Two New Sesquiterpenoids and a New Shikimic Acid Metabolite from Mangrove Sediment-Derived Fungus Roussoella sp. SCSIO 41427.

Two new sesquiterpenoid derivatives, elgonenes M (1) and N (2), and a new shikimic acid metabolite, methyl 5-O-acetyl-5-epi-shikimate (3), were isolated from the mangrove sediment-derived fungus Roussoella sp. SCSIO 41427 together with fourteen known compounds (4-17). The planar structures were elucidated through nuclear magnetic resonance (NMR) and mass spectroscopic (MS) analyses. The relative configurations of 1-3 were ascertained by NOESY experiments, while their absolute configurations were determined by electronic circular dichroism (ECD) calculation. Elgonene M (1) exhibited inhibition of interleukin-1ß (IL-1ß) mRNA, a pro-inflammatory cytokine, at a concentration of 5 µM, with an inhibitory ratio of 31.14%. On the other hand, elgonene N (2) demonstrated inhibition at a concentration of 20 µM, with inhibitory ratios of 27.57%.

Bioactive Polyketides and Benzene Derivatives from Two Mangrove Sediment-Derived Fungi in the Beibu Gulf.

To discover bioactive natural products from mangrove sediment-derived microbes, a chemical investigation of the two Beibu Gulf-derived fungi strains, Talaromyces sp. SCSIO 41050 and Penicillium sp. SCSIO 41411, led to the isolation of 23 natural products. Five of them were identified as new ones, including two polyketide derivatives with unusual acid anhydride moieties named cordyanhydride A ethyl ester (1) and maleicanhydridane (4), and three hydroxyphenylacetic acid derivatives named stachylines H-J (10-12). Their structures were determined by detailed nuclear magnetic resonance (NMR) and mass spectroscopic (MS) analyses, while the absolute configurations were established by theoretical electronic circular dichroism (ECD) calculation. A variety of bioactive screens revealed three polyketide derivatives (1-3) with obvious antifungal activities, and 4 displayed moderate cytotoxicity against cell lines A549 and WPMY-1. Compounds 1 and 6 at 10 µM exhibited obvious inhibition against phosphodiesterase 4 (PDE4) with inhibitory ratios of 49.7% and 39.6%, respectively, while 5, 10, and 11 showed the potential of inhibiting acetylcholinesterase (AChE) by an enzyme activity test, as well as in silico docking analysis.

Marine-Fungi-Derived Gliotoxin Promotes Autophagy to Suppress Mycobacteria tuberculosis Infection in Macrophage.

The Mycobacterium tuberculosis (MTB) infection causes tuberculosis (TB) and has been a long-standing public-health threat. It is urgent that we discover novel antitubercular agents to manage the increased incidence of multidrug-resistant (MDR) or extensively drug-resistant (XDR) strains of MTB and tackle the adverse effects of the first- and second-line antitubercular drugs. We previously found that gliotoxin (1), 12, 13-dihydroxy-fumitremorgin C (2), and helvolic acid (3) from the cultures of a deep-sea-derived fungus, Aspergillus sp. SCSIO Ind09F01, showed direct anti-TB effects. As macrophages represent the first line of the host defense system against a mycobacteria infection, here we showed that the gliotoxin exerted potent anti-tuberculosis effects in human THP-1-derived macrophages and mouse-macrophage-leukemia cell line RAW 264.7, using CFU assay and laser confocal scanning microscope analysis. Mechanistically, gliotoxin apparently increased the ratio of LC3-II/LC3-I and Atg5 expression, but did not influence macrophage polarization, IL-1ß, TNF-a, IL-10 production upon MTB infection, or ROS generation. Further study revealed that 3-MA could suppress gliotoxin-promoted autophagy and restore gliotoxin-inhibited MTB infection, indicating that gliotoxin-inhibited MTB infection can be treated through autophagy in macrophages. Therefore, we propose that marine fungi-derived gliotoxin holds the promise for the development of novel drugs for TB therapy.

Enhancing deep neural network training efficiency and performance through linear prediction.

Deep neural networks have achieved remarkable success in various fields. However, training an effective deep neural network still poses challenges. This paper aims to propose a method to optimize the training effectiveness of deep neural networks, with the goal of improving their performance. Firstly, based on the observation that parameters (weights and bias) of deep neural network change in certain rules during training process, the potential of parameters prediction for improving training efficiency is discovered. Secondly, the potential of parameters prediction to improve the performance of deep neural network by noise injection introduced by prediction errors is revealed. And then, considering the limitations comprehensively, a deep neural network Parameters Linear Prediction method is exploit. Finally, performance and hyperparameter sensitivity validations are carried out on some representative backbones. Experimental results show that by employing proposed Parameters Linear Prediction method, as opposed to SGD, has led to an approximate 1% increase in accuracy for optimal model, along with a reduction of about 0.01 in top-1/top-5 error. Moreover, it also exhibits stable performance under various hyperparameter settings, shown the effectiveness of the proposed method and validated its capacity in enhancing network's training efficiency and performance.