Biology subjects

Ayyappan, V.

Publications and source records attributed to Ayyappan, V..

3 recordsLinked to original sources

bioRxiv2025

Gastruloid patterning reflects division of labor among biased stem cell clones

Division of labor, whereby individuals specialize in complementary roles to collectively achieve beneficial outcomes, is a recurring phenomenon in economics, ecology, and microbiology. In development, individual cells specialize, but this specialization is thought to arise from instruction by external signaling cues upon otherwise interchangeable progenitors. It is unclear if progenitors exhibit some degree of specialization, and if so, whether that specialization is optimized for developmental outcomes. Using fluorescence-based lineage tracing in combination with spatial transcriptomics, we show that, in the gastruloid model of early development, individual stem cell clones harbored reproducible spatial propensities for anterior or posterior fates that result in a spontaneous division of labor during axial organization. Gastruloids derived from pure clones generated elongated structures less frequently than a polyclonal population, but mixing clones allowed clones to follow their inherent propensity, restoring proper axial elongation. Spatial transcriptomics reveals that pure clones show disrupted gene expression with inappropriate coexpression of anterior and posterior markers, while clone combinations restore proper spatial organization. We further show that propensities are globally utilized: a clone with a particular propensity can adopt different fates depending on what is more optimal for development as a whole. Perturbations to key developmental signaling pathways disrupted this sorting and profiling revealed molecular characteristics of propensity. Proper developmental outcomes may thus emerge from the coordinated action of intrinsically biased clonal populations.

developmental biology↗

bioRxiv2021

Allelic correlation is a marker of tradeoffs between barriers to transmission of expression variability and signal responsiveness in genetic networks

Accurately functioning genetic networks should be responsive to signals but prevent transmission of stochastic bursts of expression. Existing data in mammalian cells suggests that such transcriptional "noise" is transmitted by some genes and not others, suggesting that noise transmission is tunable, perhaps at the expense of other signal processing capabilities.However, systematic claims about noise transmission in genetic networks have been limited by the inability to directly measure noise transmission. Here we build a mathematical framework capable of modeling allelic correlation and noise transmission. We find that allelic correlation and noise transmission correspond across a broad range of model parameters and network architectures. We further find that limiting noise transmission comes with the trade-off of being unresponsive to signals, and that within the parameter regimes that are responsive to signals, there is a further trade-off between response time and basal noise transmission. Using a published allele specific single cell RNA-sequencing dataset, we found that genes with high allelic odds ratios are enriched for cell-type specific functions, and that within multiple signaling pathways, factors which are upstream in the pathway have higher allelic odds ratios than downstream factors. Overall, our findings suggest that some degree of noise transmission is required to be responsive to signals, but that minimization of noise transmission can be accomplished by trading-off for a slower response time.

systems biology↗

bioRxiv2020

ADPriboDB v2.0: An Updated Database of ADP-ribosylated Proteins

ADP-ribosylation is a protein modification responsible for biological processes such as DNA repair, RNA regulation, cell cycle, and biomolecular condensate formation. Dysregulation of ADP-ribosylation is implicated in cancer, neurodegeneration, and viral infection. We developed ADPriboDB (adpribodb.leunglab.org) to facilitate studies in uncovering insights into the mechanisms and biological significance of ADP-ribosylation. ADPriboDB 2.0 serves as a one-stop repository comprising 48,346 entries and 9,097 ADP-ribosylated proteins, of which 6,708 were newly identified since the original database release. In this updated version, we provide information regarding the sites of ADP-ribosylation in 32,946 entries. The wealth of information allows us to interrogate existing databases or newly available data. For example, we found that ADP-ribosylated substrates are significantly associated with the recently identified human protein interaction networks associated with SARS-CoV-2, which encodes a conserved protein domain called macrodomain that binds and removes ADP-ribosylation. In addition, we create a new interactive tool to visualize the local context of ADP-ribosylation, such as structural and functional features as well as other post-translational modifications (e.g., phosphorylation, methylation and ubiquitination). This information provides opportunities to explore the biology of ADP-ribosylation and generate new hypotheses for experimental testing.

bioinformatics↗