Gene Expression Analysis of the IPEC-J2 Cell Line: A Simple Model for the Inflammation-Sensitive Preterm Intestine

The IPEC-J2 cell line was studied as a simple model for investigating responses of the newborn intestinal epithelium to diets. Especially, the small intestine of immature newborns is sensitive to diet-induced inflammation. We investigated gene expression of epithelial- and immune response-related genes in IPEC-J2 cells stimulated for 2 h with milk formula (CELL-FORM), colostrum (CELL-COLOS), or growth medium (CELL-CONTR) and in distal small intestinal tissue samples from preterm pigs fed milk formula (PIG-FORM) or colostrum (PIG-COLOS). High throughput quantitative PCR analysis of 48 genes revealed the expression of 22 genes in IPEC-J2 cells and 31 genes in intestinal samples. Principal component analysis (PCA) discriminated the gene expression profile of IPEC-J2 cells from that of intestinal samples. The expression profile of intestinal tissue was separated by PCA into 2 groups according to diet, whereas no diet-dependent grouping was seen for IPEC-J2 cells. Expression differences between PIG-FORM and PIG-COLOS were found for DEFB1, CXCL10, IL1RN, and ALPI, while IL8 was upregulated in CELL-FORM compared with CELL-CONTR. These differences, between IPEC-J2 cells and intestinal tissue from preterm pigs, both used as models for the newborn intestine, underline that caution must be exercised prior to analysis and interpretation of diet-induced effects on gene expression.

A Review of Soft Computing Techniques for Gene Prediction

In the past decade, various genomes have been sequenced in both plants and animals. The falling cost of genome sequencing manifests a great impact on the research community with respect to annotation of genomes. Genome annotation helps in understanding the biological functions of the sequences of these genomes. Gene prediction is one of the most important aspects of genome annotation and it is an open research problem in bioinformatics. A large number of techniques for gene prediction have been developed over the past few years. In this paper a theoretical review of soft computing techniques for gene prediction is presented. The problem of gene prediction, along with the issues involved in it, is first described. A brief description of soft computing techniques, before discussing their application to gene prediction, is then provided. In addition, a list of different soft computing techniques for gene prediction is compiled. Finally some limitations of the current research and future research directions are presented.

Complete Mitogenomes of Euploea mulciber (Nymphalidae: Danainae) and Libythea celtis (Nymphalidae: Libytheinae) and Their Phylogenetic Implications

The complete mitochondrial genome sequences of the two butterfly species Euploea mulciber (Lepidoptera: Nymphalidae: Danainae) and Libythea celtis (Lepidoptera: Nymphalidae: Libytheinae) were determined in this study, comprising 15,166 bp and 15,164 bp, respectively. The orientation and the gene order of the two mitogenomes are identical to those of most of the other lepidopteran species. All protein-coding genes of Euploea mulciber and Libythea celtis mitogenomes start with a typical ATN codon with the exception of COI gene which uses CGA as its initial codon. All tRNA genes possess the typical cloverleaf secondary structure except for tRNASer (AGN), which has a simple loop with the absence of the DHU stem. There are short microsatellite-like repeat regions, but no conspicuous macrorepeats scattered throughout the A + T-rich regions. Phylogenetic analysis among the available butterfly species suggests that Libythea celtis (Libytheinae) is closely related to Calinaga davidis (Calinaginae), indicating that the subfamily Libytheinae may not represent a basal lineage of the Nymphalidae as previously suggested, and that Euploea mulciber stands at the base of the nymphalid tree as a sister to all other nymphalids.

Multiscale fragPIN Modularity

Modularity in protein interactome networks (PINs) is a central theme involving aspects such as the study of the resolution limit, the comparative assessment of module-finding algorithms, and the role of data integration in systems biology. It is less common to study the relationships between the topological hierarchies embedded within the same network. This occurrence is not unusual, in particular with PINs that are considered assemblies of various interactions depending on specialized biological processes. The integrated view offered so far by modularity maps represents in general a synthesis of a variety of possible interaction maps, each reflecting a certain biological level of specialization. The driving hypothesis of this work leverages on such network components. Therefore, subnetworks are generated from fragmentation, a process aimed to isolating parts of a common network source that are here called fragments, from which the acronym fragPIN is used. The characteristics of modularity in each obtained fragPIN are elucidated and compared. Finally, as it was hypothesized that different timescales may underlie the biological processes from which the fragments are computed, the analysis was centered on an example involving the fluctuation dynamics inherent to the signaling process and was aimed to show how timescales can be identified from such dynamics, in particular assigning the interactions based on selected topological properties.

Critical Analysis of Strand-Biased Somatic Mutation Signatures in TP53 versus Ig Genes, in Genome-Wide Data and the Etiology of Cancer

Previous analyses of rearranged immunoglobulin (Ig) variable genes (VDJs) concluded that the mechanism of Ig somatic hypermutation (SHM) involves the Ig pre-mRNA acting as a copying template resulting in characteristic strand biased somatic mutation patterns at A:T and G:C base pairs. We have since analysed cancer genome data and found the same mutation strand-biases, in toto or in part, in nonlymphoid cancers. Here we have analysed somatic mutations in a single well-characterised gene TP53. Our goal is to understand the genesis of the strand-biased mutation patterns in TP53—and in genome-wide data—that may arise by “endogenous” mechanisms as opposed to adduct-generated DNA-targeted strand-biased mutations caused by well-characterised “external” carcinogenic influences in cigarette smoke, UV-light, and certain dietary components. The underlying strand-biased mutation signatures in TP53, for many non-lymphoid cancers, bear a striking resemblance to the Ig SHM pattern. A similar pattern can be found in genome-wide somatic mutations in cancer genomes that have also mutated TP53. The analysis implies a role for base-modified RNA template intermediates coupled to reverse transcription in the genesis of many cancers. Thus Ig SHM may be inappropriately activated in many non-lymphoid tissues via hormonal and/or inflammation-related processes leading to cancer.