Human MLL/KMT2A gene exhibits a second breakpoint cluster region for recurrent MLL–USP2 fusions

Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: PQ-2017#305529/2017-0Deutsche Forschungsgemeinschaft, DFG: MA 1876/12-1Alexander von Humboldt-Stiftung: 88881.136091/2017-01RVO-VFN64165, 26/203.214/20172018.070.1Associazione Italiana per la Ricerca sul Cancro, AIRC: IG2015, 17593Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPESCancer Australia: PdCCRS1128727CancerfondenBarncancerfondenVetenskapsrÃ¥det, VRCrafoordska StiftelsenKnut och Alice Wallenbergs StiftelseLund University Medical Faculty FoundationXiamen University, XMU2014S0617-74-30019C7838/A15733Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNSF: 31003A_140913CNIBInstitut National Du Cancer, INCaR01 NCI CA167824National Institutes of Health, NIH: S10OD0185222016/2017, 02R/2016AU 525/1-1Deutschen Konsortium für Translationale Krebsforschung, DKTK70112951Smithsonian Institution, SIIsrael Science Foundation, ISFAustrian Science Fund, FWF: W1212SFB-F06107, SFB-F06105Acknowledgements BAL received a fellowship provided by CAPES and the Alexander von Humboldt Foundation (#88881.136091/2017-01). ME is supported by CNPq (PQ-2017#305529/2017-0) and FAPERJ-JCNE (#26/203.214/2017) research scholarships, and ZZ by grant RVO-VFN64165. GC is supported by the AIRC Investigator grant IG2015 grant no. 17593 and RS by Cancer Australia grant PdCCRS1128727. This work was supported by grants to RM from the “Georg und Franziska Speyer’sche Hochsschulstiftung”, the “Wilhelm Sander foundation” (grant 2018.070.1) and DFG grant MA 1876/12-1.Acknowledgements This work was supported by The Swedish Childhood Cancer Foundation, The Swedish Cancer Society, The Swedish Research Council, The Knut and Alice Wallenberg Foundation, BioCARE, The Crafoord Foundation, The Per-Eric and Ulla Schyberg Foundation, The Nilsson-Ehle Donations, The Wiberg Foundation, and Governmental Funding of Clinical Research within the National Health Service. Work performed at the Center for Translational Genomics, Lund University has been funded by Medical Faculty Lund University, Region Skåne and Science for Life Laboratory, Sweden.Acknowledgements This work was supported by the Fujian Provincial Natural Science Foundation 2016S016 China and Putian city Natural Science Foundation 2014S06(2), Fujian Province, China. Alexey Ste-panov and Alexander Gabibov were supported by Russian Scientific Foundation project No. 17-74-30019. Jinqi Huang was supported by a doctoral fellowship from Xiamen University, China.Acknowledgments This work was supported by the Swiss National Science Foundation (grant 31003A_140913; OH) and the Cancer Research UK Experimental Cancer Medicine Centre Network, Cardiff ECMCI, grant C7838/A15733. We thank N. Carpino for the Sts-1/2 double-KO mice.Acknowledgements This work was supported by the French National Cancer Institute (INCA) and the Fondation Française pour la Recherche contre le Myélome et les Gammapathies (FFMRG), the Intergroupe Francophone du Myélome (IFM), NCI R01 NCI CA167824 and a generous donation from Matthew Bell. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD018522. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the Association des Malades du Myélome Multiple (AF3M) for their continued support and participation. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.We are indebted to all members of our groups for useful discussions and for their critical reading of the manuscript. Special thanks go to Silke Furlan, Friederike Opitz and Bianca Killing. F.A. is supported by the Deutsche For-schungsgemeinschaft (DFG, AU 525/1-1). J.H. has been supported by the German Children’s Cancer Foundation (Translational Oncology Program 70112951), the German Carreras Foundation (DJCLS 02R/2016), Kinderkrebsstiftung (2016/2017) and ERA PerMed GEPARD. Support by Israel Science Foundation, ERA-NET and Science Ministry (SI). A. B. is supported by the German Consortium of Translational Cancer Research, DKTK. We are grateful to the Jülich Supercomputing Centre at the Forschungszemtrum Jülich for granting computing time on the supercomputer JURECA (NIC project ID HKF7) and to the “Zentrum für Informations-und Medientechnologie” (ZIM) at the Heinrich Heine University Düsseldorf for providing computational support to H. G. The study was performed in the framework of COST action CA16223 “LEGEND”.Funding The work was supported by the Austrian Science Fund FWF grant SFB-F06105 to RM and SFB-F06107 to VS and FWF grant W1212 to VS

An update on "reverse vaccinology": the pathway from genomes and epitope predictions to tailored, recombinant vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles—mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development

An update on "reverse vaccinology": the pathway from genomes and epitope predictions to tailored, recombinant vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles—mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development

Assembly and Functional Role of PACE Transporter PA2880 from Pseudomonas aeruginosa

The recently identified proteobacterial antimicrobial compound efflux (PACE) transporters are multidrug transporters energized by the electrochemical gradient of protons. Here, we present the results of phylogenetic and functional studies on the PACE family transporter PA2880 from Pseudomonas aeruginosa. A phylogenetic analysis of the PACE family revealed that PA2880 and AceI from Acinetobacter baumannii are classified into evolutionarily distinct clades, although they both transport chlorhexidine. We demonstrate that PA2880 mainly exists as a dimer in solution, which is independent of pH, and its dimeric state is essential for its proper function. Electrogenicity studies revealed that the chlorhexidine/H+ antiport process is electrogenic. The function of several highly conserved residues was investigated. These findings provide further insights into the functional features of PACE family transporters, facilitating studies on their transport mechanisms.IMPORTANCE Pseudomonas aeruginosa is a pathogen that causes hospital-acquired (nosocomial) infections, such as ventilator-associated pneumonia and sepsis syndromes. Chlorhexidine diacetate is a disinfectant used for bacterial control in various environments potentially harboring P. aeruginosa. Therefore, investigation of the mechanism of the efflux of chlorhexidine mediated by PA2880, a PACE family transporter from P. aeruginosa, is of significance to combat bacterial infections. This study improves our understanding of the transport mechanism of PACE family transporters and will facilitate the effective utilization of chlorhexidine for P. aeruginosa control

Natural product diversity associated with the nematode symbionts Photorhabdus and Xenorhabdus

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Identification of the novel class D β-lactamase OXA-679 involved in carbapenem resistance in Acinetobacter calcoaceticus

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Ocean plankton. Structure and function of the global ocean microbiome

Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional diversity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans samples from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with >40 million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched samples, containing >35,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that >73% of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems.status: publishe