Biology subjects

Shinde, S.

Publications and source records attributed to Shinde, S..

3 recordsLinked to original sources

bioRxiv2019

Glycogen metabolism jump-starts photosynthesis through the oxidative pentose phosphate pathway (OPPP) in cyanobacteria

Cyanobacteria experience drastic changes in their carbon metabolism under daily light-dark cycles. In the light, the Calvin-Benson cycle fixes CO2 and divert excess carbon into glycogen storage. At night, glycogen is degraded to support cellular respiration. Dark-light transition represents a universal environmental stress for cyanobacteria and other photosynthetic lifeforms. Recent studies in the field revealed the essential genetic background necessary for the fitness of cyanobacteria during diurnal growth. However, the metabolic engagement behind the dark-light transition is not well understood. In this study, we discovered that glycogen metabolism can jump-start photosynthesis in the cyanobacterium Synechococcus elongatus PCC 7942 when photosynthesis reactions start upon light. Compared to the wild type, the glycogen mutant ({Delta}glgC) showed much lower photosystem II efficiency and slower photosystem I-mediated cyclic electron flow rate when photosynthesis starts. Proteomics analyses indicated that glycogen is degraded through the oxidative pentose phosphate pathway (OPPP) during dark-light transition. We confirmed that the OPPP is essential for the initiation of photosynthesis, and further showed that glycogen degradation through the OPPP is likely to contribute to the activation of key Calvin-Benson cycle enzymes by modulating NADPH levels during the transition period. This ingenious strategy helps jump-start photosynthesis in cyanobacteria following dark respiration, and stabilize the Calvin-Benson cycle under fluctuating environmental conditions. It has evolutionary advantages for the survival of photosynthetic organisms using the Calvin-Benson cycle for carbon fixation.

plant biology↗

bioRxiv2019

Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinsons disease (PD). PD is characterized by loss of dopaminergic neurons in SNc and current techniques employ estimation of contrast ratios of the SNc visualized on NMS-MRI to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique based on convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. The technique not only performs with a superior cross-validation accuracy (83.7%) as well as testing accuracy (80%) as compared to contrast ratio-based classification (52.7% cross-validation and 56.5% testing accuracy) and radiomics based classifier (81.1% cross-validation and 60.3% testing accuracy); but also locates the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.

neuroscience↗

bioRxiv2018

RMI-chip: a microfluidics setup for functional imaging of microbial interactions with tree roots

Coupling microfludics with microscopy has emerged as a powerful approach to study at cellular resolution the dynamics in plant physiology and root-microbe interactions. Most devices have been designed to study the model plant Arabidopsis thaliana at higher throughput than conventional methods. However, there is a need for microfluidic devices which enable in vivo studies of root development and root-microbe interactions in woody plants. Here, we developed the RMI-chip, a simple microfluidic setup in which Populus tremuloides (aspen tree) seedlings can grow for over a month, allowing continuous microscopic observation of interactions between live roots and rhizobacteria. We find that the colonization of growing aspen roots by Pseudomonas fluorescens in the RMI-chip involves dynamic biofilm formation and dispersal, in keeping with previous observations in a different experimental set-up. Also, we find that whole-cell biosensors based on the rhizobacterium Bacillus subtilis can be used to monitor compositional changes in the rhizosphere but that the application of these biosensors is limited by their efficiency at colonizing aspen roots and persisting. These results indicate that functional imaging of dynamic root-bacteria interactions in the RMI-chip requires careful matching between the host plant and the bacterial root colonizer.

physiology↗