Biology subjects

Vijayan, S.

Publications and source records attributed to Vijayan, S..

9 recordsLinked to original sources

bioRxiv2025

Identification of novel cis-acting elements, E-box and ISRE, regulating IFNγ-IRF1 axis-mediated NLRC5 expression

NLRC5 and CIITA are the primary transcriptional regulators of MHC class I and MHC class II, respectively, and play essential roles in adaptive immunity. While the regulatory mechanisms of CIITA have been extensively characterized, the transcriptional control of NLRC5 remains incompletely understood. In this study, we identified two novel conserved cis-regulatory elements within the NLRC5 promoter, an E-box and an ISRE. Furthermore, we revealed IRF1 as a novel transcriptional regulator of NLRC5, binding directly to the ISRE within the NLRC5 promoter. Using the newly identified NLRC5 ISRE, we established a screening platform to identify modulators of the IFN{gamma}-IRF1-NLRC5 axis. This system corroborated the inhibitory effects of known viral antagonists and led to the identification of novel SARS-CoV-2 viral factors that suppress IFN{gamma}- mediated IRF1 nuclear translocation, thereby inhibiting the MHC class I pathway. By elucidating the previously unproven molecular mechanism underlying IFN{gamma}-mediated NLRC5 regulation, our study provides critical insights into viral immune evasion strategies and the modulation of antigen presentation. These findings may facilitate the development of MHC class I-targeted therapeutics by modulating the IRF1-NLRC5 axis.

immunology↗

bioRxiv2024

Examining Cognitive Performance in Mice using the Open-Source Operant Feeding Device FED3

Cognitive impairments are prevalent in various neurological disorders, including chronic pain conditions, and pose significant therapeutic challenges. Preclinical rodent models serve as valuable tools for investigating the underlying mechanisms of and treatments for cognitive dysfunction. However, factors such as stress, age, sex, and disease duration present challenges to reliably capturing cognitive deficits in rodents. Here, we present a comprehensive and high-throughput protocol utilizing the open-source operant Feeding Experimentation Device 3 (FED3) for assessing cognitive performance in mice. We developed a data pipeline to streamline data compilation and analysis, and established operating conditions for a six-test cognitive battery which can be completed in as few as 20 days. We validated our testing procedures using bilateral orbitofrontal cortical lesions to capture deficits in executive function, and demonstrated the feasibility of assessing cognitive function in aged mice of both sexes to identify genotypic and sex-specific effects. Overall, our findings demonstrate that the FED3 is a versatile tool for evaluating cognitive function in mice, offering a low-cost, high-throughput approach for preclinical studies of neurological disorders. We anticipate that this protocol will facilitate broader implementation of cognitive testing in rodent models and contribute to the understanding and treatment of cognitive dysfunction in neurological diseases.

neuroscience↗

bioRxiv2023

Modeling of whole brain Electroencephalogram (EEG) in a spatially organized oscillatory network

In this study, we model high-dimensional Electroencephalogram signals in sleep stages, using a general trainable network of Hopf oscillators. The proposed architecture has two components: a layer of oscillators with lateral connections, and a complex valued feedforward network with and without a hidden layer. The output of the Hopf oscillators, whose dynamics is described in the complex domain, is fed as input to the feedforward network and the output predicts the EEG signals. The proposed network is trained in two stages: in the 1st stage, intrinsic frequencies of the oscillators and the lateral connections are trained whereas, in the 2nd stage, the complex-valued feed-forward network is trained. Reconstruction error obtained when there is a hidden layer in the feedforward network is an order of magnitude smaller than that obtained without a hidden layer. Also, it has been shown that during testing our model is able to generate EEG signals, whose spectral properties accurately describe the corresponding target signals. In the first, the oscillators do not have any spatial localization, whereas in the second the oscillators are spatially distributed in a spherical shell geometry. The model described can be interpreted as a stepping stone towards a large-scale model of brain dynamics.

neuroscience↗

bioRxiv2022

Defensive shimmering responses in the giant honeybee Apis dorsata are triggered by dark stimuli moving against a bright background

Giant honeybees, including the open-nesting Asian giant honeybee Apis dorsata, display a spectacular collective defence behaviour - known as "shimmering" - against predators, which is characterised by travelling waves generated by individual bees flipping their abdomens in a coordinated and sequential manner across the bee curtain. We examined if shimmering is visually-mediated by presenting moving stimuli of varying sizes and contrasts to the background (dark or light) in bright and dim ambient light conditions. Shimmering was strongest under bright ambient light, and its strength declined under dim-light in this facultatively nocturnal bee. A. dorsata shimmered only when presented with the darkest stimulus against a light background, but not when this condition was reversed (light stimulus against dark background). This response did not attenuate with repeated exposure to the stimuli, suggesting that shimmering behaviour does not undergo habituation. We suggest that this is an effective anti-predatory strategy in open-nesting A. dorsata colonies which are exposed to high ambient light, as flying predators are more easily detected when they appear as dark moving objects against a bright sky. Moreover, the stimulus detection threshold (smallest visual angular size) is much smaller in this anti-predatory context (1.6{degrees} - 3.4{degrees}) than in the context of foraging (5.7{degrees}), indicating that ecological context affects visual detection threshold.

animal behavior and cognition↗

bioRxiv2021

Multitaper estimates of phase-amplitude coupling

Phase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing phase-amplitude coupling with existing statistical measures have been well-documented. The limitations of these measures include: (i) response to non-oscillatory, high-frequency, broad-band activity, (ii) response to high-frequency components of the low-frequency oscillation, (iii) adhoc selection of analysis frequency-intervals, and (iv) reliance upon data shuffling to assess statistical significance. In this work, a multitaper phase-amplitude coupling estimator is proposed that addresses issues (i)-(iv) above. Specifically, issue (i) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (ii) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (iii) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (iv).

neuroscience↗

bioRxiv2020

Spatially expandable fiber-based probes as a multifunctional deep brain interface

Understanding the cytoarchitecture and wiring of the brain requires improved methods to record and stimulate large groups of neurons with cellular specificity. This requires miniaturized neural interfaces that integrate into brain tissue without altering its properties. Existing neural interface technologies have been shown to provide high-resolution electrophysiological recording with high signal-to-noise ratio. However, with single implantation, the physical properties of these devices limit their access to one, small brain region. To overcome this limitation, we developed a platform that provides three-dimensional coverage of brain tissue through multisite multifunctional fiber-based neural probes guided in a helical scaffold. Chronic recordings from the spatially expandable fiber probes demonstrate the ability of these fiber probes capturing brain activities with a single-unit resolution for long observation times. Furthermore, using Thy1-ChR2-YFP mice we demonstrate the application of our probes in simultaneous recording and optical/chemical modulation of brain activities across distant regions. Similarly, varying electrographic brain activities from different brain regions were detected by our customizable probes in a mouse model of epilepsy, suggesting the potential of using these probes for the investigation of brain disorders such as epilepsy. Ultimately, this technique enables three-dimensional manipulation and mapping of brain activities across distant regions in the deep brain with minimal tissue damage, which can bring new insights for deciphering complex brain functions and dynamics in the near future.

bioengineering↗

bioRxiv2019

Temporal learning of bottom-up connections via spatially nonspecific top-down inputs

In the brain, high-order and low-order areas are connected via bottom-up connections (from low-order to high-order areas) and top-down connections (from high-order to low-order areas). While bottom-up signals are thought to be critical in generating perception, functions of top-down signals have not been clearly delineated. One popular theory is that top-down inputs modify the activity of specific cell assemblies to modulate responses to bottom-up inputs. However, a different line of studies proposes that not all top-down inputs are specifically delivered. As the leading theories cannot account for nonspecific top-down inputs, we seek potential functions of nonspecific top-down signals using network models in our study. Our simulation results suggest that top-down inputs can regulate low-order area responses by providing temporal information even without spatial specificity. Specifically, the temporal information in nonspecific top-down inputs can weaken the undesired bottom-up connections, contributing to bottom-up connections learning. Further, we found that cortical rhythms (synchronous oscillatory neural responses) are critical in the proposed learning process of bottom-up connections in our model.

neuroscience↗

bioRxiv2018

Look before you jump: jumping spiders discriminate different ants by visual cues

Ants, being ubiquitous, aggressive, and top predators, play a predominant role in terrestrial ecosystems. Jumping spiders are another prominent invertebrate predator that are present in similar habitats as that of ants. Most jumping spiders are thought to avoid ants, yet little is known if they discriminate among them. In this study we examined the response of jumping spider genus Plexippus towards three different ant species (Oecophylla smaragdina, the weaver ants; Camponotus sericeus the golden-back carpenter ants, and Leptogenys processionalis, the procession ants). In a behavioral assay that excluded tactile and chemical cues, we tested if spiders distinguish the three ants by visual cues alone. We recorded and analysed behaviors such as look, approach, stalk, attack, and avoidance by spiders towards ants. Our results show that the three ants differ in their color, movement and aggressive behavior. Spiders gave short looks to live ants, suggesting movement is important in detecting ants. Furthermore, spiders gave significantly more long looks to procession and golden-back ants compared to weaver ants. Spiders approached, stalked and attacked procession ants more compared to weaver ants. Numerous jumping spiders and ants overlap in their habitat, and it is advantageous to selectively avoid some ants over others. Our results suggests that jumping spiders can indeed distinguish ants that co-occur in their habitat by visual cues alone, however, the precise nature of visual cues warrants further studies.

animal behavior and cognition↗

bioRxiv2017

Cortical circuit based lossless neural integrator for perceptual decision-making

The intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. Indeed, uncertainty can be reduced by integrating (accumulating) incoming sensory evidence. It is widely thought that this accumulation is instantiated via recurrent rate-code neural networks. Yet, these networks do not fully explain important aspects of perceptual decision-making, such as a subjects ability to retain accumulated evidence during temporal gaps in the sensory evidence. Here, we utilized computational models to show that cortical circuits can switch flexibly between retention and integration modes during perceptual decision-making. Further, we found that, depending on how the sensory evidence was readout, we could simulate stepping and ramping activity patterns, which may be analogous to those seen in different studies of decision-making in the primate parietal cortex. This finding may reconcile these previous empirical studies because it suggests these two activity patterns emerge from the same mechanism.

neuroscience↗