Communications Biology最新文献

Previous reports showed that long non-coding RNA (lncRNA) participates in the development and progression of tumors. Nevertheless, the effect of LINC02139 and its mechanism on gastric cancer (GC) is still unknown. We revealed that LINC02139 is upregulated in GC cell lines and tissues and high LINC02139 expression was correlated with the advancement of GC in patients. Functionally, overexpression of LINC02139 promoted, while knockdown of LINC02139 impaired GC cell proliferation, migration, and invasion in vitro and impeded tumorigenesis in a tumor xenograft model in vivo. Mechanistically, LINC02139 directly bound to XIAP and increased the protein level by maintaining its protein stability through inhibition of the ubiquitination and proteasome-dependent degradation pathway. Importantly, the regulatory function of XIAP in LINC02139-mediated oncogenic effects was demonstrated. Both in vitro and in vivo experiments showed that LINC02139 and XIAP collaboratively modulate GC cell growth and apoptosis. Analysis of clinical GC tissues further confirmed the upregulation of XIAP and the positive association between LINC02139 and XIAP expression. These findings established LINC02139 as a driver of tumorigenesis and highlighted the crucial involvement of the LINC02139-XIAP axis in GC progression, suggesting its potential as a promising therapeutic target for combating GC advancement.

Human cognition supports complex behaviour across a range of situations, and traits (e.g. personality) influence how we react in these different contexts. Although viewing traits as situationally grounded is common in social sciences, often studies attempting to link brain activity to human traits examine brain-trait associations in a single task, or, under passive conditions like wakeful rest. These studies, often referred to as brain wide association studies (BWAS) have recently become the subject of controversy because results are often unreliable even with large sample sizes. Although there are important statistical reasons why BWAS yield inconsistent results, we hypothesised that the situation in which brain activity is measured will impact the power in detecting a reliable link to specific traits. We performed a state-space analysis where tasks from the Human Connectome Project (HCP) were organized into a low-dimensional space based on how they activated different large-scale neural systems. We examined how individuals' observed brain activity across these different contexts related to their personality. We found that for multiple personality traits, stronger associations with brain activity emerge in some tasks than others. These data highlight the importance of context-bound views for understanding how brain activity links to trait variation in human behaviour.

Trilobite cephalic shape disparity varied through geological time and was integral to their ecological niche diversity, and so is widely used for taxonomic assignments. To fully appreciate trilobite cephalic evolution, we must understand how this disparity varies and the factors responsible. We explore trilobite cephalic disparity using a dataset of 983 cephalon outlines of c. 520 species, analysing the associations between cephalic morphometry and taxonomic assignment and geological Period. Elliptical Fourier transformation visualised as a Principal Components Analysis suggests significant differences in morphospace occupation and in disparity measures between the groups. Cephalic shape disparity peaks in the Ordovician and Devonian. The Cambrian-Ordovician expansion of morphospace occupation reflects radiations to new niches, with all trilobite orders established by the late Ordovician. In comparison, the Silurian-Devonian expansion seems solely a result of within-niche diversification. Linear Discriminant Analyses cross-validation, average cephalon shapes, and centroid distances demonstrate that, except for Harpida and the Cambrian and Ordovician Periods, order and geological Period cannot be robustly predicted for an unknown trilobite. Further, k-means clustering analyses suggest the total dataset naturally subdivides into only seven groups that do not correspond with taxonomy, though k-means clusters do decrease in number through the Palaeozoic, aligning with findings of decreasing disparity.