Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.

BackgroundQuantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptide's abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets.ResultsWe applied Q-FISH to Nano-LC-MS/MS data obtained from human hepatocellular carcinoma (HCC) and normal liver tissue samples to identify differentially expressed peptides between the normal and disease samples. For a total of 44,318 spectra obtained through MS/MS analysis, Q-FISH yielded 14,747 clusters. Among these, 5,777 clusters were identified only in the HCC sample, 6,648 clusters only in the normal tissue sample, and 2,323 clusters both in the HCC and normal tissue samples. While it will be interesting to investigate peptide clusters only found from one sample, further examined spectral clusters identified both in the HCC and normal samples since our goal is to identify and assess differentially expressed peptides quantitatively. The next step was to perform a beta-binomial test to isolate differentially expressed peptides between the HCC and normal tissue samples. This test resulted in 84 peptides with significantly differential spectral counts between the HCC and normal tissue samples. We independently identified 50 and 95 peptides by SEQUEST, of which 24 and 56 peptides, respectively, were found to be known biomarkers for the human liver cancer. Comparing Q-FISH and SEQUEST results, we found 22 of the differentially expressed 84 peptides by Q-FISH were also identified by SEQUEST. Remarkably, of these 22 peptides discovered both by Q-FISH and SEQUEST, 13 peptides are known for human liver cancer and the remaining 9 peptides are known to be associated with other cancers.ConclusionsWe prop...

Background Atypical teratoid/rhabdoid tumors (AT/RTs) are highly malignant brain tumors with inactivation of the SMARCB1 gene, which play a critical role in genomic transcriptional control. In this study, we analyzed the genomic and transcriptomic profiles of human AT/RTs to discover new druggable targets. Methods Multiplanar sequencing analyses, including whole exome sequencing (WES), single nucleotide polymorphism (SNP) arrays, array comparative genomic hybridization (aCGH), and whole transcriptome sequencing (RNA-Seq), were performed on 4 AT/RT tissues. Validation of a druggable target was conducted using AT/RT cell lines. Results WES revealed that the AT/RT genome is extremely stable except for the inactivation of SMARCB1 . However, we identified 897 significantly upregulated genes and 523 significantly downregulated genes identified using RNA-Seq, indicating that the transcriptional profiles of the AT/RT tissues changed substantially. Gene set enrichment assays revealed genes related to the canonical pathways of cancers, and nucleophosmin (NPM1) was the most significantly upregulated gene in the AT/RT samples. An NPM1 inhibitor (NSC348884) effectively suppressed the viability of 7 AT/RT cell lines. Network analyses showed that genes associated with NPM1 are mainly involved in cell cycle regulation. Upon treatment with an NPM1 inhibitor, cell cycle arrest at G1 phase was observed in AT/RT cells. Conclusions We propose that NPM1 is a novel therapeutic target for AT/RTs. Electronic supplementary material The online version of this article (10.1186/s12885-019-6044-z) contains supplementary material, which is available to authorized users.

Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AML) by somatic mutation, runt-related transcription factor 1 (RUNX1) and ets variant gene 6 (ETV6), affect expression levels of other genes and how RUNX1 and ETV6 are related to each other. The SEM approach allows us to compare several candidate models from which an explanatory genetic network can be constructed.

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MS/MS experiments generate hundreds to tens of thousands of fragment ion spectra during the experiment. In the peptide identification, MS/MS spectra are often identified by database searching algorithms such as SEQUEST and Mascot. Most database searching algorithms calculate score functions to compare the experimental MS/MS spectra with theoretical MS/MS spectra of certain peptides derived from protein sequence databases. However, these indirect methods are vulnerable to potential errors. Thus, some spectra may not be assigned to peptides. To overcome these limitations, we propose a novel algorithm called Finding Identical Spectra for Homogenous peptide (FISH) by using two-stage clustering algorithm. Our proposed FISH algorithm can cluster spectra from the same peptide as a group based on the direct comparison. That is, through all possible pair-wise comparisons, our FISH method provides a set of spectra from the same peptide. To investigate the efficiency of our proposed method, we performed Nano-LC-MS/MS experiment for human tissue samples. Also, we conducted the simulation study to compare the performance of our proposed method with other database searching methods. Our simulation study showed that FISH yielded higher sensitivity than the other methods.