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Bojesen Kidd posted an update 6 months, 3 weeks ago
The newly identified PAX3 gene mutation can expand the understanding of WS1.Vincetoxicum versicolor (Bunge) Decne is the original plant species of the Chinese herbal medicine Cynanchi Atrati Radix et Rhizoma. The lack of information on the transcriptome and chloroplast genome of V. versicolor hinders its evolutionary and taxonomic studies. Here, the V. versicolor transcriptome and chloroplast genome were assembled and functionally annotated. In addition, the comparative chloroplast genome analysis was conducted between the genera Vincetoxicum and Cynanchum. A total of 49,801 transcripts were generated, and 20,943 unigenes were obtained from V. versicolor. One thousand thirty-two unigenes from V. versicolor were classified into 73 functional transcription factor families. The transcription factors bHLH and AP2/ERF were the most significantly abundant, indicating that they should be analyzed carefully in the V. versicolor ecological adaptation studies. The chloroplast genomes of Vincetoxicum and Cynanchum exhibited a typical quadripartite structure with highly conserved gene order and gene content. They shared an analogous codon bias pattern in which the codons of protein-coding genes had a preference for A/U endings. The natural selection pressure predominantly influenced the chloroplast genes. A total of 35 RNA editing sites were detected in the V. versicolor chloroplast genome by RNA sequencing (RNA-Seq) data, and one of them restored the start codon in the chloroplast ndhD of V. versicolor. Phylogenetic trees constructed with protein-coding genes supported the view that Vincetoxicum and Cynanchum were two distinct genera.Environmental surveillance is a critical tool for combatting public health threats represented by the global COVID-19 pandemic and the continuous increase of antibiotic resistance in pathogens. With its power to detect entire microbial communities, metagenomics-based methods stand out in addressing the need. However, several hurdles remain to be overcome in order to generate actionable interpretations from metagenomic sequencing data for infection prevention. Conceptually and technically, we focus on viability assessment, taxonomic resolution, and quantitative metagenomics, and discuss their current advancements, necessary precautions and directions to further development. We highlight the importance of building solid conceptual frameworks and identifying rational limits to facilitate the application of techniques. We also propose the usage of internal standards as a promising approach to overcome analytical bottlenecks introduced by low biomass samples and the inherent lack of quantitation in metagenomics. Taken together, we hope this perspective will contribute to bringing accurate and consistent metagenomics-based environmental surveillance to the ground.Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants.Chinese cabbage is one of the most important and widely consumed vegetables in China. The developmental transition from the vegetative to reproductive phase is a crucial process in the life cycle of flowering plants. In spring-sown Chinese cabbage, late bolting is desirable over early bolting. In this study, we analyzed double haploid (DH) lines of late bolting (“Y410-1” and “SY2004”) heading Chinese cabbage (Brassica rapa var. pekinensis) and early-bolting Chinese cabbage (“CX14-1”) (B. rapa ssp. chinensis var. parachinensis) by comparative transcriptome profiling using the Illumina RNA-seq platform. We assembled 721.49 million clean high-quality paired-end reads into 47,363 transcripts and 47,363 genes, including 3,144 novel unigenes. There were 12,932, 4,732, and 4,732 differentially expressed genes (DEGs) in pairwise comparisons of Y410-1 vs. CX14-1, SY2004 vs. MG149 ic50 CX14-1, and Y410-1 vs. SY2004, respectively. The RNA-seq results were confirmed by reverse transcription quantitative real-time PCR (RT-qPCR). A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs revealed significant enrichment for plant hormone and signal transduction as well as starch and sucrose metabolism pathways. Among DEGs related to plant hormone and signal transduction, six unigenes encoding the indole-3-acetic acid-induced protein ARG7 (BraA02g009130), auxin-responsive protein SAUR41 (BraA09g058230), serine/threonine-protein kinase BSK11 (BraA07g032960), auxin-induced protein 15A (BraA10g019860), and abscisic acid receptor PYR1 (BraA08g012630 and BraA01g009450), were upregulated in both late bolting Chinese cabbage lines (Y410-1 and SY2004) and were identified as putative candidates for the trait. These results improve our understanding of the molecular mechanisms underlying flowering in Chinese cabbage and provide a foundation for studies of this key trait in related species.In recent years, a substantial number of tissue microbiome studies have been published, mainly due to the recent improvements in the minimization of microbial contamination during whole transcriptome analysis. Another reason for this trend is due to the capability of next-generation sequencing (NGS) to detect microbiome composition even in low biomass samples. Several recent studies demonstrate a significant role for the tissue microbiome in the development and progression of cancer and other diseases. For example, the increase of the abundance of Proteobacteria in tumor tissues of the breast has been revealed by gene expression analysis. The link between human papillomavirus infection and cervical cancer has been known for some time, but the relationship between the microbiome and breast cancer (BC) is more novel. There are also recent attempts to investigate the possible link between the brain microbiome and the cognitive dysfunction caused by neurological diseases. Such studies pointing to the role of the brain microbiome in Huntington’s disease (HD) and Alzheimer’s disease (AD) suggest that microbial colonization is a risk factor.
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