Increased Expression of the RBPMS Splice Variants Inhibits Cell Proliferation in Ovarian Cancer Cells

RNA-Binding Protein with Multiple Splicing (RBPMS) is a member of family proteins that bind to nascent RNA transcripts and regulate their splicing, localization, and stability. Evidence indicates that RBPMS controls the activity of transcription factors associated with cell growth and proliferation, including AP-1 and Smads. Three major RBPMS protein splice variants (RBPMSA, RBPMSB, and RBPMSC) have been described in the literature. We previously reported that reduced RBPMS levels decreased the sensitivity of ovarian cancer cells to cisplatin treatment. However, little is known about the biological role of the RBPMS splice variants in ovarian cancer cells. We performed RT-PCR and Western blots and observed that both RBPMSA and RBPMSC are reduced at the mRNA and protein levels in cisplatin resistant as compared with cisplatin sensitive ovarian cancer cells. The mRNA and protein levels of RBPMSB were not detectable in any of the ovarian cancer cells tested. To better understand the biological role of each RBPMSA and RBPMSC, we transfected these two splice variants in the A2780CP20 and OVCAR3CIS cisplatin resistant ovarian cancer cells and performed cell proliferation, cell migration, and invasion assays. Compared with control clones, a significant reduction in the number of colonies, colony size, cell migration, and invasion was observed with RBPMSA and RBPMSC overexpressed cells. Moreover, A2780CP20-RBPMSA and A2780CP20-RBPMSC clones showed reduced senescence-associated β-galactosidase (β-Gal)-levels when compared with control clones. A2780CP20-RBPMSA clones were more sensitive to cisplatin treatment as compared with A2780CP20-RBPMSC clones. The A2780CP20-RBPMSA and A2780CP20-RBPMSC clones subcutaneously injected into athymic nude mice formed smaller tumors as compared with A2780CP20-EV control group. Additionally, immunohistochemical analysis showed lower proliferation (Ki67) and angiogenesis (CD31) staining in tissue sections of A2780CP20-RBPMSA and A2780CP20-RBPMSC tumors compared with controls. RNAseq studies revealed many common RNA transcripts altered in A2780CP20-RBPMSA and A2780CP20-RBPMSC clones. Unique RNA transcripts deregulated by each RBPMS variant were also observed. Kaplan–Meier (KM) plotter database information identified clinically relevant RBPMSA and RBPMSC downstream effectors. These studies suggest that increased levels of RBPMSA and RBPMSC reduce cell proliferation in ovarian cancer cells. However, only RBPMSA expression levels were associated with the sensitivity of ovarian cancer cells to cisplatin treatment

Brain Targeted Gold Liposomes Improve RNAi Delivery for Glioblastoma.

INTRODUCTION: Glioblastoma (GBM) is the most common and lethal of the central nervous system (CNS) malignancies. The initiation, progression, and infiltration ability of GBMs are attributed in part to the dysregulation of microRNAs (miRNAs). Thus, targeting dysregulated miRNAs with RNA oligonucleotides (RNA interference, RNAi) has been proposed for GBM treatment. Despite promising results in the laboratory, RNA oligonucleotides have clinical limitations that include poor RNA stability and off-target effects. RNAi therapies against GBM confront an additional obstacle, as they need to cross the blood-brain barrier (BBB). METHODS: Here, we developed gold-liposome nanoparticles conjugated with the brain targeting peptides apolipoprotein E (ApoE) and rabies virus glycoprotein (RVG). First, we functionalized gold nanoparticles with oligonucleotide miRNA inhibitors (OMIs), creating spherical nucleic acids (SNAs). Next, we encapsulated SNAs into ApoE, or RVG-conjugated liposomes, to obtain SNA-Liposome-ApoE and SNA-Liposome-RVG, respectively. We characterized each nanoparticle in terms of their size, charge, encapsulation efficiency, and delivery efficiency into U87 GBM cells in vitro. Then, they were administered intravenously (iv) in GBM syngeneic mice to evaluate their delivery efficiency to brain tumor tissue. RESULTS: SNA-Liposomes of about 30-50 nm in diameter internalized U87 GBM cells and inhibited the expression of miRNA-92b, an aberrantly overexpressed miRNA in GBM cell lines and GBM tumors. Conjugating SNA-Liposomes with ApoE or RVG peptides increased their systemic delivery to the brain tumors of GBM syngeneic mice. SNA-Liposome-ApoE demonstrated to accumulate at higher extension in brain tumor tissues, when compared with non-treated controls, SNA-Liposomes, or SNA-Liposome-RVG. DISCUSSION: SNA-Liposome-ApoE has the potential to advance the translation of miRNA-based therapies for GBM as well as other CNS disorders

Reduced RBPMS Levels Promote Cell Proliferation and Decrease Cisplatin Sensitivity in Ovarian Cancer Cells

Worldwide, the number of cancer-related deaths continues to increase due to the ability of cancer cells to become chemotherapy-resistant and metastasize. For women with ovarian cancer, a staggering 70% will become resistant to the front-line therapy, cisplatin. Although many mechanisms of cisplatin resistance have been proposed, the key mechanisms of such resistance remain elusive. The RNA binding protein with multiple splicing (RBPMS) binds to nascent RNA transcripts and regulates splicing, transport, localization, and stability. Evidence indicates that RBPMS also binds to protein members of the AP-1 transcription factor complex repressing its activity. Until now, little has been known about the biological function of RBPMS in ovarian cancer. Accordingly, we interrogated available Internet databases and found that ovarian cancer patients with high RBPMS levels live longer compared to patients with low RBPMS levels. Similarly, immunohistochemical (IHC) analysis in a tissue array of ovarian cancer patient samples showed that serous ovarian cancer tissues showed weaker RBPMS staining when compared with normal ovarian tissues. We generated clustered regularly interspaced short palindromic repeats (CRISPR)-mediated RBPMS knockout vectors that were stably transfected in the high-grade serous ovarian cancer cell line, OVCAR3. The knockout of RBPMS in these cells was confirmed via bioinformatics analysis, real-time PCR, and Western blot analysis. We found that the RBPMS knockout clones grew faster and had increased invasiveness than the control CRISPR clones. RBPMS knockout also reduced the sensitivity of the OVCAR3 cells to cisplatin treatment. Moreover, β-galactosidase (β-Gal) measurements showed that RBPMS knockdown induced senescence in ovarian cancer cells. We performed RNAseq in the RBPMS knockout clones and identified several downstream-RBPMS transcripts, including non-coding RNAs (ncRNAs) and protein-coding genes associated with alteration of the tumor microenvironment as well as those with oncogenic or tumor suppressor capabilities. Moreover, proteomic studies confirmed that RBPMS regulates the expression of proteins involved in cell detoxification, RNA processing, and cytoskeleton network and cell integrity. Interrogation of the Kaplan–Meier (KM) plotter database identified multiple downstream-RBPMS effectors that could be used as prognostic and response-to-therapy biomarkers in ovarian cancer. These studies suggest that RBPMS acts as a tumor suppressor gene and that lower levels of RBPMS promote the cisplatin resistance of ovarian cancer cells

Genome of Rhodnius prolixus, an insect vector of Chagas disease, reveals unique adaptations to hematophagy and parasite infection

O artigo apresenta nas duas primeiras páginas nota de correção.Submitted by sandra infurna ([email protected]) on 2016-03-31T12:56:45Z No. of bitstreams: 1 andre_torres_etal_IOC_2015.pdf: 1095119 bytes, checksum: df9054f950a043553746f4758ab01c35 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-03-31T15:33:31Z (GMT) No. of bitstreams: 1 andre_torres_etal_IOC_2015.pdf: 1095119 bytes, checksum: df9054f950a043553746f4758ab01c35 (MD5)Made available in DSpace on 2016-03-31T15:33:31Z (GMT). No. of bitstreams: 1 andre_torres_etal_IOC_2015.pdf: 1095119 bytes, checksum: df9054f950a043553746f4758ab01c35 (MD5) Previous issue date: 2015Universidade Federal do Rio de Janeiro. Instituto de Química. Departamento de Bioquímica. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Simon Fraser University. Biological Sciences. Burnaby, BC, Canada.Universidad Nacional de La Plata. Centro Regional de Estudios Genomicos. La Plata, Argentina / Universidad Nacional del Noroeste de Buenos Aires. Centro de Bioinvestigaciones. Pergamino, Argentina.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.Universidade Federal do Rio de Janeiro. Instituto de Biologia. Departamento de Genética. Rio de Janeiro, RJ, Brasil.Universidad de la República. Facultad de Ciencias. Sección Genética Evolutiva. Montevideo, Uruguay.European Bioinformatics Institute. European Molecular Biology Laboratory. Welcome Trust Genome Campus. Hinxton, Cambridge, United Kingdom.Universidade Federal do Rio de Janeiro. Instituto de Química. Departamento de Bioquímica. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.University of Notre Dame. Department of Biological Sciences. Notre Dame, IN.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Estadual Paulista. Departamento de Biologia. São Paulo, SP, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas René Rachou. Belo Horizonte, MG, Brasil.The Barcelona Institute of Science and Technology. Centre for Genomic Regulation. Barcelona, Spain / Universitat Pompeu Fabra. Barcelona, Spain.Institut de Recherche pour le Development. Centre National de la Recherche Scientifique. Laboratoire d`Evolution, Génome et Spéciation. Gif sur Yvette, France / Université Paris-Sud, Orsay, France.European Bioinformatics Institute. European Molecular Biology Laboratory. Welcome Trust Genome Campus. Hinxton, Cambridge, United Kingdom.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Université François Rabelais. Centre National de la Recherche Sicentifique. Institut de Recherche sur la Biologie de l`Insect. Tours, France.Université Paris-Sud, Orsay, France.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Biologia. Departamento de Genética. Rio de Janeiro, RJ, Brasil.University of Toronto. Department of Biology. Mississauga, ON, Canada.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas René Rachou. Belo Horizonte, MG, Brasil.Universidad Nacional de La Plata. Centro Regional de Estudios Genomicos. La Plata, Argentina.Centers for Disease Control and Prevention. Entomology Branch. Division of Parasitic Diseases and Malaria. Atlanta, GA, USA.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Biologia. Departamento de Genética. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas René Rachou. Belo Horizonte, MG, Brasil.Universidade Estadual do Norte Fluminense Darcy Ribeiro. Centro de Biociências e Biotecnologia. Laboratório de Química e Função de Proteínas e Peptídeos. Campos de Goytacazes, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil /Universidade Federal do Rio de Janeiro. Faculdade de Farmácia. Departamento de Biotecnologia Farmacêutica. Rio de Janeiro, RJ, Brasil.Centers for Disease Control and Prevention. Entomology Branch. Division of Parasitic Diseases and Malaria. Atlanta, GA, USA.The Barcelona Institute of Science and Technology. Centre for Genomic Regulation. Barcelona, Spain / Universitat Pompeu Fabra. Barcelona, Spain.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.European Bioinformatics Institute. European Molecular Biology Laboratory. Welcome Trust Genome Campus. Hinxton, Cambridge, United Kingdom.Universidad Nacional de La Plata. Centro Regional de Estudios Genomicos. La Plata, Argentina.Universidade Federal do Rio de Janeiro. Instituto de Química. Departamento de Bioquímica. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil.Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. oDepartment of Physiology, Biophysics and Neuroscience. Mexico City, Mexico.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Fisiologia e BIoquímica. Belo Horizonte, MG, Brasil.Florida International University. Department of Biological Sciences. Miami, FL, USA.Florida International University. Department of Biological Sciences. Miami, FL, USA.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal Rural do Rio de Janeiro. Instituto de Ciências Biológicas e da Saúde. Departamento de Biologia Animal. Seropédica, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.University of Toronto. Department of Biology. Mississauga, ON, Canada.Universidad Nacional de La Plata. Centro Regional de Estudios Genomicos. La Plata, Argentina.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidad Nacional de La Plata. Centro Regional de Estudios Genomicos. La Plata, Argentina.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal de Minas Gerais.Instituto de Ciências Biológicas. Departamento de Parasitologia. Belo Horizonte, MG, Brasil.The John Hopkins University. Bloomberg School of Public Health. Deparment of Molecular Microbiology and Immunology. Baltimore, MD, USA.Instituto Federal de Educação Ciência e Tecnologia do Rio de Janeiro. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal do Espirito Santo. Núcleo de Doenças Infecciosas. Vitória, ES, Brasil.University of Illinois at Urbana–Champaign. Department of Entomology. Urbana, IL, USA.Instituto Federal de Educação Ciência e Tecnologia do Rio de Janeiro. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.The Barcelona Institute of Science and Technology. Centre for Genomic Regulation. Barcelona, Spain / Universitat Pompeu Fabra. Barcelona, Spain.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil./ Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal de Uberlândia. Faculdade de Computação. Instituto de Genética e Bioquímica. Laboratório de Bioinformática e Análises Moleculares. Uberlândia, MG, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, BrasilUniversidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, BrasilUniversity of Santiago de Compostela. Instituto de Investigaciones Sanitarias. School of Medicine– Center for Resesarch in Molecular Medicine and Chronic Diseases. Department of Physiology. Santiago de Compostela, Spain.Virginia Polytechnic Institute. Department of Biochemistry. Blacksburg, VA, USA.University of Cambridge. Deparment of Veterinary Medicine. Cambridge, United Kingdom.Simon Fraser University. Biological Sciences. Burnaby, BC, Canada.National Institutes of Health. National Institute of Allergy and Infectious Diseases. Section of Vector Biology. Rockville, MD, USA.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Estadual do Norte Fluminense Darcy Ribeiro. Centro de Biociências e Biotecnologia. Laboratório de Química e Função de Proteínas e Peptídeos. Campos de Goytacazes, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, BrasilEuropean Bioinformatics Institute. European Molecular Biology Laboratory. Welcome Trust Genome Campus. Hinxton, Cambridge, United Kingdom.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.University of Manitoba.Department of Biological Sciences. Winnipeg, MB, Canada.Centers for Disease Control and Prevention. Entomology Branch. Division of Parasitic Diseases and Malaria. Atlanta, GA, USA.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil..Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.University of Geneva Medical School. Department of Genetic Medicine and Development. Geneva 1211, Switzerland / Swiss Institute of Bioinformatics. Geneva 1211, Switzerland / Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory. Cambridge, MA, USA / The Broad Institute of MIT and Harvard. Cambridge, MA, USA.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Grupo de Pesquisa em Ecologia de Doenças Transmissíveis na Amazônia. AM, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal de Uberlândia. Faculdade de Computação. Instituto de Genética e Bioquímica. Laboratório de Bioinformática e Análises Moleculares. Uberlândia, MG, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Ciências Biomédicas. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Parasitologia. Belo Horizonte, MG, Brasil.National Institutes of Health. National Center for Biotechnology Information. Rockville, MD, USA.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Ciências Médicas. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Química. Departamento de Bioquímica. Rio de Janeiro, RJ, Brasil / Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Universidade Estadual Paulista. Departamento de Biologia. São Paulo, SP, Brasil.European Bioinformatics Institute. European Molecular Biology Laboratory. Welcome Trust Genome Campus. Hinxton, Cambridge, United Kingdom.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Universidade Estadual do Norte Fluminense Darcy Ribeiro. Centro de Biociências e Biotecnologia. Laboratório de Química e Função de Proteínas e Peptídeos. Campos de Goytacazes, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.The John Hopkins University. Bloomberg School of Public Health. Deparment of Molecular Microbiology and Immunology. Baltimore, MD, USA.University of Notre Dame. Department of Computer Science and Engineering. Notre Dame, IN.Universidad Nacional de La Plata. Centro Regional de Estudios Genomicos. La Plata, Argentina.Universidade Federal Rural do Rio de Janeiro. Instituto de Ciências Biológicas e da Saúde. Departamento de Biologia Animal. Seropédica, RJ, Brasil.Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sérgio Arouca. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Núcleo de Pesquisas Ecológicas de Macaé. Macaé, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Ciências Biomédicas. Rio de Janeiro, RJ, Brasil.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.Washington University School of Medicine. McDonnell Genome Institute. St. Louis, MO, USA.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Instituto de Bioquímica Médica Leopoldo de Meis. Programa de Biologia Molecular e Biotecnologia. Rio de Janeiro, RJ, Brasil.Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Rio de Janeiro, RJ, Brasil.Rhodnius prolixus not only has served as a model organism for the study of insect physiology, but also is a major vector of Chagas disease, an illness that affects approximately seven million people worldwide. We sequenced the genome of R. prolixus, generated assembled sequences covering 95% of the genome (∼702 Mb), including 15,456 putative protein-coding genes, and completed comprehensive genomic analyses of this obligate blood-feeding insect. Although immunedeficiency (IMD)-mediated immune responses were observed, R. prolixus putatively lacks key components of the IMD pathway, suggesting a reorganization of the canonical immune signaling network. Although both Toll and IMD effectors controlled intestinal microbiota, neither affected Trypanosoma cruzi, the causal agent of Chagas disease, implying the existence of evasion or tolerance mechanisms. R. prolixus has experienced an extensive loss of selenoprotein genes, with its repertoire reduced to only two proteins, one of which is a selenocysteine-based glutathione peroxidase, the first found in insects. The genome contained actively transcribed, horizontally transferred genes from Wolbachia sp., which showed evidence of codon use evolution toward the insect use pattern. Comparative protein analyses revealed many lineage-specific expansions and putative gene absences in R. prolixus, including tandem expansions of genes related to chemoreception, feeding, and digestion that possibly contributed to the evolution of a blood-feeding lifestyle. The genome assembly and these associated analyses provide critical information on the physiology and evolution of this important vector species and should be instrumental for the development of innovative disease control methods

An insight into the transcriptome of the digestive tract of the bloodsucking bug, Rhodnius prolixus.

The bloodsucking hemipteran Rhodnius prolixus is a vector of Chagas' disease, which affects 7-8 million people today in Latin America. In contrast to other hematophagous insects, the triatomine gut is compartmentalized into three segments that perform different functions during blood digestion. Here we report analysis of transcriptomes for each of the segments using pyrosequencing technology. Comparison of transcript frequency in digestive libraries with a whole-body library was used to evaluate expression levels. All classes of digestive enzymes were highly expressed, with a predominance of cysteine and aspartic proteinases, the latter showing a significant expansion through gene duplication. Although no protein digestion is known to occur in the anterior midgut (AM), protease transcripts were found, suggesting secretion as pro-enzymes, being possibly activated in the posterior midgut (PM). As expected, genes related to cytoskeleton, protein synthesis apparatus, protein traffic, and secretion were abundantly transcribed. Despite the absence of a chitinous peritrophic membrane in hemipterans - which have instead a lipidic perimicrovillar membrane lining over midgut epithelia - several gut-specific peritrophin transcripts were found, suggesting that these proteins perform functions other than being a structural component of the peritrophic membrane. Among immunity-related transcripts, while lysozymes and lectins were the most highly expressed, several genes belonging to the Toll pathway - found at low levels in the gut of most insects - were identified, contrasting with a low abundance of transcripts from IMD and STAT pathways. Analysis of transcripts related to lipid metabolism indicates that lipids play multiple roles, being a major energy source, a substrate for perimicrovillar membrane formation, and a source for hydrocarbons possibly to produce the wax layer of the hindgut. Transcripts related to amino acid metabolism showed an unanticipated priority for degradation of tyrosine, phenylalanine, and tryptophan. Analysis of transcripts related to signaling pathways suggested a role for MAP kinases, GTPases, and LKBP1/AMP kinases related to control of cell shape and polarity, possibly in connection with regulation of cell survival, response of pathogens and nutrients. Together, our findings present a new view of the triatomine digestive apparatus and will help us understand trypanosome interaction and allow insights into hemipteran metabolic adaptations to a blood-based diet.Journal ArticleResearch Support, N.I.H. IntramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jSCOPUS: ar.jinfo:eu-repo/semantics/publishe

International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module

We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research