Hox genes impart segment identity to body structures along the anterior-posterior axis and are crucial for the proper development of all organisms. Multiple regulatory elements, best defined in Drosophila melanogaster, ensure that Hox expression patterns follow the spatial and temporal colinearity reflected in their tight genomic organization. However, the precise mechanisms that regulate colinear patterns of Hox gene expression remain unclear, especially in higher vertebrates where it is not fully determined how the distinct activation domains of the tightly clustered Hox genes are defined independently of each other. Here, we report the identification of a large number of novel cis-elements at mammalian Hox clusters that can help in regulating their precise expression pattern.Hox clusters that show poor association with histone H3 in chromatin immunoprecipitation (ChIP)-chip tiling arrays. The majority of these elements lie in the intergenic regions segregating adjacent Hox genes; we demonstrate that they possess efficient enhancer-blocking activity in mammalian cells. Further, we find that these histone-free intergenic regions bear GA repeat motifs and associate with the vertebrate homolog of the GAGA binding boundary factor. This suggests that they can act as GAGA factor-dependent chromatin boundaries that create independent domains, insulating each Hox gene from the influence of neighboring regulatory elements.We have identified DNA elements at all four murine Hox clusters. We further demarcate the precise location of several novel cis-elements bearing chromatin boundary activity that appear to segregate successive Hox genes. This reflects a pattern reminiscent of the organization of homeotic genes in Drosophila, where such regulatory elements have been characterized. Our findings thus provide new insights into the regulatory processes and evolutionarily conserved epigenetic mechanisms that control homeotic gene expression.Our results reveal a large number of potential regulatory elements throughout the murine  Hox genes specify segment identity during development: the unique combination of their expression pattern provides correct positional identity along the anterior-posterior (AP) body axis. The distinct organization of the Hox genes was first defined in Drosophila melanogaster[Hox complexes, however, are distinct from those seen in invertebrates. Through genome duplication events, vertebrates have at least four Hox clusters that are more organized, with all the genes transcribed in the same orientation and arranged more compactly than those in lower organisms [Hox genes. Their temporal order of activation, however, shows a difference; in Drosophila, the expression pattern of all the Hox genes is set simultaneously, while mammals show temporal colinearity by expressing their 3′ Hox genes first and then the more 5′ Hox genes sequentially from anterior to posterior during development [Hox gene regulation is highly dependent on genomic organization. It remains unclear how the colinear pattern of Hox gene expression in mammals is governed to provide discrete segment identity during development. In particular, it is not fully determined how the distinct activation domains of successive Hox genes are defined to maintain this spatial and temporal colinearity of expression. Mouse transgenes bearing regulatory regions of Hox genes faithfully recapitulate their expression domains [Hox gene expression are probably located in close proximity to the genes themselves, interspersed between coding regions within the conserved clusters. This arrangement may also allow enhancer sharing and promoter competition between the vertebrate Hox genes, providing an additional layer of regulatory control [Hox regulatory elements has not been clearly mapped in organisms other than Drosophila, where distinct regulatory domains control gene expression; most importantly, boundary elements flank each regulatory domain and define its limits in the bithorax complex [Hox clusters [anogaster and suchanogaster. Specifirganisms . The ordelopment ,5. These domains ,7, indic control . However complex . Chromat complex ,11. Home complex . Epigeneclusters .Hox clusters using custom-designed high-resolution chromatin immunoprecipitation (ChIP)-on-chip tiling arrays to reveal the presence of novel histone H3-free regions as potential regulatory elements. We show that most of these histone-depleted regions are associated with the vertebrate GAGA factor. Further, almost all intergenic regions at the Hox clusters show the presence of such histone-free regions that bear significant enhancer-blocking activity in human cells. These findings establish an association of the GAGA factor at specific intergenic chromatin structures that act as regulatory elements in mammalian Hox clusters. The observation that some of these regions can function as chromatin boundaries indicates a conserved mechanism of Hox gene expression regulated by distinct chromatin domains of each gene.Here, we have explored the chromatin regulatory features across all four murine Hox clusters, we designed a ChIP-on-chip tiling array including all 39 Hox genes along with their intergenic as well as upstream and downstream flanking regions. Synchronized G0 cells from the mouse C2C12 cell line were used to obtain a homogenous assay system for chromatin analysis, minimizing possible effects of cell type or cell cycle distribution and potential variations in the epigenetic state. Chromatin immunoprecipitation (ChIP) experiments with histone H3 antibodies were then performed for hybridization to the custom-designed tiling arrays.To obtain a complete picture of the epigenetic organization of the four murine Hox clusters in a defined pattern, as outlined for 150 kb of each of the Hox clusters in Figure Strikingly, ChIP-on-chip analysis revealed a consistent pattern of histone depletion at intergenic intervals across all arrays. The H3 ‘pan’ antibody against the carboxy terminus of histone H3  recognizes all forms of histone H3 and its modifications. We found that the binding of this antibody was depleted at many regions within the HoxA, HoxC, and HoxD clusters while the HoxB cluster showed the lowest occurrence of such regions. The HoxB cluster is different from the other Hox clusters in that the Hoxb13 gene is separated from the rest of the cluster by ~70 kb of DNA, in contrast with the other homeotic genes, which are closely spaced in all four clusters. Although the HoxB cluster maintains colinear expression of all its genes along the A-P body axis, the distant location of Hoxb13 is believed to contribute to its loss of expression along the secondary body axes, presumably owing to the absence of cis-regulatory features in the large intervening region, which also contains highly repetitive DNA sequences [HoxB cluster could explain the absence of HFRs in this part of the complex. The reason behind the low frequency of HFRs in the cluster as a whole is not clear, although it may suggest a relatively low regulatory complexity for this cluster.Nucleosome organization governs the accessibility of chromatin to regulatory complexes, increasing the binding of nonhistone proteins to target sites. Genome-wide chromatin studies have established the presence of regions with altered nucleosome occupancy, which often coincide with known regulatory elements, such as gene promoters, enhancers and transcription start and termination sites . The disequences . This unHox coding regions. A large number of the intergenic HFRs (64 HFRs) were located at intermediate positions between successive Hox genes or downstream to them. Apart from the Hox coding regions, we also checked the positions of intergenic noncoding transcripts as well as all CpG islands known at the Hox clusters. While 16 of the intergenic HFRs were found within noncoding transcribed regions, the remainder showed no overlap with any of these known intergenic features  of which only 9 were genic  enhancer and the γ-neo gene providing antibiotic resistance blocks promoter activation  that showed high enrichment with the core histone H3 antibody.To determine whether any of the newly identified HFRs marked potential boundary elements at the murine n Figure  inset, te effect . We usedboundary  as a posP < 0.0001) in the number of G-418 resistant colonies when cells were transfected with the target HFRs  and boundary elements [Hox clusters also bear such regulatory elements and this study provides a large-scale identification of such potential elements marked by their modified histone occupancy pattern. Not all the elements tested displayed strong boundary potential and it is possible that the enhancer blocking observed in some cases may be the manifestation of some other activity in their native context, as there are many ways that enhancer-promoter interactions can be disrupted [Hox gene expression in mammals.The disruption of histones exposes tructure . Reducedelements . In thisisrupted ,25. Basein vitro colony formation assay suggests their possible association with key regulatory factors at the murine Hox loci. We therefore subjected all the identified HFRs to sequence analysis and found multiple sites for the GAGA binding factor (GAF in Drosophila) at the histone-free regions, with the GAGAG motif appearing as the most frequent significant hit with a motif P < 0.0005 . Enrichment was assayed using multiple primers designed across 18 HFRs, including 13 that had shown high enhancer-blocking activity, as described in Figure Th-POK binding boundary region between Evx2-Hoxd13 characterized previously , positioned within the Hox gene bodies, did not show similarly robust association with the Th-POK antibody  is the best studied vertebrate boundary factor  and  domains . We ther cluster . CTCF ca cluster . It is pox genes .Drosophila, many cis-regulatory elements including boundaries within the homeotic complex are bound by GAF, and evidence suggests that altering the nucleosome occupancy might be a feature of such binding [Drosophila, which remain associated with HS sites regardless of a heat-shock event triggering the activation of the locus [The association of HFRs observed here appears to be specifically with the vertebrate GAGA factor and could possibly be the outcome of the nucleosome-reorganizing potential of GAF and not solely a feature of boundary activity. In  binding . To detehe locus .Hox genes. It remains to be determined if the histone depletion creates accessibility at these sites for enhancer-blocking function or if the GAF binding elements actively serve as anchors for directing the basic chromatin structure by histone disruption at the Hox complexes. dGAF plays a key role in directing chromatin architecture and driving nucleosome reorganization by recruiting remodeling complexes; its mutation has been shown to effect both histone replacement and associated boundary function at Hox complexes in Drosophila[Hox clusters. Since HFRs can provide sites for a multitude of target proteins and GAF can bind to various proteins to form regulatory complexes, looking for interacting partners at these sites will help identify other regulatory features at the HFRs. Taken together, our findings suggest a correlation between GAGA factor association at the histone-depleted regions and chromatin accessibility, providing functional significance to the presence of HFRs in the intergenic regions of mammalian Hox clusters.We hypothesize that the vertebrate GAF-associated intergenic HFRs described here may be important in maintaining the local chromatin conformation of rosophila. The preHox clusters potentially involved in higher-order chromatin regulatory mechanisms. In Drosophila, multiple elements, such as enhancers, insulators, and PREs, form regulatory units that determine and maintain the segment-specific expression pattern of the associated homeotic genes. In mammals, the regulatory elements driving colinear activation and maintenance of Hox expression have not been delineated. In this context, GAGA factor associated histone-depleted regions identified in the intergenic stretches between murine Hox genes suggest sites of potential regulatory function that could help demarcate their domains of expression. We have previously characterized a GA-rich intergenic tract between the Evx2 and Hoxd13 genes conserved among mouse, human, and zebrafish and demonstrated its functional conservation as an enhancer blocker in both transgenic flies and cultured human cells [HoxD locus [Th-POK as the vertebrate homolog of dGAF and indicated its potential to bind and regulate Hox clusters [Hox clusters with GAF recognition sites and Th-POK binding. These results indicate a strong correlation of HFRs associated with Th-POK binding as a general feature of mammalian Hox gene domain regulation and provide mechanistic insights into the possible role of the mammalian GAGA factor in nucleosome reorganization at the Hox clusters, thereby setting the stage for the binding of regulatory proteins to organize chromatin regulatory activities, including boundaries.This study used an epigenetic approach to identify a novel set of DNA elements at murine an cells . The regxD locus ; we havexD locus . We haveclusters . In thisHox genes across species but delineating the conserved organizational features of the Hox clusters can offer remarkable insights into their regulation. In this study, we have been able to identify over 90 such novel regions in the Hox clusters as HFRs and demonstrate the potential of many of the intergenic HFRs to function as chromatin regulatory elements. Our work indicates a genome-wide association for such structures and demonstrates that a lack of enrichment of histone H3 at specific genomic sites in ChIP-on-chip binding assays can serve as a marker for the identification of novel regulatory elements in a context-specific manner.Finally, sequence comparison might not directly identify homologous regulatory elements of the 5 cells/ml in DMEM containing 1.3% methyl cellulose, 20% FBS, 10 mM HEPES, and 1 × Penicillin-Streptomycin. Suspended cells were harvested after 48 h, during which time 98% of cells were arrested at the quiescent G0 stage [C2C12 skeletal muscle myoblasts were cultured as described previously . UndiffeG0 stage , by dilu7 synchronized cells. Cells were harvested as described and fixed using 1% formaldehyde (Fisher Scientific) in growth medium for 10 minutes at 37°C and quenched with 0.125 M Glycine (Sigma). Fixed cells were washed well with 1 × PBS containing protease inhibitors at 4°C and resuspended in 2 ml lysis buffer supplemented with PMSF, DTT, and protease inhibitor cocktail (Roche). Following 15 min incubation on ice, the sample was sonicated using Bioruptor (Diagnode) to obtain fragments of average size 200 bp to 600 bp. The sonicated chromatin was divided into 200 μl aliquots for chromatin immunoprecipitation (ChIP) using the ChIP assay kit  according to the manufacturer’s protocol. ChIP was performed on precleared chromatin from 106 cells using rabbit polyclonal antibodies against core histone H3 , H3K4me3 , H3K27me3 , Th-POK , or CTCF . Unenriched input fraction was retained as control in each case. ChIP and input samples were subjected to array hybridization or real time qPCR assays to determine enrichment.Chromatin was crosslinked and isolated from 10Hox clusters was designed with 60-mer probes  tiled continuously with a 10 bp overlap between consecutive probes for high resolution (Agilent Technologies). Repeat masking was used to avoid repeat sequences and other probes with cross hybridizing potential. Dual-color microarray hybridization experiments were carried out according to the manufacturer’s protocol. Briefly, samples recovered from the enriched fraction and unenriched whole cell extracts (input DNA) from two parallel experiments were independently pooled to obtain optimal yields for direct array hybridization, thus minimizing bias introduced during ligation-mediated PCR amplification of DNA fragments. The pooled enriched and input fractions were directly labeled with Cy5 and Cy3 dyes, respectively, using an Agilent Genomic DNA labeling kit (5190–0449) and hybridized onto the mouse custom arrays at 65°C for 40 h followed by washing. Scanned microarrays were submitted for background subtraction and data extraction to Agilent’s Feature Extraction. Normalized enrichment values for the probes were identified using DNA Analytics software. Probe data were normalized using default blanks subtraction and intra-array dye-bias median normalization and P values were assigned to groups of neighboring probes using the Whitehead error model.A custom mouse genome microarray chip representing 1.1 Mb of the mouse genome consisting of the four The ChIP-on-chip signals were normalized with input DNA signals hybridized to the same arrays and the NLRs of the probes thus obtained were visualized on the Mouse NCBI37/mm9 Assembly in the UCSC genome browser . AdditioHox clusters from the histone H3 tiling array were used to identify unenriched genomic stretches using an in-house PERL script. The extent of each target region was determined based on a defined cut-off for the number of consecutive probes that showed a negative NLR value with the histone H3 antibody. Briefly, the probe dataset from each cluster was divided into groups of five contiguous probes and each was classified as a ‘low enrichment’ group if at least three probes in the group had an NLR <0. Continuous blocks of low enrichment groups were extended until two or more consecutive high enrichment groups were encountered or if a genomic distance >200 bp was encountered between successive probes. These poorly enriched genomic stretches were classified as histone H3-free regions or HFRs and subjected to further analysis for ChIP-qPCR , NLR values of the probes within the Hox genes and all noncoding transcripts (downloaded from the Tromer database) using Geneious [Genomic sequences of HFR stretches from each of the clusters were extracted from the mouse reference assembly MGSCv37-C57BL/6J of NCBI build 37.2 and subjected to motif search analysis using the MEME Suite of tools . The HFRGeneious .Hox clusters were amplified as test fragments from mouse genomic DNA using primers designed with Xho 1 restriction sites at their 5′ ends  at the Hox clusters (x axis). (A) Large stretches of histone H3 unenriched probes showing negative NLR values across all the arrays are highlighted by boxed regions. Map of all the HFRs (green boxes) further identified by bioinformatics analysis of the probe binding data is presented at the bottom for each cluster. (B) H3K4me3 and H3K27me3 peaks called in the custom tiling arrays show that the HoxB and HoxD clusters are highly enriched for the H3K27me3 mark in G0 cells, as reported previously for proliferating myoblasts and myotubes [HoxA and HoxC clusters do not share this feature but show some association with H3K4me3. These trends are comparable with those seen in the ChIP-seq data from ENCODE using C2C12 cells (brown tracks). Genomic scale bar is indicated at the top. Figure S2. Map of GAGA motifs and CTCF sites across Hox clusters. Schematic map  depicting the location of all the GAGA motifs identified by sequence analysis as well as the CTCF sites identified by ChIP-seq in C2C12 cells from the mouse ENCODE project as obtained from the UCSC genome browser . Green boxes indicate HFRs while red bars mark GAGA motifs and grey bars mark CTCF sites. Blue boxes denote the Hox genes with transcriptional orientation; the large intronic regions of Hoxa3 and Hoxd3 genes have been omitted for clarity. Figure S3. DNaseI HS peaks in context of HFRs at the Hox clusters. Schematic map  depicting the location of all the peaks of DNaseI hypersensitivity identified in skeletal muscle (blue track), mesoderm (black track) and embryonic stem cells (maroon track) from the mouse ENCODE project . Green boxes indicate the HFRs while blue boxes denote the Hox genes with transcriptional orientation; the large intronic regions of Hoxa3 and Hoxd3 genes have been omitted for clarity. Table S1. Classification of HFRs identified in the three histone arrays. Table S2. List of GAGA factor binding motifs at the Hox clusters. Table S3. HFRs overlapping with DNaseI HS sites. Table S4. List of primers for ChIP-qPCR assays. Table S5. List of primers for cloning test fragments for boundary assays.HFRs across all arrays using histone H3 antibodies. Screenshots from the tiling array data obtained using pan histone H3 (blue track), H3K4me3 (pink track) and H3K27me3 (green track) antibodies, visualized using the UCSC genome browser showing normalized log ratio (NLR) values of probes (myotubes ; genes iClick here for fileNormalized log ratio values for probes at Hox clusters.Click here for file


The architects of phase I radiochemotherapy development programs impose a semblance of structured radiation “intensity” and adverse event predictability upon radiation-anticancer agent interactions whose natural complexity and improper mixing would otherwise lead to dire health consequences. It is incumbent upon radiation oncology investigators to pledge radiation quality and safety to the participants of radiochemotherapy trials. Measures of radiation quality and safety may be tools to scrutinize radiation-anticancer agent dose and schedule, as well as, radiation field design among diverse radiation delivery platforms. In this article, the merits and demerits of phase I radiochemotherapy quality and safety policies are critiqued considering the current era of rapidly evolving radiation technologies. The traditional first-in-human radiochemotherapy study or phase I investigation often determines the dose and schedule of radiation and investigational anticancer agent combination. This assessment also paints the initial descriptive palette of adverse events linked with the co-administration of the two in a dose-dependent manner. From this parti, we gain an illustrative safety portfolio for radiation-anticancer agent combinations that allow next step study of therapeutic response in phase II or III clinical trials.In an effort to maximize clinical benefit, architects of phase I radiochemotherapy development programs conduct stepwise dose-escalated trials of radiation, of anticancer agent(s), or both together. It has been held in such dose-escalated trials that one accepts an adverse event or toxicity rate of 33% or less in participants, based on the premise that the limited study subject numbers are largely representative of patient cancer groups sharing common traits. Discovery of a toxicity rate target may be done by escalating radiation or anticancer agent dose until two of six  enrollees manifest an adverse event, often a toxicity arising from a pre-determined list. For radiochemotherapy trials, investigators then drop down to the next lower radiation or anticancer agent dose level to enroll additional patients to establish a recommended phase II dose and schedule. This is coined the maximally tolerated dose (MTD). The end product is a radiation-anticancer agent combination recommended for phase II or III clinical testing. Here, we focus on stepwise dose-escalated trials but recognize that other radiation-drug discovery formats are part of the anticancer clinical development portfolio -cisplatin (30 mg/m2) regimen  is administered for cisplatin radiochemosensitization  and single-cycle bolus mitomycin C (15 mg/m2) radiochemotherapy (30 Gy in 15 2 Gy fractions) for anal cancer  plus mitomycin C  radiochemotherapy had planned adjustments in treatment intensity  and mitomycin C  radiochemotherapy investigated peer-reviewed dose-painted intensity modulated radiation therapy (IMRT)  has brought new challenges for assurance of radiation quality and safety. These technologies along with adaptive radiotherapy and image guided radiotherapy (IGRT) rely on treatment volume definitions which are still in development in many disease sites. In radiochemotherapy centers and cooperative groups known for excellence in the quality delivery of radiation, rapid review of cases allows for uniform definition of radiation volumes and dose delivered over elapsed times specified in clinical trial protocols with little variation. Real-time monitoring of these advanced radiation delivery platforms merits our serious and respectful scrutiny. Treatment volumes are not as easily defined by bony anatomy as is seen in 2 and 3-D treatment volumes and can be variable depending on differing patient anatomy. Hence these platforms should not earn our uncritical acceptance. Arguably, these platforms offer expectantly substantial reduction in radiation-related adverse events but should not be used clinically in deference to rigorous quality assurance by all users. Also, it is prudent within a single phase I radiochemotherapy trial to have a uniform radiation platform by all investigators. Enrolling trial participants into a phase I radiochemotherapy trial that allows radiation treatment by conventional radiation or by IMRT would cloud the interpretation of observed radiation-drug adverse events, possibly raising issues of falsely concluding a safe relationship exists when it truly may not. Mixing technologies on a phase I radiochemotherapy trial may also invalidate the historical radiation-specific technology adverse event data upon which the trial was predicated. Observed toxicity on down-the-line phase II or phase III studies may also be influenced by “any” form radiation delivery. More study is needed and guidelines for safety and quality assurance are emerging for these new technologies and utilization in phase I radiochemotherapy studies should only be considered once these guidelines are confirmed in other venues.Making radiation-anticancer agent discovery safer and of optimal quality is the goal of phase I radiochemotherapy programs. Implementing the layers of quality control such as dose checks, timetables, uniformity in treatment portal/treatment volume design, and brachytherapy application, and with timely critical feedback are musts in high-performing phase I radiochemotherapy programs. In the context of radiochemotherapy, the architects of clinical trials must layout plans intended to balance the safe selection and concise use of radiation-anticancer agent combinations.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Charadriiformes infected in Delaware Bay this host population does not appear to significantly contribute to the North American AIV diversity sampled in Anseriformes. In contrast, influenza viruses sampled from Anseriformes in Alberta are representative of the AIV diversity circulating in North American Anseriformes. While AIV may be restricted to specific migratory flyways over short time frames, our large-scale analysis showed that the long-term persistence of AIV was independent of bird flyways with migration between populations throughout North America. Analysis of long-term surveillance data provides vital insights to develop appropriately informed predictive models critical for pandemic preparedness and livestock protection.Wild birds have been implicated in the emergence of human and livestock influenza. The successful prediction of viral spread and disease emergence, as well as formulation of preparedness plans have been hampered by a critical lack of knowledge of viral movements between different host populations. The patterns of viral spread and subsequent risk posed by wild bird viruses therefore remain unpredictable. Here we analyze genomic data, including 287 newly sequenced avian influenza A virus (AIV) samples isolated over a 34-year period of continuous systematic surveillance of North American migratory birds. We use a Bayesian statistical framework to test hypotheses of viral migration, population structure and patterns of genetic reassortment. Our results reveal that despite the high prevalence of  Anseriformes) and shorebirds (Charadriiformes), the ecological and evolutionary dynamics of avian influenza A virus (AIV) in these hosts is poorly understood. Comparative genomic analysis of AIV data revealed that the high prevalence of Charadriiformes infected in Delaware Bay is a reservoir of AIV that is phylogenetically distinct from AIV sampled from most North American Anseriformes. In contrast, influenza viruses sampled from Anseriformes in Alberta are representative of the remaining AIV diversity sampled across North America. While AIV may be restricted to specific migratory flyways over short time frames, our large-scale analysis showed that this population genetic structure was transient and the long-term persistence of AIV was independent of bird flyways. These results suggest an introduced virus lineage may initially be restricted to one flyway, but migration to a major congregation site such as Alberta could occur followed by subsequent spread across flyways. These generalized predictions for virus movement will be critical to assess the associated risk for widespread diffusion and inform surveillance for pandemic preparedness.Despite continuous virological surveillance (1976–2009) in wild waterfowl ( Migrating wild birds have been implicated in the spread and emergence of human and livestock influenza, including pandemic influenza and highly pathogenic H5N1 avian influenza Anseriformes infected with influenza A virus have hampered migration, these hosts vector influenza viruses vast distances It is estimated worldwide that over 50 billion birds migrate annually between breeding and non-breeding areas Anseriformes and Charadriformes gene pools where birds from both continental regions commingle and therefore the threat posed by introduction of H5N1 to North America remains. However, once a virological threat has entered the North American bird population there is little information regarding how that virus may behave or diffuse between spatially distant migratory bird populations.Recent efforts to assess invasive virological threats have focused on increased surveillance and early detection of introduced viral strains ad hoc approaches. This is largely due to a critical lack of knowledge of viral movements between different host populations The prediction of viral spread and disease emergence, as well as formulation of preparedness plans has generally been based on In this study we identified 20 discrete regions in North America where influenza viruses have been systematically collected from wild birds to determine whether the viral population was structured according to host migratory flyways, and rates of gene flow between these populations. Avian influenza viruses were isolated annually throughout our surveillance in Alberta, Canada and Delaware Bay, USA and an additional 287 genomes were sequenced. Using full genome data we characterize the reassortment dynamics, spatial diffusion patterns and evolutionary genomics of influenza A viruses in North America collected over a 25-year period from migratory birds.Avian influenza H3 viruses were among the most frequently isolated influenza subtype from our surveillance in Alberta, Canada and Delaware Bay, USA AnseriformesCharadriiformes were sampled, H3 viruses were detected in only 7 years (ntax = 69) from 24 years of surveillance  and patristic distances for each gene segment (excluding NA) to test differences in reassortment between populations . In thisAnseriformes, throughout the entire surveillance period. One possibility is that this deep divergence is the product of (i) a very large host meta-population and (ii) relatively rare cross-species transmission rate when compared to annual seasonal epidemic dynamics leading to a lack of synchronicity of partial immunity across host species so that more than one lineage can effectively survive long periods of time. Although there was little evidence for geographic structuring of the virus population over extended periods, an obvious exception is a single lineage that has circulated for more than 10 years in birds sampled from Delaware Bay . The tMRCA of Lineages I and II was estimated to be ∼1942 . The mechanism for maintenance of this deep divergence remains unknown, as viruses from both lineages have co-circulated in geographically overlapping host populations, primarily ware Bay .Ancestral state reconstruction of virus geographic location suggests that the population of Lineage II was localized in southeast Alberta prior to migrating to other locations across all North American flyways . HoweverWe next estimated rates of viral migration between discrete geographic locations treating each gene as an independent dataset to capitalize on the extra historical information generated by genetic reassortment. While each gene segment analysed supported lateral diffusion between migratory flyways over time, analysis of migration paths using single gene segments yielded contradictory answers , S7, S8.th state termed “Other”, that maintained phylogenetic tree structure. The “Other” state included taxa isolated prior to 1998 where few geographic locations were sampled and locations where few isolates were encountered over the surveillance period We further analyzed all publically available PA, PB1, PB2, NP and M sequence data from wild aquatic birds isolated between 1985–2009 in North America. The HA, NA and NS gene segments were not included in this analysis due to the deep divergence between the subtypes Anseriformes' social behavior in persistence of the virus population.Our goal was to understand the migration dynamics and diffusion patterns of influenza virus in their natural hosts by utilizing over 30 years of continuous systematic surveillance data. We show that our surveillance within Alberta, which includes convergence points for all four migratory flyways Charadriiformes stopover annually to feed in highly dense congregations. Our results showed limited genetic diversity coupled with high prevalence of infection indicating an epizootic in Charadriiformes that does not play a significant role in the shaping the sampled AIV diversity within North American Anseriformes. Even though this hotspot is not representative of gene pool diversity, these viruses are ultimately derived from the same population of viruses common throughout North America. The transmission of viruses between populations of birds is most likely occurring where migratory Anseriformes and Charadriiformes commingle, possibly in South and Central America or Arctic breeding grounds. The role of Charadriiformes in the persistence and transmission of influenza A viruses therefore warrants further study, especially on a more comprehensive spatial scale.The site at Delaware Bay has been identified as a hotspot for avian influenza A viruses We show that the long-term persistence of the influenza A virus gene pool in North American wild birds may be independent of migratory flyways. Although virus migration could be restricted within a flyway over short time periods, our results show strong support for longer-term lateral diffusion of viral lineages between host populations. In our study, data points were not assigned to a flyway but discrete sites were assigned and used to inform within and between flyway migration rates using tip-dated time-dependent phylogenetic reconstructions. While this does contradict previous work by Lam et al Subtype specific host distribution, geographic state definition and host ecology may also be a source for the differences observed between the two studies The data used in our analysis included collections from resident and short distance migratory birds Using our model for virus transmission generalized predictions for movement of an introduced Eurasian virus and the associated risk for widespread diffusion can be inferred. An introduced virus lineage to Alaska might initially be restricted to the Pacific Flyway, but migration to a major congregation site such as Alberta could occur with subsequent spread across flyways occurring shortly after. While the establishment of introduced lineages into North America may be rare, introduction and reassortment events with Eurasian and North American strains probably occur more frequently than detected The development of fully resolved ecological and viral risk models depend upon the continued long-term active surveillance in major bird congregation zones. While the resolution and detection of migration events has been enhanced with increased surveillance in recent years, critical information for wild bird surveillance remains sparse. This is especially evident as no sampling in Central and South America was available for this study. A comprehensive understanding of spatial diffusion patterns of viruses introduced to wild animal populations is critical for the development of preparedness plans in response to emerging viral threats.Anas platyrhynchos), northern pintail (Anas acuta), and blue-winged teal (Anas discors) with these three species accounting for 93% of the total specimens. Other species (listed in decreasing rank order of samples obtained) include redhead (Aythya americana), green-winged teal (Anas crecca), american wigeon (Anas americana), gadwall (Anas strepera), canvasback , lesser scaup (Aythya affinis), american coot (Fulica americana), northern shoveler (Anas clypeata), bufflehead , cinnamon teal (Anas cyanoptera), common goldeneye , ruddy duck (Oxyura jamaicensis), greater scaup (Aythya marila), hooded merganser (Lophodytes cucullatus), and wood duck (Aix sponsa).Systematic influenza surveillance has been conducted in ducks in Alberta, Canada since 1976, and in shorebirds and gulls at Delaware Bay (Delaware and New Jersey) since 1985. Ducks were sampled post-breeding and prior to southern migration during July through early September at various wetlands in the following regions of Alberta: Vermilion (1976–1978), Grand Prairie/Fairview , Edmonton/Stettler , Brooks (1992–1995), and High River . Sampling occurred during duck banding operations conducted by the Canadian Wildlife Service after ducks were captured in swim-in bait traps. Birds banded in Alberta have been recovered in all four North American flyways but most mallards are recovered in the Central and pacific flyways. In 1984 samples were also collected from ducks captured in decoy traps during late April to early May in the Vermilion area. Overall, the majority of samples were obtained as cloacal swabs  and tracheal/oropharyngeal specimens accounted for most of the remaining samples . Hatch-year ducks were sampled more frequently than after-hatch-year ducks . A variety of duck species were sampled – primarily dabbling ducks. The most abundantly sampled species are mallard (Charadriiformes – shorebirds and gulls - were collected in May at Delaware Bay from ruddy turnstone (Arenaria interpres), red knot , semipalmated sandpiper , sanderling , and dunlin  starting in 1985 and continuing to the present. Samples were also obtained from breeding colonies of gulls – primarily laughing gull (Larus atricilla) and herring gull (Larus argentatus). It is during this period in May that shorebirds (waders) are migrating north from South America to their breeding grounds in the Canadian Arctic. Delaware Bay serves as a stopover point where the birds can re-fuel on the abundance of eggs deposited by the coincident spawning of horseshoe crabs (Limulus polyphemus).Fecal samples from Although most of the 10,350 samples obtained were from freshly deposited feces on beaches we also collected 213 cloacal swabs from captured birds spanning the years 1986–1989 and 2000. A subset of 440 samples was collected outside of the May surveillance period at the following times; September 1985, September and November 1986, and June-September 1988. It should be noted that from 1988 through 2002 multiple swabs  were combined to constitute a single sample vial. In the years prior to 1988 most sample vials contained an individual swab, and all samples since 2003 have been from single fecal deposits.Approximately 19 sample sites were established around Delaware Bay and varied from year-to-year. Six sites were used on the west side of Delaware Bay in Maryland and Delaware from 1985 through 1989. Sampling was performed at 13 sites on the east side of the bay in New Jersey in all years. The majority of the swabs were derived from fecal deposits and therefore it was not possible to identify the species that served as the source of the sample in over half of the specimens. However, the birds tend to congregate in groups of like species, and gull feces were easily discriminated from other bird droppings, therefore in many instances we could attribute the source of the sample to a particular species. Otherwise the sample was considered “shorebird” or “gull”.Swabs were collected using a dacron tipped applicator and placed in transport medium containing 50% phosphate buffered saline and 50% glycerol adjusted to pH 7.2 and supplemented with penicillin G, streptomycin, polymyxin B, gentamycin, and nystatin. In Alberta the duck swabs were placed immediately in liquid nitrogen and returned to the laboratory. Shorebird samples from Delaware Bay were immediately placed on ice and shipped to the laboratory within 6 days of collection. Storage of the specimens prior to testing was at −70°C.Viruses were isolated in 10-day-old embryonated chicken eggs as previously described Through exploratory examination of surveillance records from Alberta and Delaware Bay we determined that H3 subtype viruses have been most frequently isolated throughout the time period 1985–2009. We therefore focused our sequencing efforts on this time period and randomly selected 200 viruses for full genome sequencing. This data was further supplemented with an additional 100 viruses randomly selected for genomic sequencing of various subtypes.All samples were sequenced using a high-throughput Next-Generation sequencing pipeline at the JCVI that includes the 454/Roche GS-FLX and the Illumina HiSeq 2000. Viral RNA was first reverse transcribed and amplified by multi-segment RT-PCR (M-RTPCR) One library was prepared for sequencing on the 454/Roche GS-FLX platform using Titanium chemistry while the other was made into a library for sequencing on the Illumina HiSeq 2000. The sequence reads from the 454/Roche GS-FLX data were sorted by barcode, binned by sample, trimmed, searched by TBLASTX against custom nucleotide databases of full-length influenza A segments downloaded from GenBank to filter out both chimeric influenza sequences and non-influenza sequences amplified during the random hexamer-primed amplification. For each sample, the filtered 454/Roche GS-FLX reads were then binned by segment, and de novo assembled using CLC Bio's clc_novo_assemble program. The resulting contigs were searched against the corresponding custom full-length influenza segment nucleotide database to find the closest reference sequence for each segment. Because of the short read length of the sequences obtained from the barcode-trimmed Illumina, HiSeq 2000 these were not subjected to the TBLASTX filtering step. Both 454/Roche GS-FLX and Illumina HiSeq 2000 reads were then mapped to the selected reference influenza A virus segments using the clc_ref_assemble_long program.At loci where both GS-FLX and Illumina sequence data agreed on a variation (as compared to the reference sequence), the reference sequence was updated to reflect the difference. A final mapping of all next-generation sequences to the updated reference sequences was then performed. Any regions of the viral genomes that were poorly covered or ambiguous after Next Generation sequencing were PCR amplified and sequenced using standard Sanger sequencing approach.Through sequencing, some of these selected viruses have been identified as more than one isolate (“Mixed” in table S3). The direct sequencing method does not allow us to determine which internal gene segments are associated with which subtype. Furthermore, some variants could not yield unique gene sequences for each potential virus identified. Hence, some mixed variants contain more than 8 associated sequences, but fewer than 16. As such, these were not included in the analysis of genomic reassortment patterns. Other variants could not be completely sequenced and have subsequently been submitted as “Draft.” Out of the 300 variants submitted for sequencing, 287 full genomes have been completed. All data generated for this study has been made publicly available via the Influenza Virus Resource at NCBI We analyzed 1441 genomic sequences of influenza A viruses in wild birds  as this was the most commonly isolated subtype throughout the surveillance period in both Alberta and Delaware Bay. Secondly, we analyzed all publically available PB1, PB2, PA, NP, M gene segments (excluding recent introductions from Eurasia) to estimate the viral migration patterns across the entire population of birds regardless of subtype. HA, NA and NS genes were not included due to the deep divergence between subtypes. This latter analysis resulted in a dataset of more than 1300 sequences for each of the five genes included.While the phylogeny and substitution rates were separate for each gene, based on a joint migration process a single migration matrix was estimated. We used a reversible continuous-time Markov chain model to estimate the migration rates between geographical regions and the general patterns of avian influenza A virus circulation in different populations We used multidimensional scaling plots to visually assess the strength of reassortment in Alberta and Delaware Bay. In this analysis the tree-to-tree variation in branch lengths is visualized as a cloud of points where the centroid of the cloud represents the mean from the 500 trees used in the analysis. Here we assume that gene segments with similar evolutionary histories will occupy the similar locations in the 2-dimensional Euclidean space where the cloud of points should overlap. We used two metrics to assess the degree of reassortment of the influenza A virus populations in the two discrete sampling regions: the time to the most recent common ancestor (tMRCA) or patristic distances calculated from a posterior distribution of trees. From a posterior distribution of phylogenetic trees we estimated the tMRCA for influenza A viruses sampled in each location from each gene during each year and computed the correlation coefficient of the tMRCAs between each pair of trees. This method of tree to tree comparisons has been applied to seasonal influenza A viruses In our data sets there was a sparseness of sampling through time, especially in Delaware Bay. Therefore we encountered high levels of uncertainty where no clear pattern was discernable and zero distances between trees resulted in computational errors by using the tMRCA to estimate phylogenetic uncertainty between gene trees. To overcome this we computed the correlation matrix of the pairwise tree distances. Here we calculated the correlation coefficient for each pair of trees using the patristic distances between every taxon, where the patristic distance is the sum of branch lengths between two nodes. The dissimilarity matrix was obtained by calculating one minus the correlation matrix.All animal experiments were performed following Protocol Number 081 approved on August 19, 2011 by the St. Jude Children's Research Hospital Institutional Animal Care and Use Committee in compliance with the Guide for the Care and Use of Laboratory Animals, 8th Ed. These guidelines were established by the Institute of Laboratory Animal Resources and approved by the Governing Board of the U.S. National Research Council.Figure S1Neighbor joining phylogenetic tree produced from an HKY85 nucleotide substitution model optimized distance matrix from all available H3-HA data, including sequences generated in this study. The major lineages; Oceania, Eurasia, and North American Lineages I and II are indicated to the right of the tree. Bootstrap supports for these major lineages are indicated on the tree. The scale bar indicates nucleotide substitutions/site.(PDF)Click here for additional data file.Figure S2http://tree.bio.ed.ac.uk/software/figtree/). Also applies to figures S3, S4, S5, S6, S7, S8.H3 Hemagglutinin gene tree nexus file. Temporally structured maximum clade credibility phylogenetic tree showing the mixing of avian influenza A virus isolated from North American wild birds for each individual gene dataset. Ancestral state changes recovered from the discrete phylogeographic analyses are indicated by color changes at tree nodes. Purple bars on nodes indicated 95% confidence intervals of date estimates. Trees with taxon labels and node annotations can be viewed in FigTree (available from (TREE)Click here for additional data file.Figure S3PB2 gene tree nexus file.(TREE)Click here for additional data file.Figure S4PB1 gene tree nexus file.(TREE)Click here for additional data file.Figure S5PA gene tree nexus file.(TREE)Click here for additional data file.Figure S6NP gene tree nexus file.(TREE)Click here for additional data file.Figure S7M gene tree nexus file.(TREE)Click here for additional data file.Figure S8NS gene tree nexus file.(TREE)Click here for additional data file.Figure S9A) Mean migration rate per MCMC step within flyway migration rates vs Mean between flyway migration jointly estimated from a subsampled dataset of (PDF)Click here for additional data file.Figure S10Relationship of migration rate and distance. A) Mean statistically supported rates vs distance between discrete migration sites; B) Median statistically supported rates vs distance between discrete migration sites; C) All Mean migration rates vs distance between discrete migration sites; D) All Median rate indicator vs distance between discrete migration sites.(PDF)Click here for additional data file.Figure S11Interactive Google Earth Supplementary Data. GenBank Accession numbers and specific location of virus sampling for all sequences used in this study in the 5° Latitude by 5° Longitude square used to define the discrete character for ancestral state reconstruction.(KML)Click here for additional data file.Figure S12http://tree.bio.ed.ac.uk/software/figtree/). Also applies to figures S13, S14, S15, S16.PB2 gene tree nexus file used to estimate joint migration. Interactive Tree files. Temporally structured maximum clade credibility phylogenetic tree with all available data used to jointly estimate the migration patterns summarized in (TREE)Click here for additional data file.Figure S13PB1 gene tree nexus file used to estimate joint migration.(TREE)Click here for additional data file.Figure S14PA gene tree nexus file used to estimate joint migration.(TREE)Click here for additional data file.Figure S15NP gene tree nexus file used to estimate joint migration.(TREE)Click here for additional data file.Figure S16M gene tree nexus file used to estimate joint migration.(TREE)Click here for additional data file.File S1http://beast2.cs.auckland.ac.nz/index.php/Main_Page).BEAST2 executable xml file detailing the parameters for the joint estimation of the single migration rate matrix from independently generated phylogenies (BEAST2 available from (TXT)Click here for additional data file.Table S1Host Avifauna most frequently infected with influenza A virus summarized from the Centers of Excellence for Influenza Research and Surveillance North American wild bird surveillance efforts reporting from 2007.(DOC)Click here for additional data file.Table S2GenBank Accession numbers, isolation date and location of virus sampling for additional sequences from public databases used in this study.(DOC)Click here for additional data file.Table S3Associated geographic metadata and exact date of sampling of newly sequenced avian influenza A viruses.(DOC)Click here for additional data file.Table S4Number of taxa included per protein coding region to estimate average migration dynamics between discrete regions.(DOC)Click here for additional data file.Text S1Supplementary information describing flyways and bird behavior.(DOC)Click here for additional data file.


Estrogen Receptor (ER) is the defining feature of luminal breast cancers, where is functions as a transcription factor. The traditional view of ER getting recruited to promoters of target genes is too simplistic. The recent discovery of ER-DNA interaction regions from ER+ breast cancer cell lines has revealed that ER rarely associates with promoter regions of target genes and instead associates with enhancer elements significant distances from the target genes. The genomic mapping of ER binding events also revealed the enrichment of DNA motifs for Forkhead factors. The Forkhead protein FOXA1 (HNF3a) was subsequently shown to bind to ~half of the ER binding events in the genome and was required for ER to maintain interaction with DNA. We have extended on these findings to map ER binding events in primary breast cancers and distant metastases. We find context dependent ER cis-regulatory elements (cistromes) that give insight into underlying transcriptional networks. These differential ER binding profiles correlate with clinical response in ER+ breast cancers. We experimentally explore the binding dynamics between drug sensitive and resistant contexts and identify properties that govern ER binding differences. These data suggest that ER-DNA interactions are dynamic and can be modulated by changes in FOXA1. We are currently exploring mechanisms that mediate FOXA1-DNA interactions, in order to better understand ER transcriptional activity in breast cancer biology.


Treating acute ischaemic stroke patients with iv-rtPA is of overall benefit. An associated increase in the risk of symptomatic intracranial haemorrhage (SICH) may cause considerable harm. We investigated (i) whether novel or existing prediction models could predict SICH or poor functional outcome in rtPA treated patients and (ii) whether ischemic stroke patients at either a high predicted risk of SICH or poor functional outcome experienced less benefit from rtPA.We used the IST-3 trial data, an international, multicentre, open treatment randomised trial of rtPA versus control in 3035 acute ischemic stroke patients. We developed and internally evaluated a multivariate logistic regression model for SICH following rtPA including variables identified as important in a systematic review. We compared the discrimination (area under the receiver operating characteristic curve (AUROCC)) of our model with those existing in the medical literature. We calculated the absolute risk reduction of death or dependency with rtPA in patients at a low, medium or high predicted risk of SICH or poor functional outcome with each model.Our model had similar discrimination for SICH (AUROCC 0.68 95% CI: 0.63-0.74) to nine previously developed models . There was no evidence that patients at high predicted risk of SICH or poor functional outcome after stroke derived less benefit from rtPA.We found no evidence to support a stratified approach in administering rtPA to acute ischaemic stroke patients at a high predicted risk of intracranial haemorrhage or poor functional outcome.


Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. In drug discovery, the goal is to identify new compounds to alter the behavior of a protein implicated in disease. With the very large number of small molecules to test, researchers have increasingly studied fragments  because there are fewer possibilities to evaluate and they can be used to identify larger compounds. Computational tools can efficiently assess if a fragment will bind a protein target of interest. Given the large number of structures available for protein-small molecule complexes, we present in this study a data-driven computational method for fragment binding prediction called FragFEATURE. FragFEATURE predicts fragments preferred by a protein structure using a knowledge base of all previously observed protein-fragment interactions. Comparison to previous observations enables it to determine if a query structure is likely to bind particular fragments. For numerous protein structures bound to small molecules, FragFEATURE predicted fragments matching the bound entity. For multiple proteins, it also predicted fragments matching drugs known to inhibit the proteins. These fragments can therefore lead us to promising drug-like compounds to study further using computational tools or experimental resources. Fragments in this context refer to low-molecular-weight small molecules usually 120–250 Daltons in weight Fragments tend to bind in the millimolar to micromolar range and require sensitive experimental screening techniques, including protein crystallography et. al. took a ligand fragment-centric view and calculated the residue preferences of different fragments et. al. took a protein residue-centric view and determined the fragment preferences of the side chains of Asp, Glu, Arg, and His However, the growing database of structural data for protein-small molecule complexes provides an opportunity for empirical data-driven approaches to fragment binding prediction in silico fragment binding predictors/evaluators. Using information from the Protein Data Bank, we created a knowledge base linking local protein structural environments to the small molecule fragments they bind. Given structural environments from a target protein, FragFEATURE compares them to the knowledge base to find similar structural environments and identify statistically preferred fragments. For a variety of protein-ligand complexes, we demonstrate the method's ability to predict fragments that are substructures of the bound ligands. We also present six case studies (three in main text and three in supporting information) of fragment predictions corresponding to known inhibitors. These fragment predictions can help identify promising compounds from compound libraries. Such compounds have potential to form favorable interactions with the protein target due to the presence of the fragment but require additional evaluation as other moieties in the compounds may prevent binding. Predicted fragments are also starting points for fragment-based drug design. Furthermore, knowledge of fragments favored by a protein pocket could give insight to lead optimization by suggesting favorable chemical groups. FragFEATURE thus powerfully leverages empirical knowledge of protein-fragment interactions to predict fragments for a target protein structure.Here, we describe FragFEATURE, a novel fragment binding predictor that overcomes many of the limitations of existing The knowledge base is the underlying source of information for fragment predictions. We mined 34,000 protein-ligand complexes from the PDB (01/01/2013 snapshot) to create a knowledge base of local protein structural environments (microenvironments) annotated with the small molecule substructures (fragments) they bind . In the To capture protein information, we used FEATURE To capture fragment information, we divided 16,000 PDB ligands into overlapping substructures ranging from three to thirteen heavy atoms in size . These fFragFEATURE predicts fragments preferred by the microenvironments of a protein pocket . It usesWe validated FragFEATURE on four nucleotide and four non-nucleotide ligands and found strong ability to predict fragments matching the ligand bound. The nucleotides included adenine (ADE), adenosine-5′-diphosphate (ADP), flavin-adenine dinucleotide (FAD), and nicotinamide-adenine dinucleotide (NAD). The non-nucleotides included thiamin (VIB), thiamine diphosphate (TPP), pyridoxal-5′-phosphate (PLP), and triclosan (TCL). These compounds tested FragFEATURE on ligands of different frequency , flexiblFragFEATURE predicted fragments for 9,392 ligand-binding pockets  and we aFor the previous analysis, we used observed ligand-binding pockets. In other applications of FragFEATURE, the location and extent of the ligand-binding pockets may be unknown. In those cases, both pocket definition and conformation may alter the microenvironments used by FragFEATURE to make predictions. To determine the sensitivity to non-ideal microenvironments, we tested computationally predicted pockets fPocket  from botWe investigated the potential utility of FragFEATURE fragment predictions for drug discovery. In the following case studies, FragFEATURE rediscovered fragments of inhibitors for the analyzed protein targets. These “drug-relevant fragments” can inform different research approaches including virtual screening, high-throughput experimental screening, or fragment-based drug design.Pseudomonas aeruginosa is an opportunistic bacterium that infects immunocompromised patients −29 for the microenvironments proximal to the nicotinamide moiety of NAD (i: 140 nM) and is available in the PDB bound to exotoxin A . A crystal structure of staurosporine-bound DAPK1  that interact with the protein target. It relies solely on the target's 3D structure and can be applied to any protein with a solved structure or high quality homology model. Additionally, the structural space of protein pockets is small, with the PDB showing good coverage of pocket space Other methods for identifying compounds for a protein target include using information from (1) known bioactive compounds for the target, (2) compounds that bind to homologs of the target, and (3) compounds that bind to proteins non-homologous to the target but share binding specificity (i.e. bind similar endogenous ligands as the target). Such compounds can serve as valuable starting points in drug discovery Fragments predicted by FragFEATURE may overlap with available binding information for the target or be intuitive to a researcher with expertise in structure-based or fragment-based drug design. However, FragFEATURE provides a systematic statistical framework within which to interpret a fragment prediction along with the structural evidence for the prediction. FragFEATURE uses fragment information from proteins with low sequence identity to the target protein. The retrieved nearest neighbor protein structures may highlight unexpected local structural similarity. Shared fragment preferences between proteins can provide an opportunity for polypharmacology The FEATURE software Microenvironment centers correspond to residue side chains or backbone atoms. For the side chain centers, we use those defined by PocketFEATURE To determine microenvironment similarity, we adopted the approach used by PocketFEATURE x and y) as the ratio of shared properties to non-zero properties. Like PocketFEATURE, we define shared properties as those whose difference is less than the standard deviation of the property. If c is the number of shared properties and a and b the number of non-zero properties in vector x and y respectively, the Tanimoto coefficient (Tc) is as follows: We use PocketFEATURE's Tanimoto coefficient to perform similarity comparisons between microenvironments of the same type (e.g. backbone oxygen to backbone oxygen). This Tanimoto coefficient measures the similarity between a pair of microenvironment vectors , we retrieve all protein X-ray crystallography structures with resolution less than 3 Å. We then select the structures bound to small molecules, ignoring small molecules less than three heavy atoms, greater than 100 heavy atoms, or corresponding to commonly used buffers and crystallization agents. PDB ligands with over 100 heavy atoms tended to produce errors with either PUG SOAP or SMSD. For the resulting protein-ligand complexes, we calculate the location of all pseudo atoms (see Feature Microenvironments). We then use a distance cutoff to identify protein atoms/pseudo atoms “interacting” with the ligands, keeping side chain atoms/pseudo atoms within 5 Å and backbone atoms within 4 Å of a ligand heavy atom. The shorter distance cutoff for the backbone atoms helps exclude atoms participating in intra-protein hydrogen bonding and thus not involved in ligand binding. We converted the collected protein atoms/pseudo atoms into microenvironments using FEATURE and divided them into 23 types (see Feature Microenvironments). By definition, each microenvironment is “interacting” with one or more ligand heavy atoms. For each ligand heavy atom interacting with a microenvironment, we retrieve its associated fragment list and take the union of these lists as the microenvironment's associated fragments . This prGiven a query microenvironment, we compare it to knowledge base microenvironments of the same type. This allows us to sort the knowledge base by similarity to the query using the Tanimoto coefficients. We then filter the knowledge base for homology by first removing microenvironments from proteins homologous to the query. Of the remaining microenvironments, we keep only the most similar microenvironment for microenvironments from homologous proteins. This ensures the final knowledge base microenvironments are non-homologous to the query and to each other. We define homology as ≥50% sequence identity as pre-computed by the PDB. This produces a query-specific non-homologous knowledge base ordered by microenvironment similarity to the query.k most similar non-homologous microenvironments (k nearest neighbors) and their corresponding fragment list. For each fragment in these lists, we calculate a hypergeometric p-value. This requires the number of neighbors selected (k), the number of neighbors binding the fragment (m), the number of microenvironments in the non-homologous knowledge base binding the fragment (M), and the number of microenvironments in the non-homologous knowledge base (N). The hypergeometric p-value . We take the most significant fragment predicted for a microenvironment and compare it to the microenvironment's fragment list to determine its validity (correct/incorrect). We then use the predicted fragments' p-values and validity labels to compute a precision recall curve for each microenvironment type. The area under the precision recall curve (AUPR) serves as a measure of overall performance. An AUPR approaching 1.0 indicates similar microenvironments bind similar fragments.We selected eight ligands to validate FragFEATURE's performance: adenine (ADE), adenosine-5′-diphosphate (ADP), flavin-adenine dinucleotide (FAD), nicotinamide-adenine dinucleotide (NAD), pyridoxal-5′-phosphate (PLP), triclosan (TCL), thiamine diphosphate (TPP), and thiamin (VIB). For the ligand-bound structures, we retrieved all structures from the PDB in complex with the validation ligands. For the ligand-free structures, we retrieved all protein-only structures from the PDB homologous (95% sequence identity) to the ligand-bound structures.Three types of pockets are of interest: observed ligand-binding pockets, predicted pockets from ligand-bound structures, and predicted pockets from ligand-free structures. An observed ligand-binding pocket contains the side-chain atoms within 5 Å and backbone atoms within 4 Å of any validation ligand heavy atom. A predicted pocket for a ligand-bound or ligand-free structure is the atoms forming the largest pocket predicted by fPocket. fPocket performs pocket detection for each protein chain individually with the ligand information removed. Protein atoms comprising the different pockets are then passed to FEATURE to calculate their microenvironments. Microenvironments centered on a pseudo atom are calculated if any of the atoms contributing to the pseudo atom location are part of the pocket (see FEATURE Microenvironments).N spatially proximal microenvironments. However, given that some microenvironments in the set may not be informative for fragment prediction, we also create all subsets of size N–1 to 2 microenvironments . Fragments with a hypergeometric p-value for all microenvironments within a set are consensus fragments regardless of the hypergeometric p-value. We keep only consensus fragments to ensure agreement and coherence among the microenvironments of a set. For each consensus fragment, we use Fisher's method to combine the hypergeometric p-values  and the structures homologous to the query. This helps take into account that proteins may bind multiple ligands at a given site. We retrieve PDB structures sharing at least 95% sequence identity to the query and use BLAST+ Fragment predictions arise from microenvironment sets and are correct, incorrect or no information . CorrectEach fragment prediction is made by a set of microenvironments with each microenvironment using fragment information from the five nearest non-homologous neighbors. A subset of these nearest neighbors contributes to the fragment prediction (e.g. bind the predicted fragment). We calculate the percent sequence identity between the query structure and this subset of nearest neighbor protein structures. We analyze the most significant correct fragment prediction for each test structure, so test proteins without a correct fragment prediction are excluded from analysis. To determine the percent sequence identity between a pair of proteins, we use BLAST+ to align their amino acid sequences. We consider alignments that fail to cover at least 20% of the smaller protein or that possess an alignment E-value ≥10 (default) as failed alignments.Computation time varies with the size of the pocket of interest as measured by the number of microenvironments comprising the pocket. The process begins with an input list of atoms defining the pocket and ends with an output file of fragment predictions. The ligand-binding pockets generally possess five to fifty-five microenvironments. We select eleven pocket sizes to cover this range evenly and time five pockets for each pocket size to obtain computation time as a function of pocket size . ComputaFigure S1FEATURE microenvironments.(DOCX)Click here for additional data file.Figure S2Breakdown of knowledge base microenvironments.(DOCX)Click here for additional data file.Figure S3Ligand fragmentation.(DOCX)Click here for additional data file.Figure S4Breakdown of knowledge base fragments.(DOCX)Click here for additional data file.Figure S5Fragment prediction for single microenvironments.(DOCX)Click here for additional data file.Figure S6Microenvironment set definition.(DOCX)Click here for additional data file.Figure S7Chemical moieties of the validation ligands.(DOCX)Click here for additional data file.Figure S8Fragment evaluation.(DOCX)Click here for additional data file.Figure S9Sequence identity between test proteins and nearest neighbor proteins.(DOCX)Click here for additional data file.Figure S10Comparison of observed ligand binding pockets and predicted pockets.(DOCX)Click here for additional data file.Figure S11FragFEATURE performance on predicted pockets.(DOCX)Click here for additional data file.Figure S12Fragment prediction and validation for Abl tyrosine kinase.(DOCX)Click here for additional data file.Figure S13Fragment prediction and validation for protein kinase A (PKA).(DOCX)Click here for additional data file.Figure S14Alternative fragments for PDB ligands I5S, M77, and TZ1.(DOCX)Click here for additional data file.Figure S15Microenvironment similarity of nearest neighbors.(DOCX)Click here for additional data file.Figure S16FragFEATURE computation time.(DOCX)Click here for additional data file.Table S1Prevalence of validation ligands across 50% sequence identity clusters.(DOCX)Click here for additional data file.Table S2Breakdown of protein pockets tested.(DOCX)Click here for additional data file.Table S3PDB ligands supporting the benzamide prediction for exotoxin A.(DOCX)Click here for additional data file.Table S4PDB structures supporting the benzamide prediction for exotoxin A.(DOCX)Click here for additional data file.Table S5Protein names of PDB structures supporting the benzamide prediction for exotoxin A.(DOCX)Click here for additional data file.Table S6Sequence identity between PDB structures supporting the benzamide prediction for exotoxin A.(DOCX)Click here for additional data file.Table S7PDB structures supporting fragment 13509097/benzamide prediction for DAPK1.(DOCX)Click here for additional data file.Table S8Sequence identity of PDB structures supporting fragment 13509097/benzamide prediction for DAPK1.(DOCX)Click here for additional data file.Table S9PDB ligands supporting the benzamide prediction for DAPK1.(DOCX)Click here for additional data file.Table S10PDB structures supporting fragment 1049/241 prediction for aPKC.(DOCX)Click here for additional data file.Table S11Sequence identity of PDB structures supporting fragment 1049/241 prediction for aPKC.(DOCX)Click here for additional data file.Text S1Similar microenvironments bind similar fragments.(DOCX)Click here for additional data file.Text S2FragFEATURE predicts fragments of bound ligands using predicted protein pockets.(DOCX)Click here for additional data file.Text S3FragFEATURE predicts fragments of unbound ligands using predicted protein pockets.(DOCX)Click here for additional data file.Text S4Inhibitor fragment predictions for cholix toxin.(DOCX)Click here for additional data file.Text S5Inhibitor fragment predictions for Abl tyrosine kinase.(DOCX)Click here for additional data file.Text S6Inhibitor fragment predictions for protein kinase A.(DOCX)Click here for additional data file.Text S7Expanding the knowledge base beyond the information in the PDB.(DOCX)Click here for additional data file.Text S8FragFEATURE and ligand/fragment affinity.(DOCX)Click here for additional data file.Text S9Microenvironment independence assumption.(DOCX)Click here for additional data file.Text S10Multiple hypothesis correction.(DOCX)Click here for additional data file.


Integration of retroviruses occurs in most genomic regions with weak but statistical significant preferences for target site sequences. HIV-1 preferentially targets genes, particularly the transcriptionally active ones. Avian sarcoma and leukosis viruses (ASLV) integrate with only slight preference for genes while murine leukemia virus uniquely favors integration in close proximity to upstream or downstream transcription start sites. Genomic features as well as the “epigenetic landscape“ at the site of integration are crucial for the outcome of each integration event, i. e. stable long terminal repeat-driven expression of the provirus or silencing and transcriptional suppression. The current availability of assembled genome sequences with epigenomic characteristics and reliable cloning of retrovirus integration sites enables to study the interplay between transcription signals of the retrovirus and enhancing or suppressive influences of the adjacent cellular DNA.We studied the integration sites of ASLV-derived vectors integrated into normal or DNA methylation-deficient human cells. Integration sites from cell clones containing single provirus were cloned and their genomic features as well as epigenetic landscape were correlated with expression or silencing of the provirus. Dnmt3b turned out to be the major player in silencing the long terminal repeat-driven transcription. Proviruses integrated into H3K4me3-rich CpG islands associated with promoters of active genes display long-term stability of expression and are resistant to the transcriptional silencing after over expression of Dnmt3b. Stability of expression and resistance to the silencing decreases with the distance from transcription start site. By contrast, proviruses integrated into the intergenic regions tend to the spontaneous transcriptional silencing even in DNA methyltransferase-deficient cells.


Ion channels facilitate the diffusion of specific ions across neuronal membranes. If large enough, this movement of charge creates currents that may change the membrane potential. Biophysical models of membrane potential assume the trans-membrane currents flow within an “equivalent electrical circuit” in which ion channels are represented by resistors arranged in parallel. The functions representing the trans-membrane currents mediated by channels are typically written using Ohm's law. It is possible to describe channel-mediated currents by taking diffusion into consideration , but suc


Cell behavior in the presence of nanomaterials is typically explored through simple viability assays, but there is mounting evidence that nanomaterials can have more subtle effects on a variety of cellular functions. Previously our lab demonstrated that gold nanorods functionalized with polyelectrolyte multi-layers inhibited rat cardiac fibroblast-mediated remodeling of type I collagen scaffolds by altering fibroblast phenotype and the mechanical properties of the collagen network. In this work, we examine a possible mechanism for these effects: adsorption of cellular proteins by the nanorods. Mass spectrometric and gel electrophoresis of media collected from cultured cells suggests that a number of proteins, some of which mediate cell-cell and cell-matrix interactions, adsorb onto the surface of these nanoparticles in vitro. Polyethylene glycol coating of the nanorods largely mitigates protein adsorption and fibroblast-mediated collagen remodeling. These results suggest that adsorption of proteins by nanorods could have a significant effect on cell functions, including fibroblast-mediated matrix remodeling. Gold nanomaterials have received considerable attention for use in biomedical applications due to their unique optoelectronic properties Previously, we reported that gold nanorods coated with polyelectrolyte multilayers terminated with anionic poly(styrene sulfonate) (PSS) substantially altered the matrix-remodeling behavior of neonatal rat cardiac fibroblasts in type I collagen gels in vitro model for tissue and also the native ECM of the cells in the phenotype study In addition to the effects nanorods have on cells themselves, possibly via the protein corona, nanorods also alter the physical properties of the matrix that surrounds them. We quantitatively examined the effects of polyelectrolyte-coated nanorods on the polymerization and mechanical properties of type I collagen hydrogels, frequently used as an Here, we report the composition of the protein corona adsorbed onto polyelectrolyte-coated gold nanorods from culture media conditioned by rat cardiac fibroblasts. We demonstrate differential binding of proteins to nanorods that bear positively or negatively charged functional groups. Furthermore, the hypothesis that adsorbed proteins actually cause behavior changes in fibroblasts was tested using “protein-resistant” coatings on the nanoparticles as functional controls. Reduced protein adsorption to these “resistant” particles was observed along with a reduction in nanomaterial-induced cellular behavior. Taken together, these experiments support the notion that the protein corona that surrounds nanomaterials provides a molecular mechanism to influence cell behavior.4.3H20), sodium borohydride (NaBH4), ascorbid acid, poly sodium salt , poly, poly, and ultrapure hexadecyltrimethylammonium bromide (CTAB) were obtained from Sigma-Aldrich . All other chemicals were purchased from Sigma unless otherwise noted and all solutions were prepared with 18 MΩ resistivity water using a Millipore Biocel ultrafiltration system .Chloroauric acid . Transmission electron microscopy was performed on a JEOL 2100 Cryo-TEM instrument . Light scattering and zeta potential analysis were performed on a Brookhaven Instruments ZetaPALS machine .Gold nanorods  were prepared in aqueous solution using a seed-mediated surfactant-directed approach previously described and purified by centrifugation and washing 9 and 1010 rods) were incubated in conditioned media overnight at 37°C followed by centrifugation to separate them from the media. After washing with phosphate buffered saline, bound proteins were eluted using 1 M NaCl, separated on a 4–20% gradient sodium dodecylsulfate–polyacrylamide gel electrophoresis (SDS-PAGE) gel  and silver stained using the Silver Quest staining kit (Invitrogen) as per manufacturer’s instructions. To identify specific proteins bound to the nanorods, samples were sent to ProtTech  where they were reduced using dithiothreitol, alkylated with iodoacetamide (20 mM), trypsin digested, and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a system with a high performance liquid chromatography reverse phase C18 column coupled to an ion trap mass spectrometer . Protein identification was determined using the Non-Redundant Protein Database from GeneBank with ProtTech’s ProtQuest software package.Neonatal rat cardiac fibroblasts were isolated from 3–4 day old Sprague Dawley pups as previously described 9 nanorods/gel were re-suspended in the HEPES buffer prior to preparing the collagen mixture. Fibroblasts  were combined with the neutralized collagen and added in 100 µL aliquots to the wells of 96 well microtiter plates and allowed to polymerize for 1 hour at 37°C in a 5% CO2 humidified incubator. After polymerization, culture media was added to each well and the gels were manually detached from the wall of the well. Gels were photographed after 24 hours in culture with a Canon EOS Rebel digital camera and gel areas were measured using ImageJ. Statistical analysis was conducted using a Student’s T-test in Microsoft Excel.Three dimensional collagen gels containing neonatal cardiac fibroblasts seeded with PSS, PDADMAC or PEGylated gold nanorods were prepared as previously described α-smooth muscle actin (α-SMA)  and control gene acidic ribosomal phosphoprotein (ARBP) . Data were analyzed using the fold change method incorporating primer efficiency with the REST2009 program .Three gels per condition were pooled, solubilized in Trizol (Invitrogen) and RNA isolated using the RNeasy Micro RNA kit with on column DNase I digestion as recommended by the manufacturer . RNA concentrations were determined using a BioRad Experion system with standard sensitivity RNA chips and 50 ng of total RNA was used to prepare cDNA using the iScript cDNA synthesis kit . PCR reactions were carried out using iQ Supermix (BioRad) with primers specific for rat In our previous experiments, the presence of PSS-coated gold nanorods in fibroblast-seeded three-dimensional collagen gels led to reduced gel contraction by the fibroblasts and corresponding changes in gene expression 9 PEGylated (350 or 5000 MW PEG) or PSS- or PDADMAC-coated rods/gel were prepared and allowed to contract for 24 hours To confirm that protein adsorption was involved in mediating differences in collagen gel contraction, gels containing cardiac fibroblasts and 5.6×10To test our hypothesis that the protein binding characteristics of the nanoparticle coating are the mitigating factor in the observed changes in fibroblast behavior, PDADMAC-, PSS- and PEG-coated nanorods were exposed to fibroblast conditioned culture media and the adsorbed proteins identified. The procedure used to isolate and characterize nanorod-bound proteins is outlined in LC-MS/MS analysis of protein eluates revealed that the nanorods adsorbed a more diverse population of proteins than originally expected based on the electrophoresis data. On average, PSS- and PDADMAC-coated nanorods bound almost twice as many unique proteins as PEG-terminated nanorods . CompariIn a combination of four independent analyses, 37 unique proteins were identified which bound to the PSS-coated nanorods while 45 unique proteins adsorbed to PDADMAC-coated nanorods . Of thesThe data in Based on the modest effects that the PEGylated nanorods had on fibroblast-mediated gel contraction and gene expression, the protein adsorption characteristics of these particles were also examined to determine if the minimal changes in gel contraction observed were due to the non-fouling and minimally adsorptive characteristics of the PEG coating. As shown in An examination by species of origin indicated that most of the proteins identified as being associated with the nanorods were of bovine origin, and therefore due to the serum used in the culture media. However, biglycan was of rat origin, therefore produced by the fibroblasts, and was found in the protein fractions binding to both cationic and anionic nanorods but not PEGylated particles. Biglycan is a small ECM proteoglycan found in most connective tissues, and it is known to influence cell-ECM interactions and to modulate cell bioactivity by binding collagen and multiple growth factors, including members of the transforming growth factor β (TGFβ) superfamily The local concentration of biglycan in the heart has been shown to influence cardiac fibroblast development in 6-week-old mice  A number of investigators have begun to examine the role that protein adsorption onto nanomaterials has on biological effects of nanomaterials The results of this study offer insight into a possible mechanism by which polyelectrolyte-coated nanorods disrupt the matrix remodeling behavior of cardiac fibroblasts. The ability to bind proteins on the surface of the nanomaterials which can modulate the ECM remodeling behavior of fibroblasts suggests that nanoparticles could serve as novel therapeutic agents in the regulation of wound healing. If the protein adsorption process could be tailored for particular proteins, one could envision regulation of cell behavior by colloidal nanomaterials, by analogy to how fixed nanostructured substrates guide cell behavior.Figure S1Size Distribution of Adsorbed Proteins. PEG-, PSS-, and PDADMAC- coated nanorods showed a similar distribution of bound proteins based upon the protein size.(JPG)Click here for additional data file.Table S1Complete list of unique proteins identified which bound to PSS-, PDADMAC-, and PEG-coated nanorods. Three proteins identified as binding to PDADMAC-coated nanorods were of human origin and discounted as contamination since no human products were added to the cell culture media.(DOCX)Click here for additional data file.


Molecular parameters of key interest for monitoring the efficacy of anti-malaria interventions are those that quantify effects on transmission or incidence of infection and disease.molFOI is easily detected despite a background of ongoing infections.Vaccine trials provide regular follow-up samples of both comparator groups. In addition, the clinical episodes are detected and sampled. Provided the spacing of follow up surveys is in the range of 2-8 weeks, the force of infections (FOI) can be determined by genotyping all consecutive samples of all study participants. The molecular FOI is defined by new parasite clones appearing per time interval. Individual clones are identified using highly polymorphic molecular markers in conjunction with high resolution typing. Simultaneously, blood samples from trial participants become available for RNA based detection and quantification of gametocytes by qRT-PCR. Vaccine effects on gametocyte prevalence can be detected by targeting gametocyte-specific transcripts.We have evaluated RNA sampling techniques for malaria field surveys. Collecting samples directly into RNAprotect solution gave best results. Gametocytes were detected by qRT-PCR using marker pfs25.P. falciparum clones aquired per time per individual host, corrected for imperfect detectability [In our cohort studies asexual parasites were genotyped using marker msp2 and fragment sizing by capillary electrophoresis ,2. In thtability .P. falciparum infection dynamics were estimated based on high precision genotyping data from cohort studies or from a clinical trial with repeated follow up bleeds at intervals between 2 weeks and 2 months. The full time-series of presence and absence of clones in consecutive samples from one individual forms the basis from which molFOI, duration of infection, and clone detectability were estimated.Molecular parameters describing the molFOI in vaccinated children from the Combination B vaccine trial was significantly reduced in vaccine recipients only for parasites carrying a 3D7-type msp2 allele corresponding to 3D7 MSP2 component of Combination B.molFOI as an outcome measurement in vaccine trials and to collect and preserve in the field trial setting in parallel blood samples useful for RNA extractions for monitoring vaccine effects on transmission stages.We demonstrated proof of concept of this approach in a vaccine trial of the Combination B malaria vaccine. We propose to consider the