Biology subjects

Lim, P. K.

Publications and source records attributed to Lim, P. K..

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

Constructing Ensemble Gene Functional Networks Capturing Tissue/condition-specific Co-expression from Unlabled Transcriptomic Data with TEA-GCN

Gene co-expression networks (GCNs) can reveal useful gene co-functional and co-regulatory relationships. However, current GCN construction methodologies are sensitive to batch effects and sample composition, limiting their performance in generating GCNs from public RNA-seq samples abundant for many species. Here, we detail the development of TEA-GCN (Two-Tier Ensemble Aggregation-GCN), a GCN construction method that leverages unsupervised transcriptomic dataset partitioning and multi-metric co-expression scoring to derive ensemble gene co-expression. Benchmarking over 450,000 public RNA-seq samples across 12 species, TEA-GCN outperforms the state-of-the-art in predicting gene functions and inferring gene regulatory networks. Through the use of natural language processing, we also show that the biologically-relevant dataset partitions with high co-expression can identify tissue-/condition-specific co-expression in TEA-GCN, providing an unprecedented level of explainability. Furthermore, we show that TEA-GCNs exhibit enhanced conservation across species, making them suitable for multi-species comparative studies. TEA-GCN is available at https://github.com/pengkenlim/TEA-GCN.

systems biology↗

bioRxiv2024

Comparative transcriptome database for Camellia sinensis reveals genes important for flavonoid synthesis in tea plants

Tea, as one of the most popular beverages in the world, possesses a plethora of secondary metabolites that are beneficial to human health. Tea plants (Camellia sinensis) exhibit rich genetic diversity, where different cultivars can vary significantly in terms of yield, adaptability, morphology, and composition of secondary metabolites. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify valuable functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 cultivars of Camellia sinensis were assembled and combined with the coding sequences of 13 cultivars of Camellia sinensis to study the differences and similarities of gene expression and biological functions among cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that provides user-friendly tools and interactive visualizations that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform cross-cultivar comparison of co-expressed gene neighborhoods, clusters, and tissue-specific gene expression. We show that the members of the chalcone synthase (CHS) gene family, important for flavonoid synthesis, exhibit the highest expression variability, specific expression in leaves and buds, and significant modulation by methyl jasmonate (MeJA) treatment. By using comparative co-expression tools of TeaNekT, we identified multiple conserved genes involved in flavonoid biosynthesis among cultivars that have not been previously studied, warranting further research.

bioinformatics↗

bioRxiv2024

A method for mining condition-specific co-expressed genes in Camellia sinensis based on K-means clustering: A case study of "Anji Baicha" tea cultivar

As one of the worlds most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a PPR-type RNA editing factor gene (CWM1) that specifically functions during the bud-prealbinism stage of the Camellia sinensis cultivar "Anji Baicha". We hypothesize that this gene may be upregulated and play a role in inhibiting chloroplast development, ultimately resulting in albino phenotypes in "Anji Baicha".

bioinformatics↗

bioRxiv2023

Transcriptomic and metabolomic characterization of antibacterial activity of Melastoma dodecandrum

Antibacterial resistance poses a significant global threat, necessitating the discovery of new therapeutic agents. Plants are a valuable source of secondary metabolites with demonstrated anticancer and antibacterial properties. In this study, we reveal that Melastoma dodecandrum exhibits both bacteriostatic and bactericidal effects against Pseudomonas aeruginosa and Staphylococcus aureus. Treatment with plant extracts results in membrane damage and a reduction in Pseudomonas swimming and swarming motility. A comparative analysis of bacterial transcriptomes exposed to Melastoma extracts and four distinct antibiotics indicates that the extracts trigger similar transcriptomic responses as triclosan, a fatty acid inhibitor. Activity-guided fractionation suggests that the antibacterial activity is not attributable to hydrolyzable tannins, but to unidentified minor compounds. Additionally, we identified 104 specialized metabolic pathways and demonstrated a high level of transcriptional coordination between these biosynthetic pathways and phytohormones, highlighting potential regulatory mechanisms in plant metabolism.

plant biology↗

bioRxiv2023

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Structured AbstractO_ST_ABSMotivationC_ST_ABSDespite the abundance of species with transcriptomic data, a significant number of the species still lack genomes, making it difficult to study gene function and expression in these organisms. While de novo transcriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily-selected or similar experimental conditions which might fail to capture condition-specific transcripts. ResultsWe developed the Large-Scale Transcriptome Assembly Pipeline for de novo assembled transcripts (LSTrAP-denovo) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP-denovo can (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses. Availability and ImplementationLSTrAP-denovo is easy to implement, written in python, and is freely available at https://github.com/pengkenlim/LSTrAP-denovo/. Supplementary InformationSupplementary data are available in the forms of supplementary figures, supplementary tables, and supplementary methods.

bioinformatics↗

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↗

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↗

bioRxiv2021

Protist.guru: a comparative transcriptomics database for the protist kingdom

SummaryDuring the last few decades, the study of microbial ecology has been enabled by molecular and genomic data. DNA sequencing has revealed the surprising extent of microbial diversity and how microbial processes run global ecosystems. However, significant gaps in our understanding of the microbial world remain, and one example is that microbial eukaryotes, or protists, are still neglected. To address this gap, we used gene expression data from 15 distinct protist species to create protist.guru: an online database equipped with tools for identifying functional co-expression networks, gene families, and enriched gene clusters. Here, we show how our database can be used to reveal genes involved in essential pathways, such as the synthesis of secondary carotenoids in Haematococcus lacustris. We expect protist.guru to serve as a valuable resource for protistologists, as well as a catalyst for discoveries and new insights into the biological processes of microbial eukaryotes. AvailabilityThe database and co-expression networks are freely available from http://protist.guru/. The expression matrices and sample annotations are found in the supplementary data.

evolutionary biology↗

bioRxiv2021

Bacteria.guru: comparative transcriptomics and co-expression database for bacterial pathogens

SummaryThe bacterial kingdom comprises unicellular prokaryotes able to establish symbioses from mutualism to parasitism. To combat bacterial pathogenicity, we need an enhanced understanding of gene function and regulation, which will mediate the development of novel antimicrobials. Gene expression can predict gene function, but there lacks a database enabling expansive inter- and intraspecific exploration of gene expression profiles and co-expression networks for bacteria. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and studied bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, co-expression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid the discovery of multi-faceted antibiotic targets. Hence, we believe bacteria.guru will facilitate future bacterial research. AvailabilityThe database and co-expression networks are freely available from https://bacteria.guru/. The sample annotations are found in the supplemental data.

bioinformatics↗