Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae

2013 ◽  
Vol 9 (7) ◽  
pp. 1743 ◽  
Author(s):  
Tobias Maier ◽  
Josep Marcos ◽  
Judith A. H. Wodke ◽  
Bernhard Paetzold ◽  
Manuel Liebeke ◽  
...  
Keyword(s):  
Mycoplasma Pneumoniae ◽  
Large Scale ◽  
Proteomics Data ◽  
Metabolome Analysis ◽  
Genomics And Proteomics ◽  
Quantitative Integration

scholarly journals A modular toolkit to inhibit proline-rich motif–mediated protein–protein interactions

2015 ◽  
Vol 112 (16) ◽  
pp. 5011-5016 ◽  
Author(s):  
Robert Opitz ◽  
Matthias Müller ◽  
Cédric Reuter ◽  
Matthias Barone ◽  
Arne Soicke ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Focal Adhesions ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
General Applicability ◽  
Genomics And Proteomics ◽  
Domain Interactions ◽  
Src Homology ◽  
Recognition Motifs

Small-molecule competitors of protein–protein interactions are urgently needed for functional analysis of large-scale genomics and proteomics data. Particularly abundant, yet so far undruggable, targets include domains specialized in recognizing proline-rich segments, including Src-homology 3 (SH3), WW, GYF, and Drosophila enabled (Ena)/vasodilator-stimulated phosphoprotein (VASP) homology 1 (EVH1) domains. Here, we present a modular strategy to obtain an extendable toolkit of chemical fragments (ProMs) designed to replace pairs of conserved prolines in recognition motifs. As proof-of-principle, we developed a small, selective, peptidomimetic inhibitor of Ena/VASP EVH1 domain interactions. Highly invasive MDA MB 231 breast-cancer cells treated with this ligand showed displacement of VASP from focal adhesions, as well as from the front of lamellipodia, and strongly reduced cell invasion. General applicability of our strategy is illustrated by the design of an ErbB4-derived ligand containing two ProM-1 fragments, targeting the yes-associated protein 1 (YAP1)-WW domain with a fivefold higher affinity.

scholarly journals Large-scale plasma-metabolome analysis identifies potential biomarkers of psoriasis and its clinical subtypes

2021 ◽  
Author(s):  
Toshihiro Kishikawa ◽  
Noriko Arase ◽  
Shigeyoshi Tsuji ◽  
Yuichi Maeda ◽  
Takuro Nii ◽  
...  
Keyword(s):  
Large Scale ◽  
Metabolome Analysis ◽  
Plasma Metabolome ◽  
Potential Biomarkers

scholarly journals A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Harshi Weerakoon ◽  
Jeremy Potriquet ◽  
Alok K. Shah ◽  
Sarah Reed ◽  
Buddhika Jayakody ◽  
...  
Keyword(s):  
T Cell ◽  
Large Scale ◽  
Large Data ◽  
Spectral Library ◽  
Proteomics Data ◽  
Cell Library ◽  
Public Resource ◽  
Human T Cell ◽  
Theoretical Mass ◽  
Current Resource

AbstractData independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).

scholarly journals Denoising large-scale biological data using network filters

2021 ◽  
Author(s):  
Andrew J Kavran ◽  
Aaron Clauset
Keyword(s):  
Large Scale ◽  
Synthetic Data ◽  
Interaction Network ◽  
Learning Task ◽  
Biological Data ◽  
Data Sets ◽  
Proteomics Data ◽  
Life History Variation ◽  
Wide Range ◽  
Underlying Processes

Abstract Background: Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.Results: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data.Conclusions: Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

scholarly journals 2-Methyl-pentanoyl-carnitine (2-MPC): a urine biomarker for patent Ascaris lumbricoides infection

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ole Lagatie ◽  
Ann Verheyen ◽  
Stijn Van Asten ◽  
Maurice R. Odiere ◽  
Yenny Djuardi ◽  
...  
Keyword(s):  
Ascaris Lumbricoides ◽  
Large Scale ◽  
Infection Intensity ◽  
World Health ◽  
Infection Model ◽  
Metabolome Analysis ◽  
Control Programs ◽  
Urine Biomarker ◽  
Intestinal Worms ◽  
Health Organization

Abstract Infections with intestinal worms, such as Ascaris lumbricoides, affect hundreds of millions of people in all tropical and subtropical regions of the world. Through large-scale deworming programs, World Health Organization aims to reduce moderate-to-heavy intensity infections below 1%. Current diagnosis and monitoring of these control programs are solely based on the detection of worm eggs in stool. Here we describe how metabolome analysis was used to identify the A. lumbricoides-specific urine biomarker 2-methyl pentanoyl carnitine (2-MPC). This biomarker was found to be 85.7% accurate in determining infection and 90.5% accurate in determining a moderate-to-heavy infection. Our results also demonstrate that there is a correlation between 2-MPC levels in urine and A. lumbricoides DNA detected in stool. Furthermore, the levels of 2-MPC in urine were shown to rapidly and strongly decrease upon administration of a standard treatment (single oral dose of 400 mg albendazole). In an Ascaris suum infection model in pigs, it was found that, although 2-MPC levels were much lower compared to humans, there was a significant association between urinary 2-MPC levels and both worm counts (p = 0.023) and the number of eggs per gram (epg) counts (p < 0.001). This report demonstrates that urinary 2-MPC can be considered an A. lumbricoides-specific biomarker that can be used to monitor infection intensity.

scholarly journals MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Shisheng Wang ◽  
Hongwen Zhu ◽  
Hu Zhou ◽  
Jingqiu Cheng ◽  
Hao Yang
Keyword(s):  
Mass Spectrometry ◽  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Spectral Feature ◽  
Mass Spectrometry Data ◽  
Learning Approaches ◽  
Proteomics Data ◽  
Proteome Profiling ◽  
Analytical Technique

Abstract Background Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved. Results We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967). Conclusion This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches.

scholarly journals Integrating Genomics and Proteomics Data to Predict Drug Effects Using Binary Linear Programming

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e102798 ◽  
Author(s):  
Zhiwei Ji ◽  
Jing Su ◽  
Chenglin Liu ◽  
Hongyan Wang ◽  
Deshuang Huang ◽  
...  
Keyword(s):  
Linear Programming ◽  
Drug Effects ◽  
Proteomics Data ◽  
Binary Linear Programming ◽  
Genomics And Proteomics

scholarly journals Chromosome-level de novo assembly of the pig-tailed macaque genome using linked-read sequencing and HiC proximity scaffolding

GigaScience ◽  
2020 ◽  
Vol 9 (7) ◽  
Author(s):  
Morteza Roodgar ◽  
Afshin Babveyh ◽  
Lan H Nguyen ◽  
Wenyu Zhou ◽  
Rahul Sinha ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Innate Immune ◽  
Human Diseases ◽  
Macaca Nemestrina ◽  
Proximity Ligation Assay ◽  
Immune Gene ◽  
Proteomics Data ◽  
Macaque Species ◽  
Chromosome Level

Abstract Background Macaque species share &gt;93% genome homology with humans and develop many disease phenotypes similar to those of humans, making them valuable animal models for the study of human diseases (e.g., HIV and neurodegenerative diseases). However, the quality of genome assembly and annotation for several macaque species lags behind the human genome effort. Results To close this gap and enhance functional genomics approaches, we used a combination of de novo linked-read assembly and scaffolding using proximity ligation assay (HiC) to assemble the pig-tailed macaque (Macaca nemestrina) genome. This combinatorial method yielded large scaffolds at chromosome level with a scaffold N50 of 127.5 Mb; the 23 largest scaffolds covered 90% of the entire genome. This assembly revealed large-scale rearrangements between pig-tailed macaque chromosomes 7, 12, and 13 and human chromosomes 2, 14, and 15. We subsequently annotated the genome using transcriptome and proteomics data from personalized induced pluripotent stem cells derived from the same animal. Reconstruction of the evolutionary tree using whole-genome annotation and orthologous comparisons among 3 macaque species, human, and mouse genomes revealed extensive homology between human and pig-tailed macaques with regards to both pluripotent stem cell genes and innate immune gene pathways. Our results confirm that rhesus and cynomolgus macaques exhibit a closer evolutionary distance to each other than either species exhibits to humans or pig-tailed macaques. Conclusions These findings demonstrate that pig-tailed macaques can serve as an excellent animal model for the study of many human diseases particularly with regards to pluripotency and innate immune pathways.

scholarly journals Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 1535 ◽  
Author(s):  
Francisco Azuaje ◽  
Sang-Yoon Kim ◽  
Daniel Perez Hernandez ◽  
Gunnar Dittmar
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Diagnostic Value ◽  
Classification Model ◽  
Clinical Approach ◽  
Proteomics Data ◽  
Cell Renal Cell Carcinoma ◽  
Molecular Features ◽  
Genes Encoding ◽  
New Research

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.

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