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Lee, W. W.

Publications and source records attributed to Lee, W. W..

2 recordsLinked to original sources

Transcriptome analysis of developmental stages of cocoa pod borer, Conopomorpha cramerella: A polyphagous insect pest of economic importance in Southeast Asia.

The cocoa pod borer, Conopomorpha cramerella (Snellen) is a serious pest in cocoa plantations in Southeast Asia. It causes significant losses in the crop. Unfortunately, genetic resources for this insect is extremely scarce. To improve these resources, we sequenced the transcriptome of C. cramerella representing the three stages of development, larva, pupa and adult moth using Illumina NovaSeq6000. Transcriptome assembly was performed by Trinity for all the samples. A total number of 147,356,088 high quality reads were obtained. Of these, 285,882 contigs were assembled. The mean contig size was 374 bp. Protein coding sequence (CDS) was extracted from the reconstructed transcripts by TransDecoder. Subsequently, BlastX and InterProScan were applied for homology search to make a prediction of the function of CDS in unigene. Additionally, we identified a number of genes that are involved in reproduction and development such as genes involved in general function processes in the insect. Genes found to be involved in reproduction such as porin, dsx, bol and fruitless were associated with sex determination, spermatogenesis and pheromone binding. Furthermore, transcriptome changes during development were analysed. There were 2,843 differentially expressed genes (DEG) detected between the larva and pupa samples. A total of 2,861 DEG were detected between adult and larva stage whereas between adult and pupa stage, 1,953 DEG were found. In conclusion, the transcriptomes could be a valuable genetic resource for identification of genes in C. cramerella and the study will provide putative targets for RNAi pest control.

developmental biology

Data-driven analysis of COVID-19 reveals specific severity patterns distinct from the temporal immune response

Key immune signatures of SARS-CoV-2 infection may associate with either adverse immune reactions (severity) or simply an ongoing anti-viral response (temporality); how immune signatures contribute to severe manifestations and/or temporal progression of disease and whether longer disease duration correlates with severity remain unknown. Patient blood was comprehensively immunophenotyped via mass cytometry and multiplex cytokine arrays, leading to the identification of 327 basic subsets that were further stratified into more than 5000 immunotypes and correlated with 28 plasma cytokines. Low-density neutrophil abundance was closely correlated with hepatocyte growth factor levels, which in turn correlated with disease severity. Deep analysis also revealed additional players, namely conventional type 2 dendritic cells, natural killer T cells, plasmablasts and CD16+ monocytes, that can influence COVID-19 severity independent of temporal progression. Herein, we provide interactive network analysis and data visualization tools to facilitate data mining and hypothesis generation for elucidating COVID-19 pathogenesis.

immunology