A mouse model reproducing the pathophysiology of neonatal group B streptococcal infection

Group B streptococcal (GBS) meningitis remains a devastating disease. The absence of an animal model reproducing the natural infectious process has limited our understanding of the disease and, consequently, delayed the development of effective treatments. We describe here a mouse model in which bacteria are transmitted to the offspring from vaginally colonised pregnant females, the natural route of infection. We show that GBS strain BM110, belonging to the CC17 clonal complex, is more virulent in this vertical transmission model than the isogenic mutant BM110∆cylE, which is deprived of hemolysin/cytolysin. Pups exposed to the more virulent strain exhibit higher mortality rates and lung inflammation than those exposed to the attenuated strain. Moreover, pups that survive to BM110 infection present neurological developmental disability, revealed by impaired learning performance and memory in adulthood. The use of this new mouse model, that reproduces key steps of GBS infection in newborns, will promote a better understanding of the physiopathology of GBS-induced meningitis.The authors gratefully acknowledge the help of Encarnaca̧ ̃o Ribeiro for excellent technical assistance, Joana Tavares for assisting with IVIS Lumina LT, Susana Roque for the luminex instrument experiments, the Molecular Microbiology group at i3S for microscope use, and the Portuguese architect and artist Gil Ferreira da Silva for the artworks included in the last figure. This work was supported by funds from Foundation for Science and Technology (FCT), European Regional Development Fund (FEDER) and Compete under project POCI-01-0145-FEDER-016607 (PTDC/IMI-MIC/1049/2014) and from the project NORTE-01-0145-FEDER-000012, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). T.S. and A.M. were supported by Investigador FCT (IF/00875/2012 and IF/00753/2014), POPH and Fundo Social Europeu. E.B.A. and C.C.P. hold postdoctoral fellowships from FCT (PTDC/IMI-MIC/1049/2014 and SFRH/BPD/91962/2012). Ar.F. and P.T.C. were supported by Laboratoire d’Excellence (LABEX) Integrative Biology of Emerging Infectious Diseases (grant ANR-10-LABX-62-IBEID).info:eu-repo/semantics/publishedVersio

SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation data

RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.JHC was supported by NIH grants R21 HG007554 and R01 NS094637. ERA and EE were supported by the MINECO and FEDER (BIO2014-52566-R), AGAUR (SGR2014-1121), and the Sandra Ibarra Foundation for Cancer (FSI2013)