Polyfunctional response by ImmTAC (IMCgp100) redirected CD8+ and CD4+ T cells.

The success of immune system-based cancer therapies depends on a broad immune response engaging a range of effector cells and mechanisms. Immune mobilizing monoclonal T cell receptors (TCRs) against cancer (ImmTAC™ molecules: fusion proteins consisting of a soluble, affinity enhanced TCR and an anti-CD3 scFv antibody) were previously shown to redirect CD8+ and CD4+ T cells against tumours. Here we present evidence that IMCgp100 (ImmTAC recognizing a peptide derived from the melanoma-specific protein, gp100, presented by HLA-A*0201) efficiently redirects and activates effector and memory cells from both CD8+ and CD4+ repertoires. Using isolated subpopulations of T cells, we find that both terminally differentiated and effector memory CD8+ T cells redirected by IMCgp100 are potent killers of melanoma cells. Furthermore, CD4+ effector memory T cells elicit potent cytotoxic activity leading to melanoma cell killing upon redirection by IMCgp100. The majority of T cell subsets belonging to both the CD8+ and CD4+ repertoires secrete key pro-inflammatory cytokines (tumour necrosis factor-α, interferon-γ, interleukin-6) and chemokines (macrophage inflammatory protein-1α-ß, interferon-γ-inducible protein-10, monocyte chemoattractant protein-1). At an individual cell level, IMCgp100-redirected T cells display a polyfunctional phenotype, which is a hallmark of a potent anti-cancer response. This study demonstrates that IMCgp100 induces broad immune responses that extend beyond the induction of CD8+ T cell-mediated cytotoxicity. These findings are of particular importance because IMCgp100 is currently undergoing clinical trials as a single agent or in combination with check point inhibitors for patients with malignant melanoma.

Compensated pathogenic deviations: analysis of structural effects.

Pathogenic deviations (PDs) in humans are disease-causing missense mutations. However, in some cases, these disease-associated residues occur as the wild-type residues in functionally equivalent proteins in other species and these cases are termed 'compensated pathogenic deviations' (CPDs). The lack of pathogenicity in a non-human protein is presumed to be explained in most cases by the presence of compensatory mutations, most commonly within the same protein. Identification of structural features of CPDs and detection of specific compensatory events will help us to understand traversal along fitness landscape valleys in protein evolution. We divided mutations listed in the OMIM (Online Mendelian Inheritance in Man) database into PD and CPD data sets and performed two independent analyses: (i) We searched for potential compensatory mutations spatially close to the CPDs and, (ii) using our SAAPdb database, we examined likely structural effects to try to explain why mutations are pathogenic, comparing PDs and CPDs. Our data sets were obtained from a set of 245 human proteins of known structure and contained a total of 2328 mutations of which 453 (from 85 structures) were seen to be compensated in at least one functionally equivalent protein in another (non-human) species. Structural analysis results confirm previous findings that CPDs are, on average, 'milder' in their likely structural effects than uncompensated PDs and tend to be on the protein surface. We also showed that the residues surrounding the CPD residue in the folded protein are more often mutated than the residues surrounding an uncompensated mutation, supporting the hypothesis that compensation is largely a result of structurally local mutations.

The SAAPdb web resource: a large-scale structural analysis of mutant proteins.

The Single Amino Acid Polymorphism database (SAAPdb) is a new resource for the analysis and visualization of the structural effects of mutations. Our analytical approach is to map single nucleotide polymorphisms (SNPs) and pathogenic deviations (PDs) to protein structural data held within the Protein Data Bank. By mapping mutations onto protein structures, we can hypothesize whether the mutant residues will have any local structural effect that may "explain" a deleterious phenotype. Our prior work used a similar approach to analyze mutations within a single protein. An analysis of the contents of SAAPdb indicates that there are clear differences in the sequence and structural characteristics of SNPs and PDs, and that PDs are more often explained by our structural analysis. This mapping and analysis is a useful resource for the mutation community and is publicly available at http://www.bioinf.org.uk/saap/db/.

Clustering of Pseudomonas aeruginosa transcriptomes from planktonic cultures, developing and mature biofilms reveals distinct expression profiles.

BACKGROUND: Pseudomonas aeruginosa is a genetically complex bacterium which can adopt and switch between a free-living or biofilm lifestyle, a versatility that enables it to thrive in many different environments and contributes to its success as a human pathogen. RESULTS: Transcriptomes derived from growth states relevant to the lifestyle of P. aeruginosa were clustered using three different methods (K-means, K-means spectral and hierarchical clustering). The culture conditions used for this study were; biofilms incubated for 8, 14, 24 and 48 hrs, and planktonic culture (logarithmic and stationary phase). This cluster analysis revealed the existence and provided a clear illustration of distinct expression profiles present in the dataset. Moreover, it gave an insight into which genes are up-regulated in planktonic, developing biofilm and confluent biofilm states. In addition, this analysis confirmed the contribution of quorum sensing (QS) and RpoS regulated genes to the biofilm mode of growth, and enabled the identification of a 60.69 Kbp region of the genome associated with stationary phase growth (stationary phase planktonic culture and confluent biofilms). CONCLUSION: This is the first study to use clustering to separate a large P. aeruginosa microarray dataset consisting of transcriptomes obtained from diverse conditions relevant to its growth, into different expression profiles. These distinct expression profiles not only reveal novel aspects of P. aeruginosa gene expression but also provide a growth specific transcriptomic reference dataset for the research community.