Drug resistance mutations in HIV: new bioinformatics approaches and challenges.

Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatment-naive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.

Spatio-temporal dynamics of dengue in Brazil: Seasonal travelling waves and determinants of regional synchrony.

Dengue continues to be the most important vector-borne viral disease globally and in Brazil, where more than 1.4 million cases and over 500 deaths were reported in 2016. Mosquito control programmes and other interventions have not stopped the alarming trend of increasingly large epidemics in the past few years. Here, we analyzed monthly dengue cases reported in Brazil between 2001 and 2016 to better characterise the key drivers of dengue epidemics. Spatio-temporal analysis revealed recurring travelling waves of disease occurrence. Using wavelet methods, we characterised the average seasonal pattern of dengue in Brazil, which starts in the western states of Acre and Rondônia, then travels eastward to the coast before reaching the northeast of the country. Only two states in the north of Brazil (Roraima and Amapá) did not follow the countrywide pattern and had inconsistent timing of dengue epidemics throughout the study period. We also explored epidemic synchrony and timing of annual dengue cycles in Brazilian regions. Using gravity style models combined with climate factors, we showed that both human mobility and vector ecology contribute to spatial patterns of dengue occurrence. This study offers a characterization of the spatial dynamics of dengue in Brazil and its drivers, which could inform intervention strategies against dengue and other arboviruses.

MVSE: An R-package that estimates a climate-driven mosquito-borne viral suitability index.

Viruses, such as dengue, Zika, yellow fever and chikungunya, depend on mosquitoes for transmission. Their epidemics typically present periodic patterns, linked to the underlying mosquito population dynamics, which are known to be driven by natural climate fluctuations. Understanding how climate dictates the timing and potential of viral transmission is essential for preparedness of public health systems and design of control strategies. While various alternative approaches have been proposed to estimate local transmission potential of such viruses, few open-source, ready to use and freely available software tools exist.We developed the Mosquito-borne Viral Suitability Estimator (MVSE) software package for the R programming environment. MVSE estimates the index P, a novel suitability index based on a climate-driven mathematical expression for the basic reproductive number of mosquito-borne viruses. By accounting for local humidity and temperature, as well as viral, vector and human priors, the index P can be estimated for specific host and viral species in different regions of the globe.We describe the background theory, empirical support and biological interpretation of the index P. Using real-world examples spanning multiple epidemiological contexts, we further demonstrate MVSE's basic functionality, research and educational potentials.

Introdução: Os vírus da dengue, Zika, febre amarela e chikungunya mantém­se em ciclos de transmissão entre humanos e mosquitos. As epidemias desses vírus apresentam padrões oscilatórios periódicos de carácter universal, que refletem flutuações no número total de mosquitos presentes, por sua vez influenciadas por flutuações climáticas naturais que afetam o ciclo de vida do mosquito. O conhecimento de como o clima resulta em janelas de oportunidade favorável à transmissão desses vírus é essencial para o planeamento de ações de saúde pública, como campanhas de prevenção, alocação de recursos em sistemas de saúde e ações de controlo do mosquito. Enquanto que vários métodos que estimam o potencial desses vírus segundo dados climáticos tȇm sido publicados em anos recentes, ferramentas de software grátis e open­source são praticamente inexistentes. Resultados: Nós desenvolvemos o Mosquito­borne Viral Suitability Estimator (MVSE), um pacote de software para o sistema de programação R. MVSE estima o index P, uma nova medida informada por dados climáticos, baseada na equação do número reprodutivo básico (R0) de um modelo dinâmico. A expressão do R0 usada considera informação a priori para parâmetros humanos, virais e entomológicos, assim como séries temporais de temperatura e humidade. Assim sendo, o index P pode ser calculado para qualquer hospedeiro e vírus de interesse em qualquer região do globo para qual dados climáticos estão disponíveis. Conclusões: Neste manuscrito, a teoria, o suporte empírico e a interpretação biológica do novo index P são introduzidas e discutidas. Usando exemplos reais em múltiplos contextos epidemiológicos da América do Sul e Central, nós demonstramos também as funcionalidades básicas do MVSE e o seu potencial para fins académicos e educacionais.

Genomic and epidemiological characterisation of a dengue virus outbreak among blood donors in Brazil.

Outbreaks caused by Dengue, Zika and Chikungunya viruses can spread rapidly in immunologically naïve populations. By analysing 92 newly generated viral genome sequences from blood donors and recipients, we assess the dynamics of dengue virus serotype 4 during the 2012 outbreak in Rio de Janeiro. Phylogenetic analysis indicates that the outbreak was caused by genotype II, although two isolates of genotype I were also detected for the first time in Rio de Janeiro. Evolutionary analysis and modelling estimates are congruent, indicating a reproduction number above 1 between January and June, and at least two thirds of infections being unnoticed. Modelling analysis suggests that viral transmission started in early January, which is consistent with multiple introductions, most likely from the northern states of Brazil, and with an increase in within-country air travel to Rio de Janeiro. The combination of genetic and epidemiological data from blood donor banks may be useful to anticipate epidemic spread of arboviruses.