P. gingivalis-Induced TLR2 Interactome Analysis Reveals Association with PARP9.

Porphyromonas gingivalis is a Gram-negative anaerobic bacterium strongly associated with periodontal disease. Toll-like receptor 2 (TLR2) is indispensable for the host response to P. gingivalis, but P. gingivalis escapes from immune clearance via TLR2-dependent activation of phosphoinositide-3-kinase (PI3K). To probe the TLR2-dependent escape pathway of P. gingivalis, we analyzed the TLR2 interactome induced following P. gingivalis infection or activation by a synthetic lipopeptide TLR2/1 agonist on human macrophages overexpressing TLR2. Interacting proteins were stabilized by cross-linking and then immunoprecipitated and analyzed by mass spectrometry. In total, 792 proteins were recovered and network analysis enabled mapping of the TLR2 interactome at baseline and in response to infection. The P. gingivalis infection-induced TLR2 interactome included the poly (ADP-ribose) polymerase family member mono-ADP-ribosyltransferase protein 9 (PARP9) and additional members of the PARP9 complex (DTX3L and NMI). PARP9 and its complex members are highly upregulated in macrophages exposed to P. gingivalis or to the synthetic TLR2/1 ligand Pam3Cys-Ser-(Lys)4 (PAM). Consistent with its known role in virally induced interferon production, PARP9 knockdown blocked type I interferon (IFN-I) production in response to P. gingivalis and reduced inflammatory cytokine production. We found that P. gingivalis drives signal transducer and activation of transcription (STAT) 1 (S727) phosphorylation through TLR2-PARP9, explaining PARP9's role in the induction of IFN-I downstream of TLR2. Furthermore, PARP9 knockdown reduced PI3K activation by P. gingivalis, leading to improved macrophage bactericidal activity. In summary, PARP9 is a novel TLR2 interacting partner that enables IFN-I induction and P. gingivalis immune escape in macrophages downstream of TLR2 sensing.

MASS: multiple structural alignment by secondary structures.

We present a novel method for multiple alignment of protein structures and detection of structural motifs. To date, only a few methods are available for addressing this task. Most of them are based on a series of pairwise comparisons. In contrast, MASS (Multiple Alignment by Secondary Structures) considers all the given structures at the same time. Exploiting the secondary structure representation aids in filtering out noisy results and in making the method highly efficient and robust. MASS disregards the sequence order of the secondary structure elements. Thus, it can find non-sequential and even non-topological structural motifs. An important novel feature of MASS is subset alignment detection: It does not require that all the input molecules be aligned. Rather, MASS is capable of detecting structural motifs shared only by a subset of the molecules. Given its high efficiency and capability of detecting subset alignments, MASS is suitable for a broad range of challenging applications: It can handle large-scale protein ensembles (on the order of tens) that may be heterogeneous, noisy, topologically unrelated and contain structures of low resolution.