Biology subjects

Chowdhury, A.

Publications and source records attributed to Chowdhury, A..

3 recordsLinked to original sources

bioRxiv2018

Non-Canonical Activation of CREB Mediates Neuroprotection in a C. elegans Model of Excitotoxic Necrosis

Excitotoxicity, caused by exaggerated neuronal stimulation by Glutamate (Glu), is a major cause of neurodegeneration in brain ischemia. While we know that neurodegeneration is triggered by overstimulation of Glu-Receptors (GluRs), the subsequent mechanisms that lead to cellular demise remain controversial. Surprisingly, signaling downstream of GluRs can also activate neuroprotective pathways. The strongest evidence involves activation of the transcription factor cAMP Response Element Binding-protein (CREB), widely recognized for its importance in synaptic plasticity. Canonical views describe CREB as a phosphorylation-triggered transcription factor, where transcriptional activation involves CREB phosphorylation and association with CREB Binding Protein (CBP). However, given CREBs ubiquitous cross-tissue expression, the multitude of cascades leading to CREB phosphorylation, and its ability to regulate thousands of genes, it remains unclear how CREB exerts closely-tailored, differential neuroprotective responses in excitotoxicity. A non-canonical, alternative cascade for activation of CREB-mediated transcription involves the CREB co-factor cAMP-regulated transcriptional co-activator (CRTC), and may be independent of CREB phosphorylation. To identify cascades that activate CREB in excitotoxicity we use a C. elegans model of neurodegeneration by excitotoxic necrosis. We demonstrate that CREBs neuroprotective effect is conserved, and seems most effective in neurons with moderate Glu exposure. We find that factors mediating canonical CREB activation are not involved. Instead, phosphorylation-independent CREB activation in nematode excitotoxic necrosis hinges on CRTC. CREB-mediated transcription that depends on CRTC, but not on CREB phosphorylation, might lead to expression of a specific subset of neuroprotective genes. Elucidating conserved mechanisms of excitotoxicity-specific CREB activation can help us focus on core neuroprotective programs in excitotoxicity.

neuroscience↗

bioRxiv2017

Functional Compensation Between Hematopoietic Stem Cells In Vivo

In most organ systems, regeneration is a coordinated effort that involves many stem cells, but little is known about whether and how individual stem cells compensate for the functional deficiencies of other stem cells. Functional compensation between stem cells is critically important during disease progression and treatment. Here, we show how individual hematopoietic stem cells (HSCs) in a mouse heterogeneously compensate for the deficiencies of other HSCs during lymphopoiesis by increasing their clonal expansion at specific differentiation stages. This compensation rescues the overall blood supply and influences blood cell types outside of the deficient lineages in distinct patterns. We have identified the molecular regulators and signaling pathways in HSCs that are involved in this process. Our data demonstrate how stem cells interact with each other to constitute a coordinated network that is robust enough to withstand minor functional disruptions. Exploiting the innate compensation capacity of stem cell networks may improve the prognosis and treatment of many diseases.

systems biology↗

bioRxiv2017

Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular architecture and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is now more than ever accessible to researchers. This allows for collection of large amount of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs to resort to the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation.\n\nWe approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.

bioinformatics↗