Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory.

Methyl salicylate induces endogenous jasmonic acid and salicylic acid in 'Nam Dok Mai' mango to maintain postharvest ripening and quality.

Salicylic acid (SA) and jasmonic acid (JA) are indispensable phytohormones whose interaction influences the ripening process in plants. Methyl salicylate (MeSA) and methyl jasmonate (MeJA) have been utilized to elevate the endogenous levels of SA and JA in horticultural products after harvest. However, their ability to preserve mango is uncertain.

Optimization of microbial fuel cell performance application to high sulfide industrial wastewater treatment by modulating microbial function.

Microbial fuel cells (MFCs) are innovative eco-friendly technologies that advance a circular economy by enabling the conversion of both organic and inorganic substances in wastewater to electricity. While conceptually promising, there are lingering questions regarding the performance and stability of MFCs in real industrial settings. To address this research gap, we investigated the influence of specific operational settings, regarding the hydraulic retention time (HRT) and organic loading rate (OLR) on the performance of MFCs used for treating sulfide-rich wastewater from a canned pineapple factory.

Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation.

Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism.

Cluster-based photography and modeling integrated method for an efficient measurement of cassava leaf area.

Leaf area (LA) and biomass are important agronomic indicators of the growth and health of plants. Conventional methods for measuring the LA can be challenging, time-consuming, costly, and laborious, especially for a large-scale study. A hybrid approach of cluster-based photography and modeling was, thus, developed herein to improve practicality.

CO recycling by phosphopyruvate carboxylase enables cassava leaf metabolism to tolerate low water availability.

Cassava is a staple crop that acclimatizes well to dry weather and limited water availability. The drought response mechanism of quick stomatal closure observed in cassava has no explicit link to the metabolism connecting its physiological response and yield. Here, a genome-scale metabolic model of cassava photosynthetic leaves (leaf-MeCBM) was constructed to study on the metabolic response to drought and stomatal closure.

Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach.

As a sessile organism, plants hold elaborate transcriptional regulatory systems that allow them to adapt to variable surrounding environments. Current understanding of plant regulatory mechanisms is greatly constrained by limited knowledge of transcription factor (TF)-DNA interactions. To mitigate this problem, a Plant-DTI predictor (BD-FBS nteraction) was developed here as the first machine-learning model that covered the largest experimental datasets of 30 plant TF families, including 7 plant-specific DNA binding domain (DBD) types, and their transcription factor binding sites (TFBSs).

Effective Metabolic Carbon Utilization and Shoot-to-Root Partitioning Modulate Distinctive Yield in High Yielding Cassava Variety.

Increasing cassava production could mitigate one of the global food insecurity challenges by providing a sustainable food source. To improve the yield potential, physiological strategies (i.e.

Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava.

The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power.

Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava.

Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars.

Exploring dynamic protein-protein interactions in cassava through the integrative interactome network.

Protein-protein interactions (PPIs) play an essential role in cellular regulatory processes. Despite, in-depth studies to uncover the mystery of PPI-mediated regulations are still lacking. Here, an integrative interactome network (MePPI-Ux) was obtained by incorporating expression data into the improved genome-scale interactome network of cassava (MePPI-U).

Genomic and Transcriptomic Analysis Identified Novel Putative Cassava lncRNAs Involved in Cold and Drought Stress.

Long non-coding RNAs (lncRNAs) play important roles in the regulation of complex cellular processes, including transcriptional and post-transcriptional regulation of gene expression relevant for development and stress response, among others. Compared to other important crops, there is limited knowledge of cassava lncRNAs and their roles in abiotic stress adaptation. In this study, we performed a genome-wide study of ncRNAs in cassava, integrating genomics- and transcriptomics-based approaches.

Genome-Wide Identification of Putative MicroRNAs in Cassava ( Crantz) and Their Functional Landscape in Cellular Regulation.

MicroRNAs are small noncoding RNAs, involved in the regulation of many cellular processes in plants. Hundreds of miRNAs have been identified in cassava by various techniques, yet these identifications were constrained by a lack of miRNA templates and the narrow range of conditions in transcriptome study. In this research, we conducted genome-wide analysis identification, whereby miRNAs from cassava genome were thoroughly screened using bioinformatics approach independent of predefined templates and studied conditions.

Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through Flux Balance Analysis.

Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the constraint-based metabolic model of cassava storage roots, rMeCBM, was developed based on the carbon assimilation pathway of cassava. Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN.

Pan- and core- gene association networks: Integrative approaches to understanding biological regulation.

The rapid increase in transcriptome data provides an opportunity to access the complex regulatory mechanisms in cellular systems through gene association network (GAN). Nonetheless, GANs derived from single datasets generally allow us to envisage only one side of the regulatory network, even under the particular condition of study. The circumstance is well demonstrated by inconsistent GANs of individual datasets proposed for similar experimental conditions, which always leads to ambiguous interpretation.

Unlocking conserved and diverged metabolic characteristics in cassava carbon assimilation via comparative genomics approach.

Globally, cassava is an important source of starch, which is synthesized through carbon assimilation in cellular metabolism whereby harvested atmospheric carbon is assimilated into macromolecules. Although the carbon assimilation pathway is highly conserved across species, metabolic phenotypes could differ in composition, type, and quantity. To unravel the metabolic complexity and advantage of cassava over other starch crops, in terms of starch production, we investigated the carbon assimilation mechanisms in cassava through genome-based pathway reconstruction and comparative network analysis.

Prediction of cassava protein interactome based on interolog method.

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking.

Networking Omic Data to Envisage Systems Biological Regulation.

To understand how biological processes work, it is necessary to explore the systematic regulation governing the behaviour of the processes. Not only driving the normal behavior of organisms, the systematic regulation evidently underlies the temporal responses to surrounding environments (dynamics) and long-term phenotypic adaptation (evolution). The systematic regulation is, in effect, formulated from the regulatory components which collaboratively work together as a network.

Gene Co-Expression Analysis Inferring the Crosstalk of Ethylene and Gibberellin in Modulating the Transcriptional Acclimation of Cassava Root Growth in Different Seasons.

Cassava is a crop of hope for the 21st century. Great advantages of cassava over other crops are not only the capacity of carbohydrates, but it is also an easily grown crop with fast development. As a plant which is highly tolerant to a poor environment, cassava has been believed to own an effective acclimation process, an intelligent mechanism behind its survival and sustainability in a wide range of climates.

Genome-wide analysis reveals phytohormone action during cassava storage root initiation.

Development of storage roots is a process associated with a phase change from cell division and elongation to radial growth and accumulation of massive amounts of reserve substances such as starch. Knowledge of the regulation of cassava storage root formation has accumulated over time; however, gene regulation during the initiation and early stage of storage root development is still poorly understood. In this study, transcription profiling of fibrous, intermediate and storage roots at eight weeks old were investigated using a 60-mer-oligo microarray.

Starch biosynthesis in cassava: a genome-based pathway reconstruction and its exploitation in data integration.

Background: Cassava is a well-known starchy root crop utilized for food, feed and biofuel production. However, the comprehension underlying the process of starch production in cassava is not yet available.

Results: In this work, we exploited the recently released genome information and utilized the post-genomic approaches to reconstruct the metabolic pathway of starch biosynthesis in cassava using multiple plant templates.