Dynamic interplay of two molecular switches enabled by the MEK1/2–ERK1/2 and IL-6–STAT3 signaling axes controls epithelial cell migration in response to growth factors

Cell migration is an essential physiological process, and aberrant migration of epithelial cells underlies many pathological conditions. However, the molecular mechanisms governing cell migration are not fully understood. We report here that growth factor–induced epithelial cell migration is critically dependent on the crosstalk of two molecular switches, namely phosphorylation switch (P-switch) and transcriptional switch (T-switch). P-switch refers to dynamic interactions of deleted in liver cancer 1 (DLC1) and PI3K with tensin-3 (TNS3), phosphatase and tensin homolog (PTEN), C-terminal tension, and vav guanine nucleotide exchange factor 2 (VAV2) that are dictated by mitogen-activated protein kinase kinase 1/2–extracellular signal–regulated protein kinase 1/2–dependent phosphorylation of TNS3, PTEN, and VAV2. Phosphorylation of TNS3 and PTEN on specific Thr residues led to the switch of DLC1–TNS3 and PI3K–PTEN complexes to DLC1–PTEN and PI3K–TNS3 complexes, whereas Ser phosphorylation of VAV2 promotes the transition of the PI3K–TNS3/PTEN complexes to PI3K–VAV2 complex. T-switch denotes an increase in C-terminal tension transcription/ expression regulated by both extracellular signal–regulated protein kinase 1/2 and signal transducer and activator of transcription 3 (STAT3) via interleukin-6–Janus kinase–STAT3 signaling pathway. We have found that, the P-switch is indispensable for both the initiation and continuation of cell migration induced by growth factors, whereas the T-switch is only required to sustain cell migration. The interplay of the two switches facilitated by the interleukin-6–Janus kinase–STAT3 pathway governs a sequence of dynamic protein–protein interactions for sustained cell migration. That a similar mechanism is employed by both normal and tumorigenic epithelial cells to drive their respective migration suggests that the P-switch and T-switch are general regulators of epithelial cell migration and potential therapeutic targets

NUMB regulates the endocytosis and activity of the anaplastic lymphoma kinase in an isoform-specific manner

NUMB is an evolutionarily conserved protein that plays an important role in cell adhesion, migration, polarity, and cell fate determination. It has also been shown to play a role in the pathogenesis of certain cancers, although it remains controversial whether NUMB functions as an oncoprotein or tumor suppressor. Here, we show that NUMB binds to anaplastic lymphoma kinase (ALK), a receptor tyrosine kinase aberrantly activated in several forms of cancer, and this interaction regulates the endocytosis and activity of ALK. Intriguingly, the function of the NUMB-ALK interaction is isoform-dependent. While both p66-NUMB and p72-NUMB isoforms are capable of mediating the endocytosis of ALK, the former directs ALK to the lysosomal degradation pathway, thus decreasing the overall ALK level and the downstream MAP kinase signal. In contrast, the p72-NUMB isoform promotes ALK recycling back to the plasma membrane, thereby maintaining the kinase in its active state. Our work sheds light on the controversial role of different isoforms of NUMB in tumorigenesis and provides mechanistic insight into ALK regulation

High-Level PM2.5/PM10 Exposure Is Associated With Alterations in the Human Pharyngeal Microbiota Composition

Previous studies showed that high concentration of particulate matter (PM) 2.5 and PM10 carried a large number of bacterial and archaeal species, including pathogens and opportunistic pathogens. In this study, pharyngeal swabs from 83 subjects working in an open air farmer’s market were sampled before and after exposure to smog with PM2.5 and PM10 levels up to 200 and 300 μg/m3, respectively. Their microbiota were investigated using high-throughput sequencing targeting the V3–V4 regions of the 16S rRNA gene. The genus level phylotypes was increased from 649 to 767 in the post-smog pharyngeal microbiota, of which 142 were new and detected only in the post-smog microbiota. The 142 new genera were traced to sources such as soil, marine, feces, sewage sludge, freshwater, hot springs, and saline lakes. The abundance of the genera Streptococcus, Haemophilus, Moraxella, and Staphylococcus increased in the post-smog pharyngeal microbiota. All six alpha diversity indices and principal component analysis showed that the taxonomic composition of the post-smog pharyngeal microbiota was significantly different to that of the pre-smog pharyngeal microbiota. Redundancy analysis showed that the influences of PM2.5/PM10 exposure and smoking on the taxonomic composition of the pharyngeal microbiota were statistically significant (p < 0.001). Two days of exposure to high concentrations of PM2.5/PM10 changed the pharyngeal microbiota profiles, which may lead to an increase in respiratory diseases. Wearing masks could reduce the effect of high-level PM2.5/PM10 exposure on the pharyngeal microbiota

Constructing a Measurement Method of Differences in Group Preferences Based on Relative Entropy

In the research and data analysis of the differences involved in group preferences, conventional statistical methods cannot reflect the integrity and preferences of human minds; in particular, it is difficult to exclude humans’ irrational factors. This paper introduces a preference amount model based on relative entropy theory. A related expansion is made based on the characteristics of the questionnaire data, and we also construct the parameters to measure differences in the data distribution of different groups on the whole. In this paper, this parameter is called the center distance, and it effectively reflects the preferences of human minds. Using the survey data of securities market participants as an example, this paper analyzes differences in market participants’ attitudes toward the effectiveness of securities regulation. Based on this method, differences between groups that were overlooked by analysis of variance are found, and certain aspects obscured by general data characteristics are also found

Deep Reinforcement Learning Car-Following Model Considering Longitudinal and Lateral Control

The lateral control of the vehicle is significant for reducing the rollover risk of high-speed cars and improving the stability of the following vehicle. However, the existing car-following (CF) models rarely consider lateral control. Therefore, a CF model with combined longitudinal and lateral control is constructed based on the three degrees of freedom vehicle dynamics model and reinforcement learning method. First, 100 CF segments were selected from the OpenACC database, including 50 straight and 50 curved road trajectories. Afterward, the deep deterministic policy gradient (DDPG) car-following model and multi-agent deep deterministic policy gradient (MADDPG) car-following model were constructed based on the deterministic policy gradient theory. Finally, the models are trained with the extracted trajectory data and verified by comparison with the observed data. The results indicate that the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model are both safer and more comfortable than the human-driven vehicle (HDV) on straight roads and curved roads. Under the premise of safety, the vehicle under the control of the MADDPG model has the highest road traffic flow efficiency. The maximum lateral offset of the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model in straight road conditions is respectively reduced by 80.86% and 71.92%, compared with the HDV, and the maximum lateral offset in the curved road conditions is lessened by 83.67% and 78.95%. The proposed car following model can provide a reference for developing an adaptive cruise control system considering lateral stability