A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes

Protein-coding genes in eukaryotes are interrupted by introns, but intron densities widely differ between eukaryotic lineages. Vertebrates, some invertebrates and green plants have intron-rich genes, with 6–7 introns per kilobase of coding sequence, whereas most of the other eukaryotes have intron-poor genes. We reconstructed the history of intron gain and loss using a probabilistic Markov model (Markov Chain Monte Carlo, MCMC) on 245 orthologous genes from 99 genomes representing the three of the five supergroups of eukaryotes for which multiple genome sequences are available. Intron-rich ancestors are confidently reconstructed for each major group, with 53 to 74% of the human intron density inferred with 95% confidence for the Last Eukaryotic Common Ancestor (LECA). The results of the MCMC reconstruction are compared with the reconstructions obtained using Maximum Likelihood (ML) and Dollo parsimony methods. An excellent agreement between the MCMC and ML inferences is demonstrated whereas Dollo parsimony introduces a noticeable bias in the estimations, typically yielding lower ancestral intron densities than MCMC and ML. Evolution of eukaryotic genes was dominated by intron loss, with substantial gain only at the bases of several major branches including plants and animals. The highest intron density, 120 to 130% of the human value, is inferred for the last common ancestor of animals. The reconstruction shows that the entire line of descent from LECA to mammals was intron-rich, a state conducive to the evolution of alternative splicing

A molecular mechanism for bacterial susceptibility to zinc

Transition row metal ions are both essential and toxic to microorganisms. Zinc in excess has significant toxicity to bacteria, and host release of Zn(II) at mucosal surfaces is an important innate defence mechanism. However, the molecular mechanisms by which Zn(II) affords protection have not been defined. We show that in Streptococcus pneumonia extracellular Zn(II) inhibits the acquisition of the essential metal Mn(II) by competing for binding to the solute binding protein PsaA. We show that, although Mn(II) is the high-affinity substrate for PsaA, Zn(II) can still bind, albeit with a difference in affinity of nearly two orders of magnitude. Despite the difference in metal ion affinities, high-resolution structures of PsaA in complex with Mn(II) or Zn(II) showed almost no difference. However, Zn(II)-PsaA is significantly more thermally stable than Mn(II)-PsaA, suggesting that Zn(II) binding may be irreversible. In vitro growth analyses show that extracellular Zn(II) is able to inhibit Mn(II) intracellular accumulation with little effect on intracellular Zn(II). The phenotype of S. pneumoniae grown at high Zn(II):Mn(II) ratios, i.e. induced Mn(II) starvation, closely mimicked a DpsaA mutant, which is unable to accumulate Mn(II). S. pneumoniae infection in vivo elicits massive elevation of the Zn(II):Mn(II) ratio and, in vitro, these Zn(II):Mn(II) ratios inhibited growth due to Mn(II) starvation, resulting in heightened sensitivity to oxidative stress and polymorphonuclear leucocyte killing. These results demonstrate that microbial susceptibility to Zn(II) toxicity is mediated by extracellular cation competition and that this can be harnessed by the innate immune response.Christopher A. McDevitt, Abiodun D. Ogunniyi, Eugene Valkov, Michael C. Lawrence, Bostjan Kobe, Alastair G. McEwan and James C. Pato

Very early ART initiation during acute HIV infection is associated with normalization of cerebrospinal fluid but not plasma markers of immune activation

BackgroundChronic immune activation in the blood and central nervous system is a consequence of human immunodeficiency virus (HIV) infection that contributes to disease morbidity and can occur despite virally suppressive antiretroviral therapy (ART). The trajectory of HIV-related inflammation may vary with the timing of ART initiation. We examined immune activation markers in cerebrospinal fluid (CSF) and blood specimens collected over 96 weeks from participants who initiated ART during acute HIV infection (AHI).MethodsRV254/SEARCH010 study participants with AHI underwent CSF (n = 89) and plasma (n = 146) sampling before initiating ART and at weeks 24 and 96 of treatment. A majority participants (64.4%) received a standard ART regimen (hereafter, "standard ART"), with some (34.7%) also receiving maraviroc and raltegravir for the first 24 weeks (hereafter, "ART plus"). We compared neopterin, CXCL10, CCL2, and interleukin 6 (IL-6) levels in the AHI group to those in 18 healthy, uninfected controls.ResultsFollowing 24 and 96 weeks of treatment, levels of all CSF markers normalized while levels of several plasma markers remained elevated in the AHI group (P < .001). Participants receiving the ART-plus regimen had lower median plasma CCL2 levels at week 24 and lower plasma neopterin levels at week 96.ConclusionsART initiation during AHI differentially impacts the brain compartment, with markers of inflammation returning to normal levels in the CSF, where they were sustained at week 96, but not in plasma

Discovering Social Events through Online Attention

Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011