Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, 'digital mRNA counting' is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.     

Gene Expression Commons: an open platform for absolute gene expression profiling

Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments. However, the unknown and variable sensitivities of each probeset have rendered the absolute expression of any given gene nearly impossible to estimate. We have overcome this limitation by using a very large number (>10,000) of varied microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be reliably discovered through meta-analysis. This strategy is implemented in a web-based platform named "Gene Expression Commons" (https://gexc.stanford.edu/) which contains data of 39 distinct highly purified mouse hematopoietic stem/progenitor/differentiated cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, investigators can explore the expression level of any gene, search by expression patterns of interest, submit their own microarray data, and design their own working models representing biological relationship among samples.     

Enhanced identification and biological validation of differential gene expression via Illumina whole-genome expression arrays through the use of the model-based background correction methodology

Despite the tremendous growth of microarray usage in scientific studies, there is a lack of standards for background correction methodologies, especially in single-color microarray platforms. Traditional background subtraction methods often generate negative signals and thus cause large amounts of data loss. Hence, some researchers prefer to avoid background corrections, which typically result in the underestimation of differential expression. Here, by utilizing nonspecific negative control features integrated into Illumina whole genome expression arrays, we have developed a method of model-based background correction for BeadArrays (MBCB). We compared the MBCB with a method adapted from the Affymetrix robust multi-array analysis algorithm and with no background subtraction, using a mouse acute myeloid leukemia (AML) dataset. We demonstrated that differential expression ratios obtained by using the MBCB had the best correlation with quantitative RT–PCR. MBCB also achieved better sensitivity in detecting differentially expressed genes with biological significance. For example, we demonstrated that the differential regulation of Tnfr2, Ikk and NF-kappaB, the death receptor pathway, in the AML samples, could only be detected by using data after MBCB implementation. We conclude that MBCB is a robust background correction method that will lead to more precise determination of gene expression and better biological interpretation of Illumina BeadArray data.     

Genetic variations strongly influence phenotypic outcome in the mouse retina

Variation in genetic background can significantly influence the phenotypic outcome of both disease and non-disease associated traits. Additionally, differences in temporal and strain specific gene expression can also contribute to phenotypes in the mammalian retina. This is the first report of microarray based cross-strain analysis of gene expression in the retina investigating genetic background effects. Microarray analyses were performed on retinas from the following mouse strains: C57BL6/J, AKR/J, CAST/EiJ, and NOD.NON-H2(-nb1) at embryonic day 18.5 (E18.5) and postnatal day 30.5 (P30.5). Over 3000 differentially expressed genes were identified between strains and developmental stages. Differential gene expression was confirmed by qRT-PCR, Western blot, and immunohistochemistry. Three major gene networks were identified that function to regulate retinal or photoreceptor development, visual perception, cellular transport, and signal transduction. Many of the genes in these networks are implicated in retinal diseases such as bradyopsia, night-blindness, and cone-rod dystrophy. Our analysis revealed strain specific variations in cone photoreceptor cell patterning and retinal function. This study highlights the substantial impact of genetic background on both development and function of the retina and the level of gene expression differences tolerated for normal retinal function. These strain specific genetic variations may also be present in other tissues. In addition, this study will provide valuable insight for the development of more accurate models for human retinal diseases.     

Mouse phenome research: implications of genetic background

Now that sequencing of the mouse genome has been completed, the function of each gene remains to be elucidated through phenotypic analysis. The "genetic background" (in which each gene functions) is defined as the genotype of all other related genes that may interact with the gene of interest, and therefore potentially influences the specific phenotype. To understand the nature and importance of genetic background on phenotypic expression of specific genes, it is necessary to know the origin and evolutionary history of the laboratory mouse genome. Molecular analysis has indicated that the fancy mice of Japan and Europe contributed significantly to the origin of today's laboratory mice. The genetic background of present-day laboratory mice varies by mouse strain, but is mainly derived from the European domesticus subspecies group and to a lesser degree from Asian mice, probably Japanese fancy mice, which belong to the musculus subspecies group. Inbred laboratory mouse strains are genetically uniform due to extensive inbreeding, and they have greatly contributed to the genetic analysis of many Mendelian traits. Meanwhile, for a variety of practical reasons, many transgenic and targeted mutant mice have been created in mice of mixed genetic backgrounds to elucidate the function of the genes, although efforts have been made to create inbred transgenic mice and targeted mutant mice with coisogenic embryonic stem cell lines. Inbred mouse strains have provided uniform genetic background for accurate evaluation of specific genes phenotypes, thus eliminating the phenotypic variations caused by mixed genetic backgrounds. However, the process of inbreeding and selection of various inbred strain characteristics has resulted in inadvertent selection of other undesirable genetic characteristics and mutations that may influence the genotype and preclude effective phenotypic analysis. Because many of the common inbred mouse stains have been established from relatively small gene pools, common inbred strains have limitations in their genetic polymorphisms and phenotypic variations. Wild-derived mouse strains can complement deficiencies of common inbred mouse strains, providing novel allelic variants and phenotypes. Although wild-derived strains are not as tame as the common laboratory strains, their genetic characteristics are attractive for the future study of gene function.     

Nonuniform expression of a mouse mammary tumor virus-driven int-2/Fgf-3 transgene in pregnancy-responsive breast tumors.

We have developed transgenic mice in which expression of the mouse int-2/Fgf-3 gene is regulated by a single long terminal repeat from mouse mammary tumor virus. Such mice contain and transmit a replica of the activated int-2/Fgf-3 allele present in a spontaneous mammary tumor from a BR6 mouse. Although free of infectious mouse mammary tumor virus and with a different genetic background, the transgenic mice develop pregnancy-responsive mammary epithelial proliferations that are similar to the early stages of tumorigenesis in the BR6 strain. Histological examination revealed that most of these tumors showed pronounced tubular and acinar structures, features usually associated with morphological differentiation. In some cases, the tumors were locally invasive, causing disruption of the dermis which manifested itself as local hair loss. In situ hybridization showed that patterns of transgene expression in the abnormal glands were markedly nonuniform. In contrast, mouse mammary tumor virus-induced neoplasms showed more uniform expression of int-2/Fgf-3, as did the urogenital epithelial proliferations that occur among males of this transgenic line. These data suggest that mammary tumors in virally infected animals may depend primarily on autocrine stimulation by the int-2/Fgf-3 gene product, whereas both autocrine and paracrine mechanisms may contribute to tumors and hyperplasias found in transgenic animals.     

How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?

Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.     

Genetic variables that influence phenotype

Characterization of genetically engineered mice requires consideration of the gene of interest and the genetic background on which the mutation is maintained. A fundamental prerequisite to deciphering the genetic factors that influence the phenotype of a mutant mouse is an understanding of genetic nomenclature. Mutations and transgenes are often maintained on segregating or mixed backgrounds of often-unspecified origin. Minimizing the importance of strain and substrain differences, especially among 129 strains, can lead to poor experimental design or faulty interpretations of data. Genetic factors that influence phenotype can be categorized as traits that are unique to the background strain, unique to the gene of interest, or an interaction of both the background strain and the gene of interest. The commonly used inbred strains are generally well characterized and understood; however, specific genetic alterations combined with genes unique to the background inbred strain may lead to unexpected results. Genetic background effects can be analyzed and controlled for by using specific targeting and breeding strategies. Selection of appropriate experimental controls is critical. Ideally, mutations or transgenes should be characterized on more than one genetic background and in hybrids of the two progenitor strains. This approach may lead to the identification of novel genetic modifiers of the "gene of interest." Conditional mutagenesis technologies increase the options for controlling genetic background effects in addition to permitting the study of developmental and temporal changes in gene and protein expression and thus phenotype.     

Prediction of gene expression in embryonic structures of Drosophila melanogaster

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.     

Comprehensive expression analysis of rice phospholipase D gene family during abiotic stresses and development

Phospholipase D is one of the crucial enzymes involved in lipid mediated signaling, triggered during various developmental and physiological processes. Different members of PLD gene family have been known to be induced under different abiotic stresses and during developmental processes in various plant species. In this report, we are presenting a detailed microarray based expression analysis and expression profiles of entire set of PLD genes in rice genome, under three abiotic stresses (salt, cold and drought) and different developmental stages (3-vegetative stages and 11-reproductive stages). Seven and nine PLD genes were identified, which were expressed differentially under abiotic stresses and during reproductive developmental stages, respectively. PLD genes, which were expressed significantly under abiotic stresses exhibited an overlapping expression pattern and were also differentially expressed during developmental stages. Moreover, expression pattern for a set of stress induced genes was validated by real time PCR and it supported the microarray expression data. These findings emphasize the role of PLDs in abiotic stress signaling and development in rice. In addition, expression profiling for duplicated PLD genes revealed a functional divergence between the duplicated genes and signify the role of gene duplication in the evolution of this gene family in rice. This expressional study will provide an important platform in future for the functional characterization of PLDs in crop plants.     

Nonadditive expression and parent-of-origin effects identified by microarray and allele-specific expression profiling of maize endosperm

Plant endosperm cells have a nuclear ratio of two maternal genomes to one paternal genome. This 2 to 1 dosage relationship provides a unique system for studying the additivity of gene expression levels in reciprocal hybrids. A combination of microarray profiling and allele-specific expression analysis was performed using RNA isolated from endosperm tissues of maize (Zea mays) inbred lines B73 and Mo17 and their reciprocal hybrids at two developmental stages, 13 and 19 d after pollination. The majority of genes exhibited additive expression in reciprocal hybrids based on microarray analyses. However, a substantial number of genes exhibited nonadditive expression patterns, including maternal like, paternal like, high parent like, low parent like, and expression patterns outside the range of the parental inbreds. The frequency of hybrid expression patterns outside of the parental range in maize endosperm tissue is much higher than that observed for vegetative tissues. For a set of 90 genes, allele-specific expression assays were employed to monitor allelic bias and regulatory variation. Eight of these genes exhibited evidence for maternally or paternally biased expression at multiple stages of endosperm development and are potential examples of differential imprinting. Our data indicate that parental effects on gene expression are much stronger in endosperm than in vegetative tissues.