Biology subjects

Kumar, A.

Publications and source records attributed to Kumar, A..

651 records · Page 37Linked to original sources

bioRxiv2017

Reactive Oxygen Species Regulate the Inflammatory Function of NKT Cells through Promyelocytic Leukemia Zinc Finger

Reactive oxygen species (ROS) are byproducts of aerobic metabolism and contribute to both physiological and pathological conditions as second messengers. ROS are essential for antigen specific activation of T cells, but little is known about what role ROS play in NKT cells. In the current study, we investigated the role of ROS in NKT cell function. We found that ROS levels are similar among CD4, CD8 and NKT cell subsets in the thymus. However, NKT cells, but neither CD4 nor CD8 T cells, showed dramatically increased ROS in the spleen and liver but not in adipose tissues. ROS in the peripheral NKT cells were primarily produced by NADPH oxidases not mitochondria. Accordingly, ROS-high NKT cells were susceptible to oxidative stress and underwent apoptotic cell death. Furthermore, ROS play an important role in regulating the inflammatory function of NKT cells because antioxidant treatment of NKT cells showed reduced frequencies of IFN-{gamma}+ and IL-17+ cells. In line with this, freshly isolated ROS-high NKT cells had more NKT1 and NKT17 cells but less NKT2 than ROS-low cells. These characteristics are regulated by promyelocytic leukemia zinc finger (PLZF) as evidenced by low ROS in NKT cells from PLZF haplodeficient mice and also from adipose tissues that do not express PLZF. Conversely, ROS were highly elevated in CD4 T cells from mice ectopically expressing PLZF. Together, our study revealed for the first time that ROS regulate NKT cell functions through PLZF.

immunology↗

bioRxiv2017

Low Dimensional Activity in Spiking Neuronal Networks

Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. Surprisingly, the structure of the intrinsic manifold of the network activity puts constraints on learning. For instance, animals find it difficult to perform tasks that may require a change in the intrinsic manifold. Here, we demonstrate that the Neural Engineering Framework (NEF) can be adapted to design a biologically plausible spiking neuronal network that exhibit low dimensional activity. Consistent with experimental observations, the resulting synaptic weight distribution is heavy-tailed (log-normal). In our model, a change in the intrinsic manifold of the network activity requires rewiring of the whole network, which may be either not possible or a very slow process. This observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.\n\nSignificance statementA network in the brain consists of thousands of neurons. A priori, we expect that the network will have as many degrees of freedom as its number of neurons. Surprisingly, experimental evidence suggests that local brain activity is confined to a space spanned by 10 variables. Here, we describe an approach to construct spiking neuronal networks that exhibit low-dimensional activity and address the question: how the intrinsic dimensionality of the network activity restricts the learning as suggested by recent experiments? Specifically, we show that tasks that requires animals to change the network activity outside the intrinsic space would entail large changes in the neuronal connectivity, and therefore, animals are either slow or not able to acquire such tasks.

neuroscience↗

bioRxiv2016

Activity Dynamics and Signal Representation in Striatal Network Model with Distance-dependent Connectivity

Striatum is predominantly inhibitory and the main input nucleus of the basal ganglia. A functional characterization of its activity dynamics is crucial for understanding the mechanisms underlying phenomenon such as action selection and initiation. Here, we investigated the effects of the spatial connectivity structure on the emergence and maintenance of localized bumps of activity in large-scale striatal networks (~10,000 neurons). We show that in striatal network model in which the distance-dependent connection probability varies in a Gaussian fashion (Gaussian networks), the activity remains asynchronous irregular (AI) and spatially homogeneous, independent of the background input. By contrast, when the distance-dependent connectivity varies according to a Gamma distribution (Gamma networks), with short-range connectivity suppressed, a repertoire of activity dynamics can be observed: While weak background inputs induce spatially homogeneous AI activity, stronger background inputs induce stable, spatially localized activity bumps as in winner-take-all (WTA) dynamics. Interestingly, for intermediate background inputs, the networks exhibit spatially localized, but unstable activity bumps (Transition Activity, TA), resembling the experimentally observed neuronal assembly dynamics in the striatum.\n\nAmong the three main regimes of network activity (AI, WTA, TA) we found that in the AI and TA regimes, network dynamics are flexible and can be easily modified by external stimuli. Moreover, the dynamical state of the network returns to the baseline after the stimulus is removed. By contrast, the dynamics in the WTA state are rigid and can only be changed by very strong external stimuli. These results support the hypothesis that the flexibility of the striatal network state in response to stimuli is important for its normal function and the rigid network states (WTA) correspond to brain disorders such as Parkinsons disease, where the striatum looses its repertoire of dynamic states and is only receptive to very strong inputs.

neuroscience↗