Search

In the U.S. and Canada, Fusarium graminearum (Fg) is the predominant etiological agent of Fusarium head blight (FHB), an economically devastating fungal disease of wheat and other small grains. Besides yield losses, FHB leads to grain contamination with trichothecene mycotoxins that are harmful to plant, human, and livestock health. Three genetic North American populations of Fg, differing in their predominant trichothecene chemotype (i.e., NA1/15ADON, NA2/3ADON, and NA3/NX-2), have been identified. To improve our understanding of the newly discovered population NA3 and how population-level diversity influences FHB outcomes, we inoculated heads of the moderately resistant wheat cultivar Alsen with 15 representative strains from each population and evaluated disease progression, mycotoxin accumulation, and mycotoxin production per unit Fg biomass. Additionally, we evaluated population-specific differences in induced host defense responses. The NA3 population was significantly less aggressive than the NA1 and NA2 populations but posed a similar mycotoxigenic potential. Multi-omics analyses revealed patterns in mycotoxin production per unit Fg biomass, expression of Fg aggressiveness-associated genes, and host defense responses that did not always correlate with the NA3-specific severity difference. Our comparative disease assay of NA3/NX-2 and admixed NA1/NX-2 strains indicated that the reduced NA3 aggressiveness is not due solely to the NX-2 chemotype. Notably, the NA1 and NA2 populations did not show a significant advantage over NA3 in perithecia production, a fitness-related trait. Together, our data highlight that the disease outcomes were not due to mycotoxin production or host defense alone, indicating that other virulence factors and/or host defense mechanisms are likely involved.

Motivation Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography–electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes. Results We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. Availability and implementation BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. Supplementary information Supplementary data are available at Bioinformatics online.

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities.Author summaryCompared with small-molecule drugs, aptamer drugs are short RNAs/DNAs that can specifically bind to targets with high strength. With the interest of discovering novel aptamer drugs as an alternative to address the long-lasting COVID-19 pandemic, in this research, we developed an artificial intelligence (AI) framework for the in silico design of novel aptamer drugs that can prevent the SARS-CoV-2 virus from entering human cells. Our research is valuable as we explore a novel approach for the treatment of SARS-CoV-2 infection and the AI framework could be applied to address future health crises.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Delirium is an acute change in attention and cognition occurring in ~65% of severe SARS-CoV-2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium-susceptibility in the postoperative setting using metabolomic profiling of cerebrospinal fluid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found significant concentration 2 differences in several amino acids, acylcarnitines and polyamines linking delirium-prone patients to known factors in Alzheimer’s disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein-protein docking analysis showed that there potentially is strong binding of SARS-CoV-2 spike protein to MAOB. The possibility that SARS-CoV-2 influences MAOB activity leading to the observed neurological and platelet-based complications of SARS-CoV-2 infection requires further investigation.

Cell cultures are one of the most useful tools in life science research and biotechnology, irreplaceable for applications as diverse as testing of drugs or toxins, development of gene and cell therapies, investigation of the function of biomolecules, production of biologics or vaccine particles. Metabolomics and lipidomics with their closest link to cell phenotype and as indicators of functional effects and changes in biological systems provide a highly valuable window into many applications of cell cultures. These omics tools can be used for the real time monitoring of cell culture growth and provide detailed, nonbiased analysis of changes within cells or in secreted media or extracellular vesicles. Metabolomics can be used to optimize cell growth for enhanced production in bioreactor applications or for monitoring cell health, for example, in cell therapy development. In this chapter, we outline some examples and methods used in metabolomics and lipidomics of cell cultures including analysis of cells, media, and extracellular vesicles. Experimental method presentation is followed by introduction to analysis and modeling tools including statistical, mechanistic, and machine learning methods with particular relevance in the applications of metabolomics and lipidomics in cell cultures.

Background:: The current therapeutic options available to patients diagnosed with Amyotrophic Lateral Sclerosis (ALS) are limited and edaravone is a compound that has gained significant interest for its therapeutic potential in this condition. Objectives: : The current work was thus undertaken to synthesize and characterize a series of edaravone analogues. Methods: A total of 17 analogues were synthesized and characterized for their antioxidant properties, radical scavenging potential and copper-chelating capabilities. Results: Radical scavenging and copper-chelating properties were notably observed for edaravone. Analogues bearing hydrogen in position 1 and a phenyl at position 3 and a phenyl in both positions of pyrazol-5 (4H)-one displayed substantial radical scavenging, antioxidants and copper-chelating properties. High accessibility of electronegative groups combined with higher electronegativity and partial charge of the carbonyl moiety in edaravone might explain the observed difference in the activity of edaravone relative to the closely related analogues 6 and 7 bearing hydrogen at position 1 and a phenyl at position 3 (6) and a phenyl in both positions (7). Conclusion: Overall, this study reveals a subset of edaravone analogues with interesting properties. Further investigation of these compounds is foreseen in relevant models of oxidative stress-associated diseases in order to assess their therapeutic potential in such conditions.

Kidney cancer is one of the top ten cancer diagnosed worldwide and its incidence has increased the last 20 years. Clear Cell Renal Cell Carcinoma (ccRCC) are characterized by mutations that inactivate the von Hippel-Lindau (VHL) tumor suppressor gene and evidence indicated alterations in metabolic pathways, particularly in glutamine metabolism. We previously identified a small molecule, STF-62247, which target VHL-deficient renal tumors by affecting late-stages of autophagy and lysosomal signaling. In this study, we investigated ccRCC metabolism in VHL-deficient and proficient cells exposed to the small molecule. Metabolomics profiling using 1H NMR demonstrated that STF-62247 increases levels of glucose, pyruvate, glycerol 3-phosphate while glutamate, asparagine, and glutathione significantly decreased. Diminution of glutamate and glutamine was further investigated using mass spectrometry, western blot analyses, enzymatic activities, and viability assays. We found that expression of SLC1A5 increases in VHL-deficient cells treated with STF-62247, possibly to stimulate glutamine uptake intracellularly to counteract the diminution of this amino acid. However, exogenous addition of glutamine was not able to rescue cell viability induced by the small molecule. Instead, our results showed that VHL-deficient cells utilize glutamine to produce fatty acid in response to STF-62247. Surprisingly, this occurs through oxidative phosphorylation in STF-treated cells while control cells use reductive carboxylation to sustain lipogenesis. We also demonstrated that STF-62247 stimulated expression of stearoyl-CoA desaturase (SCD1) and peripilin2 (PLIN2) to generate accumulation of lipid droplets in VHL-deficient cells. Moreover, the carnitine palmitoyltransferase 1A (CPT1A), which control the entry of fatty acid into mitochondria for β-oxidation, also increased in response to STF-62247. CPT1A overexpression in ccRCC is known to limit tumor growth. Together, our results demonstrated that STF-62247 modulates cellular metabolism of glutamine, an amino acid involved in the autophagy-lysosome process, to support lipogenesis, which could be implicated in the signaling driving to cell death.

BackgroundAntibodies raised against human seasonal coronaviruses (sCoVs), which are responsible for the common cold, are known to cross-react with SARS-CoV-2 antigens. This prompts questions about their protective role against SARS-CoV-2 infections and COVID-19 severity. However, the relationship between sCoV exposure and SARS-CoV-2 correlates of protection are not clearly identified.MethodsWe performed a cross-sectional analysis of cross-reactivity and cross-neutralization to SARS-CoV-2 antigens (S-RBD, S-trimer, N) using pre-pandemic serum from four different groups: pediatrics and adolescents, persons 21 to 70 years of age, older than 70 years of age, and persons living with HCV or HIV.FindingsAntibody cross-reactivity to SARS-CoV-2 antigens varied between 1.6% and 15.3% depending on the cohort and the isotype-antigen pair analyzed. We also show a range of neutralizing activity (0-45%) in serum that interferes with SARS-CoV-2 spike attachment to ACE2. While the abundance of sCoV antibodies did not directly correlate with neutralization, we show that neutralizing activity is rather dependent on relative ratios of IgGs in sera directed to all four sCoV spike proteins. More specifically, we identified antibodies to NL63 and OC43 as being the most important predictors of neutralization.InterpretationOur data support that exposure to sCoVs triggers antibody responses that influence the efficiency of SARS-CoV-2 spike binding to ACE2, and may also impact COVID-19 disease severity through other latent variables.Research in ContextEvidence before this studyThere is a growing body of evidence showing that within the population there are varying levels of pre-existing immunity to SARS-CoV-2 infection and possibly COVID-19 disease severity. This immunity is believed to be attributable to prior infection by four prevalent seasonal coronaviruses (sCoVs) responsible for the common cold. Pre-existing immunity can be assessed in part by antibodies directed to sCoVs that also cross-react to SARS-CoV-2 antigens. The SARS-CoV-2 spike and, more specifically, the receptor binding domain are the primary targets for neutralizing antibodies. It is unclear if cross-reactive antibodies to SARS-CoV-2 are neutralizing and are also responsible for the broad spectrum of COVID-19 disease severity, from asymptomatic to critical, observed in the infected population.Added-value of this studyHere we carried out a detailed analysis of sCoV prevalence in samples acquired before the pandemic from individuals of various age groups and in people living with HIV and HCV. We then analyzed the frequency of all the different types of antibodies that cross-react to three SARS-CoV-2 antigens. We found a high level of people with cross-reactive antibodies, surprisingly we also detected that some people have antibodies that block the SARS-CoV-2 spike from binding to its human receptor, ACE2. By using machine learning, we were able to accurate predict which individuals can neutralize SARS-CoV-2 spike-ACE2 interactions based on their relative ratios of antibodies against the four sCoVs.Implications of all the available evidenceWe demonstrate that it not absolute levels of sCoVs antibodies that are predictive of neutralization but the relative ratios to all four sCoVs, with NL63 being the most weighted for this prediction. Machine learning also highlighted the existence of latent variables that contribute to the neutralization and that may be related to the type of cellular immune response triggered by the infection to certain sCoVs. This study is one of the first to identify a functional relationship between prior-exposure to sCoV and the establishment of a certain degree of immunity to SARS-CoV-2 by way of a cross-reactive antibody response.Graphical Abstract

MotivationIdentifying pathological metabolic changes in complex disease such as Dementia with Lewy Bodies (DLB) requires a deep understanding of functional modifications in the context of metabolic networks. Network determination and analysis from metabolomics and lipidomics data remains a major challenge due to sparse experimental coverage, a variety of different functional relationships between metabolites and lipids, and only sporadically described reaction networks.ResultsDistance correlation, measuring linear and non-linear dependences between variables as well as correlation between vectors of different lengths, e.g. different sample sizes, is presented as an approach for data-driven metabolic network development. Additionally, novel approaches for the analysis of changes in pair-wise correlation as well as overall correlations for metabolites in different conditions are introduced and demonstrated on DLB data. Distance correlation and signed distance correlation was utilized to determine metabolic network in brain in DLB patients and matching controls and results for the two groups are compared in order to identify metabolites with the largest functional change in their network in the disease state. Novel correlation network analysis showed alterations in the metabolic network in DLB brains relative to the controls, with the largest differences observed in O-phosphocholine, fructose, propylene-glycol, pantothenate, thereby providing novel insights into DLB pathology only made apparent through network investigation with presented methods.

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

Accurate and efficient segmentation of live-cell images is critical in maximising data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labelling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub. https://github.com/kaernlab/YeastNet

Glutathione (GSH) is the most abundant non-protein thiol present at millimolar concentrations in mammalian tissues. As an important intracellular antioxidant, it acts as a regulator of cellular redox state protecting cells from damage caused by lipid peroxides, reactive oxygen and nitrogen species, and xenobiotics. Recent studies have highlighted the importance of GSH in key signal transduction reactions as a controller of cell differentiation, proliferation, apoptosis, ferroptosis and immune function. Molecular changes in the GSH antioxidant system and disturbances in GSH homeostasis have been implicated in tumor initiation, progression, and treatment response. Hence, GSH has both protective and pathogenic roles. Although in healthy cells it is crucial for the removal and detoxification of carcinogens, elevated GSH levels in tumor cells are associated with tumor progression and increased resistance to chemotherapeutic drugs. Recently, several novel therapies have been developed to target the GSH antioxidant system in tumors as a means for increased response and decreased drug resistance. In this comprehensive review we explore mechanisms of GSH functionalities and different therapeutic approaches that either target GSH directly, indirectly or use GSH-based prodrugs. Consideration is also given to the computational methods used to describe GSH related processes for in silico testing of treatment effects.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a range of extra-respiratory signs and symptoms. One such manifestation is delirium, an acute confusional state occurring in 60-70% of severe SARS-CoV-2 cases. Delirium is also a common clinical syndrome following planned orthopedic surgery. This investigation initially explored the underlying role of metabolism in delirium-susceptibility in this setting. Metabolomics profiles of cerebrospinal fluid (CSF) and blood taken prior to surgery found significant concentration differences of several amino acids, acylcarnitines and polyamines were observed in delirium-prone patients. Phenethylamine (PEA) concentrations in delirium-prone patients was significantly lower in CSF than in blood, whilst in age- and gender-matched controls the opposite was observed (adjusted p values: 1.8x10-6 (control) and 1.788x10-10 (delirium)). PEA is metabolised by monoamine oxidase B (MAOB), a putative enzyme target for the treatment of Alzheimers disease, Parkinsons disease and depression. Our computational structural comparisons of MAOB and angiotensin converting enzyme (ACE) 2 found high similarity, specifically within the SARS-CoV-2 spike protein. MAOB structural alignment to ACE2 was 51% overall, but this was over 95% in the ACE2-spike protein binding region. Thus, it is possible that the spike protein interacts with MAOB on a molecular level. A previously published metabolomic dataset of control subjects and patients with either mild or severe COVID-19 was then analysed. Major concentration differences in some metabolites attributed to altered MAO activity were detected. Therefore, our hypothesis is that the SARS-CoV-2 influences MAOB activity, which is one potential cause for the many observed neurological and platelet based complications of SARS-CoV-2 infection.

The development of effective therapies as well as early, molecular diagnosis of Alzheimer’s disease is impeded by the lack of understanding of the underlying pathological mechanisms. Metabolomics studies of body fluids as well as brain tissues have shown major changes in metabolic profiles of Alzheimer’s patients. However, with analysis performed at the late stages of the disease it is not possible to distinguish causes and consequence. The mouse model APP/PS1 expresses a mutant amyloid precursor protein resulting in early Amyloid β (Aβ) accumulation as well as many resulting physiological changes including changes in metabolic profile and metabolism. Analysis of metabolic profile of cerebrospinal fluid (CSF) and blood of APP/PS1 mouse model can provide information about metabolic changes in these body fluids caused by Aβ accumulation. Using our novel method for analysis of correlation and mathematical ranking of significant correlations between metabolites in CSF and blood, we have explored changes in metabolite correlation and connectedness in APP/PS1 and wild type mice. Metabolites concentration and correlation changes in CSF, blood and across the blood brain barrier determined in this work are affected by the production of amyloid plaque. Metabolite changes observed in the APP/PS1 mouse model are the response to the mutation causing plaque formation, not the cause for the plaque suggesting that they are less relevant in the context of early treatment and prevention then the metabolic changes observed only in humans.

Glioblastoma (GBM) is one of the most aggressive cancers of the central nervous system. Despite current advances in non-invasive imaging and the advent of novel therapeutic modalities, patient survival remains very low. There is a critical need for the development of effective biomarkers for GBM diagnosis and therapeutic monitoring. Extracellular vesicles (EVs) produced by GBM tumors have been shown to play an important role in cellular communication and modulation of the tumor microenvironment. As GBM-derived EVs contain specific &ldquo, molecular signatures&rdquo, of their parental cells and are able to transmigrate across the blood&ndash, brain barrier into biofluids such as the blood and cerebrospinal fluid (CSF), they are considered as a valuable source of potential diagnostic biomarkers. Given the relatively harsh extracellular environment of blood and CSF, EVs have to endure and adapt to different conditions. The ability of EVs to adjust and function depends on their lipid bilayer, metabolic content and enzymes and transport proteins. The knowledge of EVs metabolic characteristics and adaptability is essential for their utilization as diagnostic and therapeutic tools. The main aim of this study was to determine the metabolome of small EVs or exosomes derived from different GBM cells and compare to the metabolic profile of their parental cells using NMR spectroscopy. In addition, a possible flux of metabolic processes in GBM-derived EVs was simulated using constraint-based modeling from published proteomics information. Our results showed a clear difference between the metabolic profiles of GBM cells, EVs and media. Machine learning analysis of EV metabolomics, as well as flux simulation, supports the notion of active metabolism within EVs, including enzymatic reactions and the transfer of metabolites through the EV membrane. These results are discussed in the context of novel GBM diagnostics and therapeutic monitoring.

Diseases of agricultural crops caused by fungi have devastating economic and health effects. Fusarium head blight (FHB) is one of the most damaging diseases of wheat and other small grain cereals. FHB reduces agricultural yield while also affecting food supply and safety through deposition of toxins (mycotoxins/phytotoxins). Control of FHB growth and toxin accumulation in grains remain major challenges. While the ultimate goal in the battle against FHB is the development of resistant wheat varieties, the actual use of fully resistant plants that preclude any need for treatment with fungicides remains out of sight. Current antifungals being applied against FHB are generally azole‐based inhibitors. However, usage of these azole‐based fungicides is being complicated by the facts that these are active only during specific short‐lived developmental time periods, fungi are developing increased resistance to them and they are having significant environmental impacts. As such, there is a great need for more targeted, specific and effective antifungal agents to address the significant threat of FHB. This review provides an overview of some of the more promising fungal targets that are currently being investigated for antifungal development.

Introduction: Although age is a major risk factor for Alzheimer’s disease (AD), it is not an inevitable consequence of aging nor is it exclusively an old-age disease. Several other major risk factors for AD are strongly associated with metabolism and include lack of exercise, obesity, diabetes, high blood pressure and cholesterol, over-consumption of alcohol and depression in addition to low educational level, social isolation, and cognitive inactivity. Approaches for Alzheimer prevention and treatment through manipulation of metabolism and utilization of active metabolites have great potential either as a primary or secondary treatment avenue or as a preventative strategy in high-risk individuals. Areas covered: This review outlines the current knowledge concerning the relationship between AD and metabolism and the novel treatments attempting to correct changes in AD patients determined through metabolomics or lipidomic analyses. Expert opinion: Metabolites are one of the main driving factors and indicators of AD and can offer many possible avenues for prevention and treatment. However, with the highly interconnected effects of metabolites and metabolism, as well as the many different routes for metabolism dysfunction, successful treatment would have to include the correction of metabolic errors as well as errors in transport and metabolite processing in order to affect and revert AD progression.

Adipose Triglyceride Lipase (ATGL) performs the first and rate-limiting step in lipolysis by hydrolyzing triacylglycerols stored in lipid droplets to diacylglycerols. By mediating lipolysis in adipose and non-adipose tissues, ATGL is a major regulator of overall energy metabolism and plasma lipid levels. Since chronically high levels of plasma lipids are linked to metabolic disorders including insulin resistance and type 2 diabetes, ATGL is an interesting therapeutic target. In the present study, fourteen closely related analogues of Atglistatin (1), a newly discovered ATGL inhibitor, were synthesized, and their ATGL inhibitory activity was evaluated. The effect of these analogues on lipolysis in 3T3-L1 adipocytes clearly shows that inhibition of the enzyme by Atglistatin (1) is due to the presence of the carbamate and N,N-dimethyl moieties on the biaryl central core at meta and para position, respectively. Mono carbamate-substituted analogue C2, in which the carbamate group was in the meta position as in Atglistatin (1), showed slight inhibition. Low dipole moment of Atglistatin (1) compared to the synthesized analogues possibly explains the lower inhibitory activities.