Caenorhabditis elegans, RNAi is systemic; i.e., RNAi silencing signals can move between cells and tissues. Furthermore, RNAi effects can be inherited transgenerationally and may last for many generations. Neither the biological relevance of systemic RNAi nor transgenerational RNAi is currently understood. Here we examined the role of both pathways in the protection of C. elegans from viral infection. We studied the Orsay virus, a positive-strand RNA virus related to Nodaviridae and the first and only virus known to infect C. elegans. Immunity to Orsay virus infection requires the RNAi pathway. Surprisingly, we found that genes required for systemic or transgenerational RNAi did not have a role in antiviral defense. Furthermore, we found that Orsay virus infection did not elicit a systemic RNAi response even when a target for RNAi was provided by using transgenes. Finally, we show that viral siRNAs, the effectors of RNAi, are not inherited to a level that provides any significant resistance to viral infection in the next generation. We conclude that systemic or transgenerational RNAi does not play a role in the defense against natural Orsay virus infection. Furthermore, our data suggest that there is a qualitative difference between experimental RNAi and antiviral RNAi. Our data are consistent with a model of systemic and transgenerational RNAi that requires a nuclear or germ line component that is lacking in almost all RNA virus infections.Antiviral RNA-mediated silencing (RNA interference [RNAi]) acts as a powerful innate immunity defense in plants, invertebrates, and mammals. In IMPORTANCE Since its discovery in Caenorhabditis elegans, RNAi has proven a valuable scientific tool in many organisms. In C. elegans, exogenous RNAi spreads throughout the organism and can be passed between generations; however, there has been controversy as to the endogenous role(s) that the RNAi pathway plays. One endogenous role for which spreading both within the infected organism and between generations would be advantageous is a role in viral defense. In plants, antiviral RNAi is systemic and the spread of RNAi between cells provides protection against subsequent viral infection. Here we investigated this by using the only naturally occurring virus known to infect C. elegans, Orsay virus, and surprisingly found that, in contrast to the exogenous RNAi pathway, the antiviral RNAi response targeted against this virus does not spread systemically throughout the organism and cannot be passed between generations. These results suggest that there are differences between the two pathways that remain to be discovered. RNA interference (RNAi) is a mechanism of gene silencing that is broadly conserved across eukaryotes. RNAi is initiated by the cleavage of long double-stranded RNA (dsRNA) by the RNase III enzyme Dicer into short 20- to 24-nucleotide (nt) small interfering RNAs (siRNAs) . These sCaenorhabditis elegans, there is an additional amplification step in the pathway. siRNAs generated by Dicer (DCR-1) form complexes with Argonaute proteins that recruit RNA-dependent RNA polymerases (RdRps) to the target mRNA. The RdRps produce an abundant class of siRNAs ], HC271 [ccIs4251 qtIs3 sid-2(qt42) mIs11], RB2519 [drh-1(ok3495)], SX2836 (mjIs242[psur-5::GFP::OrsayRNA2::tbb-2 + psur-5::mCherry::unc-54]), SX2839 {mjIs242[psur-5::GFP::OrsayRNA2::tbb-2 + psur-5::mCherry::unc-54] sid-1(qt2)}, SX2838 {mjIs242[psur-5::GFP::OrsayRNA2::tbb-2 + psur-5::mCherry::unc-54] drh-1(ok3495)}, and SX2813 (mjEx565[psur-5::GFP::OrsayRNA1::tbb-2] mjEx566[psur-5::mCherry::unc-54]).istol N2 . The strThe psur-5::GFP::OrsayRNA2::tbb-2 and psur-5::GFP::OrsayRNA1::tbb-2 constructs were generated by using the MultiSite Gateway Three-Fragment Vector Construction kit (Life Technologies). psur-5 in the first position was a gift from the Ahringer laboratory, and green fluorescent protein  was placed in the second position. PCR fusion was used to generate the OrsayRNA::tbb-2 fragments that were placed in the third position. All three fragments were combined in an LR reaction into the pCFJ150 vector. The sequences of the primers used to amplify viral segments from cDNA are available on request.Viral segments were PCR amplified from cDNA by using primers with appropriate tails for BP cloning into pDONR221 (Gateway cloning). LR reactions were performed to place viral segments in a Gateway modified RNAi vector L4440. The sequences of the primers used are available on request.Virus filtrate was prepared as described previously .For all strains, two or three young adults were inoculated with 20 μl of viral filtrate for 4 days at 20°C in 55-mm plates.Four days after infection, all animals were collected in M9; RNA extraction and quantitative reverse transcription (qRT)-PCR was performed as described previously , 30. AliFor liquid culture-based infection, 200 L2 animals were rotated for 1 h at 20°C in 300 μl of M9, 100 μl of HB101 in LB broth, and 100 μl of Orsay virus filtrate (nondiluted or diluted in M9 10 or 100 times). After 1 h, the animals were collected and washed three times in M9 before transfer to 50-mm plates.For agar-based infection, 200 L2 animals were transferred to a 50-mm plate per individual infection. A 100-μl volume of Orsay virus filtrate was added (nondiluted or diluted in M9 10 or 100 times). Infections were performed in five biological replicates.For all strains, 200 L2 animals were infected and collected 48 h postinfection for detection of viral RNA as described above.d-thiogalactopyranoside  and carbenicillin (25 μg/ml). After drying overnight, two or three animals were added and then grown at 20°C for 4 days, at which point they were collected for RNA extraction  or their phenotype was scored . For the analysis of phenotypes of animals treated with RNAi against the unc-22 (ZK617.1) or dpy-11 (F46E10.9) gene  and used to build genomes with Bowtie-build to which small RNAs were aligned while allowing 0 mismatches.Small RNA libraries were sequenced by using Illumina MiSeq and/or HiSeq. Processing and alignment of high-throughput sequencing data to the Orsay virus were carried out as described previously, by using Bowtie for all sequence alignments and allowing up to one mismatch to compensate for divergence in the viral sequence . To geneGSE60020.The raw high-throughput functional genomics data obtained in this study are available under GEO accession number C. elegans to dsRNA through feeding, injection, or viral infection results in the generation of two classes of siRNAs that bring about RNAi  and compared them with their uninfected offspring (N2 F1)  to E. Asdpy-11 . si. sidpy-1 animals  and J. Bffspring . Notablydrh-1 animals and found that agar-based infection was more reproducible and resulted in higher levels of infection than liquid culture-based infection over a range of viral concentrations  (psur-5::GFP::OrsayRNA2::tbb-2). The animals also carry the same promoter driving mCherry expression with an alternate 3′ UTR (psur-5::mCherry::unc-54)  . In thise sensor .OrsayRNA2 fragment. This treatment silences the sensor robustly in all animals, with residual GFP expression in the pharynx only (the pharynx is known to be somewhat RNAi resistant)  , confirmsistant) .sid-1 mutant , whereas drh-1 mutant F1 animals showed significantly more GFP silencing than the wild-type sensor (P < 0.001) . Orsay virus is not transmitted vertically , so the < 0.001) . These dWe have shown that exposure of parents to the Orsay virus does not confer resistance in F1 progeny and that Orsay virus-induced sensor silencing does not seem to be inherited. RNAi-induced silencing against the viral sensor can however be passed on to the F1 generation; therefore, we asked whether RNAi-induced silencing in the presence of the Orsay virus sensor could protect the P0 generation or F1 offspring against Orsay virus infection.OrsayRNA1 than after RNAi against OrsayRNA2 (We tested a series of regions of the Orsay virus RNA genome for the ability to protect against Orsay virus replication in RNAi feeding experiments . IntrigursayRNA2 . This diOrsayRNA1 sensor instead of the previously used OrsayRNA2 sensor. The sensor was extrachromosomal instead of integrated but otherwise identically constructed. Interestingly, while Orsay virus RNAi reduced the levels of Orsay virus RNA detected in N2, the presence of the RNA1 sensor resulted in even more RNAi-induced protection against Orsay virus replication (We then asked whether there was also a reduction in Orsay virus RNA levels following concurrent RNAi and viral infection in N2 or an Orsay virus sensor background . Becauselication . It is tTo test whether the combination of RNAi against the Orsay virus and the sensor could confer protection on the F1 offspring, we bleached the adults from the previous experiment to generate uninfected embryos and then infected them with the Orsay virus . DespiteThese data show that even in the most extreme case of RNAi and a transgenic viral portion, there is still no evidence of deposition in the F1 offspring of functional small RNA molecules that can protect against Orsay virus infection.C. elegans initiated in response to exposure to dsRNA matching endogenous genes are its ability to spread throughout the animal and its ability to act transgenerationally. There has been much speculation on what the function of these properties is for animals in the wild, with the proposed role of RNAi in antiviral silencing a key candidate. Here we have shown that infection of C. elegans with the Orsay virus instigates neither transgenerational nor systemic silencing. In the absence of any other known naturally occurring C. elegans virus, it is possible that systemic RNAi may function in defense against an as-yet-undiscovered infection. Nevertheless, our results have important implications for both the mechanism of systemic RNAi and the biology of small RNA pathways in C. elegans.Two of the most notable aspects of the siRNA pathway in C. elegans (The fact that antiviral siRNA induced by Orsay virus infection, in contrast to siRNA induced by exposure to dsRNA, does not spread between cells may be explained by differences in the intermediates produced by the two pathways. Importantly, 22G RNAs, produced by both pathways, are unlikely to transfer RNAi in  elegans , perhaps elegans , 39. How elegans . Both RD elegans . Furtherdpy-11 gene and inheritance of small RNAs, albeit without an observable phenotype, in silencing of the unc-22 gene. The lack of systemic RNAi after infection with the Orsay virus thus prevents small RNAs from entering embryos to a sufficient extent to transmit the silencing effect.Our observations on the nonsystemic nature of the antiviral RNAi response to infection with the Orsay virus are fully consistent with the facts that we detected no transgenerational protection against infection and did not detect a strong small RNA signal mapping to the virus in the embryos derived from infected individuals. This is in contrast to the robust inheritance of both small RNAs and the silencing phenotype in the case of RNAi-induced silencing of the C. elegans RNAi pathway is employed in targeting the Orsay virus, despite an absolute requirement for cell-autonomous RNAi in antiviral defense. It remains possible that other naturally occurring viruses will be discovered that can instigate systemic or transgenerational responses, in particular, viruses that could infect the germ line or DNA viruses, which, in plants, appear to be targeted by transgenerational silencing through RNA-directed DNA methylation. It is also possible that, as noted above, the Orsay virus itself has evolved to prevent systemic silencing, although it is worth noting that RNAi-competent strain N2 is unlikely to be the natural host of the Orsay virus. The Orsay virus was discovered in strain JU1580, which is deficient in antiviral RNAi (C. elegans evolved for completely different reasons, linked potentially to its unusual ability to take up dsRNA from its environment. The answers to these questions require deeper sampling of C. elegans in its natural environment to understand better the selective forces acting on the RNAi pathway in the wild.The function of RNAi in antiviral defense in plants, fungi, and animals, including mammals, has led to the proposal that viral infection was the major driving force behind its evolution. Systemic silencing, which potentially allows antiviral siRNAs to move ahead of the spread of virus, and transgenerational vaccination of the next generation might seem to be ideal components of an effective antiviral pathway. It is therefore interesting that neither of these two aspects of the ral RNAi , 30; thu


Significant efforts have recently been put into the investigation of the spatial organization and the chromatin-interaction networks of genomes. Chromosome conformation capture (3C) technology and its derivatives are important tools used in this effort. However, many of these have limitations, such as being limited to one viewpoint, expensive with moderate to low resolution, and/or requiring a large sequencing effort. Techniques like Hi-C provide a genome-wide analysis. However, it requires massive sequencing effort with considerable costs. Here we describe a new technique termed Targeted Chromatin Capture (T2C), to interrogate large selected regions of the genome. T2C provides an unbiased view of the spatial organization of selected loci at superior resolution  at much lower costs than Hi-C due to the lower sequencing effort.β-globin locus and the human H19/IGF2 locus. In both cases we identified all known chromatin interactions. Furthermore, we compared the human H19/IGF2 locus data obtained from different chromatin conformation capturing methods with T2C data. We observed the same compartmentalization of the locus, but at a much higher resolution (single restriction fragments vs. the common 40 kbp bins) and higher coverage. Moreover, we compared the β-globin locus in two different biological samples , where it is either actively transcribed or not, to identify possible transcriptional dependent interactions. We identified the known interactions in the β-globin locus and the same topological domains in both mouse primary erythroid cells and in mouse fetal brain with the latter having fewer interactions probably due to the inactivity of the locus. Furthermore, we show that interactions due to the important chromatin proteins, Ldb1 and Ctcf, in both tissues can be analyzed easily to reveal their role on transcriptional interactions and genome folding.We applied T2C on well-known model regions, the mouse T2C is an efficient, easy, and affordable with high (restriction fragment) resolution tool to address both genome compartmentalization and chromatin-interaction networks for specific genomic regions at high resolution for both clinical and non-clinical research. A number of recent studies have shown that the genome is organized in self-associating domains  that areShh[One loop is defined as two distant chromatin regions coming, spatially, into close proximity (interact with each other), thereby creating DNA loops. Such ‘long-range interactions’ have been first observed between promoters and distant enhancers  technique which allowed the rapid identification of chromatin regions residing in close proximity ,20. The The analysis of the interactions of several viewpoints with the aforementioned techniques in 3C and 4C is possible, but the choice for several viewpoints will increase the costs and work effort linearly. However, the number of viewpoints can also be limited due to the (often) limiting amount of available cell material. 5C is demanding in primer design and allows the analysis of interactions only among the primer designed fragments. Furthermore, genome-wide coverage is not possible. Hi-C is very expensive as it requires extremely deep sequencing in order to cover the whole genome, even at a relatively low resolution of 40 kbp. The most recent Hi-C data analysis has used a new algorithm and provided a genome-wide interaction map of 10 kbp resolution. However, an enormous amount of sequencing is required  . Such efH19/IGF2 locus and compared the results of our new method with data from other chromatin conformation capturing techniques. Using the mouse β-globin locus we demonstrated that the method can reliably identify chromatin structural changes between different tissues and also allows the study of the role of individual transcription factors in the chromatin architecture.Here we present Targeted Chromatin Capture (T2C), a new 3C method, which does not involve a massive sequencing effort, but which results in a high resolution map of interactions for particular loci of interest. We used the well-studied human To overcome the aforementioned problems of the 5C and Hi-C techniques we have developed the novel method T2C. The method has the advantage that it allows the analysis of the structure of the genome and all the interactions of selected regions of the genome at high resolution (single restriction fragments) without a massive sequencing effort and associated costs.T2C employs a selective enrichment of the 3C ligation products in preselected regions of interest in order to identify their interactions within a domain as well as the compartmentalization of one or several specific regions of the genome. These regions can be continuous Mb sized genomic regions, but could also be a collection of smaller regions (a few kbp each). Every captured restriction fragment can be used as a single ‘4C-seq viewpoint’ and analyzed accordingly. The results of T2C provide a local interaction map at a restriction fragment-level resolution accompanied with a lower sequencing effort and less intricate bioinformatics analysis than Hi-C. T2C also overcomes the limits of 5C since it identifies not only interactions within the targeted region(s), but also interactions between the targeted region(s) and with regions outside of them.Mm - HindIII + NlaIII digest, Hs - BglII + NlaIII digest) in our regions of interest, the mouse β-globin locus and the human H19/IGF2 locus (see Methods). Alternative to continuous regions, separate genomic regions within one (or more) chromosomes could be analyzed simultaneously. The oligonucleotides are spotted on an array or can alternatively be captured on beads. Some fragment ends cannot be captured by a designed oligonucleotide due to the presence of repeat elements or the insufficient size of the restriction fragment end. Repetitive sequences are a general problem in all 3C-based methods, including Hi-C. The size limitation of the fragment end can be circumvented if necessary by a backup procedure with different enzymes (changing either the first or the second restriction enzyme or both), which generates a new set of end fragments or by mechanically shearing of the chromatin (instead of the second restriction enzyme digestion) which can result in fragment sizes of different length (see Discussion).In brief, we have designed sets of unique oligonucleotide probes (ranging from 62 to 90 nucleotides) specific for all the restriction fragments and as close as possible to the end of the first restriction site .To demonstrate that T2C reveals a similar overall interaction pattern and compartmentalization of the locus as observed by Hi-C in IMR90 cells  we firstus IRM90  with a SHowever, with T2C we obtained a chromatin interaction map at restriction fragment resolution  and that when T2C is performed for a continuous region over 2 Mb it can reproduce the overall topological domain structure that was observed by Hi-C.β-globin locus as a model system to show that the T2C method can detect reliably conformational changes due to activation of the genes in vivo at high resolution  with the next domain starting near the end of the selected sequences   and between the LCR-3′HS1 are clearly visualized [β-globin genes and HS1 at the 3′end of the locus in fetal liver cells but not in brain [β-globin promoter we identify a few additional interactions further away than the ones previously reported. These are located even approximately 1 Mb far from the β-globin promoter  gene and the LCR also contact a number of positions on other chromosomes.The analysis of the hybridized fragments shows that almost the entire 2.1 Mb appears to be part of one topological domain .The importance of the role of chromatin interactions in the regulation of the gene transcription is well established ,39-42. HH19/IGF2 locus  involved in the interactions. Ldb1 and Ctcf are important proteins which mediate chromatin interactions. Ldb1 is an important transcription factor necessary for primitive mouse hematopoiesis and for the development of megakaryocytes ,44 and cin cells ,20,35 anin cells  . That yielded after comprehensive data analysis and 271,177 and 557,763 paired-reads within the limits of the region of interest excluding self-ligations and uncut fragments for both fetal brain and liver (see Methods). These reads represented 2,369 and 4,057 distinct interactions with 114 and 137 reads per interaction on average for fetal brain and liver, respectively  to extract safe conclusions about the local chromatin interactome and the compartmentalization of the genome.We conclude that T2C can be used as an affordable, cost-effective, diagnostic tool with single restriction fragment resolution to explore the local spatial organization of the genome and chromatin interactions without requiring laborious procedures or massive sequencing efforts.H19/IGF2 locus unique oligonucleotides were designed close to BglII restriction sites  spanning an area of 2.1 Mb . For the The oligonucleotides were designed with the following criteria, they should be: (1) as close as possible to the first restriction site; (2) a unique DNA sequence within the area of interest and preferably in the entire genome; (3) similar melting temperatures, but with different base composition and the length; (4) oligonucleotides which exceed the second restriction site due to very small end fragments, were trimmed keeping in mind to stay close to the same melting temperature.H19/IGF2 locus and for the β-globin locus containing for each one the oligonucleotides which satisfy the aforementioned criteria. The oligonucleotides, 525 for the H19/IGF2 locus and 800 for the β-globin locus, were replicated proportionally and equally up to 2.1 M in total for each design, that is, for the β-globin locus each of the 800 oligonucleotides was spotted in 2,625 spots.A custom-made NimbleGen Sequence Capture 2.1 M capture array is produced separately for the 7 mouse primary erythroid cells from mouse fetal liver E12.5, mouse fetal brain cells E12.5, and a human breast endothelial cell line (HB2) were isolated, cross-linked  quenched with 1 M glycine and were re-suspended in lysis buffer (10 mM Tris–HCl (pH 8.0), 10 mM NaCl, 0.2% (vol/vol) NP-40 and 1× protease inhibitor solution). The chromatin was digested with a 6-cutter (400 units of HindIII for mouse cells and BglII for the HB2 cells) and ligated using 100 units of T4 DNA ligase (Promega) under conditions favoring intramolecular ligation events. After reversing the cross-link at 65°C overnight, 50 μg of the resulting DNA chromatin library were digested with a frequent 4-cutter . All these steps were performed according to the initial steps of 3C-seq protocol, as described previously [Nuclei from approximately 10eviously .http://www.illumina.com). In short, the digested library is purified using AMPure XP beads (Beckman Coulter), end-repaired, and cleaned using AMPure XP beads. The now blunt-ended fragments were A-tailed using the Klenow exo enzyme in the presence of ATP and purified again using AMPure XP beads. Then indexed adapters provided by Illumina were ligated to the A-tailed DNA fragments with subsequent purification using AMPure XP beads.The final library is prepared for analysis on the Illumina Cluster Station and HiSeq 2000 Sequencer according to the Illumina TruSeq DNA protocol with modifications (http://www.nimblegen.com/seqcapez) on the NimbleGen Hybridization System. The captured DNA fragments are eluted from the capture array and purified using MinElute columns (Qiagen). The yield for a positive region (a fragment inside the region of interest) and a negative region (a fragment outside the region of interest) differ by >30-fold on average. The captured DNA fragments are amplified by 12 PCR cycles. PCR products are purified using AMPure XP beads and eluted in 30 μL of re-suspension buffer. One microliter is loaded on an Agilent Technologies 2100 Bioanalyzer using a DNA 1000 assay to determine the library concentration and to check for quality.The resulting adapter-modified DNA library (300 to 500 ng) was hybridized in 35 μL for 64 h at 42°C on a custom-made NimbleGen Sequence Capture 2.1 M capture array according to NimbleGen Sequence Capture array protocol (http://www.illumina.com). Briefly, 1 μL of a 10 nM TruSeq DNA library stock DNA is denatured with NaOH, diluted to 9-10 pM and hybridized onto the flowcell. The hybridized fragments are sequentially amplified, linearized, and end-blocked according to the Illumina Paired-end Sequencing user guide protocol. After hybridization of the sequencing primer, sequencing by synthesis is performed using the HiSeq 2000 sequencer with a 101 cycle protocol according to manufacturer’s instructions. The sequenced fragments were denatured with NaOH using the HiSeq 2000 and the index-primer was hybridized onto the fragments. The index was sequenced with a seven-cycle protocol. The fragments are denatured with NaOH, sequentially amplified, linearized, and end-blocked. After hybridization of the sequencing primer, sequencing by synthesis of the third read is performed using the HiSeq 2000 sequencer with a 101-cycle protocol.Cluster generation is performed according to the Illumina Cluster Reagents preparation protocol (HindIII for the mouse derived reads and BglII for the human derived reads) For each read with one or more enzyme recognition sites, the DNA sequence after the 3′ end of the first site was removed, that is, after the trimming procedure the trimmed reads contained and ended with a single restriction recognition site. Subsequently, consecutive bases with a quality score lower than 10 were cut off from the ends of all the reads and the reads that contained less than 12 bases were omitted using Trimmomatic [NlaIII-NlaIII restriction fragments were removed using BEDtools [The generated HiSeq 2000 sequencing reads were trimmed if the reads contained the first enzyme restriction recognition site (mmomatic . We usedmmomatic . AlignedBEDtools .In the alignment, paired reads were removed if one of the reads was not uniquely mapped. Furthermore, paired reads that were a result of a self-ligation event, non-digestion/re-ligation event, or a ligation of identical ends were removed from the analysis, since these paired reads introduce a common bias in chromosome conformation capture techniques ,51. The Published ChIP-seq datasets ,38 were The authors declare that they have no competing interest.PK, KSW, TAK, and FG designed the experiments. PK, JZ, and KSW carried out the experiments. HJGvdW performed the bioinformatics analysis. NK and RWWB conducted the initial steps of bioinformatics analysis. CEMK and WFJvI carried out the Illumina sequencing. PK, HJGvdW and FG wrote the manuscript. All authors read and approved the final manuscript.


The complete genome sequence  of an emerging cucumber green mottle mosaic virus (CGMMV) isolate on cucumber in North America was determined through deep sequencing of small (sRNA) and rapid amplification of cDNA ends. The virus shares 99% nucleotide sequence identity with the Asian genotype but only 90% with the European genotype. Tobamovirus in the family Virgaviridae . The small RNA (sRNA) was separated from the total RNA by polyacrylamide gel electrophoresis, and the sRNA library was constructed according to the published protocol (The cucumber green mottle mosaic virus (CGMMV) belongs to the genus aviridae . CGMMV iaviridae –4, Asia viridae ––7, and tiridae ––, 9. Sinciridae –– and in firidae ––. Currentiridae ––. The geniridae –– was obtaprotocol  and sequprotocol . A singlKP772568.The full genomic sequence of CGMMV isolate ABCA13-01 was deposited in GenBank under the accession no.


Apodemus ponticus. We evaluated 62 patients in Russia with Sochi virus infection. Most clinical cases were severe, and the case-fatality rate was as high as 14.5%.Sochi virus was recently identified as a new hantavirus genotype carried by the Black Sea field mouse,  Apodemus ponticus) and a patient with fulminant hantavirus disease who died of shock and combined kidney and lung failure . Both syndromes can lead to cardiopulmonary and renal failure . For 26 patients, sufficient volumes of follow-up serum were available for additional focus reduction neutralization assays to specify neutralizing antibodies. All serum samples exhibited substantially higher neutralizing titers toward DOBV than toward Puumala virus, Hantaan virus, and Seoul virus. When the neutralizing effect of DOBV-positive patients’ serum were compared against the different human pathogenic genotypes of DOBV , all serum predominantly reacted with the Sochi genotype  Table 1.A. ponticus animals  were amplified. The sequences obtained were deposited in GenBank under accession nos. KM192207–09 and KP878308–10 (L segment) and KP878311–13 (S segment)  segment sequences from 2 patients . In the neighborhood of the residence of patient no. 59, mice were trapped, and hantaviral L and small (S) segment regions from 2 segment)  Table 2.A. ponticus–derived sequences 43/Ap, 79/Ap, 10636/Ap, 10645/Ap, and Sochi/Ap  and lymph node , whereas the least virus RNA (10–100 copies/ng RNA) was detected in lung, brain, and liver .–9). Moreover, severe disease developed in most affected male patients (66.7%) but in only 35.7% of affected female patients (p = 0.037). The fact that only 2 of 9 fatal cases occurred in female patients , which occurs naturally in the Transcaucasian region between the Black and Caspian Seas, including a part of southern European Russia. In anecdotal field studies in the coast region near Sochi, A. ponticus was the most abundant mouse species ; moreover, 14% of trapped A. ponticus mice were serologically proven to be DOBV infected , which has a CFR of up to 10%–12% (A. agrarius mice) is associated with a CFR of only 0.3%–0.9% (Among the related viruses of the DOBV species, Sochi virus seems to have the highest level of virulence, similar to Dobrava virus (carried by Details of serodiagnostic data of 62 Sochi virus–infected patients investigated, Russia; list of virus sequences and corresponding GenBank accession numbers used in the phylogenetic analyses; and classification criteria of clinical severity of hemorrhagic fever with renal syndrome.


We describe the kinetics of Zika virus (ZIKV) detection in serum and urine samples of 6 patients. Urine samples were positive for ZIKV >10 days after onset of disease, which was a notably longer period than for serum samples. This finding supports the conclusion that urine samples are useful for diagnosis of ZIKV infections. Flaviviridae, genus Flavivirus) that was isolated in 1947 from a rhesus monkey in the Zika forest in Uganda  is an emerging mosquito-borne pathogen ; arthralgia, notably of small joints of hands and feet; myalgia, headache; retroorbital pain; conjunctivitis; and cutaneous maculopapular rash. ZIKV infection is believed to be asymptomatic or mildly symptomatic in most cases . Although IgM against ZIKV can be detected by ELISA, few laboratories have this ability. Thus, in addition to the nonspecific clinical features of infection with ZIKV, laboratory diagnosis is challenging because of low viremia and cross-reactivity of ZIKV antibodies with other flaviviruses (including dengue), which require confirmation by neutralization assays , we used both sets of primers/probe specific for ZIKV . Urine s,Partial sequences of the gene for ZIKV nonstructural protein were obtained  after onset of symptoms, which was >7 days after it was not detected in serum samples.Other groups have reported that other flavivirus genomes, such as those of dengue virus . However, urine was preferred for virus detection. We observed a slight increase in ZIKV RNA from urine over the first few days after disease onset and rash . We therThis study investigated the diagnostic utility of urine as a source for detection of ZIKV RNA by real-time RT-PCR. Results suggest that urine might be useful for confirmation of ZIKV infection because virus was detected at higher titers and for a longer period in urine samples than in serum samples. Although these results need confirmation in larger cohorts, they strongly suggest the suitability of urine as a specimen for ZIKV detection and screening in large-scale investigations or other epidemiologic contexts .Ae. albopictus mosquitoes are a competent vector for ZIKV (In industrialized regions, where local transmission of arboviruses, such as dengue virus or chikungunya virus has been reported, physicians should test patients who return from tropical regions for ZIKV when a case of dengue-like infection is suspected. Travelers might be a source of local transmission because


Objective: To determine the relative bioavailability of ethinyl estradiol (EE) and gestodene (GSD) after application of a novel transdermal contraceptive patch vs. a standard combined oral contraceptive (COC) pill (study 1), and to evaluate the pharmacokinetics (PK) of EE after application of the EE/GSD patch compared with an EE/norelgestromin (NGMN) patch (study 2). Materials: Participants were healthy, non-obese women aged 18 – 45 years (study 1) or 18 – 35 years (study 2). Compositions of study treatments were as follows: 0.55 mg EE/2.1 mg GSD (EE/GSD patch); 0.02 mg EE/0.075 mg GSD (standard COC); 0.6 mg EE/6 mg NGMN (EE/NGMN patch). Methods: In study 1, which consisted of 3 treatment periods (each followed by 7 patch- or pill-free days), treatments were administered in one of two randomized orders: either P–M–E (EE/GSD patch (P) every 7 days for 28 days → COC (M) once-daily for 21 days → two 7-day patch-wearing periods followed by one 10-day patch-wearing phase (E)), or the same treatments administered in sequence M–P–E. For study 2, participants received either the EE/GSD patch or EE/NGMN patch for seven treatment cycles . Results: In study 1, average daily exposure to EE was similar for treatments P and M; the mean daily area under the concentration-time curve (AUC) ratio of treatment P vs. treatment M for EE was 1.06 : 0.964 – 1.16), indicating average daily delivery similar to oral administration of 0.019 – 0.023 mg EE. For unbound GSD, average daily exposure was lower for treatment P vs. treatment M. The mean AUC ratio of treatment P vs. treatment M for unbound GSD was 0.820 (90% CI: 0.760 – 0.885), indicating average daily delivery from the patch of 0.057 – 0.066 mg GSD. Prolonged patch wearing did not result in a distinct decline in GSD and EE serum concentrations. In study 2, AUC at steady state , average steady-state serum concentration, and maximum steady-state serum concentration for EE was 2.0 – 2.7-fold higher for the EE/NGMN patch vs. the EE/GSD patch. The EE/GSD patch was well tolerated in both studies. Conclusions: Based on the 90% CI of the AUC ratio of oral treatment vs. patch application for unbound GSD and EE, the daily doses of GSD and EE released from the EE/GSD patch over the 7-day application period provided the same systemic exposure as those recorded after daily oral administration of a COC containing 0.02 mg EE and 0.06 mg GSD. The EE/GSD patch showed reduced EE exposure compared with the EE/NGMN patch. Together with its good tolerability, these properties support the EE/GSD patch as an effective and well-tolerated alternative to available transdermal and oral contraceptives.  Clinical trials registration no.: NCT00984789, EudraCT no.: 2009-016972-66, EUDRA CTno.: 2008-007308-27; NCT00984789 The systemic delivery of steroid hormones through the transdermal route is a well-established treatment for post-menopausal women, using patches that contain estrogen either alone or in combination with a progestin . Transde*In the US, a slightly different formulation was approved by the Food and Drug Administration in November 2001. Daily use of COCs represents the most common form of contraception used by women in the developed world , and daiIn recent years, a novel, once-a-week, transparent, transdermal contraceptive patch has been developed, which may have benefits over COCs . Transdermal contraceptives offer several advantages over COCs, including effective hormone absorption and the provision of relatively constant serum concentrations , 8, 9. WBoth EE and the progestin gestodene (GSD) are well absorbed through the skin and are, therefore, appropriate agents for inclusion in a transdermal contraceptive formulation , 8, 10. ®). The latter assessment was based on sparse blood sampling in a large Phase IIIa trial  and the application of a population PK approach to describe the PK characteristics of EE. Additionally, also the PK characteristics of GSD for the EE/GSD patch were studied. In this paper, we report the results of two studies that examined the PK profile of a novel transdermal contraceptive patch containing 0.55 mg EE and 2.1 mg GSD. The first study aimed to determine the relative bioavailability of these hormones after application of the EE/GSD patch in comparison with a standard COC containing 0.02 mg EE and 0.075 mg GSD. In the second study, the PK of EE following application of the EE/GSD patch was compared with that of a transdermal contraceptive patch containing 0.6 mg EE and 6 mg NGMN  and to estimate the dose delivery of EE and GSD from the EE/GSD patch. A secondary objective was to assess serum concentrations of EE and GSD when the patch was worn for an additional 3 days beyond the designated week. The study comprised 6 periods: screening, pre-dose, treatment period 1, treatment period 2, treatment period 3, and follow-up. Each woman received both contraceptive formulations in one of two orders: either treatment P , followed by treatment M , followed by treatment E , referred to here as sequence P–M–E; or the same treatments administered in sequence M–P–E. The duration of treatment P was 28 days, rather than the 21 days envisioned in practice, to ensure steady-state serum concentrations of EE, GSD, and sex hormone-binding globulin (SHBG) at the time of PK analysis. Patches were applied to the lower abdomen. Study 1 was an open-label, randomized, intra-individual, crossover phase I study in healthy women. The primary objective was to compare the relative bioavailability of EE and GSD from the EE/GSD patch with that of a standard oral COC containing 0.02 mg EE and 0.075 mg GSD . Treatment consisted of 7 consecutive treatment cycles . The primary objective was to investigate the bleeding pattern and cycle control parameters of the EE/GSD patch compared with a transdermal patch containing 0.6 mg EE and 6 mg NGMN; these data will be reported elsewhere. Secondary objectives, reported here, included a comparison of the PK of EE following application of either the EE/GSD patch or EE/NGMN patch, a description of the PK of EE, SHBG, and GSD following application of the EE/GSD patch for up to 7 treatment cycles using a population PK approach, a covariate analysis of the influence of demographic and physiologic covariates  on the PK of EE and GSD, and an examination of compliance and adverse events (AEs). Written informed consent was obtained from all participants in both studies. The studies were conducted in accordance with the ethical principles set out in the Declaration of Helsinki and the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use Guideline E6: Good Clinical Practice. Both studies were approved by the individual Ethics Committees of the participating sites. ®, Perkin Elmer Life and Analytical Sciences, Turku, Finland) with a lower limit of quantification of < 10 nmol/L. The accuracy and precision of the analyses of EE, GSD and SHBG were monitored using appropriate quality control samples. These analyses were carried out under the supervision of the sponsor’s Drug Metabolism and Pharmacokinetics department. The calibration range of the procedure for determination of EE was 2.49 – 124.30 ng/L upper limit of quantification (ULOQ); lower limit of quantification (LLOQ) was set to 2.50 ng/L. Mean inter-assay accuracy of back-calculated concentrations in calibrators was 99.36 – 101.00%, and precision was ≤ 4.23%. Quality control samples in the concentration range 7.46 – 93.23 ng/L were determined with an accuracy of 98.69 – 101.00% and a precision of 2.62 – 4.08%. The calibration range of the procedure for determination of GSD was 0.04958 – 19.832 µg/L (ULOQ) and the LLOQ was set to 0.050 µg/L. Mean inter-assay accuracy of back-calculated concentrations in calibrators was 98.13 – 102.57%, and precision was ≤ 5.88%. Quality control samples in the concentration range 0.14874 – 14.874 µg/L were determined with an accuracy of 99.43 – 102.17% and a precision of 2.40 – 5.05%. The unbound concentration of GSD was calculated based on the total concentration of GSD and the respective concentration of SHBG and albumin. The primary PK variable was the average daily area under the concentration–time curve (AUC) for EE and unbound GSD – treatment P: AUC from 0 to 168 hours after application of the fourth patch (AUC0–168) divided by 7; treatment M: AUC from the last pill intake until 24 hours thereafter (AUC0–24). Secondary PK variables included AUC from 0 to 240 hours after application of the third patch (AUC0–240) for treatment E, and the maximum and average serum concentrations  during week 4 for treatment P, day 21 for treatment M, or week 3 for treatment E. AEs were also monitored. In study 1, blood samples for treatment P were taken pre-treatment and before patch application on days 8, 15, and 22. Additional samples were taken on days 22 – 29 at various points after application of the fourth patch, then on days 29 – 34 after removal of the last patch. For treatment M, blood samples for PK analysis were taken pre-treatment and before pill administration on days 8, 15, 18, and 21. Additional samples were taken between days 21 and 26 after last intake. For treatment E, blood samples were taken pre-treatment and before patch application on days 8 and 15. Additional samples were taken between days 15 and 25 after application of the third patch, and between days 25 and 30 after removal of the third patch. The concentrations of EE and GSD in serum were determined by a validated liquid chromatography/tandem mass spectrometry method, with lower limits of quantification of 2.5 and 50 ng/L, respectively. The concentration of SHBG was determined by a commercially available validated immunoassay method , preferably in treatment cycles 3 and 7. Two further samples were taken during the patch-free interval of these treatment cycles, preferably within 12 hours after patch removal (day 22) for treatment cycles 3 and 7 at 1-hour intervals. Concentrations of SHBG, EE, and GSD in serum were assessed using the methods described for study 1. The accuracy and precision of the analyses of SHBG, EE, and GSD were monitored using appropriate quality control samples. 0–168 at steady state (ss), average drug concentration , and maximum drug concentration , were estimated. In addition to the PK analyses, AEs were monitored throughout the study. A visual predictive check was performed in order to assess whether the final population PK model adequately described the observations to fit the purpose of predicting the exposure. For each subject, the serum concentration–time profiles were simulated 500 times by means of Monte Carlo simulation. Briefly, in a Monte Carlo simulation, random values are drawn from the distributions of the identified random effects. The 5th, 50th, and 95th percentiles of the predictions were then calculated and compared with the observations. The population PK analysis focused on characterization of the PK of EE and GSD, including the interaction with SHBG, for women in the EE/GSD patch group, and characterization of the PK of EE for women in the EE/NGMN patch group. PK data were analyzed using non-linear mixed-effects models . These m0–168 divided by 7 for treatment P and AUC0–24 for treatment M, assuming log-normally distributed data. Logarithms of these characteristics were studied by analysis of variance (ANOVA), including sequence, subject (sequence), period, and treatment effects. Based on these analyses, point estimates (least square (LS) means) and exploratory 90% confidence intervals (CIs) for the patch : pill ratio of PK parameters was calculated by re-transformation of the logarithmic results given by the ANOVA using the intra-individual standard deviation (SD). The 90% CIs were used to estimate the doses of EE and GSD delivered by the patch, by multiplying the interval limits by a factor of 20 for EE and a factor of 75 for GSD, respectively (based on the COC formulation 0.02 mg EE and 0.075 mg GSD). Study 1 was an exploratory study, thus, no formal determination of sample size was carried out. Summary statistics for PK parameters included the geometric mean, coefficient of variation (CV), range, and number of measurements. The relative bioavailability of the EE/GSD patch was assessed vs. a standard COC by analyzing EE, unbound GSD and total GSD in terms of AUCIn study 2, all variables were analyzed using descriptive statistical methods. As the analyses were purely descriptive, no formal sample size calculation was undertaken. Statistical evaluations were performed by using the software package SAS release 9.1 or higher . In study 1, treatments P and E both used the EE/GSD patch containing 0.55 mg EE and 2.1 mg GSD. Patches were applied and removed under supervision of a member of the investigator’s team between 6:00 AM and 10:00 AM. Treatment M was a COC pill containing 0.02 mg EE and 0.075 mg GSD, to be taken daily between 6:00 AM and 10:00 AM. All women kept a diary detailing intake of pills and patch application. In study 2, participants were randomized 1 : 1 to use one of two transdermal contraceptive patches: either a patch containing 0.6 mg EE/6 mg NGMN , or the EE/GSD patch containing 0.55 mg EE/2.1 mg GSD. Patches were applied to the upper arm, abdomen, or buttocks. Treatment compliance and patch adhesion were evaluated based on participant diaries, with women asked to record application deviations (defined as patch loss for ≥ 24 hours or patch replacement delayed for ≥ 48 hours), the reason for patch removal, and the dates they did not wear a patch. 2) women, aged 18 – 45 years (study 1) and 18 – 35 years (study 2). In the latter study, women were not using any other methods of contraception. Exclusion criteria for both studies included: a positive pregnancy test; any disease, condition, or drug use that could affect the PK of the study agents or be worsened under hormonal treatment; misuse of alcohol/drugs/medicines; or delivery, abortion, or lactation in the previous 3 months; undiagnosed abnormal genital bleeding; or hypersensitivity to any ingredient of the study drug or significant skin reaction to transdermal preparations. Additional exclusion criteria for study 1 included: contraindications for use of hormonal contraceptives ; clinically relevant findings in the physical or gynecologic examination or standard laboratory tests or vital sign measurements; smoking for women aged 31 – 45 years (or smoking > 10 cigarettes/day for women aged 18 – 30 years). Participants in study 1 were also required to accept a synchronizing cycle for the purpose of study logistics, during which they were required to use a hormonal contraceptive for 15 – 42 days and then cease use 7 days before the first treatment. Participants were healthy, non-obese  having no major protocol deviations. Baseline characteristics are shown in Over the treatment period, mean EE concentrations reached steady state at day 7 during all three treatments. As expected, peak serum EE concentrations were identical for treatments P and E. Maximum EE concentration was higher for treatment M (COC) compared with treatment P (EE/GSD patch) and treatment E (EE/GSD patch with extended application phase). Average serum EE concentrations were similar during each treatment, with a trend towards slightly lower average concentrations for treatment E. Unbound GSD trough concentrations also reached steady state at day 7 during all three treatments. Peak serum concentrations of unbound GSD were again identical for treatment P and treatment E, and ~ 1.7-fold higher for treatment M. Average serum concentrations were slightly lower for treatment E compared with treatment P. Average concentrations after treatment M (COC) were slightly higher compared with treatment P . Mean total GSD trough concentrations during treatment P reached steady state after 2 – 3 weeks. During treatment M and treatment E, total GSD trough concentrations reached a plateau at day 14. Maximal mean serum concentrations of total GSD were similar during treatment P  and treatment E , and were lower than during treatment M . Average serum total GSD concentrations were relatively similar across the treatments . With treatment P, mean serum SHBG concentrations increased from pre-dose to day 14, increasing only slightly thereafter to reach steady state on day 28. Following pill intake (treatment M), SHBG concentrations reached a steady state after 2 weeks of intake. The increase in mean SHBG serum concentrations during treatment E showed a similar time course to treatment P, reaching a plateau between days 14 and 21. 0–168/7) was in the same range as that after administration of a COC containing 0.02 mg EE (AUC0–24), resulting in an AUC ratio for treatment P vs. treatment M of 1.06 , which clearly fulfills bioequivalence criteria  than for treatment M. Based on these data, the mean daily dose of EE and GSD delivered from the EE/GSD patch was calculated to result in the same systemic exposure as the daily oral administration of 0.0212 mg EE (90% CI: 0.0193 – 0.0233 mg) and 0.0615 mg GSD . In the population PK models EE and EE/SHBG/GSD, EE serum concentrations were described with a three-compartment model where the volume of the second peripheral compartment was set to that of the central compartment. A two-compartment model was considered not to be sufficient for a good description of the data, but the data did not allow the estimation of the volume of the second peripheral compartment. A time-dependent release rate from the patch compartment to the skin compartment and a first-order absorption rate from the skin compartment to the central compartment were used to describe EE release and absorption. Inter-individual variability (IIV) was identified for the clearance of EE. GSD serum concentrations were described with a two-compartment model with binding to albumin (assumed to be constant) and SHBG in the central compartment, from which only unbound GSD could be eliminated or transported to the peripheral compartment. For EE, only total concentrations were considered since total EE concentrations are not dependent on SHBG concentrations , and the fraction unbound does not change in relation to the total EE concentration. Additionally, elimination of GSD from the central compartment was described by non-linear Michaelis-Menten elimination. Release and absorption of transdermally administered GSD was described as for EE. IIV was identified for a parameter in the non-linear Michaelis-Menten elimination. A correlation was identified between this parameter and the clearance of EE. Serum concentrations of endogenous SHBG were described using a one-compartment model. SHBG concentrations were indirectly up-regulated by EE and down-regulated by GSD. The effect of EE and GSD on SHBG concentrations was delayed using a single-delay compartment. IIV was identified for the SHBG baseline parameter. In all cases, a log-normal distribution for the IIV and a proportional residual error model were assumed. 0–168,ss, Cav,ss, and Cmax,ss suggested a similar application-site effect for the EE/NGMN patch. This is probably due to the high variability of EE after EE/NGMN patch application compared with that for the EE/GSD patch (th (49 kg) and 95th (79.9 kg) percentiles of the body weight distribution, EE clearance values were 91% and 113% of the typical clearance (at median weight of 60 kg), respectively. In the population PK model EE, an application-site effect was found for the EE/GSD patch: a lower release parameter was estimated for abdomen compared with arm or buttocks. For the EE/NGMN patch, only one release parameter for all application sites was kept since a strong overlap in CIs of the release parameter estimates per application site could be observed, even though comparison of the geometric means of AUCSD patch . In the rd week of treatment cycles 1, 3, and 7, and showed that AUC0–168,ss, Cav,ss, and Cmax,ss values for EE were greater with the EE/NGMN patch than with the EE/GSD patch for all three application sites ; in 21 women these were drug related. Three women prematurely discontinued study participation due to a TEAE . The most frequent TEAEs were headache, application-site reaction, and nasopharyngitis. All AEs were mild to moderate in severity, and all were resolved. There were no serious AEs or deaths. In study 2, 95 women (47.5%) using the EE/GSD patch and 79 (39.9%) using the EE/NGMN patch reported at least one TEAE during the study. Drug-related AEs were reported by 24.0% of women using the EE/GSD patch and 20.7% using the EE/NGMN patch. With the EE/GSD patch, the most common treatment-related AEs were application-site reaction (7.5%), application-site pruritus (5.5%), cervical dysplasia (5.5%), and metrorrhagia (4.0%). In the EE/NGMN group, the most common events were headache (4.0%), application-site reaction (4.0%), metrorrhagia (4.0%), and breast pain (4.0%). Serious TEAEs were reported by 6 women; however, none was considered related to study medication. No deaths were reported. The pharmacokinetic parameters and statistical evaluation of study 1 show that, during steady state, the average daily AUC for EE after oral intake of a COC containing 0.02 mg EE and 0.075 mg GSD (treatment M) is the same as the average daily AUC during the 7-day dermal application of EE/GSD patch (treatment P). The mean AUC ratio of treatment P vs. treatment M for EE after patch application  was cleaBased on the average daily AUC of unbound GSD, the amount of GSD released by the EE/GSD patch during the 7-day wearing period resulted in a systemic exposure similar to that after daily oral administration of a GSD dose of ~ 0.06 mg (mean AUC ratio patch vs. 0.075 mg GSD COC was 82%). As GSD is almost completely bioavailable after oral administration , 17, theAlthough the EE/GSD patch and the EE/NGMN patch assessed in study 2 contain similar amounts of EE , the findings of this study show that systemic exposure to EE with the EE/GSD patch is approximately half (or less) than that seen with the EE/NGMN patch. This applies to all three application sites. Other studies with the EE/NGMN patch reported lower EE exposure than reported in the current study for the direct comparison between the EE/GSD patch and the EE/NGMN patch , 19. TheExposure of EE with the EE/GSD patch when applied for 7 days was also lower than that reported in a recent study describing the PK parameters of a patch containing EE and levonorgestrel . This coOur results also show that EE exposure with the EE/GSD patch was bioequivalent to that which was seen with a currently available reference COC containing a combination of 0.02 mg EE and 0.075 mg GSD, while unbound GSD exposure was slightly lower. In addition, there was negligible influence from demographic covariates, and although body weight had a small but borderline significant effect on EE clearance, this was considered unlikely to be clinically meaningful. The EE/GSD patch resulted in a reduced exposure to EE compared with the EE/NGMN transdermal contraceptive patch, and in the same systemic exposure that would be achieved by the oral administration of a COC containing 0.02 mg EE and 0.06 mg GSD. Prolonged wearing of the patch for up to 10 days did not result in a distinct decline in EE or GSD serum concentrations. In addition, the EE/GSD patch also had a good safety profile. These findings, set alongside those of previous studies, provide further support for the EE/GSD patch as an effective and well-tolerated alternative to both currently available transdermal contraceptives and COCs containing EE and GSD. The population PK analysis was performed under supervision of Bayer Pharma AG  by Maurice Ahsman and Jean Smeets, Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands. Statistical analyses were performed by Matthias Ludwig, Bayer Pharma AG. Editorial assistance was provided by Ogilvy 4D and was funded by Bayer Pharma AG. All authors are employees of Bayer Pharma AG.


In 2010, a vaccinia virus isolate caused an atypically severe outbreak that affected humans and cattle in Brazil. Of 26 rural workers affected, 12 were hospitalized. Our data raise questions about the risk factors related to the increasing number and severity of vaccinia virus infections. Orthopoxvirus, which includes cowpox virus in Europe; monkeypox virus, which occurs naturally in Africa and of which 1 introduction was event reported in the United States; and vaccinia virus (VACV) in Asia and South America  infections have increased worldwide, and the immunologic status of the population against OPV is a major risk factor for its reemergence , Minas Gerais State, Brazil. This region is characterized by small rural properties, where cattle are kept for milk production. Outbreaks of VACV infection had been reported in the neighboring counties in previous dry seasons. In dairy cattle, typical lesions had developed on teats and udders that caused a decrease in milk production; however, the source (index case) was not identified. The reported virulence of the disease in cattle was not atypical and was similar to previously described cases . Patients reported that in case of multiple lesions, autoinoculation probably occurred from lesions on hands, the first site of infection; therefore, we have no clinical evidence of “generalized vaccinia.” Three patients also had convulsion, vomiting, diarrhea, and mental confusion.The patients received clinical support and remained hospitalized for 3–18 days. They had no history of immunologic disorders and took no medications that could cause this severe clinical condition. The patients were 15–26 years of age, and none had a history of smallpox vaccination; 1 patient reported having similar clinical illness in 2009. Our investigations also identified 14 additional rural workers who were occupationally infected but not hospitalized; 7 were >40 years old and probably vaccinated against smallpox.To characterize the etiologic agent of this outbreak, we collected serum from 4 infected cows, scabs from 3 cows, and swab samples from the lesions of 4 hospitalized patients and 1 nonhospitalized patient. The serum samples were submitted to plaque reduction–neutralizing tests as previously described (http://www.megasoftware.net). We named these isolates VACV DOR2010 .The viral DNA from the A56R (hemagglutinin) gene was amplified and sequenced from all isolated viruses  . DOR20106 plaque-forming units, as described previously . Sixteen BALB/c mice were divided into 4 groups of 4 mice each. We intranasally inoculated 4 mice with 10-μL doses of viral suspensions containing 10Our biologic, epidemiologic, and molecular data indicate that the VACV isolate DOR2010 was associated with an outbreak of severe, exanthematous vaccinia virus infection that resulted in the hospitalization of 12 workers in a rural area in Brazil. During the past decade, VACV has spread to all regions of Brazil, and no specific official national programs are in place to prevent the disease , vaccina


Many studies have estimated national chronic disease costs, but state-level estimates are limited. The Centers for Disease Control and Prevention developed the Chronic Disease Cost Calculator (CDCC), which estimates state-level costs for arthritis, asthma, cancer, congestive heart failure, coronary heart disease, hypertension, stroke, other heart diseases, depression, and diabetes.Using publicly available and restricted secondary data from multiple national data sets from 2004 through 2008, disease-attributable annual per-person medical and absenteeism costs were estimated. Total state medical and absenteeism costs were derived by multiplying per person costs from regressions by the number of people in the state treated for each disease. Medical costs were estimated for all payers and separately for Medicaid, Medicare, and private insurers. Projected medical costs for all payers (2010 through 2020) were calculated using medical costs and projected state population counts.Median state-specific medical costs ranged from $410 million (asthma) to $1.8 billion (diabetes); median absenteeism costs ranged from $5 million (congestive heart failure) to $217 million (arthritis).CDCC provides methodologically rigorous chronic disease cost estimates. These estimates highlight possible areas of cost savings achievable through targeted prevention efforts or research into new interventions and treatments. Costsculator/ .italics) state-level medical costs, absenteeism costs, and 10-year medical cost projections for arthritis, asthma, cancer, congestive heart failure (CHF), coronary heart disease (CHD), hypertension, stroke, other heart diseases, depression, and diabetes  and separately for those covered by specific payers . For each state, payer, chronic disease, and subpopulation selected, the CDCC generates a standardized report presenting the number of people treated for the disease, annual payer and absenteeism costs attributable to the disease , and 10-year medical cost projections for all payers combined. These estimates supplement other disease burden measures of chronic disease impact  and may identify opportunities for targeting prevention programs to reduce risk factors and chronic diseases. CDCC users can also generate customized state-level cost estimates for selected chronic diseases by modifying key inputs. For example, the financial impact of prevention programs could be simulated by inputting treated population prevalence from other sources. The purpose of this article is to demonstrate the CDCC by providing an example across all states and all payers.The first version of the CDCC, released in 2009, estimated state-level Medicaid costs for 6 chronic diseases  state-specific payer populations, 2) treated population prevalence, 3) per-person medical and absenteeism costs, and 4) medical cost projections . The cosDefault state-specific payer populations  were estimated for all payers combined and by specific payer category. Payer populations were not mutually exclusive . Payer populations were calculated by using the following data: 2008 US Census Bureau for all payers combined, fiscal year 2008 Medicaid Statistical Information Statistics for Medicaid, Kaiser Family Foundation 2008 Medicare Health and Prescription Drug Plan Tracker for Medicare, and 2008 Current Population Survey (CPS) for the privately insured .For each payer group, we estimated the proportion in each of the 6 unique age  and sex groups. We included children  for asthma and depression only because these conditions are common among them. Age and sex distribution for all payers and private payers were calculated using 2008 US Census Bureau and 2008 CPS data, respectively. We estimated Medicaid and Medicare enrolment by age and sex using 2007 through 2009 CPS data. Unlike the CMS population counts, CPS represents the noninstitutionalized population only. We derived each age- and sex-specific state-level payer population by multiplying state payer populations by the age- and sex-specific proportions.International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). We defined each disease using codes from the AHRQ Clinical Classification System or CDC definitions (both based on ICD-9-CM codes)  was estimated in 2 steps. In step 2A, we estimated treated prevalence for the noninstitutionalized population using data from the 2004–2008 Medical Expenditure Panel Survey (MEPS) . MEPS isM codes)  14). Tr. TrInteri in payer population p, we estimated the probability of disease d:For each payer population in the noninstitutionalized population, we used separate logistic regressions to predict the probability of having each disease (4 payers × 10 chronic conditions = 40 models). For each individual Because MEPS does not survey the noninstitutionalized population, we adjusted estimates by the most recent (2004) National Nursing Home Survey (NNHS)  so that costs include treatment received by nursing home residents (step 2B) . CDCC doWe calculated the number who were treated for each disease in 2008 by multiplying the estimated treatment population prevalence for each payer/age/sex/region group by the estimated number in the corresponding payer/age/sex/state specific category from Component 1 .“All payers” analysis contained all medical costs in the 2004–2008 MEPS . Each payer’s analysis used only costs paid by that payer.For each payer, we estimated default chronic disease medical costs using a 5-part process. In step 3A(i), we estimated per-person medical costs using a logistic regression model that predicted the probability that a person incurred any medical costs and a generalized linear model with a gamma distribution and log link that estimated annual medical costs for those incurring such costs.Hypertension and diabetes are risk factors for other chronic conditions, and costs for these conditions were estimated in separate models that did not control for these sequelae conditions; that is, the hypertension and diabetes models did not control for CHF, CHD, stroke, other heart diseases, and renal failure. Additionally, the diabetes model did not adjust for hypertension and dyslipidemia. Therefore, the CDCC’s default estimates for hypertension and diabetes include the costs of sequelae. We caution users that summing costs across selected diseases that include hypertension and diabetes will overestimate costs for all the selected diseases. The CDCC provides estimates for diseases of the heart and total cardiovascular disease but avoids double-counting of costs across diseases using methods described below (step 3A[ii]).In step 3A(ii), we calculated each disease’s attributable costs by comparing predicted costs for people with each unique combination of diseases with predicted costs for people without that combination of diseases, while holding all other characteristics constant. For example, we calculated costs of treated cancer alone and cancer with hypertension as 2 different disease combinations. Then we divided the total costs attributable to the disease combinations back to the constituent diseases . In this combination of diseases, the greater share of the costs is apportioned to the disease with the larger regression coefficient . We estiIn step 3A(iii), we used 2004 NNHS  and NatiIn step 3A(iv), we used restricted-access MEPS data to generate state-specific cost adjustments for the 30 largest states and 9 Census divisions for the smallest 20 states and the District of Columbia. Among respondents with any medical costs, we regressed medical costs  on state/census division and adjustment variables  using a 1-part model. We estimated state-specific estimates, we multiplied the per-person costs from step 3A(iii) by the coefficients from step 3A(iv). For example, if per-person costs from the national model 3A(iii) were $1,000 and the state coefficient from 3A(iv) was 1.10, state-specific per-person costs were $1,100 .In step 3A(v), we adjusted MEPS cost data to 2010 dollars using the gross domestic product price deflator  and multWe separately estimated workdays lost for working adults and implied lost workdays for parents of children who lost schooldays. We predicted annual workdays missed by working adults using a negative binomial model. The dependent variable was annual workdays missed due to illness or injury. Independent variables were identical to medical cost regressions, including use of alternate models for hypertension and diabetes. Among children, we estimated annual number of school days missed due to illness or injury using negative binomial models. We assumed that one working parent also misses work on the days that his or her child is absent from school. We calculated workdays missed per person attributable to each disease using the same method as that used for medical costs . We estiWe calculated projected chronic disease medical costs for all payers combined through 2020 using default cost estimates and projected 2010 through 2020 US Census Bureau state population counts . For eacAmong the 10 specific conditions reported in the CDCC, median state-specific medical costs ranged from $410 million for asthma to $1.8 billion for diabetes; median absenteeism costs ranged from $5 million for CHF to $217 million for arthritis . Across Public health officials often lack the resources to estimate the economic burden of chronic diseases. The CDCC estimates medical and absenteeism costs of chronic diseases without additional data requirements. Decision makers can generate customized estimates for local populations of interest using accurate local data for these populations.The CDCC’s default estimates may differ from other cost estimates because of variation in data sources, settings, populations studied, disease prevalence definitions, cost measurement, and study methods. For example, the Behavioral Risk Factor Surveillance System (BRFSS) prevalence definition is based on a lifetime disease history  whereas CDCC estimates may differ from other chronic disease cost estimates reported elsewhere. There is substantial heterogeneity in methods used to derive medical cost estimates, and the most appropriate approach to estimating disease costs depends on the users’ needs, available data sources, and cost definitions. Some costs are based on summing of payments only for claims with diagnosis codes for the disease of interest ,3 or sumCDCC results are not comparable across states for at least 2 reasons. First, they were derived from data from different levels of geographic detail , and second, MEPS survey weights are not designed to be representative of state populations. CDCC does not estimate productivity losses through caregiving to adults with chronic disease, impaired productivity while at work, premature mortality, and reductions in the quality of life.CDCC’s medical cost projections reflect historical medical cost growth and demographic changes but do not account for changes from Patient Protection and Affordable Care Act implementation. However, projections provide a useful baseline for gauging impact of current and future policies and may inform budgetary allocations for chronic disease prevention and early detection investments.The CDCC provides state-level estimates of the economic burden of selected chronic diseases, because many policy decisions are made at the state level. Estimates highlight possible areas of cost savings achievable through targeted prevention efforts or research into new interventions and treatments. This information is vital for program decisions and resource allocations for chronic disease prevention and disease management programs.


We also detected SOX9 and S100P expression in a tissue microarray with 90 colon cancer cases to provide their clinical relevance. There was a strong correlation between SOX9 and S100P expression in colon carcinomas. In conclusion, our results suggest that SOX9 promotes tumor metastasis and invasion through regulation of S100P expression.Elevated expression of S100P has been detected in several tumor types and suggested to be responsible for tumor metastasis and invasion, but the upstream regulatory mechanisms promoting S100P overexpression are largely unknown. Here, we report that SOX9 was predicted and verified as a transcription factor of S100P. SOX9 and S100P were both overexpressed in colon cancer. SOX9 bound to and activated the S100P promoter. Knockdown of SOX9 expression down-regulated S100P expression, resulting in reduced invasiveness and metastasis of colon cancer cells by inhibiting the activation of receptor for advanced glycation end products (RAGE)/ERK signaling and epithelial-mesenchymal transition (EMT). Further, decreased expression of SOX9 dramatically inhibited the tumor growth and peritoneal metastasis in nude mice. More importantly, S100P was found to be critical for SOX9-mediated metastasis and invasion in colon cancer. Knockdown of S100P in SOX9-overexpressing colon cancer cells dramatically suppressed metastasis and invasion both  Bioinformatic analysis and literature searches identified SOX9 as a potential transcription factor. SOX9, a high mobility group (HMG) box transcription factor, is highly up-regulated in many premalignant lesions and in tumor tissues and has been proposed to play an oncogenic role in tumor development , 3. The In line with this hypothesis, we demonstrated that overexpression of SOX9 induces the expression of S100P in both a colon cancer cell line and human samples, leading to increased cell invasiveness and metastasis as well as activation of epithelial-mesenchymal transition (EMT). Expression of SOX9 and S100P is strongly correlated in primary colon cancer tissues and is associated with adverse prognosis.P = 0.038) and S100P (P < 0.001) mRNA was observed in colon cancer. Western blot analysis showed that SOX9 and S100P proteins were both up-regulated in colon cancer  by designing biotin-labeled, unlabeled, and mutant probes based on the predicted transcription factor-binding site (TFBS) of SOX9 for the S100P promoter . To identify if SOX9 binds to the S100P promoter r Figure , upper. r Figure , lane 2,r Figure , lane 3.r Figure , lane 4.r Figure , lane 5.S100P promoter. For these studies, reporter genes containing wild type (S100P-promoter-WT) and mutant (S100P-promoter-MU) sequences of the S100P promoter were generated  and HCT116-iNC cells were implanted subcutaneously into nude mice, and the growth of the resultant primary tumors was monitored. Mice injected with SOX9 knockdown cells developed smaller tumors than those injected with HCT116-iNC cells  and HCT116-iNC cells were injected into the abdominal cavities of mice, and the mice were subsequently scanned for metastasis using a whole-body visualization system. Fewer metastatic foci were observed in the abdominal cavities of mice injected with HCT116-SOX9(−) cells compared with those injected with HCT116-iNC cells  and invasion (P < 0.001) of HCT116-SOX9(+) cells were significantly greater than that of HCT116-oNC cells. However, the migration (P = 0.003) and invasive (P = 0.001) responses were decreased significantly in SOX9-overexpressing cells after S100P expression was suppressed ) and its negative control (HCT116-oNC). We then utilized the lentivirus expressing S100P-specific siRNA to knockdown S100P expression in HCT116-SOX9(+) cells, thus establishing a SOX9-overexpressing and S100P knockdown colon cancer cell line (HCT116-SOX9(+)/S100P(−)). Using these stable cell lines, we performed cell migration and invasion assays, and we found that cell migration , HCT116-SOX9(+)/S100P(−), and HCT116-oNC cells were injected subcutaneously into nude mice. As showed in Figure P = 0.012). However, xenografted tumor growth was suppressed in mice injected with HCT116-SOX9(+)/S100P(−) cells compared to HCT116-SOX9(+) cells . More importantly, knockdown of S100P dramatically reduced the number of metastatic foci in the abdominal cavity of mice (P = 0.003)  Figure . Therefo2 = 0.782, P < 0.001) in primary colon cancer  or S100P (P = 0.020) was correlated with poor survival , differentiation , colonic wall invasion , nodal metastasis , AJCC (American Joint Commitee on Cancer) stage , S100P expression , and SOX9 expression . Based on the results of the univariate survival analysis, multivariate survival analysis was performed. After adjustment, tumor size, tumor differentiation, AJCC stage, SOX9 expression, and S100P expression were identified as covariates. Colonic wall invasion and nodal metastasis were excluded from the multivariate survival analysis due to their interactions with AJCC stage. AJCC stage was considered to be an independent risk predictor  for poor overall survival but not for SOX9 or S100P expression  containing 90 pathologically annotated cases of colon cancer. The results showed that the expression of SOX9 and S100P in colon cancer had a high concordance Figure . After cr Figure . Based ol Figure . UnivariS100P promoter (AAACAAAAG). The direct regulation of S100P transcription by SOX9 was corroborated by a dual-luciferase reporter assay. Mutation analysis of the promoter-binding site indicated that overexpression of SOX9 activated S100P promoter activity. However, it remains unknown whether the binding specificity is determined by the adjacent sequences or other unknown cofactors.This study contributes to our understanding of the molecular mechanism by which S100P overexpression in colon cancer promotes tumor progression. Overexpression of S100P has been detected in several types of cancers –17. To uTo explore the biological function of SOX9 in colon cancer, we knocked down SOX9 expression in HCT116 cells via lentiviral-mediated transfection. The invasion and migration capacities of HCT116 cells were significantly decreased as a result of the down-regulation of SOX9 expression. In addition, low SOX9 expression was associated with decreased tumor growth and metastasis in the animal model. Western blot analysis showed that SOX9 knockdown was accompanied by down-regulation of S100P, down-regulation of RAGE, decreased phosphorylation of ERK1/2, and inhibition of the EMT process. S100P promotion of cancer progression via its specific role in cell proliferation, survival, angiogenesis, and metastasis in human cancers has been reported –21. Signin vivo assay demonstrated that SOX9-promoted peritoneal metastasis was blocked by S100P knockdown, indicating that up-regulation of S100P expression at least partially accounts for the function of SOX9 in promotion of tumor progression in vivo. Moreover, the linear regression between SOX9 and S100P expression was pronounced in colon cancer TMA analysis. Further survival analysis indicated that overexpression of SOX9 or S100P predicted poor prognosis. Although overexpression of SOX9 or S100P could be a survival risk factor in the Cox univariate analysis, the prognosis predictive power of SOX9 or S100P expression could not exceed the classical AJCC stage in the Cox multivariate analysis. However, the clinical utility of SOX9 or S100P as a prognostic biomarker compared with conventional factors remains to be verified in prospective studies analyzing many more cases.Our study showed that SOX9 regulation of S100P promotes cancer cell migration and invasion as well as inducing EMT. However, it was unclear if S100P is necessary for the functions of SOX9 in cancer progression. By constructing a SOX9-overexpressing and S100P knockdown colon cancer cell line, we confirmed that knockdown of S100P expression can dramatically decrease SOX9-mediated migration and invasion abilities. An In conclusion, we demonstrated that SOX9 and S100P are both overexpressed in colon cancer. SOX9, as a transcription factor of S100P, up-regulates the transcription activity of S100P and promotes metastasis, invasion and poor survival of colon cancer by regulating S100P expression. More importantly, S100P plays a pivotal role in SOX9-induced metastasis and invasion of colon cancer.2 incubator.Human colon cancer tissue samples were obtained from the Department of General Surgery at Nanfang Hospital  with approval by the Institute Research Medical Ethics Committee of Nanfang Hospital. Tissue samples were collected from patients who were diagnosed with colon adenocarcinoma. Adjacent normal tissues that were at least 5 cm away from the tumor margins and confirmed to be free of tumor deposits were used as normal control in this study. HCT 116 cells were obtained from the Cell Bank of Type Culture Collection  and were cultured in DMEM (HyClone) supplemented with 10% fetal bovine serum , 100 U/mL penicillin, and 100 μg/mL streptomycin at 37°C in a humidified 5% COProteins were separated on SDS-PAGE gels followed by transfer to polyvinylidene fluoride membranes. The following antibodies were used for Western blotting: monoclonal rabbit S100P antibody and polyclonal rabbit anti-SOX9 obtained from Abcam; and RAGE, p-ERK1/2, ERK1/2, E-cadherin, N-cadherin, Vimentin, Snail, and GAPDH antibodies obtained from Cell Signaling Technology. Blots were incubated with the appropriate primary antibody in TBS-Tween 20 at 4°C overnight. Following incubation with an anti-mouse or anti-rabbit secondary antibody conjugated to horseradish peroxidase, the blots were visualized using the Luminata Chemiluminescent Detection Kit (Millipore). cDNA was synthesized from 1 μg of total RNA for reverse transcription using a PrimeScript RT Master Mix Perfect Real Time Kit (Takara). Oligo (dT) primers were synthesized by Takara . Q-PCR wImmunofluorescence, electrophoretic mobility shift assay (EMSA), chromatin-immunoprecipitation (ChIP) assay, and dual-luciferase reporter assay were performed to test if SOX9 can bind and transcriptionally activate the S100P gene promoter. Biotin-labeled, unlabeled, and mutant probes were designed for EMSA. A SOX9-overexpressing plasmid and luciferase reporter plasmids containing wild type (S100P-promoter-WT) or mutant (S100P-promoter-MU) S100P promoter sequences were constructed for the dual-luciferase reporter assay. All of the experiments followed standard protocols. See SOX9 and S100P genes.An optimized SOX9 knockdown lentivirus expressing SOX9-siR-2167  and the 5) were seeded into the cell culture inserts in serum-free medium, and the lower chamber was filled with medium containing 15% FBS. The cells were allowed to migrate for 48 h at 37°C. After removing the cells that remained in the top chamber, the top surface of each membrane was cleared of cells with a cotton swab. Cells that had penetrated to the bottom side of the membrane were then fixed in paraformaldehyde, stained with crystal violet, and counted in nine randomly selected microscope fields (×200) per well.For migration assays, cell culture inserts equipped with 8-μm membranes were used (BD Biosciences). For invasion assays, BioCoat Matrigel invasion chambers (BD Biosciences) were used according to the manufacturer's protocol. Cells . Tumors were measured with calipers from day 5 to day 31 after injection, and the tumor volumes were calculated according to the following formula: 0.5 × length × width2 [7 cells in 0.2 mL of serum-free DMEM (three mice in each group). One month later, the mice were sacrificed, and intraperitoneal tumor formation was assessed using the In-Vivo F Imaging System (Kodak). All animal experiments were undertaken in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals with the approval of the Laboratory Animal Centre of the Southern Medical University in Guangzhou, China. All animals were housed in a virus-free facility and maintained in a standard temperature- and light-controlled animal facility.To evaluate × width2 . For theThe TMA containing a total of 90 colon cancer patients, together with the data of pathological staging in accordance with TNM classification of the American Joint Committee on Cancer  and survival time after surgery for all cases , was obtThe standard two-step immunohistochemistry (IHC) technique was used in the present study. Sections of the TMA were subjected to IHC with anti-S100P (Abcam) and anti-SOX9 (Abcam) antibodies. SOX9 and S100P expression was analyzed independently by three observers  using the same light microscope (OLYMPUS BX51). Staining was evaluated according to the criteria described by previous studies –43. The Statistical analysis was performed using the SPSS statistical software package . T-tests were used to evaluate differences between established stable cell lines. Linear regression analysis was performed to assess the relationship between SOX9 and S100P in the TMA. Survival curves were obtained using the Kaplan–Meier method. Cox proportional hazards regression was performed to identify independent factors with a significant impact on patient survival. Probability values from the two-tailed test less than 0.05 were considered significant.


Within hosts, RNA viruses form populations that are genetically and phenotypically complex. Heterogeneity in RNA virus genomes arises due to error-prone replication and is reduced by stochastic and selective mechanisms that are incompletely understood. Defining how natural selection shapes RNA virus populations is critical because it can inform treatment paradigms and enhance control efforts. We allowed West Nile virus (WNV) to replicate in wild-caught American crows, house sparrows and American robins to assess how natural selection shapes RNA virus populations in ecologically relevant hosts that differ in susceptibility to virus-induced mortality. After five sequential passages in each bird species, we examined the phenotype and population diversity of WNV through fitness competition assays and next generation sequencing. We demonstrate that fitness gains occur in a species-specific manner, with the greatest replicative fitness gains in robin-passaged WNV and the least in WNV passaged in crows. Sequencing data revealed that intrahost WNV populations were strongly influenced by purifying selection and the overall complexity of the viral populations was similar among passaged hosts. However, the selective pressures that control WNV populations seem to be bird species-dependent. Specifically, crow-passaged WNV populations contained the most unique mutations  and defective genomes , but the lowest average mutation frequency . Therefore, our data suggest that WNV replication in the most disease-susceptible bird species is positively associated with virus mutational tolerance, likely via complementation, and negatively associated with the strength of selection. These differences in genetic composition most likely have distinct phenotypic consequences for the virus populations. Taken together, these results reveal important insights into how different hosts may contribute to the emergence of RNA viruses. Viruses are constantly emerging into new areas and pose significant challenges to public health. Chikungunya and West Nile viruses (WNV), both mosquito-borne RNA viruses, are quintessential examples of how increased globalization has facilitated the expansion of viruses into new territories. Rapid evolution of both of these agents has contributed to their rapid spread and health burden. Thus, characterizing how selection shapes zoonotic RNA viruses in their natural hosts is important to understand their emergence. As an ecological generalist able to infect hundreds of bird species, WNV is an excellent tool to study how different animal hosts can differentially drive virus evolution. We examined the genetic composition and fitness of WNV produced during replication in wild-caught American crows, house sparrows and American robins, species that range in mortality following WNV infection . We demonstrate host-dependent effects on WNV population structure and fitness. Our study provides insights on how different virus-animal interactions can influence the success of a virus in the next host and ultimately the success of virus emergence into new host systems. Orthomyxoviridae) in China /s2, using R (data did not fit a beta distribution). An iSNV frequency >0.02 was determined to be in the upper 5% of the gamma distribution and was used to define high frequency SNVs detected through WNV passage in birds . The sequencing reads from p0, p1 and p5 were aligned to the WNV genome using mpileup from the VarScan2 software package /ml of serum) and survival were compared by two-way ANOVAs with Tukey’s corrections for multiple comparisons. Left: *, crow-passage 5 replicate “b” (p5b) vs p0 at 2, 3 and 4 days post infection (dpi), P = 0.0206, 0.0382 and 0.0185, respectively. Right inset: *, p5b vs p0, P = 0.0309. However, this significance is likely due to lower than expected WNVic p0 virus titers (100× lower at 3 dpi than the first passage of WNVic shown in B) Wild-caught sparrows infected with sparrow-passaged WNV were assessed as described in (A) and led to no significant differences in viremia and survival compared to p0 viruses (n = 4 each). (C) WNV competitive fitness was measured by the change of mean proportion of the competitor (i.e. p5 WNV) from the inocula compared to after 3 days post infection (dpi) in crows and sparrows by unpaired t-tests . The biological replicates are shown as magenta squares (replicate ‘a’), yellow triangles (replicate ‘b’) and teal circles (replicate ‘c’). (D) Crow p5 WNV competitive fitness in Culex quinqefasciatus mosquitoes 14 dpi by oral inoculation was determined as described in (C) .((TIF)Click here for additional data file.S2 FigA) WNV sequencing coverage plotted by genome position for the input virus used to initiate passaging  and after five passages in wild-caught crows, sparrow and robin viruses . (B) Intrahost virus population sizes measured by genome equivalents (GE)/ml of bird serum after each sequential passage. (C) Correlations of sequencing coverage of the WNV genome to the intra-host virus population sizes from each bird species using individuals were made by the Pearson’s correlation coefficient .((TIF)Click here for additional data file.S3 FigCulex quinquefasciatus mosquitoes and are represented by the number of single nucleotide variants (SNVs) per haplotype (Hamming distance from the p0 haplotype).Haplotypes were reconstructed from the high frequency iSNVs (i.e. > 0.02) from input WNV and after one passage in young chickens and (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


PHS Guideline for Reducing Human Immunodeficiency Virus, Hepatitis B Virus, and Hepatitis C Virus Transmission Through Organ Transplantation  Guidelines for Preventing Transmission of Human Immunodeficiency Virus Through Transplantation of Human Tissue and Organs  recommendations that organ donors be screened for hepatitis B virus (HBV) and hepatitis C virus (HCV) in addition to human immunodeficiency virus (HIV); 2) recommendations for new, more sensitive laboratory testing; and 3) inclusion of a revised set of risk factors for HIV, HBV, and HCV infection. CDC’s http://www.cdc.gov/transplantsafety.In addition, CDC recently launched a website devoted to transplant safety, which features 1) the role of CDC and other agencies and organizations involved in organ and tissue safety; 2) donor screening and testing requirements; and 3) information for health-care providers. The website can be accessed at