Biology subjects

Mutwil, M.

Publications and source records attributed to Mutwil, M..

At least 37 records · Page 2Linked to original sources

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↗

bioRxiv2022

Environmental gradients reveal stress hubs predating plant terrestrialization

Plant terrestrialization brought forth the land plants (embryophytes). Embryophytes account for most of the biomass on land and evolved from streptophyte algae in a singular event. Recent advances have unraveled the first full genomes of the closest algal relatives of land plants; among the first such species was Mesotaenium endlicherianum. Here, we used fine-combed RNAseq in tandem with photophysiological assessment on Mesotaenium exposed to a continuous range of temperature and light cues. Our data establish a grid of 42 different conditions, resulting in 128 transcriptomes and ~1.5 Tbp (~9.9 billion reads) of data to study combinatory effects of stress response using clustering along gradients. We describe major hubs in genetic networks underpinning stress response and acclimation in the molecular physiology of Mesotaenium. Our data suggest that lipid droplet formation, plastid and cell wall-derived signals denominate molecular programs since more than 600 million years of streptophyte evolution--before plants made their first steps on land.

evolutionary 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↗

bioRxiv2022

Feature Importance Network reveals novel functional relationships between biological features in Arabidopsis thaliana

Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it has the ability to find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes, and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools (http://finder.plant.tools/).

bioinformatics↗

bioRxiv2022

The conserved transcriptional program of metazoan male germ cells uncovers ancient origins of human infertility

Male germ cells share a common origin across animal species, therefore they likely retain a conserved genetic program that defines their cellular identity. However, the unique evolutionary dynamics of male germ cells coupled with their widespread leaky transcription pose significant obstacles to the identification of the core spermatogenic program. Through network analysis of the spermatocyte transcriptome of vertebrate and invertebrate species, we describe the conserved evolutionary origin of metazoan male germ cells at the molecular level. We estimate the average functional requirement of a metazoan male germ cell to correspond to the expression of approximately 10,000 protein-coding genes, a third of which defines a genetic scaffold of deeply conserved genes that has been retained throughout evolution. Such scaffold contains a set of 79 functional associations between 104 gene expression regulators that represent a core component of the conserved genetic program of metazoan spermatogenesis. By genetically interfering with the acquisition and maintenance of male germ cell identity, we uncover 161 previously unknown spermatogenesis genes and three new potential genetic causes of human infertility. These findings emphasize the importance of evolutionary history on human reproductive disease and establish a cross-species analytical pipeline that can be repurposed to other cell types and pathologies.

systems 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↗

bioRxiv2021

LSTrAP-Kingdom: an automated pipeline to generate annotated gene expression atlases for kingdoms of life

SummaryThere are now more than two million RNA sequencing experiments for plants, animals, bacteria and fungi publicly available, allowing us to study gene expression within and across species and kingdoms. However, the tools allowing the download, quality control and annotation of this data for more than one species at a time are currently missing. To remedy this, we present the Large-Scale Transcriptomic Analysis Pipeline in Kingdom of Life (LSTrAP-Kingdom) pipeline, which we used to process 134,521 RNA-seq samples, achieving ~12,000 processed samples per day. Our pipeline generated quality-controlled, annotated gene expression matrices that rival the manually curated gene expression data in identifying functionally-related genes. Availability and implementationLSTrAP-Kingdom is available from: https://github.com/wirriamm/plants-pipeline and is fully implemented in Python and Bash.

bioinformatics↗

bioRxiv2020

Comparative transcriptomic analysis reveals conserved transcriptional programs underpinning organogenesis and reproduction in land plants

The evolution of plant organs, including leaves, stems, roots, and flowers, mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. Furthermore, the fertilization products of angiosperms, seeds serve as the basis for most of our food. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes, and the development of novel regulatory programs. However, our knowledge of these events is limited, as no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we have generated gene expression atlases for various organs and gametes of 10 plant species comprising bryophytes, vascular plants, gymnosperms, and flowering plants. Comparative analysis of the atlases identified hundreds of organ- and gamete-specific gene families and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, the appearance of organ-specific gene families does not coincide with the corresponding organs appearance, suggesting that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, suggesting that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function. To provide easy access to the expression atlases and these comparative analyses, we provide an online database, www.evorepro.plant.tools, that allows the exploration of expression profiles, organ-specific genes, phylogenetic trees, co-expression networks, and others.

evolutionary biology↗

bioRxiv2020

Fungi.guru: comparative genomic and transcriptomic database for the Fungi kingdom

The fungi kingdom is composed of eukaryotic heterotrophs, which are responsible for balancing the ecosystem and play a major role as decomposers. They also produce a vast diversity of secondary metabolites, which have antibiotic or pharmacological properties. However, our lack of knowledge of gene function in fungi precludes us from tailoring them to our needs and tapping into their metabolic diversity. To remedy this, we gathered genomic and gene expression data of 19 most widely-researched fungi to build a database, fungi.guru, which contains tools for cross-species identification of conserved pathways, functional gene modules, and gene families. We exemplify how our database can elucidate the molecular function, biological process and cellular component of genes involved in various biological processes, by identifying a secondary metabolite pathway producing gliotoxin in Aspergillus fumigatus, the catabolic pathway of cellulose in Coprinopsis cinerea and the conserved DNA replication pathway in Fusarium graminearum and Pyricularia oryzae. The database is available at www.fungi.guru.

bioinformatics↗

bioRxiv2020

Computationally-inferred structural landscape of the complete genomes of Dengue serotypes and other viral hemorrhagic fevers

BackgroundWith more than 300 million potentially infected people every year, and with the expanded habitat of mosquitoes due to climate change, dengue cannot be considered anymore only a tropical disease. The RNA secondary structure is a functional characteristic of RNA viruses, and together with the accumulated high-throughput sequencing data could provide general insights towards understanding virus biology. Here, we profiled the RNA secondary structure of >7500 complete viral genomes from 11 different species of viral hemorrhagic fevers, including dengue serotypes, ebola, and yellow fever. ResultsWe achieved hig prediction scores (AUC up to 0.85 with experimental data), and computed consensus secondary structure profiles using hundreds of structural in silico models. We observed that virulent viruses such as DENV-2 and ebola tend to be less structured than the other viruses. Furthermore, we observed virus-specific correlations between secondary structure and the number of interaction sites with human proteins, reaching a correlation of 0.89 in the case of zika. We demonstrate that the secondary structure and presence of protein-binding domains in the genomes can be used as intrinsic signature to further classify the viruses. We also used structural data to study the geographical distribution of dengue, finding a significant difference between DENV-3 from Asia and South-America, which could imply different evolutionary routes of this subtype. ConclusionsOur massive computational analysis provided novel results regarding the secondary structure and the interaction with human proteins, not only for Dengue serotypes, but also for other viral hemorrhagic fevers. We also provided a new approach to classify viruses according ot their structure, which could be useful for future cassifications. We envision that these approaches can be used by the scientific community to further classify and characterise these complex viruses.

bioinformatics↗

bioRxiv2020

Removing auto-activators from yeast-two-hybrid assays by conditional negative selection

Yeast-two-hybrid (Y2H) is widely used as a strategy to detect protein-protein interactions (PPIs). Recent advancements have made it possible to generate and analyse genome-wide PPI networks en masse by coupling Y2H with next-generation sequencing technology. However, one of the major challenges of yeast two-hybrid assay is the large amount of false-positive hits caused by auto-activators (AAs), which are proteins that activate the reporter genes without the presence of an interacting protein partner. Here, we have developed a negative selection to minimize these auto-activators by integrating the pGAL2-URA3 fragment into the yeast genome. Upon activation of the pGAL2 promoter by an AA, yeast cells expressing URA3 cannot grow in media supplemented with 5-Fluoroorotic acid (5-FOA). Hence, we selectively inhibit the growth of yeast cells expressing auto-activators and thus minimizing the amount of false-positive hits. Here, we have demonstrated that auto-activators can be successfully removed from a Marchantia polymorpha cDNA library using pGAL2-URA3 and 5-FOA treatment, in liquid and solid-grown cultures. Furthermore, since URA3 can also serve as a marker for uracil autotrophy, we propose that our approach is a valuable addition to any large-scale Y2H screen.

molecular biology↗

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

Expression atlas of Selaginella moellendorffii provides insights into the evolution of vasculature, secondary metabolism and roots

O_LIThe lycophyte Selaginella moellendorffii represents early vascular plants and is studied to understand the evolution of higher plant traits such as the vasculature, leaves, stems, roots, and secondary metabolism. However, little is known about the gene expression and transcriptional coordination of Selaginella genes, which precludes us from understanding the evolution of transcriptional programs behind these traits.\nC_LIO_LIWe here present a gene expression atlas comprising all major organs, tissue types, and the diurnal gene expression profiles for S. moellendorffii. The atlas is part of the CoNekT-Plants database (conekt.plant.tools), which enables comparative transcriptomic analyses across two algae and seven land plants.\nC_LIO_LIWe show that the transcriptional gene module responsible for the biosynthesis of lignocellulose evolved in the ancestor of vascular plants, and pinpoint the duplication and subfunctionalization events that generated multiple gene modules involved in the biosynthesis of various cell wall types. We further demonstrate how secondary metabolism is transcriptionally coordinated and integrated with other cellular pathways. Finally, we identify root-specific genes in vascular plants and show that the evolution of roots did not coincide with an increased appearance of gene families, suggesting that the existing genetic material was sufficient to generate new organs.\nC_LIO_LIOur updated database at conekt.plant.tools provides a unique resource to study the evolution of genes, gene families, transcriptomes, and functional gene modules in the Archaeplastida kingdom.\nC_LI

plant biology↗

bioRxiv2019

Gene expression analysis of Cyanophora paradoxa reveals conserved abiotic stress responses between basal algae and flowering plants

O_LIThe glaucophyte Cyanophora paradoxa represents the most basal member of the Archaeplastida kingdom, however the function and expression of most of its genes are unknown. This information is needed to uncover how functional gene modules, i.e. groups of genes performing a given function, evolved in the plant kingdom.\nC_LIO_LIWe have generated a gene expression atlas capturing responses of Cyanophora to various abiotic stresses. This data was included in the CoNekT-Plants database, enabling comparative transcriptomic analyses across two algae and six land plants.\nC_LIO_LIWe demonstrate how the database can be used to study gene expression, co-expression networks and gene function in Cyanophora, and how conserved transcriptional programs can be identified. We identified gene modules involved in phycobilisome biosynthesis, response to high light and cell division. While we observed no correlation between the number of differentially expressed genes and the impact on growth of Cyanophora, we found that the response to stress involves a conserved, kingdom-wide transcriptional reprogramming, which is activated upon most stresses in algae and land plants.\nC_LIO_LIThe Cyanophora stress gene expression atlas and the tools found in https://conekt.plant.tools/ database provide a useful resource to reveal functionally related genes and stress responses in the plant kingdom.\nC_LI

plant biology↗

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↗