Kinetic modelling of phospholipid synthesis in Plasmodium knowlesi unravels crucial steps and relative importance of multiple pathways

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Very Long Chain Polyunsaturated Fatty Acids Accumulated in Triacylglycerol Are Channeled From Phosphatidylcholine in Thraustochytrium

Thraustochytrium is a marine protist that can accumulate a large amount of very long chain polyunsaturated fatty acids (VLCPUFA) in triacylglycerols (TAG). How these freshly synthesized VLCPUFAs are channeled into TAG remains unknown. In this study, the glycerolipid profile of Thraustochytrium at log and stationary growth stages was first analyzed by lipidomic tools, and then 14C-acetate and 14C-glycerol were used to trace the flux of fatty acids and backbone in glycerolipids. Lipidomic analysis showed that VLCPUFAs were mostly allocated to phosphatidylcholine (PC) and TAG. PC possessed a relatively stable profile of VLCPUFAs, whereas TAG carrying VLCPUFAs were significantly increased at the stationary phase. 14C-acetate labeled VLCPUFAs were predominately incorporated into PC initially but were mostly found in TAG at later time of labeling. Positional analysis showed that PC had either one VLCPUFA at its sn-2 position (PC1) or two VLCPUFAs (PC2), while TAG incorporated VLCPUFAs almost exclusively at the sn-2 position. Similarly, 14C-glycerol was more efficiently incorporated into PC1 than TAG initially but was mostly found in TAG at later time of labeling, and diacylglycerol and PC1 shared a similar incorporation pattern. These results indicate that VLCPUFAs in TAG are mainly channeled from PC likely through diacylglycerol as the intermediate.

Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

Structural determinants of the catalytic mechanism of Plasmodium CCT, a key enzyme of malaria lipid biosynthesis

The development of the malaria parasite, Plasmodium falciparum, in the human erythrocyte, relies on phospholipid metabolism to fulfil the massive need for membrane biogenesis. Phosphatidylcholine (PC) is the most abundant phospholipid in Plasmodium membranes. PC biosynthesis is mainly ensured by the de novo Kennedy pathway that is considered as an antimalarial drug target. The CTP:phosphocholine cytidylyltransferase (CCT) catalyses the rate-limiting step of the Kennedy pathway. Here we report a series of structural snapshots of the PfCCT catalytic domain in its free, substrate- and product-complexed states that demonstrate the conformational changes during the catalytic mechanism. Structural data show the ligand-dependent conformational variations of a flexible lysine. Combined kinetic and ligand-binding analyses confirm the catalytic roles of this lysine and of two threonine residues of the helix αE. Finally, we assessed the variations in active site residues between Plasmodium and mammalian CCT which could be exploited for future antimalarial drug design.

Heterologous expression of CTP:phosphocholine cytidylyltransferase from Plasmodium falciparum rescues Chinese Hamster Ovary cells deficient in the Kennedy phosphatidylcholine biosynthesis pathway

The plasmodial CTP:phosphocholine cytidylyltransferase (PfCCT) is a promising antimalarial target, which can be inhibited to exploit the need for increased lipid biosynthesis during the erythrocytic life stage of Plasmodium falciparum. Notable structural and regulatory differences of plasmodial and mammalian CCTs offer the possibility to develop species-specific inhibitors. The aim of this study was to use CHO-MT58 cells expressing a temperature-sensitive mutant CCT for the functional characterization of PfCCT. We show that heterologous expression of wild type PfCCT restores the viability of CHO-MT58 cells at non-permissive (40 °C) temperatures, whereas catalytically perturbed or structurally destabilized PfCCT variants fail to provide rescue. Detailed in vitro characterization indicates that the H630N mutation diminishes the catalytic rate constant of PfCCT. The flow cytometry-based rescue assay provides a quantitative readout of the PfCCT function opening the possibility for the functional analysis of PfCCT and the high throughput screening of antimalarial compounds targeting plasmodial CCT.

Lipid analysis of Eimeria sporozoites reveals exclusive phospholipids, a phylogenetic mosaic of endogenous synthesis, and a host-independent lifestyle

Successful inter-host transmission of most apicomplexan parasites requires the formation of infective sporozoites within the oocysts. Unlike all other infective stages that are strictly intracellular and depend on host resources, the sporozoite stage develops outside the host cells, but little is known about its self-governing metabolism. This study deployed Eimeria falciformis, a parasite infecting the mouse as its natural host, to investigate the process of phospholipid biogenesis in sporozoites. Lipidomic analyses demonstrated the occurrence of prototypical phospholipids along with abundant expression of at least two exclusive lipids, phosphatidylthreonine (PtdThr) and inositol phosphorylceramide with a phytosphingosine backbone, in sporozoites. To produce them de novo, the parasite harbors nearly the entire biogenesis network, which is an evolutionary mosaic of eukaryotic-type and prokaryotic-type enzymes. Notably, many have no phylogenetic counterpart or functional equivalent in the mammalian host. Using Toxoplasma gondii as a gene-tractable surrogate to examine Eimeria enzymes, we show a highly compartmentalized network of lipid synthesis spread primarily in the apicoplast, endoplasmic reticulum, mitochondrion, and Golgi complex. Likewise, trans-genera complementation of a Toxoplasma mutant with the PtdThr synthase from Eimeria reveals a convergent role of PtdThr in fostering the lytic cycle of coccidian parasites. Taken together, our work establishes a model of autonomous membrane biogenesis involving significant inter-organelle cooperation and lipid trafficking in sporozoites. Phylogenetic divergence of certain pathways offers attractive drug targets to block the sporulation and subsequent transmission. Not least, our results vindicate the possession of an entire de novo lipid synthesis network in a representative protist adapted to an obligate intracellular parasitic lifestyle.

Evaluation and assessment of read-mapping by multiple next-generation sequencing aligners based on genome-wide characteristics

Massive data produced due to the advent of next-generation sequencing (NGS) technology is widely used for biological researches and medical diagnosis. The crucial step in NGS analysis is read alignment or mapping which is computationally intensive and complex. The mapping bias tends to affect the downstream analysis, including detection of polymorphisms. In order to provide guidelines to the biologist for suitable selection of aligners; we have evaluated and benchmarked 5 different aligners (BWA, Bowtie2, NovoAlign, Smalt and Stampy) and their mapping bias based on characteristics of 5 microbial genomes. Two million simulated read pairs of various sizes (36bp, 50bp, 72bp, 100bp, 125bp, 150bp, 200bp, 250bp and 300bp) were aligned. Specific alignment features such as sensitivity of mapping, percentage of properly paired reads, alignment time and effect of tandem repeats on incorrectly mapped reads were evaluated. BWA showed faster alignment followed by Bowtie2 and Smalt. NovoAlign and Stampy were comparatively slower. Most of the aligners showed high sensitivity towards long reads (>100bp) mapping. On the other hand NovoAlign showed higher sensitivity towards both short reads (36bp, 50bp, 72bp) and long reads (>100bp) mappings; It also showed higher sensitivity towards mapping a complex genome like Plasmodium falciparum. The percentage of properly paired reads aligned by NovoAlign, BWA and Stampy were markedly higher. None of the aligners outperforms the others in the benchmark, however the aligners perform differently with genome characteristics. We expect that the results from this study will be useful for the end user to choose aligner, thus enhance the accuracy of read mapping.

Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes

The overarching ambition of kinetic metabolic modeling is to capture the dynamic behavior of metabolism to such an extent that systems and synthetic biology strategies can reliably be tested in silico. The lack of kinetic data hampers the development of kinetic models, and most of the current models use ad hoc reduced stoichiometry or oversimplified kinetic rate expressions, which may limit their predictive strength. There is a need to introduce the community-level standards that will organize and accelerate the future developments in this area. We introduce here a set of requirements that will ensure the model quality, we examine the current kinetic models with respect to these requirements, and we propose a general workflow for constructing models that satisfy these requirements.