Biology subjects

Soto, M. J.

Publications and source records attributed to Soto, M. J..

3 recordsLinked to original sources

bioRxiv2025

The effect of Sinorhizobium meliloti volatilomes and synthetic long-chain methylketones on soil and Medicago truncatula microbiomes

Bacterial volatile compounds play important roles in intra- and interkingdom interactions but very little is known about their effects on soil and plant microbiomes. The legume symbiont Sinorhizobium meliloti (Sm) releases volatile methylketones (MKs), one of which acts as an infochemical in bacteria and hampers plant-bacteria interactions. MK production in Sm is modestly increased in the absence of the long-chain-fatty-acyl-coenzyme-A (CoA) synthetase FadD. To explore further the ecological role of MKs on soil and plant bacterial communities, we aimed at obtaining an MK-overproducer Sm strain by deleting the 3-oxo-acyl-CoA-thiolase-encoding fadA gene. Analyses of the Sm wild type (wt), and fad mutant volatilomes identified seventeen compounds consisting mostly of MKs and fatty acid methyl esters (FAMEs) and revealed that the fadA mutant produced more MKs than the fadD mutant and much more than the wt, while in the fadD mutant FAME emission was increased. When natural soil or the rhizosphere of Medicago truncatula were exposed to wt and fadA volatilomes or synthetic MKs, bacterial alpha- or beta-diversity were not strongly affected but specific genera were identified which responded differentially to each condition. Interestingly, Sm volatilomes had a significant effect on root endosphere Ensifer/Sinorhizobium populations by maintaining their abundance over time in contrast to control conditions or exposure to synthetic MKs. This study provides new insights on the synthesis of rhizobial volatile compounds and represents the first exploration of the effects of bacterial volatilomes on plant bacterial communities, contributing to increase our knowledge on the complex molecular bases underlying plant-bacteria interactions.

microbiology↗

bioRxiv2022

Sensitivity analysis of genome-scale metabolic flux prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with Transcription Factor (TF)-gene regulatory network (TRN), where the TRN is modeled via Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction considering potential model uncertainty in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty when learning from given transcriptomic expression data and analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yield of interest. The obtained sensitivity analyses can provide a useful guidance for Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and effectively achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed simulation experiments to demonstrate the effectiveness of our developed sensitivity analysis strategy and its potential to effectively guide OED. ACM Reference FormatPuhua Niu, Maria J. Soto, Shuai Huang, Byung-Jun Yoon, Edward R. Dougherty,, Francis J. Alexander, Ian Blaby, Xiaoning Qian. 2018. Sensitivity analysis of genome-scale metabolic flux prediction. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (CNB-MAC 2022). ACM, New York, NY, USA, 9 pages. https://doi.org/XXXXXXX.XXXXXXX

bioinformatics↗

bioRxiv2021

TRIMER: Transcription Regulation Integrated with MEtabolic Regulation

Advances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMERs applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.

bioinformatics↗