Composition and Regulation of the Cellular Repertoire of SCF Ubiquitin Ligases.

SCF (Skp1-Cullin-F-box) ubiquitin ligases comprise several dozen modular enzymes that have diverse roles in biological regulation. SCF enzymes share a common catalytic core containing Cul1⋅Rbx1, which is directed toward different substrates by a variable substrate receptor (SR) module comprising 1 of 69 F-box proteins bound to Skp1. Despite the broad cellular impact of SCF enzymes, important questions remain about the architecture and regulation of the SCF repertoire, including whether SRs compete for Cul1 and, if so, how this competition is managed. Here, we devise methods that preserve the in vivo assemblages of SCF complexes and apply quantitative mass spectrometry to perform a census of these complexes (the "SCFome") in various states. We show that Nedd8 conjugation and the SR exchange factor Cand1 have a profound effect on shaping the SCFome. Together, these factors enable rapid remodeling of SCF complexes to promote biased assembly of SR modules bound to substrate.

Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide.

Emerging studies have linked the ribosome to more selective control of gene regulation. However, an outstanding question is whether ribosome heterogeneity at the level of core ribosomal proteins (RPs) exists and enables ribosomes to preferentially translate specific mRNAs genome-wide. Here, we measured the absolute abundance of RPs in translating ribosomes and profiled transcripts that are enriched or depleted from select subsets of ribosomes within embryonic stem cells. We find that heterogeneity in RP composition endows ribosomes with differential selectivity for translating subpools of transcripts, including those controlling metabolism, cell cycle, and development. As an example, mRNAs enriched in binding to RPL10A/uL1-containing ribosomes are shown to require RPL10A/uL1 for their efficient translation. Within several of these transcripts, this level of regulation is mediated, at least in part, by internal ribosome entry sites. Together, these results reveal a critical functional link between ribosome heterogeneity and the post-transcriptional circuitry of gene expression.

Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling.

Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.

Neural signatures of second language proficiency in narrative processing.

Making sense of speech in a second language relies on multiple abilities. Differences in brain activity related to proficiency in language tasks have often been attributed to processing demands. However, during naturalistic narrative comprehension, listeners at different proficiency levels may form different representations of the same speech. We hypothesized that the intersubject synchronization of these representations could be used to measure second-language proficiency. Using a searchlight-shared response model, we found highly proficient participants showed synchronization in regions similar to those of native speakers, including in the default mode network and the lateral prefrontal cortex. In contrast, participants with low proficiency showed more synchronization in auditory cortex and word-level semantic processing areas in the temporal lobe. Moderate proficiency showed the greatest neural diversity, suggesting lower consistency in the source of this partial proficiency. Based on these synchronization differences, we were able to classify the proficiency level or predict behavioral performance on an independent English test in held-out participants, suggesting the identified neural systems represented proficiency-sensitive information that was generalizable to other individuals. These findings suggest higher second-language proficiency leads to more native-like neural processing of naturalistic language, including in systems beyond the cognitive control network or the core language network.

Prospective exploration of the role of combined internalizing symptoms in self-reported memory among older adults during the COVID-19 pandemic.

OBJECTIVES: A growing literature suggests depression and anxiety increase risk of cognitive decline. However, few studies have examined their combined effects on cognition, among older adults, especially during periods of high stress. METHOD: Based on a sample of community dwelling older adults (N = 576), we evaluated the effects of pre-pandemic anxiety and depressive symptoms, obtained in September 2018, to changes in self-reported memory (SRM) assessed 3 months into the COVID-19 pandemic. RESULTS: In separate models, we found participants with depression scores at least 1-SD above the mean and participants with anxiety scores at least 2-SD above the mean to report a significant decline in SRM. Moderation analyses revealed those with high depressive symptoms (at or above the mean) showed a decrease in SRM regardless of anxiety. The extent to which high pre-pandemic anxiety symptoms influenced SRM is dependent on whether pre-pandemic depression was at or above the mean. CONCLUSIONS: Pre-pandemic depression predicted a decline in SRM regardless of anxiety. Moderation analyses revealed that the extent to which anxiety symptoms influenced SRM was dependent on depression being at or above the mean. Those with high anxiety and depression are at highest risk of experiencing cognitive consequences related to stressful exposures like COVID-19.

Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms.

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors.

BACKGROUND: Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS: A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS: A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS: The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Estimation of measurement uncertainty for the quantification of protein by ID-LC-MS/MS.

The emergence of mass spectrometry (MS)-based methods to quantify proteins for clinical applications has led to the need for accurate and consistent measurements. To meet the clinical needs of MS-based protein results, it is important that the results are traceable to higher-order standards and methods and have defined uncertainty values. Therefore, we outline a comprehensive approach for the estimation of measurement uncertainty of a MS-based procedure for the quantification of a protein biomarker. Using a bottom-up approach, which is the model outlined in the "Guide to the Expression of Uncertainty of Measurement" (GUM), we evaluated the uncertainty components of a MS-based measurement procedure for a protein biomarker in a complex matrix. The cause-and-effect diagram of the procedure is used to identify each uncertainty component, and statistical equations are derived to determine the overall combined uncertainty. Evaluation of the uncertainty components not only enables the calculation of the measurement uncertainty but can also be used to determine if the procedure needs improvement. To demonstrate the use of the bottom-up approach, the overall combined uncertainty is estimated for the National Institute of Standards and Technology (NIST) candidate reference measurement procedure for albumin in human urine. The results of the uncertainty approach are applied to the determination of uncertainty for the certified value for albumin in candidate NIST Standard Reference Material® (SRM) 3666. This study provides a framework for measurement uncertainty estimation of a MS-based protein procedure by identifying the uncertainty components of the procedure to derive the overall combined uncertainty.

An Introduction to Advanced Targeted Acquisition Methods.

Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field.

Combined nanometric and phylogenetic analysis of unique endocytic compartments in Giardia lamblia sheds light on the evolution of endocytosis in Metamonada.

BACKGROUND: Giardia lamblia, a parasitic protist of the Metamonada supergroup, has evolved one of the most diverged endocytic compartment systems investigated so far. Peripheral endocytic compartments, currently known as peripheral vesicles or vacuoles (PVs), perform bulk uptake of fluid phase material which is then digested and sorted either to the cell cytosol or back to the extracellular space. RESULTS: Here, we present a quantitative morphological characterization of these organelles using volumetric electron microscopy and super-resolution microscopy (SRM). We defined a morphological classification for the heterogenous population of PVs and performed a comparative analysis of PVs and endosome-like organelles in representatives of phylogenetically related taxa, Spironucleus spp. and Tritrichomonas foetus. To investigate the as-yet insufficiently understood connection between PVs and clathrin assemblies in G. lamblia, we further performed an in-depth search for two key elements of the endocytic machinery, clathrin heavy chain (CHC) and clathrin light chain (CLC), across different lineages in Metamonada. Our data point to the loss of a bona fide CLC in the last Fornicata common ancestor (LFCA) with the emergence of a protein analogous to CLC (GlACLC) in the Giardia genus. Finally, the location of clathrin in the various compartments was quantified. CONCLUSIONS: Taken together, this provides the first comprehensive nanometric view of Giardia's endocytic system architecture and sheds light on the evolution of GlACLC analogues in the Fornicata supergroup and, specific to Giardia, as a possible adaptation to the formation and maintenance of stable clathrin assemblies at PVs.

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module.

Because of its ability to objectively reflect people's emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, ß, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51-15.43% compared with existing methods.

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways.

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

Proteomic Signature of Extracellular Vesicles Associated with Colorectal Cancer.

The proteins of extracellular vesicles (EVs) provide proteomic signatures that reflect molecular features of EV-producing cells, including cancer cells. Detection of cancer cell EV proteins is of great interest due to the development of novel predictive diagnostic approaches. Using targeted mass spectrometry with stable-isotope-labeled peptide standards (SIS), we measured in this study the levels of 34 EV-associated proteins in vesicles and whole lysate derived from the colorectal cancer (CRC) cell lines Caco-2, HT29 and HCT116. We also evaluated the abundance of 13 EV-associated proteins (FN1, TLN1, ITGB3, HSPA8, TUBA4A, CD9, CD63, HSPG2, ITGB1, GNAI2, TSG101, PACSIN2, and CDC42) in EVs isolated from blood plasma samples from 11 CRC patients and 20 healthy volunteers. Downregulation of TLN1, ITGB3, and TUBA4A with simultaneous upregulation of HSPG2 protein were observed in cancer samples compared to healthy controls. The proteomic cargo of the EVs associated with CRC represents a promising source of potential prognostic markers.

Analytical comparison of absolute quantification strategies to investigate the insulin signaling pathway in fat cells.

So far, mass spectrometry-based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best accuracy and coefficient of variation was achieved by ratio to ratio calibration curves. We applied the ratio to ratio quantification approach to analyze very low abundant insulin signaling proteins such as PIK3RA (0.10-0.93 fmol/µg), AKT1 (0.1-0.39 fmol/µg), and the insulin receptor (0.22-2.62 fmol/µg) in a fat cell model and demonstrated the adaptation of this pathway at different states of insulin sensitivity.

Addressing the Protease Bias in Quantitative Proteomics.

Protein quantification strategies using multiple proteases have been shown to deliver poor interprotease accuracy in label-free mass spectrometry experiments. By utilizing six different proteases with different cleavage sites, this study explores the protease bias and its effect on accuracy and precision by using recombinant protein standards. We established 557 SRM assays, using a recombinant protein standard resource, toward 10 proteins in human plasma and determined their concentration with multiple proteases. The quantified peptides of these plasma proteins spanned 3 orders of magnitude (0.02-70 µM). In total, 60 peptides were used for absolute quantification and the majority of the peptides showed high robustness. The retained reproducibility was achieved by quantifying plasma proteins using spiked stable isotope standard recombinant proteins in a targeted proteomics workflow.

Direct Measurement of ATP7B Peptides Is Highly Effective in the Diagnosis of Wilson Disease.

BACKGROUND & AIMS: Both existing clinical criteria and genetic testing have significant limitations for the diagnosis of Wilson disease (WD), often creating ambiguities in patient identification and leading to delayed diagnosis and ineffective management. ATP7B protein concentration, indicated by direct measurement of surrogate peptides from patient dried blood spot samples, could provide primary evidence of WD. ATP7B concentrations were measured in patient samples from diverse backgrounds, diagnostic potential is determined, and results are compared with biochemical and genetic results from individual patients. METHODS: Two hundred and sixty-four samples from biorepositories at 3 international and 2 domestic academic centers and 150 normal controls were obtained after Institutional Review Board approval. Genetically or clinically confirmed WD patients with a Leipzig score >3 and obligate heterozygote (carriers) from affected family members were included. ATP7B peptide measurements were made by immunoaffinity enrichment mass spectrometry. RESULTS: Two ATP7B peptides were used to measure ATP7B protein concentration. Receiver operating characteristics curve analysis generates an area under the curve of 0.98. ATP7B peptide analysis of the sequence ATP7B 887 was found to have a sensitivity of 91.2%, specificity of 98.1%, positive predictive value of 98.0%, and a negative predictive value of 91.5%. In patients with normal ceruloplasmin concentrations (>20 mg/dL), 14 of 16 (87.5%) were ATP7B-deficient. In patients without clear genetic results, 94% were ATP7B-deficient. CONCLUSIONS: Quantification of ATP7B peptide effectively identified WD patients in 92.1% of presented cases and reduced ambiguities resulting from ceruloplasmin and genetic analysis. Clarity is brought to patients with ambiguous genetic results, significantly aiding in noninvasive diagnosis. A proposed diagnostic score and algorithm incorporating ATP7B peptide concentrations can be rapidly diagnostic and supplemental to current Leipzig scoring systems.

What if using certified reference materials (CRMs) was a requirement to publish in analytical/bioanalytical chemistry journals?

Certified reference materials (CRMs) are routinely used by analytical chemists to validate new analytical methods and to demonstrate the quality of their quantitative measurements. Even though CRMs for trace element and trace organic analysis have been available and widely used for over 50 years, the majority of papers published in analytical chemistry journals do not mention the use of CRMs. What if analytical/bioanalytical chemistry journals required the use of CRMs to publish a paper? This feature article attempts to address this question by providing examples of recent papers that have made exceptional use of CRMs to validate new analytical methods and to describe novel, alternative uses of CRMs that provide new characterization of the CRM. The potential benefits of using a CRM even when it does not have certified values for the analytes of interest are presented.

From urban dust and marine sediment to Ginkgo biloba and human serum-a top ten list of Standard Reference Materials (SRMs).

During the past 40 years, the National Institute of Standards and Technology (NIST) has developed over 180 natural matrix Standard Reference Materials® (SRMs) for the determination of trace organic constituents in environmental, clinical, food, and dietary supplement matrices. A list of the Top Ten SRMs intended for organic analysis was identified based on selection criteria including analytical challenge to assign certified values, challenges in material preparation, novel matrices, longevity, widespread use, and unique design concept or intended use. The environmental matrix SRMs include air particulate matter, marine sediment, mussel tissue, and human serum with the focus on contaminants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), chlorinated pesticides, and polybrominated diphenyl ethers (PBDEs). Human serum and plasma SRMs for clinical diagnostic markers including vitamin D metabolites represent clinical analysis, whereas infant formula, multivitamin/multielement tablets, and Ginkgo biloba constitute the food and dietary supplement matrices on the list. Each of the SRMs on the Top Ten list is discussed relative to the selection criteria and significance of the material, and several overall lessons learned are summarized.

Mission-driven research for stratospheric aerosol geoengineering.

The last decade has seen broad exploratory research into stratospheric aerosol (SA) geoengineering, motivated by concern that reducing greenhouse gas emissions may be insufficient to avoid significant impacts from climate change. Based on this research, it is plausible that a limited deployment of SA geoengineering, provided it is used in addition to cutting emissions, could reduce many climate risks for most people. However, "plausible" is an insufficient basis on which to support future decisions. Developing the necessary knowledge requires a transition toward mission-driven research that has the explicit goal of supporting informed decisions. We highlight two important observations that follow from considering such a comprehensive, prioritized natural-science research effort. First, while field experiments may eventually be needed to reduce some of the uncertainties, we expect that the next phase of research will continue to be primarily model-based, with one outcome being to assess and prioritize which uncertainties need to be reduced (and, as a corollary, which field experiments can reduce those uncertainties). Second, we anticipate a clear separation in scale and character between small-scale experimental research to resolve specific process uncertainties and global-scale activities. We argue that the latter, even if the radiative forcing is negligible, should more appropriately be considered after a decision regarding whether and how to deploy SA geoengineering, rather than within the scope of "research" activities.

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns.

Most of the existing methods focus mainly on the extraction of shape-based, rotation-based, and motion-based features, usually neglecting the relationship between hands and body parts, which can provide significant information to address the problem of similar sign words based on the backhand approach. Therefore, this paper proposes four feature-based models. The spatial-temporal body parts and hand relationship patterns are the main feature. The second model consists of the spatial-temporal finger joint angle patterns. The third model consists of the spatial-temporal 3D hand motion trajectory patterns. The fourth model consists of the spatial-temporal double-hand relationship patterns. Then, a two-layer bidirectional long short-term memory method is used to deal with time-independent data as a classifier. The performance of the method was evaluated and compared with the existing works using 26 ASL letters, with an accuracy and F1-score of 97.34% and 97.36%, respectively. The method was further evaluated using 40 double-hand ASL words and achieved an accuracy and F1-score of 98.52% and 98.54%, respectively. The results demonstrated that the proposed method outperformed the existing works under consideration. However, in the analysis of 72 new ASL words, including single- and double-hand words from 10 participants, the accuracy and F1-score were approximately 96.99% and 97.00%, respectively.