Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell\u27s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. Predicting metabolomes from gene expression data is a key challenge in understanding the genotype-phenotype relationship. Studying the enzyme expression proteome in kinase knockouts, we reveal the importance of a so far overlooked metabolism-regulatory mechanism. Enzyme expression changes are impacting on metabolite levels through many changes acting in concert. We show that one can map regulatory enzyme expression patterns using machine learning and use them to predict the metabolome of kinase-deficient cells on the basis of their enzyme expression proteome. Our study quantifies the role of enzyme abundance in the regulation of metabolism and by doing so reveals the potential of machine learning in gaining understanding about complex metabolism regulation

Distal motor neuropathy associated with novel EMILIN1 mutation

Abstract Elastin microfibril interface-located proteins (EMILINs) are extracellular matrix glycoproteins implicated in elastogenesis and cell proliferation. Recently, a missense mutation in the EMILIN1 gene has been associated with autosomal dominant connective tissue disorder and motor-sensory neuropathy in a single family. We identified by whole exome sequencing a novel heterozygous EMILIN1 mutation c.748C>T [p.R250C] located in the coiled coil forming region of the protein, in four affected members of an autosomal dominant family presenting a distal motor neuropathy phenotype. In affected patient a sensory nerve biopsy showed slight and unspecific changes in the number and morphology of myelinated fibers. Immunofluorescence study of a motor nerve within a muscle biopsy documented the presence of EMILIN-1 in nerve structures. Skin section and skin derived fibroblasts displayed a reduced extracellular deposition of EMILIN-1 protein with a disorganized network of poorly ramified fibers in comparison with controls. Downregulation of emilin1a in zebrafish displayed developmental delay, locomotion defects, and abnormal axonal arborization from spinal cord motor neurons. The phenotype was complemented by wild-type zebrafish emilin1a, and partially the human wild-type EMILIN1 cRNA, but not by the cRNA harboring the novel c.748C>T [p.R250C]. These data suggest a role of EMILIN-1 in the pathogenesis of diseases affecting the peripheral nervous system

Multicentre harmonisation of a six-colour flow cytometry panel for naïve/memory T cell immunomonitoring

Background. Personalised medicine in oncology needs standardised immunological assays. Flow cytometry (FCM) methods represent an essential tool for immunomonitoring, and their harmonisation is crucial to obtain comparable data in multicentre clinical trials. The objective of this study was to design a harmonisation workflow able to address the most effective issues contributing to intra- and interoperator variabilities in a multicentre project. Methods. The Italian National Institute of Health (Istituto Superiore di Sanita, ISS) managed a multiparametric flow cytometric panel harmonisation among thirteen operators belonging to five clinical and research centres of Lazio region (Italy). The panel was based on a backbone mixture of dried antibodies (anti-CD3, anti-CD4, anti-CD8, anti-CD45RA, and anti-CCR7) to detect naive/memory T cells, recognised as potential prognostic/predictive immunological biomarkers in cancer immunotherapies. The coordinating centre distributed frozen peripheral blood mononuclear cells (PBMCs) and fresh whole blood (WB) samples from healthy donors, reagents, and Standard Operating Procedures (SOPs) to participants who performed experiments by their own equipment, in order to mimic a real-life scenario. Operators returned raw and locally analysed data to ISS for central analysis and statistical elaboration. Results. Harmonised and reproducible results were obtained by sharing experimental set-up and procedures along with centralising data analysis, leading to a reduction of cross-centre variability for naive/memory subset frequencies particularly in the whole blood setting. Conclusion. Our experimental and analytical working process proved to be suitable for the harmonisation of FCM assays in a multicentre setting, where high-quality data are required to evaluate potential immunological markers, which may contribute to select better therapeutic options

Global analysis of cytosine and adenine DNA modifications across the tree of life.

Funder: Medical Research CouncilInterpreting the function and metabolism of enzymatic DNA modifications requires both position-specific and global quantities. Sequencing-based techniques that deliver the former have become broadly accessible, but analytical methods for the global quantification of DNA modifications have thus far been applied mostly to individual problems. We established a mass spectrometric method for the sensitive and accurate quantification of multiple enzymatic DNA modifications. Then, we isolated DNA from 124 archean, bacterial, fungal, plant, and mammalian species, and several tissues and created a resource of global DNA modification quantities. Our dataset provides insights into the general nature of enzymatic DNA modifications, reveals unique biological cases, and provides complementary quantitative information to normalize and assess the accuracy of sequencing-based detection of DNA modifications. We report that only three of the studied DNA modifications, methylcytosine (5mdC), methyladenine (N6mdA) and hydroxymethylcytosine (5hmdC), were detected above a picomolar detection limit across species, and dominated in higher eukaryotes (5mdC), in bacteria (N6mdA), or the vertebrate central nervous systems (5hmdC). All three modifications were detected simultaneously in only one of the tested species, Raphanus sativus. In contrast, these modifications were either absent or detected only at trace quantities, across all yeasts and insect genomes studied. Further, we reveal interesting biological cases. For instance, in Allium cepa, Helianthus annuus, or Andropogon gerardi, more than 35% of cytosines were methylated. Additionally, next to the mammlian CNS, 5hmdC was also detected in plants like Lepidium sativum and was found on 8% of cytosines in the Garra barreimiae brain samples. Thus, identifying unexpected levels of DNA modifications in several wild species, our resource underscores the need to address biological diversity for studying DNA modifications

The metabolic background is a global player in Saccharomyces gene expression epistasis

The regulation of gene expression in response to nutrient availability is fundamental to the genotype–phenotype relationship. The metabolic–genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic–genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale