Transcriptome profiling of gonad-stimulating factors in thoracic ganglia and a potential role of Indian hedgehog gene in vitellogenesis of banana shrimp Fenneropenaeus merguiensis.

Shrimp reproduction is controlled by several factors. Central nervous tissues, especially thoracic ganglia and brain, are known sources of gonad stimulating factors (GSFs) in crustaceans, but the GSFs in shrimp have not yet been clarified. Hence, we aimed to characterize and study putative GSFs from thoracic ganglia of adult female Fenneropenaeus merguiensis. An analysis of thoracic ganglia transcriptome revealed 3224 putative GSFs of a total 77,681 unigenes. Only 376 putative GSFs were differentially expressed during ovarian developmental stages. Eight candidate GSFs were validated for their expression patterns in thoracic ganglia, including the Indian hedgehog gene. F. merguiensis Indian hedgehog (FmIHH) was then investigated for its role in vitellogenesis. The obtained full-length cDNA of FmIHH was similar to other crustacean IHHs rather than Sonic and Desert HHs. The FmIHH was dominantly expressed in thoracic ganglia, and its expression was significantly increased in the vitellogenic stages before being downregulated at the mature stage of ovarian development. Injection of the recombinant FmIHH (His-TF-IHH) protein stimulated vitellogenin expression in ovaries on day 3 and 7, and also increased the gonadosomatic index. In addition, crustacean hyperglycemic hormone expression and total sugar were significantly decreased in eyestalks and hemolymph, respectively, after injection of His-TF-IHH, while lactic acid was increased. Both total sugar and lactic acid were unchanged in ovaries of His-TF-IHH injected shrimp. These results suggested that FmIHH plays a crucial role in vitellogenesis and regulate sugar uptake during ovarian development.

Diversity of Human Enterovirus Co-Circulations in Five Kindergartens in Bangkok between July 2019 and January 2020.

Human enterovirus causes various clinical manifestations in the form of rashes, febrile illness, flu-like illness, uveitis, hand-foot-mouth disease (HFMD), herpangina, meningitis, and encephalitis. Enterovirus A71 and coxsackievirus are significant causes of epidemic HFMD worldwide, especially in children aged from birth to five years old. The enterovirus genotype variants causing HFMD epidemics have been reported increasingly worldwide in the last decade. We aim to use simple and robust molecular tools to investigate human enteroviruses circulating among kindergarten students at genotype and subgenotype levels. With the partial 5'-UTR sequencing analysis as a low-resolution preliminary grouping tool, ten enterovirus A71 (EV-A71) and coxsackievirus clusters were identified among 18 symptomatic cases and 14 asymptomatic cases in five kindergartens in Bangkok, Thailand, between July 2019 and January 2020. Two occurrences of a single clone causing an infection cluster were identified (EV-A71 C1-like subgenotype and coxsackievirus A6). Random amplification-based sequencing using MinION (Oxford Nanopore Technology) helped identify viral transmission between two closely related clones. Diverse genotypes co-circulating among children in kindergartens are reservoirs for new genotype variants emerging, which might be more virulent or better at immune escape. Surveillance of highly contagious enterovirus in communities is essential for disease notifications and controls.

bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data.

Background: Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it. Methods: We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project. Results: The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

RNA-Seq dataset of thoracic ganglia transcriptome across four ovarian development stages in Fenneropenaeus merguiensis.

Banana shrimp (Fenneropenaeus merguiensis) is an economically important shrimp in marine aquaculture. Although there is plenty of transcriptome research for this species, the molecular mechanisms in thoracic ganglia of banana shrimp during ovarian maturation have not yet been investigated. Here we report the transcriptomic data of female banana shrimp obtained from thoracic ganglia during ovarian developmental stages. The samples were collected from four stages of ovarian development with two individual shrimps per stage. Total RNA was extracted and used to prepare the sequencing library. Approximately 188 million pair-end raw reads, ranging from 21 to 31 million reads for each library, were generated using an Illumina HiSeq 2500 platform. Quality control was applied to the raw reads before the assembly process. After de novo assembly, the final transcript assembly was generated by vector decontamination, coding regions prediction, redundancy reduction, and foreign sequence depletion. A total of 77,681 transcripts, ranging between 255 and 23,016 bp with an N50 value of 1,167 were obtained to the final assembly. Finally, the final transcripts assembly was evaluated by calculated assembly completeness with Arthropoda orthologous genes dataset. A total of 92.1% of Arthropoda orthologous genes were found in our final assembly. These data might provide benefits for gene discovery, gene annotation, transcript profiling, and other research topics in the context of banana shrimp.