Biology subjects

Tan, Q. W.

Publications and source records attributed to Tan, Q. W..

10 recordsLinked to original sources

bioRxiv2024

Comparative transcriptomics in ferns reveals key innovations and divergent evolution of secondary cell wall

Despite ferns being crucial to understanding plant evolution, their large and complex genomes has kept their genetic landscape largely uncharted, with only a handful of genomes sequenced and sparse transcriptomic data. Addressing this gap, we generated extensive RNA-sequencing data for multiple organs across 22 representative species over the fern phylogeny, assembling high-quality transcriptomes. These data facilitated the construction of a time-calibrated fern phylogeny covering all major clades, revealing numerous whole-genome duplications and highlighting the uniqueness of fern genetics, with half of the uncovered gene families being fern-specific. Our investigation into fern cell walls through biochemical and immunological analyses identified occurrences of the lignin syringyl unit and its independent evolution in ferns. Moreover, the discovery of an unusual sugar in fern cell walls hints at a divergent evolutionary path in cell wall biochemistry, potentially driven by gene duplication and sub-functionalization. We provide an online database preloaded with genomic and transcriptomic data for ferns and other land plants, which we used to identify an independent evolution of lignocellulosic gene modules in ferns. Our data provide a framework for the unique evolutionary path that ferns have navigated since they split from the last common ancestor of euphyllophytes more than 360 million years ago.

plant biology↗

bioRxiv2024

Diurnal.plant.tools in 2024: expanding to Marchantia polymorpha and four angiosperms

Diurnal gene expression is a pervasive phenomenon occurring across all kingdoms of life, orchestrating adaptive responses to daily environmental fluctuations and thus enhancing organismal fitness. Our understanding of the plant circadian clock is primarily derived from studies in Arabidopsis and direct comparisons are difficult due to differences in gene family sizes. To this end, the identification of functional orthologs based on diurnal and tissue expression is necessary. The diurnal.plant.tools database constitutes a repository of gene expression profiles from 17 members of the Archaeplastida lineage, with built-in tools facilitating cross-species comparisons. In this database update, we expand the dataset with diurnal gene expression from 4 agriculturally significant crop species and Marchantia, a plant of evolutionary significance. Notably, the inclusion of diurnal gene expression data for Marchantia enables researchers to glean insights into the evolutionary trajectories of the circadian clock and other biological processes spanning from algae to angiosperms. Moreover, by integrating data from tissue, and perturbation gene expression datasets for Marchantia from other databases, the diurnal dataset complements it to offer a comprehensive overview of gene functions across varied biological contexts. This expanded database serves as a valuable resource for elucidating the intricacies of diurnal gene regulation and its evolutionary underpinnings in plant biology.

plant biology↗

bioRxiv2023

Multi-omic dissection of ancestral heat stress memory responses in Brachypodium distachyon

Stressful environmental conditions, including heat stress (HS), are a major limiting factor in crop yield. Understanding the molecular mechanisms of plant stress memory and resilience is important for engineering more resistant plants and improving crop yield. To study how the different gene regulatory layers change upon repeated HS and how these layers are interconnected, we performed a dense temporal atlas of gene expression, alternative splicing, small and long noncoding RNAs, and DNA methylation in Brachypodium distachyon. Results show that a second HS induces changes in coding and noncoding RNA expression and alternative splicing and that DNA demethylation is responsible for mediating differential gene expression. We identified a long noncoding RNA regulatory network and provided evidence that lncRNAs positively regulate gene expression, while miRNAs are implicated in alternative splicing events. We reconstructed the ancestral heat memory network of flowering plants by comparing the dynamic responses of Arabidopsis thaliana and Brachypodium distachyon. These findings enhance our understanding of the complex inter-layer cross-talk governing HS resilience and memory and identify novel genes essential for these processes.

plant biology↗

bioRxiv2022

Genomic, transcriptomic, and metabolomic analysis of Traditional Chinese Medicine plant Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites

Natural products from traditional medicinal plants are valuable candidates for clinical cancer therapy. Plants from the Oldenlandia-Hedyotis complex are popular ingredients of Traditional Chinese Medicine (TCM), however a major hurdle in the plant bioprospecting process of TCM plants is that the active metabolites, their biosynthetic pathways, and mode of action are often unknown. We show that Oldenlandia corymbosa extracts are active against breast cancer cell lines. To study the genes involved in the biosynthesis of active compounds in this medicinal plant, we assembled a high-quality genome. We show that the main active compound is ursolic acid and that abiotic stresses cause changes in anti-cancer activity, metabolite composition, and gene expression of plants. To reveal the mode of action of ursolic acid, we show that cancer cells undergo mitotic catastrophe, and we identify three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking.

plant biology↗

bioRxiv2021

Marchantia polymorpha gene expression atlas reveals the hierarchy of abiotic stress responses and conservation of diurnal gene expression

Abiotic stresses negatively impact ecosystems and the yield of crops, and climate change will increase their frequency and intensity. Despite progress in understanding how plants respond to individual stresses, our knowledge of plant acclimatization to combined stresses-typically occurring in nature is still lacking. Here, we used a plant with minimal regulatory network redundancy, Marchantia polymorpha, to study how seven abiotic stresses, alone and in 19 pairwise combinations, affect the phenotype, gene expression, and activity of cellular pathways. We found a high divergence of transcriptomic stress responses between Arabidopsis and Marchantia, suggesting that the stress-specific gene regulatory networks (GRNs) between bryophytes and angiosperms are not strongly conserved. The reconstructed high-confidence GRNs demonstrated that the response to specific stresses dominates those of others by relying on a large ensemble of transcription factors. We also showed that a regression model could accurately predict the gene expression under combined stresses, indicating that Marchantia performs arithmetic addition to respond to multiple stresses. Finally, we provide two online resources (https://conekt.plant.tools and http://bar.utoronto.ca/efp_marchantia/cgi-bin/efpWeb.cgi) to facilitate the study of gene expression in Marchantia exposed to abiotic stresses.

plant biology↗

bioRxiv2020

Prediction of novel components of bacterial ribosomes by crowd-sourced analysis of RNA sequencing data

Bacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

bioinformatics↗

bioRxiv2020

LSTrAP-Cloud: A User-friendly Cloud ComputingPipeline to Infer Co-functional and RegulatoryNetworks

As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes that they might be interested in. LSTrAP-Cloud is based on Google Colaboratory and provides user-friendly tools that process and quality-control RNA sequencing data streamed from the European Sequencing Archive. LSTRAP-Cloud outputs a gene co-expression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters and transcription factors involved in the synthesis, transport and regulation of nicotine can be identified using our pipeline.

bioinformatics↗

bioRxiv2019

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

0.Prediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as the major bottleneck in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for less than 50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics.\n\nHighlightsO_LIProcessing of large scale transcriptomic data with affordable single-board computers\nC_LIO_LITranscription factors can be found in the same network as their targets\nC_LIO_LICo-expression of transcription factors and genes in secondary cell wall biosynthesis\nC_LIO_LICo-expression of transcription factors and genes involved in artemisinin biosynthesis\nC_LI

bioinformatics↗

bioRxiv2019

Diurnal.plant.tools: comparative transcriptomic and co-expression analyses of diurnal gene expression of the Archaeplastida kingdom

Almost all organisms coordinate some aspects of their biology through the diurnal cycle. Photosynthetic organisms, and plants especially, have established complex programs that coordinate physiological, metabolic and developmental processes with the changing light. The diurnal regulation of the underlying transcriptional processes is observed when groups of functionally related genes (gene modules) are expressed at a specific time of the day. However, studying the diurnal regulation of these gene modules in the plant kingdom was hampered by the large amount of data required for the analyses. To meet this need, we used gene expression data from 17 diurnal studies spanning the whole Archaeplastida kingdom (Plantae kingdom in the broad sense) to make an online diurnal database. We have equipped the database with tools that allow user-friendly cross-species comparisons of gene expression profiles, entire co-expression networks, co-expressed clusters (involved in specific biological processes), time-specific gene expression, and others. We exemplify how these tools can be used by studying three important biological questions: (i) the evolution of cell division, (ii) the diurnal control of gene modules in algae and (iii) the conservation of diurnally-controlled modules across species. The database is freely available at http://diurnal.plant.tools/.

plant biology↗

bioRxiv2019

Malaria.tools: comparative genomic and transcriptomic database for Plasmodium species

Malaria is a tropical parasitic disease caused by the Plasmodium genus, which resulted in an estimated 219 million cases of malaria and 435,000 malaria-related deaths in 2017. Despite the availability of the P. falciparum genome since 2002, almost 50% of the genes remain unannotated. To remedy this paucity of functional information, we used transcriptomic data to build gene co-expression networks for two Plasmodium species (P. falciparum and P. berghei), and included genomic data of four other Plasmodium species, P. yoleii, P. knowlesi, P. vivax and P. cynomolgi, as well as two non-Plasmodium species from the Apicomplexa, Toxoplasma gondii and Theileria parva. The database is preloaded with tools that allow the identification and cross-species comparison of co-expressed gene neighborhoods, clusters, and life stage-specific expression, thus providing sophisticated tools to predict gene function. Moreover, we exemplify how the tools can be used to easily identify genes relevant for pathogenicity and various life stages of the malaria parasite. The database is freely available at www.malaria.tools.

bioinformatics↗