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Aftab, A. M.

Publications and source records attributed to Aftab, A. M..

2 recordsLinked to original sources

Fidelity in co-diversified symbiosis

Obligate co-dependence can arise in symbiosis, yielding heritable partnerships. These interactions are considered to be highly specific, but partner fidelity is difficult to quantify owing to the experimental constraints of symbiont exchange between host species. Here, we overcome this challenge by leveraging the unique transmission dynamics of Stammera capleta, the obligate digestive symbiont of tortoise beetles (Chrysomelidae: Cassidinae). Despite its extracellular localization, S. capleta possesses a drastically reduced genome ([~]0.25 Mb) and is vertically transmitted through egg-associated spheres. Manipulating these spheres allowed us to experimentally exchange S. capleta between beetle species to determine their impact on host development. We show that non-native S. capleta can successfully colonize the symbiotic organs of a novel host, but that the interaction outcome correlates with genetic relatedness to the native symbiont. Genetically distant species trigger a more pronounced transcriptional response and can only partially rescue host development. While more closely related symbionts proliferate similarly to the native one and induce a comparable host response, they fail to propagate to the next generation, underscoring how transmission fidelity, host-symbiont compatibility, and local adaptation can further specificity within a Paleocene-aged partnership.

evolutionary biology↗

TP53-META, a meta-analysis tool for comparative transcriptomics of TP53 dependency: Examples from target silencing and liver fibrosis

BackgroundTP53 is the most frequently mutated transcription factor (TF) in sporadic cancers; and its targets exhibit dysregulation at the level of expression in both cancer and non-cancer pathologies. However, there is not yet a web-based tool that enables meta-analysis and visualization of TP53-related gene expression datasets, although several databases exist to access and annotate TP53 target information. To address this gap, we developed TP53-META, an interactive R Shiny-based web tool that allows users to upload and simultaneously analyze users or integrated public RNA-seq datasets for effects of TP53 depletion and/or induction on the transcriptome. ResultsTP53-META can be used to visualize significant expression clusters as well as TF-TF, pathway-pathway, disease-gene and treatment-gene networks to determine TP53 dependency of selected treatment contrasts. We demonstrated the utility of TP53-META through two case studies. In the first, using an in-house RNA-seq data from MCF7 cells treated with siRNAs against CHRNA5 and TP53, we identified TP53-independent and dependent transcriptomic changes by CHRNA5 depletion by comparing with selected public datasets in TP53-META. In the second, we demonstrated the user data upload functionality of TP53-META before meta-analysis and extracted commonly modulated TP53-related genes in liver fibrosis using public RNA-seq datasets. TP53-META is available at http://konulabapps.bilkent.edu.tr:3838/TP53-Meta1.5/ ConclusionsBy facilitating meta-analysis, clustering, and network-based visualizations, TP53-META enables researchers to efficiently integrate and explore TP53-related transcriptomic datasets from diverse sources, and help uncover robust expression patterns, and investigate context-specific TP53 functions.

bioinformatics↗