Biology subjects

Agrawal, S.

Publications and source records attributed to Agrawal, S..

3 recordsLinked to original sources

bioRxiv2018

Modulation of TOR kinase activity in Chlamydomonas reinhardtii: Effect of N-starvation and changing carbon pool

Target Of Rapamycin (TOR) kinase is a sensor as well as a central integrator of internal and external metabolic cues. The upstream and downstream signals of this kinase are very well characterized in animals. However, in algae, higher plants and other photosynthetic organisms, the components of the TOR kinase-signaling are yet to be characterized. Here, we establish an assay system to study TOR kinase activity in C.reinhardtii using the phosphorylation status of its downstream target, CrS6K. We further use this assay to monitor TOR kinase activity under various physiological states such as photoautotrophy, heterotrophy, mixotrophy and nitrogen starvation. We observe that autotrophy in light (and not in dark) leads to TOR kinase attenuation during N starvation while the same is not observed in mixotrophy. Importantly, we show that the external carbon source glucose is sensed and uptaken by C.reinhardtii cells only in the presence of light and not in the dark. And such exogenously added glucose, as the photoassimilate carbon mimic, results in enhanced production of ROS, induction of autophagy and concomitant drop in TOR kinase activity, creating N-starvation-like cellular state even in N+ conditions. Interestingly, dose dependent addition of glucose revealed TOR kinase activation in low glucose regime (ROS independent) followed by attenuation of TOR kinase (ROS dependent) at high glucose levels.\n\nSummaryTOR kinase activity in C.reinhardtii is modulated by available carbon source especially glucose, where low levels of glucose cause an increase whereas high levels cause a reduction in TOR kinase activity.

plant biology↗

bioRxiv2018

The conservation landscape of the human ribosomal RNA gene repeats

Ribosomal RNA gene repeats (rDNA) encode ribosomal RNA, a major component of ribosomes. Ribosome biogenesis is central to cellular metabolic regulation, and several diseases are associated with rDNA dysfunction, notably cancer, However, its highly repetitive nature has severely limited characterization of the elements responsible for rDNA function. Here we make use of phylogenetic footprinting to provide a comprehensive list of novel, potentially functional elements in the human rDNA. Complete rDNA sequences for six non-human primate species were constructed using de novo whole genome assemblies. These new sequences were used to determine the conservation profile of the human rDNA, revealing 49 conserved regions in the rDNA intergenic spacer (IGS). To provide insights into the potential roles of these conserved regions, the conservation profile was integrated with functional genomics datasets. We find two major zones that contain conserved elements characterised by enrichment of transcription-associated chromatin factors, and transcription. Conservation of some IGS transcripts in the apes underpins the potential functional significance of these transcripts and the elements controlling their expression. Our results characterize the conservation landscape of the human IGS, and suggest that noncoding transcription and chromatin elements are conserved and important features of this unique genomic region.

genomics↗

bioRxiv2018

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience↗