Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila

From lignocellulosic biomass to single cell oil for sustainable biomanufacturing: Current advances and prospects

As global temperatures rise and arid climates intensify, the reserves of Earth's resources and the future development of humankind are under unprecedented pressure. Traditional methods of food production are increasingly inadequate in meeting the demands of human life while remaining environmentally sustainable and resource-efficient. Consequently, the sustainable supply of lipids is expected to become a pivotal area for future food development. Lignocellulose biomass (LB), as the most abundant and cost-effective renewable resource, has garnered significant attention from researchers worldwide. Thus, bioprocessing based on LB is appearing as a sustainable model for mitigating the depletion of energy reserves and reducing carbon footprints. Currently, the transformation of LB primarily focuses on producing biofuels, such as bioethanol, biobutanol, and biodiesel, to address the energy crisis. However, there are limited reports on the production of single cell oil (SCO) from LB. This review, therefore, provides a comprehensive summary of the research progress in lignocellulosic pretreatment. Subsequently, it describes how the capability for lignocellulosic use can be conferred to cells through genetic engineering. Additionally, the current status of saccharification and fermentation of LB is outlined. The article also highlights the advances in synthetic biology aimed at driving the development of oil-producing microorganism (OPM), including genetic transformation, chassis modification, and metabolic pathway optimization. Finally, the limitations currently faced in SCO production from straw are discussed, and future directions for achieving high SCO yields from various perspectives are proposed. This review aims to provide a valuable reference for the industrial application of green SCO production.

A temperature-induced metabolic shift in the emerging human pathogen Photorhabdus asymbiotica

Photorhabdus is a bacterial genus containing both insect and emerging human pathogens. Most insect-restricted species display temperature restriction, unable to grow above 34°C, while Photorhabdus asymbiotica can grow at 37°C to infect mammalian hosts and cause Photorhabdosis. Metabolic adaptations have been proposed to facilitate the survival of this pathogen at higher temperatures, yet the biological mechanisms underlying these are poorly understood. We have reconstructed an extensively manually curated genome-scale metabolic model of P. asymbiotica (iEC1073, BioModels ID MODEL2309110001), validated through in silico gene knockout and nutrient utilization experiments with an excellent agreement between experimental data and model predictions. Integration of iEC1073 with transcriptomics data obtained for P. asymbiotica at temperatures of 28°C and 37°C allowed the development of temperature-specific reconstructions representing metabolic adaptations the pathogen undergoes when shifting to a higher temperature in a mammalian compared to insect host. Analysis of these temperature-specific reconstructions reveals that nucleotide metabolism is enriched with predicted upregulated and downregulated reactions. iEC1073 could be used as a powerful tool to study the metabolism of P. asymbiotica, in different genetic or environmental conditions.

IMPORTANCE

Photorhabdus bacterial species contain both human and insect pathogens, and most of these species cannot grow in higher temperatures. However, Photorhabdus asymbiotica, which infects both humans and insects, can grow in higher temperatures and undergoes metabolic adaptations at a temperature of 37°C compared to that of insect body temperature. Therefore, it is important to examine how this bacterial species can metabolically adapt to survive in higher temperatures. In this work, using a mathematical model, we have examined the metabolic shift that takes place when the bacteria switch from growth conditions in 28°C to 37°C. We show that P. asymbiotica potentially experiences predicted temperature-induced metabolic adaptations at 37°C predominantly clustered within the nucleotide metabolism pathway.

13 C-MFA helps to identify metabolic bottlenecks for improving malic acid production in Myceliophthora thermophila

BACKGROUND

Myceliophthora thermophila has been engineered as a significant cell factory for malic acid production, yet strategies to further enhance production remain unclear and lack rational guidance. 13C-MFA (13C metabolic flux analysis) offers a means to analyze cellular metabolic mechanisms and pinpoint critical nodes for improving product synthesis. Here, we employed 13C-MFA to investigate the metabolic flux distribution of a high-malic acid-producing strain of M. thermophila and attempted to decipher the crucial bottlenecks in the metabolic pathways.

RESULTS

Compared with the wild-type strain, the high-Malic acid-producing strain M. thermophila JG207 exhibited greater glucose uptake and carbon dioxide evolution rates but lower oxygen uptake rates and biomass yields. Consistent with these phenotypes, the 13C-MFA results showed that JG207 displayed elevated flux through the EMP pathway and downstream TCA cycle, along with reduced oxidative phosphorylation flux, thereby providing more precursors and NADH for malic acid synthesis. Furthermore, based on the 13C-MFA results, we conducted oxygen-limited culture and nicotinamide nucleotide transhydrogenase (NNT) gene knockout experiments to increase the cytoplasmic NADH level, both of which were shown to be beneficial for malic acid accumulation.

CONCLUSIONS

This work elucidates and validates the key node for achieving high malic acid production in M. thermophila. We propose effective fermentation strategies and genetic modifications for enhancing malic acid production. These findings offer valuable guidance for the rational design of future cell factories aimed at improving malic acid yields.

Exploring the hidden hot world of long non-coding RNAs in thermophilic fungus using a robust computational pipeline

Long noncoding RNAs (lncRNAs) are versatile RNA molecules recently identified as key regulators of gene expression in response to environmental stress. Our primary focus in this study was to develop a robust computational pipeline for identifying structurally identical lncRNAs across replicates from publicly available bulk RNA-seq datasets. In order to demonstrate the effectiveness of the pipeline, we utilized the transcriptome of the thermophilic fungus Thermothelomyces thermophilus and assessed the expression pattern of lncRNAs in conjunction with Heat Shock Proteins (HSP), a well-known protein family critical for the cell's response to high temperatures. Our findings demonstrate that the identification of structurally identical transcripts among replicates in this thermophilic fungus ensures the reliability and accuracy of RNA studies, contributing to the validity of biological interpretations. Furthermore, the majority of lncRNAs exhibited a distinct expression pattern compared to HSPs. Our study contributes to advancing the understanding of the biological mechanisms comprising lncRNAs in thermophilic fungi.

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kcat data

BACKGROUND

Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study.

RESULTS

Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based kcat data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct kcat collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted kcat values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila.

CONCLUSIONS

In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based kcat data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism.

Improvement of thermostability and catalytic efficiency of xylanase from Myceliophthora thermophilar by N-terminal and C-terminal truncation

Introduction

Extracting xylanase from thermophilic filamentous fungi is a feasible way to obtain xylanase with good thermal stability.

Methods

The transcriptomic data of Myceliophthora thermophilic destructive ATCC42464 were differentially expressed and enriched. By comparing the sequences of Mtxylan2 and more than 10 xylanases, the N-terminal and C-terminal of Mtxylan2 were truncated, and three mutants 28N, 28C and 28NC were constructed.

Results and discussion

GH11 xylan Mtxylan2 was identified by transcriptomic analysis, the specific enzyme activity of Mtxylan2 was 104.67 U/mg, and the optimal temperature was 65°C. Molecular modification of Mtxylan2 showed that the catalytic activity of the mutants was enhanced. Among them, the catalytic activity of 28C was increased by 9.3 times, the optimal temperature was increased by 5°C, and the residual enzyme activity remained above 80% after 30 min at 50-65°C, indicating that redundant C-terminal truncation can improve the thermal stability and catalytic performance of GH11 xylanase.

Rewiring metabolic flux to simultaneously improve malate production and eliminate by-product succinate accumulation by Myceliophthora thermophila

Although a high titre of malic acid is achieved by filamentous fungi, by-product succinic acid accumulation leads to a low yield of malic acid and is unfavourable for downstream processing. Herein, we conducted a series of metabolic rewiring strategies in a previously constructed Myceliophthora thermophila to successfully improve malate production and abolish succinic acid accumulation. First, a pyruvate carboxylase CgPYC variant with increased activity was obtained using a high-throughput system and introduced to improve malic acid synthesis. Subsequently, shifting metabolic flux to malate synthesis from mitochondrial metabolism by deleing mitochondrial carriers of pyruvate and malate, led to a 53.7% reduction in succinic acid accumulation. The acceleration of importing cytosolic succinic acid into the mitochondria for consumption further decreased succinic acid formation by 53.3%, to 2.12 g/L. Finally, the importer of succinic acid was discovered and used to eliminate by-product accumulation. In total, malic acid production was increased by 26.5%, relative to the start strain JG424, to 85.23 g/L and 89.02 g/L on glucose and Avicel, respectively, in the flasks. In a 5-L fermenter, the titre of malic acid reached 182.7 g/L using glucose and 115.8 g/L using raw corncob, without any by-product accumulation. This study would accelerate the industrial production of biobased malic acid from renewable plant biomass.

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Recent Development of Advanced Biotechnology in the Oleaginous Fungi for Arachidonic Acid Production

Arachidonic acid is an essential ω-6 polyunsaturated fatty acid, which plays a significant role in cardiovascular health and neurological development, leading to its wide use in the food and pharmaceutical industries. Traditionally, ARA is obtained from deep-sea fish oil. However, this source is limited by season and is depleting the already threatened global fish stocks. With the rapid development of synthetic biology in recent years, oleaginous fungi have gradually attracted increasing attention as promising microbial sources for large-scale ARA production. Numerous advanced technologies including metabolic engineering, dynamic regulation of fermentation conditions, and multiomics analysis were successfully adapted to increase ARA synthesis. This review summarizes recent advances in the bioengineering of oleaginous fungi for ARA production. Finally, perspectives for future engineering approaches are proposed to further improve the titer yield and productivity of ARA.