IFITM proteins drive type 2 T helper cell differentiation and exacerbate allergic airway inflammation.

The interferon-inducible transmembrane (Ifitm/Fragilis) genes encode homologous proteins that are induced by IFNs. Here, we show that IFITM proteins regulate murine CD4 Th cell differentiation. Ifitm2 and Ifitm3 are expressed in wild-type (WT) CD4 T cells.

A genomic atlas of human adrenal and gonad development.

Background: In humans, the adrenal glands and gonads undergo distinct biological events between 6-10 weeks post conception (wpc), such as testis determination, the onset of steroidogenesis and primordial germ cell development. However, relatively little is currently known about the genetic mechanisms underlying these processes. We therefore aimed to generate a detailed genomic atlas of adrenal and gonad development across these critical stages of human embryonic and fetal development.

A genome wide transcriptional model of the complex response to pre-TCR signalling during thymocyte differentiation.

Developing thymocytes require pre-TCR signalling to differentiate from CD4-CD8- double negative to CD4+CD8+ double positive cell. Here we followed the transcriptional response to pre-TCR signalling in a synchronised population of differentiating double negative thymocytes. This time series analysis revealed a complex transcriptional response, in which thousands of genes were up and down-regulated before changes in cell surface phenotype were detected.

Revealing individual signatures of human T cell CDR3 sequence repertoires with Kidera Factors.

The recent development of High Throughput Sequencing technologies has enabled an individual's TCR repertoire to be efficiently analysed at the nucleotide level. However, with unique clonotypes ranging in the tens of millions per individual, this approach gives a surfeit of information that is difficult to analyse and interpret in a biological context and gives little information about TCR structural diversity. Using publicly available TCR CDR3 sequence data, we analysed TCR repertoires by converting the encoded CDR3 amino acid sequences into Kidera Factors, a set of orthogonal physico-chemical properties that reflect protein structure.

Genome-wide analysis of alternative splicing in medulloblastoma identifies splicing patterns characteristic of normal cerebellar development.

Alternative splicing is an important mechanism for the generation of protein diversity at a post-transcriptional level. Modifications in the splicing patterns of several genes have been shown to contribute to the malignant transformation of different tissue types. In this study, we used the Affymetrix Exon arrays to investigate patterns of differential splicing between pediatric medulloblastomas and normal cerebellum on a genome-wide scale.

Dissection of a complex transcriptional response using genome-wide transcriptional modelling.

Modern genomics technologies generate huge data sets creating a demand for systems level, experimentally verified, analysis techniques. We examined the transcriptional response to DNA damage in a human T cell line (MOLT4) using microarrays. By measuring both mRNA accumulation and degradation over a short time course, we were able to construct a mechanistic model of the transcriptional response.

Fitting ordinary differential equations to short time course data.

Ordinary differential equations (ODEs) are widely used to model many systems in physics, chemistry, engineering and biology. Often one wants to compare such equations with observed time course data, and use this to estimate parameters. Surprisingly, practical algorithms for doing this are relatively poorly developed, particularly in comparison with the sophistication of numerical methods for solving both initial and boundary value problems for differential equations, and for locating and analysing bifurcations.

Correction of scaling mismatches in oligonucleotide microarray data.

Background: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses.

Ranked prediction of p53 targets using hidden variable dynamic modeling.

Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA.