MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics.

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in posttranslational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site and consider all the relevant sources of confounding and variation. In this manuscript, we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with PTMs and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types, and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale.

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

A high-fat diet increases hepatic mitochondrial turnover through restricted acetylation in a NAFLD mouse model.

Lysine acetylation of proteins has emerged as a key posttranslational modification (PTM) that regulates mitochondrial metabolism. Acetylation may regulate energy metabolism by inhibiting and affecting the stability of metabolic enzymes and oxidative phosphorylation (OxPhos) subunits. Although protein turnover can be easily measured, due to the low abundance of modified proteins, it has been difficult to evaluate the effect of acetylation on the stability of proteins in vivo. We applied 2H2O-metabolic labeling coupled with immunoaffinity and high-resolution mass spectrometry method to measure the stability of acetylated proteins in mouse liver based on their turnover rates. As a proof-of-concept, we assessed the consequence of high-fat diet (HFD)-induced altered acetylation in protein turnover in LDL receptor-deficient (LDLR-/-) mice susceptible to diet-induced nonalcoholic fatty liver disease (NAFLD). HFD feeding for 12 wk led to steatosis, the early stage of NAFLD. A significant reduction in acetylation of hepatic proteins was observed in NAFLD mice, based on immunoblot analysis and label-free quantification with mass spectrometry. Compared with control mice on a normal diet, NAFLD mice had overall increased turnover rates of hepatic proteins, including mitochondrial metabolic enzymes (0.159 ± 0.079 vs. 0.132 ± 0.068 day-1), suggesting their reduced stability. Also, acetylated proteins had slower turnover rates (increased stability) than native proteins in both groups (0.096 ± 0.056 vs. 0.170 ± 0.059 day-1 in control, and 0.111 ± 0.050 vs. 0.208 ± 0.074 day-1 in NAFLD). Furthermore, association analysis revealed a relationship between the HFD-induced decrease in acetylation and increased turnover rates for hepatic proteins in NAFLD mice. These changes were associated with increased expressions of the hepatic mitochondrial transcriptional factor (TFAM) and complex II subunit without any changes to other OxPhos proteins, suggesting that enhanced mitochondrial biogenesis prevented restricted acetylation-mediated depletion of mitochondrial proteins. We conclude that decreased acetylation of mitochondrial proteins may contribute to adaptive improved hepatic mitochondrial function in the early stages of NAFLD.NEW & NOTEWORTHY This is the first method to quantify acetylome dynamics in vivo. This method revealed acetylation-mediated altered hepatic mitochondrial protein turnover in response to a high-fat diet in a mouse model of NAFLD.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets.

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

Measuring acetyl-CoA and acetylated histone turnover in vivo: Effect of a high fat diet.

Cellular availability of acetyl-CoA, a central intermediate of metabolism, regulates histone acetylation. The impact of a high-fat diet (HFD) on the turnover rates of acetyl-CoA and acetylated histones is unknown. We developed a method for simultaneous measurement of acetyl-CoA and acetylated histones kinetics using a single 2H2O tracer, and used it to examine effect of HFD-induced perturbations on hepatic histone acetylation in LDLR-/- mice, a mouse model of non-alcoholic fatty liver disease (NAFLD). Mice were given 2H2O in the drinking water and the kinetics of hepatic acetyl-CoA, histones, and acetylated histones were quantified based on their 2H-labeling. Consumption of a high fat Western-diet (WD) for twelve weeks led to decreased acetylation of hepatic histones (p< 0.05), as compared to a control diet. These changes were associated with 1.5-3-fold increased turnover rates of histones without any change in acetyl-CoA flux. Acetylation significantly reduced the stability of histones and the turnover rates of acetylated peptides were correlated with the number of acetyl groups in neighboring lysine sites. We conclude that 2H2O-method can be used to study metabolically controlled histone acetylation and acetylated histone turnover in vivo.

A case report of delayed lower intestinal bleeding after organophosphate poisoning.

BACKGROUND: Organophosphate poisoning is a serious issue and it results in significant casualties in developing countries. Since agriculture remains an important and necessary sector of human society and organophosphate are commonly used in agriculture, it is difficult to prevent organophosphate poisoning. Gastrointestinal bleeding is not a common but life threatening symptom of organophosphate poisoning. We report a rare case of gastrointestine bleeding due to organophosphate poisoning. CASE PRESENTATION: A 78-year-old woman presented to our hospital approximately 12 h after ingesting a mouthful of organophosphate and benzodiazepines in a suicide attempt. Six weeks after successful medical treatment for respiratory failure, she developed recurring melena. Colonoscopy and esophagogastroduodenoscopy findings were negative for ulcers or bleeding. Enteroscopy revealed severe circumferential ulcers with luminal narrowing 10 cm proximal to the ileocecal valve. The patient underwent a 100-cm ileum resection after failed medical treatment and recovered uneventfully. The resected terminal ileum demonstrated severe inflammation and a sharp transitional zone between the healthy and injured mucosa approximately 50 cm proximal to the ileocecal valve. Pathological examination revealed an injured mucosa with inflammatory cell infiltration and structural damage. This case highlights a rare event of OP poisoning with late-onset lower gastrointestinal bleeding, which prolonged the patient's recovery course and parenteral alimentation period. CONCLUSION: We report a rare case of a patient with organophosphate poisoning, with late-onset lower GI tract bleeding, which raised clinical awareness regarding the organophosphate poisoning that induce intestinal symptoms.

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

BACKGROUND: A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. METHODS: We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. RESULTS: The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (<7 %) between estimation and ground truth. By applying the topic model-based purification to mass spectrometric data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. CONCLUSIONS: We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed that incorporation of scan-level features have the potential to lead to more accurate purification results by alleviating the loss in information as a result of integrating peaks. We believe cancer biomarker discovery studies that use mass spectrometric analysis of human biospecimens can greatly benefit from topic model-based purification of the data prior to statistical and pathway analyses.

LC-MS/MS-based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma.

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label-free proteomic analysis by LC-MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC-associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 (http://proteomecentral.proteomexchange.org/dataset/PXD001171).

LC-MS profiling of N-Glycans derived from human serum samples for biomarker discovery in hepatocellular carcinoma.

Defining clinically relevant biomarkers for early stage hepatocellular carcinoma (HCC) in a high-risk population of cirrhotic patients has potentially far-reaching implications for disease management and patient health. Changes in glycan levels have been associated with the onset of numerous diseases including cancer. In the present study, we used liquid chromatography coupled with electrospray ionization mass spectrometry (LC-ESI-MS) to analyze N-glycans in sera from 183 participants recruited in Egypt and the U.S. and identified candidate biomarkers that distinguish HCC cases from cirrhotic controls. N-Glycans were released from serum proteins and permethylated prior to the LC-ESI-MS analysis. Through two complementary LC-ESI-MS quantitation approaches, global profiling and targeted quantitation, we identified 11 N-glycans with statistically significant differences between HCC cases and cirrhotic controls. These glycans can further be categorized into four structurally related clusters, matching closely with the implications of important glycosyltransferases in cancer progression and metastasis. The results of this study illustrate the power of the integrative approach combining complementary LC-ESI-MS based quantitation approaches to investigate changes in N-glycan levels between HCC cases and patients with liver cirrhosis.

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. RESULTS: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. AVAILABILITY AND IMPLEMENTATION: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html. CONTACT: hwr@georgetown.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A self-driven, microfluidic, integrated-circuit biosensing chip for detecting four cardiovascular disease biomarkers.

Cardiovascular diseases (CVDs) claimed the lives of nearly 21 million people worldwide in 2021, accounting for 30% of global deaths. However, one in five CVD patients is unaware that they have the disease, emphasizing the need for accurate biomarker monitoring. Herein we developed an integrated microfluidic system (IMS) for rapid quantification of four CVD biomarkers, including N-terminal pro B-type natriuretic peptide (NT-proBNP), fibrinogen, cardiac troponin I (cTnI), and C-reactive protein (CRP)- via aptamer-coated interdigitated electrodes (IDE) with integrated circuits (IC) and a self-driven IMS for sample treatment. The device was composed of plasma filtration, metering, and fluidic delay modules, and the former could extract 45% of plasma from a 20-µL blood sample; the metering module could quantify 5 µL of plasma within 90 s. Subsequently, the plasma was transported to a detection chamber, where IC-based IDE sensors made measurements within 5 min. The entire 15-min process allowed us to evaluate biomarkers across a wide dynamic range: NT-proBNP (0.1-10,000 pg/mL), fibrinogen (50-1,000 mg/dL), cTnI (0.1-10,000 pg/mL), and CRP (0.5-9 mg/L). Given that spiked blood samples were measured with reasonable accuracy (>80%), the IMS could see utility in CVD risk assessment and personalized medicine.

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications.

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications.

LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort.

Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3-5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3ß, 6ß-dihydroxy-5ß-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC-MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.

Using a spike-in experiment to evaluate analysis of LC-MS data.

BACKGROUND: Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. RESULTS: The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose. CONCLUSIONS: The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.

Detecting Cochlear Synaptopathy Through Curvature Quantification of the Auditory Brainstem Response.

The sound-evoked electrical compound potential known as auditory brainstem response (ABR) represents the firing of a heterogenous population of auditory neurons in response to sound stimuli, and is often used for clinical diagnosis based on wave amplitude and latency. However, recent ABR applications to detect human cochlear synaptopathy have led to inconsistent results, mainly due to the high variability of ABR wave-1 amplitude. Here, rather than focusing on the amplitude of ABR wave 1, we evaluated the use of ABR wave curvature to detect cochlear synaptic loss. We first compared four curvature quantification methods using simulated ABR waves, and identified that the cubic spline method using five data points produced the most accurate quantification. We next evaluated this quantification method with ABR data from an established mouse model with cochlear synaptopathy. The data clearly demonstrated that curvature measurement is more sensitive and consistent in identifying cochlear synaptic loss in mice compared to the amplitude and latency measurements. We further tested this curvature method in a different mouse model presenting with otitis media. The change in curvature profile due to middle ear infection in otitis media is different from the profile of mice with cochlear synaptopathy. Thus, our study suggests that curvature quantification can be used to address the current ABR variability issue, and may lead to additional applications in the clinic diagnosis of hearing disorders.

Coexistence of disseminated Kaposi sarcoma and multicentric Castleman disease in an HIV-infected patient under viral suppression.

Coexistence of multicentric Castleman disease and Kaposi sarcoma is rare and might be missed without an experienced pathologists' interpretation. A 46-year-old man had been diagnosed with HIV infection and treated with combination antiretroviral therapy of dolutegravir/abacavir/lamivudine (Triumeq) for one year. The latest viral load was 49 copies/mL and CD4 T-cell count was 192 cells/uL. He was admitted due to fever off and on, splenomegaly, general lymphadenopathy, and severe thrombocytopenia for two months. Biopsy of a purplish skin lesion and gastric tissue showed Kaposi sarcoma. The pathology of inguinal lymph nodes revealed coexistence of Kaposi sarcoma and multicentric Castleman disease. The plasma Kaposi sarcoma herpesvirus viral load was 365,000 copies/mL. During hospitalization, progressive pancytopenia and spiking fever persisted, and he died of multi-organ failure before completion of chemotherapeutic treatments with rituximab plus liposomal doxorubicin.

Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response.

Defective autophagy is strongly associated with chronic inflammation. Loss-of-function of the core autophagy gene Atg16l1 increases risk for Crohn's disease in part by enhancing innate immunity through myeloid cells such as macrophages. However, autophagy is also recognized as a mechanism for clearance of certain intracellular pathogens. These divergent observations prompted a re-evaluation of ATG16L1 in innate antimicrobial immunity. In this study, we found that loss of Atg16l1 in myeloid cells enhanced the killing of virulent Shigella flexneri (S.flexneri), a clinically relevant enteric bacterium that resides within the cytosol by escaping from membrane-bound compartments. Quantitative multiplexed proteomics of murine bone marrow-derived macrophages revealed that ATG16L1 deficiency significantly upregulated proteins involved in the glutathione-mediated antioxidant response to compensate for elevated oxidative stress, which simultaneously promoted S.flexneri killing. Consistent with this, myeloid-specific deletion of Atg16l1 in mice accelerated bacterial clearance in vitro and in vivo. Pharmacological induction of oxidative stress through suppression of cysteine import enhanced microbial clearance by macrophages. Conversely, antioxidant treatment of macrophages permitted S.flexneri proliferation. These findings demonstrate that control of oxidative stress by ATG16L1 and autophagy regulates antimicrobial immunity against intracellular pathogens.

Fiber optic nanogold-linked immunosorbent assay for rapid detection of procalcitonin at femtomolar concentration level.

A rapid and ultrasensitive biosensing method based on fiber optic nanogold-linked immunosorbent assay is reported. The method employs an immobilized capture probe on the fiber core surface of an optical fiber and a detection probe conjugated to gold nanoparticles (AuNPs) in a solution. Introduction of a sample containing an analyte and the detection probe into a biosensor chip leads to the formation of a sandwich-like complex of capture probe-analyte-detection probe on the fiber core surface, through which nanoplasmonic absorption of the fiber optic evanescent wave occurs. The performance of this method has been evaluated by its application to the detection of procalcitonin (PCT), an important biomarker for sepsis. In this study, anti-PCT capture antibody is functionalized on an unclad segment of an optical fiber to yield a fiber sensor and anti-PCT detection antibody is conjugated to AuNPs to afford nanoplasmonic probes. The method provides a wide linear response range from 1 pg/mL to 100 ng/mL (5 orders) and an extremely low limit of detection of 95 fg/mL (7.3 fM) for PCT. In addition, the method shows a good correlation in determining PCT in blood plasma with the clinically validated electrochemiluninescent immunoassay. Furthermore, the method is quick (analysis time ≤15 min), requires low-cost instrumentation and sensor chips, and is also potentially applicable to the detection of many other biomarkers.

Succinate Dehydrogenase-Deficient Renal Cell Carcinoma.

Succinate dehydrogenase (SDH)-deficient renal cell carcinoma is a recently recognized distinct subtype of renal cell carcinoma in the 2016 World Health Organization classification. It is associated with SDH gene germline mutations, which also cause paraganglioma/pheochromocytoma, gastrointestinal stromal tumor, and pituitary adenoma. The tumor most commonly presents in young adulthood. The tumors are arranged in solid nests or in tubules and frequently show cystic change. The tumors are composed of cuboidal to oval cells with round nuclei, dispersed chromatin, and inconspicuous nucleoli. The cytoplasm is eosinophilic or flocculent but not truly oncocytic. The most distinctive histologic feature is the presence of cytoplasmic vacuoles or inclusions. Loss of SDH subunit B immunostaining is needed for a definite diagnosis. The prognosis is good for low-grade tumors but worse for tumors with high-grade nuclei, sarcomatoid change, or coagulative necrosis. Long-term follow-up is indicated.