North Star project report

This scoping project explored the potential value of a new research platform, North Star, for the University of Cambridge’s academics and researchers which could simplify the publication process for academics and help them promote themselves and their research output via a single profile, while also acting as a shopfront for the University’s world-leading research. It was thought that such a platform might remove the complexity of the bewildering array of platforms and processes with which academics and researchers currently have to engage. Over 20 Cambridge University academics from a variety of disciplines were interviewed in depth about their research processes. Previous research into academic behaviour and a series of design workshops also fed into the final prototype which was tested with academics. An ‘Experience Blueprint’ was also created as part of the project to define how the platform would work for different user groups. Although the research showed that academics and research staff involved in the project were open to the concept there are no current plans to pursue North Star further

Canonical pathways associated with transcripts commonly dysregulated among distinct SLE patient subsets.

<p>Canonical pathways associated with transcripts commonly dysregulated among distinct SLE patient subsets.</p

Clinical profile of SLE patients.

<p>Clinical profile of SLE patients.</p

RNA-seq Analysis Reveals Unique Transcriptome Signatures in Systemic Lupus Erythematosus Patients with Distinct Autoantibody Specificities

<div><p>Systemic lupus erythematosus (SLE) patients exhibit immense heterogeneity which is challenging from the diagnostic perspective. Emerging high throughput sequencing technologies have been proved to be a useful platform to understand the complex and dynamic disease processes. SLE patients categorised based on autoantibody specificities are reported to have differential immuno-regulatory mechanisms. Therefore, we performed RNA-seq analysis to identify transcriptomics of SLE patients with distinguished autoantibody specificities. The SLE patients were segregated into three subsets based on the type of autoantibodies present in their sera (anti-dsDNA⁺ group with anti-dsDNA autoantibody alone; anti-ENA⁺ group having autoantibodies against extractable nuclear antigens (ENA) only, and anti-dsDNA⁺ENA⁺ group having autoantibodies to both dsDNA and ENA). Global transcriptome profiling for each SLE patients subsets was performed using Illumina® Hiseq-2000 platform. The biological relevance of dysregulated transcripts in each SLE subsets was assessed by ingenuity pathway analysis (IPA) software. We observed that dysregulation in the transcriptome expression pattern was clearly distinct in each SLE patients subsets. IPA analysis of transcripts uniquely expressed in different SLE groups revealed specific biological pathways to be affected in each SLE subsets. Multiple <i>cytokine signaling</i> pathways were specifically dysregulated in anti-dsDNA⁺ patients whereas <i>Interferon signaling</i> was predominantly dysregulated in anti-ENA⁺ patients. In anti-dsDNA⁺ENA⁺ patients <i>regulation of actin based motility by Rho</i> pathway was significantly affected. The granulocyte gene signature was a common feature to all SLE subsets; however, anti-dsDNA⁺ group showed relatively predominant expression of these genes. Dysregulation of Plasma cell related transcripts were higher in anti-dsDNA⁺ and anti-ENA⁺ patients as compared to anti-dsDNA⁺ ENA⁺. Association of specific canonical pathways with the uniquely expressed transcripts in each SLE subgroup indicates that specific immunological disease mechanisms are operative in distinct SLE patients’ subsets. This ‘sub-grouping’ approach could further be useful for clinical evaluation of SLE patients and devising targeted therapeutics.</p></div

Immunoglobulin gene transcript distribution in different SLE patients’ subsets.

<p>Immunoglobulin gene transcript distribution in different SLE patients’ subsets.</p

Plasma cell signature transcripts in each subset of SLE patients.

<p>Plasma cell signature transcripts in each subset of SLE patients.</p

Validation of differentially expressed transcripts in distinct SLE patients’ subsets by real time PCR.

<p>A. CCL20 was significantly overexpressed in anti-dsDNA⁺ patients (p value 0.009) B. CCNA1 specifically overexpressed in anti-ENA⁺ patients (p value 0.001) C. EPHB2 expression was observed to be significantly overexpressed in anti-dsDNA⁺ENA⁺ patients (p value 0.01) and D. ELANE was significantly overexpressed in all patient subsets (anti-dsDNA+ patients p value 0.001, anti-ENA⁺ patients p value 0.02 and anti-dsDNA⁺ENA⁺ patients’ p value 0.01) but had higher expression in patients with anti-dsDNA autoantibody.</p

Decreased Pattern Recognition Receptor Signaling, Interferon-Signature, and Bactericidal/Permeability-Increasing Protein Gene Expression in Cord Blood of Term Low Birth Weight Human Newborns

<div><p>Background</p><p>Morbidity and mortality rates of low birth weight (LBW) newborns at term are higher than rates in normal birth weight (NBW) newborns. LBW newborns are at greater risk to acquire recurrent bacterial and viral infections during their first few weeks of life possibly as an outcome of compromised innate immune functions. As adaptive immunity is in a naive state, increased risk of infection of LBW as compared to NBW newborns may reflect impairments in innate immunity.</p><p>Methodology</p><p>To characterize the increased susceptibility to infections in LBW newborns we used microarray technology to identify differences in gene expression in LBW newborns (n = 8) compared to NBW newborns (n = 4) using cord blood. The results obtained from the microarray study were validated on a larger number of samples using real time RT-PCR (LBW = 22, NBW = 18) and western blotting (LBW = 12, NBW = 12). The Interferome database was used to identify interferon (IFN) signature genes and ingenuity pathway analysis identified canonical pathways and biological functions associated with the differentially expressed genes in LBW newborns. ELISAs for IFNs and bactericidal/permeability-increasing protein were performed in both LBW and NBW newborns and in adults (LBW = 18, NBW = 18, Adults  = 8).</p><p>Principal Findings</p><p>Upon microarray analysis, we identified 1,391 differentially expressed genes, of which, 1,065 genes were down-regulated and 326 genes were up-regulated in the LBW compared to NBW newborns. Of note, 70 IFN-signature genes were found to be significantly down-regulated in LBW compared to NBW newborns. Ingenuity pathway analysis revealed pattern recognition receptors signaling including Toll-Like Receptors (TLRs) -1, -5, and -8 genes and IFN signaling as the most significantly impacted pathways. Respiratory infectious diseases were the most significantly affected bio-functions in LBW newborns.</p><p>Conclusion and Significance</p><p>Diminished PRRs, IFN-signature, and BPI gene expression raises the possibility that impairments in these pathways contribute to the susceptibility of LBW term infants to infection.</p></div

Transcriptome characterization in different SLE patients’ subsets.

<p>The pie chart at the centre represents the percentage of coding RNA, non-coding RNA, Ig transcripts and other transcripts (pseudogenes, antisense transcripts, processed transcripts etc.) in SLE patients compared to healthy individuals. Each transcript types was further analysed for each subset of SLE patients. The percentage of coding RNA and Ig transcripts vary significantly in distinct subsets whereas the expression of non-coding RNA and other transcripts was comparable among different subgroups.</p

Unsupervised analysis of individual samples that belongs to distinct SLE patients’ subsets.

<p>A. Principal component analysis of each SLE patients. Individual dot on scatter pot represent specific SLE patient that were spatially separated based on their transcripts rather than expression values. Red dots represent anti-dsDNA⁺ Group; Green dots belong to anti-ENA⁺ Group and Blue dots belong to anti-dsDNA⁺ENA⁺ Group. B. Dendrogram derived from a hierarchical clustering analysis represents the similarity and distinction among the samples based on distance between datasets (represented as the height of the branches).</p