MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics.

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Use of ionic liquids in amidation reactions for proteolysis targeting chimera synthesis.

Selective degradation of disease-causing proteins using proteolysis targeting chimeras (PROTACs) has gained great attention, thanks to its several advantages over traditional therapeutic modalities. Despite the advances made so far, the structural chemical complexity of PROTACs poses challenges in their synthetic approaches. PROTACs are typically prepared through a convergent approach, first synthesizing two fragments separately (target protein and E3 ligase ligands) and then coupling them to produce a fully assembled PROTAC. The amidation reaction represents the most common coupling exploited in PROTACs synthesis. Unfortunately, the overall isolated yields of such synthetic procedures are usually low due to one or more purification steps to obtain the final PROTAC with acceptable purity. In this work, we focused our attention on the optimization of the final amidation step for the synthesis of an anti-SARS-CoV-2 PROTAC by investigating different amidation coupling reagents and a range of alternative solvents, including ionic liquids (ILs). Among the ILs screened, [OMIM][ClO4] emerged as a successful replacement for the commonly used DMF within the HATU-mediated amidation reaction, thus allowing the synthesis of the target PROTAC under mild and sustainable conditions in very high isolated yields. With the optimised conditions in hand, we explored the scalability of the synthetic approach and the substrate scope of the reaction by employing different E3 ligase ligand (VHL and CRBN)-based intermediates containing linkers of different lengths and compositions or by using different target protein ligands. Interestingly, in all cases, we obtained high isolated yields and complete conversion in short reaction times.

DeepGRID: Deep Learning Using GRID Descriptors for BBB Prediction.

Deep Learning approaches are able to automatically extract relevant features from the input data and capture nonlinear relationships between the input and output. In this work, we present the GRID-derived AI (GrAId) descriptors, a simple modification to GRID MIFs that facilitate their use in combination with Convolutional Neural Networks (CNNs) to build Deep Learning models in a rotationally, conformationally, and alignment-independent approach we are calling DeepGRID. To our knowledge, this is the first time that GRID MIFs have been combined with CNNs in a Deep Learning approach. We applied the approach to build regression and classification models for blood-brain barrier permeation, an important factor when designing CNS drugs and conversely when designing to avoid off-target effects for CNS-inactive drugs. The VolSurf approach was one of the first to successfully model this property from three-dimensional structures, using descriptors derived from their GRID Molecular Interaction Fields (MIFs) in combination with PLS. We compared the DeepGRID models with others built using the hand-crafted VolSurf descriptors in combination with both PLS and Random Forest (RF). Both the DeepGRID and RF regression models performed best according to the % of compounds with a Geometric Mean Fold Error (GMFE) within 2-fold of the experimental data. Applying these regression models as classifiers, for the smaller 332 and 416 compound data sets all models performed well with ROC AUC values of ∼0.9 on the external test set. For the larger 2105 compound data set, the DeepGRID classifier performed the best with an AUC of 0.87 on the external test set with the RF model having an AUC of 0.84 and the original VolSurf lgBB model having an AUC of 0.83.

Endoplasmic reticulum stress and NF-kB activation in SARS-CoV-2 infected cells and their response to antiviral therapy.

Unfolded protein response (UPR) and endoplasmic reticulum (ER) stress are aspects of SARS-CoV-2-host cell interaction with proposed role in the cytopathic and inflammatory pathogenesis of this viral infection. The role of the NF-kB pathway in these cellular processes remains poorly characterized. When investigated in VERO-E6 cells, SARS-CoV-2 infection was found to markedly stimulate NF-kB protein expression and activity. NF-kB activation occurs early in the infection process (6 hpi) and it is associated with increased MAPK signaling and expression of the UPR inducer IRE-1α. These signal transduction processes characterize the cellular stress response to the virus promoting a pro-inflammatory environment and caspase activation in the host cell. Inhibition of viral replication by the viral protease inhibitor Nelfinavir reverts all these molecular changes also stimulating c-Jun expression, a key component of the JNK/AP-1 pathway with important role in the IRE-1α-mediated transcriptional regulation of stress response genes with anti-inflammatory and cytoprotection function. The present study demonstrates that UPR signaling and its interaction with cellular MAPKs and the NF-kB activity are important aspects of SARS-CoV-2-host cell interaction that deserve further investigation to identify more efficient therapies for this viral infection.

Resistance to PI3Kδ inhibitors in marginal zone lymphoma can be reverted by targeting the IL-6/PDGFRA axis.

PI3Kδ inhibitors are active in patients with lymphoid neoplasms and a first series of them have been approved for the treatment of multiple types of B-cell lymphoid tumors, including marginal zone lymphoma (MZL). The identification of the mechanisms underlying either primary or secondary resistance is fundamental to optimize the use of novel drugs. Here we present a model of secondary resistance to PI3Kδ inhibitors obtained by prolonged exposure of a splenic MZL cell line to idelalisib. The VL51 cell line was kept under continuous exposure to idelalisib. The study included detailed characterization of the model, pharmacological screens, silencing experiments, and validation experiments on multiple cell lines and on clinical specimens. VL51 developed resistance to idelalisib, copanlisib, duvelisib, and umbralisib. An integrative analysis of transcriptome and methylation data highlighted an enrichment of upregulated transcripts and low-methylated promoters in resistant cells, including IL-6/STAT3- and PDGFRA-related genes and surface CD19 expression, alongside the repression of the let-7 family of miRNA, and miR-125, miR-130, miR-193 and miR-20. The IL-6R blocking antibody tocilizumab, the STAT3 inhibitor stattic, the LIN28 inhibitor LIN1632, the PDGFR inhibitor masitinib and the anti-CD19 antibody drug conjugate loncastuximab tesirine were active compounds in the resistant cells as single agents and/or in combination with PI3Kδ inhibition. Findings were validated on additional in vitro lymphoma models and on clinical specimens. A novel model of resistance obtained from splenic MZL allowed the identification of therapeutic approaches able to improve the antitumor activity of PI3Kδ inhibitors in B-cell lymphoid tumors.

Melatonin modulates Nrf2 activity to protect porcine pre-pubertal Sertoli cells from the abnormal H2 O2 generation and reductive stress effects of cadmium.

Melatonin (MLT) is a cytoprotective agent holding potential to prevent cadmium (Cd) toxicity and its impact in testicular function and fertility. In this study, we explored such potential in porcine pre-pubertal Sertoli cells (SCs). Cd toxicity resulted in impaired SC viability and function, abnormal cellular H2 O2 generation and efflux, and induction of reductive stress by the upregulation of Nrf2 expression and activity, cystine uptake and glutathione biosynthesis, glutathione-S-transferase P (GSTP) expression, and protein glutathionylation inhibition. Cd toxicity also stimulated the activity of cellular kinases (MAPK-ERK1/2 and Akt) and NFkB transcription factor, and cJun expression was increased. MLT produced a potent cytoprotective effect when co-administered with Cd to SCs; its efficacy and the molecular mechanism behind its cytoprotective function varied according to Cd concentrations. However, a significant restoration of cell viability and function, and of H2 O2 levels, was observed both at 5 and 10 µM Cd. Mechanistically, these effects of MLT were associated with a significant reduction of the Cd-induced activation of Nrf2 and GSTP expression at all Cd concentrations. CAT and MAPK-ERK1/2 activity upregulation was associated with these effects at 5 µM Cd, whereas glutathione biosynthesis and efflux were involved at 10 µM Cd together with an increased expression of the cystine transporter xCT, of cJun and Akt and NFkB activity. MLT protects SCs from Cd toxicity reducing its H2 O2 generation and reductive stress effects. A reduced activity of Nrf2 and the modulation of other molecular players of MLT signaling, provide a mechanistic rational for the cytoprotective effect of this molecule in SCs.

FragExplorer: GRID-Based Fragment Growing and Replacement.

Understanding which chemical modifications can be made to known ligands is a key aspect of structure-based drug design and one that was pioneered by the software GRID. We developed FragExplorer with the explicit aim of showing GRID users which fragments would best match the GRID molecular interaction fields in a protein binding site, given a bound ligand as a starting point. Users can grow ligands or replace existing moieties; the R-Group Exploration mode identifies all potential R-Groups and searches for replacements automatically; the Scaffold Exploration mode does the same for all potential scaffolds. For a ligand with three points of variation, R-Group Exploration will typically explore a chemical space of 1016 potential molecules; including Scaffold Exploration increases this to 1022. FragExplorer was designed to be integrated within an interactive 3D Editor/Designer; therefore, the speed of computation was an important consideration; a typical fragment search takes 20 seconds. In a fragment reprediction test, FragExplorer demonstrates an overall fragment retrieval rate of 55%, increasing to 69% for smaller fragments. At a 90% substructural match, the retrieval rate increases to ∼80%. We also show how the approach could have been used to hop from olmesartan to azilsartan or to optimize a p38 MAP kinase lead to a compound that bears similarity to a known nanomolar inhibitor.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions.

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening.

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

CROMATIC: Cross-Relationship Map of Cavities from Coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19 disease, has rapidly imposed an urgent need to identify effective drug candidates. In this context, the high resolution and non-redundant beta-Coronavirus protein cavities database is pivotal to help virtual screening protocols. Furthermore, the cross-relationship among cavities can lead to highlighting multitarget therapy chances. Here, we first collect all protein cavities on SARS-CoV-2, SARS-CoV, and MERS-CoV X-ray structures, and then, we compute a similarity map by using molecular interaction fields (MIFs). All the results come together in CROMATIC (CROss-relationship MAp of CaviTIes from Coronaviruses). CROMATIC encloses both a comprehensive and a non-redundant version of the cavities collection and a similarity map revealing, on the one hand, cavities that are conserved among the three Coronaviruses and, on the other hand, unexpected similarities among cavities that can represent a key starting point for multitarget therapy strategies. Similarity analysis was also performed for the available structures of SARS-CoV-2 spike variants, linking sequence mutations to three-dimensional interaction alterations. The CROMATIC repository is freely available to the scientific community at https://github.com/moldiscovery/sars-cromatic.

Discovery of a new class of triazole based inhibitors of acetyl transferase KAT2A.

We have recently developed a new synthetic methodology that provided both N-aryl-5-hydroxytriazoles and N-pyridine-4-alkyl triazoles. A selection of these products was carried through virtual screening towards targets that are contemporary and validated for drug discovery and development. This study determined a number of potential structure target dyads of which N-pyridinium-4-carboxylic-5-alkyl triazole displayed the highest score specificity towards KAT2A. Binding affinity tests of abovementioned triazole and related analogs towards KAT2A confirmed the predictions of the in-silico assay. Finally, we have run in vitro inhibition assays of selected triazoles towards KAT2A; the ensemble of binding and inhibition assays delivered pyridyl-triazoles carboxylates as the prototype of a new class of inhibitors of KAT2A.

Exploring Ligand Binding Domain Dynamics in the NRs Superfamily.

Nuclear receptors (NRs) are transcription factors that play an important role in multiple diseases, such as cancer, inflammation, and metabolic disorders. They share a common structural organization composed of five domains, of which the ligand-binding domain (LBD) can adopt different conformations in response to substrate, agonist, and antagonist binding, leading to distinct transcription effects. A key feature of NRs is, indeed, their intrinsic dynamics that make them a challenging target in drug discovery. This work aims to provide a meaningful investigation of NR structural variability to outline a dynamic profile for each of them. To do that, we propose a methodology based on the computation and comparison of protein cavities among the crystallographic structures of NR LBDs. First, pockets were detected with the FLAPsite algorithm and then an "all against all" approach was applied by comparing each pair of pockets within the same sub-family on the basis of their similarity score. The analysis concerned all the detectable cavities in NRs, with particular attention paid to the active site pockets. This approach can guide the investigation of NR intrinsic dynamics, the selection of reference structures to be used in drug design and the easy identification of alternative binding sites.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications.

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well-established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen-bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint-based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT-derived EP, and that there is correlation of the new potentials with hydrogen-bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non-HB atoms, can also be used to estimate sites of reaction.

Binding of Androgen- and Estrogen-Like Flavonoids to Their Cognate (Non)Nuclear Receptors: A Comparison by Computational Prediction.

Flavonoids are plant bioactives that are recognized as hormone-like polyphenols because of their similarity to the endogenous sex steroids 17ß-estradiol and testosterone, and to their estrogen- and androgen-like activity. Most efforts to verify flavonoid binding to nuclear receptors (NRs) and explain their action have been focused on ERα, while less attention has been paid to other nuclear and non-nuclear membrane androgen and estrogen receptors. Here, we investigate six flavonoids (apigenin, genistein, luteolin, naringenin, quercetin, and resveratrol) that are widely present in fruits and vegetables, and often used as replacement therapy in menopause. We performed comparative computational docking simulations to predict their capability of binding nuclear receptors ERα, ERß, ERRß, ERRγ, androgen receptor (AR), and its variant ART877A and membrane receptors for androgens, i.e., ZIP9, GPRC6A, OXER1, TRPM8, and estrogens, i.e., G Protein-Coupled Estrogen Receptor (GPER). In agreement with data reported in literature, our results suggest that these flavonoids show a relevant degree of complementarity with both estrogen and androgen NR binding sites, likely triggering genomic-mediated effects. It is noteworthy that reliable protein-ligand complexes and estimated interaction energies were also obtained for some suggested estrogen and androgen membrane receptors, indicating that flavonoids could also exert non-genomic actions. Further investigations are needed to clarify flavonoid multiple genomic and non-genomic effects. Caution in their administration could be necessary, until the safe assumption of these natural molecules that are largely present in food is assured.

Use of Phenotypically Poor Metabolizer Individual Donor Human Liver Microsomes To Identify Selective Substrates of UGT2B10.

UDP-glucuronosyltransferase (UGT)1A4 and UGT2B10 are the human UGT isoforms most frequently involved in N-glucuronidation of drugs. UGT2B10 exhibits higher affinity than UGT1A4 for numerous substrates, making it potentially the more important enzyme for metabolism of these compounds in vivo. Clinically relevant UGT2B10 polymorphisms, including a null activity splice site mutation common in African populations, can lead to large exposure differences for UGT2B10 substrates that may limit their developability as marketed drugs. UGT phenotyping approaches using recombinantly expressed UGTs are limited by low enzyme activity and lack of validation of scaling to in vivo. In this study, we describe the use of an efficient experimental protocol for identification of UGT2B10-selective substrates (i.e., those with high fraction metabolized by UGT2B10), which exploits the activity difference between pooled human liver microsomes (HLM) and HLM from a phenotypically UGT2B10 poor metabolizer donor. Following characterization of the approach with eight known UGT2B10 substrates, we used ligand-based virtual screening and literature precedents to select 24 potential UGT2B10 substrates of 140 UGT-metabolized drugs for testing. Of these, dothiepin, cidoxepin, cyclobenzaprine, azatadine, cyproheptadine, bifonazole, and asenapine were indicated to be selective UGT2B10 substrates that have not previously been described. UGT phenotyping experiments and tests comparing conjugative and oxidative clearance were then used to confirm these findings. These approaches provide rapid and sensitive ways to evaluate whether a potential drug candidate cleared via glucuronidation will be sensitive to UGT2B10 polymorphisms in vivo. SIGNIFICANCE STATEMENT: The role of highly polymorphic UDP-glucuronosyltransferase (UGT)2B10 is likely to be underestimated currently for many compounds cleared via N-glucuronidation due to high test concentrations often used in vitro and low activity of UGT2B10 preparations. The methodology described in this study can be combined with the assessment of UGT versus oxidative in vitro metabolism to rapidly identify compounds likely to be sensitive to UGT2B10 polymorphism (high fraction metabolized by UGT2B10), enabling either chemical modification or polymorphism risk assessment before candidate selection.

A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues.

The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies. The "omics", such as genomics, proteomics, and metabolomics, have recently entered pharmaceutical research in both drug discovery and drug development, but to the best of our knowledge, no applications in high-throughput safety risk assessment have been attempted so far. This paper reports an innovative method to anticipate adverse drug effects in an early discovery phase based on lipid fingerprints using human three-dimensional microtissues. The risk of clinical hepatotoxicity potential was evaluated for a data set of 22 drugs belonging to five different therapeutic chemical classes and with various drug-induced liver injury effect. The treatment of microtissues with repeated doses of each drug allowed collecting lipid fingerprints for five time points (2, 4, 7, 9, and 11 days), and multivariate statistical analysis was applied to search for correlations with the hepatotoxic effect. The method allowed clustering of the drugs based on their hepatotoxic effect, and the observed lipid impairments for a number of drugs was confirmed by literature sources. Compared to traditional screening methods, here multiple interconnected variables (lipids) are measured simultaneously, providing a snapshot of the cellular status from the lipid perspective at a molecular level. Applied here to hepatotoxicity, the proposed workflow can be applied to several tissues, being tridimensional microtissues from various origins.

Structure-metabolism relationships in human-AOX: Chemical insights from a large database of aza-aromatic and amide compounds.

Aldehyde oxidase (AOX) is a metabolic enzyme catalyzing the oxidation of aldehyde and aza-aromatic compounds and the hydrolysis of amides, moieties frequently shared by the majority of drugs. Despite its key role in human metabolism, to date only fragmentary information about the chemical features responsible for AOX susceptibility are reported and only "very local" structure-metabolism relationships based on a small number of similar compounds have been developed. This study reports a more comprehensive coverage of the chemical space of structures with a high risk of AOX phase I metabolism in humans. More than 270 compounds were studied to identify the site of metabolism and the metabolite(s). Both electronic [supported by density functional theory (DFT) calculations] and exposure effects were considered when rationalizing the structure-metabolism relationship.

How computational chemistry develops: a tribute to Peter Goodford.

This editorial discusses the foundation of aspects of computational chemistry and is a tribute to Peter Goodford, one of those founders, who recently passed away. Several colleagues describe Professor Goodford's work and the person himself.

Interaction of Mycotoxin Alternariol with Serum Albumin.

Alternariol (AOH) is a mycotoxin produced by Alternaria species. In vitro studies suggest the genotoxic, mutagenic, and endocrine disruptor effects of AOH, and an increased incidence of esophageal cancer has been reported related to higher AOH exposure. Human serum albumin (HSA) is the most abundant plasma protein in the circulation, it is able to affect toxicokinetic properties of numerous xenobiotics. HSA forms stable complexes with several mycotoxins, however, the interaction of AOH with albumin has not been examined. In this study, the complex formation of AOH with HSA was tested, employing fluorescence spectroscopy, ultrafiltration, and molecular modeling. Each spectroscopic measurement shows the formation of stable AOH-HSA complexes (K = 4 × 105 L/mol). Investigations with site markers (in spectroscopic and ultrafiltration models) as well as modeling studies suggest that AOH occupies Sudlow's site I as a high-affinity binding site in HSA. The binding affinity of AOH towards bovine, porcine, and rat albumins was also tested, suggesting that AOH binds to rat albumin with considerably higher affinity than other albumins tested. Our results demonstrate the strong interaction of AOH with serum albumins, suggesting the potential in vivo importance of these interactions.

Delving into the Polar Lipidome by Optimized Chromatographic Separation, High-Resolution Mass Spectrometry, and Comprehensive Identification with Lipostar: Microalgae as Case Study.

The work describes the chromatographic separation optimization of polar lipids on Kinetex-EVO, particularly focusing on sulfolipids in spirulina microalgae ( Arthrospira platensis). Gradient shape and mobile-phase modifiers (pH and buffer) were tested on lipid standards. Different conditions were evaluated, and resolution, peak capacity, and peak shape were calculated both in negative mode, for sulfolipids and phospholipids, and in positive mode, for glycolipids. A high-confidence lipid identification strategy was also applied. In collaboration with software creators and developers, Lipostar was implemented to improve the identification of phosphoglycerolipids and to allow the identification of glycosylmonoradyl- and glycosyldiradyl-glycerols classes, the last being the main focus of this work. By this approach, an untargeted screening also for searching lipids not yet reported in the literature could be accomplished. The optimized chromatographic conditions and database search were tested for lipid identification first on the standard mixture, then on the polar lipid extract of spirulina microalgae, for which 205 lipids were identified.