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Barbosa-Xavier, K.

Publications and source records attributed to Barbosa-Xavier, K..

4 recordsLinked to original sources

Computational Identification of Candidate Gene Families for Volatile Sulfur Compound Biosynthesis in Cannabis sativa Using Profile Hidden Markov Models

Cannabis is well known for its pungent, skunk-like aroma. Recent chemical studies have identified prenylated and C6 volatile sulfur compounds as contributors to its skunky and citrus-like aromas, but the pathways that produce these compounds remain unknown. This gap limits efforts to explain variation in sulfur-aroma traits and to selectively enhance or reduce those traits. To address this gap, we used the known chemistry of sulfur-containing volatiles in Cannabis and characterized sulfur and volatile biosynthetic pathways in other plant species to select candidate enzyme groups. Because the GMO cultivar is anecdotally associated with a pronounced sulfurous aroma, reference protein sequences and profile hidden Markov models were used to search its version 1 (v1) primary high-confidence protein set of 55,790 sequences. These searches recovered 975 unique proteins. Sequence screening retained 941 candidates across 20 reporting categories; 939 contained all expected domains, while the two candidates assigned to the methionine gamma-lyase (MGL)-nearest category had no category-specific expected-domain rule. The largest reporting category comprised 359 proteins containing a cytochrome P450 domain, recovered through a search motivated by cytochrome P450 family 74 (CYP74) enzymes involved in oxylipin and plant volatile formation. Thirteen of these proteins were also recovered by at least one full-length CYP74 reference search. Other large reporting categories included 218 sugar-transferase, 83 glutathione-transferase, and 61 alcohol dehydrogenase candidates. Comparison with the Cannabis Expression Atlas linked 168 candidates to 128 annotated genes through 100%-identity amino-acid matches spanning at least 80% of each GMO v1 candidate protein. Twenty-nine genes were tissue-specific, including 13 root-specific and 6 trichome-specific genes. These results define candidates for biochemical testing and direct searches for additional enzymes acting upstream and downstream in Cannabis sulfur-volatile pathways.

plant biology↗

Transcription-based dissection of floral identity and trichome biosynthesis pathways in Cannabis sativa L.

Cannabis sativa L. has a long history of medicinal and industrial use, with female flowers being the primary source of bioactive compounds such as cannabinoids and terpenoids, which are synthesized in glandular trichomes. Despite its importance, the genetic mechanisms governing flower development, sex determination, and the biosynthesis of these valuable secondary metabolites remain only partially understood. In this study, we conducted an in-depth transcriptomic and comparative genomic analysis to elucidate the molecular networks underlying these key traits. By integrating 117 RNA-Seq datasets and performing phylogenetic analyses across nine distinct C. sativa genomes, we identified 31 orthogroups of MADS-box transcription factors. Expression profiling of these genes revealed distinct sets of candidates associated with male and female flower identity, consistent with the established ABCDE model of floral development. Specifically, genes from the AP3, PI/GLO, and MIKCS clades showed preferential expression in male flowers, while genes from the AGL6, FLC-like, and Bsister clades were highly expressed in female flowers. Furthermore, our investigation into genes related to pollen development highlighted the significant role of sugar metabolism and transport in male fertility. Finally, our analysis of cannabinoid and terpenoid biosynthetic genes confirmed their pronounced expression in trichomes and highlighted a key distinction: while the upstream polyketide, MEV, and MEP pathways and the terpenoid pathway showed conserved expression across chemotypes, the cannabinoid pathway exhibited chemotype-specific expression profiles. Collectively, our findings provide a comprehensive molecular framework for understanding floral development and secondary metabolism in C. sativa, offering valuable targets for future functional studies and advanced breeding programs aimed at optimizing desirable agronomic and medicinal traits.

plant biology↗

Investigating Cannabis sativa L. gene expression through housekeeping genes and gene coexpression networks

Cannabis sativa L. is a versatile crop with applications ranging from medicinal products to industrial materials. Despite growing interest in cannabis transcriptomics, comprehensive studies of gene expression in this species remain limited. Here, we explore cannabis transcriptomics through two complementary approaches: the identification and prioritization of housekeeping (HK) genes for qRT-PCR normalization and the construction of a genome-wide gene coexpression network (GCN) using publicly available RNA-Seq data. Based on expression stability criteria (i.e. log mean TPM [&ge;] 6, log variance < 0.3), we proposed the optimal HK genes and designed qRT-PCR primers for them. Primer specificity was verified in silico using the Jamaican Lion mother + Y genome. In parallel, to explore the non HK gene expression, we constructed a GCN comprising 32 coexpression modules, which was then analyzed for tissue-specific expression patterns, transcription factor (TF), and functional enrichment. Several modules were significantly correlated with cannabis tissues such as flowers and bast fibres and were linked to key processes, including photoperiod sensitivity, stress responses, and vegetative-to-reproductive phase transitions. Together, our results help understand the functions and maximize the use of HK genes in molecular biology and reveal coexpression modules that constitute critical genetic circuits underlying cannabis physiology.

plant biology↗

Cannabis Expression Atlas: a comprehensive resource for integrative analysis of Cannabis sativa L. gene expression

Cannabis sativa L., a plant originating from Central Asia, is a versatile crop with applications spanning textiles, construction, pharmaceuticals, and food products. This study aimed to compile and analyze publicly available Cannabis RNA-Seq data and develop an integrated database tool to help advance Cannabis research in various topics such as fiber production, cannabinoid biosynthesis, sex determination, and plant development. We identified 515 publicly available RNA-Seq samples that, after stringent quality control, resulted in a high-quality dataset of 394 samples. Utilizing the Jamaican Lion genome as reference, we constructed a comprehensive database and developed the Cannabis Expression Atlas (https://cannatlas.venanciogroup.uenf.br/), a web application for visualization of gene expression, annotation, and functional classification. Key findings include the quantification of 27,640 Cannabis genes and their classification into seven expression categories: not-expressed, low-expressed, housekeeping, tissue-specific, group-enriched, mixed, and expressed-in-all tissues. The study revealed substantial variability and coherence in gene expression across different tissues and chemotypes. We found 2,382 tissue-specific genes, including 177 transcription factors.. The Cannabis Expression Atlas constitutes a valuable tool for exploring gene expression patterns and offers insights into Cannabis biology, supporting research in plant breeding, genetic engineering, biochemistry, and functional genomics.

plant biology↗