Pax1/E2a double-mutant mice develop non-lethal neural tube defects that resemble human malformations

Mouse as a model for multifactorial inheritance of neural tube defects

Neural tube defects (NTDs) such as spina bifida and anencephaly are some of the most common structural birth defects found in humans. These defects occur due to failures of neurulation, a process where the flat neural plate rolls into a tube. In spite of their prevalence, the causes of NTDs are poorly understood. The multifactorial threshold model best describes the pattern of inheritance of NTDs where multiple undefined gene variants interact with environmental factors to cause an NTD. To date, mouse models have implicated a multitude of genes as required for neurulation, providing a mechanistic understanding of the cellular and molecular pathways that control neurulation. However, the majority of these mouse models exhibit NTDs with a Mendelian pattern of inheritance. Still, many examples of multifactorial inheritance have been demonstrated in mouse models of NTDs. These include null and hypomorphic alleles of neurulation genes that interact in a complex fashion with other genetic mutations or environmental factors to cause NTDs. These models have implicated several genes and pathways for testing as candidates for the genetic basis of NTDs in humans, resulting in identification of putative pathogenic mutations in some patients. Mouse models also provide an experimental paradigm to gain a mechanistic understanding of the environmental factors that influence NTD occurrence, such as folic acid and maternal diabetes, and have led to the discovery of additional preventative nutritional supplements such as inositol. This review provides examples of how multifactorial inheritance of NTDs can be modeled in the mouse.

TF-Cluster: a pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM)

BACKGROUND

Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed.

RESULTS

We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and Arabidopsis roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest.

CONCLUSIONS

TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.

Neural tube defects in mice with reduced levels of inositol 1,3,4-trisphosphate 5/6-kinase

Inositol 1,3,4-trisphosphate 5/6-kinase (ITPK1) is a key regulatory enzyme at the branch point for the synthesis of inositol hexakisphosphate (IP(6)), an intracellular signaling molecule implicated in the regulation of ion channels, endocytosis, exocytosis, transcription, DNA repair, and RNA export from the nucleus. IP(6) also has been shown to be an integral structural component of several proteins. We have generated a mouse strain harboring a beta-galactosidase (betagal) gene trap cassette in the second intron of the Itpk1 gene. Animals homozygous for this gene trap are viable, fertile, and produce less ITPK1 protein than wild-type and heterozygous animals. Thus, the gene trap represents a hypomorphic rather than a null allele. Using a combination of immunohistochemistry, in situ hybridization, and betagal staining of mice heterozygous for the hypomorphic allele, we found high expression of Itpk1 in the developing central and peripheral nervous systems and in the paraxial mesoderm. Examination of embryos resulting from homozygous matings uncovered neural tube defects (NTDs) in some animals and axial skeletal defects or growth retardation in others. On a C57BL/6 x 129(P2)Ola background, 12% of mid-gestation embryos had spina bifida and/or exencephaly, whereas wild-type animals of the same genetic background had no NTDs. We conclude that ITPK1 is required for proper development of the neural tube and axial mesoderm.

Modeling neural tube defects in the mouse

Neural tube defects (NTDs) are among the most common structural birth defects observed in humans. Mouse models provide an excellent experimental system to study the underlying causes of NTDs. These models not only allow for identification of the genes required for neurulation, they provide tractable systems for uncovering the developmental, pathological and molecular mechanisms underlying NTDs. In addition, mouse models are essential for elucidating the mechanisms of gene-environment and gene-gene interactions that contribute to the multifactorial inheritance of NTDs. In some cases these studies have led to development of approaches to prevent NTDs and provide an understanding of the underlying molecular mechanism of these therapies prevent NTDs.