Biology subjects

Tibocha-Bonilla, J. D.

Publications and source records attributed to Tibocha-Bonilla, J. D..

4 recordsLinked to original sources

bioRxiv2026

Genome-scale mapping of plant transcription factor effector domains reveals conserved regulatory principles across eukaryotes

Eukaryotic life spans species that diverged over a billion years ago, yet our molecular view of gene regulation remains heavily skewed toward Opisthokonta model systems, particularly yeast and humans. The degree to which the protein features governing transcriptional regulation are conserved across eukaryotes or have diversified within individual eukaryotic supergroups remains unresolved. To provide a distinct evolutionary reference point, we used a massively parallel reporter assay in plant cells to identify transcriptional effector domains and define their sequence constraints, measuring ~26,000 protein fragments tiling nearly all Arabidopsis transcription factors (1,859 proteins). Comparison with the activity of the same peptide sequences in yeast revealed that activation is governed by a mixture of deeply conserved biochemical features and lineage-dependent functional differences across widely divergent eukaryotic lineages. Likewise, plant repression domains are governed by flexible sequence grammars that extend beyond conserved repression motifs. Together, these results reveal both broadly conserved and clade-specific principles governing eukaryotic transcriptional regulation.

plant biology↗

bioRxiv2025

Multi-strain Analysis of Pseudomonas putida Reveals the Metabolic and Genetic Diversity of the Species.

Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains. We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a [~]8% increase to the available genomic data for P. putida. Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes such as PcaI and PcaJ revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.

bioengineering↗

bioRxiv2024

Proteome allocation of the microbiome reveals how diet and metabolic dysbiosis impact disease

The gut microbiome plays a critical role in human health, spurring extensive research into host-microbe interactions using multi-omic technologies. Although these tools offer valuable insights, they often fall short in capturing the complexity of microbial interactions and assigning causality to disease onset, progression, and treatment. Proteome allocation is intertwined with microbial interactions, yet there is little mechanistic knowledge. Here, we deploy models of metabolism and gene expression (ME-models) that predict proteome allocation. Despite their potential, their deployment has been limited by the laborious reconstruction process. To address this, we reconstructed 495 ME-models for human gut microbes using an automated pipeline. By integrating ME-models with multi-omics data from patients with inflammatory bowel disease (IBD), we identified taxa responsible for variations in amino acids, short-chain fatty acids, and pH in the gut of IBD patients. Thus, this approach provides a mechanistic understanding and paves the way for new intervention strategies.

systems biology↗

bioRxiv2021

A multiscale model predicts the sensitivity of Chlorella vulgaris to light and nitrogen levels in photobioreactors

The maximization of lipid productivity in microalgae is crucial for the biofuel industry, and it can be achieved by manipulating their metabolism. However, little efforts have been made to apply metabolic models in a dynamic framework to predict possible outcomes to scenarios observed at an industrial scale. Here, we present a dynamic framework for the simulation of large-scale photobioreactors. The framework was generated by merging the genome-scale metabolic model of Chlorella vulgaris (iCZ843) with reactor-scale parameters, thus yielding a multiscale model. This multiscale model was employed to predict the sensitivity of growth and composition variation of C. vulgaris on light and nitrogen levels. Simulations of lipid accumulation quantified the tradeoff between growth and lipid biosynthesis under nitrogen limitation. Moreover, our modeling approach quantitatively predicted the dependence of microalgal metabolism on light intensity and circadian oscillations. Finally, we use the model to design a reactor irradiance profile that maximized lipid accumulation, thus achieving a lipid productivity increase of 46% at a constant intensity of 966 E m-2 s-1. Our modeling framework elucidated how metabolism and external factors can be combined to predict optimized parameters for industrial applications.

bioengineering↗