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Zeng, A.

Publications and source records attributed to Zeng, A..

22 records · Page 2Linked to original sources

CRYPTOCHROME suppresses the circadian proteome and promotes protein homeostasis

The daily organisation of most mammalian cellular functions is attributed to circadian regulation of clock-controlled protein expression, driven by daily cycles of CRYPTOCHROME-dependent transcriptional feedback repression. To test this, we compared the circadian proteome and phosphoproteome of wild type and CRY-deficient fibroblast cells. Strikingly, CRY-deficient cells showed a two-fold increase in circadian-regulated proteins, phosphopeptides, and K+ transport. This was accompanied by extensive remodelling of the cellular proteome overall, including reduced phosphatase and proteasome subunit expression. These adaptations rendered CRY-deficient cells more sensitive to stress, which may account for their reduced circadian robustness and contribute to the wide-ranging phenotypes of CRY-deficient mice. We suggest that CRY ultimately functions to suppress, rather than generate, daily rhythms in cellular protein abundance, thereby maintaining protein and osmotic homeostasis.

cell biology↗

CRYPTOCHROMES confer robustness, not rhythmicity, to circadian timekeeping

SummaryCircadian (approximately daily) rhythms are a pervasive property of mammalian cells, tissues, and behaviour, ensuring physiological and metabolic adaptation to solar time. Models of daily cellular timekeeping revolve around transcriptional feedback repression, whereby CLOCK and BMAL1 activate the expression of clock proteins PERIOD (PER) and CRYPTOCHROME (CRY), which in turn repress CLOCK/BMAL1 activity. CRY proteins are thus considered essential negative regulators of the oscillation; a function supported by behavioural arrhythmicity of CRY-deficient mice when kept under constant conditions. Challenging this interpretation, however, we find evidence for persistent circadian rhythms in mouse behaviour and cellular PER2 levels when CRY is absent. CRY-less oscillations are variable in their expression and have a shorter period than wild type controls. Importantly, we find classic circadian hallmarks such as temperature compensation and determination of period by casein kinase 1{delta}/{varepsilon} activity to be maintained. In the absence of CRY-mediated transcriptional feedback repression and rhythmic Per2 transcription, PER2 protein rhythms are sustained for several cycles, accompanied by circadian variation in protein stability. We suggest that, whereas circadian transcriptional feedback imparts robustness and functionality onto biological clocks, the core timekeeping mechanism is post-translational. Our findings suggest that PER proteins normally act as signalling hubs that transduce timing information to the nucleus, imparting daily rhythms upon the activity of transcriptional effectors. Highlights[tpltrtarr] PER/CRY-mediated negative feedback is dispensable for mammalian circadian timekeeping [tpltrtarr]Circadian variation in PER2 levels persists in the absence of rhythmic Per2 transcription [tpltrtarr]CK1 and GSK3 are plausible mechanistic components of a cytoscillator mechanism [tpltrtarr]CRY-mediated feedback repression imparts robustness to biological timekeeping In briefCircadian turnover of mammalian clock protein PERIOD2 persists in the absence of canonical transcriptional feedback repression and rhythmic clock gene activity, demanding a re-evaluation of cellular clock function and evolution.

cell biology↗

Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model

Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, DeepSignalingSynergy, for drug combination prediction. Compared with existing models that use a large number of chemical-structure and genomics features in densely connected layers, we built the model on a small set of cancer signaling pathways, which can mimic the integration of multi-omics data and drug target/mechanism in a more biological meaningful and explainable manner. The evaluation results of the model using the NCI ALMANAC drug combination screening data indicated the feasibility of drug combination prediction using a small set of signaling pathways. Interestingly, the model analysis suggested the importance of heterogeneity of the 46 signaling pathways, which indicates that some new signaling pathways should be targeted to discover novel synergistic drug combinations.

bioinformatics↗

Hormone Levels Are Related to Altered Functional Connectivity in Prolactinomas

Background and ObjectiveProlactinomas may cause drastic hormone fluctuations throughout the body. It is not fully understood how endogenous hormone disorders such as prolactinomas reshape the patients brain. By employing the resting-state functional magnetic resonance imaging technique, we aimed to investigate the whole-brain functional connectivity (FC) and its relationship with hormone levels in patients with prolactinomas.\n\nMethodsUsing whole-brain and seed-based functional connectivity analyses, we compared FC metrics between 33 prolactinoma patients and 31 healthy controls matched with age, sex, and handedness. Then we performed partial correlation analysis to examine the relationship between FC metrics and hormone levels.\n\nResultsCompared to healthy controls, we found that prolactinoma patients showed significantly increased thalamocortical (visual cortex) and cerebellar-cerebral connectivity. In addition, endogenous hormone levels were positively correlated with the increased FC, and the hormone-FC relationships showed sex difference in prolactinoma patients.\n\nConclusionsOur findings are the first to reveal the altered FC patterns and sex-dependent hormone-FC relationships in prolactinoma patients, indicating the important role of hormone levels in the neural mechanism of brain reorganization and hyperactive intrinsic connections in prolactinomas.

neuroscience↗