In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production

A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM

Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.

Common operational issues and possible solutions for sustainable biosurfactant production from lignocellulosic feedstock

Surfactants are compounds with high surface activity and emulsifying property. These compounds find application in food, medical, pharmaceutical, and petroleum industries, as well as in agriculture, bioremediation, cleaning, cosmetics, and personal care product formulations. Due to their widespread use and environmental persistence, ensuring biodegradability and sustainability is necessary so as not to harm the environment. Biosurfactants, i.e., surfactants of plant or microbial origin produced from lignocellulosic feedstock, perform better than their petrochemically derived counterparts on the scale of net-carbon-negativity. Although many biosurfactants are commercially available, their high cost of production justifies their application only in expensive pharmaceuticals and cosmetics. Besides, the annual number of new biosurfactant compounds reported is less, compared to that of chemical surfactants. Multiple operational issues persist in the biosurfactant value chain. In this review, we have categorized some of these issues based on their relative position in the value chain - hurdles occurring during planning, upstream processes, production stage, and downstream processes - alongside plausible solutions. Moreover, we have presented the available paths forward for this industry in terms of process development and integrated pretreatment, combining conventional tried-and-tested strategies, such as reactor designing and statistical optimization with cutting-edge technologies including metabolic modeling and artificial intelligence. The development of techno-economically feasible biosurfactant production processes would be instrumental in the complete substitution of petrochemical surfactants, rather than mere supplementation.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Advances in the co-production of biosurfactant and other biomolecules: statistical approaches for process optimization

An efficient microbial conversion for simultaneous synthesis of multiple high-value compounds, such as biosurfactants and enzymes, is one of the most promising aspects for an economical bioprocess leading to a marked reduction in production cost. Although biosurfactant and enzyme production separately have been much explored, there are limited reports on the predictions and optimization studies on simultaneous production of biosurfactants and other industrially important enzymes, including lipase, protease, and amylase. Enzymes are suited for an integrated production process with biosurfactants as multiple common industrial processes and applications are catalysed by these molecules. However, the complexity in microbial metabolism complicates the production process. This study details the work done on biosurfactant and enzyme co-production and explores the application and scope of various statistical tools and methodologies in this area of research. The use of advanced computational tools is yet to be explored for the optimization of downstream strategies in the co-production process. Given the complexity of the co-production process and with various new methodologies based on artificial intelligence (AI) being invented, the scope of AI in shaping the biosurfactant-enzyme co-production process is immense and would lead to not only efficient and rapid optimization, but economical extraction of multiple biomolecules as well.

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity

Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact-printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights into surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles.

Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions

Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.

Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

A review of the role of biosurfactants in the biodegradation of hydrophobic organopollutants: production, mode of action, biosynthesis and applications

The increasing influence of human activity and industrialization has adversely impacted the environment via pollution with organic contaminants, which are minimally soluble in water. These hydrophobic organopollutants may be present in sediment, water or biota and have created concern due to their toxic effects in mammals. The ability of microorganisms to degrade pollutants makes their use the most effective, inexpensive and ecofriendly method for environmental remediation. Microorganisms have the ability to produce natural surfactants (biosurfactants) that increase the bioavailability of hydrophobic organopollutants, which enables their use as carbon and energy sources. Due to microbial diversity in production, and the biodegradability, nontoxicity, stability and specific activity of the surfactants, the use of microbial surfactants has the potential to overcome problems associated with contamination by hydrophobic organopollutants.This review provides an overview of the current state of knowledge regarding microbial surfactant production, mode of action in the biodegradation of hydrophobic organopollutants and biosynthetic pathways as well as their applications using emergent strategy tools to remove organopollutants from the environment. It is also specified for the first time that biosurfactants are produced either as growth-associated products or secondary metabolites, and are produced in different amounts by a wide range of microorganisms.

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

A navigation guide of synthetic biology tools for Pseudomonas putida

Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.

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A pipeline for metabolic modeling in Synechococcus sp. PCC 7002 is presented

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Metabolic fluxes display clear differences in pathway activity across conditions

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Omic-informed GSMMs provide critical mechanistic details within machine learning

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Combining GSMM and machine learning improves methods based on transcriptomics alone

The relevance of enzyme specificity for coenzymes and the presence of 6-phosphogluconate dehydrogenase for polyhydroxyalkanoates production in the metabolism of Pseudomonas sp. LFM046

Reconstruction of genome-based metabolic model is a useful approach for the assessment of metabolic pathways, genes and proteins involved in the environmental fitness capabilities or pathogenic potential as well as for biotechnological processes development. Pseudomonas sp. LFM046 was selected as a good polyhydroxyalkanoates (PHA) producer from carbohydrates and plant oils. Its complete genome sequence and metabolic model were obtained. Analysis revealed that the gnd gene, encoding 6-phosphogluconate dehydrogenase, is absent in Pseudomonas sp. LFM046 genome. In order to improve the knowledge about LFM046 metabolism, the coenzyme specificities of different enzymes was evaluated. Furthermore, the heterologous expression of gnd genes from Pseudomonas putida KT2440 (NAD⁺ dependent) and Escherichia coli MG1655 (NADP⁺ dependent) in LFM046 was carried out and provoke a delay on cell growth and a reduction in PHA yield, respectively. The results indicate that the adjustment in cyclic Entner-Doudoroff pathway may be an interesting strategy for it and other bacteria to simultaneously meet divergent cell needs during cultivation phases of growth and PHA production.

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.