Signal Peptidase Complex Subunit 1 and Hydroxyacyl-CoA Dehydrogenase Beta Subunit Are Suitable Reference Genes in Human Lungs

Palmitic acid induces intestinal lipid metabolism disorder, endoplasmic reticulum stress and inflammation by affecting phosphatidylethanolamine content in large yellow croaker Larimichthys crocea

In the 21^st century, intestinal homeostatic imbalance has emerged as a growing health challenge worldwide. Accumulating evidence reveals that excessive intake of saturated fatty acid (SFA) induces intestinal homeostatic imbalance. However, the potential molecular mechanism is still unclear. In the present study, we found that palm oil or palmitic acid (PA) treatment disturbed lipid metabolism homeostasis and triggered endoplasmic reticulum (ER) stress and inflammation in the intestine or intestinal cells of large yellow croaker (Larimichthys crocea). Interestingly, PA treatment significantly decreased phosphatidylethanolamine (PE) content in the intestinal cells. PE supplementation decreased triglyceride content in the intestinal cells induced by PA treatment by inhibiting fatty acid uptake and lipogenesis. PE supplementation suppressed ER stress. Meanwhile, PE supplementation alleviated inflammatory response through p38 MAPK-p65 pathway, reducing the damage of intestinal cells caused by PA treatment to some extent. Our work revealed that intestinal homeostatic imbalance caused by PA treatment was partly due to the decrease of PE content. PE consumption might be a nutritional strategy to regulate intestinal homeostasis in fish and even human beings.

Un-biased housekeeping gene panel selection for high-validity gene expression analysis

Differential gene expression normalised to a single housekeeping (HK) is used to identify disease mechanisms and therapeutic targets. HK gene selection is often arbitrary, potentially introducing systematic error and discordant results. Here we examine these risks in a disease model of brain hypoxia. We first identified the eight most frequently used HK genes through a systematic review. However, we observe that in both ex-vivo and in vivo, their expression levels varied considerably between conditions. When applying these genes to normalise expression levels of the validated stroke target gene, inducible Nox4, we obtained opposing results. As an alternative tool for unbiased HK gene selection, software tools exist but are limited to individual datasets lacking genome-wide search capability and user-friendly interfaces. We, therefore, developed the HouseKeepR algorithm to rapidly analyse multiple gene expression datasets in a disease-specific manner and rank HK gene candidates according to stability in an unbiased manner. Using a panel of de novo top-ranked HK genes for brain hypoxia, but not single genes, Nox4 induction was consistently reproduced. Thus, differential gene expression analysis is best normalised against a HK gene panel selected in an unbiased manner. HouseKeepR is the first user-friendly, bias-free, and broadly applicable tool to automatically propose suitable HK genes in a tissue- and disease-dependent manner.

Consensus reference gene(s) for gene expression studies in human cancers: end of the tunnel visible?

BACKGROUND

Gene expression studies are increasingly used to provide valuable information on the diagnosis and prognosis of human cancers. Also, for in vitro and in vivo experimental cancer models gene expression studies are widely used. The complex algorithms of differential gene expression analyses require normalization of data against a reference or normalizer gene, or a set of such genes. For this purpose, mostly invariant housekeeping genes are used. Unfortunately, however, there are no consensus (housekeeping) genes that serve as reference or normalizer for different human cancers. In fact, scientists have employed a wide range of reference genes across different types of cancer for normalization of gene expression data. As a consequence, comparisons of these data and/or data harmonizations are difficult to perform and challenging. In addition, an inadequate choice for a reference gene may obscure genuine changes and/or result in erroneous gene expression data comparisons.

METHODS

In our effort to highlight the importance of selecting the most appropriate reference gene(s), we have screened the literature for gene expression studies published since the turn of the century on thirteen of the most prevalent human cancers worldwide.

CONCLUSIONS

Based on the analysis of the data at hand, we firstly recommend that in each study the suitability of candidate reference gene(s) should carefully be evaluated in order to yield reliable differential gene expression data. Secondly, we recommend that a combination of PPIA and either GAPDH, ACTB, HPRT and TBP, or appropriate combinations of two or three of these genes, should be employed in future studies, to ensure that results from different studies on different human cancers can be harmonized. This approach will ultimately increase the depth of our understanding of gene expression signatures across human cancers.

Correlation and variation-based method for identifying reference genes from large datasets

BACKGROUND

Reference genes are assumed to be stably expressed under most circumstances. Previous studies have shown that identification of potential reference genes using common algorithms, such as NormFinder, geNorm, and BestKeeper, are not suitable for microarray-sized datasets. The aim of this study was to evaluate existing methods and develop methods for identifying reference genes from microarray datasets.

METHODS

We evaluated the correlation between outputs from 7 published methods for identifying reference genes, including NormFinder, geNorm, and BestKeeper, using subsets of published microarray data. From these results, seven novel combinations of published methods for identifying reference genes were evaluated.

RESULTS

Our results showed that NormFinder's and geNorm's indices had high correlations (R(2) = 0.987, P < 0.0001), which is consistent with the findings of previous studies. However, NormFinder's and BestKeeper's indices (R(2) = 0.489, 0.01 < P < 0.05) and NormFinder's coefficient of variance (CV) suggested a lower correlation (R(2) = 0.483, 0.01 < P < 0.05). We developed two novel methods with high correlations with NormFinder (R(2) values of both methods were 0.796, P < 0.0001). In addition, computational times required by the two novel methods were linear with the size of the dataset.

CONCLUSION

Our findings suggested that both of our novel methods can be used as alternatives to NormFinder, geNorm, and BestKeeper for identifying reference genes from large datasets. These methods were implemented as a tool, OLIgonucleotide Variable Expression Ranker (OLIVER), which can be downloaded from http://sourceforge.net/projects/bactome/files/OLIVER/OLIVER_1.zip.

Transcriptome Analysis of Spermophilus lateralis and Spermophilus tridecemlineatus Liver Does Not Suggest the Presence of Spermophilus-Liver-Specific Reference Genes

The expressions of reference genes used in gene expression studies are assumed to be stable under most circumstances. However, studies had demonstrated that genes assumed to be stably expressed in a species are not necessarily stably expressed in other organisms. This study aims to evaluate the likelihood of genus-specific reference genes for liver using comparable microarray datasets from Spermophilus lateralis and Spermophilus tridecemlineatus. The coefficient of variance (CV) of each probe was calculated and there were 178 probes common between the lowest 10% CV of both datasets (n = 1258). All 3 lists were analysed by NormFinder. Our results suggest that the most invariant probe for S. tridecemlineatus was 02n12, while that for S. lateralis was 24j21. However, our results showed that Probes 02n12 and 24j21 are ranked 8644 and 926 in terms of invariancy for S. lateralis and S. tridecemlineatus respectively. This suggests the lack of common liver-specific reference probes for both S. lateralis and S. tridecemlineatus. Given that S. lateralis and S. tridecemlineatus are closely related species and the datasets are comparable, our results do not support the presence of genus-specific reference genes.

Selection of suitable reference genes for normalization of quantitative real-time PCR in cartilage tissue injury and repair in rabbits

When studying the altered expression of genes associated with cartilage regeneration by quantitative real-time RT-PCR (RT-qPCR), reference genes with highly stable expression during different stages of chondrocyte developmental are necessary to normalize gene expression accurately. Until now, no reports evaluating expression changes of commonly used reference genes in rabbit articular cartilage have been published. In this study, defects were made in rabbit articular cartilage, with or without insulin-like growth factor 1 (IGF-1) treatment, to create different chondrocyte living environments. The stability and intensity of the expressions of the candidate reference genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18S Ribosomal RNA (18S rRNA), cyclophilin (CYP), hypoxanthine phosphoribosyl transferase (HPRT1), and beta-2-microglobulin (B2M) were evaluated. The data were analyzed by geNorm and NormFinder. B2M and 18S rRNA were identified to be suitable reference genes for rabbit cartilage tissues.