Molecular mechanism of abscisic acid signaling response factor VcbZIP55 to promote anthocyanin biosynthesis in blueberry (Vaccinium corymbosum).

A high content of anthocyanin in blueberry (Vaccinium corymbosum) is an important indicator to evaluate fruit quality. Abscisic acid (ABA) can promote anthocyanin biosynthesis, but since the molecular mechanism is unclear, clarifying the mechanism will improve for blueberry breeding and cultivation regulation. VcbZIP55 regulating anthocyanin synthesis in blueberry were screened and mined using the published Isoform-sequencing, RNA-Seq and qRT-PCR at different fruit developmental stages. Blueberry genetic transformation and transgenic experiments confirmed that VcbZIP55 could promote anthocyanin biosynthesis in blueberry adventitious buds, tobacco leaves, blueberry leaves and blueberry fruit. VcbZIP55 responded to ABA signals and its expression was upregulated in blueberry fruit. In addition, using VcbZIP55 for Yeast one hybrid assay (Y1H) and transient expression in tobacco leaves demonstrated an interaction between VcbZIP55 and a G-Box motif on the VcMYB1 promoter to activate the expression of VcMYB1. This study will lay the theoretical foundation for the molecular mechanisms of phytohormone regulation responsible for anthocyanin synthesis and provide theoretical support for blueberry quality improvement.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Transcriptome changes associated with apple (Malus domestica) root defense response after Fusarium proliferatum f. sp. malus domestica infection.

BACKGROUND: Apple replant disease is a soilborne disease caused by Fusarium proliferatum f. sp. malus domestica strain MR5 (abbreviated hereafter as Fpmd MR5) in China. This pathogen causes root tissue rot and wilting leaves in apple seedlings, leading to plant death. A comparative transcriptome analysis was conducted using the Illumina Novaseq platform to identify the molecular defense mechanisms of the susceptible M.26 and the resistant M9T337 apple rootstocks to Fpmd MR5 infection. RESULTS: Approximately 518.1 million high-quality reads were generated using RNA sequencing (RNA-seq). Comparative analysis between the mock-inoculated and Fpmd MR5 infected apple rootstocks revealed 28,196 significantly differentially expressed genes (DEGs), including 14,572 up-regulated and 13,624 down-regulated genes. Among them, the transcriptomes in the roots of the susceptible genotype M.26 were reflected by overrepresented DEGs. MapMan analysis indicated that a large number of DEGs were involved in the response of apple plants to Fpmd MR5 stress. The important functional groups identified via gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were responsible for fundamental biological regulation, secondary metabolism, plant-pathogen recognition, and plant hormone signal transduction (ethylene and jasmonate). Furthermore, the expression of 33 up-regulated candidate genes (12 related to WRKY DNA-binding proteins, one encoding endochitinase, two encoding beta-glucosidases, ten related to pathogenesis-related proteins, and eight encoding ethylene-responsive transcription factors) were validated by quantitative real-time PCR. CONCLUSION: RNA-seq profiling was performed for the first time to analyze response of apple root to Fpmd MR5 infection. We found that the production of antimicrobial compounds and antioxidants enhanced plant resistance to pathogens, and pathogenesis-related protein (PR10 homologs, chitinase, and beta-glucosidase) may play unique roles in the defense response. These results provide new insights into the mechanisms of the apple root response to Fpmd MR5 infection.

Microblog Topic-Words Detection Model for Earthquake Emergency Responses Based on Information Classification Hierarchy.

Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies.