Search bioRxiv⌕ Search

Biology subjects

Meyer, J. G.

Publications and source records attributed to Meyer, J. G..

33 records · Page 2Linked to original sources

High Throughput Proteomic Analysis of Heterogeneous Cell Populations with NanoDTSC

Identification and proteomic characterization of rare cell types within complex organ derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 minutes of total run time per cell with peptides quantified over 11.5 minutes using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single-cells per day. At this throughput, nanoDTSC quantified over 1,000 proteins in individual cardiomyocytes and heterogenous populations of single cells from aorta. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/522908v4_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@352d49org.highwire.dtl.DTLVardef@1304d2corg.highwire.dtl.DTLVardef@1a9df5org.highwire.dtl.DTLVardef@1785464_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Regulation of urea cycle by reversible high stoichiometry lysine succinylation

The post-translational modification, lysine succinylation is implicated in the regulation of various metabolic pathways. However, its biological relevance remains uncertain due to methodological difficulties in determining high-impact succinylation sites. In the present study, using stable isotope labeling and data-independent acquisition mass spectrometry, we quantified lysine succinylation stoichiometries in mouse livers. Despite the low overall stoichiometry of lysine succinylation, several high stoichiometry sites were identified, especially upon deletion of the desuccinylase SIRT5. In particular, multiple high stoichiometry lysine sites identified in argininosuccinate synthase (ASS1), a key enzyme in urea cycle, are regulated by SIRT5. Mutation of the high stoichiometry lysine in ASS1 to succinyl-mimetic glutamic acid significantly decreased its enzymatic activity. Metabolomics profiling confirms that SIRT5 deficiency decreases urea cycle activity in liver. Importantly, SIRT5 deficiency compromises ammonia tolerance and reduces locomotor and exploratory activity in male mice upon high-ammonium diet feeding. Therefore, lysine succinylation is functionally important in ammonia metabolism.

molecular biology↗

Coenzyme A binding sites induce proximal acylation across protein families

Lysine N{varepsilon}-acylations, such as acetylation or succinylation, are post-translational modifications that regulate protein function. In mitochondria, lysine acylation is predominantly non-enzymatic, and only a specific subset of the proteome is acylated. Coenzyme A (CoA) can act as an acyl group carrier via a thioester bond, but what controls the acylation of mitochondrial lysines remains poorly understood. Using published datasets, here we found that proteins with a CoA-binding site are more likely to be acetylated, succinylated, and glutarylated. Using computational modeling, we show that lysine residues near the CoA-binding pocket are highly acylated compared to those farther away. We hypothesized that acyl-CoA binding enhances acylation of nearby lysine residues. To test this hypothesis, we co-incubated enoyl-CoA hydratase short chain 1 (ECHS1), a CoA-binding mitochondrial protein, with succinyl-CoA and CoA. Using mass spectrometry, we found that succinyl-CoA induced widespread lysine succinylation and that CoA competitively inhibited ECHS1 succinylation. CoA-induced inhibition at a particular lysine site correlated inversely with the distance between that lysine and the CoA-binding pocket. Our study indicated that CoA acts as a competitive inhibitor of ECHS1 succinylation by binding to the CoA-binding pocket. Together, this suggests that proximal acylation at CoA-binding sites is a primary mechanism for lysine acylation in the mitochondria.

biochemistry↗

Multi-Omic Integration by Machine Learning (MIMaL) Reveals Protein-Metabolite Connections and New Gene Functions

Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow the ability to gather information of cellular states of its components, measuring abundances of transcripts, their translation, the accumulation of proteins, lipids and metabolites. These highly complex datasets reflect the state of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through integration of these data remains challenging. Here we show that connections between omic layers can be discovered through a combination of machine learning and model interpretation. We find that model interpretation values connecting proteins to metabolites are valid experimentally and reveal also largely new connections. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of gene five gene functions, each of which was validated experimentally. We accurately predicted that two uncharacterized genes in yeast modulate mitochondrial translation, YJR120W and YLD157C.We also predict and validate functions for several incompletely characterized genes, including SDH9, ISC1, and FMP52. Our work demonstrates that multi-omic analysis with machine learning (MIMaL) views multi-omic data through a new lens to reveal new insight that was not possible using existing methods.

bioinformatics↗

EpyNN: Educational python for Neural Networks

SummaryArtificial Neural Networks (ANNs) have achieved unequaled performance for numerous problems in many areas of Science, Business, Public Policy, and more. While experts are familiar with performance-oriented software and underlying theory, ANNs are difficult to comprehend for non-experts because it requires skills in programming, background in mathematics and knowledge of terminology and concepts. In this work, we release EpyNN, an educational python resource meant for a public willing to understand key concepts and practical implementation of scalable ANN architectures from concise, homogeneous and idiomatic source code. EpyNN contains an educational Application Programming Interface (API), educational workflows from data preparation to ANN training and a documentation website setting side-by-side code, mathematics, graphical representation and text to facilitate learning and provide teaching material. Overall, EpyNN provides basics for python-fluent individuals who wish to learn, teach or develop from scratch. AvailabilityEpyNN documentation is available at https://epynn.net and repository can be retrieved from https://github.com/synthaze/epynn. ContactStephanie Olivier-Van-Stichelen, solivier@mcw.edu. Supplementary InformationSupplementary files and listings.

scientific communication and education↗

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

New mass spectrometry data collection methods require new computational tools. Direct Infusion Shotgun Proteome Analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here we introduce CsoDIAq, a user-friendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity, and a new spectrum-spectrum match score called match count and cosine (MaCC), which improves target discrimination in a target-decoy analysis. We further show that reanalysis after fragment mass tolerance correction increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC-MS DIA, and show that it outperforms other spectrum-spectrum matching software.

biochemistry↗

Positional SHAP for Interpretation of Deep Learning Models Trained from Biological Sequences

Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neural network architectures. Here, we introduce a framework called "Positional SHAP" (PoSHAP) to interpret models trained from biological sequences by utilizing SHapely Additive exPlanations (SHAP) to generate positional model interpretations. We demonstrate this using three long short-term memory (LSTM) regression models that predict peptide properties, including binding affinity to major histocompatibility complexes (MHC), and collisional cross section (CCS) measured by ion mobility spectrometry. Interpretation of these models with PoSHAP reproduced MHC class I (rhesus macaque Mamu-A1*001 and human A*11:01) peptide binding motifs, reflected known properties of peptide CCS, and provided new insights into interpositional dependencies of amino acid interactions. PoSHAP should have widespread utility for interpreting a variety of models trained from biological sequences.

bioinformatics↗

Detection of Discordant Peptide Quantities in Shotgun Proteomics Data by Peptide Correlation Analysis (PeCorA)

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies, which are produced by cleavage of all isolated protein by a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false for biological or technical reasons. Here, I describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. Simple linear models are used to assess whether the slope of a peptides change across treatment groups differs from the slope of all other peptides assigned to the same protein. Reanalysis of proteomic data from primary mouse microglia with PeCorA revealed that about 15% of proteins contain one discordant peptide. Inspection of the discordant peptides shows utility of PeCorA for direct and indirect detection of regulated PTMs, and also for discovery of poorly quantified peptides that should be excluded. PeCorA can be applied to an arbitrary list of quantified peptides, and is freely available as a script written in R.

bioinformatics↗

Protein Acetylation Induced by Dichloroacetate (DCA) Treatment is Associated with Decreased Respiration in Cultured Hepatocytes: Preliminary Results

Protein post-translational modification (PTM) by acetylation at the {varepsilon}-amine on lysine residues in proteins regulates various cellular processes including transcription and metabolism. Several metabolic and genetic perturbations are known to increase acetylation of various proteins. Hyper-acetylation can also be induced using deacetylase inhibitors. While there is much interest in discovering drugs that can reverse protein acetylation, pharmacological tools that increase non-enzymatic protein acetylation are needed in order to understand the physiological role of excess protein acetylation. In this study, I assessed whether inhibition of pyruvate dehydrogenase kinase (PDHK) could cause protein hyper-acetylation due to excess production of acetyl-CoA by pyruvate dehydrogenase (PDH). Western blot of total protein from dichloroacetate (DCA) treated hepatocytes with anti-acetyl-lysine antibody showed increased protein acetylation, and seahorse respirometry of DCA pretreated hepatocytes indicated a subtle decrease in basal and maximal respiratory capacity.

biochemistry↗

Impact of RNA Extraction and Target Capture Methods on RNA Sequencing Using Formalin-Fixed, Paraffin Embedded Tissues

Formalin fixed paraffin embedded (FFPE) tissues are commonly used biospecimen for clinical diagnosis. However, RNA degradation is extensive when isolated from FFPE blocks making it challenging for whole transcriptome profiling (RNA-seq). Here, we examined RNA isolation methods, quality metrics, and the performance of RNA-seq using different approaches with RNA isolated from FFPE and fresh frozen (FF) tissues. We evaluated FFPE RNA extraction methods using six different tissues and five different methods. The reproducibility and quality of the prepared libraries from these RNAs were assessed by RNA-seq. We next examined the performance and reproducibility of RNA-seq for gene expression profiling with FFPE and FF samples using targeted (Kinome capture) and whole transcriptome capture based sequencing. Finally, we assessed Agilent SureSelect All-Exon V6+UTR capture and the Illumina TruSeq RNA Access protocols for their ability to detect known gene fusions in FFPE RNA samples. Although the overall yield of RNA varied among extraction methods, gene expression profiles generated by RNA-seq were highly correlated (>90%) when the input RNA was of sufficient quality ([≥]DV200 30%) and quantity ([≥] 100 ng). Using gene capture, we observed a linear relationship between gene expression levels for shared genes that were captured using either All-Exon or Kinome kits. Gene expression correlations between the two capture-based approaches were similar using RNA from FFPE and FF samples. However, TruSeq RNA Access protocol provided significantly higher exon and junction reads when compared to the SureSelect All-Exon capture kit and was more sensitive for fusion gene detection. Our study established pre and post library construction QC parameters that are essential to reproducible RNA-seq profiling using FFPE samples. We show that gene capture based NGS sequencing is an efficient and highly reproducible strategy for gene expression measurements as well as fusion gene detection.

genomics↗

Proteome and Secretome Dynamics of Stem Cell-Derived Retinal Pigmented Epithelium in Response to Acute and Chronic ROS

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries, and is characterized by slow retinal degeneration linked to chronic oxidative stress in the retinal pigmented epithelium (RPE). The exact molecular mechanisms that lead to RPE death and dysfunction in response to chronic reactive oxygen species (ROS) are still unclear. In this work, human stem cell-derived RPE samples were treated with a low dose of paraquat (PQ) for 1 week or 3 weeks to induce chronic reactive oxygen species (ROS) stress. Cells were then harvested and both the intracellular and secreted RPE proteomes were quantified by mass spectrometry. Inside the RPE, chronic ROS caused concerted increase of glycolytic proteins but decreased mitochondrial proteins, as well as decreased extracellular matrix proteins and membrane proteins required for endocytosis. From the secreted proteins, we found that stressed RPE secrete over 1,000 detectable proteins, and the composition of the proteins secreted from RPE changes due to chronic ROS. Notably, secreted APOE is decreased 4-fold due to 3 weeks of chronic ROS stress, and urotensin-II, the strongest known vasoconstrictor, doubles. Further, secreted TGF-beta is increased, and its cognate signaler BMP1 decreased in the secretome. Together, these alterations of the RPE proteome and protein secretome paint a detailed molecular picture of the retinal stress response in space and time.

systems biology↗

Learning Molecule Drug Function from Structure Representations with Deep Neural Networks or Random Forests

Empirical testing of chemicals for desirable properties, such as drug efficacy, toxicity, solubility, and thermal conductivity, costs many billions of dollars every year. Further, the choice of molecules for testing relies on expert knowledge and intuition in the relevant domain of application. The ability to predict the action of a molecule in silico would greatly increase the speed and decrease the cost of prioritizing molecules with desirable function for experimental testing. We explore the use of molecule structures to predict their drug classification without any explicit biological model (e.g. protein structure or cell system). Two approaches were compared: (1) a two-dimensional image representation of molecule structure with transfer learning from a pre-trained convolutional neural network (CNN), and (2) Morgan molecular fingerprints (MFP) with a random forest (RF). Both methods using only molecule information as input achieved significantly better accuracy than previous work that used transcriptomic measurements of molecular effects. Because these data suggest that much of a molecules function is encoded by its chemical structure, we explore which general molecular properties are drug class-specific and how misclassification of structures might provide drug repurposing opportunities.

bioinformatics↗

Identification of novel protein lysine acetyltransferasesin Escherichia coli

Post-translational modifications, such as N{varepsilon}-lysine acetylation, regulate protein function. N{varepsilon}-lysine acetylation can occur either non-enzymatically or enzymatically. The non-enzymatic mechanism uses acetyl phosphate (AcP) or acetyl coenzyme A (AcCoA) as acetyl donors to modify an N{varepsilon}-lysine residue of a protein. The enzymatic mechanism uses N{varepsilon}-lysine acetyltransferases (KATs) to specifically transfer an acetyl group from AcCoA to N{varepsilon}-lysine residues on proteins. To date, only one KAT (YfiQ, also known as Pka and PatZ) has been identified in E. coli. Here, we demonstrate the existence of 4 additional E. coli KATs: RimI, YiaC, YjaB, and PhnO. In a genetic background devoid of all known acetylation mechanisms (most notably AcP and YfiQ) and one deacetylase (CobB), overexpression of these putative KATs elicited unique patterns of protein acetylation. We mutated key active site residues and found that most of them eliminated enzymatic acetylation activity. We used mass spectrometry to identify and quantify the specificity of YfiQ and the four novel KATs. Surprisingly, our analysis revealed a high degree of substrate specificity. The overlap between KAT-dependent and AcP-dependent acetylation was extremely limited, supporting the hypothesis that these two acetylation mechanisms play distinct roles in the post-translational modification of bacterial proteins. We further showed that these novel KATs are conserved across broad swaths of bacterial phylogeny. Finally, we determined that one of the novel KATs (YiaC) and the known KAT (YfiQ) can negatively regulate bacterial migration. Together, these results emphasize distinct and specific non-enzymatic and enzymatic protein acetylation mechanisms present in bacteria.\n\nImportanceN{varepsilon}-lysine acetylation is one of the most abundant and important post-translational modifications across all domains of life. One of the best-studied effects of acetylation occurs in eukaryotes, where acetylation of histone tails activates gene transcription. Although bacteria do not have true histones, N{varepsilon}-lysine acetylation is prevalent; however, the role of these modifications is mostly unknown. We constructed an E. coli strain that lacked both known acetylation mechanisms to identify four new N{varepsilon}-lysine acetyltransferases (RimI, YiaC, YjaB, and PhnO). We used mass spectrometry to determine the substrate specificity of these acetyltransferases. Structural analysis of selected substrate proteins revealed site-specific preferences for enzymatic acetylation that had little overlap with the preferences of the previously reported acetyl-phosphate non-enzymatic acetylation mechanism. Finally, YiaC and YfiQ appear to regulate flagellar-based motility, a phenotype critical for pathogenesis of many organisms. These acetyltransferases are highly conserved and reveal deeper and more complex roles for bacterial post-translational modification.

microbiology↗

Multi-Omic Profiling Reveals the Opposing Forces of Excess Dietary Sugar and Fat on Liver Mitochondria Protein Acetylation and Succinylation

Dietary macronutrient composition alters metabolism through several mechanisms, including post-translational modification (PTM) of proteins. To connect diet and molecular changes, here we performed short- and long-term feeding of mice with standard chow diet (SCD) and high-fat diet (HFD), with or without glucose or fructose supplementation, and quantified liver metabolites, 861 proteins, and 1,815 protein level-corrected mitochondrial acetylation and succinylation sites. Nearly half the acylation sites were altered by at least one diet; nutrient-specific changes in protein acylation sometimes encompass entire pathways. Although acetyl-CoA is an intermediate in both sugar and fat metabolism, acetyl-CoA had a dichotomous fate depending on its source; chronic feeding of dietary sugars induced protein hyperacetylation, whereas the same duration of HFD did not. Instead, HFD resulted in citrate accumulation, anaplerotic metabolism of amino acids, and protein hypo-succinylation. Together, our results demonstrate novel connections between dietary macronutrients, protein post-translational modifications, and regulation of fuel selection in liver.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/263426_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (53K):\norg.highwire.dtl.DTLVardef@1419c64org.highwire.dtl.DTLVardef@82a874org.highwire.dtl.DTLVardef@16023org.highwire.dtl.DTLVardef@4ed44e_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

systems biology↗

PIQED: Automated Identification And Quantification Of Protein Modifications From DIA-MS Data

Label-free quantification using data-independent acquisition (DIA) is a robust method for deep and accurate proteome quantification1,2. However, when lacking a pre-existing spectral library, as is often the case with studies of novel post-translational modifications (PTMs), samples are typically analyzed several times: one or more data dependent acquisitions (DDA) are used to generate a spectral library followed by DIA for quantification. This type of multi-injection analysis results in significant cost with regard to sample consumption and instrument time for each new PTM study, and may not be possible when sample amount is limiting and/or studies require a large number of biological replicates. Recently developed software (e.g. DIA-Umpire) has enabled combined peptide identification and quantification from a data-independent acquisition without any pre-existing spectral library3,4. Still, these tools are designed for protein level quantification. Here we demonstrate a software tool and workflow that extends DIA-Umpire to allow automated identification and quantification of PTM peptides from DIA. We accomplish this using a custom, open-source graphical user interface DIA-Pipe (https://github.com/jgmeyerucsd/PIQEDia/releases/tag/v0.1.2) (figure 1a).\n\nO_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC=\"FIGDIR/small/141382_fig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@41b8f3org.highwire.dtl.DTLVardef@d59733org.highwire.dtl.DTLVardef@b9a722org.highwire.dtl.DTLVardef@8be50c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Automated Qualitative and Quantitative Analysis of Post-Translational Modifications using DIA-Pipe. (a) Workflow of the all-DIA strategy for identification and quantification of PTMs. Modified peptides enriched from biological samples are analyzed by data-independent acquisition. All data analysis steps starting from instrument. wiff files, acquired on a TripleTOF 5600, can be completed using the DIA-Pipe GUI, including: (1) file conversion and pseudo-MS/MS spectra generation using DIA-Umpire, (2) database searching by MS-GF+, X! Tandem, and Comet followed by results refinement and combination using PeptideProphet/iProphet/PTMProphet, (3) automated spectral library generation and fragment area extraction using SkylineRunner, and finally, (4) Skyline report filtering and formatting for significance testing with mapDIA. (b) mProphet composite score distributions of target and second-best peaks picked by Skylineshowing essentially error-free peak picking by Skyline of peptides identified using pseudo-MS/MS spectra. (c) Distribution of coefficient of variations observed from three technical replicates for 1,182 acetylation sites. (d) Observed distributions of log2(fold change) computed by mapDIA using three technical replicates of 1X injection volume compared to three technical replicates of 0.5X injection volume; expected log2(fold change) value =1. (e) Number of identified peptides from each single replicate injection and from the combination of two or three replicates. An average of 1,655 acetylated peptides were identified per replicate. The combination of two or three replicates increased the number of identifications by 19% or 29%, respectively.\n\nC_FIG

systems biology↗