Histone deacetylase 6 (HDAC6) is currently being discussed as a promising therapeutic target for the treatment of Alzheimer's disease (AD). Mounting evidence indicates that increased HDAC6 expression may contribute to AD-associated neurodegeneration, although beneficial effects have also been identified. In the present study, we tested the potential of two selective HDAC6 inhibitors, tubastatin A and ACY-1215, to rescue cognitive deficits in a mouse model of AD. We found that both tubastatin A and ACY-1215 alleviated behavioral deficits, altered amyloid-β (Aβ) load, and reduced tau hyperphosphorylation in AD mice without obvious adverse effects. Our data suggested that tubastatin A and ACY-1215 not only promoted tubulin acetylation, but also reduced production and facilitated autophagic clearance of Aβ and hyperphosphorylated tau. Further, the decreased hyperphosphorylated tau and increased tubulin acetylation may account for the improved microtubule stability in AD mice after tubastatin A/ACY-1215 treatment. These preclinical results support the detrimental role of HDAC6 in AD, and offer prospective approaches for using tubastatin A/ACY-1215 as potential therapeutic strategy for AD.

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Bacterial endophytes with the capacity to degrade petroleum hydrocarbons and promote plant growth may facilitate phytoremediation for the removal of petroleum hydrocarbons from contaminated soils. A hydrocarbon-degrading, biosurfactant-producing, and plant-growth-promoting endophytic bacterium, Pseudomonas aeruginosa L10, was isolated from the roots of a reed, Phragmites australis, in the Yellow River Delta, Shandong, China. P. aeruginosa L10 efficiently degraded C10–C26 n-alkanes from diesel oil, as well as common polycyclic aromatic hydrocarbons (PAHs) such as naphthalene, phenanthrene, and pyrene. In addition, P. aeruginosa L10 could produce biosurfactant, which was confirmed by the oil spreading method, and surface tension determination of inocula. Moreover, P. aeruginosa L10 had plant growth-stimulating attributes, including siderophore and indole-3-acetic acid (IAA) release, along with 1-aminocyclopropane-1-carboxylic (ACC) deaminase activity. To explore the mechanisms underlying the phenotypic traits of endophytic P. aeruginosa L10, we sequenced its complete genome. From the genome, we identified genes related to petroleum hydrocarbon degradation, such as putative genes encoding monooxygenase, dioxygenase, alcohol dehydrogenase, and aldehyde dehydrogenase. Genome annotation revealed that P. aeruginosa L10 contained a gene cluster involved in the biosynthesis of rhamnolipids, rhlABRI, which should be responsible for the observed biosurfactant activity. We also identified two clusters of genes involved in the biosynthesis of siderophore (pvcABCD and pchABCDREFG). The genome also harbored tryptophan biosynthetic genes (trpAB, trpDC, trpE, trpF, and trpG) that are responsible for IAA synthesis. Moreover, the genome contained the ACC deaminase gene essential for ACC deaminase activity. This study will facilitate applications of endophytic P. aeruginosa L10 to phytoremediation by advancing the understanding of hydrocarbon degradation, biosurfactant synthesis, and mutualistic interactions between endophytes and host plants.

Oral submucous fibrosis (OSF) is a high-risk precancerous condition of the oral cavity. Areca nut chewing is its key etiologic factor, but the full pathogenesis is still obscure. In this study, microarray analysis was used to characterize the mRNA changes of 14,500 genes in four OSF and four normal buccal mucosa samples to identify novel biomarkers of OSF. Five candidate genes with the most differential changes were chosen for validation. The correlation between clinicopathologic variables of 66 OSF patients and the expression of each gene was assessed by immunohistochemistry. The microarray analysis showed that 661 genes were up-regulated (fold value >2) and 129 genes were downregulated (fold value <0.5) in OSF (q < 0.01). The top three up-regulated genes [Loricrin, Cartilage oligomeric matrix protein (COMP), Cys-X-Cys ligand 9 (CXCL9)] with the largest fold changes and the top two downregulated genes [keratin 19 (KRT19), cytochrome P450 3A5 (CYP 3A5)] with the most significantly differential changes in OSF were chosen as candidate biomarkers. In immunohistochemical results, the expression of Loricrin and COMP showed statistically significant association with histologic grade of OSF (P = 0.03 and 0.006, respectively). COMP was found to be overexpressed frequently in patients with the habit of areca nut chewing for more than 4 years (P = 0.002). CYP 3A5 was revealed an inverse correlation with histologic grade (P = 0.04). This pilot study showed that five novel genes might play important roles in the pathogenesis of OSF and may be clinically useful for early detection of OSF. (Cancer Epidemiol Biomarkers Prev 2008;17(9):2249 -59)

Cutting off the glucose supply by glucose oxidase (GOx) has been regarded as an emerging strategy in cancer starvation therapy. However, the standalone GOx delivery suffered suboptimal potency for tumor elimination and potential risks of damaging vasculatures and normal organs during transportation. To enhance therapeutic efficacy and tumor specificity, a site-specific activated dual-catalytic nanoreactor was herein constructed by embedding GOx and ferrocene in hyaluronic acid (HA)-enveloped dendritic mesoporous silica nanoparticles to promote intratumoral oxidative stress in cancer starvation. In this nanoreactor, the encapsulated GOx served as the primary catalyst that accelerated oxidation of glucose and generation of H 2 O 2 , while the covalently linked ferrocene worked as the secondary catalyst for converting the upstream H 2 O 2 to more toxic hydroxyl radicals ( • OH) via a classic Fenton reaction. The outmost HA shell not only offered a shielding layer for preventing blood glucose from oxidation during nanoreactor transportation, thus minimizing the probable oxidative damage to normal tissues, but also imparted the nanoreactor with targeting ability for facilitating its internalization into CD44-overexpressing tumor cells. After the nanoreactor was endocytosed by target cells, the HA shell underwent hyaluronidase-triggered degradation in lysosomes and switched on the cascade catalytic reaction mediated by GOx and ferrocene. The resulting glucose exhaustion and • OH accumulation would effectively kill cancer cells and suppress tumor growth via combination of starvation and oxidative stress enhancement. Both in vitro and in vivo results indicated the significantly amplified therapeutic effects of this synergistic therapeutic strategy based on the dual-catalytic nanoreactor. Our study provides a new avenue for engineering therapeutic nanoreactors that take effect in a tumor-specific and orchestrated fashion for cancer starvation therapy.

BACKGROUND Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the fourth leading cause of cancer-related death worldwide. Sarcomatoid HCC, which contains poorly differentiated carcinomatous and sarcomatous components, is a rare histological subtype of HCC that differs from conventional HCC. It is highly aggressive and has a poor prognosis. Its clinicopathological characteristics, surgical outcomes and underlying mechanisms of its highly aggressive nature have not been fully elucidated. AIM To examine the clinicopathological characteristics and surgical outcomes of sarcomatoid HCC and explore the histogenesis of sarcomatoid HCC. METHODS In total, 196 patients [41 sarcomatoid HCC and 155 high-grade (Edmondson-Steiner grade III or IV) HCC] who underwent surgical resection between 2007 and 2017 were retrospectively reviewed. The characteristics and surgical outcomes of sarcomatoid HCC were compared with those of patients with high-grade HCC. The histological composition of invasive and metastatic sarcomatoid HCCs was evaluated. RESULTS Sarcomatoid HCC was more frequently diagnosed at an advanced stage with a larger tumor and higher rates of nonspecific symptom, adjacent organ invasion and lymph node metastasis than high-grade HCC (all P < 0.05). Compared with high-grade HCC patients, sarcomatoid HCC patients are less likely to have typical dynamic imaging features of HCC (44.4% vs 72.7%, P = 0.001) and elevated serum alpha-fetoprotein levels (> 20 ng/mL; 36.6% vs 78.7%, P < 0.001). The sarcomatoid group had a significantly shorter median recurrence-free survival (5.6 mo vs 16.4 mo, log-rank P < 0.0001) and overall survival (10.5 mo vs 48.1 mo, log-rank P < 0.0001) than the high-grade group. After controlling for confounding factors, the sarcomatoid subtype was identified as an independent predictor of poor prognosis. Pathological analyses indicated that invasive and metastatic lesions were mainly composed of carcinomatous components. CONCLUSION Sarcomatoid HCC was associated with a more advanced stage, atypical dynamic imaging, lower serum alpha-fetoprotein levels and a worse prognosis. The highly aggressive nature of sarcomatoid HCC is perhaps mediated by carcinomatous components.

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.

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