Probiotic lactic acid bacteria alleviate pediatric IBD and remodel gut microbiota by modulating macrophage polarization and suppressing epithelial apoptosis.

Introduction: The incidence of pediatric inflammatory bowel disease (PIBD) continues to rise. It was reported that the probiotic lactic acid bacteria Pediococcus pentosaceus (P. pentosaceus) can interfere with intestinal immunity, but it is still unknown whether it can alleviate PIBD and the concrete mechanism of immune regulation is unclear. Methods: For this study, 3-week-old juvenile mice were selected for modeling the development of PIBD. The mice treated with 2% DSS were randomly divided into two groups, which were given P. pentosaceus CECT8330 and equal amounts of solvent, respectively. The feces and intestinal tissue were collected for the mechanism exploration in vivo. THP-1 and NCM460 cells were used to investigate the effects of P. pentosaceus CECT8330 on macrophage polarization, epithelial cell apoptosis, and their crosstalk in vitro. Results: P. pentosaceus CECT8330 obviously alleviated colitis symptoms of juvenile mice, including weight loss, colon length shortening, spleen swelling, and intestinal barrier function. Mechanistically, P. pentosaceus CECT8330 could inhibit intestinal epithelial apoptosis by suppressing the NF-κB signaling pathway. Meanwhile, it reprogramed macrophages from a pro-inflammatory M1 phenotype to an anti-inflammatory M2 phenotype, leading to a decreased secretion of IL-1ß which contributes to the reduction in ROS production and epithelial apoptosis. Additionally, the 16S rRNA sequence analysis revealed that P. pentosaceus CECT8330 could recover the balance of gut microbiota, and a significantly increased content of Akkermansia muciniphila was particularly observed. Conclusion: P. pentosaceus CECT8330 shifts macrophage polarization toward an anti-inflammatory M2 phenotype. The decreased production of IL-1ß leads to a reduction in ROS, NF-κB activation, and apoptosis in the intestinal epithelium, all of which help to repair the intestinal barrier and adjust gut microbiota in juvenile colitis mice.

Identification of FCN1 as a novel macrophage infiltration-associated biomarker for diagnosis of pediatric inflammatory bowel diseases.

BACKGROUND: The incidence of pediatric inflammatory bowel disease (PIBD) has been steadily increasing globally. Delayed diagnosis of PIBD increases the risk of complications and contributes to growth retardation. To improve long-term outcomes, there is a pressing need to identify novel markers for early diagnosis of PIBD. METHODS: The candidate biomarkers for PIBD were identified from the GSE117993 dataset by two machine learning algorithms, namely LASSO and mSVM-RFE, and externally validated in the GSE126124 dataset and our PIBD cohort. The role of ficolin-1 (FCN1) in PIBD and its association with macrophage infiltration was investigated using the CIBERSORT method and enrichment analysis of the single-cell dataset GSE121380, and further validated using immunoblotting, qRT-PCR, and immunostaining in colon biopsies from PIBD patients, a juvenile murine DSS-induced colitis model, and THP-1-derived macrophages. RESULTS: FCN1 showed great diagnostic performance for PIBD in an independent clinical cohort with the AUC of 0.986. FCN1 expression was upregulated in both colorectal biopsies and blood samples from PIBD patients. Functionally, FCN1 was associated with immune-related processes in the colonic mucosa of PIBD patients, and correlated with increased proinflammatory M1 macrophage infiltration. Furthermore, single-cell transcriptome analysis and immunostaining revealed that FCN1 was almost exclusively expressed in macrophages infiltrating the colonic mucosa of PIBD patients, and these FCN1+ macrophages were related to hyper-inflammation. Notably, proinflammatory M1 macrophages derived from THP-1 expressed high levels of FCN1 and IL-1ß, and FCN1 overexpression in THP-1-derived macrophages strongly promoted LPS-induced activation of the proinflammatory cytokine IL-1ß via the NLRP3-caspase-1 axis. CONCLUSIONS: FCN1 is a novel and promising diagnostic biomarker for PIBD. FCN1+ macrophages enriched in the colonic mucosa of PIBD exhibit proinflammatory phenotypes, and FCN1 promotes IL-1ß maturation in macrophages via the NLRP3-caspase-1 axis.

MIIP functions as a novel ligand for ITGB3 to inhibit angiogenesis and tumorigenesis of triple-negative breast cancer.

Migration and invasion inhibitory protein (MIIP) has been identified as a tumor suppressor in various cancer types. Although MIIP is reported to exert tumor suppressive functions by repressing proliferation and metastasis of cancer cells, the detailed mechanism is poorly understood. In the present study, we found MIIP is a favorable indicator of prognosis in triple-negative breast cancer. MIIP could inhibit tumor angiogenesis, proliferation, and metastasis of triple-negative breast cancer cells in vivo and in vitro. Mechanistically, MIIP directly interacted with ITGB3 and suppressed its downstream signaling. As a result, ß-catenin was reduced due to elevated ubiquitin-mediated degradation, leading to downregulated VEGFA production and epithelial mesenchymal transition. More importantly, we found RGD motif is essential for MIIP binding with ITGB3 and executing efficient tumor-suppressing effect. Our findings unravel a novel mechanism by which MIIP suppresses tumorigenesis in triple-negative breast cancer, and MIIP is thus a promising molecular biomarker or therapeutic target for the disease.

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics.

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

ERp29 forms a feedback regulation loop with microRNA-135a-5p and promotes progression of colorectal cancer.

Expression of endoplasmic reticulum (ER) stress-associated genes is often dysregulated in cancer progression. ER protein 29 (ERp29) is abnormally expressed in many neoplasms and plays an important role in tumorigenesis. Here, we showed ERp29 is a novel target for microRNA-135a-5p (miR-135a-5p) to inhibit the progression of colorectal cancer (CRC); correspondingly, ERp29 acts as an oncoprotein in CRC by promoting proliferation and metastasis of CRC cells, and suppressing apoptosis of the cells. More importantly, we found that miR-135a-5p expression is reversely upregulated by ERp29 through suppressing IL-1ß-elicited methylation of miR-135a-5p promoter region, a process for enterocyte to maintain a balance between miR-135a-5p and ERp29 but dysregulated in CRC. Our study reveals a novel feedback regulation loop between miR-135a-5p and ERp29 that is critical for maintaining appropriate level of each of them, but partially imbalanced in CRC, resulting in abnormal expression of miR-135a-5p and ERp29, which further accelerates CRC progression. We provide supporting evidence for ERp29 and miR-135a-5p as potential biomarkers for diagnosis and treatment of CRC.

Information Losses in Neural Classifiers From Sampling.

This article considers the subject of information losses arising from the finite data sets used in the training of neural classifiers. It proves a relationship between such losses as the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then bounds this expected total variation as a function of the size of randomly sampled data sets in a fairly general setting, and without bringing in any additional dependence on model complexity. It ultimately obtains bounds on information losses that are less sensitive to input compression and in general much smaller than existing bounds. This article then uses these bounds to explain some recent experimental findings of information compression in neural networks that cannot be explained by previous work. Finally, this article shows that not only are these bounds much smaller than existing ones, but they also correspond well with experiments.

Pnserpin: A Novel Serine Protease Inhibitor from Extremophile Pyrobaculum neutrophilum.

Serine protease inhibitors (serpins) are native inhibitors of serine proteases, constituting a large protein family with members spread over eukaryotes and prokaryotes. However, only very few prokaryotic serpins, especially from extremophiles, have been characterized to date. In this study, Pnserpin, a putative serine protease inhibitor from the thermophile Pyrobaculum neutrophilum, was overexpressed in Escherichia coli for purification and characterization. It irreversibly inhibits chymotrypsin-, trypsin-, elastase-, and subtilisin-like proteases in a temperature range from 20 to 100 °C in a concentration-dependent manner. The stoichiometry of inhibition (SI) of Pnserpin for proteases decreases as the temperature increases, indicating that the inhibitory activity of Pnserpin increases with the temperature. SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) showed that Pnserpin inhibits proteases by forming a SDS-resistant covalent complex. Homology modeling and molecular dynamic simulations predicted that Pnserpin can form a stable common serpin fold. Results of the present work will help in understanding the structural and functional characteristics of thermophilic serpin and will broaden the current knowledge about serpins from extremophiles.

Isolation of a novel beta-cypermethrin degrading strain Bacillus subtilis BSF01 and its biodegradation pathway.

Continuous use of the pyrethroid insecticide beta-cypermethrin (beta-cp) has resulted in serious environmental contamination problems. We report here that a novel bacterial strain BSF01, which was isolated from activated sludge and identified as Bacillus subtilis (collection number: CCTCC AB 2014103), showed high efficiency in degrading beta-cp. Strain BSF01 was able to utilize beta-cp as the sole carbon source for growth and degraded 89.4 % of 50 mg L(-1) beta-cp within 7 days. The optimal conditions for beta-cp degradation were determined to be 34.5 °C, pH 6.7, and inocula amount 0.11 g dry wt L(-1) using response surface methodology. The kinetic parameters q max, K s, and K i were established to be 2.19 day(-1), 76.37 mg L(-1), and 54.14 mg L(-1), respectively. The critical inhibitor concentration was determined to be 64.30 mg L(-1). Seven metabolites were identified by gas chromatography-mass spectrometry. Furthermore, a novel biodegradation pathway for beta-cp was proposed on the basis of analysis of the metabolites. This strain was also capable of degrading a wide range of pyrethroid insecticides including cypermethrin, deltamethrin, cyhalothrin, and beta-cyfluthrin, which similar to beta-cp are hazardous chemicals. Taken together, our results depict the biodegradation pathway of beta-cp and highlight the promising potentials of strain BSF01 in bioremediation of pyrethroid-contaminated environments.