Zika virus (ZIKV) is a mosquito-borne flavivirus first isolated in Uganda in 1947. Although entomological and virologic surveillance have reported ZIKV enzootic activity in diverse countries of Africa and Asia, few human cases were reported until 2007, when a Zika fever epidemic took place in Micronesia. In the context of West Africa, the WHO Collaborating Centre for Arboviruses and Hemorrhagic Fever at Institut Pasteur of Dakar (http://www.pasteur.fr/recherche/banques/CRORA/) reports the periodic circulation of ZIKV since 1968. Despite several reports on ZIKV, the genetic relationships among viral strains from West Africa remain poorly understood. To evaluate the viral spread and its molecular epidemiology, we investigated 37 ZIKV isolates collected from 1968 to 2002 in six localities in Senegal and Côte d'Ivoire. In addition, we included strains from six other countries. Our results suggested that these two countries in West Africa experienced at least two independent introductions of ZIKV during the 20th century, and that apparently these viral lineages were not restricted by mosquito vector species. Moreover, we present evidence that ZIKV has possibly undergone recombination in nature and that a loss of the N154 glycosylation site in the envelope protein was a possible adaptive response to the Aedes dalzieli vector.

Many aspects of the evolutionary process of tumorigenesis that are fundamental to cancer biology and targeted treatment have been challenging to reveal, such as the divergence times and genetic clonality of metastatic lineages. To address these challenges, we performed tumor phylogenetics using molecular evolutionary models, reconstructed ancestral states of somatic mutations, and inferred cancer chronograms to yield three conclusions. First, in contrast to a linear model of cancer progression, metastases can originate from divergent lineages within primary tumors. Evolved genetic changes in cancer lineages likely affect only the proclivity toward metastasis. Single genetic changes are unlikely to be necessary or sufficient for metastasis. Second, metastatic lineages can arise early in tumor development, sometimes long before diagnosis. The early genetic divergence of some metastatic lineages directs attention toward research on driver genes that are mutated early in cancer evolution. Last, the temporal order of occurrence of driver mutations can be inferred from phylogenetic analysis of cancer chronograms, guiding development of targeted therapeutics effective against primary tumors and metastases.tumor phylogenetics | ancestral reconstruction | cancer | chronograms | oncogenes I t has long been understood that tumorigenesis is an evolutionary process (1) associated with the accumulation of somatic mutations (2). However, many aspects of that process that are fundamental to cancer biology and targeted treatment have been challenging to reveal, such as the divergence times and genetic clonality of metastatic lineages (3, 4). Somatic mutations have revealed tumor type-specific drivers by comparison of primary tumor and normal tissues (5, 6), and studies examining the evolutionary process of cancer across multiple sites have used a handful of subjects to identify ubiquitous, shared, and private mutations (1) and to reconstruct a number of tumor phylogenies using parsimony or unweighted pair group methods with arithmetic mean (1, 7) but have lacked the power to generalize about the tumorigenic or metastatic process across cancer types (1).Tumor phylogenetics, using a larger sample with explicit evolutionary models, can be applied using molecular evolutionary models to reconstruct ancestral states of somatic mutations and infer cancer chronograms, revealing novel information about the timing of gene mutations and their contributions to tumorigenesis and metastasis and addressing three fundamental aspects of cancer biology. First, the topology of divergence of primary and metastatic lineages can differentiate between a linear model of cancer progression, in which all metastatic tumors are descended from a single original primary cell such that all metastases are more closely related to each other than they are to any tissue in the primary tumor, and a branched model, in which metastases can originate from divergent lineages within primary tumors. Second, molecular evolutionary trees and chronograms can quantify how e...

Increasing public interest in science information in a digital and 2.0 science era promotes a dramatically, rapid and deep change in science itself. The emergence and expansion of new technologies and internet-based tools is leading to new means to improve scientific methodology and communication, assessment, promotion and certification. It allows methods of acquisition, manipulation and storage, generating vast quantities of data that can further facilitate the research process. It also improves access to scientific results through information sharing and discussion. Content previously restricted only to specialists is now available to a wider audience. This context requires new management systems to make scientific knowledge more accessible and useable, including new measures to evaluate the reach of scientific information. The new science and research quality measures are strongly related to the new online technologies and services based in social media. Tools such as blogs, social bookmarks and online reference managers, Twitter and others offer alternative, transparent and more comprehensive information about the active interest, usage and reach of scientific publications. Another of these new filters is the Research Blogging platform, which was created in 2007 and now has over 1,230 active blogs, with over 26,960 entries posted about peer-reviewed research on subjects ranging from Anthropology to Zoology. This study takes a closer look at RB, in order to get insights into its contribution to the rapidly changing landscape of scientific communication.

Hepatitis C virus (HCV) infects 170 million people worldwide, and is a major public health problem in Brazil, where over 1% of the population may be infected and where multiple viral genotypes co-circulate. Chronically infected individuals are both the source of transmission to others and are at risk for HCV-related diseases, such as liver cancer and cirrhosis. Before the adoption of anti-HCV control measures in blood banks, this virus was mainly transmitted via blood transfusion. Today, needle sharing among injecting drug users is the most common form of HCV transmission. Of particular importance is that HCV prevalence is growing in non-risk groups. Since there is no vaccine against HCV, it is important to determine the factors that control viral transmission in order to develop more efficient control measures. However, despite the health costs associated with HCV, the factors that determine the spread of virus at the epidemiological scale are often poorly understood. Here, we sequenced partial NS5b gene sequences sampled from blood samples collected from 591 patients in São Paulo state, Brazil. We show that different viral genotypes entered São Paulo at different times, grew at different rates, and are associated with different age groups and risk behaviors. In particular, subtype 1b is older and grew more slowly than subtypes 1a and 3a, and is associated with multiple age classes. In contrast, subtypes 1a and 3b are associated with younger people infected more recently, possibly with higher rates of sexual transmission. The transmission dynamics of HCV in São Paulo therefore vary by subtype and are determined by a combination of age, risk exposure and underlying social network. We conclude that social factors may play a key role in determining the rate and pattern of HCV spread, and should influence future intervention policies.

Using Ebolavirus genomic and epidemiological data, we conducted the first joint analysis in which both data types were used to fit dynamic transmission models for an ongoing outbreak. Our results indicate that transmission is clustered, highlighting a potential bias in medical demand forecasts, and provide the first empirical estimate of underreporting.