An important mechanism of resistance to β-lactam antibiotics is via their β-lactamase–catalyzed hydrolysis. Recent work has shown that, in addition to the established hydrolysis products, the reaction of the class D nucleophilic serine β-lactamases (SBLs) with carbapenems also produces β-lactones. We report studies on the factors determining β-lactone formation by class D SBLs. We show that variations in hydrophobic residues at the active site of class D SBLs (i.e. Trp105, Val120, and Leu158, using OXA-48 numbering) impact on the relative levels of β-lactones and hydrolysis products formed. Some variants, i.e. the OXA-48 V120L and OXA-23 V128L variants, catalyze increased β-lactone formation compared with the WT enzymes. The results of kinetic and product studies reveal that variations of residues other than those directly involved in catalysis, including those arising from clinically observed mutations, can alter the reaction outcome of class D SBL catalysis. NMR studies show that some class D SBL variants catalyze formation of β-lactones from all clinically relevant carbapenems regardless of the presence or absence of a 1β-methyl substituent. Analysis of reported crystal structures for carbapenem-derived acyl-enzyme complexes reveals preferred conformations for hydrolysis and β-lactone formation. The observation of increased β-lactone formation by class D SBL variants, including the clinically observed carbapenemase OXA-48 V120L, supports the proposal that class D SBL-catalyzed rearrangement of β-lactams to β-lactones is important as a resistance mechanism.

Boris Macek
Feb 1, 2008; 7:299-307
Research

Tamar Geiger
Mar 1, 2012; 11:M111.014050-M111.014050
Special Issue: Prospects in Space and Time

Lawrence E. Ostrowski
Jun 1, 2002; 1:451-465
Research

Qiang Tian
Oct 1, 2004; 3:960-969
Research

Claudio P. Albuquerque
Jul 1, 2008; 7:1389-1396
Research

Ludovic C. Gillet
Jun 1, 2012; 11:O111.016717-O111.016717
Research

Linn Fagerberg
Feb 1, 2014; 13:397-406
Research

Expert

In animals, the response to chronic hypoxia is mediated by prolyl hydroxylases (PHDs) that regulate the levels of hypoxia-inducible transcription factor α (HIFα). PHD homologues exist in other types of eukaryotes and prokaryotes where they act on non HIF substrates. To gain insight into the factors underlying different PHD substrates and properties, we carried out biochemical and biophysical studies on PHD homologues from the cellular slime mold, Dictyostelium discoideum, and the protozoan parasite, Toxoplasma gondii, both lacking HIF. The respective prolyl-hydroxylases (DdPhyA and TgPhyA) catalyze prolyl-hydroxylation of S-phase kinase-associated protein 1 (Skp1), a reaction enabling adaptation to different dioxygen availability. Assays with full-length Skp1 substrates reveal substantial differences in the kinetic properties of DdPhyA and TgPhyA, both with respect to each other and compared with human PHD2; consistent with cellular studies, TgPhyA is more active at low dioxygen concentrations than DdPhyA. TgSkp1 is a DdPhyA substrate and DdSkp1 is a TgPhyA substrate. No cross-reactivity was detected between DdPhyA/TgPhyA substrates and human PHD2. The human Skp1 E147P variant is a DdPhyA and TgPhyA substrate, suggesting some retention of ancestral interactions. Crystallographic analysis of DdPhyA enables comparisons with homologues from humans, Trichoplax adhaerens, and prokaryotes, informing on differences in mobile elements involved in substrate binding and catalysis. In DdPhyA, two mobile loops that enclose substrates in the PHDs are conserved, but the C-terminal helix of the PHDs is strikingly absent. The combined results support the proposal that PHD homologues have evolved kinetic and structural features suited to their specific sensing roles.

A. I. Aptekarev and M. L. Yattselev
Trans. Moscow Math. Soc. 83 (), 251-268.
Abstract, references and article information

M. A. Stepanova
Trans. Moscow Math. Soc. 83 (), 1-13.
Abstract, references and article information

Haiyong Wang
Math. Comp. 93 (), 2861-2884.
Abstract, references and article information

Katherine Baker, Lehel Banjai and Mariya Ptashnyk
Math. Comp. 93 (), 2711-2743.
Abstract, references and article information

T. De Ryck and S. Mishra
Math. Comp. 93 (), 2643-2677.
Abstract, references and article information

Constantin Carle and Marlis Hochbruck
Math. Comp. 93 (), 2611-2641.
Abstract, references and article information

Donghyeok Lim and Christian Maire
Proc. Amer. Math. Soc. 152 (), 5013-5024.
Abstract, references and article information

N. Kuznetsov
St. Petersburg Math. J. 35 (), 467-472.
Abstract, references and article information

When using the distinct count function in SAS Visual Analytics reports, you might find that a negative value is displayed instead of the actual distinct count: imgalt="distinct_count" src="{fusion_66562_1_disti

Programmed cell death protein 1 (PD-1) is a critical inhibitory receptor that limits excessive T cell responses. Cancer cells have evolved to evade these immunoregulatory mechanisms by upregulating PD-1 ligands and preventing T cell–mediated anti-tumor responses. Consequently, therapeutic blockade of PD-1 enhances T cell–mediated anti-tumor immunity, but many patients do not respond and a significant proportion develop inflammatory toxicities. To improve anti-cancer therapy, it is critical to reveal the mechanisms by which PD-1 regulates T cell responses. We performed global quantitative phosphoproteomic interrogation of PD-1 signaling in T cells. By complementing our analysis with functional validation assays, we show that PD-1 targets tyrosine phosphosites that mediate proximal T cell receptor signaling, cytoskeletal organization, and immune synapse formation. PD-1 ligation also led to differential phosphorylation of serine and threonine sites within proteins regulating T cell activation, gene expression, and protein translation. In silico predictions revealed that kinase/substrate relationships engaged downstream of PD-1 ligation. These insights uncover the phosphoproteomic landscape of PD-1–triggered pathways and reveal novel PD-1 substrates that modulate diverse T cell functions and may serve as future therapeutic targets. These data are a useful resource in the design of future PD-1–targeting therapeutic approaches.

Clostridium difficile is an anaerobic and spore-forming bacterium responsible for 15–25% of postantibiotic diarrhea and 95% of pseudomembranous colitis. Peptidoglycan is a crucial element of the bacterial cell wall that is exposed to the host, making it an important target for the innate immune system. The C. difficile peptidoglycan is largely N-deacetylated on its glucosamine (93% of muropeptides) through the activity of enzymes known as N-deacetylases, and this N-deacetylation modulates host–pathogen interactions, such as resistance to the bacteriolytic activity of lysozyme, virulence, and host innate immune responses. C. difficile genome analysis showed that 12 genes potentially encode N-deacetylases; however, which of these N-deacetylases are involved in peptidoglycan N-deacetylation remains unknown. Here, we report the enzymes responsible for peptidoglycan N-deacetylation and their respective regulation. Through peptidoglycan analysis of several mutants, we found that the N-deacetylases PdaV and PgdA act in synergy. Together they are responsible for the high level of peptidoglycan N-deacetylation in C. difficile and the consequent resistance to lysozyme. We also characterized a third enzyme, PgdB, as a glucosamine N-deacetylase. However, its impact on N-deacetylation and lysozyme resistance is limited, and its physiological role remains to be dissected. Finally, given the influence of peptidoglycan N-deacetylation on host defense against pathogens, we investigated the virulence and colonization ability of the mutants. Unlike what has been shown in other pathogenic bacteria, a lack of N-deacetylation in C. difficile is not linked to a decrease in virulence.

Pyrroline-5-carboxylate reductase 1 (PYCR1) catalyzes the biosynthetic half-reaction of the proline cycle by reducing Δ1-pyrroline-5-carboxylate (P5C) to proline through the oxidation of NAD(P)H. Many cancers alter their proline metabolism by up-regulating the proline cycle and proline biosynthesis, and knockdowns of PYCR1 lead to decreased cell proliferation. Thus, evidence is growing for PYCR1 as a potential cancer therapy target. Inhibitors of cancer targets are useful as chemical probes for studying cancer mechanisms and starting compounds for drug discovery; however, there is a notable lack of validated inhibitors for PYCR1. To fill this gap, we performed a small-scale focused screen of proline analogs using X-ray crystallography. Five inhibitors of human PYCR1 were discovered: l-tetrahydro-2-furoic acid, cyclopentanecarboxylate, l-thiazolidine-4-carboxylate, l-thiazolidine-2-carboxylate, and N-formyl l-proline (NFLP). The most potent inhibitor was NFLP, which had a competitive (with P5C) inhibition constant of 100 μm. The structure of PYCR1 complexed with NFLP shows that inhibitor binding is accompanied by conformational changes in the active site, including the translation of an α-helix by 1 Å. These changes are unique to NFLP and enable additional hydrogen bonds with the enzyme. NFLP was also shown to phenocopy the PYCR1 knockdown in MCF10A H-RASV12 breast cancer cells by inhibiting de novo proline biosynthesis and impairing spheroidal growth. In summary, we generated the first validated chemical probe of PYCR1 and demonstrated proof-of-concept for screening proline analogs to discover inhibitors of the proline cycle.

Visual Abstract

Toma Keser
Dec 29, 2020; 0:RA120.002433v1-mcp.RA120.002433
Research

Litong Nie
Dec 1, 2020; 19:2015-2029
Research

David Durán
Nov 19, 2020; 0:RA120.002276v1-mcp.RA120.002276
Research

Xinting Zhang
Dec 8, 2020; 0:RA120.002306v1-mcp.RA120.002306
Research

Juntuo Zhou
Nov 30, 2020; 0:RA120.002384v1-mcp.RA120.002384
Research

Colin T. McDowell
Nov 24, 2020; 0:RA120.002256v1-mcp.RA120.002256
Research

Daniel J. Geiszler
Dec 1, 2020; 0:TIR120.002216v1-mcp.TIR120.002216
Technological Innovation and Resources

Johannes Griss
Dec 1, 2020; 19:2115-2124
Technological Innovation and Resources

Protein quality control is maintained by a number of integrated cellular pathways that monitor the folding and functionality of the cellular proteome. Defects in these pathways lead to the accumulation of misfolded or faulty proteins that may become insoluble and aggregate over time. Protein aggregates significantly contribute to the development of a number of human diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Alzheimer's disease. In vitro, imaging-based, cellular studies have defined key biomolecular components that recognize and clear aggregates; however, no unifying method is available to quantify cellular aggregates, limiting our ability to reproducibly and accurately quantify these structures. Here we describe an ImageJ macro called AggreCount to identify and measure protein aggregates in cells. AggreCount is designed to be intuitive, easy to use, and customizable for different types of aggregates observed in cells. Minimal experience in coding is required to utilize the script. Based on a user-defined image, AggreCount will report a number of metrics: (i) total number of cellular aggregates, (ii) percentage of cells with aggregates, (iii) aggregates per cell, (iv) area of aggregates, and (v) localization of aggregates (cytosol, perinuclear, or nuclear). A data table of aggregate information on a per cell basis, as well as a summary table, is provided for further data analysis. We demonstrate the versatility of AggreCount by analyzing a number of different cellular aggregates including aggresomes, stress granules, and inclusion bodies caused by huntingtin polyglutamine expansion.

Montgomery Blencowe
Dec 23, 2020; 0:jlr.RA120000713v1-jlr.RA120000713
Research Articles

In SAS Life Science Analytics Framework 4.6, group membership removal fails with an exception if a user is set as assignee, a candidate, or a notification recipient in a user task for a Process Flow . The Process

SAS  Episode Analytics 3.1 requires the ability to capture user interactions with the user interface for auditing purposes. To support the required functionality a new table has been add

When viewing a SAS Visual Analytics report, you might intermittently see an error that includes content similar to the following:

Genome-wide association studies (GWAS) have implicated ~380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in ‘interferon signaling’, ‘autoimmune/immune activation’, ‘visual transduction’, and ‘protein catabolism’ were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

The analysis of T cell lipid raft proteome is challenging due to the highly dynamic nature of rafts and the hydrophobic character of raft-resident proteins. We explored an innovative strategy for bottom-up lipid raftomics based on suspension-trapping (S-Trap) sample preparation. Mouse T cells were prepared from splenocytes by negative immunoselection, and rafts were isolated by a detergent-free method and OptiPrep gradient ultracentrifugation. Microdomains enriched in flotillin-1, LAT, and cholesterol were subjected to proteomic analysis through an optimized protocol based on S-Trap and high pH fractionation, followed by nano-LC-MS/MS. Using this method, we identified 2,680 proteins in the raft-rich fraction and established a database of 894 T cell raft proteins. We then performed a differential analysis on the raft-rich fraction from nonstimulated versus anti-CD3/CD28 T cell receptor (TCR)-stimulated T cells. Our results revealed 42 proteins present in one condition and absent in the other. For the first time, we performed a proteomic analysis on rafts from ex vivo T cells obtained from individual mice, before and after TCR activation. This work demonstrates that the proposed method utilizing an S-Trap-based approach for sample preparation increases the specificity and sensitivity of lipid raftomics.

Ion mobility brings an additional dimension of separation to LC–MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (~30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (~10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.

In-depth coverage of proteomic analysis could enhance our understanding to the mechanism of the protein functions. Unfortunately, many highly hydrophobic proteins and low-abundance proteins, which play critical roles in signaling networks, are easily lost during sample preparation, mainly attributed to the fact that very few extractants can simultaneously satisfy the requirements on strong solubilizing ability to membrane proteins and good enzyme compatibility. Thus, it is urgent to screen out ideal extractant from the huge compound libraries in a fast and effective way. Herein, by investigating the interior mechanism of extractants on the membrane proteins solubilization and trypsin compatibility, a molecular dynamics simulation system was established as complement to the experimental procedure to narrow down the scope of candidates for proteomics analysis. The simulation data shows that the van der Waals interaction between cation group of ionic liquid and membrane protein is the dominant factor in determining protein solubilization. In combination with the experimental data, 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) is on the shortlist for the suitable candidates from comprehensive aspects. Inspired by the advantages of C12Im-Cl, an ionic liquid-based filter-aided sample preparation (i-FASP) method was developed. Using this strategy, over 3,300 proteins were confidently identified from 10³ HeLa cells (~100 ng proteins) in a single run, an improvement of 53% over the conventional FASP method. Then the i-FASP method was further successfully applied to the label-free relative quantitation of human liver cancer and para-carcinoma tissues with obviously improved accuracy, reproducibility and coverage than the commonly used urea-based FASP method. The above results demonstrated that the i-FASP method could be performed as a versatile tool for the in-depth coverage proteomic analysis of biological samples.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers and known for its extensive genetic heterogeneity, high therapeutic resistance, and strong variation in intrinsic radiosensitivity. To understand the molecular mechanisms underlying radioresistance, we screened the phenotypic response of 38 PDAC cell lines to ionizing radiation. Subsequent phosphoproteomic analysis of two representative sensitive and resistant lines led to the reproducible identification of 7,800 proteins and 13,000 phosphorylation sites (p-sites). Approximately 700 p-sites on 400 proteins showed abundance changes after radiation in all cell lines regardless of their phenotypic sensitivity. Apart from recapitulating known radiation response phosphorylation markers such as on proteins involved in DNA damage repair, the analysis uncovered many novel members of a radiation-responsive signaling network that was apparent only at the level of protein phosphorylation. These regulated p-sites were enriched in potential ATM substrates and in vitro kinase assays corroborated 10 of these. Comparing the proteomes and phosphoproteomes of radiosensitive and -resistant cells pointed to additional tractable radioresistance mechanisms involving apoptotic proteins. For instance, elevated NADPH quinine oxidoreductase 1 (NQO1) expression in radioresistant cells may aid in clearing harmful reactive oxygen species. Resistant cells also showed elevated phosphorylation levels of proteins involved in cytoskeleton organization including actin dynamics and focal adhesion kinase (FAK) activity and one resistant cell line showed a strong migration phenotype. Pharmacological inhibition of the kinases FAK by Defactinib and of CHEK1 by Rabusertib showed a statistically significant sensitization to radiation in radioresistant PDAC cells. Together, the presented data map a comprehensive molecular network of radiation-induced signaling, improves the understanding of radioresistance and provides avenues for developing radiotherapeutic strategies.

Co-fractionation MS (CF-MS) is a technique with potential to characterize endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published Saccharomyces cerevisiae CF-MS data sets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending 'Guilt-by-Association' by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution data set, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies – Spearman and Kendall correlations – and the recently introduced Co-apex metric frequently maximize recall, whereas a popular metric—Euclidean distance—delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying nonmodel organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimize false discovery. These assessments are summarized in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation.

High-speed analysis of large (prote)omics sample sets at the rate of thousands or millions of samples per day on a single platform has been a challenge since the beginning of proteomics. For many years, ESI-based MS methods have dominated proteomics because of their high sensitivity and great depth in analyzing complex proteomes. However, despite improvements in speed, ESI-based MS methods are fundamentally limited by their sample introduction, which excludes off-line sample preparation/fractionation because of the time required to switch between individual samples/sample fractions, and therefore being dependent on the speed of on-line sample preparation methods such as liquid chromatography. Laser-based ionization methods have the advantage of moving from one sample to the next without these limitations, being mainly restricted by the speed of modern sample stages, i.e. 10 ms or less between samples. This speed matches the data acquisition speed of modern high-performing mass spectrometers whereas the pulse repetition rate of the lasers (>1 kHz) provides a sufficient number of desorption/ionization events for successful ion signal detection from each sample at the above speed of the sample stages. Other advantages of laser-based ionization methods include the generally higher tolerance to sample additives and contamination compared with ESI MS, and the contact-less and pulsed nature of the laser used for desorption, reducing the risk of cross-contamination. Furthermore, new developments in MALDI have expanded its analytical capabilities, now being able to fully exploit high-performing hybrid mass analyzers and their strengths in sensitivity and MS/MS analysis by generating an ESI-like stable yield of multiply charged analyte ions. Thus, these new developments and the intrinsically high speed of laser-based methods now provide a good basis for tackling extreme sample analysis speed in the omics.

Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.

Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.

We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.