Mayaro virus (MAYV) is responsible for a mosquito-borne tropical disease with clinical symptoms similar to dengue or chikungunya virus fevers. In addition to the recent territorial expansion of MAYV, ...this virus may be responsible for an increasing number of outbreaks. Currently, no vaccine is available. Aedes aegypti is promiscuous in its viral transmission and thus an interesting model to understand MAYV-vector interactions. While the life-cycle of MAYV is known, the mechanisms by which this arbovirus affects mosquito host cells are not clearly understood.
After defining the best conditions for cell culture harvesting using the highest virus titer, Ae. aegypti Aag-2 cells were infected with a Brazilian MAYV isolate at a MOI of 1 in order to perform a comparative proteomic analysis of MAYV-infected Aag-2 cells by using a label-free semi-quantitative bottom-up proteomic analysis. Time-course analyses were performed at 12 and 48 h post-infection (hpi). After spectrum alignment between the triplicates of each time point and changes of the relative abundance level calculation, the identified proteins were annotated and using Gene Ontology database and protein pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes.
After three reproducible biological replicates, the total proteome analysis allowed for the identification of 5330 peptides and the mapping of 459, 376 and 251 protein groups, at time 0, 12 hpi and 48 hpi, respectively. A total of 161 mosquito proteins were found to be differentially abundant during the time-course, mostly related to host cell processes, including redox metabolism, translation, energy metabolism, and host cell defense. MAYV infection also increased host protein expression implicated in viral replication.
To our knowledge, this first proteomic time-course analysis of MAYV-infected mosquito cells sheds light on the molecular basis of the viral infection process and host cell response during the first 48 hpi. Our data highlight several mosquito proteins modulated by the virus, revealing that MAYV manipulates mosquito cell metabolism for its propagation.
Chikungunya virus (CHIKV) is a single-stranded positive RNA virus that belongs to the genus
Alphavirus
and is transmitted to humans by infected
Aedes aegypti
and
Aedes albopictus
bites. In humans, ...CHIKV usually causes painful symptoms during acute and chronic stages of infection. Conversely, virus–vector interaction does not disturb the mosquito’s fitness, allowing a persistent infection. Herein, we studied CHIKV infection of
Ae. aegypti
Aag-2 cells (multiplicity of infection (MOI) of 0.1) for 48 h through label-free quantitative proteomic analysis and transmission electron microscopy (TEM). TEM images showed a high load of intracellular viral cargo at 48 h postinfection (hpi), as well as an unusual elongated mitochondria morphology that might indicate a mitochondrial imbalance. Proteome analysis revealed 196 regulated protein groups upon infection, which are related to protein synthesis, energy metabolism, signaling pathways, and apoptosis. These Aag-2 proteins regulated during CHIKV infection might have roles in antiviral and/or proviral mechanisms and the balance between viral propagation and the survival of host cells, possibly leading to the persistent infection.
Although RNA viruses have high mutation rates, host cells and organisms work as selective environments, maintaining the viability of virus populations by eliminating deleterious genotypes. In serial ...passages of RNA viruses in a single cell line, most of these selective bottlenecks are absent, with no virus circulation and replication in different tissues or host alternation. In this work,
Aag-2 cells were accidentally infected with Chikungunya virus (CHIKV) and Mayaro virus (MAYV). After numerous passages to achieve infection persistency, the infectivity of these viruses was evaluated in
C6/36 cells, African green monkey Vero cells and primary-cultured human fibroblasts. While these CHIKV and MAYV isolates were still infectious to mosquito cells, they lost their ability to infect mammalian cells. After genome sequencing, it was observed that CHIKV accumulated many nonsynonymous mutations and a significant deletion in the coding sequence of the hypervariable domain in the
gene. Since MAYV showed very low titres, it was not sequenced successfully. Persistently infected Aag-2 cells also accumulated high loads of short and recombinant CHIKV RNAs, which seemed to have been originated from virus-derived DNAs. In conclusion, the genome of this CHIKV isolate could guide mutagenesis strategies for the production of attenuated or non-infectious (to mammals) CHIKV vaccine candidates. Our results also reinforce that a paradox is expected during passages of cells persistently infected by RNA viruses: more loosening for the development of more diverse virus genotypes and more pressure for virus specialization to this constant cellular environment.
ObjectivesTo analyze the chronology of diagnosis and determine whether clinical and epidemiological variables have an influence on diagnostic delay at two referral centers.MethodsThe medical records ...of all patients older than 18 years diagnosed with oral/oropharyngeal cancer from June 2005 to June 2013 were analyzed using SPSS® 20. The association between epidemiological and clinical variables with patient and professional delay was performed using ANOVA, Student’s t-test, and Kruskal-Wallis test.ResultsIn total, 121 medical records were included in the study. Patients were predominantly brown, male, illiterate, living in country towns, smokers, and heavy drinkers (mean age 64.3 years, SD=12.94). The majority (85.1%) of patients were diagnosed at advanced stages of their disease. The greatest delay was patient-related, mean 197.8 days (SD=323.9). Delay in establishing the medical diagnosis averaged 20 days (SD=25.9), and health care system-related delay was 71.1 days (SD=71.7). There was no association of clinical and epidemiological variables with delayed diagnosis (patient and professional).ConclusionData from the present study suggest that clinical and epidemiological variables do not influence diagnostic delay.
Resumo Objetivo Estimar se variáveis clínicas e epidemiológicas influenciam no atraso do diagnóstico em dois centros de referência. Métodos Foi realizado um estudo analítico longitudinal ...retrospectivo. Todos os prontuários de pacientes maiores de 18 anos diagnosticados no período de junho de 2005 a junho de 2013 foram analisados por meio do SPSS® 20. Para testar associações entre as variáveis epidemiológicas e clínicas com os atrasos do paciente e do profissional, foram utilizados os testes: ANOVA, t de Student e Kruskal-Wallis. Resultados Foram incluídos no estudo 121 prontuários. Prevaleceram pacientes do sexo masculino, com idade média de 64,3 anos (DP=12,94), pardos, procedentes do interior, analfabetos, tabagistas e etilistas. A grande maioria (85,1%) foi diagnosticada nos estádios avançados. O maior atraso estava relacionado ao paciente, com média de tempo de 197,8 dias (DP=323,9). O atraso no diagnóstico profissional foi de 20 dias (DP=25,9), e aquele relacionado ao sistema de saúde foi de 71,1 dias (DP=71,7). Não houve associação entre as variáveis clínicas/epidemiológicas e o atraso no diagnóstico (do paciente e do profissional). Conclusão De acordo com os resultados do presente estudo, as variáveis clínicas e epidemiológicas não influenciam no atraso do diagnóstico.
Abstract Objectives To analyze the chronology of diagnosis and determine whether clinical and epidemiological variables have an influence on diagnostic delay at two referral centers. Methods The medical records of all patients older than 18 years diagnosed with oral/oropharyngeal cancer from June 2005 to June 2013 were analyzed using SPSS® 20. The association between epidemiological and clinical variables with patient and professional delay was performed using ANOVA, Student’s t-test, and Kruskal-Wallis test. Results In total, 121 medical records were included in the study. Patients were predominantly brown, male, illiterate, living in country towns, smokers, and heavy drinkers (mean age 64.3 years, SD=12.94). The majority (85.1%) of patients were diagnosed at advanced stages of their disease. The greatest delay was patient-related, mean 197.8 days (SD=323.9). Delay in establishing the medical diagnosis averaged 20 days (SD=25.9), and health care system-related delay was 71.1 days (SD=71.7). There was no association of clinical and epidemiological variables with delayed diagnosis (patient and professional). Conclusion Data from the present study suggest that clinical and epidemiological variables do not influence diagnostic delay.
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