Mapping and analyzing the human liver proteome: progress and potential

Yu, Hongxiu; Wang, Fang; Li, Gang; Cao, Weiqian; Liu, Yinkun; Qin, Lun–Xiu; Lu, Haojie; He, Fuchu; Shen, Huali; Yang, Pengyuan

doi:10.1080/14789450.2016.1213132

Cited by 7 publications

(3 citation statements)

References 78 publications

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“…The advancement of analytical technologies like shotgun proteomics opened the way for global snapshots of the molecular makeup of healthy tissues and tumours. 31 , 32 The increased sensitivity of these technologies together with improved data reproducibility enable for the first time to map the biochemical network in its totality. 9 However, due to (i) enormous plasticity and dynamics, (ii) multi-level regulatory mechanisms and (iii) a highly complex network of reactions, cellular metabolic processes are difficult to study.…”

Section: Discussionmentioning

confidence: 99%

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

et al. 2019

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BACKGROUND: Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. METHODS: We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. RESULTS: We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing. CONCLUSIONS: Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

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Section: Discussionmentioning

confidence: 99%

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

et al. 2019

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“…Apparently, for a substantial part of Chr18 encoded proteins, their abundance in samples from the human liver and HepG2 cells is too low for confident detection by SRM-SIS. This is not something that is particular to Chr18 because, for example, for chromosome 8 with 663 established protein-coding genes (neXtProt 2019-01-11), only 271, 330, and 325 proteins were revealed by the MS-based proteomic profiling in stomach, colon, and liver tissue samples, respectively . Moreover, the recent comprehensive (not bound to a chromosome) proteomic profiling of human hepatocytes and HepG2 cells by MS/MS resulted in the detection of 9400 proteins, which accounts for <50% of predicted proteins .…”

mentioning

confidence: 99%

“…This is not something that is particular to Chr18 because, for example, for chromosome 8 with 663 established protein-coding genes (neXtProt 2019-01-11), only 271, 330, and 325 proteins were revealed by the MS-based proteomic profiling in stomach, colon, and liver tissue samples, respectively. 11 Moreover, the recent comprehensive (not bound to a chromosome) proteomic profiling of human hepatocytes and HepG2 cells by MS/MS resulted in the detection of 9400 proteins, which accounts for <50% of predicted proteins. 12 Apart from technical problems, there may be a biological reason for the lack of proteins, namely, the lack of gene transcription.…”

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confidence: 99%

The “Missing” Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes

et al. 2019

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The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.

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Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Clark

Kwitek

2021

Comprehensive Physiology

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Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS.As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genomewide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments.

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Mapping and analyzing the human liver proteome: progress and potential

Cited by 7 publications

References 78 publications

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

The “Missing” Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes

Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

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