The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility

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.

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

A Lifelike guided journey through the pathophysiology of pulmonary hypertension-from measured metabolites to the mechanism of action of drugs

Introduction

Pulmonary hypertension (PH) is a pathological condition that affects approximately 1% of the population. The prognosis for many patients is poor, even after treatment. Our knowledge about the pathophysiological mechanisms that cause or are involved in the progression of PH is incomplete. Additionally, the mechanism of action of many drugs used to treat pulmonary hypertension, including sotatercept, requires elucidation.

Methods

Using our graph-powered knowledge mining software Lifelike in combination with a very small patient metabolite data set, we demonstrate how we derive detailed mechanistic hypotheses on the mechanisms of PH pathophysiology and clinical drugs.

Results

In PH patients, the concentration of hypoxanthine, 12(S)-HETE, glutamic acid, and sphingosine 1 phosphate is significantly higher, while the concentration of L-arginine and L-histidine is lower than in healthy controls. Using the graph-based data analysis, gene ontology, and semantic association capabilities of Lifelike, led us to connect the differentially expressed metabolites with G-protein signaling and SRC. Then, we associated SRC with IL6 signaling. Subsequently, we found associations that connect SRC, and IL6 to activin and BMP signaling. Lastly, we analyzed the mechanisms of action of several existing and novel pharmacological treatments for PH. Lifelike elucidated the interplay between G-protein, IL6, activin, and BMP signaling. Those pathways regulate hallmark pathophysiological processes of PH, including vasoconstriction, endothelial barrier function, cell proliferation, and apoptosis.

Discussion

The results highlight the importance of SRC, ERK1, AKT, and MLC activity in PH. The molecular pathways affected by existing and novel treatments for PH also converge on these molecules. Importantly, sotatercept affects SRC, ERK1, AKT, and MLC simultaneously. The present study shows the power of mining knowledge graphs using Lifelike's diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions. We believe that Lifelike and our presented approach will be valuable for future mechanistic studies of PH, other diseases, and drugs.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.