Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Abstract Background Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls. Results With a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample. Conclusion Decon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs. Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/

Spatial and Functional Relationships Among Pol V-Associated Loci, Pol IV-Dependent siRNAs, and Cytosine Methylation in the \u3cem\u3eArabidopsis\u3c/em\u3e Epigenome

Multisubunit RNA polymerases IV and V (Pols IV and V) mediate RNA-directed DNA methylation and transcriptional silencing of retrotransposons and heterochromatic repeats in plants. We identified genomic sites of Pol V occupancy in parallel with siRNA deep sequencing and methylcytosine mapping, comparing wild-type plants with mutants defective for Pol IV, Pol V, or both Pols IV and V. Approximately 60% of Pol V-associated regions encompass regions of 24-nucleotide (nt) siRNA complementarity and cytosine methylation, consistent with cytosine methylation being guided by base-pairing of Pol IV-dependent siRNAs with Pol V transcripts. However, 27% of Pol V peaks do not overlap sites of 24-nt siRNA biogenesis or cytosine methylation, indicating that Pol V alone does not specify sites of cytosine methylation. Surprisingly, the number of methylated CHH motifs, a hallmark of RNA-directed de novo methylation, is similar in wild-type plants and Pol IV or Pol V mutants. In the mutants, methylation is lost at 50%–60% of the CHH sites that are methylated in the wild type but is gained at new CHH positions, primarily in pericentromeric regions. These results indicate that Pol IV and Pol V are not required for cytosine methyltransferase activity but shape the epigenome by guiding CHH methylation to specific genomic sites

PICU Survivorship: Factors Affecting Feasibility and Cohort Retention in a Long-Term Outcomes Study

Our understanding of longitudinal outcomes of Pediatric Intensive Care Unit (PICU) survivors is limited by the heterogeneity of follow-up intervals, populations, and outcomes assessed. We sought to demonstrate (1) the feasibility of longitudinal multidimensional outcome assessment and (2) methods to promote cohort retention. The objective of this presented study was to provide details of follow-up methodology in a PICU survivor cohort and not to present the outcomes at long-term follow-up for this cohort. We enrolled 152 children aged 0 to 17 years admitted to the PICU in a prospective longitudinal cohort study. We examined resource utilization, family impact of critical illness, and neurodevelopment using the PICU Outcomes Portfolio (POP) Survey which included a study-specific survey and validated tools: 1. Functional Status Scale, 2. Pediatric Evaluation of Disability Inventory Computer Adaptive Test, 3. Pediatric Quality of Life Inventory, 4. Strengths and Difficulties Questionnaire, and 5. Vanderbilt Assessment Scales for Attention Deficit-Hyperactivity Disorder. POP Survey completion rates were 89%, 78%, and 84% at 1, 3, and 6 months. Follow-up rates at 1, 2, and 3 years were 80%, 55%, and 43%. Implementing a longitudinal multidimensional outcome portfolio for PICU survivors is feasible within an urban, tertiary-care, academic hospital. Our attrition after one year demonstrates the long-term follow-up challenges in this population. Our findings inform ongoing efforts to implement core outcome sets after pediatric critical illness

Combining patient visual timelines with deep learning to predict mortality

Background: Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. Methods and findings: All adult consecutive patient admissions from 2008–2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P Conclusions: We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.</p

Combining patient visual timelines with deep learning to predict mortality.

BackgroundDeep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality.Methods and findingsAll adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P ConclusionsWe converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine

Predicting transfers to intensive care in children using CEWT and other early warning systems

Background and Objective: The Children’s Early Warning Tool (CEWT), developed in Australia, is widely used in many countries to monitor the risk of deterioration in hospitalized children. Our objective was to compare CEWT prediction performance against a version of the Bedside Pediatric Early Warning Score (Bedside PEWS), Between the Flags (BTF), and the pediatric Calculated Assessment of Risk and Triage (pCART). Methods: We conducted a retrospective observational study of all patient admissions to the Comer Children’s Hospital at the University of Chicago between 2009–2019. We compared performance for predicting the primary outcome of a direct ward-to-intensive care unit (ICU) transfer within the next 12 h using the area under the receiver operating characteristic curve (AUC). Alert rates at various score thresholds were also compared. Results: Of 50,815 ward admissions, 1,874 (3.7%) experienced the primary outcome. Among patients in Cohort 1 (years 2009–2017, on which the machine learning-based pCART was trained), CEWT performed slightly worse than Bedside PEWS but better than BTF (CEWT AUC 0.74 vs. Bedside PEWS 0.76, P Conclusion: CEWT has good discrimination for predicting which patients will likely be transferred to the ICU, while pCART performed the best.</p

Glycoproteomics: Identifying the Glycosylation of Prostate Specific Antigen at Normal and High Isoelectric Points by LC–MS/MS

Prostate specific antigen (PSA) is currently used as a biomarker to diagnose prostate cancer. PSA testing has been widely used to detect and screen prostate cancer. However, in the diagnostic gray zone, the PSA test does not clearly distinguish between benign prostate hypertrophy and prostate cancer due to their overlap. To develop more specific and sensitive candidate biomarkers for prostate cancer, an in-depth understanding of the biochemical characteristics of PSA (such as glycosylation) is needed. PSA has a single glycosylation site at Asn69, with glycans constituting approximately 8% of the protein by weight. Here, we report the comprehensive identification and quantitation of N-glycans from two PSA isoforms using LC–MS/MS. There were 56 N-glycans associated with PSA, whereas 57 N-glycans were observed in the case of the PSA-high isoelectric point (pI) isoform (PSAH). Three sulfated/phosphorylated glycopeptides were detected, the identification of which was supported by tandem MS data. One of these sulfated/phosphorylated N-glycans, HexNAc5Hex4dHex1s/p1 was identified in both PSA and PSAH at relative intensities of 0.52 and 0.28%, respectively. Quantitatively, the variations were monitored between these two isoforms. Because we were one of the laboratories participating in the 2012 ABRF Glycoprotein Research Group (gPRG) study, those results were compared to that presented in this study. Our qualitative and quantitative results summarized here were comparable to those that were summarized in the interlaboratory study

The quantitative changes in the yeast Hsp70 and Hsp90 interactomes upon DNA damage

The molecular chaperones Hsp70 and Hsp90 participate in many important cellular processes, including how cells respond to DNA damage. Here we show the results of applied quantitative affinity-purification mass spectrometry (AP-MS) proteomics to understand the protein network through which Hsp70 and Hsp90 exert their effects on the DNA damage response (DDR). We characterized the interactomes of the yeast Hsp70 isoform Ssa1 and Hsp90 isoform Hsp82 before and after exposure to methyl methanesulfonate. We identified 256 chaperone interactors, 146 of which are novel. Although the majority of chaperone interaction remained constant under DNA damage, 5 proteins (Coq5, Ast1, Cys3, Ydr210c and Rnr4) increased in interaction with Ssa1 and/or Hsp82. This data presented here are related to [1] (Truman et al., in press). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (Vizcaino et al. (2013) [2]) with the dataset identifier PXD001284