Biology subjects

Kannan, L.

Publications and source records attributed to Kannan, L..

3 recordsLinked to original sources

bioRxiv2025

Transcriptional control of neuronal maintenance by SOX2 during inner ear innervation

The cochlear sensory epithelium and the spiral ganglion neurons it supports arise from a common pool of otic progenitors, yet the neurons remain dependent on the epithelium for guidance and survival long after the two lineages diverge. How that dependency is sustained as the progenitor pool generates increasingly restricted cell types is unknown. Here we show that SOX2 maintains this regulatory continuity. Deletion of Sox2 from the cochlear epithelium after cochlear neurogenesis does not affect neuroblast generation, delamination and proliferation; however, peripheral axons failed to reach the epithelium and neurons were progressively lost, identifying a non-cell-autonomous requirement for epithelial SOX2. Transcriptomic profiling revealed a SOX2-dependent epithelial programme enriched for neurotrophic and axon-guidance genes, including Ntf3 and Bdnf. In cochlear organoids, acute Sox2 deletion reduced H3K27ac at regulatory elements while H3K4me1 remained stable, consistent with SOX2 maintaining the activity of previously marked enhancers. Ntf3 and Bdnf exemplified distinct regulatory trajectories. At Ntf3, SOX2 occupied an enhancer whose activity depended on Sox2, whereas at Bdnf it occupied promoter-proximal elements in progenitors before ATOH1 engaged these and a hair-cell-active distal enhancer. Thus, SOX2 links sensory fate specification to epithelial-neuronal communication. More generally, our findings suggest that regulatory competence can persist through lineage restriction, while changing transcription-factor inputs partition its output among descendants to coordinate their development.

developmental biology↗

bioRxiv2019

Gibbs Process Determines Survival and Reveals Contact-Inhibition Genes in Glioblastoma Multiforme

Tumor growth is a spatiotemporal birth-and-death process with loss of heterotypic contact-inhibition of locomotion (CIL) of tumor cells promoting invasion and metastasis. Therefore, representing tumor cells as two-dimensional points, we can expect the tumor tissues in histology slides to reflect realizations of spatial birth-and-death process which can be mathematically modeled to reveal molecular mechanisms of CIL, provided the mathematics models the inhibitory interactions. Gibbs process as an inhibitory point process is a natural choice since it is an equilibrium process of the spatial birth-and-death process. That is if the tumor cells maintain homotypic contact inhibition, the spatial distributions of tumor cells will result in Gibbs hard core process over long time scales. In order to verify if this is the case, we applied the Gibbs process to 411 TCGA Glioblastoma multiforme patient images. Our imaging dataset included all cases for which diagnostic slide images were available. The model revealed two clusters, one of which - the "Gibbs cluster," showed the convergence of the Gibbs process with significant survival difference. Further smoothing the discretized (and noisy) inhibition metric, for both increasing and randomized survival time, we found a significant association of the patients in the Gibbs cluster with increasing survival time. The mean inhibition metric also revealed the point at which the homotypic CIL establishes in tumor cells. Besides, RNAseq analysis between patients with loss of heterotypic CIL and intact homotypic CIL in the Gibbs cluster unveiled cell movement gene signatures and differences in Actin cytoskeleton and RhoA signaling pathways as key molecular alterations. These genes and pathways have established roles in CIL. Taken together, our integrated analysis of patient images and RNAseq data provides for the first time a mathematical basis for CIL in tumors, explains survival as well as uncovers the underlying molecular landscape for this key tumor invasion and metastatic phenomenon.

cancer biology↗

bioRxiv2017

Consensus on Molecular Subtypes of Ovarian Cancer

INTRODUCTIONVarious computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.\n\nMETHODSWe use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.\n\nRESULTSEach subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.\n\nCONCLUSIONSOur analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.

cancer biology↗