Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways.

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.

Illuminating the functions of the understudied Fructosamine-3-kinase (FN3K) using a multi-omics approach reveals new links to lipid, carbon, and co-factor metabolic pathways.

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the tree of life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3Ks and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. By identifying a potential link between FN3Ks, redox regulation, and NAD-dependent metabolic processes, our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance is altered.

Informatic challenges and advances in illuminating the druggable proteome.

The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.

Computational analysis of variation in C. elegans ugts.

Caenorhabditis elegans are free-living nematodes with a relatively short life cycle and a wealth of genomic information across multiple databases. Uridine diphosphate-glycosyltransferases (UGTs) are a family of enzymes involved in Phase II modification of xenobiotics in C. elegans , which is the addition of a sizeable water-soluble molecule to a xenobiotic to allow for its excretion out of a cell. Little is known about the variation in UGTs across wild isolates and how that might affect their innate immune response. We analyzed the diversity in ugt genes across C. elegans isolates from different geographical locations from the Caenorhabditis elegans Natural Diversity Resource (CaeNDR) database. This was accomplished using whole genome data and data identifying genome regions as hyper-divergent for each isotype. We implemented three steps to identify ugt genes and make inferences based on their variation. First, we created a catalog of UGTs in the N2 reference strain and used them to create a phylogenetic tree that depicts the relationships between the UGT protein sequences. We then quantified ugt variation using the strains from the CaeNDR database and used their data to remove hyper-divergent ugt genes. The third step was to catalog the occurrence of minor allele frequency (MAF) > 0.05 for all the ugts to compare how that aligned with genes classified as hyper-divergent by CaeNDR. Of the 67 ugt genes analyzed, 18 were hyper-divergent. This research will help improve our understanding of ugt variation in C. elegans .

Structural and biochemical insight into a modular ß-1,4-galactan synthase in plants.

Rhamnogalacturonan I (RGI) is a structurally complex pectic polysaccharide with a backbone of alternating rhamnose and galacturonic acid residues substituted with arabinan and galactan side chains. Galactan synthase 1 (GalS1) transfers galactose and arabinose to either extend or cap the ß-1,4-galactan side chains of RGI, respectively. Here we report the structure of GalS1 from Populus trichocarpa, showing a modular protein consisting of an N-terminal domain that represents the founding member of a new family of carbohydrate-binding module, CBM95, and a C-terminal glycosyltransferase family 92 (GT92) catalytic domain that adopts a GT-A fold. GalS1 exists as a dimer in vitro, with stem domains interacting across the chains in a 'handshake' orientation that is essential for maintaining stability and activity. In addition to understanding the enzymatic mechanism of GalS1, we gained insight into the donor and acceptor substrate binding sites using deep evolutionary analysis, molecular simulations and biochemical studies. Combining all the results, a mechanism for GalS1 catalysis and a new model for pectic galactan side-chain addition are proposed.

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies.

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

Polymerization of the backbone of the pectic polysaccharide rhamnogalacturonan I.

Rhamnogalacturonan I (RG-I) is a major plant cell wall pectic polysaccharide defined by its repeating disaccharide backbone structure of [4)-α-D-GalA-(1,2)-α-L-Rha-(1,]. A family of RG-I:Rhamnosyltransferases (RRT) has previously been identified, but synthesis of the RG-I backbone has not been demonstrated in vitro because the identity of Rhamnogalacturonan I:Galaturonosyltransferase (RG-I:GalAT) was unknown. Here a putative glycosyltransferase, At1g28240/MUCI70, is shown to be an RG-I:GalAT. The name RGGAT1 is proposed to reflect the catalytic activity of this enzyme. When incubated together with the rhamnosyltransferase RRT4, the combined activities of RGGAT1 and RRT4 result in elongation of RG-I acceptors in vitro into a polymeric product. RGGAT1 is a member of a new GT family categorized as GT116, which does not group into existing GT-A clades and is phylogenetically distinct from the GALACTURONOSYLTRANSFERASE (GAUT) family of GalA transferases that synthesize the backbone of the pectin homogalacturonan. RGGAT1 has a predicted GT-A fold structure but employs a metal-independent catalytic mechanism that is rare among glycosyltransferases with this fold type. The identification of RGGAT1 and the 8-member Arabidopsis GT116 family provides a new avenue for studying the mechanism of RG-I synthesis and the function of RG-I in plants.

Modularity of the hydrophobic core and evolution of functional diversity in fold A glycosyltransferases.

Hydrophobic cores are fundamental structural properties of proteins typically associated with protein folding and stability; however, how the hydrophobic core shapes protein evolution and function is poorly understood. Here, we investigated the role of conserved hydrophobic cores in fold-A glycosyltransferases (GT-As), a large superfamily of enzymes that catalyze formation of glycosidic linkages between diverse donor and acceptor substrates through distinct catalytic mechanisms (inverting versus retaining). Using hidden Markov models and protein structural alignments, we identify similarities in the phosphate-binding cassette (PBC) of GT-As and unrelated nucleotide-binding proteins, such as UDP-sugar pyrophosphorylases. We demonstrate that GT-As have diverged from other nucleotide-binding proteins through structural elaboration of the PBC and its unique hydrophobic tethering to the F-helix, which harbors the catalytic base (xED-Asp). While the hydrophobic tethering is conserved across diverse GT-A fold enzymes, some families, such as B3GNT2, display variations in tethering interactions and core packing. We evaluated the structural and functional impact of these core variations through experimental mutational analysis and molecular dynamics simulations and find that some of the core mutations (T336I in B3GNT2) increase catalytic efficiency by modulating the conformational occupancy of the catalytic base between "D-in" and acceptor-accessible "D-out" conformation. Taken together, our studies support a model of evolution in which the GT-A core evolved progressively through elaboration upon an ancient PBC found in diverse nucleotide-binding proteins, and malleability of this core provided the structural framework for evolving new catalytic and substrate-binding functions in extant GT-A fold enzymes.

Mapping the glycosyltransferase fold landscape using interpretable deep learning.

Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversification of GTs presents a major challenge in mapping the relationships connecting sequence, structure, fold and function using traditional bioinformatics approaches. Here, we present a convolutional neural network with attention (CNN-attention) based deep learning model that leverages simple secondary structure representations generated from primary sequences to provide GT fold prediction with high accuracy. The model learns distinguishing secondary structure features free of primary sequence alignment constraints and is highly interpretable. It delineates sequence and structural features characteristic of individual fold types, while classifying them into distinct clusters that group evolutionarily divergent families based on shared secondary structural features. We further extend our model to classify GT families of unknown folds and variants of known folds. By identifying families that are likely to adopt novel folds such as GT91, GT96 and GT97, our studies expand the GT fold landscape and prioritize targets for future structural studies.

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases.

BACKGROUND: Protein kinases are among the largest druggable family of signaling proteins, involved in various human diseases, including cancers and neurodegenerative disorders. Despite their clinical relevance, nearly 30% of the 545 human protein kinases remain highly understudied. Comparative genomics is a powerful approach for predicting and investigating the functions of understudied kinases. However, an incomplete knowledge of kinase orthologs across fully sequenced kinomes severely limits the application of comparative genomics approaches for illuminating understudied kinases. Here, we introduce KinOrtho, a query- and graph-based orthology inference method that combines full-length and domain-based approaches to map one-to-one kinase orthologs across 17 thousand species. RESULTS: Using multiple metrics, we show that KinOrtho performed better than existing methods in identifying kinase orthologs across evolutionarily divergent species and eliminated potential false positives by flagging sequences without a proper kinase domain for further evaluation. We demonstrate the advantage of using domain-based approaches for identifying domain fusion events, highlighting a case between an understudied serine/threonine kinase TAOK1 and a metabolic kinase PIK3C2A with high co-expression in human cells. We also identify evolutionary fission events involving the understudied OBSCN kinase domains, further highlighting the value of domain-based orthology inference approaches. Using KinOrtho-defined orthologs, Gene Ontology annotations, and machine learning, we propose putative biological functions of several understudied kinases, including the role of TP53RK in cell cycle checkpoint(s), the involvement of TSSK3 and TSSK6 in acrosomal vesicle localization, and potential functions for the ULK4 pseudokinase in neuronal development. CONCLUSIONS: In sum, KinOrtho presents a novel query-based tool to identify one-to-one orthologous relationships across thousands of proteomes that can be applied to any protein family of interest. We exploit KinOrtho here to identify kinase orthologs and show that its well-curated kinome ortholog set can serve as a valuable resource for illuminating understudied kinases, and the KinOrtho framework can be extended to any protein-family of interest.

Evolution of Functional Diversity in the Holozoan Tyrosine Kinome.

The emergence of multicellularity is strongly correlated with the expansion of tyrosine kinases, a conserved family of signaling enzymes that regulates pathways essential for cell-to-cell communication. Although tyrosine kinases have been classified from several model organisms, a molecular-level understanding of tyrosine kinase evolution across all holozoans is currently lacking. Using a hierarchical sequence constraint-based classification of diverse holozoan tyrosine kinases, we construct a new phylogenetic tree that identifies two ancient clades of cytoplasmic and receptor tyrosine kinases separated by the presence of an extended insert segment in the kinase domain connecting the D and E-helices. Present in nearly all receptor tyrosine kinases, this fast-evolving insertion imparts diverse functionalities, such as post-translational modification sites and regulatory interactions. Eph and EGFR receptor tyrosine kinases are two exceptions which lack this insert, each forming an independent lineage characterized by unique functional features. We also identify common constraints shared across multiple tyrosine kinase families which warrant the designation of three new subgroups: Src module (SrcM), insulin receptor kinase-like (IRKL), and fibroblast, platelet-derived, vascular, and growth factor receptors (FPVR). Subgroup-specific constraints reflect shared autoinhibitory interactions involved in kinase conformational regulation. Conservation analyses describe how diverse tyrosine kinase signaling functions arose through the addition of family-specific motifs upon subgroup-specific features and coevolving protein domains. We propose the oldest tyrosine kinases, IRKL, SrcM, and Csk, originated from unicellular premetazoans and were coopted for complex multicellular functions. The increased frequency of oncogenic variants in more recent tyrosine kinases suggests that lineage-specific functionalities are selectively altered in human cancers.

GTXplorer: A portal to navigate and visualize the evolutionary information encoded in fold A glycosyltransferases.

Glycosyltransferases (GTs) play a central role in sustaining all forms of life through the biosynthesis of complex carbohydrates. Despite significant strides made in recent years to establish computational resources, databases and tools to understand the nature and role of carbohydrates and related glycoenzymes, a data analytics framework that connects the sequence-structure-function relationships to the evolution of GTs is currently lacking. This hinders the characterization of understudied GTs and the synthetic design of GTs for medical and biotechnology applications. Here, we present GTXplorer as an integrated platform that presents evolutionary information of GTs adopting a GT-A fold in an intuitive format enabling in silico investigation through comparative sequence analysis to derive informed hypotheses about their function. The tree view mode provides an overview of the evolutionary relationships of GT-A families and allows users to select phylogenetically relevant families for comparisons. The selected families can then be compared in the alignment view at the residue level using annotated weblogo stacks of the GT-A core specific to the selected clade, family, or subfamily. All data are easily accessible and can be downloaded for further analysis. GTXplorer can be accessed at https://vulcan.cs.uga.edu/gtxplorer/ or from GitHub at https://github.com/esbgkannan/GTxplorer to deploy locally. By packaging multiple data streams into an accessible, user-friendly format, GTXplorer presents the first evolutionary data analytics platform for comparative glycomics.

Peters plus syndrome mutations affect the function and stability of human ß1,3-glucosyltransferase.

Peters Plus Syndrome (PTRPLS OMIM #261540) is a severe congenital disorder of glycosylation where patients have multiple structural anomalies, including Peters anomaly of the eye (anterior segment dysgenesis), disproportionate short stature, brachydactyly, dysmorphic facial features, developmental delay, and variable additional abnormalities. PTRPLS patients and some Peters Plus-like (PTRPLS-like) patients (who only have a subset of PTRPLS phenotypes) have mutations in the gene encoding ß1,3-glucosyltransferase (B3GLCT). B3GLCT catalyzes the transfer of glucose to O-linked fucose on thrombospondin type-1 repeats. Most B3GLCT substrate proteins belong to the ADAMTS superfamily and play critical roles in extracellular matrix. We sought to determine whether the PTRPLS or PTRPLS-like mutations abrogated B3GLCT activity. B3GLCT has two putative active sites, one in the N-terminal region and the other in the C-terminal glycosyltransferase domain. Using sequence analysis and in vitro activity assays, we demonstrated that the C-terminal domain catalyzes transfer of glucose to O-linked fucose. We also generated a homology model of B3GLCT and identified D421 as the catalytic base. PTRPLS and PTRPLS-like mutations were individually introduced into B3GLCT, and the mutated enzymes were evaluated using in vitro enzyme assays and cell-based functional assays. Our results demonstrated that PTRPLS mutations caused loss of B3GLCT enzymatic activity and/or significantly reduced protein stability. In contrast, B3GLCT with PTRPLS-like mutations retained enzymatic activity, although some showed a minor destabilizing effect. Overall, our data supports the hypothesis that loss of glucose from B3GLCT substrate proteins is responsible for the defects observed in PTRPLS patients, but not for those observed in PTRPLS-like patients.

Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases.

Glycosyltransferases (GTs) are prevalent across the tree of life and regulate nearly all aspects of cellular functions. The evolutionary basis for their complex and diverse modes of catalytic functions remain enigmatic. Here, based on deep mining of over half million GT-A fold sequences, we define a minimal core component shared among functionally diverse enzymes. We find that variations in the common core and emergence of hypervariable loops extending from the core contributed to GT-A diversity. We provide a phylogenetic framework relating diverse GT-A fold families for the first time and show that inverting and retaining mechanisms emerged multiple times independently during evolution. Using evolutionary information encoded in primary sequences, we trained a machine learning classifier to predict donor specificity with nearly 90% accuracy and deployed it for the annotation of understudied GTs. Our studies provide an evolutionary framework for investigating complex relationships connecting GT-A fold sequence, structure, function and regulation.

Carbohydrates are one of the major groups of large biological molecules that regulate nearly all aspects of life. Yet, unlike DNA or proteins, carbohydrates are made without a template to follow. Instead, these molecules are built from a set of sugar-based building blocks by the intricate activities of a large and diverse family of enzymes known as glycosyltransferases. An incomplete understanding of how glycosyltransferases recognize and build diverse carbohydrates presents a major bottleneck in developing therapeutic strategies for diseases associated with abnormalities in these enzymes. It also limits efforts to engineer these enzymes for biotechnology applications and biofuel production. Taujale et al. have now used evolutionary approaches to map the evolution of a major subset of glycosyltransferases from species across the tree of life to understand how these enzymes evolved such precise mechanisms to build diverse carbohydrates. First, a minimal structural unit was defined based on being shared among a group of over half a million unique glycosyltransferase enzymes with different activities. Further analysis then showed that the diverse activities of these enzymes evolved through the accumulation of mutations within this structural unit, as well as in much more variable regions in the enzyme that extend from the minimal unit. Taujale et al. then built an extended family tree for this collection of glycosyltransferases and details of the evolutionary relationships between the enzymes helped them to create a machine learning framework that could predict which sugar-containing molecules were the raw materials for a given glycosyltransferase. This framework could make predictions with nearly 90% accuracy based only on information that can be deciphered from the gene for that enzyme. These findings will provide scientists with new hypotheses for investigating the complex relationships connecting the genetic information about glycosyltransferases with their structures and activities. Further refinement of the machine learning framework may eventually enable the design of enzymes with properties that are desirable for applications in biotechnology.

Conservation of Atypical Allostery in C. elegans UDP-Glucose Dehydrogenase.

Human UDP-glucose dehydrogenase (hUGDH) oxidizes uridine diphosphate (UDP)-glucose to UDP-glucuronic acid, an essential substrate in the phase II metabolism of drugs. The activity of hUGDH is controlled by an atypical allosteric mechanism in which the feedback inhibitor UDP-xylose competes with the substrate for the active site and triggers a buried allosteric switch to produce an inactive complex (EΩ). Previous comparisons with a nonallosteric UGDH identified six large-to-small substitutions that produce packing defects in the protein core and provide the conformational flexibility necessary for the allosteric transition. Here, we test the hypothesis that these large-to-small substitutions form a motif that can be used to identify allosteric UGDHs. Caenorhabditis elegans UGDH (cUGDH) conserves this motif with the exception of an Ala-to-Pro substitution in position 109. The crystal structures of unliganded and UDP-xylose bound cUGDH show that the A109P substitution is accommodated by an Asn-to-Ser substitution at position 290. Steady-state analysis and sedimentation velocity studies show that the allosteric transition is conserved in cUGDH. The enzyme also exhibits hysteresis in progress curves and negative cooperativity with respect to NAD+ binding. Both of these phenomena are conserved in the human enzyme, which is strong evidence that these represent fundamental features of atypical allostery in UGDH. A phylogenetic analysis of UGDH shows that the atypical allostery motif is ancient and identifies a potential transition point in the evolution of the UGDH family.

Tracing the origin and evolution of pseudokinases across the tree of life.

Protein phosphorylation by eukaryotic protein kinases (ePKs) is a fundamental mechanism of cell signaling in all organisms. In model vertebrates, ~10% of ePKs are classified as pseudokinases, which have amino acid changes within the catalytic machinery of the kinase domain that distinguish them from their canonical kinase counterparts. However, pseudokinases still regulate various signaling pathways, usually doing so in the absence of their own catalytic output. To investigate the prevalence, evolutionary relationships, and biological diversity of these pseudoenzymes, we performed a comprehensive analysis of putative pseudokinase sequences in available eukaryotic, bacterial, and archaeal proteomes. We found that pseudokinases are present across all domains of life, and we classified nearly 30,000 eukaryotic, 1500 bacterial, and 20 archaeal pseudokinase sequences into 86 pseudokinase families, including ~30 families that were previously unknown. We uncovered a rich variety of pseudokinases with notable expansions not only in animals but also in plants, fungi, and bacteria, where pseudokinases have previously received cursory attention. These expansions are accompanied by domain shuffling, which suggests roles for pseudokinases in plant innate immunity, plant-fungal interactions, and bacterial signaling. Mechanistically, the ancestral kinase fold has diverged in many distinct ways through the enrichment of unique sequence motifs to generate new families of pseudokinases in which the kinase domain is repurposed for noncanonical nucleotide binding or to stabilize unique, inactive kinase conformations. We further provide a collection of annotated pseudokinase sequences in the Protein Kinase Ontology (ProKinO) as a new mineable resource for the signaling community.

A Toxoplasma Prolyl Hydroxylase Mediates Oxygen Stress Responses by Regulating Translation Elongation.

As the protozoan parasite Toxoplasma gondii disseminates through its host, it responds to environmental changes by altering its gene expression, metabolism, and other processes. Oxygen is one variable environmental factor, and properly adapting to changes in oxygen levels is critical to prevent the accumulation of reactive oxygen species and other cytotoxic factors. Thus, oxygen-sensing proteins are important, and among these, 2-oxoglutarate-dependent prolyl hydroxylases are highly conserved throughout evolution. Toxoplasma expresses two such enzymes, TgPHYa, which regulates the SCF-ubiquitin ligase complex, and TgPHYb. To characterize TgPHYb, we created a Toxoplasma strain that conditionally expresses TgPHYb and report that TgPHYb is required for optimal parasite growth under normal growth conditions. However, exposing TgPHYb-depleted parasites to extracellular stress leads to severe decreases in parasite invasion, which is likely due to decreased abundance of parasite adhesins. Adhesin protein abundance is reduced in TgPHYb-depleted parasites as a result of inactivation of the protein synthesis elongation factor eEF2 that is accompanied by decreased rates of translational elongation. In contrast to most other oxygen-sensing proteins that mediate cellular responses to low O2, TgPHYb is specifically required for parasite growth and protein synthesis at high, but not low, O2 tensions as well as resistance to reactive oxygen species. In vivo, reduced TgPHYb expression leads to lower parasite burdens in oxygen-rich tissues. Taken together, these data identify TgPHYb as a sensor of high O2 levels, in contrast to TgPHYa, which supports the parasite at low O2IMPORTANCE Because oxygen plays a key role in the growth of many organisms, cells must know how much oxygen is available. O2-sensing proteins are therefore critical cellular factors, and prolyl hydroxylases are the best-studied type of O2-sensing proteins. In general, prolyl hydroxylases trigger cellular responses to decreased oxygen availability. But, how does a cell react to high levels of oxygen? Using the protozoan parasite Toxoplasma gondii, we discovered a prolyl hydroxylase that allows the parasite to grow at elevated oxygen levels and does so by regulating protein synthesis. Loss of this enzyme also reduces parasite burden in oxygen-rich tissues, indicating that sensing both high and low levels of oxygen impacts the growth and physiology of Toxoplasma.

Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.-Hil.

INTRODUCTION: Molecular networks are now established as the method of choice for tandem mass spectrometry dereplication and similarity-based structure elucidation. Node identification can be used to start the propagation of the structure elucidation of unknown compounds progressively. OBJECTIVE: To demonstrate the capabilities of using the LipidXplorer data results along with molecular networking to identify nodes and aid sequential structure elucidation of unknown compounds. MATERIAL AND METHODS: Molecular fragmentation query language (MFQL) files were written to identify glycoalkaloids based on known structures described for Solanum species. A dataset generated from liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of Solanum pseudoquina sample were submitted to dereplication on both LipidXplorer software and Global Natural Products Social Molecular Network (GNPS) online system. The resulting attribute table from GNPS calculations was merged with the LipidXplorer results and this merged file was used for network visualisation in Cytoscape. Nodes in the molecular network were labelled using the LipidXplorer identifiers, thus assisting the structure elucidation of unidentified compounds. RESULTS: The combination of the LipidXplorer glycoalkaloids list and GNPS analysis was used in Cytoscape to label nodes in the molecular network. The analysis of the network using these labelled starting points triggered the structure elucidation of closely related nodes leading to the identification of 30 compounds using the LipidXplorer output and four purified and structure elucidated compounds, including a new glycoalkaloids identified as 3-O-(ß-d-xylopyranosyl)-(20R,25S)-22,26-epimino-16-acetyl-cholesta-5,22(N)-diene. CONCLUSION: A significant compound identification completely based on molecular formula and fragmentation queries was achieved. This new and effective approach could help researches to expand the identification rate of compounds in dereplication studies using molecular networks.

Dereplication of plant phenolics using a mass-spectrometry database independent method.

INTRODUCTION: Dereplication, an approach to sidestep the efforts involved in the isolation of known compounds, is generally accepted as being the first stage of novel discoveries in natural product research. It is based on metabolite profiling analysis of complex natural extracts. OBJECTIVE: To present the application of LipidXplorer for automatic targeted dereplication of phenolics in plant crude extracts based on direct infusion high-resolution tandem mass spectrometry data. MATERIAL AND METHODS: LipidXplorer uses a user-defined molecular fragmentation query language (MFQL) to search for specific characteristic fragmentation patterns in large data sets and highlight the corresponding metabolites. To this end, MFQL files were written to dereplicate common phenolics occurring in plant extracts. Complementary MFQL files were used for validation purposes. RESULTS: New MFQL files with molecular formula restrictions for common classes of phenolic natural products were generated for the metabolite profiling of different representative crude plant extracts. This method was evaluated against an open-source software for mass-spectrometry data processing (MZMine®) and against manual annotation based on published data. CONCLUSION: The targeted LipidXplorer method implemented using common phenolic fragmentation patterns, was found to be able to annotate more phenolics than MZMine® that is based on automated queries on the available databases. Additionally, screening for ascarosides, natural products with unrelated structures to plant phenolics collected from the nematode Caenorhabditis elegans, demonstrated the specificity of this method by cross-testing both groups of chemicals in both plants and nematodes.