Integration of Aspergillus niger transcriptomic profile with metabolic model identifies potential targets to optimise citric acid production from lignocellulosic hydrolysate

Background Citric acid is typically produced industrially by Aspergillus niger-mediated fermentation of a sucrose-based feedstock, such as molasses. The fungus Aspergillus niger has the potential to utilise lignocellulosic biomass, such as bagasse, for industrial-scale citric acid production, but realising this potential requires strain optimisation. Systems biology can accelerate strain engineering by systematic target identification, facilitated by methods for the integration of omics data into a high-quality metabolic model. In this work, we perform transcriptomic analysis to determine the temporal expression changes during fermentation of bagasse hydrolysate and develop an evolutionary algorithm to integrate the transcriptomic data with the available metabolic model to identify potential targets for strain engineering. Results The novel integrated procedure matures our understanding of suboptimal citric acid production and reveals potential targets for strain engineering, including targets consistent with the literature such as the up-regulation of citrate export and pyruvate carboxylase as well as novel targets such as the down-regulation of inorganic diphosphatase. Conclusions In this study, we demonstrate the production of citric acid from lignocellulosic hydrolysate and show how transcriptomic data across multiple timepoints can be coupled with evolutionary and metabolic modelling to identify potential targets for further engineering to maximise productivity from a chosen feedstock. The in silico strategies employed in this study can be applied to other biotechnological goals, assisting efforts to harness the potential of microorganisms for bio-based production of valuable chemicals.

Hindcasting Ecosystem Functioning Change in an Anthropogenized Estuary: Implications for an Era of Global Change

Understanding how altered hydrodynamics related to climate change and anthropogenic modifications affect ecosystem integrity of shallow coastal soft-sediment environments requires a sound integration of how species populations influence ecosystem functioning across heterogeneous spatial scales. Here, we hindcasted how intertidal habitat loss and altered hydrodynamic regimes between 1955 and 2010 associated with geomorphological change to accommodate expansion in anthropogenic activities in the Western Scheldt altered spatial patterns and basin-wide estimates of ecosystem functioning. To this end we combined an empirically derived metabolic model for the effect of the common ragworm Hediste diversicolor on sediment biogeochemistry (measured as sediment oxygen uptake) with a hydrodynamic and population biomass distribution model. Our integrative modeling approach predicted an overall decrease by 304 tons in ragworm biomass between 1955 and 2010, accounting for a reduction by 28% in stimulated sediment oxygen uptake at the landscape scale. Local gains or losses in habitat suitability and ecosystem functioning were primarily driven by changes in maximal current velocities and inundation regimes resulting from deepening, dredging and disposal practices. By looking into the past, we have demonstrated how hydro- and morphodynamic changes affect soft-sediment ecology and highlight the applicability of the integrative framework to upscale anticipated population effects on ecosystem functioning.

Metabolic Model of the Nitrogen-Fixing Obligate Aerobe Azotobacter vinelandii Predicts Its Adaptation to Oxygen Concentration and Metal Availability

The world’s dependence on industrially produced nitrogenous fertilizers has created a dichotomy of issues. First, parts of the globe lack access to fertilizers, leading to poor crop yields that significantly limit nutrition while contributing to disease and starvation.

OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

Computational tools have been widely adopted for strain optimisation in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximisation of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout) leading to high biochemical production. The usefulness 1and capabilities of OptDesign are demonstrated for the production of three biochemicals in E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

A Metabolomics Approach to Increasing Chinese Hamster Ovary (CHO) Cell Productivity

Much progress has been made in improving the viable cell density of bioreactor cultures in monoclonal antibody production from Chinese hamster ovary (CHO) cells; however, specific productivity (qP) has not been increased to the same degree. In this work, we analyzed a library of 24 antibody-expressing CHO cell clones to identify metabolites that positively associate with qP and could be used for clone selection or medium supplementation. An initial library of 12 clones, each producing one of two antibodies, was analyzed using untargeted LC-MS experiments. Metabolic model-based annotation followed by correlation analysis detected 73 metabolites that significantly correlated with growth, qP, or both. Of these, metabolites in the alanine, aspartate, and glutamate metabolism pathway, and the TCA cycle showed the strongest association with qP. To evaluate whether these metabolites could be used as indicators to identify clones with potential for high productivity, we performed targeted LC-MS experiments on a second library of 12 clones expressing a third antibody. These experiments found that aspartate and cystine were positively correlated with qP, confirming the results from untargeted analysis. To investigate whether qP correlated metabolites reflected endogenous metabolic activity beneficial for productivity, several of these metabolites were tested as medium additives during cell culture. Medium supplementation with citrate improved qP by up to 490% and more than doubled the titer. Together, these studies demonstrate the potential for using metabolomics to discover novel metabolite additives that yield higher volumetric productivity in biologics production processes.

Nitrogen-metabolising Microorganism Analysis in Rapid Sand Filters From Drinking Water Treatment Plant by Culturing and Metagenomics

Sand filter (SFs) are common treatment processes for nitrogen pollutants removal in drinking water treatment plants (DWTPs). However, the mechanisms on the nitrogen-cycling role of SFs are still unclear. In this study, 16S rRNA gene amplicon sequencing was used to characterise the diversity and composition of the bacterial community in SFs from DWTPs. Additionally, metagenomics approach was used to determine the functional microorganisms involved in nitrogen cycle in SFs. Our results showed that Proteobacteria, Acidobacteria, Nitrospirae, and Chloroflexi dominated in SFs. Subsequently, 85 high-quality metagenome-assembled genomes (MAGs) were retrieved from metagenome datasets of selected SFs involving nitrification, assimilatory nitrogen reduction, and denitrification processes. Read mapping to reference genomes of Nitrospira and the phylogenetic tree of the ammonia monooxygenase subunit A gene, amoA, suggested that Nitrospira is abundantly found in SFs. Furthermore, according to their genetic content, a nitrogen metabolic model in SFs was proposed using representative MAGs and pure culture isolates. Quantitative real-time polymerase chain reaction (PCR) showed that ammonia-oxidising bacteria (AOB) and archaea (AOA), and complete ammonia oxidisers (comammox) were ubiquitous in the SFs, with the abundance of comammox being higher than that of AOA and AOB. Moreover, we identified a bacterial strain with a high NO3-N removal rate as Pseudomonas sp., which could be applied in the bioremediation of micro-polluted drinking water sources. Our study provides insights into functional nitrogen-metabolising microbes in SFs of DWTPs.

Study on Heart Rate and Energy Expenditure in College Sports Training Based on Multisensor Perception

Heart rate is one of the important indices to calculate and evaluate the intensity and quality of motion. It can scientifically and objectively reflect the intensity level of momentum and exercise in the process of exercise. It is an important index of athletic strength and physical fitness of athletes in college sports training. This paper analyzes the basic metabolic model of the human body and energy supply and demand model and constructs a scientific energy consumption test model for special sports activity. An algorithm for heart rate detection and energy consumption based on acceleration data acquisition is proposed. Therefore, it is proposed to calculate motion acceleration using bone point data obtained from a portable telephone camera sensor for special sports activity and to calculate kinetic energy consumption through detection data. A model for evaluating the proposed algorithm is also established.

XomicsToModel: Multiomics data integration and generation of thermodynamically consistent metabolic models

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in a given context. However, existing model extraction algorithms are unable to ensure that the context-specific model is thermodynamically feasible. This protocol introduces XomicsToModel, a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model, but it enables multi-omic data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. With all input data fully prepared, algorithmic completion of the pipeline takes ~10 min, however manual review of intermediate results may also be required, e.g., when inconsistent input data lead to an infeasible model.

GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction

Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.