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Cheng, T.

Publications and source records attributed to Cheng, T..

57 records · Page 4Linked to original sources

Feeling left out or just surprised? Neural correlates of social exclusion and expectancy violations in adolescence

Social belonging and affiliation are important human drives that impact decision-making and health outcomes. While neural responses to social exclusion are increasingly well-characterized, studies rarely identify whether these responses reflect the affective distress or expectancy violation associated with exclusion. The present study compares neural responses to exclusion and over-inclusion, a similarly unexpected social occurrence that violates fair play expectations, but does not involve rejection, with a focus on implications for models of dorsal anterior cingulate cortex (dACC) function. In an fMRI adaptation of the Cyberball paradigm with human adolescents 11.1-17.7 years of age (N=69), we employed parametric modulators to examine scaling of neural signal with cumulative exclusion and inclusion events. The use of parametric modulators overcomes arbitrary definitions of condition onsets and offsets imposed on fluid, continuous gameplay. In support of the notion that the dACC plays a role in processing affective distress, this region exhibited greater signal with cumulative exclusion than cumulative inclusion events. However, this difference was partly driven by diminished signal accompanying cumulative inclusion, a finding that does not conform to either affective distress or expectancy violation model predictions. Additionally, we found that signal in the insula and dorsomedial prefrontal cortex scaled uniquely with social exclusion and over-inclusion, respectively, while conjunction analyses revealed that the rostromedial prefrontal cortex and left intraparietal sulcus responded similarly to both conditions. These findings shed light on which neural regions may exhibit patterns of differential sensitivity to exclusion or over-inclusion, as well as those that are more broadly engaged by both types of social interaction.

neuroscience↗

Single cell transcriptomes reveal characteristics of miRNA in gene expression noise reduction

Isogenic cells growing in identical environments show cell-to-cell variations because of stochastic gene expression. The high level of variation or noise could disrupt robust gene expression and result in tremendous consequences on cell behaviors. In this work, we showed evidence that microRNAs (miRNAs) could reduce gene expression noise in mRNA level of mouse cells based on single-cell RNA-sequencing data analysis. We identified that miRNA expression level, number of targets, targets pool abundance and interaction strength of miRNA with its targets are the key features contributing to noise repression. MiRNAs tend to work together as cooperative sub-networks to repress target noise synergistically in a cell type specific manner. Using a physical model of post-transcriptional regulation, we demonstrated that the accelerated degradation with elevated transcriptional activation of miRNA target provides resistance to extrinsic fluctuations. Together, through the integration analysis of single-cell RNA and miRNA expression profiles. We demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

bioinformatics↗

Regulation by competition: a hidden layer of gene regulatory network

Molecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

systems biology↗