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1.
Adapting dCas9-APEX2 for subnuclear proteomic profiling.
Gao, XD, Rodríguez, TC, Sontheimer, EJ
Methods in enzymology. 2019;:365-383
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Abstract
Genome organization and subnuclear protein localization are essential for normal cellular function and have been implicated in the control of gene expression, DNA replication, and genomic stability. The coupling of chromatin conformation capture (3C), chromatin immunoprecipitation and sequencing, and related techniques have continuously improved our understanding of genome architecture. To profile site-specifically DNA-associated proteins in a high-throughput and unbiased manner, the RNA-programmable CRISPR-Cas9 platform has recently been combined with an enzymatic labeling system to allow proteomic landscapes at repetitive and nonrepetitive loci to be defined with unprecedented ease and resolution. In this chapter, we describe the dCas9-APEX2 experimental approach for specifically targeting a DNA sequence, enzymatically labeling local proteins with biotin, and quantitatively analyzing the labeled proteome. We also discuss the optimization and extension of this pipeline to facilitate its use in understanding nuclear and chromosome biology.
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2.
Serum Protein Biomarkers of Fibrosis Aid in Risk Stratification of Future Stricturing Complications in Pediatric Crohn's Disease.
Wu, J, Lubman, DM, Kugathasan, S, Denson, LA, Hyams, JS, Dubinsky, MC, Griffiths, AM, Baldassano, RN, Noe, JD, Rabizadeh, S, et al
The American journal of gastroenterology. 2019;(5):777-785
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Abstract
OBJECTIVES Avoiding fibrostenotic complications is of paramount concern in the management of Crohn's disease (CD). We sought to investigate the association of candidate biomarkers of fibrosis collected at diagnosis with the future development of fibrostenotic CD. METHODS Using the Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn's Disease cohort, a multicenter prospective observational pediatric inception cohort, subjects with an inflammatory phenotype (B1) at diagnosis who later converted to a stricturing phenotype (B2) within 3 years were compared with those who remained B1. Serum collected at diagnosis underwent both parallel reaction monitoring-targeted proteomic analysis and conventional enzyme-linked immunosorbent assay for 10 candidate biomarkers of intestinal fibrosis. Cox proportional hazard regression was used for multivariable analysis of time-dependent outcomes. RESULTS In 116 subjects 58 subjects with verified B1 phenotype at diagnosis who later converted to B2 disease were compared with 58 subjects who remained B1 over 3 years of follow-up. Extracellular matrix protein 1 (ECM1) levels in the upper quartile (hazard ratio [HR] 3.43, 95% confidence limit [CL] 1.33, 8.42) were associated with future fibrostenotic disease. ASCA IgA (HR 4.99, 95% CL 1.50, 16.68) and CBir levels (HR 5.19, 95% CL 1.83, 14.74) were also associated with future intestinal fibrostenosis, although ECM1 continued to demonstrate independent association with conversion to B2 even with adjustment for serologies in multivariable analysis (HR 5.33, 95% CL 1.29, 22.13). CONCLUSIONS ECM1 and other biomarkers of fibrosis may aid in determining the risk of uncomplicated inflammatory disease converting to B2 stricturing phenotypes in children with CD. Prospective validation studies to verify test performance and optimize clinical utilization are needed before clinical implementation.
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Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning.
Guan, S, Moran, MF, Ma, B
Molecular & cellular proteomics : MCP. 2019;(10):2099-2107
Abstract
Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed. The LC-MS/MS properties or behaviors are indexed retention times (iRT), MS1 or survey scan charge state distributions, and sequence ion intensities of HCD spectra. A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models. Two featurization schemes were proposed and demonstrated to allow for efficient encoding of modifications. The iRT and charge state distribution models were trained with on order of 105 data points each. An HCD sequence ion prediction model was trained with 2 × 106 experimental spectra. The iRT prediction model and HCD sequence ion prediction model provide improved accuracies over the start-of-the-art models available in literature. The MS1 charge state distribution prediction model offers excellent performance. The prediction models can be used to enhance peptide identification and quantification in data-dependent acquisition and data-independent acquisition (DIA) experiments as well as to assist MRM (multiple reaction monitoring) and PRM (parallel reaction monitoring) experiment design.
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2018 YPIC Challenge: A Case Study in Characterizing an Unknown Protein Sample.
Pino, L, Lin, A, Bittremieux, W
Journal of proteome research. 2019;(11):3936-3943
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Abstract
For the 2018 YPIC Challenge, contestants were invited to try to decipher two unknown English questions encoded by a synthetic protein expressed in Escherichia coli. In addition to deciphering the sentence, contestants were asked to determine the three-dimensional structure and detect any post-translation modifications left by the host organism. We present our experimental and computational strategy to characterize this sample by identifying the unknown protein sequence and detecting the presence of post-translational modifications. The sample was acquired with dynamic exclusion disabled to increase the signal-to-noise ratio of the measured molecules, after which spectral clustering was used to generate high-quality consensus spectra. De novo spectrum identification was used to determine the synthetic protein sequence, and any post-translational modifications introduced by E. coli on the synthetic protein were analyzed via spectral networking. This workflow resulted in a de novo sequence coverage of 70%, on par with sequence database searching performance. Additionally, the spectral networking analysis indicated that no systematic modifications were introduced on the synthetic protein by E. coli. The strategy presented here can be directly used to analyze samples for which no protein sequence information is available or when the identity of the sample is unknown. All software and code to perform the bioinformatics analysis is available as open source, and self-contained Jupyter notebooks are provided to fully recreate the analysis.
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Plant-Microbe Symbiosis: What Has Proteomics Taught Us?
Khatabi, B, Gharechahi, J, Ghaffari, MR, Liu, D, Haynes, PA, McKay, MJ, Mirzaei, M, Salekdeh, GH
Proteomics. 2019;(16):e1800105
Abstract
Beneficial microbes have a positive impact on the productivity and fitness of the host plant. A better understanding of the biological impacts and underlying mechanisms by which the host derives these benefits will help to address concerns around global food production and security. The recent development of omics-based technologies has broadened our understanding of the molecular aspects of beneficial plant-microbe symbiosis. Specifically, proteomics has led to the identification and characterization of several novel symbiosis-specific and symbiosis-related proteins and post-translational modifications that play a critical role in mediating symbiotic plant-microbe interactions and have helped assess the underlying molecular aspects of the symbiotic relationship. Integration of proteomic data with other "omics" data can provide valuable information to assess hypotheses regarding the underlying mechanism of symbiosis and help define the factors affecting the outcome of symbiosis. Herein, an update is provided on the current and potential applications of symbiosis-based "omic" approaches to dissect different aspects of symbiotic plant interactions. The application of proteomics, metaproteomics, and secretomics as enabling approaches for the functional analysis of plant-associated microbial communities is also discussed.
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The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs).
Yakubu, RR, Nieves, E, Weiss, LM
Advances in experimental medicine and biology. 2019;:169-198
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Abstract
Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.
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Global phosphoproteomic analysis reveals ARMC10 as an AMPK substrate that regulates mitochondrial dynamics.
Chen, Z, Lei, C, Wang, C, Li, N, Srivastava, M, Tang, M, Zhang, H, Choi, JM, Jung, SY, Qin, J, et al
Nature communications. 2019;(1):104
Abstract
AMP-activated protein kinase (AMPK) is a key regulator of cellular energy homeostasis. Although AMPK has been studied extensively in cellular processes, understanding of its substrates and downstream functional network, and their contributions to cell fate and disease development, remains incomplete. To elucidate the AMPK-dependent signaling pathways, we performed global quantitative phosphoproteomic analysis using wild-type and AMPKα1/α2-double knockout cells and discovered 160 AMPK-dependent phosphorylation sites. Further analysis using an AMPK consensus phosphorylation motif indicated that 32 of these sites are likely direct AMPK phosphorylation sites. We validated one uncharacterized protein, ARMC10, and demonstrated that the S45 site of ARMC10 can be phosphorylated by AMPK both in vitro and in vivo. Moreover, ARMC10 overexpression was sufficient to promote mitochondrial fission, whereas ARMC10 knockout prevented AMPK-mediated mitochondrial fission. These results demonstrate that ARMC10 is an effector of AMPK that participates in dynamic regulation of mitochondrial fission and fusion.
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Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) for Quantitative Proteomics.
Hoedt, E, Zhang, G, Neubert, TA
Advances in experimental medicine and biology. 2019;:531-539
Abstract
Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful approach for high-throughput quantitative proteomics. SILAC allows highly accurate protein quantitation through metabolic encoding of whole cell proteomes using stable isotope labeled amino acids. Since its introduction in 2002, SILAC has become increasingly popular. In this chapter we review the methodology and application of SILAC, with an emphasis on three research areas: dynamics of posttranslational modifications, protein-protein interactions, and protein turnover.
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Multi-omics approaches for strategic improvement of stress tolerance in underutilized crop species: A climate change perspective.
Muthamilarasan, M, Singh, NK, Prasad, M
Advances in genetics. 2019;:1-38
Abstract
For several decades, researchers are working toward improving the "major" crops for better adaptability and tolerance to environmental stresses. However, little or no research attention is given toward neglected and underutilized crop species (NUCS) which hold the potential to ensure food and nutritional security among the ever-growing global population. NUCS are predominantly climate resilient, but their yield and quality are compromised due to selective breeding. In this context, the importance of omics technologies namely genomics, transcriptomics, proteomics, phenomics and ionomics in delineating the complex molecular machinery governing growth, development and stress responses of NUCS is underlined. However, gaining insights through individual omics approaches will not be sufficient to address the research questions, whereas integrating these technologies could be an effective strategy to decipher the gene function, genome structures, biological pathways, metabolic and regulatory networks underlying complex traits. Given this, the chapter enlists the importance of NUCS in food and nutritional security and provides an overview of deploying omics approaches to study the NUCS. Also, the chapter enumerates the status of crop improvement programs in NUCS and suggests implementing "integrating omics" for gaining a better understanding of crops' response to abiotic and biotic stresses.
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Small-protein Enrichment Assay Enables the Rapid, Unbiased Analysis of Over 100 Low Abundance Factors from Human Plasma.
Harney, DJ, Hutchison, AT, Su, Z, Hatchwell, L, Heilbronn, LK, Hocking, S, James, DE, Larance, M
Molecular & cellular proteomics : MCP. 2019;(9):1899-1915
Abstract
Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.