Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.

Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.

MiR-1303 Regulates Mycobacteria Induced Autophagy by Targeting Atg2B.

MicroRNAs are emerging post-transcriptional regulators of gene expressions in both innate immunity and adaptive immunity. In mycobacteria infection, autophagy plays an important role in innate defense mechanism and is tightly regulated by the autophagy-related proteins. Here, we show that Atg2B is involved in the regulation of mycobacteria-induced autophagy. MiR-1303, which function is not defined yet, is found to negatively regulate mycobacteria-induced Atg2B protein production, ultimately down-regulate mycobacteria-induced autophagy. MiR-1303 production is shown to be upregulated during BCG infection and its production is regulated by PI3K and NFκB. It is also demonstrated that miR-1303 targets putative target sites on Atg2B and possibly represses its translation.

A role for interleukin-17A in modulating intracellular survival of Mycobacterium bovis bacillus Calmette-Guérin in murine macrophages.

Interleukin 17A IL-17A is a crucial immunomodulator in various chronic immunological diseases including rheumatoid arthritis and inflammatory bowel disease. The cytokine has also been demonstrated to control the pathogenesis of the Mycobacterium tuberculosis by dysregulating production of cytokines and chemokines and promoting granuloma formation. Whether IL-17A regulates innate defence mechanisms of macrophages in response to mycobacterial infection remains to be elucidated. In the current report, we investigated the effects of IL-17A on modulating the intracellular survival of Mycobacterium bovis bacillus Calmette-Guérin (BCG) in RAW264.7 murine macrophages. We observed that IL-17A pre-treatment for 24 hr was able to synergistically enhance BCG-induced nitric oxide (NO) production and inducible nitric oxide synthase expression in dose- and time-dependent manners. We further delineated the mechanisms involved in this synergistic reaction. IL-17A was found to specifically enhanced BCG-induced phosphorylation of Jun N-terminal kinase (JNK), but not of extracellular signal-regulated kinase 1/2 and p38 mitogen-activated protein kinase. By using a specific JNK inhibitor (SP600125), we found that the production of NO in BCG-infected macrophages was significantly suppressed. Taken together, we confirmed the involvement of the JNK pathway in IL-17A-enhanced NO production in BCG-infected macrophages. We further demonstrated that IL-17A significantly enhanced the clearance of intracellular BCG by macrophages through an NO-dependent killing mechanism. In conclusion, our study revealed an anti-mycobacterial role of IL-17A through priming the macrophages to produce NO in response to mycobacterial infection.

Identification of liver proteins and their roles associated with carbon tetrachloride-induced hepatotoxicity.

Carbon tetrachloride (CCl(4)) is a common hepatotoxin used in experimental models to elicit liver injury. To identify the proteins involved in CCl(4)-induced hepatotoxicity, two-dimensional gel electrophoresis was employed followed by mass spectrometry - mass spectrometry (MS/MS) to study the differentially expressed proteins during CCl(4) exposure in the Fischer 344 rat liver proteome for 5 weeks. Ten spots with notable changes between the Control and CCl(4) groups were successfully identified. Among them, four proteins with significant up-regulation, namely calcium-binding protein 1, protein disulfide isomerase, mitochondrial aldehyde dehydrogenase precursor, and, glutathione-S-transferase mu1 and six proteins with significant down-regulation, namely catechol-O-methyltransferase, hemoglobin-alpha-2-chain, hemopexin precursor, methionine sulfoxide reductase A, catalase and carbonic anhydrase 3, were identified. The data indicates that CCl(4) causes hepatotoxicity by depleting oxygen radical scavengers in the hepatocytes. In this rat model, we profiled hepatic proteome alterations in response to CCl(4) intoxication. The findings should facilitate understanding of the mechanism of CCl(4)-induced liver injury.