Role of Chitosan in the Regulation of the Growth,Antioxidant System and Photosynthetic Characteristics of Maize Seedlings under Cadmium Stress

Russian Journal of Plant Physiology - Cadmium (Cd) pollution was becoming more and more serious; there is an urgent need for an effective solution to inhibit the harm of cadmium stress. Chitosan...     

Effect of coupled reduced irrigation and nitrogen fertilizer on soil mite community composition in a wheat field

Groundwater and nitrogen fertilizer overuse severely threatens crop productions; thus, current ecological agriculture requires low irrigation and nitrogen fertilizer inputs. The effects of combined reduced irrigation and nitrogen fertilizer addition on soil organism (e.g., mite) community and biodiversity remain poorly understood. We analyzed soil mite community composition, wheat grain yield, and soil characteristics in a 10‐year manipulation experiment with two levels of irrigation (reduced and conventional irrigation) and five nitrogen fertilizer levels (0, 70, 140, 210, and 280 kg N/ha). Reduced irrigation (20% reduction, from 280 to 220 mm) and nitrogen fertilizer (25% reduction, from 280 to 210 kg N/ha) addition did not significantly influence soil mite community and wheat yield. The relative abundances of fungivores and predators showed negative quadratic relationships with wheat yield, while that of plant parasites showed a positive relationship. The relationships between soil mite trophic groups and wheat yield revealed that we can evaluate the impacts of reduced irrigation and nitrogen fertilizer addition from the perspective of soil fauna. Soil mite community composition was altered by soil abiotic factors prior to reduced irrigation and nitrogen fertilizer addition. Overall, moderate reductions of irrigation and nitrogen fertilizer may not threaten to soil mite community and diversity or decrease crop production; in contrast, such reductions will benefit mite community development and the sustainable agriculture.     

Development of high‐resolution DNA barcodes for Dioscorea species discrimination and phylogenetic analysis

The genus Dioscorea is widely distributed in tropical and subtropical regions, and is economically important in terms of food supply and pharmaceutical applications. However, DNA barcodes are relatively unsuccessful in discriminating between Dioscorea species, with the highest discrimination rate (23.26%) derived from matK sequences. In this study, we compared genic and intergenic regions of three Dioscorea chloroplast genomes and found that the density of SNPs and indels in intergenic sites was about twice and seven times higher than that of SNPs and indels in the genic regions, respectively. A total of 52 primer pairs covering highly variable regions were designed and seven pairs of primers had 80%–100% PCR success rate. PCR amplicons of 73 Dioscorea individuals and assembled sequences of 47 Dioscorea SRAs were used for estimating intraspecific and interspecific divergence for the seven loci: The rpoB‐trnC locus had the highest interspecific divergence. Automatic barcoding gap discovery (ABGD), Poisson tree processes (PTP), and generalized mixed Yule coalescence (GMYC) analysis were applied for species delimitation based on the seven loci and successfully identified the majority of species, except for species in the Enantiophyllum section. Phylogenetic analysis of 51 Dioscorea individuals (28 species) showed that most individuals belonging to the same species tended to cluster in the same group. Our results suggest that the variable loci derived from comparative analysis of plastid genome sequences could be good DNA barcode candidates for taxonomic analysis and species delimitation.     

DHHC4 and DHHC5 Facilitate Fatty Acid Uptake by Palmitoylating and Targeting CD36 to the Plasma Membrane

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PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq

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Probiotic Therapy (BIO-THREE) Mitigates Intestinal Microbial Imbalance and Intestinal Damage Caused by Oxaliplatin

Probiotics and Antimicrobial Proteins - Gastrointestinal mucositis associated with the use of chemotherapeutic drugs can seriously affect the quality of life of patients. In this study, a probiotic...     

Accurate cancer classification using expressions of very few genes

We aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. The significance of finding the minimum gene subsets is three-fold: 1) it greatly reduces the computational burden and "noise" arising from irrelevant genes. In the examples studied in this paper, finding the minimum gene subsets even allows for extraction of simple diagnostic rules which lead to accurate diagnosis without the need for any classifiers, 2) it simplifies gene expression tests to include only a very small number of genes rather than thousands of genes, which can bring down the cost for cancer testing significantly, 3) it calls for further investigation into the possible biological relationship between these small numbers of genes and cancer development and treatment. Our simple yet very effective method involves two steps. In the first step, we choose some important genes using a feature importance ranking scheme. In the second step, we test the classification capability of all simple combinations of those important genes by using a good classifier. For three "small" and "simple" data sets with two, three, and four cancer (sub)types, our approach obtained very high accuracy with only two or three genes. For a "large" and "complex" data set with 14 cancer types, we divided the whole problem into a group of binary classification problems and applied the 2-step approach to each of these binary classification problems. Through this "divide-and-conquer" approach, we obtained accuracy comparable to previously reported results but with only 28 genes rather than 16,063 genes. In general, our method can significantly reduce the number of genes required for highly reliable diagnosis     

Downregulation of lysyl oxidase-like 4 LOXL4 by miR-135a-5p promotes lung cancer progression in vitro and in vivo

Aberrant microRNAs are widely identified in multiple cancers, including lung cancer. miR-135a-5p can function as a significant tumor regulator in diverse cancers via impacting multiple genes in oncogenic pathways. Nevertheless, the biological role of miR-135a-5p in lung cancer is poorly known. Here, we investigated its function in lung cancer. As exhibited, miR-135a-5p was elevated in lung cancer cells in contrast to BEAS-2B cells. Then, we inhibited miR-135a-5p expression by transfecting LV-anti-miR-135a-5p into lung cancer cells. As displayed, miR-135a-5p was obviously reduced in A549 and H1299 cells. Knockdown of miR-135a-5p repressed lung cancer cell growth and cell proliferation. Meanwhile, cell colony formation capacity was depressed, cell apoptosis was enhanced and cell cycle progression was blocked in G1 phase by inhibition of miR-135a-5p in vitro. Additionally, the migration and invasion of A549 and H1299 cells was strongly depressed by LV-anti-miR-135a-5p. For another, by using informatics analysis, lysyl oxidase-like 4 (LOXL4) was speculated as the downstream target of miR-135a-5p. We validated their direct correlation and moreover, overexpression of miR-135a-5p restrained LOXL4 levels in lung cancer cells. Subsequently, we proved that miR-135a-5p promoted lung cancer development via targeting LOXL4 by carrying out the in vivo assays. Taken these together, our study revealed miR-135a-5p might be indicated as a perspective for lung cancer via targeting LOXL4.     

Purple sweet potato color attenuated NLRP3 inflammasome by inducing autophagy to delay endothelial senescence

Autophagy is a vital negative factor regulating cellular senescence. Purple sweet potato color (PSPC), one type of flavonoid, has been demonstrated to suppress endothelial senescence and restore endothelial function in diabetic mice by inhibiting the nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing protein 3 (NLRP3) inflammasome. However, the roles of autophagy in the inflammatory response during endothelial senescence are unknown. Here, we found that PSPC augmented autophagy to restrict high-glucose-induced premature endothelial senescence. In addition, PSPC administration impaired endothelium aging in diabetic mice by increasing autophagy. Inhibition of autophagy accelerated endothelial senescence, while enhancement of autophagy delayed senescence. Moreover, deactivation of the NLRP3 inflammasome triggered by PSPC was autophagy-dependent. Autophagy receptor microtubule-associated protein 1 light chain 3 and p62 interacted with the inflammasome component NLRP3, suggesting that autophagosomes target the NLRP3 inflammasome and deliver it to the lysosome for degradation. Altogether, PSPC amplified cellular autophagy, subsequently attenuated NLRP3 inflammasome activity and finally delayed endothelial senescence to ameliorate cardiovascular complication. These results suggest a potential therapeutic target in senescence-related cardiovascular diseases.