We found that CD105 (endoglin) had a strongly significant association with poor prognosis on multivariate analysis (p = <0.001). Supervised hierarchical clustering of genome wide gene expression data identified 13 genes associated with differential degrees of vascularity in the cohort. The novel angiogenesis-associated genes identified in this analysis (including MIPOL-1 and ENPP5) were validated by realtime polymerase chain reaction. We also demonstrate that CD105 positive blood vessels associate with CD133 positive tumour cells and that a proportion of CD105 positive vessel cells demonstrates co-positivity for CD133, suggesting that the recently described phenomenon of vasculogenic mimicry occurs in pHGG. Together, the data suggest that targeting angiogenesis, and in particular CD105, is a valid therapeutic strategy for pHGG.Paediatric high grade glioma (pHGG)  remains poor prognosis tumours, with a median survival of only 15 months following diagnosis. Current investigation of anti-angiogenic strategies has focused on adult glioblastoma multiforme (GBM) with phase III trials targeting vascular endothelial growth factor continuing. In this study we investigated whether the degree of vascularity correlated with prognosis in a large cohort of pHGG (The online version of this article (doi:10.1007/s00401-012-0952-1) contains supplementary material, which is available to authorized users. In children high grade gliomas (HGG) (WHO astrocytoma grade III and IV) represent around 10% of intracranial neoplasms  and contCurrent phase III trials in adult GBM are targeting the pro-angiogenic ligand vascular endothelial growth factor (VEGF) via monoclonal antibody or small molecule inhibitor therapy . Recent Glioma vasculature formation occurs through at least three distinct processes. Angiogenesis is the process of generating new blood vessels from rerouting and remodelling of pre-existing vessels . VasculoAmong solid tumours, glioblastoma multiforme displays the most angiogenic features and the highest degree of vascular proliferation and endothelial cell hyperplasia . MalignaConventional immunohistochemical measurement of microvessel density as a proxy marker for angiogenic activity has utilised markers of mature endothelial cells such as CD31 (PECAM), CD34 or factor VIII. Variable results have been achieved, both in HGG  and otheCD105 (cluster of differentiation molecule 105) (endoglin) is a transmembrane homodimer protein of 180 kDa which is characteristically found on endothelial (blood vessel) walls . It is aAs a marker for blood vessels CD105 is associated with immature vessels, and some studies have suggested it could be a marker that preferentially stains for novel angiogenic vessels. It has also been proposed that CD105 is a marker for mesenchymal stem cells . In someBecause of its association with developing blood vessels, we hypothesised that CD105 may be a more representative measure of angiogenic activity in pHGG tissue, and that genetically characterising tumours with differing levels of CD105 may identify novel genes involved in paediatric angiogenesis. The micro-environment in immediate proximity to blood vessels has been suggested as an area enriched for, and supportive of, tumour initiating cells [www.CBTRC.org). All samples were surgically collected ante-mortem at UK paediatric neurosurgical centres and are from paediatric high grade gliomas  with diagnosis confirmed by central pathological review (JL and Keith Robson). The cohort consisted of 102 children diagnosed with GBM, 29 with anaplastic astrocytoma (AA), 10 with HGG not otherwise specified, 8 with anaplastic oligodendroglioma and 1 with anaplastic pleomorphic xanthoastrocytoma. Full consent and ethical approval has been obtained for their use in this study, from the UK Children’s Cancer and Leukaemia Group and local ethical and Trent MREC approval (06/MRE04/86). A full clinical dataset, including demographic and treatment details, is available for each of the samples in this cohort. Three cores from representative regions of each tumour were included on the tissue microarray.The tissue microarrays utilised in this paper were built from 150 samples banked by the Children’s Cancer Leukaemia Group (UK CCLG) through Nottingham Children’s Brain Tumour Research Centre  were all utilised at 1:50 concentration. Anti-KI 67 was applied for 30 min, all other antibodies were applied for 1 h at room temperature. Secondary antibody (100 μl Dako horse radish peroxidase conjugated rabbit anti-mouse) was applied for 30 min at room temperature. Finally, 2 μl of 3,3′-Diaminobenzidine chromogen in 98 μl of Dako REAL substrate buffer was applied before haemotoxylin counterstaining and mounting. At least three TMA cores were analysed for each tumour for each antibody, with the area of strongest staining in each core being assessed. For Ki67, scoring was performed by taking the most positive high powered field for each core and counting the number of positive nuclei as a percentage of the total number of nuclei present. When assessing Ki67 staining in vessel wall cells, all the blood vessels definitively identifiable as such in the core were first selected. The number of cell nuclei in the vessel wall staining positive for Ki67 were then counted as a percentage of the total number of nuclei in the vessel wall. Mitotic index was calculated by counting the number of mitotic figures per high powered field for at least three representative fields for each tumour. For CD31 and CD105, the total number of positive vessels per TMA core were counted. On whole sections of 12 paediatric HGG, defined areas of geographic necrosis were selected and the distance to the 10 nearest blood vessels staining positive for CD31 or CD105 was measured. For VEGF, as a cytoplasmic stain, scoring was undertaken by assessing the approximate percentage of cells staining positive .Immunohistochemistry against the tissue microarrays commenced with de-paraffinisation in ethanol and xylene, followed by antigen retrieval in sodium citrate in a steamer for 40 min . For CD105, a proteinase K antigen retrieval method was utilised. After deparaffinisation, sections were covered with 100 μl of proteinase K solution (Sigma-Aldrich UK) (20 μg/ml). Specimens were then placed in a humidification chamber and then warmed to 37°C for 15 min in an oven. Anti-KI 67 , anti-CD31 , anti-CD105 , and anti-VEGF  buffer, adjusted to pH 8.0, heated in a steamer for 40 min. Blocking solution was applied , 0.1% Triton X-100 in PBS, 1% bovine serum albumin) for 1 h in the dark at room temperature. Primary antibodies  were applied overnight in the dark at 4°C. Secondary antibody combinations of Alexa488-conjugated goat anti-rabbit (1:200) and Alexa555-conjugated goat anti-mouse (1:200), diluted in 2% NGS antibody diluent were then applied for 2 h in the dark at room temperature. Vectashield with DAPI mounting medium  was used. Images were taken using a Nikon ECLIPSE 90i light microscope fitted with a Hamamatsu OCRA-ER camera, using three fluorescent light filters; DAPI (excitation 340–380 nm), FITC (Ex 405–495 nm) and Texas-red (Ex 540–580 nm) and Volocity 5.0 imaging software. On average three images were taken per core wherever positive staining for CD31 or CD105 blood vessels was visible. Measurements were performed using the line measurement tool in Volocity; the distance between the centres of the nuclei for CD133+ cells was measured to the edge of the closest CD31+ or CD105+ blood vessel (see online resource 1). This was also performed for all CD133− cells in each image.http://www.ncbi.nlm.nih.gov/geo/, accession No.GSE19578).As previously detailed , analysi2 First Strand Kit (Qiagen). Target genes identified on array analysis were validated with quantitative real-time PCR using a CFX96 realtime system . SYBR Green Supermix  was used with the following primer sequences (5′–3′) CD109—GAAGCTGTCCTCCTGTGACC; TCACAGCCAAAGTTCCATAAAG; ENPP5—TTGCACCCATGTCACAGAAT; CACTTATCACCTTCTTCTTCTCTCA MIPOL1—TGGTCATGCATGGAGTCTTG; ACTTACCTGCACCTCCCAGA GAPDH—CTGACTTCAACAGCGACA; TGCTGTAGCCAAATTCGTTG. The following programme was used: initial activation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s, annealing at 59°C for 30 s and extension at 72°C for 30 s followed by fluorescence read. Primer efficiency was calculated and the expression of each gene in low and high vascularity tumours calculated relative to normal adult brain RNA  using the modified Pfaffl equation R = Etarget ∆CT (CT target gene control−CT target gene sample)/Econtrol ∆CT (CT control gene control−CT control gene sample) with GAPDH as the control gene.RNA was isolated from representative tumour specimens using the mirVana RNA isolation kit . cDNA was produced with the RTT test. Survival analysis was performed using a Cox regression multivariate model and Kaplan–Meier plots (log-rank) for discrete groups. Array analysis was performed using Genespring software  with multiple unpaired T tests between groups and Benjamini–Hochberg multiple test correction applied throughout. P values of less than 0.05 were considered significant.Analysis was performed using SPSS 16 with comparison between groups undertaken using unpaired two-tailed p = 0.065).The tumour cohort consisted of 150 pHGG with diagnosis confirmed by central pathological review, ranging in age (at diagnosis) from 2 days to 21 years, with a mean age of 7 years and 10 months. The cohort consisted of 88 males, 54 females and 8 cases where sex was not recorded, a male to female ratio of 1.6:1. Further details of the cohort have been previously published . One hunp = 0.880)  (see supplementary resource 4). Mitotic index across the wider tumour also had a significant correlation with survival on univariate analysis (p = 0.048).Ki67 staining data were acquired for 127 tumours with survival data available for 100 patients . The data were then re-analysed with very short-term survivors removed from the analysis  whose short survival may have been determined by surgical complications rather than tumour biology. There was now a significant relationship between MVD CD31 and survival (p = 0.008 hazard ratio = 0.934 Cox regression) with patients tending to survive longer if their tumour had a higher density of CD31 positive vessels .There was no significant relationship between CD31 microvessel density (MVD CD31) and survival  than vessels staining positive for CD31. It was also noticeable that some structures that histologically appeared as blood vessels did not stain positive for CD105. For individual tumours mean number of positively stained vessels ranged from 0 to 36 vessels/core, with single cores ranging from 0 to 59 vessels/core. Tumours were grouped into three levels of microvessel density for Kaplan–Meier analysis—those with less than 3 vessels/core (low), those with 3–8 (medium) and those with more than 8 vessels/core (high). As with CD31 IHC, no previous papers have used objective scoring systems, so the groupings are based on creating three groups of equal sample numbers.p = 0.001 Cox regression, with hazard ratio of 1.05). All long-term survivors (>8 years), for whom data were available, were in the lowest group of MVD CD105 (<3 vessels/core). This relationship was identical on log rank testing by groups (p = <0.001) and if the short-term survivors were removed as for the CD31 analysis. The same relationship was also found using the individual peak core count for each tumour. There was a weak correlation between the peak CD31 and CD105 values for each tumour (p = 0.022 Spearman) but not the mean values. There was no relationship between mitotic index and microvessel density and the levels of CD105 staining were the same in GBM and AA . There was a significantly shorter mean distance between areas of necrosis and CD105 positive vessels (121.9 μm) than between areas of necrosis and CD31 positive vessels (193.1 μm) (p = 0.001 T test).There was a highly significant relationship between microvessel density as defined by CD105 immunostaining (MVD CD105) and survival Fig. . The grep = 0.818 Cox regression).VEGF immunohistochemistry data were available for 102 tumours, with survival data available for 79 of these patients. VEGF staining was variable with some cores being strongly positive and some completely negative. Even with a single core, expression could vary significantly, with VEGF positivity highest at the interface between necrotic and viable regions of cells , extent of surgery and administration of radiotherapy. The effect of MVD CD105 was slightly enhanced with hazard ratio increased to 1.084. The effect of MVD CD31 also achieved significance with an increased value correlating with improved survival chances (hazard ratio = 0.891). There was no significant association between age and any of the other factors studied.When multivariate analysis was performed for the 75 cases Table  where inT testing with multiple test correction was performed, analysing the difference between samples on the basis of MVD CD31 no significant genetic differences were found. However, when the samples were analysed on the basis of MVD CD105, 13 genes were found to be significantly differentially expressed between samples in the low (<3 vessels/core) group and tumours in the medium and high groups , a candidate tumour suppressor gene and ENPP5 (a closely-related molecule to ENPP2 (autotaxin)). In view of their known tumour-related function these genes were selected for validation of the gene expression array data. On rtPCR significant differences in expression level relative to control  were confirmed for MIPOL-1 and ENPP5 (online resource 3).The genes that have been highlighted by this analysis include mirror image polydactyly-1 . 21 tumours were analysed from the co-staining of CD133 and CD105, with 9,525 cells measured. On average, CD133+ cells were also significantly closer than CD133− cells to CD105+ blood vessels, by 15.14 μm , with the differences between CD133± cells greater for CD105 vessels than CD31 vessels (p = <0.001) . CD133− cells were significantly further away from CD31+ vessels than CD105+ vessels. The basis for the increased differential for CD133± for CD105 vessels therefore seems to be an increased separation of CD133− cells from the blood vessel rather than increased proximity of CD133+ cells.There was no significant difference between the mean distance from CD133+ cells to CD31 vessels or CD105 vessels . In contrast, CD133+ cells were significantly closer to CD31+ blood vessels in the 36 months or under group . An increased proportion of CD133+ cells in an individual’s tumour showed a trend towards correlation with decreased survival but did not achieve statistical significance (p = 0.107 Cox regression).To investigate whether the proximity of CD133+ cells to CD105+ vessels was affected by the patient’s age, patients who were over 36-month-old at the time of diagnosis were compared with those 36 months or under. In the >36-month-group, CD133+ cells were on average, ~0.43 μm closer to CD105+ blood vessels than CD31+ vessels, however, the result was not statistically significant (p = <0.001). Compared to AA, CD133+ cells in GBM were significantly closer to both CD31+ blood vessels  and CD105+ blood vessels . There was no significant association (p = 0.107) found between survival and proportion of CD133 expression though patient numbers were small.To determine if the tumour type correlated with the distance of CD133+ cells from CD31+ and CD105+ blood vessels, measurements for patients with GBM and AA were analysed separately. The average proportion of CD133+ cells in GBM tumours (42.56%) was significantly greater than AA tumours (24.62%)  and also for CD105   that provide resistance to radiotherapy or chemotherapy . This stA third possible mechanism is also linked to the cancer stem cell hypothesis. CD105 is well recognised as a marker for mesenchymal stem cells . Some hiThere was a weak positive correlation between MVD CD31 and overall survival once very short-term survivors were removed from the analysis. This could be explained by inclusion of normal brain parenchymal vessels into the tumour and thus into the surgical specimen. In this case the vessels could be a sign that the tumour is still at an early stage of spread through the brain and that normal tissue continues to survive within the tumour. At a later stage of tumour growth the ischaemic conditions generated by the cancer would lead to death of the normal tissue and selection for hypoxia-resistant tumour cells, along with tumour-induced CD105 positive blood vessels.An alternative explanation is that the vessels staining positive for CD31 are indeed formed as part of tumour growth and are not simply bystander vessels incorporated into the tumour as it invade surrounding brain. In this case the slight survival benefit may be explained due to the level of vessel maturity. Tumours that are more differentiated may in turn produce vessels that are better differentiated and will thus likely display higher levels of the mature vessel wall marker CD31. Tumours with higher levels of mature cells and less anaplasia will be more likely to have a better prognosis as they may grow more slowly and invade less extensively into surrounding brain. Alternatively, CD31 positive vessels may be more efficient in oxygenating the tumour parenchyma due to their greater maturity and increased diameter. The improved tissue oxygenation would then result in a decreased drive for tumour cells to migrate out of hypoxic areas and invade surrounding brain. Improved tumour oxygenation could also potentially have other effects such as fuelling higher tumour cell metabolism and turnover, or by influencing the level of stem-like behaviour shown by tumour cells.The lack of significant correlation between clinical factors, MiB-1 and VEGF may also have various explanations. One might expect mitotic index to correlate well with prognosis as faster growing tumours should logically lead to shorter survival, and indeed this has been found to be the case in some CNS tumour types . It is cIt may be that the sampling carried out in this study was simply not extensive enough to accurately gauge the true proliferation rate for each tumour and negative Ki67 staining may not necessarily imply quiescence, just that negative cells have not been stained at the particular phase of mitosis when the target protein is present. There is also an issue with the scoring system as to how one incorporates a small area of highly active tissue which may be the most prognostically significant area into the overall assessment of a tumour which may have large areas of less proliferative tissue. By contrast, scoring proliferation by counting mitotic figures across multiple high powered fields showed a significant relationship with survival, suggesting that rate of cellular proliferation has an important influence on the behaviour of these tumours. The interesting finding that proliferation index in the perivascular population is significantly correlated with survival supports the idea that it is the proliferation rate of sub-populations within the tumour rather than the total cell proliferation that is critical. Tumours with increased perivascular proliferation may have the capacity to increase their nutrient supply more quickly than tumours with slower rates of blood vessel growth. This increased proliferation could also be generating favourable microvascular niche environments at a faster rate, thus enhancing tumour growth and possibly promoting the stem-like fraction of cancer cells. Indeed, it may even be that the proliferative cells identified in the vessel walls are tumour cells undergoing a vasculogenic mimicry type process. It is worth noting that proliferation index is not currently part of the WHO grading criteria for astrocytomas because of the difficulties in obtaining a consistent inter-observer value for individual tumours.VEGF is also locally and specifically expressed at the hypoxic/normoxic interface where ischaemic conditions trigger the angiogenic switch. Even within a single core, a band of expression could often be seen with low levels on either side of VEGF staining in the necrotic core and in the viable periphery. It again seems that results for VEGF are likely to be highly dependent on the precise location within the tumour from where the sample came. In some tumours this region of maximal expression will not be visualised as the specimen cores will have been extracted from other regions of the tumour. Equally, on molecular biological studies, only a small part of the tumour is analysed and molecular differences may well exist within different regions of the same tumour as well as between different patients’ tumours.MIPOL1) is located on 14q and was initially identified in a patient with this deformity [MIPOL1 was implicated as a putative tumour suppressor gene in nasopharyngeal carcinoma (NPC), where chromosome 14 loss (including the MIPOL1 locus) is common [MIPOL1 in NPC cell lines greatly reduced their ability to proliferate or form tumours in xenograft experiments. In our study MIPOL1 was found to be 2.4-fold downregulated in the higher vascularity tumours. MIPOL1 protein is located in the nucleus and seems to exert its effects by up regulating expression of p27 (WAF1/CIP1) and p21 (KIP1), which are also implicated as angiogenic markers in other malignancies.The list of genes showing varying levels of expression between tumours with high and low microvessel density as measured by CD105 immunohistochemistry shows several candidate genes of potential interest. Mirror image polydactyly 1 , a cell surface antigen which was initially studied in CD34 positive acute myeloid leukaemia cell lines, but has subsequently been found to be expressed on a variety of haematopoietic cells and endothelial structures [CD109 suppressed malignant growth and CD109 shed from the cell surface has a pro-growth function), squamous cell carcinoma of the oral cavity [CD109 was found to have a 1.9× fold change between high and low vascularity tumours.Another gene of interest highlighted by this analysis is ructures . It has ructures . It has ructures  . This molecule has not been widely studied but the closely-related molecule ENPP2 (autotaxin) has been widely implicated in neoplasia and is the focus of considerable efforts to develop small molecule inhibitors [MRPL12, a downstream target of the REIC/Dkk3 tumour suppressor in prostate cancer [CIB2  [NEK1 (down regulation of which may worsen the ability of the cell to repair DNA damage) [DLST (a component of the respiratory chain). These genes will be further investigated in novel three dimensional cell culture models of angiogenesis we are currently developing.hibitors . It prode cancer , CIB2 (aathways) , NEK1 (d damage)  and DLSTThis study also investigated whether cells marked with the putative stem cell marker, CD133, were found in closer proximity to the glioma vasculature than CD133− cells. The results indicated that CD133+ cells were found significantly closer to both CD105+ and CD31+ blood vessels than CD133− cells; this result is consistent with other studies which have shown that glioma cells expressing other stem cell markers, such as Nestin, were located closer to brain tumour vessels than Nestin negative cells , 13, 46.It was observed that the difference of the proximity of CD133+ cells/CD133− cells to CD105+ vessels was greater than that to CD31+ blood vessels. Furthermore, when comparing GBM and AA tumour samples, CD133+ cells were found significantly closer to CD105+ blood vessels in GBM than in AA, but this difference between GBM and AA was much less for CD31+ blood vessels. This suggests that CD105+ vessels may have a more important role than CD31+ vessels in the vascular microenvironment for stem-like cells, especially in grade IV tumours. This could be related to the presence of necrosis in the grade IV tumours and the greater proximity of CD105 positive vessels to necrosis (compared to CD31 positive vessels) suggesting a role for CD105 in hypoxic areas.An interesting finding was that the average proportion of total cells counted that were CD133+ cells in GBM which was greater than in AA. This may be associated with the higher histological grading of GBM, thus supporting studies that suggest CD133 expression can be used as a prognostic factor , 24, 32.Some CD31 and CD105 endothelial cells co-expressed CD133, as part of the vessel wall or as single cells. CSC’s in GBM tumours are suggested to be able to produce differentiated neoplastic endothelial cells, forming part of the hyperplastic GBM vasculature . Our worThis study demonstrates that CD105 positive vessels have a significant role in the biology of pHGG and exert an influence over prognosis even in a heterogeneously treated cohort. Expression of different levels of CD105 by tumours is associated with wider changes in gene expression, allowing the identification of novel genes not previously implicated in CNS tumour angiogenesis. One possible explanation for the prognostic influence of CD105 is through its role in developing the microvascular niche in which stem-like cancer cells preferentially inhabit. Our data provide evidence that this supportive micro-environment may be preferentially found in the proximity of CD105 positive vessels as opposed to blood vessels stained by other endothelial markers. This suggests that CD105 or other components of the TGF beta pathways may be potential therapeutic targets for the treatment of pHGG. Future studies of the HGG microvascular niche should focus on the environs of CD105+ vessels and their interplay with, and possible development from, CD133+ tumour cells.Supplementary material 1 (TIFF 17945 kb)Below is the link to the electronic supplementary material.Online resource 1 Central blood vessel stained positively for CD105 (red) with illustration of measurement technique (multiple lines) to nuclei of surrounding cells (DAPI blue stained)Supplementary material 2 (TIFF 4068 kb)Online resource 2 Supervised hierarchical clustering of 16 pHGG demonstrating separation of high and low MVD CD105 tumours based on expression of differentially expressed genesSupplementary material 3 (TIFF 6156 kb)Online resource 3 Validation by realtime polymerase chain reaction for three selected genes – ENPP5, CD109, MIPOL-1 with ENPP5 and MIPOL1 significantly differentially expressedSupplementary material 4 (TIFF 1101 kb)Online resource 4 Kaplan–Meier plot stratified by level of vessel wall cell Ki67 positivity, demonstrating reduced overall survival in tumours with high levels of vessel wall cell proliferation.


HIV-1 genomic RNA has a noncoding 5′ region containing sequential conserved structural motifs that control many parts of the life cycle. Very limited data exist on their three-dimensional (3D) conformation and, hence, how they work structurally. To assemble a working model, we experimentally reassessed secondary structure elements of a 240-nt region and used single-molecule distances, derived from fluorescence resonance energy transfer, between defined locations in these elements as restraints to drive folding of the secondary structure into a 3D model with an estimated resolution below 10 Å. The folded 3D model satisfying the data is consensual with short nuclear-magnetic-resonance-solved regions and reveals previously unpredicted motifs, offering insight into earlier functional assays. It is a 3D representation of this entire region, with implications for RNA dimerization and protein binding during regulatory steps. The structural information of this highly conserved region of the virus has the potential to reveal promising therapeutic targets.  •The 2D structure of the HIV-1 5′ UTR RNA has been elucidated in a monomerized form•The low-resolution 3D structure has been determined by FRET and simulated annealing•Modeling has revealed an unpredicted kink turn The highly conserved packaging signal region of HIV-1 RNA represents a potential therapeutic target. Stephenson et al. present a monomerized 2D structure of the region and a 3D model elucidated by FRET and simulated annealing which reveals an unpredicted kink turn. Effective treatments now exist to suppress HIV replication, but high sequence variability and mutational escape contribute to the lack of an effective vaccine. Regions of high RNA sequence conservation provide attractive therapeutic targets. One such sequence is the 5′ untranslated region (UTR), present in all genomic HIV transcripts whose stringent conservation is attributable to the presence of many regulatory regions controlling reverse transcription , transcrSeveral methodologies have been used to elucidate the secondary structure of the HIV-1 5′ UTR , includiThe 3D structures of several small isolated fragments of the 5′ UTR have been elucidated by nuclear magnetic resonance (NMR) , but it In order to avoid the confusing signals caused by mixed inter- and intramolecular RNA interactions in the dimer, we competed out dimeric RNA to produce a homogeneous monomeric species and, in close to intracellular conditions, solved the secondary structure of the major packaging signal region of HIV-1. We then used through-space distances derived from single-molecule fluorescence resonance energy transfer (SmFRET) experiments to build a 3D structure by molecular modeling. The resulting structure fits well with the known functions of this region and also with known short NMR-derived substructures. Apart from revealing the relative orientations and flexibilities of previously documented helix loop motifs, modeling reproducibly demonstrates an unpredicted kink-turn motif at the core of the structure, which is suggestive of a protein binding site and which explains the effects on genome packaging of previous mutations. This demonstration of this methodology to generate a large viral RNA structure in three dimensions indicates the power and versatility of the technique. The information on flexibility and general helix position of the HIV-1 5′ UTR RNA presented here has the potential to facilitate structural analysis of protein binding and to aid intelligent drug design.psi) region of HIV-1 into a pure monomeric species using a blocking locked nucleic acid (LNA) (5′ GCGCUUC 3′), complementary to 4 of the 6 nt  digestion and reverse transcriptase pausing , was perAlthough the 2D model is useful in indicating local Watson-Crick interactions, other interactions such as loop-loop interactions, base stacking, and sugar edge interactions mean that RNA structures are not well represented on a plane. Without biochemical data on the distances between RNA positions through space, these interactions cannot be modeled. However, previous knowledge regarding geometric constraints of bond angles and lengths, as well as the geometry of helices can be used to generate a crude 3D representation of the 2D model. We used the software RNA2D3D  to conveStarting the modeling process from our initial 3D representation may have limited the amount of searchable structure space; for example, a helix may never be able to orient itself successfully if sterically blocked by another part of the structure. In order to increase the proportion of structure space searched and thus to maximize the probability of achieving the ideal solution; random starting structures were constructed by applying sets of pseudoexperimental restraints using the Crystallography and NMR system (CNS) , as explIn order to predict the most likely relative orientations of features in the RNA, distances were required between several points. FRET has been used previously for this purpose in solving the human telomerase core RNA structure . The priFluorophores Atto488 (donor) and Atto647 (acceptor) were bound via linkers to 11–13 nt peptide nucleic acid (PNA) oligonucleotides, which in turn had sequences complementary to the RNA target regions. Fluorophore positions were chosen so that the pairs could not be separated by more than 10 nm (the longest useful FRET distance) and also to provide maximum coverage of the structure. The sites were also sequence unique from one another, had favorable binding energies, and caused minimal structure change as determined in silico D. The spUnlike in previous FRET RNA analyses, many of our PNAs were designed to anneal to base-paired regions of the RNA. This has generally been avoided in previous studies for fear of inducing a global structural change. Several reasons make this approach reasonable in our case. Most important is the fact that the PNAs are added after the RNA has already folded into a stable structure. It has been shown previously that PNA oligonucleotides can displace an RNA helix  due to tSmFRET experiments were performed using all combinations of donor Atto488  and acceptor Atto647  pairs except for pair D3/A4, which anneal to the same sequence . The lowEapp, of each burst were calculated according to Eapp = nA/(nA + γnD), where nA and nD are the acceptor and donor counts, respectively.Apparent FRET efficiencies, AηA)/(ϕDηD) is a factor accounting for the difference in the quantum yields, ϕA and ϕD, and detection efficiencies, ηA and ηD, for the acceptor and donor channels, respectively. This factor has been previously measured to be close to 1 for our set-up. Prior to calculating interfluorophore distances from FRET efficiencies, each repeat measurement was weighted by its signal:noise ratio in order to decrease the influence of more ambiguous measurements.γ = 6], with R0 being the Förster distance (6.68 nm) of the Atto488 and Atto647 pairing as calculated from experimental anisotropy measurements  histograms. As the linker is identical in all cases, we suggest that the flexibility of the RNA linking the two dyes in the monomer may also contribute to widening of the histogram. We therefore used the area:height ratio of the histograms to calculate a distance tolerance for each pair used during the modeling where dynamic pairs were permitted a greater range of distances centered around their mean distance .Torsional simulated annealing was initiated in CNS from these 10 random starting models concurrently multiple times with random starting trajectories (five separate outputs were collected for each set of starting coordinates). The large differences in each starting structure allowed a sizable proportion of structure space to be searched, and the five initial trajectories from each increased the search space further. Conventionally, more starting structures are used , but theFollowing high-temperature simulated annealing, each structure was subjected to a further annealing at room temperature in order to investigate the local structure space more thoroughly  distance from the real FRET distances by the energy penalty remaining. The best representative structure from each of the 10 starting models is shown in the FRET Restrained columns in Simulated annealing does not guarantee finding the global optimum solution, as searching every possible structural conformation is currently computationally intractable. The final structures from the random start models may be considered local optima (some of which may have converged). Although we cannot know whether any of these is the global optimum, we make the assumption that the most likely candidate is the most energetically minimized local solution (structure 1 in 4AJQ.The structures (10–19) were ranked in the same way as structures 0–9; all but one had a better model fit than structures 0 and 2–9. However, none were better than structure 1 , so structure 1 was taken as the working model as displayed in To get an estimate for the average structural variation caused by modeling, the 10 refined structures 10–19 B were alWe assessed the overall quality of our working model in order to consider the confidence assignable to features in the coordinates. Previously solved small subdomain NMR structures for stem loop 1 (SL1) , stem loIn order to consider the evolutionary likeliness of our working model, we aligned 1,493 HIV-1 sequences and scored nucleotide positions by conservation using the open source software Score Sequence Converter. The colored visualization C highligIt may appear unreasonable to expect useful detail in a model of 240 nt based on sparse FRET experimental distance restraints. However, the connectivity of the structure and especially the independently confirmed secondary structure represent strong constraints on the final assembled conformation. Our final model is one of several possible ones that are in the overall space constrained by the FRET distances; the structure represented is that of the lowest energy. Detailed features involving unpaired residues will have less certainty but derive from the energy minimization steps. As a result, these are physically reasonable conformations consistent with the overall fold of the RNA.lys do not show base pairing within the PBS, the sequence CCCUUUU (150–156) could base pair alternatively with 202–206 GAAAG or 209–214 AAAGG. This metastability,  and 230–233 (UCUC) are complementary in sequence and proximal in space H. This mThis investigation of a large virus RNA by FRET has presented much valuable data at this accessible and informative resolution. Information on intramolecular interactions and regions of flexibility provide insight into possible natural ligand binding locations. The technique could be expanded to investigate the change in structure when the sequence is modified, oligonucleotides are added to outcompete tertiary interactions or ligands, natural or designed are added, with potentially therapeutic implications. Fluorophore labeling of ligands will allow further 3D mapping of their binding to the RNA. Structural changes involved in dimerization can also be investigated. It is important to note that, although the species we have solved is purely monomeric, we cannot distinguish whether this is the natural monomer structure or that of a “hemidimer.” Further and more complex analysis of larger RNAs would be needed to define that. However, the solution of either structure can give us critical insights into the functioning of this large viral RNA. The ability to manipulate the system and its versatility give it valuable advantages in RNA structural mapping.2HPO4/KH2PO4, 25 mM HEPES, 2 mM EGTA, 5 mM MgCl2, pH 7.6) for SHAPE and SmFRET experiments and 1× structure buffer (Ambion) for RNase probing. The integrity and purity of RNAs was determined by native agarose and denaturing PAGE. For SmFRET and RNase mapping experiments, PNA pairs at 10-fold molar excess were annealed to the RNA by overnight incubation at 4°C. Unbound PNAs were removed by gel filtration . PNAs were N-terminally labeled  with Atto488  or Atto647  via a 1.3-nm-long glycol linker, H2N(CH2CH2O)2CH2CO. For monomerization, a 10-fold molar excess of LNA  was added, and data were collected for a further 2 hr.RNA was transcribed in 20 μl reaction volumes using T7 RNA polymerase (Ambion Megascript T7), according to manufacturer’s instructions, at a template concentration of 15 pM. DNA template was prepared by PCR amplification of pBamH1ΔBglII  using prSHAPE was performed and analyzed as described by Structures were modeled using the software RNAstructure ; SHAPE p33P-dTTP (Perkin Elmer). Samples were electrophoresed on denaturing 10% polyacrylamide gels, in an equal volume of denaturing loading buffer . Gels were transferred to blotting paper, exposed to X-ray films (Kodak Biomax MR) at −80°C, and developed with the Xograph Compact X4 Automatic X-ray Film Processor.RNA (2 μg) was digested for 15 min at 37°C with RNase A (50 pg/ml–5 ng/ml), RNase I (5 U/μl–10 U/μl), RNase T1 (1 mU/μl–100 mU/μl), or RNase CV1 (2.5–10 mU/μl), all from Ambion, and precipitated according to the manufacturer’s protocol. Reverse transcription was performed as in Harrison and Lever (1992), using The 2D arrangement of structural elements was used to produce an initial 3D topology for the native RNA step 2;  using RNPseudorestraints used to randomize starting models had an average length equal to the experimental set but with each actual distance randomly assigned to pairs. Simulated annealing steps with the secondary structure restraints combined with the randomly assigned distance restraints generated the multiple starting conformations with large displacements of secondary structure elements.A home-built dual-channel confocal fluorescence microscope was used to detect freely diffusing single molecules . The donSmFRET measurements were performed in FRET buffer for 2 hr. For monomerization, a 10-fold molar excess of LNA was added 5′GCGCUUC3′ (Exiqon), and data were collected for a further 2 hr. A minimum of five repeat measurements for each pair from different RNA preparations on different occasions reduced condition-specific experimental bias.Anisotropy was measured for single and pairs of fluorophores as described in Gaussian curves were fitted to frequency/efficiency FRET histograms  using thThe weighted peak efficiency was calculated from the average intensity of signals above the threshold (five times the mean) divided by twice the mean. The interpair distances were calculated using the equations from Ten different starting models were generated by the application of the randomized FRET pairs step 3; , and eacThe satisfaction of FRET distances was calculated using the PyMOL through-space distance measure. The models were minimized using CNS “minimize.inp,” and the remaining energy penalty was extracted from the output, which allowed our working model to be chosen. A further simulated annealing cycle was then run from the best starting model from the previous step. The similarity of these models was evaluated by aligning models pairwise using the PyMOL “align” function and then storing the results as a similarity matrix. A dendrogram was constructed by neighbor joining from the distance matrix  using MEGA .Model resolution was investigated by considering the average distance between atoms in our working model  and thosThe conservation colored model in The RMSD between NMR structures and our model was calculated using the PyMOL built-in “align” function. Although NMR data were used to assess the model similarity to high resolution data, they were not included in the final model because the necessary minimization step to form a natural and continuous backbone altered both the high resolution structures and decreased the model fit.


Biased motion of motile cells in a concentration gradient of a chemoattractant is frequently studied on the population level. This approach has been particularly employed in human sperm chemotactic assays, where the fraction of responsive cells is low and detection of biased motion depends on subtle differences. In these assays, statistical measures such as population odds ratios of swimming directions can be employed to infer chemotactic performance. Here, we report on an improved method to assess statistical significance of experimentally determined odds ratios and discuss the strong impact of data correlations that arise from the directional persistence of sperm swimming. Chemotaxis refers to biased motion of cells or organisms in a concentration gradient of an external chemical factor, a so-called chemoattractant. Chemotaxis requires a fine-tuned interplay of motility and sensation and different navigation strategies are implemented by nature: bacterial chemotaxis relies on biased random walks for which the frequency of random tumbling events is regulated in response to temporal changes of chemoattractant concentration, which are perceived during swimming in a spatial concentration gradient Up to now it has not been possible to isolate the chemotactically responsive cells and human sperm chemotaxis assays are usually done on a population level that comprises both responsive and (many) non-responsive cells. This makes considerable demands on the statistical handling of the experimental data.et al.In this report, we reconsider the earlier approach introduced by Gakamsky N+/N− is a common measure for the non-uniformity of a large data set of angles and is defined as the ratio of the number N+ of angles pointing up-gradient (−45°<ψ<45°) divided by the corresponding number N− of angles pointing down-gradient (135°<ψ<225°), where angles ψ are measured relative to the gradient direction . This method of bootstrapping is reviewed in 2-value. Special care has to be taken, however, if successive data points are correlated. In our particular case, angle data from different sperm tracks can be considered independent, whereas successive swimming direction angles from the same sperm track are correlated (see above).Gakamsky N, our algorithm constructs subsamples comprising approximately N angles that consist of all the angles corresponding to a random selection of tracks . Odds ratios are then computed for random pairs of sub-samples. Multiple repetition of this procedure yields a unimodal control distribution of odds ratios  with that for control conditions and found very good agreement (not shown). We emphasize that the block construction of sub-samples during bootstrapping is pivotal to account for the correlations that are hidden in the data and to predict correct significance thresholds.We checked our test protocol for consistency of test design by determining the frequency of false-positives (“type-I-errors”). We first computed odds ratios for 28 disjoint pairs of control data sets not used before. 95% = 1.30 for N = 50,000). One out of the 28 odds ratios exceeded this threshold. This absolute frequency of false-positives amounts to a relative frequency of 3.6%  that undergoes rotational diffusion with some rotational diffusion coefficient Drot (that has units of an inverse time). Mathematically, the motion of a persistent random walker in two-dimensional space is given byv0 is the (constant) swimming speed and ξ(t) denotes Gaussian white noise with variance 1. Planar persistent random walks are characterized by a decay of directional persistence on a characteristic time-scale set by the persistence time τ = 1/DrotDeff = v02 τ.A simple mathematical model for animal swimming paths is provided by the persistent random walk x-axis at a rate β/τ such that its swimming direction angle now obeysP(ψ)∼exp[ß cos(ψ)]. The motion of the persistent random walker is biased with a net drift in x-direction <Δx> = v0 Δt I1(ß)/I0(ß) where I0 and I1 are modified Bessel functions of the first kind.We now assume that the persistent random walker has the ability to align its path with respect to a uniform external field parallel to the positive n of these biased persistent random walks of duration T each and samples each track at constant time intervals Δt. As a control, n unbiased persistent random walks are also sampled. This experimentalist now wishes to decide about a possible bias of motion and therefore computes an odds ratio based on a bin count of frame-to-frame direction angles as described above, see eq. (1). To assess statistical significance of an observed odds value, significance thresholds are required. These can be derived by block bootstrapping as described above for a large simulated control data set of unbiased persistent random walks. 95% as a function of sample size N = +N+N− and correlation time τ. Interestingly, all curves superpose with a single master curve, Δ95% = Δ*95%(N*), when renormalizing sample size N→N*, see inset. N* can be regarded as an effective number of independent data points in an experimental sample of size N. The renormalization factor N*/N is close to one for τ<Δt (low correlations between successive angles), and approximately scales as Δt/τ for larger τ with Δt<τ<T, (clusters of correlated angles have size ∼τ/Δt)  .t  equal to the significance level of 5%. The rate of type-II-errors  decreases with sample size N, but increases with the persistence time τ/Δt .2-value as used in In this report, we presented a test for biased motion on the population level. We compute significance thresholds for a measure of biased motion, the odds ratio, by using block bootstrapping based on an extensive control data set. The key point of our work and difference to the earlier approach Block bootstrapping is an established technique in the quantitative disciplines to empirically determine robust statistical measures of dependent data 5 cells/ml in HTF medium containing 0.3% HSA and 3.5% polyvinylpyrrolidone . The sperm suspensions were incubated under an atmosphere of 5% CO2 at 37°C for an additional 1 h  to obtain capacitated spermatozoa Human semen samples were obtained from ten healthy donors after 3 days of sexual abstinence. Informed consent was obtained in written form from each donor. Ethics approval was granted in written form by the Bioethics and Embryonic Stem Cell Research Oversight Committee of the Weizmann Institute of Science. Semen samples with normal sperm density, motility and morphology /2. By repeated selection of pairs of subsamples, one obtains a distribution of χ2-values, from which a 95% percentile can be read off. Interestingly, applying this bootstrapping protocol to different pairs of control data gave quite different χ2-distributions, and thus a set of different values for the respective 95% percentiles. In For the convenience of the reader we cite the bootstrapping protocol from an earlier work Text S1Supporting Information Text contains a detailed comparison of a previously published statistical test for biased motion  and the new test proposed here together with an analysis for the assoicated rate of false-positive-testing.(DOCX)Click here for additional data file.


The number of wireless sensor network (WSN) applications is rapidly increasing and becoming an integral part of sensor nodes. These applications have been widely developed on TinyOS operating system using the nesC programming language. However, due to the tight integration to physical world, limited node power and resources (CPU and memory) and complexity of combining components into an application, to build such applications is not a trivial task. In this context, we present an approach for treating with this complexity adopting a formal description technique, namely LOTOS, for formalising the WSN applications ‘behaviour. The formalisation has three main benefits: better understanding on how the application actually works, checking of desired properties of the application's behaviour, and simulation facilities. In order to illustrate the proposed approach, we apply it to two nesC traditional applications, namely BLink and Sense. Those applications that were originally military ones, have been spread out through several areas, including environment, habitat, disaster monitoring, healthcare applications, home automation, and traffic control , 2.WSNs applications have been widely developed on TinyOS operating system  and writDespite the current support to develop WSN applications provided by TinyOS, the tight integration to physical world, limited node power and resources (CPU and memory), and complexity of combining components into an application become the construction of an application a non trivial task. In particular, the usual great number of components that compose the application together the way they interact put some challenges on understanding how the application actually works.In this context, we present an approach for treating with the complexity of building WSN applications through adopting a formal description technique, namely LOTOS , for forThe choice of LOTOS has been based on some facts. Firstly, it is a ISO standard specially designed for describing the behaviour of concurrent systems like nesC applications. Secondly, the way LOTOS applications are structured  is very similar to the way the applications are organised in nesC. Finally, the expressiviness of LOTOS enables us to specify the application in many different levels of abstraction, which facilitates to treat with the complexity of nesC applications.Current approaches for building WSN applications includes those to formally design embedded systems , a practIn addition to the this introduction, this paper is organised into four sections. Section 2 introduces basic concepts of LOTOS and TinyOs prior to present the proposed formalisation approach. Next, Section 3 presents the proposed approach to model TinyOS applications in LOTOS. Section 4 shows how the modelling approach may be applied to two well-known TinyOS applications, namely Blink and Sense. Finally, last section presents conclusions and some future work.2.Prior to present our approach to formalise WSN applications, next subsections introduce basic concepts of LOTOS and TinyOS.2.1.A LOTOS  , 9 speciwhere” clause following the expression :noexit(2)behaviour (3)Client  | |Server  (4)where (5)process Client : noexit :=  (6)request; reply; Client    (7)endproc (8)process Server :noexit:= (9)hide processRequest in  (10)request;   (11)processRequest;   (12)reply;   (13)Server    (14)endproc  (15)endspec An essential component of a specification or process definition is its behaviour expression. A behaviour expression is built by applying an operator  performs two actions, namely request and reply (6), and then reinstantiates. The action-prefix operator  defines the temporal ordering of the actions request and reply (the action request occurs before the action reply) in the Client. Informally, the Server (8) receives a request (10), processes it (11) and then sends a reply (12) to the process Client.The top-level specification (3) is a parallel composition (operator '‖') of the processes Client and Server i.e., every action externally observable executed by the process It is worth pointing out that LOTOS specifications may be compared in order to check their behavioural equivalences such as strong equivalence, observational equivalence and safety equivalence. All of them are checked through the CADP Toolbox2.2.TinyOS  is the fconfiguration and module. A configuration simply defines how components are put together (wired), whilst modules represent the implementation itself. A component provides and uses interfaces that declare a set of functions (commands) the component must implement and functions (events) that the user of the interface must implement. Every nesC application is described by a top-level configuration that wires components. Additionally, two basic elements of TinyOS applications include: the Main component that must be implemented in every application; and the standard interface named StdControl used to initialise/start/stop TinyOS components, which includes three operations: start, init and stop.nesC (network embedded system C)  is a str3.The proposed approach for specifying WSN applications in LOTOS considers some basic principles that act as a guideline in the specification process. Firstly, as a specification language, LOTOS is used to describe the behaviour of nesC applications. Hence, typical constructions of imperative language such as loop, decision and assignment commands are not present in the specification. Secondly, the specification concentrates mainly on identifying and modelling interactions between components. In particular, interactions in LOTOS are always synchronous. Thirdly, the LOTOS state-oriented specification style  is extenTo identify the nesC components (modules and configurations) that made up the application. These components become LOTOS processes at the end of this step;To identify how components (modules and configurations) found in Step 1 are wired and which interfaces are used to connect them. The way the components are wired defines how the LOTOS processes are composed, whilst the interfaces become synchronisation ports in LOTOS;To identify operations (commands and events) of each interface identified in Step 2. For each interface, it is defined a LOTOS ADT (Abstract Data Type) whose operations represent the nesC commands and events; andTo identify interactions inside nesC modules' implementation. After identifying the modules in Step 1, their composition in Step 2, and the operations available to be invoked by each module in Step 3, it is time to precisely identify the order the operations are invoked inside the modules. When a nesC operation is called, this indicates that an interaction takes place between the nesC modules. Hence, in LOTOS, these interactions represent synchronisation events between two processes.Following these principles, the LOTOS modelling process is carried out in four main steps:Next sections present how nesC first-order elements identified in each previous step are modelled in LOTOS.3.1.As mentioned before, nesC applications are built out of components with well-defined interfaces, which represent points of access to the components. The interaction between components occurs through the invocation of functions in the interface. In LOTOS, processes interact through their synchronisation ports (see Section 2.1). In this way, the nesC interface is defined as a LOTOS synchronisation port e.g., if a component provides three interfaces it has three ports. Furthermore, as LOTOS ports have no direction (there is not the notion of output/input ports), provided and used interfaces are simply modelled as ports. The actions that occur in the port give an idea if the interface is a used or provided interface.interface-name> leads to the definition of the LOTOS ADT “I”<interface-name>. The command <command-name> and event <event-name> are defined as type operations “c_”<command-name> and “e_”<event-name>, respectively. In order to illustrate this approach, the interface SendMsg is presented in As mentioned in Section 2.2, each nesC interface has a set of functions (commands and events). As we are only interested in the module interactions or temporal ordering of events , these functions are defined as LOTOS operations of an abstract data type (ADT) defined for each interface. In this way, the nesC interface <SendMsg is modelled as the LOTOS abstract data type ISendMsg, whilst its command send and event sendDone are defined as the operations c_send and c_event of type ISendMsg. As mentioned before, the semantics of the operation together their input/output parameters have no meaning in LOTOS specification.In this example, the nesC interface 3.2.A module is a first-class element of nesC language and has of two basic parts: the set of interfaces provided and used by the module; and the implementation. The interfaces are modelled as defined in Section 3.1. Meanwhile, as the basic abstraction in LOTOS is a process, the nesC module is specified as a LOTOS process see . The impStdControl interface (see Section 3.1) are defined using the state-oriented specification style. In this case, four states are defined: the module initiates in an initial state (state 0) prior to execute the operation StdControl.init; the second state (state 1) is reached when the module is initialised but not started yet (after the execution of StdControl.start); the state when the module is already initialized and ready to execute the functions defined in the interface (state 3); and finally, the last state (state 9), when the module has been stopped after the execution of the function StdControl.stop. Being reached the state 9, the module is not able to execute any other action.Modules that implement the StdControl (provides) and Int1 (uses). These interfaces becomes LOTOS synchronisation ports. The interface StdControl has three operations, namely init, start, and stop , defined as choices in the LOTOS choice operator (). The interface Int1 is used by this module, which means that the module must implement the events defined in the interface. In this particular case, only the event event1 is defined. Finally, as this module implements the StdControl interface, the state-oriented LOTOS style has been used.It is possible to observe in the nesC programming that this module interacts with other modules (defined in the configuration where it is placed) through the interfaces MOD is in the state 0 ([s eq 0]) then a synchronisation action may occur in the port StdControl if the value c_init is offered (StdControl !c_init). Then the module MOD is instantiate and the new state is set to 1.The LOTOS specification ahead expresses that if process 3.3.A configuration is a first-class element of nesC that defines used and provided interfaces, the components that made up it and the way they are wired (implementation). A configuration is present in a nesC application in two situations: the initial configuration of an application; or part of another configuration. In the first case, the configuration is modelled as the LOTOS top-level process in the process hierarchy, which is defined as “specification” (see Section 2.1). In the second case, it is simply modelled as a LOTOS process. In both cases, the module's implementation defines how the modules that made up the configuration are wired and the interfaces used to connect them.If two modules within the configuration are wired through an interface, it means that they interact each other. Hence, these two modules are placed in a LOTOS parallel composition with synchronisation . Otherwise, the parallel interleaving LOTOS operator (| | |) is used to model the situation when two components are not wired.CONF {configuration }implementation {Mod1, Mod2, Mod3, Mod4;components  Mod1.Int1 −> Mod3.Int1; Mod1.Int1 −> Mod2.Int1; Mod2.Int2 −> Mod3.Int2; Mod2.Int3 −> Mod4.Int3; }In order to illustrate these elements, the specification ahead presents a nesC configuration:(1)CONF  : noexitspecification (2)behaviour(3)Mod1 [lnt1] (4)|[lnt1]|  (5)Mod2  (6)||   (7)where(9)Mod1 [lnt1]: noexitprocess  (10):= (* behaviour specification *) endproc (11)Mod2 : noexitprocess  (12):= Mod3 : noexitprocess  (14):= Mod4 [lnt1]: noexitprocess  (16):= endspecIts respective LOTOS specification is as follows:CONF is defined as the LOTOS top-level specification (line 1) and consists of four components: Mod1 (line 9), Mod2 (line 11), Mod3 (line 13) and Mod4 (line 15). According to the proposed approach, each nesC component is defined as a LOTOS process. These components are wired according to the nesC implementation clause: Mod1 is wired to Mod3/Mod2 through the interface Int1; Mod2 is wired to Mod3 through Int2; and Mod2 is wired to Mod4 through Int3. In LOTOS, it means that Mod1 synchronises simultaneously with Mod2 and Mod3 in port Int1 (line 4); Mod2 synchronises with Mod1 in the port Int1 (line 4), with Mod3 in the port Int2 (line 6) and with Mod4 in the port Int3 (line 6); and Mod4 and Mod3 are not connected .The nesC configuration 4.In order to illustrate the proposed modelling approach, two traditional applications of the TinyOS have been modelled, namely Blink and Application. Both of them are detailed in .4.1.BLink, SingleTimer and TimerC) that make up the whole application. The modules can be identified in the clauses “components” within the configurations.Blink is a basic application that toggles the leds on the mote on every clock interrupt. The clock interrupt is scheduled to occur every second. configuration Blink is modelled as the top-level specification (line 1) and it is defined as a parallel composition (lines 4-10) of four components: module Main (line 12), module LedsC (line 14), module BlinkM (line 16) and configuration SingleTimer (line 18). As can been seen in SingleTimer (18) is a configuration that only contains the configuration TimerC (line 19). TimerC includes four components (lines 23-30): module TimerM (32), module NoLeds (35), configuration ClockC (37), module HPLPowerManagementM (line 42). The configuration ClockC only contains the module HPLClock (not shown in specification for lack of space).According to Section 3, the first step in the modelling process is to identify the modules and configurations that compose the application. The Main is wired to SingleTimer (Main.StdControl −> SingleTimer.StdControl) and BLinkM (Main.StdControl −> BLinkM.StdControl) through StdControl (line 5); BlinkM is connected to both LedsC and SingleTimer through Leds and Timer, respectively (line 7); TimerM is connected to NoLeds through Leds (line 25); TimerM and ClockC through Clock (line 25); and TimerM to HPLPowerManagementM through PowerManagement (line 25). It worth observing that LedsC and SingleTimer are not connected Blink :noexitspecification (2)(* Abstract Data Type definition *)(3)behaviour(4)Main [StdControl] (5)| [StdControl] |  (6)BlinkM  (0)(7)|| (8)(LedsC [Leds](9)| | |   (10)SingleTimer ;  (11)where(12)Main [StdControl] : noexitprocess  (13):= LedsC [Leds] : noexitprocess  (15):= BlinkM (s:Nat) :process  (17)noexit := SingleTimer :noexit:=process  (19)TimerC   (20)where  (21)TimerC  : noexit :=process    (22)hide Leds, Clock, PowerManagement in    (23)TimerM  ||     (26)(NoLeds [Leds]    (27)| | |      (28)ClockC [Clock]    (29)| | |      (30)HPLPowerManagementM [PowerManagement];    (31)where    (32)TimerM := NoLeds [Leds] : noexitprocess      (36):= ClockC [Clock] : noexitprocess      (38):= HPLClock (s:Nat):process      (40)noexit     (41):= HPLPowerManagementM [PowerManagement]:process      (43)noexit     (44):=endspecStdControl (see Section 2.2):type IStdControl issorts IStdControlopnsc_init (*! constructor *) : −> IStdControl c_start (*! constructor *) : −> > IStdControl c_stop (*! constructor *) : −> IStdControl endtypeIn the Step 3, command and events of the interfaces must be identified. For each interface, a LOTOS abstract data type is defined and each type operation models a command/event. Next, we present the LOTOS specification of the nesC interface IStdControl) has three commands and no events: c_init initializes the component and its subcomponents; c_start starts the component and its subcomponents; and c_stop stops the component and pertinent subcomponents.This interface BlinkM  (s:Nat)process (2): noexit :=(3)StdControl !c_init;[s eq 0] −> >  (4)Leds !c_initLeds;  (5)BlinkM   (7)StdControl !c_start;[s eq 1] −>  (8)Timer !c_startTimer;  (9)BlinkM   (11)StdControl !c stop;[s eq 2] −>  (12)Timer !c_stopTimer;  (13)BlinkM  (15)Timer !e fired;[s eq 2] −> (16)Leds !c_redToggle;  (17)BlinkM  endprocThe corresponding LOTOS specification is defined as follows:The module BlinkM provides the interface StdControl  /* EXECUTION OF INIT */“I” (STDCONTROL [208]) /* EXECUTION OF START */“I” (STDCONTROL [208]) /* EXECUTION OF STOP */YEAR = {2004}<DEADLOCK>After being completely specified, several simulations have been carried out using the CADP ToolBox in order to validate the specification. Additionally, two properties have been also checked against the specification: deadlock and livelock. Next, we present the CADP output of checking deadlock freedom:StdControl.stop leads the state machine of the module that implements the StdControl interface to a state (state 9) where it is not possible to execute any further action. In fact, this behaviour reflects the nesC implementation, which also has a similar deadlock. Additionally, no livelock sequence was found in the specification.This sequence indicates the presence of a deadlock. It was expected as the function 4.2.Sense is an application that periodically samples the photo sensor and displays the highest 3 bits of the raw ADC light reading to the leds, with red being the most signficant bit and yellow being the least significant bit.Main (line 16), module SenseM (line 18), module LedsC (line 21), configuration TimerC (the same as in the Blink application), and configuration Photo (line 25). The configuration Photo only includes a module, namely PhotoTemp, which is a configuration. PhotoTemp is composed by the module PhotoTempM and the configuration ADCC. Finally, the configuration ADCC is made up of modules ADCCM and HPLADCC.Similarly to the BLink, the steps defined in Section 3 have also been followed in the specification of Sense application. Hence, the modules and configurations that compose the Sense application were initially identified see : module Sense, in which we have the following connections: Main to SenseM and TimeC through StdControl (line 6); SenseM to LedsC through Leds (line 8); SenseM to TimerC through Timer (line 8); SenseM to Photo through ADC (line 8), ADCControl and StdControl (line 8). LedsC, TimerC and Photo are not connected (lines 11 and 13).As the modules and configurations have been identified, it is necessary to defiine how they are wired. For simplicity, the specification ahead only shows the top-level specification (1)Sense  : noexit     (3)(* Abstract Data Type behaviour *)(4)behaviour(5)Main [StdControl] (6)[StdControl]|  |(7)SenseM ||  (9)((10)LedsC [Leds] (11)| | |  (12)TimerC  (13)| | |  (14)Photo ; (15)where(16)Main [StdControl] : noexitprocess  (17)= SenseM = LedsC [Leds] : noexit :=process  (22):=TimerC  : noexitprocess  (24):=Photo:noexitprocess  (26):=endspec(1)process SenseM [s eq 0] −> (4)StdControl !c init; (5)Leds !c initLeds; (6)ADC !c init; (7)SenseM (9)[s eq 1] −>(10)StdControl !c_start; (11)Timer !c_startTimer; (12)SenseM (14)[s eq 2] −>(15)StdControl !c_stop; (16)Timer !c_stopTimer; (17)SenseM (19)[s eq 2] −>(20)Timer !e_fired; (21)ADCControl !c_getData; (22)ADCControl !e_dataReady; (23)SenseM endprocAs the main component of the application, the module SenseM is defined as follows:StdControl interface, it is also defined using the state-oriented style . For example, when the process SenseM is in the state 0 (line 3), the only possible action is StdControl ! c_init (line 4). Meanwhile, when the process reaches the state 9 (line 17) after being stopped (line 15), no further actions are possible. An interface that must be mentioned is the ADC:type IADC issorts IADC opns c_getData (*! constructor *) : −> IADCc-getContinuosData   /* init operation */“I” (STDCONTROL [244]) /* START OPERATION */“I” (STDCONTROL [244]) /* STOP OPERATION */<DEADLOCK>The properties deadlock and livelock have also been checked against this specification. As expected, this specification has a deadlock, which it was also expected, as the nesC application itself stops in a deadlock situation. The sequence that leads to the deadlock is shown in the following:TimerC  were already present in the BLink application. Due to the approach defined to structure the LOTOS specification, these modules were integrally reused in the Sense specification and no further change was necessary to reuse them.Finally, it is worth observing that the modules and configurations associated to 5.This paper has proposed an approach useful to formalise the behaviour of WSN applications in LOTOS. The approach consists of moddelling nesC and TinyOS elements in LOTOS. In order to carry ou this task, we defined a set of specification steps together an approach to specify the main concepts of nesC in LOTOS, namely interfaces, modules and configurations.The approach for formalising nesC applications has some interesting benefits to those who build WSN applications in nesC. Firstly, the specification process improves the understandability of the nesC application itself. Secondly, the emphasis on the interaction of components  in the LOTOS specification provides a better understanding of the way the components collaborates. Thirdly, the strategy adopted to specify the nesC applications favoured the high reuse of module specifications. Finally, the formalisation allows us to check useful properties of the specification.We are now extending the proposed set of abstractions including more sophisticated communication and concurrent elements. Meanwhile, it is also planned to include the specification of middleware services in such way that composition constraints may also consider middleware service composition. Another interesting future work that is now being devised refers to the modelling of the operating system itself, which acts as an engine for executing the application.


The HydE, HydF, and HydG maturases assist in assembling the H-cluster and maturing hydrogenases into their catalytically active form. Characterization of these maturases and in vitro hydrogenase activation methods have helped elucidate steps in the H-cluster biosynthetic pathway such as the HydG-catalyzed generation of the CO and CN– ligands from free tyrosine. We have refined our cell-free approach for H-cluster synthesis and hydrogenase maturation by using separately expressed and purified HydE, HydF, and HydG. In this report, we illustrate how substrates and protein constituents influence hydrogenase activation, and for the first time, we show that each maturase can function catalytically during the maturation process. With precise control over the biomolecular components, we also provide evidence for H-cluster synthesis in the absence of either HydE or HydF, and we further show that hydrogenase activation can occur without exogenous tyrosine. Given these findings, we suggest a new reaction sequence for the [FeFe] hydrogenase maturation pathway. In our model, HydG independently synthesizes an iron-based compound with CO and CN– ligands that is a precursor to the H-cluster [2Fe]H subunit, and which we have termed HydG-co. We further propose that HydF is a transferase that stabilizes HydG-co and also shuttles the complete [2Fe]H subcluster to the hydrogenase, a translocation process that may be catalyzed by HydE. In summary, this report describes the first example of reconstructing the [FeFe] hydrogenase maturation pathway using purified maturases and subsequently utilizing this in vitro system to better understand the roles of HydE, HydF, and HydG.[FeFe] hydrogenases catalyze H The [FeFe] hydrogenase H-cluster consists of a [4Fe–4S] cubane cluster (the [4Fe–4S]H subcluster) that is connected by a cysteinyl sulfur ligand to a catalytic two-iron unit (the [2Fe]H subcluster) that coordinates multiple nonprotein ligands: three CO, two CN–, and a dithiol bridge (DTMX) that is arguably dithiomethylamine or dithiomethylether Metalloenzymes such as hydrogenases and nitrogenases harbor complex metal clusters that catalyze industrially relevant reactions. Hydrogenase cofactors such as the [FeFe]-based H-cluster catalyze the redox interconversion of protons and HH subcluster and the maturation of [FeFe] hydrogenases S-adenosyl methionine (SAM) enzyme that catalyzes the formation of CO and CN– from free tyrosine – [2Fe]H subcluster ligands – adducts in vitroH subcluster, onto which CO and CN– molecules generated by HydG along with the DTMX ligand are assembled H subcluster is first assembled, followed by [2Fe]H subcluster transfer from HydF to the hydrogenase, producing the active enzyme HydE, HydF, and HydG – termed the Hyd maturases – are the three [FeS] cluster accessory proteins that directly participate in the assembly of the [2Fe]In vitro protein biochemistry studies are a valuable means to identify cofactors and substrates as well as to characterize enzyme functionality. While investigating a single protein can be straightforward, reconstituting a multi-component reaction sequence requires an understanding of the necessary proteins and the influential small molecular weight substrates. In vitro assembly protocols that use a minimal set of purified enzymes have been developed for complex metal clusters such as the nitrogenase iron-molybdenum cofactor (FeMo-co), and subsequent experimentation has then provided insights to further define the activation sequence in vitro [FeFe] hydrogenase maturation have been established that utilize purified HydF with a bound [2Fe]H subcluster, although 100-fold excess HydF was required to mature roughly 15% of the hydrogenase in vitro systems in which exogenous substrates have enhanced hydrogenase maturation, 100% of the hydrogenase has been activated, and cell-free synthesis of the [2Fe]H subcluster has been demonstrated in vitro approach that uses the maturases in their pure forms has so far been detailed.in vitro system for activating [FeFe] hydrogenases using individually expressed and purified HydE, HydF, and HydG. Reconstructing H-cluster biosynthesis in a cell-free environment with purified maturases allows for precise control over the presence and the concentrations of these biomolecular constituents. We detail how excluding small molecule substrates, an Escherichia coli lysate from the strain BL21(DE3) ΔiscR, and each maturase impact activation of the CpI hydrogenase, the HydA enzyme from Clostridium pasteurianum. We also explore how maturase concentrations affect CpI activation rates, providing the first evidence that HydE, HydF, and HydG each function catalytically during the maturation reaction. Together, our results lend new insights regarding the roles of the three Hyd maturases, from which we propose a new model for H-cluster synthesis and [FeFe] hydrogenase maturation.In this work, we describe the first in vitro hydrogenase activation system 2+), sodium sulfide (S2–), dithiothreitol (DTT), and sodium dithionite (DTH) each had beneficial effects. The addition of all of these substrates resulted in complete activation, but either lower or no hydrogenase activities were seen when certain molecules were not included ). For these experiments, the E. coli lysates from the ΔiscR strain were buffer exchanged with desalting columns to significantly reduce the probability that adventitious small molecular weight substrates were influencing hydrogenase maturation.Upon modifying our first included . For exain vitro maturation was expected since this amino acid is required for HydG-catalyzed radical SAM chemistry – ligands coordinated to the [2Fe]H subcluster in vivo to assemble a HydF-bound iron cluster containing the CO and CN– adducts prior to the cell-free reactions – ligands were provided by components in the ΔL-Tyr reactions, most likely associated with HydG since this is the only maturase for which tyrosine is an identified substrate. It is also possible that adventitious tyrosine accumulated during the maturation reaction as a result of protein degradation, or that purified HydG contained adventitious tyrosine.The positive effect of free tyrosine on – ligands via a radical SAM-mediated reaction with tyrosine. We have termed this putative iron species as HydG-co. The capability of HydG to assemble HydG-co is supported by recent evidence of CO and CN– sequestration within the maturase; in these previous studies, the detectable quantity of CO (10 µM) that derived from excess tyrosine (1 mM) was substoichiometric relative to the HydG concentration (60 µM), and denaturation of HydG using perchloric acid was required before the CN– synthesized from tyrosine could be detected and quantified We propose that HydG can individually synthesize an iron complex with both CO and CN– adducts since these species absorb in an infrared region that is not obscured by other protein-related substituents Strep-tag II–HydG in the as-isolated state (i.e. the purified HydG was neither concentrated nor reconstituted) did not reveal vibrational bands indicating the presence of either CO or CN–. We speculate that only a small percentage of the as-isolated maturase contained HydG-co, which would result in concentrations of iron-coordinated CO and CN– below the detectable limits of FTIR spectroscopy. Such low concentrations of as-isolated HydG with pre-synthesized HydG-co, however, could be sufficient for partial maturation as standard reaction mixtures contained 25-fold excess HydG relative to CpI, and approximately 30% CpI was activated in the ΔL-Tyr reactions. Interestingly, using stopped-flow FTIR spectroscopy, we recently investigated the HydG-catalyzed radical SAM reaction with SAM, tyrosine, and DTH as substrates. The formation of HydG-specific Fe–CO and Fe–CN vibrational bands was observed in real-time during the first 15 minutes of the reaction. These findings, which are the focus of a separate ongoing study, strongly support our suggestion that HydG can synthesize and harbor an iron cluster with CO and CN– ligands.FTIR spectroscopy has historically been a useful technique to analyze metalloenzymes with iron clusters containing CO and CN– adducts, we speculate that HydG-co may contain the DTMX bridge, which would indicate that HydG-co is the [2Fe]H subcluster precursor that is transferred to the hydrogenase via HydF. Regarding the assembly of the [2Fe]H subunit, the mechanisms for CO and CN– synthesis and their attachment to an iron cluster have not been established. Dehydroglycine or a glycyl radical have each been suggested as a reactive intermediate synthesized from tyrosine H subcluster.Besides having CO and CNin vitro biochemical evidence exists for additional substrates used by the HydE-catalyzed radical SAM chemistry.Similar to the effects of tyrosine, the positive influence of exogenous SAM on CpI maturation was expected as both HydE and HydG are radical SAM proteins 2+, S2–, cysteine, DTT, and DTH, we observed that DTT, DTH, or Fe2+ plus S2– enabled significant CpI activation when cysteine was also added to the reaction mixtures. That is, the following omissions were permissive for maturation: Δ, Δ, and Δ. The enhanced maturation without exogenous Fe2+ and S2– observed in this study is different than we previously saw with our first in vitro system 2-sensitive proteins with intact metal clusters, thus decreasing the benefits of in vitro [FeS] cluster reconstitution 2+ and S2– demonstrates the first evidence that purified HydE, HydF, and HydG are functional without the need for in vitro [FeS] cluster reconstitution, since the maturases are able to assemble the [2Fe]H subcluster and mature the hydrogenase in a cell-free environment. It should be noted, however, that the maturase concentrations in these studies exceeded that of the CpI apoprotein by 25-fold for HydG and 2.5-fold for HydE and HydF.We also examined the effects of various chemicals commonly associated with [FeS] cluster biochemistry, since both the hydrogenase as well as all three Hyd maturases harbor [FeS] centers. When exploring permutations of Fein vitro studies that involve [FeS] cluster biochemistry typically employ a reducing agent such as DTT or DTH, the latter of which is also used to provide electrons for radical SAM reactions like those occurring in our hydrogenase activation system in vitro nitrogenase activation Anaerobic in vitro hydrogenase maturation. Many proteins utilizes PLP as a cofactor for a diverse set of reactions E. coli lysate, which presumably has cysteine desulfurases, supports this theory. However, we do not rule out that PLP may be a cofactor of one of the radical SAM maturases We have yet to establish how PLP specifically influences E. coli proteins required for CpI maturation, and this is further discussed in the following section.The addition of GTP was tested given the classification of HydF as a GTPase iscR E. coli lysate devoid of the three Hyd proteins (ΔLysate). Complete activation was seen when combining all three maturases along with either a clarified lysate or a desalted lysate, while no hydrogenase was matured in the ΔLysate reactions. We also varied maturase concentrations, and nearly complete activation was seen with substoichiometric quantities of each maturase when the other two were in excess  or a Δn excess . These rE. coli extract clearly indicates that the lysate is supplying components essential to the maturation process. Moreover, the complete activation of CpI that we observed when using a desalted cell extract  HydE and HydF, (ii) HydE and HydG, or (iii) HydF and HydG using established in vivo expression methods in vivo and in vitro maturation capability under ΔHydE and ΔHydF conditions. For example, high initial concentrations of GTP or ATP (15 mM) are important for in vitro hydrogenase activation, and such intracellular NTP concentrations are unlikely to exist during anaerobic growth and metabolism.We also investigated maturation when CpI was co-expressed in –, and DTMX adducts are ligated, resulting in the formation of the [2Fe]H subcluster. Considering the complexity and uniqueness of the H-cluster cofactor, and also that E. coli has neither a native [FeFe] hydrogenase nor the respective Hyd maturases, we believe it is unlikely that a protein from the lysate is replacing HydF to provide an [FeS] cluster scaffold for the [2Fe]H subunit and also to transfer this cluster to the CpI enzyme.Maturation of the CpI hydrogenase in the absence of either HydE or HydF is surprising as all three maturases are presumably essential to mature [FeFe] hydrogenases H subcluster is based upon spectroscopic studies of this maturase. Specifically, HydF was isolated following heterologous co-expression with HydE and HydG, and FTIR spectroscopy of the purified maturase revealed Fe–CO and Fe–CN vibrational modes, lending the conclusion that HydF is able to coordinate an iron center resembling the [2Fe]H subcluster Clostridium acetobutylicum. One S = ½ axial signal was assigned to a [4Fe–4S]+ cluster, and the second S = ½ signal was characterized as a [2Fe–2S]+ cluster, which the authors concluded was the [FeS] framework onto which HydE and HydG synthesize and assemble the H-cluster nonprotein ligands in vitro synthesis of CO, CN–, or DTMX ligands onto a HydF-bound [2Fe–2S] cluster was not demonstrated. Moreover, in a separate study, Fontecave and coworkers found that DTH-reduced HydF from Thermotoga maritima only coordinates a single [4Fe–4S]+ cluster The suggested role of HydF in the assembly of the [2Fe]in vitro hydrogenase activation can occur without HydF as well as the incongruence between previous EPR studies of the maturase, we sought to spectroscopically characterize the HydF protein used in this work. Similar to the studies by the Broderick and Fontecave groups, HydF was heterologously expressed in a genetic background devoid of the HydE and HydG proteins, and therefore, the HydF maturase should not harbor a [2Fe]H subcluster with CO and CN– ligands. In S = ½ species. The signal for the as-isolated HydF has g-values of 1.971, 2.009, and 2.052. More importantly, the line shape resembles the signal that Broderick and coworkers characterized as a [2Fe–2S]+ cluster and interpreted to be the iron cluster scaffold for the [2Fe]H subunit. However, our as-isolated HydF sample did not contain any exogenous reducing agents, the number of S = ½ spins was very low (0.02 spins per protein), and the S = ½ signal centered at g = 2.009 disappeared upon reduction with DTH, which indicates that it does not represent a [2Fe–2S]+ center. For the DTH-reduced HydF maturase, the 20 K spectrum shows an axial S = ½ signal  consistent with the [4Fe–4S]+ cluster identified in the previous HydF studies in vitro, we did not observe a second signal indicative of this type of [FeS] center.Given our findings that H-cluster synthesis and in vitro hydrogenase maturation data for ΔHydF conditions, we propose that HydF neither coordinates a [2Fe–2S] cluster when anaerobically produced in the absence of HydE and HydG, nor does HydF supply an [FeS] cluster framework onto which the H-cluster nonprotein ligands are assembled. It is worth noting that with our cell-free system, the three maturases activated the hydrogenase without extrinsic Fe2+ and S2– as well as with substoichiometric quantities of HydF relative to CpI, together suggesting that the [FeS] cluster framework for the [2Fe]H subcluster already exists on either the purified HydE or HydG maturase. We also believe that the as-isolated HydF paramagnetic species observed in this study, which appears to be the same signal characterized by Broderick and coworkers as that for a [2Fe–2S]+ cluster + cluster. A similar S = ½ species, identified as a cysteinyldisulfide radical, was observed upon Fe(CN)63– oxidation of the Azotobacter vinelandii ferredoxin I + cluster accounts for this signal S = ½ signal centered at g = 2.009 for the as-isolated HydF maturase.Based on the EPR spectroscopy, the Fe content results (3.3 Fe per HydF), and the in vitro maturation lends us to conclude that these two maturases neither synthesize the CO, CN–, and DTMX ligands nor provide the iron cluster scaffold for the [2Fe]H precursor. Rather, as illustrated in the – adducts (i.e. HydG-co). The HydG maturase has two [FeS] clusters: an N-terminal domain (NTD) [4Fe–4S] radical SAM cluster and a C-terminal domain (CTD) cluster that is also presumably a [4Fe–4S] center H precursor. If HydG-co is a [2Fe] cluster that derives from the CTD [4Fe–4S] center, we believe that the presence of a dithiol bridge would be required before CO and CN– would coordinate to form the [2Fe]H subunit, which is perhaps synthesized from tyrosine by the previously suggested mechanism – ligands. As previously stated, the characterization of HydG-co is ongoing work and the subject of a separate study.The surprising observations that HydE and HydF – purportedly essential proteins for producing active [FeFe] hydrogenases – are not required for H precursor from HydF to the hydrogenase apoprotein We propose that HydF functions as a transferase to stabilize HydG-co and catalyze its translocation from HydG to the hydrogenase apoprotein. This hypothesis is based on (i) our observation that CpI is matured in the ΔHydF reactions, (ii) our EPR spectroscopic evidence that HydF expressed in the absence of HydE and HydG does not coordinate a [2Fe–2S] cluster, and (iii) our suggested roles for the HydG maturase. HydG may also be able to transfer HydG-co directly to the hydrogenase, which could explain the CpI activation in reactions without HydF. Although GTP does not affect transfer of the [2Fe]H subunit, and upon translocation to HydF, both the HydE and HydF maturases work together to assemble the DTMX bridge. Yet, given our result that CpI maturation can occur in the absence of HydE, it seems likely that another protein creates the DTMX bridging ligand. We therefore postulate that HydE assists with translocation of the [2Fe]H precursors amongst HydG, HydF, and the hydrogenase apoprotein , we are currently investigating these unknowns in order to reconstruct the reaction sequence with a defined set of proteins and substrates. Nevertheless, our studies have rendered several key insights. We illustrate that in vitro hydrogenase maturation can proceed without HydE, HydF, or exogenous tyrosine. From these findings, we hypothesize a new model for [FeFe] hydrogenase activation, in which HydG assembles an iron complex with CO and CN– ligands that is a precursor to the H-cluster [2Fe] subunit. Future studies can also utilize this cell-free system in combination with biochemical analyses such as HPLC and LC-MS to focus on characterizing maturase interactions and elucidating the complete biosynthetic reaction sequence of the H-cluster cofactor.In this work, we describe the first use of separately produced and purified Hyd maturases for combined cell-free H-cluster synthesis and [FeFe] hydrogenase maturation. By providing evidence for maturase turnover, we establish the validity of this model system, which enables us to reliably characterize how different components influence hydrogenase activation. Although we have yet to identify the required –1 ferric ammonium citrate, and the appropriate antibiotics E. coli strain BL21(DE3) ΔiscR::kan and subsequently purified as previously described Cell cultures were performed as 4 L batch fermentations using a 5 L BioFlo 3000 fermentor (New Brunswick Scientific). Growth medium consisted of LB Miller with 50 mM MOPS/KOH buffer (pH 7.4), 25 mM glucose, 500 mg·LE. coli strain BL21(DE3) ΔiscR::kan and the growth medium described above were also used to produce lysates that did not contain the heterologous maturases, and all steps were done under aerobic conditions. Cells were grown at 25°C until an OD600 of ∼2, harvested, pelleted, and lysed using BugBuster® Master Mix (Novagen) supplemented with 50 mM Hepes buffer (pH 7.6). Clarified lysates were produced by centrifuging crude lysates at 30,000×g for 30 min. Desalted lysates were made by buffer exchanging the clarified lysate twice with PD-10 desalting columns  and subsequently concentrating the desalted lysate using a stirred cell and a 5 kDa MWCO membrane (Amicon).hydA–Strep-tag II, pET-21(b) hydE–Strep-tag II, pET-21(b) hydF–Strep-tag II, and pET-21(b) Strep-tag II–hydG constructs were used for the anaerobic production of the Clostridium pasteurianum HydA (CpI) apoprotein and the Shewanella oneidensis HydE, HydF, and HydG maturases Strep-tag II apoprotein was anaerobically isolated and concentrated to 2 mg·mL–1 (30 µM) using a stirred cell and a 30 kDa MWCO membrane. Concentrated CpI was anaerobically sealed, flash frozen using liquid nitrogen, and stored at –80°C. The anaerobically purified maturases were not concentrated since protein precipitation was occasionally observed during this step. Instead, the maturases were anaerobically sealed and flash frozen at their as-purified concentrations: 2 mg·mL–1 HydE–Strep-tag II (40 µM), 2 mg·mL–1 HydF–Strep-tag II (40 µM), and 12 mg·mL–1Strep-tag II–HydG (150 µM). Protein concentrations were estimated using the Bradford assay in vitro system that utilizes non-purified maturases without Strep-Tactin purification tags Previously described pET-21(b) In vitro activation of the CpI apohydrogenase was carried out at 26°C in an anaerobic glove box (Coy Laboratory Products), and reaction volumes were 50–100 µL Strep-tag II, 5 µM HydF–Strep-tag II, 50 µM Strep-tag II–HydG, 20% vol·vol–1 lysate  from E. coli strain BL21(DE3) ΔiscR::kan, 1 mM Fe2+, 1 mM S2–, 1 mM DTT, 2 mM DTH, 2 mM SAM, 2 mM L-cysteine, 2 mM L-tyrosine, 15 mM GTP, 2 mM PLP, and 1–2 µM CpI apoprotein. CpI activities were quantified using a methyl viologen-based H2 uptake assay, and data are presented as hydrogenase specific activities. We previously determined that a specific activity of ∼675 µmol H2 consumed·min–1·mg–1 of CpI indicates complete hydrogenase maturation E. coli strain BL21(DE3) ΔiscR::kan2 uptake assay. The maturase expression constructs for these experiments were pACYCDuet-1–hydGx–hydF, pACYCDuet-1–(empty cloning site)–hydEF, pACYCDuet-1–HydGx–hydE, and pACYCDuet-1–hydGx–hydEF.CpI was co-expressed with two or three Hyd maturases using previously described methods for high-yield hydrogenase expression in the 011-mode resonator (SHQE-W), an ESR-900 liquid helium cryostat, and an Oxford Instruments ITC-5 temperature controller. Measurement parameters were typically as follows: temperatures, 10–120 K; microwave powers, 10 µW–100 mW; modulation amplitudes, 0.2–0.5 mT; sweep widths, 300–400 mT; conversion time, 80 ms; sweep time, 82 s. Spin quantitation was based on Cu-EDTA standards measured at 80 K and 25 µW microwave power. HydF samples for EPR spectroscopy were prepared using the described expression and purification procedures. Without freezing and thawing the protein after purification, the as-isolated HydF was concentrated to 1.2 mM using 500 µL centrifugal ultrafiltration units (Amicon) with a 30 kDa MWCO membrane.EPR spectroscopy was done at the CalEPR Center in the Department of Chemistry at the University of California, Davis. Continuous-wave spectra were collected at an X-band frequency (9.39 GHz) with a Bruker Biospin Elexsys E500 spectrometer equipped with a cylindrical TE