Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry

Kitata, Reta Birhanu; Dimayacyac-Esleta, Baby Rorielyn T.; Choong, Wai-Kok; Tsai, Chia Feng; Lin, Tai Du; Tsou, Chih-Chiang; Weng, Shao Hsing; Chen, Yi Ju; Yang, Pan Chyr; Arco, Susan D.; Nesvizhskii, Alexey I.; Sung, Ting Yi; Chen, Yu‐Ju

doi:10.1021/acs.jproteome.5b00477

Cited by 26 publications

(36 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At least two transitions per peptide from the protein of interest were selected for quantification, and then the quantitative value of the peptide was estimated based on peak areas of selected transitions. The reliability of the quantitative value was confirmed by peak features as described previously (51). The quantitative value was normalized by areas of peptides estimated using internal standard proteins.…”

Section: Methodsmentioning

confidence: 99%

Increased Dynamics of Tricarboxylic Acid Cycle and Glutamate Synthesis in Obese Adipose Tissue

Nagao¹,

Nishizawa²,

Bamba

et al. 2017

Journal of Biological Chemistry

View full text Add to dashboard Cite

Obesity is closely associated with various metabolic disorders. However, little is known about abnormalities in the metabolic change of obese adipose tissue. Here we use static metabolic analysis and metabolic turnover analysis to assess metabolic dynamics in obese mice. The static metabolic analyses showed that glutamate and constitutive metabolites of the TCA cycle were increased in the white adipose tissue (WAT) of ob/ob and diet-induced obesity mice but not in the liver or skeletal muscle of these obese mice. Moreover, metabolic turnover analyses demonstrated that these glucose-derived metabolites were dynamically and specifically produced in obese WAT compared with lean WAT. Glutamate rise in obese WAT was associated with down-regulation of glutamate aspartate transporter (GLAST), a major glutamate transporter for adipocytes, and low uptake of glutamate into adipose tissue. In adipocytes, glutamate treatment reduced adiponectin secretion and insulin-mediated glucose uptake and phosphorylation of Akt. These data suggest that a high intra-adipocyte glutamate level potentially relates to adipocyte dysfunction in obesity. This study provides novel insights into metabolic dysfunction in obesity through comprehensive application of metabolic turnover analysis in two obese animal models.

show abstract

Section: Methodsmentioning

confidence: 99%

Increased Dynamics of Tricarboxylic Acid Cycle and Glutamate Synthesis in Obese Adipose Tissue

Nagao¹,

Nishizawa²,

Bamba

et al. 2017

Journal of Biological Chemistry

View full text Add to dashboard Cite

show abstract

“…A C18 RP StageTip was prepared using a small piece of C8 EmporeTM disk extracted by a 16‐gage blunt end needle, which was fit into the top of D200 tips (Gilson, Middletown, USA) . Five milligram of C18 resin (ReproSil‐Pur 120 C18, 5 μm) was dissolved in 100 μL of buffer A (1.62% FA and 3.48% NH 4 OH, pH 10) and 100 μL of buffer B (100% ACN) and was added to the C8 disk‐fitted tips.…”

Section: Methodsmentioning

confidence: 99%

Chemotherapy Immunophenoprofiles in Non‐Small‐Cell Lung Cancer by Personalized Membrane Proteomics

Putri

Feng

Hsu

et al. 2018

Proteomics Clinical Apps

Self Cite

View full text Add to dashboard Cite

show abstract

“…al. performed an experiment based on high-PH reverse-phase StageTip fractionation to confirm that PHTF1 is a membrane protein [36]. According to our analysis, PHTF1 contains 7 transmembrane helices (Figure 8).…”

Section: Application To Human Proteomementioning

confidence: 93%

PureseqTM: efficient and accurate prediction of transmembrane topology from amino acid sequence only

Wang

et al. 2019

Preprint

View full text Add to dashboard Cite

MotivationRapid and accurate identification of transmembrane (TM) topology is well suited for the annotation of the entire membrane proteome. It is the initial step of predicting the structure and function of membrane proteins. However, existing methods that utilize only amino acid sequence information suffer from low prediction accuracy, whereas methods that exploit sequence profile or consensus need too much computational time. MethodHere we propose a deep learning framework DeepCNF that predicts TM topology from amino acid sequence only. Compared to previous sequence-based approaches that use hidden Markov models or dynamic Bayesian networks, DeepCNF is able to incorporate much more contextual information by a hierarchical deep neural network, while simultaneously modeling the interdependency between adjacent topology labels. ResultExperimental results show that PureseqTM not only outperforms existing sequence-based methods, but also reaches or even surpasses the profile/consensus methods. On the 39 newly released membrane proteins, our approach successfully identifies the correct TM segments and boundaries for at least 3 cases while all existing methods fail to do so. When applied to the entire human proteome, our method can identify the incorrect annotations of TM regions by UniProt and discover the membrane-related proteins that are not manually curated as membrane proteins.Availability http://pureseqtm.predmp.com/ =============================================================================== =========== Introduction: =========== Transmembrane proteins (TMPs) are key players in energy production, material transport, and communication between cells [1]. TMPs are encoded by ~30% genes in the various genomes [2] and have been targeted by ~50% of therapeutic drugs [3]. Despite their abundance and importance, the number of solved TMPs structures is relatively low compared to that of non-transmembrane proteins (non-TMPs). In particular, under the 40% sequence identity cutoff, there are only about 1500 non-redundant TMPs whereas the number of non-redundant non-TMPs is more than 34000. The underlying reason is that the experimental determination of TMPs is challenging as membrane proteins are often too large for NMR spectroscopy and difficult to be crystallized for X-ray crystallography [4]. Thus, it is critical to develop computational methods for the prediction of TMP structures from amino acid sequences, and the initial step is the accurate identification of the transmembrane topology [5].As shown in the left part of Figure 1, transmembrane (TM) topology refers to the locations of the membrane-spanning segments, which could be represented as a 1D 0/1 string to indicate the location of each residue to reside in (label 1) or out of (label 0) the membrane. This simple but direct definition of TM topology is consistent with the 3-label definition used by many other works that divide non-TM regions (i.e., label 0) into inner or outer classes [6][7][8][9][10]. In this work, we only focus on the prediction of TM topol...

show abstract

Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry

Cited by 26 publications

References 54 publications

Increased Dynamics of Tricarboxylic Acid Cycle and Glutamate Synthesis in Obese Adipose Tissue

Increased Dynamics of Tricarboxylic Acid Cycle and Glutamate Synthesis in Obese Adipose Tissue

Chemotherapy Immunophenoprofiles in Non‐Small‐Cell Lung Cancer by Personalized Membrane Proteomics

PureseqTM: efficient and accurate prediction of transmembrane topology from amino acid sequence only

Contact Info

Product

Resources

About