Biology subjects

Riano-Pachon, D. M.

Publications and source records attributed to Riano-Pachon, D. M..

9 recordsLinked to original sources

bioRxiv2026

A haplotype-resolved, near telomere-to-telomere genome assembly of the desiccation-tolerant plant Vellozia tubiflora

Vegetative desiccation tolerance allows plants to survive the loss of up to 95% of their cellular water content and recover upon rehydration. The family Velloziaceae contains the largest number of desiccation-tolerant species among flowering plants, yet genomic resources for this lineage remain extremely limited. Here we report the first reference genome of the genus Vellozia, the most species-rich genus of the family. Using PacBio HiFi sequencing and Omni-C chromatin conformation data, we generated a haplotype-resolved genome assembly of Vellozia tubiflora with near telomere-to-telomere continuity. Hap1 and Hap2 span 423.2 Mb and 426.4 Mb, respectively, with both assemblies successfully anchored into nine chromosome-length scaffolds. The former presents a scaffold N50 of 49.8 Mb and 96.5% BUSCO completeness, while the latter reaches 50.0 Mb and 96.8%. Genome annotation predicted 23,352 and 23,549 protein-coding genes in Hap1 and Hap2, respectively. This resource is anticipated to foster new insights for comparative and evolutionary studies of vegetative desiccation tolerance across angiosperms and expands the genomic representation of plants from the Brazilian campos rupestres, a threatened biodiversity hotspot characterized by extreme environmental conditions.

plant biology↗

bioRxiv2025

Identifying associations between maize leaf transcriptome and bacteriome during different diurnal periods

O_LIBacterial communities play important roles in the plant phyllosphere. Both microbial communities and their hosts have circadian rhythms and are subject to diurnal environmental changes. However, the interaction between the host and microbiome is still poorly understood. Here, we exploit paired sequencing data of host transcriptome and microbiome derived maize genotypes in field conditions and under two contrasting diurnal periods. C_LIO_LIGene expression patterns of circadian cycle genes confirmed expected diurnal dynamics. Groups of co-expressed genes that responded to diurnal periods were associated with nucleic acid-binding, heat stress responses, and photosynthesis. C_LIO_LIMicrobiome analysis revealed only modest differences in alpha diversity between midday and midnight samples. However, beta diversity indicated a significant shift in community composition. Co-occurrence network analysis identified keystone taxa specific to each time point, suggesting time-dependent ecological roles within the phyllosphere microbiome. C_LIO_LICross-correlation analyses between host gene expression and bacterial taxon abundance revealed a greater number of host-microbe associations during the night. Several maize genes involved in circadian regulation significantly correlated with microbial taxa. Our findings provide initial evidence for diurnal-associated relationships between host gene expression and leaf-associated bacteriome, suggesting that maize circadian regulation may play a role in shaping the composition and functional potential of the phyllosphere microbiome. C_LI

bioinformatics↗

bioRxiv2025

Gene co-expression networks highlight key nodes associated with Ammonium nitrate in sugarcane

Gene co-expression network analysis offers valuable insights into complex biological processes in plants. In this study, we investigated the molecular mechanisms underlying nitrogen utilization efficiency (NUE) in sugarcane by analyzing transcriptome data from two contrasting genotypes (responsive and non-responsive) across different leaf segments under varying nitrogen conditions. RNA-seq analysis of 48 samples identified 2,723 differentially expressed genes. The responsive genotype showed enrichment in carbon metabolism and defense response pathways under high nitrogen conditions, while the non-responsive genotype prioritized photosynthesis and stress response mechanisms. Differentially expressed genes were used to construct a co-expression network which revealed 199 distinct modules. 44 modules correlating to nitrogen availability and 20 to genotype. Notably, Module 20, containing a high proportion of MYB/MYB-related transcription factors, emerged as a potential key regulator of nitrogen response in both genotypes. Several modules showed correlation with important metabolic markers such as RUBISCO, chlorophyll, and PEPCASE. Additionally, we identified novel candidate genes, including uncharacterized transcripts with strong genotype-specific expression patterns. These findings provide potential targets for understanding and improving nitrogen use efficiency in sugarcane breeding programs.

bioinformatics↗

bioRxiv2025

First chromosome scale genome of Acrocomia aculeata

The Arecaceae family comprises economically, and ecologically significant palm species widely distributed across tropical and subtropical regions. Among them, Acrocomia aculeata, commonly known as Macauba, has gained attention due to its high oil yield, environmental adaptability, and potential applications in sustainable agriculture and bioenergy production. This study presents the first chromosome scale genome assembly of A. aculeata and the first reference genome of the genus, using Oxford Nanopore Technologies sequencing, PacBio HiFi and Hi-C proximity ligation data. The genome covers 1.94 Gbp in the 15 pseudochromosomes, with N50 of 143.43 Mbp, achieving a base quality (QV) of 76.4 and a k-mer completeness of 90.58% of PacBio HiFi data, with k=21. Interspersed repetitive regions represent 77.36% of the total genome, with long terminal repeat (LTRs) accounting for 54.98%. A total of 28.215 protein-coding genes were predicted. The genome was validated with BUSCO, achieving a completeness of 98.9% for predicted proteins. The first chromosome scale genome of Macauba provide new insights and breakthroughs for studies of conservation genomics and plant breeding programs.

genomics↗

bioRxiv2024

Bridging the Python Training Gap for Bioscientists in Brazil: Improvements and Challenges

The rapid evolution of high-throughput technologies in biosciences has generated diverse and voluminous datasets, requiring bioscientists to develop data manipulation and analysis skills. Python, known for its versatility and powerful libraries, has become a crucial tool for managing these datasets. However, there is a significant lack of programming training for bioscientists in many countries. To address this knowledge gap among scientists in Brazil, the Brazilian Python Workshop for Biological Data was introduced several years ago, focusing on basic programming concepts and data handling techniques using popular Python libraries. Despite the progress and positive feedback from earlier editions, challenges persisted, necessitating continuous adaptation and improvement to meet the evolving needs of bioscientists.This work describes the advancements made in the 2021 and 2022 editions of the workshop and discusses new suggestions for its ongoing enhancement. Key innovations were introduced in the workshop structure and coordination, including the creation of new committees and the establishment of a code of conduct. Feedback forms were updated to enable real-time adjustments during the event, improving its overall effectiveness. The workshop also expanded its reach by increasing geographical diversity among participants. New didactic strategies, such as pair-teaching, code clubs, and the integration of information and communication technologies (ICTs), were implemented to enhance learning outcomes. Programming best practices and scientific reproducibility were emphasized through talks and hands-on activities, guided by PEP8 conventions. Furthermore, efforts to enhance scientific dissemination were intensified, with an increased presence on social media and participation in international scientific events and communication networks. Finally, we present updated recommendations for students, researchers, and educators interested in organizing and promoting similar events, building on those previously described.

scientific communication and education↗

bioRxiv2022

An insight into cellulolytic capacity of the Trichoderma harzianum P49P11 revealed by omics approaches

Cellulases are a group of enzymes with several applications in biofuel production, and the paper, food, pharmaceutical, and chemical industries. Trichoderma harzianum P49P11 secrete all cellulases with high efficiency, representing an alternative to the current filamentous fungi in biotechnological industries. In this study, the cellulolytic mechanisms employed by the strain P49P11 to degrade crystalline cellulose in batch fermentation culture mode were elucidated by combining genome and secretome analysis. The strain P49P11 encodes nineteen cellulase genes from five different CAZyme families (GH5, GH6, GH7, GH12, and GH45), followed by several enzyme families for hemicellulose, pectin, and alpha-and beta-glucans degradation. The diverse CAZymes were also observed in the secretome, including cellulases, hemicellulases, and glucanases. In addition, {beta}-glucosidases and xylanase activities detected during the fermentation process validated our secretome analysis. Taken together, our results revealed all enzymatic machinery used by the T. harzianum P49P11 to degrade cellulose in batch fermentation mode. HighlightsO_LIWe described a high-quality genome assembly and annotation of the T. harzianum P49P11. C_LIO_LIThe T. harzianum P49P11 genome possesses a complete set of genes for lignocellulose degradation. C_LIO_LIThe first report on T. harzianum P49P11 secretome obtained from batch fermentation strategy. C_LIO_LIT. harzianum P49P11 produced cellulases, lignocellulases, and auxiliary enzymes produced in response to crystalline cellulose. C_LI

microbiology↗

bioRxiv2021

PhycoMine: A Microalgae Data Warehouse

PhycoMine is data warehouse system created to fostering the analysis of complex and integrated data from microalgae species in a single computational environment. The PhycoMine was developed on top of the InterMine software system, and it has implemented an extended database model, containing a series of tools that help the users in the analysis and mining of individual data and group data. The platform has widgets created to facilitate simultaneous data mining of different datasets. Among the widgets implemented in PhycoMine, there are options for mining chromosome distribution, gene expression variation via transcriptomics, proteomics sets, Gene Onthology enrichment, KEGG enrichment, publication enrichment, EggNOG, Transcription factors and transcriptional regulators enrichment and phenotypical data. These widgets were created to facilitate data visualization of the gene expression levels in different experimental setups, for which RNA-seq experimental data is available in data repositories. For comparative purposes, we have reanalyzed 200 RNA-seq datasets from Chlamydomonas reinhardtii, a model unicellular microalga, for optimizing the performance and accuracy of data comparisons. We have also implemented widgets for metabolic pathway analysis of selected genes and proteins and options for biological network analysis. The option for analysis of orthologue genes was also included. With this platform, the users can perform data mining for a list of genes or proteins of interest in an integrated way through accessing the data from different sources and visualizing them in graphics and by exporting the data into table formats. The PhycoMine platform is freely available and can be visited through the URL https://PhycoMine.iq.usp.br.

systems biology↗

bioRxiv2020

An integrative approach points to membrane composition as a key factor in E. coli persistence

Many diverse bacteria can enter non- or slow-growing states where they are transiently tolerant to antibiotics. Despite its medical importance, the genetic mechanisms underlying this persistence remain largely unknown, especially for spontaneous (type II) persistence that arise during exponential growth in rich medium. To address this challenge, here we combine genomic, transcriptomic and lipidomic analysis to identify the persistence mechanisms. We first analyzed the genome of the high-persistence mutant Escherichia coli DS1 (hipQ) to identify candidate genes for the high persistence phenotype. We then compared the gene expression profile of spontaneous persisters to normally growing cells with RNAseq and find that the activation of stress response mechanisms is likely not very important in the entrance into hipQ-driven spontaneous persistence. Transcriptomic results also suggest that modifications in the cell membrane play an important role, as further corroborated by lipidomic profiles showing a higher level of unsaturated fatty acids in spontaneous persisters compared to induced persisters or normally growing cells. Taken together, our results indicate that changing membrane composition is a key process in persistence, and further our understanding of spontaneous persister cells from the DS1 (hipQ) context.

microbiology↗

bioRxiv2016

ploidyNGS: Visually exploring ploidy with Next Generation Sequencing data

SummaryploidyNGS is a model-free, open source tool to visualize and explore ploidy levels in a newly sequenced genome, exploiting short read data. We tested ploidyNGS using both simulated and real NGS data of the model yeast Saccharomyces cerevisiae. ploidyNGS allows the identification of the ploidy level of a newly sequenced genome in a visual way.\n\nAvailability and implementationploidyNGS is available under the GNU General Public License (GPL) at https://github.com/diriano/ploidyNGS. ploidyNGS is implemented in Python and R.\n\nContactdiego.riano@bioetanol.org.br

bioinformatics↗