The volatile organic compounds generated from the Maillard reaction between l-ascorbic acid and l-cysteine in hot compressed water.

BACKGROUND: Hot compressed water (HCW), also known as subcritical water (SCW), refers to high-temperature compressed water in a special physical and chemical state. It is an emerging technology for natural product extraction. The volatile organic compounds (VOCs) generated from the Maillard reaction between l-ascorbic acid (ASA) and l-cysteine (Cys) have attracted significant interest in the flavor and fragrance industry. This study aimed to explore the formation mechanism of VOCs from ASA and Cys and examine the effects of reaction parameters such as temperature, time, and pH in HCW. RESULTS: The identified VOCs were predominantly thiophene derivatives, polysulfides, and pyrazine derivatives in HCW. The findings indicated that thiophene derivatives were formed under various pH conditions, with polysulfide formation favored under acidic conditions and pyrazine derivative formation preferred under weak alkaline conditions, specifically at pH 8.0. CONCLUSION: The Maillard reaction between ASA and Cys mainly produced thiophene derivatives, polysulfides, and pyrazine derivatives in HCW. The generation mechanism was significantly dependent on the surrounding pH conditions. © 2024 Society of Chemical Industry.

The 2019 novel coronavirus resource.

An ongoing outbreak of a novel coronavirus infection in Wuhan, China since December 2019 has led to 31,516 infected persons and 638 deaths across 25 countries (till 16:00 on February 7, 2020). The virus causing this pneumonia was then named as the 2019 novel coronavirus (2019-nCoV) by the World Health Organization. To promote the data sharing and make all relevant information of 2019-nCoV publicly available, we construct the 2019 Novel Coronavirus Resource (2019nCoVR, https://bigd.big.ac.cn/ncov). 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the Global Initiative on Sharing All Influenza Data, National Center for Biotechnology Information, China National GeneBank, National Microbiology Data Center and China National Center for Bioinformation (CNCB)/National Genomics Data Center (NGDC). It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains. Moreover, by linking seamlessly with related databases in CNCB/NGDC, 2019nCoVR offers virus data submission and sharing services for raw sequence reads and assembled sequences. In this report, we provide comprehensive descriptions on data deposition, management, release and utility in 2019nCoVR, laying important foundations in aid of studies on virus classification and origin, genome variation and evolution, fast detection, drug development and pneumonia precision prevention and therapy.

[Prospects for national biological big data centers].

In the era of big data, scientific big data have become the new driving force for both science and technology innovation and social and economic development. China is a powerhouse in generating vast quantities of biological data, which are an essential strategic resource for population health and national security. The current situation of data loss due to the isolated data storage and the lack of systematic data monitoring and management, and the heavy dependency on international biological data centers urgently calls for China's own life big data storage and management system at the national level. Taking NCBI as an example, this article introduces the development history and present situation of the international biological big data centers. In addition, the importance, urgency, current historical opportunity and prospect of establishing a national biological big data center in China are also expounded in detail. In order to promote the development of the national center and improve China's international competitiveness and influence in life science research, the BIG Data Center at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, has taken many efforts on big data deposition, integration and translation and achieved initial progress.

Single-cell transcriptome analysis reveals widespread monoallelic gene expression in individual rice mesophyll cells.

Monoallelic gene expression refers to the phenomenon that all transcripts of a gene in a cell are expressed from only one of the two alleles in a diploid organism. Although monoallelic gene expression has been occasionally reported with bulk transcriptome analysis in plants, how prevalent it is in individual plant cells remains unknown. Here, we developed a single-cell RNA-seq protocol in rice and investigated allelic expression patterns in mesophyll cells of indica (93-11) and japonica (Nipponbare) inbred lines, as well as their F1 reciprocal hybrids. We observed pervasive monoallelic gene expression in individual mesophyll cells, which could be largely explained by stochastic and independent transcription of two alleles. By contrast, two mechanisms that were proposed previously based on bulk transcriptome analyses, parent-of-origin effects and allelic repression, were not well supported by our data. Furthermore, monoallelically expressed genes exhibited a number of characteristics, such as lower expression levels, narrower H3K4me3/H3K9ac/H3K27me3 peaks, and larger expression divergences between 93-11 and Nipponbare. Taken together, the development of a single-cell RNA-seq protocol in this study offers us an excellent opportunity to investigate the origins and prevalence of monoallelic gene expression in plant cells.

ISRNA: an integrative online toolkit for short reads from high-throughput sequencing data.

UNLABELLED: Integrative Short Reads NAvigator (ISRNA) is an online toolkit for analyzing high-throughput small RNA sequencing data. Besides the high-speed genome mapping function, ISRNA provides statistics for genomic location, length distribution and nucleotide composition bias analysis of sequence reads. Number of reads mapped to known microRNAs and other classes of short non-coding RNAs, coverage of short reads on genes, expression abundance of sequence reads as well as some other analysis functions are also supported. The versatile search functions enable users to select sequence reads according to their sub-sequences, expression abundance, genomic location, relationship to genes, etc. A specialized genome browser is integrated to visualize the genomic distribution of short reads. ISRNA also supports management and comparison among multiple datasets. AVAILABILITY: ISRNA is implemented in Java/C++/Perl/MySQL and can be freely accessed at http://omicslab.genetics.ac.cn/ISRNA/.

PsRobot: a web-based plant small RNA meta-analysis toolbox.

Small RNAs (smRNAs) in plants, mainly microRNAs and small interfering RNAs, play important roles in both transcriptional and post-transcriptional gene regulation. The broad application of high-throughput sequencing technology has made routinely generation of bulk smRNA sequences in laboratories possible, thus has significantly increased the need for batch analysis tools. PsRobot is a web-based easy-to-use tool dedicated to the identification of smRNAs with stem-loop shaped precursors (such as microRNAs and short hairpin RNAs) and their target genes/transcripts. It performs fast analysis to identify smRNAs with stem-loop shaped precursors among batch input data and predicts their targets using a modified Smith-Waterman algorithm. PsRobot integrates the expression data of smRNAs in major plant smRNA biogenesis gene mutants and smRNA-associated protein complexes to give clues to the smRNA generation and functional processes. Besides improved specificity, the reliability of smRNA target prediction results can also be evaluated by mRNA cleavage (degradome) data. The cross species conservation statuses and the multiplicity of smRNA target sites are also provided. PsRobot is freely accessible at http://omicslab.genetics.ac.cn/psRobot/.