeys, and could also solve other complex classification tasks such as caller identification. Our results suggest that high-level neural representations of sounds are based on task-dependent features optimized for specific computational goals.

neuroscience↗