Biology subjects

Peter, A. S.

Publications and source records attributed to Peter, A. S..

6 recordsLinked to original sources

bioRxiv2026

An image-computable characterization of the non-conditioned linkage of visual drive and valence in the primate amygdala

The amygdala is a key node in linking high-level representations of visual stimuli to affective state, and much research has focused on its role in learning artificial stimulus-value associations. By comparison, the primate amygdalas encoding of natural, non-conditioned visual stimuli is less well understood. Here, we report that some amygdala neurons have visual selectivity that is systematically linked to their valence tuning, without laboratory conditioning. First, electrophysiological recordings revealed that the firing rate responses of many amygdala recording sites are selective across arbitrary non-conditioned natural visual stimuli. Second, this selectivity was well-predicted by contemporary, image-computable models of the visually-driven selectivity of high-level ventral stream neurons that provide major afferents to the amygdala. Third, a subpopulation of visually selective amygdala units also coded valence, as defined in prior work. Fourth, these valence preferences were correlated with their visual tuning, in that the image-computable models predicted which visual stimuli tended to drive positive-valence sites and which tended to drive negative-valence sites. Taken together, these results suggest that the visual drive provided from the ventral visual stream into the amygdala is strong, and that it is not unlinked or randomly linked to valence coding. Furthermore, the results establish baseline image-computable models of visual encoding in the primate amygdala. SignificanceOur understanding of how the primate amygdala links natural visual input to affective state remains limited. Here, we report that visual selectivity in the amygdala is not random but is systematically coupled to valence encoding, even without explicit conditioning. Amygdala neurons exhibit reliable visual selectivity for natural stimuli, and this selectivity is predicted by image-computable models of the upstream ventral visual system. Interestingly, a subpopulation of these visually selective units also coded valence, and their valence preference correlated directly with their visual preferences. This establishes a predictive model of visual and valence encoding, suggesting that visual information is wired to influence affective processing and potentially enabling noninvasive modulation of limbic circuits.

neuroscience↗

bioRxiv2025

Computational design and experimental characterization of mini-protein binders targeting Nipah, Langya and Measles virus receptor-binding domains

A lack of reagents represents a major bottleneck in pandemic preparedness and rapid vaccine development. It is therefore important to enable the design of reagents for use in the treatment and diagnosis of emerging viral diseases. Ideally, the design and identification platform is fast, can be performed by testing only a small number of candidates and enables a generally applicable strategy. In this study, we assessed the ability of recently developed computational protein design tools to establish such a workflow for validating paramyxovirus receptor-binding protein de novo binders as such reagents. The family Paramyxoviridae includes various members that cause severe disease and exhibit re-occurring zoonotic spillover events, with documented human infections over the past decades. We successfully designed, identified, and characterized mini-proteins targeting the receptor binding proteins of Nipah virus, Langya virus, and Measles virus while screening as few as 10-16 designs per target. The resulting functional binders have moderate to low nanomolar affinities and display high on-target specificity. We further showed that our most promising Nipah virus receptor-binding protein binder is able to inhibit human receptor binding in vitro and competes for an epitope that overlaps with that of the neutralizing antibody HENV-117. However, despite these promising results, this Nipah binder is only weakly neutralizing, preventing therapeutic applications. Nevertheless, we established a platform, applicable to rapidly generate diagnostically relevant proteins from only a small number of candidates, and developed novel reagents for the Paramyxoviridae family.

bioinformatics↗

bioRxiv2021

Protective mucosal immunity against SARS-CoV-2 after heterologous systemic RNA-mucosal adenoviral vector immunization

Several effective SARS-CoV-2 vaccines are currently in use, but in the light of waning immunity and the emergence of novel variants, effective boost modalities are needed in order to maintain or even increase immunity. Here we report that intranasal vaccinations with adenovirus 5 and 19a vectored vaccines following a systemic DNA or mRNA priming result in strong systemic and mucosal immunity in mice. In contrast to two intramuscular injections with an mRNA vaccine, the mucosal boost with adenoviral vectors induced high levels of IgA and tissue-resident memory T cells in the respiratory tract. Mucosal neutralization of virus variants of concern was also enhanced by the intranasal boosts. Importantly, priming with mRNA provoked a more comprehensive T cell response consisting of circulating and tissue-resident memory T cells after the boost, while a DNA priming induced mostly mucosal T cells. Concomitantly, the intranasal boost strategies provided protection against symptomatic disease. Therefore, a mucosal booster immunization after mRNA priming is a promising approach to establish mucosal immunity in addition to systemic responses.

immunology↗

bioRxiv2021

A pair of non-competing neutralizing human monoclonal antibodies protecting from disease in a SARS-CoV-2 infection model

TRIANNI mice carry an entire set of human immunoglobulin V region gene segments and are a powerful tool to rapidly generate human monoclonal antibodies. After immunizing these mice against the spike protein of SARS-CoV-2, we identified 29 hybridoma antibodies that reacted with the SARS-CoV-2 spike protein. Nine antibodies neutralized SARS-CoV-2 infection at IC50 values in the subnanomolar range. ELISA-binding studies and DNA sequence analyses revealed one cluster of clonally related neutralizing antibodies that target the receptor-binding domain and compete with the cellular receptor hACE2. A second cluster of neutralizing antibodies binds to the N-terminal domain of the spike protein without competing with the binding of hACE2 or cluster 1 antibodies. SARS-CoV-2 mutants selected for resistance to an antibody from one cluster are still neutralized by an antibody from the other cluster. Antibodies from both clusters markedly reduced viral spread in mice transgenic for human ACE2 and protected the animals from SARS-CoV-2 induced weight loss. Thus, we report two clusters of potent non-competing SARS-CoV-2 neutralizing antibodies providing potential candidates for therapy and prophylaxis of COVID-19. The study further supports the use of transgenic animals with human immunoglobulin gene repertoires in pandemic preparedness initiatives.

immunology↗

bioRxiv2020

Stimulus-specific plasticity of macaque V1 spike rates and gamma

When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.

neuroscience↗

bioRxiv2020

Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that neural activity results from an integration and comparison of bottom-up inputs with contextual predictions, a process in which firing rates and synchronization may play distinct roles. Here, we quantified stimulus predictability for natural images based on self-supervised, generative neural networks. When the precise pixel structure of a stimulus falling into the V1 receptive field (RF) was predicted by the spatial context, V1 exhibited characteristic{gamma} -synchronization (30-80Hz), despite no detectable modulation of firing rates. In contrast to{gamma} , {beta}-synchronization emerged exclusively for unpredictable stimuli. Natural images with high structural predictability were characterized by high compressibility and low dimensionality. Yet, perceptual similarity was mainly determined by higher-level features of natural stimuli, not by the precise pixel structure. When higher-level features of the stimulus in the receptive field were predicted by the context, neurons showed a strong reduction in firing rates and an increase in surround suppression that was dissociated from synchronization patterns. These findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.

neuroscience↗