Bacterial infection promotes tumorigenesis of colorectal cancer via regulating CDC42 acetylation.

Increasing evidence highlights the role of bacteria in promoting tumorigenesis. The underlying mechanisms may be diverse and remain poorly understood. Here, we report that Salmonella infection leads to extensive de/acetylation changes in host cell proteins. The acetylation of mammalian cell division cycle 42 (CDC42), a member of the Rho family of GTPases involved in many crucial signaling pathways in cancer cells, is drastically reduced after bacterial infection. CDC42 is deacetylated by SIRT2 and acetylated by p300/CBP. Non-acetylated CDC42 at lysine 153 shows an impaired binding of its downstream effector PAK4 and an attenuated phosphorylation of p38 and JNK, consequently reduces cell apoptosis. The reduction in K153 acetylation also enhances the migration and invasion ability of colon cancer cells. The low level of K153 acetylation in patients with colorectal cancer (CRC) predicts a poor prognosis. Taken together, our findings suggest a new mechanism of bacterial infection-induced promotion of colorectal tumorigenesis by modulation of the CDC42-PAK axis through manipulation of CDC42 acetylation.

[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.

[Prediction of potential distribution area of the spruce forest on the central Tianshan Mountain based on site index.] / 基于生境指数的天山中部云杉林潜在分布区预测.

This paper predicted the potential distribution area of the spruce forest on the central Tianshan Mountain, through determining the site index thresholds, using 3S and spatial modeling technologies to build a site index model with water, heat, terrain and other factors, based on the current site conditions there. The results showed that the potential distribution area simulated by the site index model was not significantly different with the current actual distribution area, but had a significant difference with the non-spruce forest area. Based on the built site index model, there was a large potential distribution area, which occupied 6.4% of the total research area, i.e., 7.01×103 hm2 of forest lower boundary zones, glades, shady and half shady slope areas, and could be used for the future forest development.