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Sundaram, G.

Publications and source records attributed to Sundaram, G..

2 recordsLinked to original sources

A novel mechanism of morphogenetic transition in Schizosaccharomyces pombe dependent on the MAPK Spc1 and the transcription factor Atf1 associated with sensing of optimal growth environment

Fission yeast Schizosaccharomyces pombe are rod shaped eukaryotic cells that grow by tip elongation and divide medially by formation of cell wall septum. The coordination between cell length and cell cycle phase transitions in this simple eukaryote makes it an excellent model system for studying morphogenetic events associated with both intrinsic and extrinsic disturbances in cell cycle regulation. Cell cycle progression in S. pombe is dependent on the coordinated functions of two bZIP family transcription factors Atf1 and Pcr1. In an attempt to understand this coordination, we stumbled upon the startling observation that overexpression of Atf1 in cells lacking Pcr1 led to drastic morphogenetic alteration whereby the cells became hyper elongated. In this report we present the evidences of dependence of this phenotype on metabolic status of the cell. We also show that the phenotype is dependent on the activity of the Mitogen Activated Protein Kinase, Spc1 and that it is associated with the global gene expression alterations of cells resulting from the perturbed balance of Atf1 and Pcr1 activities. We found that change in the carbon source, alterations in iron concentration and the redox environment of the cell can affect the extent of morphogenesis, to the extent of even abolishing it at times. Our results also indicate that this phenotype has a strong association with maintenance of optimum growth conditions of the cell. Taken together, our observations reveal a completely novel mechanism of morphogenetic transition in fission yeast involving the MAPK Spc1 and its downstream effector molecule Atf1, which can be tuned by the cells metabolic environment.

microbiology↗

Visualization of Incrementally Learned Projection Trajectories for Longitudinal Data

Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (Incrementally Learned Visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal Multi-Electrode Array data from brain cortical organoids when exposed to different levels of Quinolinic Acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimers disease, and its blocking antibody. We uncover valuable insights into the organoids electrophysiological maturation and response patterns over time under these conditions.

bioinformatics↗