The α-enolase (ENO1) plays pivotal roles in several types of cancer, but its clinical significance, functional role, and possible mechanism in colorectal cancer (CRC) have remained unclear. Expression level of ENO1 in CRC tissues was examined by qRT-PCR, Western blot, and immunohistochemistry. The effects of ENO1 on cell growth were investigated by MTT, colony formation, flow cytometry assays, and in vivo tumorigenic capacity analysis. The impacts of ENO1 on cell migration and invasion were also explored by scratch-healing, Transwell or Matrigel chamber assays, and in vivo metastatic capacity analysis. Our results showed that the expression level of ENO1 was significantly elevated in CRC tissues. High expression level of ENO1 was associated with disease progression in CRC patients. Overexpression of ENO1 in HCT116 cell line promoted cell proliferation, migration, and invasion in vitro as well as tumorigenesis and metastasis in vivo. In other hand, ablation of ENO1 in HCT116 cells led to totally reverse effects. Mechanistically, we revealed ENO1 could regulate AMPK/mTOR signaling pathway. AMPK pathway activation or mTOR pathway suppression blocked these ENO1 induced alterations. Together, our results demonstrated that ENO1 is a potent promoter of CRC genesis and metastasis at least in part though regulating AMPK/mTOR pathway. These findings also suggested that ENO1 may be a promising therapeutic target in CRC patients.

High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.

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Ubiquitin specific protease 35 (USP35) is a member of deubiquitylases (DUBs). It remains largely unknown about the biological role and the regulation mechanism of USP35. Here, we first identified miR let-7a as a positive regulator of USP35 expression and showed that USP35 expression positively correlates with miR let-7a expression in different cancer cell lines and tissues. Then, we showed that USP35 expression was decreased dramatically in the tumor tissues compared with the adjacent non-cancerous tissues. USP35 overexpression inhibited cell proliferation in vitro and inhibited xenograft tumor growth in vivo. Furthermore, we revealed that USP35 acts as a functional DUB and stabilizes TNFAIP3 interacting protein 2 (ABIN-2) by promoting its deubiquitination. Functionally, both ABIN-2 and USP35 could inhibit TNFα-induced NF-κB activation and overexpression of ABIN-2 alleviated USP35-loss induced activation of NF-κB. Collectively, our data indicated that miR let-7a-regulated USP35 can inhibit NF-κB activation by deubiquitination and stabilization of ABIN-2 protein and eventually inhibit cell proliferation. Overall, our study provides a novel rationale of targeting miR let-7a-USP35-ABIN-2 pathway for the therapy of cancer patients.

Gut microbiota dysbiosis induced by antibiotics is strongly connected with health concerns. Studying the mechanisms underlying antibiotic-induced gut microbiota dysbiosis could help to identify effective drugs and prevent many serious diseases. In this study, in rats with antibiotic-induced gut microbiota dysbiosis treated with total alkaloids of Corydalis saxicola Bunting (TACS), urinary and fecal biochemical changes and cecum microbial diversity were investigated using 16S rRNA gene sequencing analysis and untargeted metabolomics. The microbial diversity results showed that 10 genera were disturbed by the antibiotic treatment, and two of them were obviously restored by TACS. The untargeted metabolomics analysis identified 34 potential biomarkers in urine and feces that may be the metabolites that are most related to the mechanisms underlying antibiotic-induced gut microbiota dysbiosis and the therapeutic effects of TACS treatment. The biomarkers were involved in six metabolic pathways, comprising pathways related to branched-chain amino acid (BCAA), bile acid, arginine and proline, purine, aromatic amino acid, and amino sugar and nucleotide sugar metabolism. Notably, there was a strong correlation between these metabolic pathways and two gut microbiota genera ( g__Blautia and g__Intestinibacter ). The correlation analysis suggested that TACS might synergistically affect four of these metabolic pathways (BCAA, bile acid, arginine and proline, and purine metabolism), thereby modulating gut microbiota dysbiosis. Furthermore, we performed a molecular docking analysis involving simulating high-precision docking and using molecular pathway maps to illuminate the way that ligands (the five main alkaloid components of TACS) act on a complex molecular network, using CYP27A1 (a key enzyme in the bile acid synthesis pathway) as the target protein. This study provides a comprehensive overview of the intervening effects of TACS on the host metabolic phenotype and gut microbiome in rats with gut microbiota dysbiosis, and it presents new insights for the discovery of effective drugs and the best therapeutic approaches.