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.

Illness perceptions in cochlear implant users - a longitudinal study.

PURPOSE: 'Illness perceptions' refers to the thoughts and ideas a person has about an illness. According to Leventhal's Self-Regulatory Model (SRM), changing the threatening illness perceptions of cochlear implant (CI) recipients can be a further step in optimizing hearing outcomes with the CI. The aims of the present study were to assess users' illness perceptions and to determine whether perceptions change during six months of CI rehabilitation. METHODS: One hundred and thirty-eight participants completed the Brief Illness Perception Questionnaire (Brief IPQ), assessing their illness perceptions on nine scales. Data were collected at a German CI center at first CI fitting and six-month follow-up. After first fitting, participants underwent intensive rehabilitation including auditory training, medical, audiological and psychological treatments. RESULTS: At both assessments, participants tended to view their hearing impairment as a severe threat. On the Brief IPQ, the 'consequences' assessment improved during CI rehabilitation, which can be explained by the CI-induced hearing improvement. However, 'understanding' and 'identity' assessments worsened. This could be because CI recipients only come to realize the full complexity of their hearing impairment during rehabilitation. The other scales and the total score remained unaffected. CONCLUSIONS: Current practice in CI rehabilitation seems to be insufficient to improve threatening illness perceptions (except for perceived consequences). This may be because standard information often fails to reach the patients. The development and empirical validation of an intervention program to address individual illness perceptions in CI recipients could be helpful in this context. Further research will be needed to confirm the results.

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.