Glycine metabolomic changes induced by anticancer agents in A549 cells

Amino Acids - Glycine plays a key role in rapidly proliferating cancer cells such as A549 cells. Targeting glycine metabolism is considered as a potential means for cancer treatment. However, the...     

Systematic profiling of staralog response to acquired drug resistant kinase gatekeeper mutations in targeted cancer therapy

Amino Acids - Kinase-targeted therapy has been widely used as a lifesaving strategy for cancer patients. However, many patients treated with targeted cancer drugs are clinically observed to rapidly...     

The Chimonanthus salicifolius genome provides insight into magnoliid evolution and flavonoid biosynthesis

Chimonanthus salicifolius, a member of the Calycanthaceae of magnoliids, is one of the most famous medicinal plants in Eastern China. Here, we report a chromosome‐level genome assembly of C. salicifolius, comprising 820.1 Mb of genomic sequence with a contig N50 of 2.3 Mb and containing 36 651 annotated protein‐coding genes. Phylogenetic analyses revealed that magnoliids were sister to the eudicots. Two rounds of ancient whole‐genome duplication were inferred in the C. salicifolious genome. One is shared by Calycanthaceae after its divergence with Lauraceae, and the other is in the ancestry of Magnoliales and Laurales. Notably, long genes with > 20 kb in length were much more prevalent in the magnoliid genomes compared with other angiosperms, which could be caused by the length expansion of introns inserted by transposon elements. Homologous genes within the flavonoid pathway for C. salicifolius were identified, and correlation of the gene expression and the contents of flavonoid metabolites revealed potential critical genes involved in flavonoids biosynthesis. This study not only provides an additional whole‐genome sequence from the magnoliids, but also opens the door to functional genomic research and molecular breeding of C. salicifolius.     

Mass loss and nutrient release during the decomposition of sixteen types of plant litter with contrasting quality under three precipitation regimes

Mass loss and nutrient release during litter decomposition drive biogeochemical cycling in terrestrial ecosystems. However, the relationship between the litter decomposition process and the decomposition stage, precipitation, and litter quality has rarely been addressed, precluding our understanding of how litter decomposition regulates nutrient cycling in various ecosystems and their responses to climate change. In this study, we measured mass loss as well as carbon and nutrient releases during the decomposition of 16 types of leaf litter under three precipitation treatments over 12 months in a common garden experiment (i.e., using standardized soil and climatic conditions). Sixteen types of leaves were divided into three functional groups (evergreen, deciduous, and herbaceous). The objectives were to understand the effects of decomposition stages and precipitation regimes on litter decomposition and to examine the relationship between this effect and chemical properties. The mass loss and release of nitrogen and potassium were significantly higher in the 6‐ to 12‐month stage of decomposition (high temperature and humidity) than in the 0‐ to 6‐month stage. Phosphorus was relatively enriched in evergreen leaves after 6 months of decomposition. The rates of mass loss and nutrient release were significantly greater in herbaceous than in deciduous and evergreen leaves. Increasing precipitation from 400 to 800 mm accelerated mass loss and potassium release but decreased phosphorus release in the 0‐ to 6‐month stage of decomposition. These results highlighted the contribution to and complexity of litter chemical properties in litter decomposition.     

The correlation of microRNA-499 rs3746444 T>C locus with the susceptibility of gastric cancer: from a case–control study to a meta-analysis

The relationship between rs3746444 T>C single-nucleotide polymorphism (SNP) in microRNA (mir)-499 and risk of gastric cancer (GC) has been widely investigated. However, the association was still unconfirmed. Here, we first recruited 490 GC patients and 1476 controls, and conducted a case-control study. And we did not find any association between rs3746444 T>C SNP polymorphism and risk of GC. Subsequently, we conducted a meta-analysis to explore the association of mir-499 rs3746444 polymorphism with GC development. Two authors searched the PubMed and EMBASE databases up to October 15, 2019 independently. Finally, nine literatures involving 12 independent studies were included. In total, 3954 GC cases and 9745 controls were recruited for meta-analysis. The results suggested that allele model, homozygote model and recessive model could increase the risk of overall GC (P = 0.002, 0.009 and 0.013, respectively). When we excluded the studies violated HWE, this association was also found in allele model (P = 0.020) and dominant model (P= 0.044). In subgroup analyses, we identified that rs3746444 SNP in mir-499 increased the risk of GC in Asians and gastric cardiac adenocarcinoma (GCA) subgroups. No significant bias of selection was found (all P>0.1). Test of sensitivity analysis indicated that our findings were stable. Additionally, we found that the power value was 0.891 in the allele model, suggesting the reliability of our findings. In summary, our analysis confirmed the association between rs3746444 and the risk of GC, especially in Asians and in patients with GCA.     

Variation in rare earth element (REE), aluminium (Al) and silicon (Si) accumulation among populations of the hyperaccumulator Dicranopteris linearis in southern China

Plant and Soil - Dicranopteris linearis is a rare earth element (REE), aluminium (Al) and silicon (Si) hyperaccumulator plant which occurs in southern China. To date, there have been no studies on...     

Development and characterization of 33 polymorphic microsatellite markers in Trachurus japonicus by SLAF-seq technology

Trachurus japonicus is an economically important fish in the northwestern Pacific Ocean. However, its resources have declined seriously and there is an urgent need for a wide-range of investigations of the existing genetic resources. This requires a large number of diverse molecular markers with high discriminating power. In this study, we identified 43,264 perfect SSRs in T. japonicus genome using SLAF-seq technology. Of these, we randomly selected 106 SSRs (tri-nucleotide to hexa-nucleotide) to test for polymorphism. Eventually, we successfully developed a total of 33 loci including 8 tri-nucleotide and 25 long repeat motifs (tetra-nucleotide to hexa-nucleotide). The number of alleles (Na) of these loci ranged from 4 to 24 (mean 12.6). The observed heterozygosity (Ho) and expected heterozygosity (He) varied from 0.258 to 0.969 (mean 0.723) and from 0.452 to 0.962 (mean 0.827), respectively. All loci except TJ6-7 were highly informative (PIC > 0.5). These results showed that the shortlisted 33 loci exhibited moderate to relatively high genetic diversity, of which 18 were regarded as highly polymorphic and well-resolved. In summary, these diverse and potential microsatellites detected in our study provide substantial genetic basis for the screening of polymorphic SSR markers of T. japonicus and also provide a powerful tool to perform further studies on the genetic resource assessment and conservation of T. japonicus.     

MAGE-C2/CT10 promotes growth and metastasis through upregulating c-Myc expression in prostate cancer

Molecular and Cellular Biochemistry - Prostate cancer (PC) is the most common reproductive cancer in men and the third leading cause of cancer death among men worldwide. Recently targeted therapy...     

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.