A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to “better” vs. “worse” outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU

SAMPI: Protein Identification with Mass Spectra Alignments

BACKGROUND: Mass spectrometry based peptide mass fingerprints (PMFs) offer a fast, efficient, and robust method for protein identification. A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database. However, existing tools for analyzing PMFs often suffer from missing or heuristic analysis of the significance of search results and insufficient handling of missing and additional peaks. RESULTS: We present an unified framework for analyzing Peptide Mass Fingerprints that offers a number of advantages over existing methods: First, comparison of mass spectra is based on a scoring function that can be custom-designed for certain applications and explicitly takes missing and additional peaks into account. The method is able to simulate almost every additive scoring scheme. Second, we present an efficient deterministic method for assessing the significance of a protein hit, independent of the underlying scoring function and sequence database. We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot. CONCLUSION: The proposed framework for analyzing Peptide Mass Fingerprints shows performance comparable to Mascot on small peak lists. Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes

CLPM: A Cross-Linked Peptide Mapping Algorithm for Mass Spectrometric Analysis

BACKGROUND: Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies. RESULT: We designed a Cross-Linked Peptide Mapping (CLPM) algorithm which takes advantage of all of the information available in the experiment including the amino acid sequence from each protein, the identity of the crosslinker, the identity of the digesting enzyme, the level of missed cleavage, and possible chemical modifications. The algorithm does in silico digestion and crosslinking, calculates all possible mass values and matches the theoretical data to the actual experimental data provided by the mass spectrometry analysis to identify the crosslinked peptides. CONCLUSION: Identifying peptides by their masses can be an efficient starting point for direct sequence confirmation. The CLPM algorithm provides a powerful tool in identifying these potential interaction sites in combination with chemical crosslinking and mass spectrometry. Through this cost-effective approach, subsequent efforts can quickly focus attention on investigating these specific interaction sites

Observation of Hadronic W Decays in t-tbar Events with the Collider Detector at Fermilab

We observe hadronic W decays in t-tbar -> W (-> l nu) + >= 4 jet events using a 109 pb-1 data sample of p-pbar collisions at sqrt{s} = 1.8 TeV collected with the Collider Detector at Fermilab (CDF). A peak in the dijet invariant mass distribution is obtained that is consistent with W decay and inconsistent with the background prediction by 3.3 standard deviations. From this peak we measure the W mass to be 77.2 +- 4.6 (stat+syst) GeV/c^2. This result demonstrates the presence of two W bosons in t-tbar candidates in the W (-> l nu) + >= 4 jet channel.Comment: 20 pages, 4 figures, submitted to PR

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Genetically enhanced asynapsis of autosomal chromatin promotes transcriptional dysregulation and meiotic failure

During meiosis, pairing of homologous chromosomes and their synapsis are essential prerequisites for normal male gametogenesis. Even limited autosomal asynapsis often leads to spermatogenic impairment, the mechanism of which is not fully understood. The present study was aimed at deliberately increasing the size of partial autosomal asynapsis and analysis of its impact on male meiosis. For this purpose, we studied the effect of t12 haplotype encompassing four inversions on chromosome 17 on mouse autosomal translocation T(16;17)43H (abbreviated T43H). The T43H/T43H homozygotes were fully fertile in both sexes, while +/T43H heterozygous males, but not females, were sterile with meiotic arrest at late pachynema. Inclusion of the t12 haplotype in trans to the T43H translocation resulted in enhanced asynapsis of the translocated autosome, ectopic phosphorylation of histone H2AX, persistence of RAD51 foci, and increased gene silencing around the translocation break. Increase was also on colocalization of unsynapsed chromatin with sex body. Remarkably, we found that transcriptional silencing of the unsynapsed autosomal chromatin precedes silencing of sex chromosomes. Based on the present knowledge, we conclude that interference of meiotic silencing of unsynapsed autosomes with meiotic sex chromosome inactivation is the most likely cause of asynapsis-related male sterility