Garcinia kola seeds have been shown to possess anti- inflammatory properties which has not been explored in diabetes. To our knowledge, this is the first study to investigate the effect of KV on pro-inflammatory proteins in the liver of diabetic rats.Chronic inflammation plays a crucial role in hyperglycemia-induced liver injury. Kolaviron (KV), a natural biflavonoid from Diabetes was induced by a single intraperitoneal injection of streptozotocin (STZ) (50 mg/kg) in male Wistar rats. Kolaviron (100 mg/kg) was administered orally five times a week for six weeks. The concentrations of cytokines and chemokine were measured using Bio-plex Pro™ magnetic bead-based assays . Plasma glucose and serum biomarkers of liver dysfunction were analyzed with diagnostic kits in an automated clinical chemistry analyzer. Insulin concentration was estimated by radioimmunoassay (RIA).Kolaviron (100mg/kg) treatment significantly ameliorated hyperglycemia and liver dysfunction. Serum levels of hepatic marker enzymes were significantly reduced in kolaviron treated diabetic rats. Kolaviron prevented diabetes induced increase in the hepatic levels of proinflammatory cytokines; interleukin (IL)-1beta, IL-6, tumour necrosis factor (TNF-α) and monocyte chemotactic protein (MCP-1).The results of this study demonstrate that the hepatoprotective effects of kolaviron in diabetic rats may be partly associated with its modulating effect on inflammatory responses. Type 1 diabetes mellitus (DM) is an autoimmune disorder involving immune mediated recognition of pancreatic β cells by auto-reactive T cells with subsequent release of pro- inflammatory cytokines that worsen the disease state  results To a large extent, the effect of hyperglycemia is mediated by an elevation in the levels of pro-inflammatory proteins. Over-production of several inflammatory mediators such as growth factors, pro-inflammatory cytokines and chemokines has been documented in DM -11.Type 1 DM is considered as an inflammatory process in which a significant increase of cytokines was found in the blood of patients with this disease. The response of hepatocytes to pro-inflammatory cytokines promotes the expression of genes that mediate the inflammatory process . FurtherGarcinia kola) belongs to the family of plants called Guttiferae and the genus Garcinia. Garcinia kola seeds have been shown to contain a complex mixture of polyphenolic compounds, biflavonoids, prenylated benzophenones and xanthones which account for the majority of its effects olaviron  were conGarcinia biflavonoid, GBI, GB2 and kolaflavones has been demonstrated for its, hypoglycemic, antioxidative, anti-inflammatory and antigenotoxic effects [Limitations of the currently used drugs on glycemic regulation have raised the need for the development of new drugs which can act as alternative and/or complementary therapy. Interest in natural plant products as anti-diabetic agents has increased over the years due to their low side effects and multidimensional mode of action . Kolavir effects ,25.The concept that chronic low grade inflammation is important in the development and progression of diabetes and its associated complication is not new. Prolonged hyperglycemia can generate an inflammatory state leading to an increment in cytokine production and pancreatic beta cell destruction . Anti-inRegulation of blood glucose either in the fasting state and/or postprandially is an important factor in diabetic therapy. Sustained glycemic control decreases the risk of developing microvascular and macrovascular complications ,33. The It has been shown that absolute or relative insulin deficiency coupled with decreased ATP production accounts for low protein synthesis . The decAmino transferases, aspartate transaminase (AST) and alanine transaminase (ALT) catalyse amino transfer reactions and are used as markers of hepatic injury . DeleterInflammation has been reported to cause direct organ damage in diabetic rats ,45 and hMCP-1 is a chemo-attractant which promotes monocyte and macrophage migration and activation at the site of injury. Over-expression of MCP-1 exerts various damaging effects via increased production of superoxide radicals from macrophages, release of lysosomal enzymes, cytokines, growth factors and cellular adhesion molecules ,50. AnimHepatic infiltrating macrophages and Kupffer cells are sources of pro-inflammatory cytokines such as TNF-α, IL-1 and IL-6 in the liver . Our stu2 and TNF-α production in macrophage-like cell line [TNF-α is one of the major cytokines upregulated in diabetic liver which can promote the activation of NF-κB through interaction with the TNF-α receptor resulting in liver inflammation and apoptosis . The invell line . Kolavirell line . The antGarcinia biflavonoid complex in diabetes. Further investigation may also be necessary for complete elucidation of the structure of kolaviron.Although the results of our study demonstrated the beneficial effects of kolaviron on inflammatory response and hepatic injury in the liver of diabetic rats, future studies can address other possible mechanisms of action of kolaviron and the underlying molecular targets of In summary, our study revealed that kolaviron treatment ameliorated hyperglycemia-induced increase in the levels of proinflammatory cytokines and chemokine in rat’s liver and may therefore act as a useful agent in retarding the progression of diabetic liver complications. Another important outcome of this study is the discovery of a new compound from kolaviron. This new compound along with previously identified compounds of kolaviron may partly explain its beneficial effects in diabetes.The authors declare that they have no competing interests.ORA was responsible for the conception and design, carried out all experiment, performed data analysis and drafted the manuscript. NNC collaborated in the antiinflammatory studies and made contribution to the revision of the manuscript. NLB and OOO made contribution to the conception and revised the manuscript critically for intellectual content. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6882/13/363/prepub


Many studies have utilized diffusion tensor imaging (DTI) and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF), which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR) technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI) techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.Diffusion-weighted MRI (DW-MRI), the only non-invasive technique for probing human brain white matter structures  Recent advances in diffusion tensor imaging (DTI) techniques and white matter tractography have made it possible to visualize the fiber tracts comprised of coherently oriented axons and to map the anatomical connectivity patterns of healthy and diseased human brains Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only available tool for non-invasively probing human brain tissue microstructure and the microanatomical organization of human brain white matter To reduce the influence of CSF on the measurement of diffusivity and to increase the accuracy of the tractography, at least two kinds of solutions have been proposed to mitigate any CSF contamination. One is to improve the DTI sequence by suppressing the CSF signal using a radio-frequency pulse inversion recovery during DTI acquisition, such as the Fluid-Attenuated Inversion Recovery (FLAIR) technique Even though measures of diffusivity and tractography derived from the FLAIR-DTI technique should be more useful for analyzing the property of brain tissue and brain connectivity, no study to date has investigated the differences between human brain anatomical networks derived from FLAIR-DTI and those derived using conventional DTI (non-FLAIR-DTI) techniques. In this study, we acquired DTI datasets from twenty-two normal, healthy volunteers using both FLAIR-DTI and conventional DTI sequences, constructed anatomical networks using a deterministic tractography method, analyzed the topological properties of the networks, and compared the statistical differences between the anatomical networks constructed from the two types of DTI datasets. We hypothesized that the brain anatomical networks derived from the FLAIR-DTI datasets would have more fibers as edges because this method can capture the “real” anatomical connectivity patterns of the human brain due to the reduced CSF contamination. We expected that the brain anatomical networks constructed from the FLAIR-DTI datasets would exhibit more efficient small-world properties and that the topological properties of brain regions with high concentrations of CSF would show significant differences from those constructed using the conventional DTI dataset.SD = 20.05±2.73 yrs) participated in this study. None of the volunteers had a history of neurological or psychiatric disease or brain injury. The protocols were approved by the Review Board of the Institute of Cognitive Neuroscience and Learning at Beijing Normal University (BNU). Informed written consent was obtained from each participant prior to the MR scanning.Twenty-two right-handed healthy volunteers  = 256 mm×248 mm and data matrix = 128×124, 30 directions with b = 1000 s/mm2 and a b = 0 volume, and 68 transverse slices without gap covering the whole brain. When we acquired the conventional DTI and FLAIR-DTI datasets, we arranged the 30 diffusion-sensitive gradient directions in an icosahedral scheme. The parameters of the FLAIR-DTI sequence were same as those of the conventional DTI sequence except the inversion time (TI) = 2250 ms, and TR = 18000 ms.Each of the twenty-two subjects was scanned on a 3T Siemens Trio MR scanner using a conventional DTI sequence and a FLAIR-DTI sequence with a twelve-channel phased array head coil with the implementation of the parallel imaging scheme GRAPPA  and an acceleration factor of 2. Both the conventional DTI and the FLAIR-DTI scans were performed using a single-shot twice-refocused spin-echo diffusion-weighted EPI sequence 3, 176 sagittal slices).In addition, we also acquired 3D brain anatomical images using a T1-weighted MP-RAGE sequence for each subject  before further processing.Considering the fact that FLAIR-DTI yields lower signal-to-noise ratio (SNR) in the acquired raw images than those in the Conventional-DTI, we calculated the SNR values in each DTI dataset and adjusted its effect to explore the pure CSF effect on the properties of brain anatomical networks. SNR calculations were performed using the most commonly used “two region” method (SNRmean) described by Dietrich We followed the procedures described in previous studies The nodes were defined according to the automated anatomical labeling (AAL) template b = 0 image as the reference volume and using an affine registration from the FSL-FDT Toolbox (http://www.fmrib.ox.ac.uk/fsl). The corrected DTI datasets were then split and averaged to generate the averaged 31 volumes of DTI datasets that were subsequently used in fiber tracking. We reconstructed the fibers for the whole brain based on the Fiber Assignment by Continuous Tracking (FACT) algorithm, which was implemented in DtiStudio. Fiber tracking was terminated at voxels with a fractional anisotropy (FA)<0.20 and a tract turning-angle>45°.The white matter fibers of the whole brain for each subject were reconstructed using the DtiStudio software package (Version 3.0.3) The edges of the anatomical networks were defined as the anatomical connections between any pair of nodes. Given two ROIs Taking each ROI as a node and the weighted index between any pair of nodes as the edge, we obtained a weighted brain anatomical network for each subject for each type of DTI data.S), to characterize the global topological properties of the brain networks. Their definitions are provided in the We used seven global network parameters, the weighted clustering coefficient  greater than the average normalized betweenness of the network, or SDThe hub regions of the brain anatomical networks were identified according to the normalized betweenness, Following the methodology used in previous studies t-test was performed on each network metric. The effect of SNR was adjusted for all of these analyses. Several previous studies p = 0.05. To solve the problem of multiple comparisons, a height statistical standard  p<0.01 (uncorrected) was adopted. To justify that the sample size in this study is enough to infer a statistically significant difference, we performed a power analysis according to a previous study To determine whether the CSF had significant effects on the topological properties of human brain anatomical networks formed using the FLAIR-DTI and conventional DTI datasets, a multiple linear regression analysis with a paired S) of the anatomical networks as well as results of the statistical comparisons. Compared with the anatomical network from the conventional DTI datasets, we found statistically significant higher values of S but a statistically significant lower value of We calculated the values of p<0.01, uncorrected). For the nodal global efficiency . From Statistical analyses revealed no significant differences in ence in  , Fig. 2.We first calculated each regional parameter for a subject and then averaged the regional parameter across all subjects. Thus, for the four regional parameters  to detect any statistically significant difference between the edges in the anatomical networks corresponding to the FLAIR-DTI and conventional DTI datasets, but no statistically significant difference was found.We compared the number of edges in the networks derived from the FLAIR-DTI and conventional DTI datasets. We found that the connectivity patterns of the anatomical networks derived from the two types of DTI datasets were similar to each other. However, we noticed more edges in the anatomical networks corresponding to the FLAIR-DTI datasets. In this study, we compared the topological properties of human brain anatomical networks constructed using graph theory and utilizing datasets collected by conventional DTI and FLAIR-DTI techniques. The anatomical network derived from the FLAIR-DTI datasets showed statistically significant high global efficiency , as well as shorter characteristic path lengths  were detected from the anatomical networks corresponding to both types of DTI datasets. The spatial pattern of the hubs detected from the anatomical networks corresponding to both the types of DTI datasets was consistent with the similarity of the betweenness nodes derived from both types of DTI datasets . The addIn order to compare the identified hubs in the present study with other studies, we also listed network hubs reported in previous studies . From TaOur results showed that the FLAIR-DTI technique is superior to the conventional DTI technique in global and local network efficiency metrics, especially in CSF-filled regions. However, several issues need to be addressed in the present study. First, the lower SNR and the longer scan time required for the FLAIR-DTI technique are drawbacks that need to be considered. The longer scan time for the FLAIR-DTI technique was not an important issue in this study because the subjects enrolled were healthy and cooperative during image acquisition. We controlled the SNR and found effects of CSF contamination on the topological properties of the anatomical networks. Therefore, the increased scan time was tolerable and the use of FLAIR is recommended for studying human brain anatomical networks, especially in normal healthy people. Actually, two kinds of approaches have been proposed to mitigate CSF contamination, one is the FLAIR-DTI technique and the other is the two tensor model. The two tensor model uses a data postprocessing approach to remove the CSF signal, while the FLAIR-DTI is a MRI technique to obtain the signals free from CSF contamination. Although the FLAIR-DTI technique may have suffered from disadvantages arising from a lower SNR and a longer scanning time, in this study we were able to find the effects of CSF on the properties of the anatomical network.q-space derived model to describe hindered and restricted (fast/slow) diffusion in brain tissue. GDTI uses higher order tensor model, such as 4th order tensor, to analyses the data. Although these DW-MRI strategies can deal with the crossing tracts problem in the low spatial resolution (2 mm isotropic) diffusion data in some degree, none of them has the capacity to distinguish the fibers crossing, twisting or kissing within a voxel, to determine exactly origins and terminations of fibers within the gray matter of a cortical area, and to distinguish efferent/afferent fibers or mixed projections. The single tensor model is a basic one to describe the DW-MRI signal behavior for low values of diffusion weighting . It does not appear to be consistently accurate in describing the signal behavior for higher values of diffusion-weighting .The single tensor model is still the basic one to process DTI data and is known to have problems with both CSF and crossing tracts At present, a growing number of DW-MRI strategies have been developed for measuring and interpreting complex diffusion properties of brain white matter. The methods vary in their acquisition sampling and analysis approaches Second, the tractography method also influences the accuracy of the calculated network properties. We only selected the FACT algorithm Third, no ground truth exists for which anatomical network is in fact “true”. No definitive answer exists about how to select the nodes in constructing human brain anatomical networks. A study has verified that selecting different brain templates to define network nodes influences the topological properties of the brain networks Using graph theoretical approaches, we explored the influence of CSF on human brain anatomical networks. Our study suggests that human brain anatomical networks derived from FLAIR-DTI datasets have higher network efficiency than those derived from conventional DTI datasets. CSF contamination influences not only the nodal properties of the brain regions close to the CSF-filled space but also the number and location of the identified hub regions in human brain anatomical networks. In addition, CSF contamination influences the backbone network derived from conventional DTI datasets. Because human brain anatomical networks have previously been constructed primarily from diffusion datasets acquired using the conventional DTI technique with the single tensor model, our findings may have implications for human brain anatomical networks and tractographic methods in studies of normal brain development and clinical applications. In order to pinpoint the differences in human brain anatomical networks between different study groups, we suggest that, if possible, selecting CSF suppression techniques such as the FLAIR-DTI sequence to eliminate the CSF signal will increase the accuracy of human brain anatomical networks.Figure S1Normal probability plot of the global parameters ((TIF)Click here for additional data file.Figure S2Normal probability plot of the global parameters (S).(TIF)Click here for additional data file.Table S1The signal-to-noise ratio (SNR) for the selected ROIs, fornix and splenium of the callosum (SCC), for each subject.(DOC)Click here for additional data file.Table S2Regions of the automated anatomical labeling (AAL) template and the corresponding abbreviations used in this study.(DOC)Click here for additional data file.Table S3Power analysis and normality test for different global parameters. Note: The bold digital number means that the value of the global parameters does not obey the normal distribution.(DOC)Click here for additional data file.Table S4Power analysis for nodal parameters in significant different regions.(DOC)Click here for additional data file.Table S5Normality test for the nodal parameters in the significant different regions. Note: The bold digital number means that the value of the nodal parameter ((DOC)Click here for additional data file.Table S6Global parameters of the brain anatomical networks derived from the conventional DTI and FLAIR-DTI datasets using the nonparametric permutation test.(DOC)Click here for additional data file.Table S7Statistically significant differences in the nodal parameters of the brain anatomical networks using the nonparametric permutation test. Note: Bold text indicates the brain regions showing significant differences in both nodal parameters, (DOC)Click here for additional data file.Text S1Definitions of nodal parameters and global parameters.(DOC)Click here for additional data file.Text S2Power analysis and normality test.(DOC)Click here for additional data file.Text S3Non-parametric permutation test.(DOC)Click here for additional data file.Text S4Results from the non-parametric permutation test.(DOC)Click here for additional data file.


Novel research has suggested that altered miRNA expression in the placenta is associated with adverse pregnancy outcomes and with potentially harmful xenobiotic exposures. We hypothesized that aberrant expression of miRNA in the placenta is associated with fetal growth, a measurable phenotype resulting from a number of intrauterine factors, and one which is significantly predictive of later life outcomes.miR-16, miR-21, miR-93, miR-135b, miR-146a, and miR-182. The expression of miR-16 and miR-21 was markedly reduced in infants with the lowest birthweights (p<0.05). Logistic regression models suggested that low expression of miR-16 in the placenta predicts an over 4-fold increased odds of small for gestational age (SGA) status . Moreover, having both low miR-16 and low miR-21 expression in the placenta predicts a greater increase in odds for SGA than having just low miR-16 or miR-21 expression (p<0.02), suggesting an additive effect of both of these miRNA.We analyzed 107 primary, term, human placentas for expression of 6 miRNA reported to be expressed in the placenta and to regulate cell growth and development pathways: in utero exposures and markers of fetal growth is essential for better understanding the epigenetic mechanisms underlying the developmental origins of health and disease.Our study is one of the first to investigate placental miRNA expression profiles associated with birthweight and SGA status. Future research on miRNA whose expression is associated with  Fetal development represents a critical period during which perturbations to the intrauterine environment through various factors in the extrauterine environment can have major ramifications on not only the proper growth and development of the fetus but also on risk for disease later in life in utero. It is involved in providing the fetus with nutrients and is involved in waste and gas exchange. The placenta's metabolic activity is crucial for protecting the fetus from potentially harmful maternal factors and xenobiotic toxicants that may alter fetal growth and development. The environment during pregnancy is thought to impact the appropriate function of the placenta during development, thus identification of alterations to the placenta and to placental gene expression may serve as a record of in utero exposures and of the intrauterine and extrauterine environments during pregnancy The placenta is of critical importance to ensure the proper growth and development of the fetus while in utero exposures The mechanisms by which in utero exposures may dysregulate the regulatory mechanisms of the placenta continue to be studied. One mode of alteration may be through the aberrant expression of microRNA (miRNA), 21–25 nucloeotide long non-coding RNA involved in post-transcriptional gene regulation miR-16miR-21miR-93miR-135bmiR-146amiR-182Since miRNA have been described as playing important roles in development and are susceptible to the environment, we sought to further characterize the expression of six candidate miRNA previously shown to be expressed in the placenta and previously reported to target genes in pathways crucial for regulating key cell processes – miR-16, miR-21, miR-93, miR-135b, miR-146a, and miR-182. The demographics of the sample population are given in . Of note, the study population was oversampled for small for gestational age (SGA) infants, defined as infants whose birth weight was the lowest 10th percentile for their gestational age, calculated as in Fenton miR-146a and miR-182 exhibiting expression in the 0.001–3.95 amol range, while miR-16 and miR-21 expression was 2 orders of magnitude greater with expression ranging from 3.53–434.74 amol range. Within each miRNA, the majority of the samples exhibited relatively homogenous low levels of expression, but the distributions of expression exhibited a right skew, suggesting there were still a number of samples with relatively moderate to high expression. As these data were not normally distributed, and as we hypothesized that aberrant high or low expression may be associated with exposures or outcomes, the expression profiles were split into quartiles, and birthweight percentile was plotted by quartile of miRNA expression  of the miRNA and infants considered small for gestational age (SGA), using logistic regression to control for potential confounders. SGA status served as a more clinically-relevant marker of the multitude of in utero conditions which may comprise an adverse intrauterine environment which may be ultimately associated with low birthweight. These models  compared to infants with high placenta miR-16 expression, controlled for confounders. Additionally, low miR-21 expression in the placenta predicts an intriguing but nonetheless statistically insignificant, 2.43-fold increased odds for SGA  compared to infants with high placenta miR-21 expression controlled for confounders. Maternal cigarette smoking during pregnancy, as expected, also demonstrated greatly increased risks for growth restriction in both models.Observing that expression in the lowest quartiles of e models Table 3 miR-21 only or low miR-16 only had non-significant elevation in SGA risk, but infants exhibiting reduced expression of both miR-16 and miR-21 were significantly more likely to be classified as SGA . Although a likelihood ratio test suggested no significant multiplicative interaction between miR-16 and miR-21 (p>0.05), there was a significant trend for increased risk of being classified as SGA from having only miR-21 reduced or only miR-16 reduced in expression to having both reduced in expression (p<0.02).As both miRNA demonstrated independent correlations with SGA status, we further examined if there was an interaction between expression of these 2 miRNA in their association with infant growth outcome. This model Table 4 miR-21 in a variety of cancers miR-21 in TCL-1 placental cells, a third trimester human placental cell line, and used Western blot to assess PTEN protein levels in cells overexpressing miR-21 versus cells transfected with negative control. Cells overexpressing miR-21 had approximately 50% less PTEN protein relative to tubulin than cells transfected with negative control  may have experienced a number of heterogeneous environmental and placental conditions associated with describe the complex phenotype that is SGA. Because of the complexity of the conditions that may be associated with SGA status, we used SGA status as a marker suggestive of the complex milieu of conditions that may be associated with small fetal growth. Fetal malnutrition linked to growth restriction has been shown occur in a variety of conditions, including but not limited to poverty, pregnancy in women with eating disorders, and pregnancy in high altitude As described described above, the multitude of factors that may contribute to altered fetal growth contribute to the relative complexity of the intrauterine environment. As concluded by Avila et al., site-to-site variability of gene expression does exist in the human placenta Mouillet and colleagues published important work investigating circulating levels of a set of trophoblast miRNA found in plasma and their association with fetal growth miR-16 and miR-21 in the placenta is associated with poor fetal growth may have many important implications. Our data are an important step in discovering miRNA expression profiles associated with low birthweight which may be powerful predictors of risk for disease later in life, such as coronary heart disease, diabetes, and hypercholesterolemia. This study is an important stepping stone in that it establishes that miRNA have the potential to predict future health outcomes based, in part, on their altered expression in the placentas of low birthweight infants. More work is needed to investigate how particular insults to the fetal environment may associate with alterations to placental miRNA expression and how these aberrant expression profiles may be associated with differential fetal growth. Future work to determine the roles of miRNA in specific pathways leading to altered fetal growth will be key to better understanding fetal growth as both a marker of the intrauterine environment as well as a developmental outcome and in better comprehending the developmental origins of health and disease.In summary, our data suggesting that low expression of th percentile) based on birthweight and gestational age and calculated from the Fenton growth chart All placenta samples used were collected as part of the Rhode Island Child Health Study (RICHS), an ongoing, population-based birth cohort at Women and Infants' Hospital in Providence, RI. The ongoing Rhode Island Child Health Study enrolls mother-infant pairs following delivery at Women and Infants Hospital in Providence, Rhode Island, USA. Term infants born small for gestational age  and manufacturer protocols as described previously Expression of mature miRNAs was measured using commercially available TaqMan microRNA Assays or TaqMan Gene Expression Assays  on an Applied Biosystems 7900HT Real-Time PCR system and analyzed with 7900HT System Software. Absolute quantitation of miRNA was calculated using a standard curve generated from serial dilutions of miRNA-specific pre-miR oligonucleotides (Ambion) run on each plate for each miRNA of interest, as previously described for array analyses by Bissels and colleagues TCL-1 placental cells were cultured as described previously miR-21 resulted in decreased protein levels of PTEN compared to cells transfected with negative control. Following cell harvest and lysate collection, total protein was quantified using the BCA assay (Thermo Scientific) and manufacturer protocols. Cell lysates were then separated electrophoretically using 10% Tris-HCl gels (Bio-Rad). Proteins were transferred to Immuno-Blot PVDF membranes (Bio-Rad) through overnight night transfer at 4 degrees C and following transfer, membranes were washed with TBSt (Boston Bioproducts) and blocked with 5% milk in TBSt. For Western blot analysis, antibodies against PTEN (Invitrogen) and gamma-Tubulin  were used to visualize corresponding proteins following established protocols for staining and washing. Proteins were visualized using the Amersham ECL Western Blotting Analysis System  following the manusfacturer's protocol. Resulting images were scanned and protein quantitation was performed using Image J software.Western blots were used to confirm that overexpression of Birthweight percentile was calculated using the Fenton growth chart


Large registries of potential unrelated stem cell donors have been established in order to enable stem cell transplantation for patients without HLA-identical related donors. Donor search is complicated by the fact that the stored HLA information of many registered donors is incomplete. We carried out a project that was aimed to improve chances of patients with ongoing donor searches to find an HLA-matched unrelated donor. For that purpose, we carried out additional donor center-initiated HLA-DRB1 typing of donors who were only typed for the HLA loci A and B so far and were potential matches for patients in need of a stem cell transplant. In total, 8,861 donors were contacted for donor center-initiated HLA-DRB1 typing within 1,089 donor searches. 12 of these donors have donated stem cells so far, 8 thereof for their respective target patients. We conclude that chances of patients with ongoing donor searches to find an HLA-matched unrelated donor can indeed be improved by donor-center initiated typing that is carried out in addition to the standard donor search process. Our results also raise questions regarding the appropriate use of incompletely typed donors within unrelated donor searches. Allogeneic hematopoietic stem cell transplantation is a well-established and increasingly used therapy for hematological malignancies and other severe diseases of the blood Full HLA typing, i.e., high-resolution typing of at least the HLA loci A, B, C and DRB1, of newly registered potential stem cell donors is advantageous as it reflects the current standard of donor-recipient matching Available HLA phenotype data of registered donors are routinely extended either by donor center-initiated HLA typing (prospective typing), generally based on selection criteria as donor age or gender or the HLA information available so far, or by patient-related typing requests within actual donor searches. A patient-related typing request is initiated by the responsible transplant physician, forwarded by the national registries involved, and executed by the donor center that has recruited the respective donor. We analyzed 9,478 donor searches between November 2009 and September 2010 that we became aware of due to patient-related HLA typing requests for donors of DKMS German Bone Marrow Donor Center. We then carried out additional donor center-initiated HLA-DRB1 typing of selected donors who were so far only typed for the HLA genes A, B and optionally C but not DRB1 . Donors We did not take into account potentially matching donors outside the DKMS donor file in the worldwide registry as we had no information if these donors were available, were already requested for patient-related HLA typing, or were intentionally not considered by the responsible transplant physicians.HLA typing results of project donors were submitted to registries without references to the specific patients for whom the donors had been included in the project. Furthermore, we did not inform the responsible transplant physicians about the donor center-initiated typing efforts carried out in order to support the donor searches of their patients. This approach was chosen to avoid interferences with the standard search process. Transplant physicians became aware of the extended HLA information of project donors by routinely rechecking registry data during donor searches.Donors who were selected for donor center-initiated typing within the project were followed-up with respect to subsequent requests for confirmatory typing , donor work-up , and stem cell donation.At stem cell donor registration, each donor signed an informed consent form covering sample collection and storage, HLA typing of the sample, data storage and transmission of anonymous data to domestic and foreign registries. All donors who were included in the analyzed donor center-initiated typing project were informed by mail about the continuative HLA typing.In order to avoid delays of the donor search, we initiated HLA typing and informed donors at the same time. Donors who withdrew their willingness to donate stem cells, were temporarily unavailable, or unable to donate for health-related reasons could mark respective statements on a response sheet that was attached to the mail. According to their feedback, they were temporarily or permanently excluded from the donor file. HLA typing results of these donors were included in the analysis regarding 3-locus low-resolution matched donors who were identified within the project.No ethics committee approval was obtained as donor center-initiated typing projects are standard procedures of donor centers that are covered by the consent form signed at recruitment.8,861 donors were contacted for additional donor center-initiated HLA-DRB1 typing within 1,089 of the 9,478 analyzed donor searches. Within 28 days after contact, 382 donors (4.3%) declared their unwillingness or lack of ability to donate stem cells or were excluded from the donor file for other reasons.The maximum number of donors who were contacted for a specific patient as they were potential matches based on their HLA-A, -B low-resolution phenotypes was 70. For 179 patients, we identified in total 236 donors who matched on the 3-locus low-resolution level with their respective target patients.76 confirmatory typing and 17 work-up requests were submitted for donors with donor center-initiated HLA typing, and 12 of these donors donated stem cells so far. 4 donor work-ups are currently processed, one donor was unable to donate for medical reasons. Of these requests and donations, 35 confirmatory typing requests and 10 work-up requests were made for the original target patients. 8 donors donated for their respective target patients, one donor was unable to donate, and one work-up for the target patient is currently processed.Donations for target patients took place between 70 and 357 days  after the respective donor center-initiated typing requests. Interval lengths of the various steps of the donor search process are shown in Details regarding the 8 donors who donated for their target patients are displayed in Our practical study was intended to provide a proof of principle if donor center-initiated HLA typing can improve the success chances of ongoing donor searches. This proof was successfully made.The median and average time from donor center-initiated typing to stem cell donation  suggest that donor-center initiated typing might often be too time-consuming to support donor searches as many stem cell transplantations are urgent. The detailed analysis of sub-intervals in Eight donations resulted from donor center-initiated HLA typing of more than 8,000 donors within the study. These figures raise questions regarding the cost-benefit ratio of the analyzed HLA typing efforts. We did not carry out a formal cost-benefit analysis. Generally, cost-benefit analyses of ongoing donor recruitment or continuative HLA typing of already registered donors are complicated by several practical and ethical problems The 8 donating study donors who were obviously – after additional donor center-initiated HLA typing, confirmatory typing and medical clearing – assessed as best available donors for their target patients by the responsible transplant physicians had originally not been requested for additional typing based on their available HLA information that did not include HLA-DRB1. It is, therefore, doubtful if they had advanced to donation without donor center-initiated HLA typing.This finding raises the question if there are deficiencies of the existing donor search process regarding the use of incompletely typed donors, especially of donors who are typed for HLA-A and -B only. An infrequent use of donors with this typing profile has been reported before The restriction to 3-locus low-resolution matching in the donor selection process and the non-availability of data on patient-related requests for non-DKMS donors within the considered donor searches are the major limitations of the study. It is, therefore, not possible to draw definite conclusions regarding the efficiency of the donor search process from our results. Due to the high relevance of this issue for optimal donor selection and thus for the best possible patient care, further analyses of this question are urgently required.It is obvious that full HLA typing at donor recruitment reduces the probability of non-finding the optimal stem cell donor. As new donors are increasingly typed more completely due to advances in typing technology and related cost reductions, the probability of non-finding the optimal donor should decrease in the future. However, there will be millions of partially typed donors in the worldwide donor file for many years to come. There is a need for strategies that make sure that these donors are utilized properly in actual donor searches. Haplotype frequency-based search algorithms Many donor centers regularly run prospective typing projects in order to increase the completeness of HLA information of their registered donors. Our results suggest it might be indicated to use the funds available for such projects at least partly for donor center-initiated HLA typing as support for currently ongoing donor searches as described in this work. We plan, therefore, to continue with this project with a refined donor selection process that is based on probabilities for 4-locus high-resolution instead of 3-locus low-resolution matching.


Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making. Perceptual decision-making involves not only simple transformation of sensory information to a motor decision, but can also be modulated by high-level cognition. For example, the latter may include strategic allocation of limited attentional resources over time in a decision task to improve performance. At the neurophysiological level, there is evidence supporting attention-induced neuronal gain modulation of both excitatory and inhibitory cortical neurons. In the context of perceptual discrimination tasks performed by animals, we make use of a biologically inspired computational model of decision-making to understand the computational capabilities of such co-modulation of neuronal gains. We find that dynamic co-modulation of both excitatory and inhibitory neurons is important for flexible, and cognitively demanding decision-making while also enhancing robustness in the decision circuit's functions. Our model captures the neuronal activity and behavioural data in the animal experiments remarkably well. Decision performance in a reaction time task can be optimized, maximizing the rate of receiving reward by using fast gain recruitment. Our experimentally fitted timescale is near the optimal one, suggesting that the animals performed almost optimally. By providing both computational simulations and theoretical analyses, our computational model sheds light into the multiple functions of rapid co-modulation of neuronal gains during decision-making. Perceptual decision-making often requires temporal integration of sensory information and its subsequent transformation to a categorical motor choice In difficult sensory discrimination tasks, for example, with near ambiguous stimuli, the optimal approach would be to integrate sensory evidence over a long period of time. However, long temporal integration is seldom observed in experiments, and there is evidence suggesting a temporally increasing urgency signal during decision formation Previous computational and theoretical models have incorporated urgency signals or dynamic gain modulation in their decision-making models In this work, we incorporate a time-varying excitatory-inhibitory gain modulation mechanism into our previous cortical microcircuit model of decision-making The classic experiments of We used a neural circuit model of decision-making As in eter see  of excitFollowing ectively .  and  aFollowing the deduction of The input current due to the encoded target  can be mFiring rates of upstream MT neurons, selective for a particular direction of motion, can be assumed to increase (decrease) roughly linearly with the motion strength, when the motion is in the preferred (anti-preferred) direction of the neuron, in the regimes of motion strength experimentally tested herence .We assume the simplest form of gain modulation, a “gain field” mplitude :(4)whereSetting Note that while representing the gain modulation of the explicit excitatory selective populations is straightforward see , the effThe slowest decay time constant in the model is that of NMDA receptors  is triggered when the higher population firing rate crossed a prescribed threshold at As shown in This activity timecourse of our two variable  attractors allow stable, steady, equal firing rates of both selective populations, which prevents decision-making and categorical choice. A symmetric unstable steady state on the other hand can force the network's state to move off-diagonally, causing the firing rate of one (winning) population to increase while that of the other (losing) to decrease, enabling decision-making and categorical choice. Asymmetric, ‘choice’ attractors ensure that the firing rates of the winning (losing) populations reach a stable steady state, and do not increase (decrease) without bound. Our modus operandi then is to let the network reach symmetric attractors during the fixation and fixation-target periods and then using gain modulation, approach  a symmetric unstable steady state during the fixation-target-gain-motion period see .During the fixation period .basin of attraction of this symmetric attractor. Trajectories starting in this region are attracted into this symmetric attractor. Although additional asymmetric attractors are present, the large basin of attraction of this symmetric attractor shows that this symmetric attractor is very stable. Even with noise . The higher firing rates for the grey upper stable branch can more easily allow a fixed motor threshold to be crossed, i.e. motor action to be initiated. It is precisely this flexible mechanism that allows it to be also used for other behavioural task paradigm e.g. the FD task, which we will later show. It is further noted that these wide range of firing rates can also allow this threshold (currently fixed at 70 Hz) to vary more widely, adding another dimension towards more flexible decision-making strategy for speed-accuracy trade-off To further demonstrate the inflexibility of either excitatory or inhibitory gain modulation as opposed to their co-modulation, we plot the stability diagram of a single selective excitatory population with respect to each (excitatory or inhibitory) gain parameter.   from 1 . This me  from 1 .Conversely, we may decrease (increase) excitatory (inhibitory) gains in isolation. The selective populations transition through the decision-making (DM) regime (where symmetric unstable steady states coexist with asymmetric attractors) to a single low stable branch . Increasing the inhibitory gain leads to an additional regime with multiple steady states including a low spontaneous stable state (LMS). The decision-making regime has a lower firing rate than that during the target period. Consequently, the firing rates of the selective populations diverge at a lower firing rate than the adapted target firing rate. Thus a decrease (increase) excitatory (inhibitory) gain in isolation can enable decision-making but at a smaller dynamic range (e.g. lower firing rates) and slower decisions  than fouTo more completely understand the interplay between stimulus inputs and gain modulation parameters, we extend the stability analysis of During the fixation epoch ,  Hz, tht upward . In partWith both choice target and motion stimulus onset , participants may not need to search for a trade-off between response time and accuracy, and emphasize only on the latter In the FD task experiments, the neural firing rates during the 1 second motion stimulus epoch are found to be generally lower in the FD task than in the RT task . FurtherIn Previous work has suggested that strong recurrent excitation is necessary for the formation of a decision and its maintainence in working memory during a delay period Although there is no intrinsic hysteresis in its stability diagram (i.e. no LMS), the network can still sustain its decision formed throughout the delay period  as long as the target stimulus remains . However, if coherences are known, higher reward rates may more likely be achieved by the optimal recruitment of gain modulation for each individual coherence  ms . For lowThis task-difficulty dependent gain modulation strategy has a reward rate (averaged over the motion coherences) that is higher than that of the previous task-difficulty independent gain modulation strategy. In fact, this difficulty-dependent strategy can lead to an average reward rate of 9.66 rewards per minute, which is dulation  throughoThe optimal time constants are applicable when we use the realistic temporal delays of rate see  for resprate see  even forTop-down cognitive control such as attention has been suggested to form an integral component in perceptual decision-making Specifically focusing on the two highly-studied reaction time (RT) and fixed delay (FD) decision task paradigms, we use both computational simulations and dynamical systems analysis of a biologically inspired decision-making model to address this issue. In our study, we have shown that simultaneous dynamic gain modulation of both excitatory and inhibitory neurons is capable of reproducing the experimentally observed dynamic range of neural activities throughout an entire trial. Our model is able to robustly reproduce realistic temporal dynamics including the signature dip phenomenon in the firing rates (shortly after motion stimulus onset) without artificially lowering the overall stimulus input as implemented in previous modelling work Without specifying any particular neural mechanism, our excitatory-inhibitory gain modulation can allow the same local cortical circuit to flexibly adapt over time to different decision-making task demands. By adopting higher gains in the RT than FD task, we were able to capture not only the behavioural data as in previous models When both excitatory and inhibitory neuronal gains are co-modulated, our model becomes more robust to small changes with respect to the stimulus input . FurtherIn the absence of gain modulation, weak recurrent excitation can lead to decisions made not being stored in working memory , and can prevent the subsequent motor action from being triggered  Our gain modulation mechanism is thus more flexible than conventional decision-making models Previous decision-making models have also incorporated time-varying gain modulations or urgency signals to allow flexible reconfiguration in the model dynamics, or in some cases, attempted to capture the characteristics of  neuronal firing rates throughout a decision-making trial Our work also demonstrates, for the first time, how dynamic excitatory-inhibitory gain modulation in a biologically realistic model can give rise to optimal decision performance. Using realistic temporal delays from the experiment of ilar to   curve, iThe first part of our optimal performance study is based on the assumption that subjects employ the same ‘strategy’, and thus a fixed timescale of gain modulation throughout the whole block of trials. However, in experiments in which coherence is fixed within a block val e.g.  and longFinally, it would be interesting to test our proposed simultaneous excitatory-inhibitory gain modulation mechanism in behaving animals performing perceptual decision-making tasks. For example, the task can include a fraction of trials with some cue to capture the subject's attention, while putative excitatory and inhibitory neurons are recorded. The neural activities and behavioural performance could then be compared between trials with or without the attentional cue.Following For the inhibitory interneuronal population, we assume that We can now reduce our model into an effectively two variable one by implicitly embedding the inhibitory coupling We performed noisy simulations of our model using MATLAB using a forward Euler-Marayama numerical scheme The activity timecourse of our model can be best understood by analysing its dynamics on the two dimensional state/phase space called the phase plane see . We set Intersections of the nullclines give the steady states (fixed points) of the network. For our purposes, these steady states can be stable, i.e. point attractors  or semi. In a noThe loci of all steady states (stable and unstable) as a function of a parameter yields a stability (bifurcation) diagram. Phase-plane and stability (bifurcation) analyses of our two-variable network model were done using XPPAUT Apart from the new parameters mentioned in n-making . We thenFigure S1Two dimensional state/phase space called phase plane. The firing rate stable steady state (symmetric attractor). Trajectories starting near this steady state are attracted into it, with local velocities given by the arrows. This set of arrows is called the vector field. The set of all starting points for trajectories attracted into this attractor is called its basin of attraction. In this figure this is the entire phase plane. (B) A symmetric unstable steady state, called a (symmetric) saddle point. Only trajectories starting on a unique curve (shown in light blue) are attracted into it. This curve is called the stable manifold of the unstable steady state. Trajectories on all other parts of the plane are eventually repelled away from the unstable steady state, to another curve (shown in yellow), called its unstable manifold. There are also two asymmetric attractors. The stable manifold of the unstable steady state separates the basins of attraction of these two attractors.(TIF)Click here for additional data file.Figure S2Post-decision shutdown. After the firing rate of one of the selective populations has crossed the motor threshold (70 Hz) for saccade initiation, the gains of both excitatory and inhibitory neurons are allowed to decay towards 0. (A) Phase plane at the end of a trial. The firing rate (TIF)Click here for additional data file.Figure S3Task dependent optimal timescale of gain modulation for maximizing reward rate for individual coherences. (A) using the realistic temporal delays from Roitman and Shadlen (2002). (B) in a reaction time task with a fixed inter-trial interval (ITI). Upper, middle and lower panels show reward rate (RR), accuracy and trial duration (A) or (TIF)Click here for additional data file.Figure S4Shallower slopes in relation to motion coherence in the anti-preferred direction leads to slightly longer reaction times and poorer accuracy. (A) Timecourse of input currents for equal slopes (black) and 2 times shallower slopes (red) of input current out of the response field, in relation to motion coherence. (B) Activity timecourse of model, averaged over multiple trials, with different motion coherences for (left) equal slopes and (right) 2 times shallower slope of input current in relation to motion coherence in the anti-preferred direction. Response threshold at ript see  and anot(TIF)Click here for additional data file.Figure S5Optimal timescale of gain modulation for maximizing mean reward rate for different noise levels. Network model performance as a function of the time constant of gain modulation ript see  and anot(TIF)Click here for additional data file.Movie S1Dynamics of decision-making over an entire trial. View slideshow to play. Upper and lower panels show phase-planes and activity timecourses, respectively, throughout the various epochs of a trial for coherence (PPTX)Click here for additional data file.Movie S2Larger dynamic ranges allow robust decision-making for co-modulation of excitatory and inhibitory gains. Stability diagrams are shown as a function of excitatory gain (AVI)Click here for additional data file.


Yersinia . Genes responsible for biosynthesis, transport, and regulation of the yersiniabactin (ybt) production are clustered on a mobile genetic element, the High-Pathogenicity Island (HPI) that is responsible for broad dissemination of the ybt genes in Enterobacteriaceae. However, the ybt gene cluster is absent from nearly half of Y. pseudotuberculosis O3 isolates and epidemic Y. pseudotuberculosis O1 isolates responsible for the Far East Scarlet-like Fever. Several potential siderophore-mediated iron uptake gene clusters are documented in Yersinia genomes, however, neither of them have been proven to be functional. It has been suggested that at least two siderophores alternative to Ybt may operate in the highly virulent Yersinia pestis/Y. pseudotuberculosis group, and are referred to as pseudochelin (Pch) and yersiniachelin (Ych). Furthermore, most sporadic Y. pseudotuberculosis O1 strains possess gene clusters encoding all three iron scavenging systems. Thus, the Ybt system appears not to be the sole endogenous siderophore iron uptake system in the highly virulent yersiniae and may be efficiently substituted and/or supplemented by alternative iron siderophore scavenging systems.Low molecular weight siderophores are used by many living organisms to scavenge scarcely available ferric iron. Presence of at least a single siderophore-based iron acquisition system is usually acknowledged as a virulence-associated trait and a pre-requisite to become an efficient and successful pathogen. Currently, it is assumed that yersiniabactin (Ybt) is the solely functional endogenous siderophore iron uptake system in highly virulent  The pathogenic Yersinia can be divided into three main pathogroups, with Y. pestis, Y. pseudotuberculosis, and Y. enterocolitica biogroup 1B making up the highly virulent group (strains able to kill mice at low infection doses); low pathogenic Y. enterocolitica biogroups 2–5, and normally apathogenic Y. enterocolitica biogroup 1A  and polyketide synthetase (PKS) mediate biosynthesis. The strategy follows modular assembly of the siderophore from salicylate, a group from malonyl coenzyme A, three cysteine molecules and three methyl groups . HMWP2 possesses six predicted NRPS domains involved in initial cyclization and condensation reactions. Irp3 (YbtU) reduces the internal thiazoline ring to a thiozolidine structure while the first five domains of HMWP1 (encoded by irp1) switch from NRPS-type assembly line molecules to a PKS-strategy. Irp4 (YbtT) contains a thioesterase domain to remove aberrant structures from the enzymatic complex and displays an editing function together with terminal HMWP1 domains. The mechanism of Ybt secretion into the environment is currently unknown. Irp8 (YbtX in Y. pestis) was supposed to be involved in Ybt export  and the inner cell membrane proteins Irp6 and Irp7 (YbtQ and YbtP in Y. pestis) , a genomic mobile element that exhibits site-specific recombination activity of the P4-like integrase -protein and activated by the AraC-type YbtA transcriptional activator that also represses its own transcription   in the Far East Russia and Japan  alternative to Ybt are active in ybt gene cluster was discovered in all sequenced strains of Y. pestis and Y. pseudotuberculosis but not in Y. enterocolitica and has been designated the Yersinia non-ribosomal peptide (ynp)   equivalent, suggesting that the putative siderophore may not use salicylate as a biosynthetic precursor. Based on the enzymatic domains encoded within the YPTB3296 and YPTB3297 genes, the structure of the putative Ynp siderophore was predicted to contain three thiazoline rings and have a molecular mass of 404.4/460.37 m/z with and without Fe3+, respectively . Nevertheless, it still demonstrates the CAS-positive phenotype suggesting that either some additional siderophore(s) mediate this activity or the truncated protein retains its function.Differential distribution of the Y. pseudotuberculosis strains, the epidemic Y. pestis strains contain at least two mutations in the ynp locus. The NRPS (YPTB3297) is split by an IS100 mobile element into two separate parts and contains a frameshift mutation in the C-terminal region  and N-terminal part of YPTB3297 while YPO0770-0778 contains the remaining genes of the cluster (YPTB3290-3296). These observations have put the functionality of the ynp locus in Y.pestis into question .In contrast to the “sporadic” in vitro to induce the expression of iron uptake systems and to identify iron regulated genes. The expression of the ynp genes has been supported by both in vivo and in vitro studies using microarray and quantitative real time PCR. Since a Fur-like binding region is located 184 bp upstream of the putative ynp receptor transcriptional start site in Y. pestis, expression of this locus is likely Fur-dependent and activated under iron limited conditions  in Y. pestis and Y. pseudotuberculosis  were up-regulated relative to growth in LB media  purified from the surface of the Ybt-negative strain Y. pestis EV76 , which has no genes coding for Ybt synthesis. Chemical analysis of the novel siderophore, designated yersiniachelin (Ych), revealed that it contained hydroxamate groups. In available genome sequences of Y. pestis, two gene clusters are able to code for hydroxamate siderophores. The first, homologous to the aerobactin siderophore cluster, carries several mutations in biosynthetic genes and was shown to be non-functional in Y. pestis  designated ysu (Yersinia siderophore uptake), which contains hydroxamate siderophore synthesis and transport genes, has no visible mutations and thus is predicted to encode for the biosynthesis of a functional siderophore  were deleted  followed by a N-hydroxylation by AlcA and N-acetylation of the hydroxylamine group by AlcB. Then the product is C-hydroxylated by AlcE and undergoes dimerization and macrocyclization performed by AlcC.The involvement of the r Figure  coding fysu and alc clusters shows similarities in DNA and protein sequences of a number of genes and differences in genetic organization and content. The homology is obvious in the four siderophore biosynthetic genes , the ferri-siderophore outer membrane receptors (ysuR and fauA), as well as the ferric iron reductase genes  whose role in Alc siderophore function is currently unknown. Though homologous in the siderophore biosynthetic and receptor genes, the two clusters have several apparent differences. First, a homolog of the alcE biosynthetic gene coding for an iron-sulfur protein involved in C-hydroxylation of precursor molecules during Alc biosynthesis, is absent from the ysu-cluster and Y. pestis genome. This suggests that the structure of Ych may be slightly different from Alc. Nevertheless, due to the obvious homology of the four siderophore biosynthetic genes several possible structures of the ysu-encoded putative macrocyclic hydroxamate siderophore have recently been proposed -like protein shown to be involved in the Alc siderophore efflux by Bordetella cells  coding for ferri-siderophore transporters through the periplasm and cytoplasmic membrane which are absent from the alc cluster. This step of the siderophore-based iron acquisition in Bordetella has been suggested to be performed by an alternative mechanism  was shown to transport iron liberated in the periplasm from multiple structurally distinct siderophores through the cytoplasmic membrane. Fourth, the ysu-cluster lacks the alcR homolog coding for the AraC-type transcriptional regulator AlcR which, in complex with ferri-Alc, activates the expression of the siderophore biosynthesis and transport genes by Bordetella  were down-regulated suggesting that in macrophages Y. pestis is not starved for iron.The ysu genes were not expressed in vivo in the flea vector  and siderophore receptor YsuR (FauA homolog), were identified in the membrane proteome of Y. pestis KIM6 grown to stationary phase at 26°C in chemically defined PMH2 medium. The YsuR protein associated with the outer membrane was increased in abundance in iron-deficient conditions (Pieper et al., The production of the Yersinia the expression of the third siderophore system encoded by the ysu locus is substantiated by the mutagenesis of the ysu genes, transcriptomic and proteomic data, as well as by the purification of the Ych siderophore from the ybt-negative Y. pestis strain. The up-regulation of the ysu genes in iron-deplete conditions in vitro point to the possible involvement of Ych in iron acquisition. However, the exposure of Ych associated with the Hcp-like protein YPO502 on the bacterial surface in iron-replete conditions and the up-regulation of its biosynthetic genes by high osmotic and salt stress suggest an additional role for Ych in Y. pestis physiology.Thus, in highly virulent Yersinia pestis /Y. pseudotuberculosis possess several alternative endogenous siderophore-mediated iron acquisition systems. A complete functional understanding of these novel siderophore systems is currently lacking in the literature and future studies should focus on parsing out the impact these systems have on bacterial physiology.Like most pathogenic bacteria, highly-virulent The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


However, as the protein size increases, sampling again becomes limiting; for example, the standard Rosetta protocol involving Monte Carlo fragment insertion starting from an extended chain fails to converge for proteins over 150 amino acids even with guidance from chemical shifts (CS-Rosetta) and other NMR data. The primary limitation of this protocol—that every folding trajectory is completely independent of every other—was recently overcome with the development of a new approach involving resolution-adapted structural recombination (RASREC). Here we describe the RASREC approach in detail and compare it to standard CS-Rosetta. We show that the improved sampling of RASREC is essential in obtaining accurate structures over a benchmark set of 11 proteins in the 15-25 kDa size range using chemical shifts, backbone RDCs and HN-HN NOE data; in a number of cases the improved sampling methodology makes a larger contribution than incorporation of additional experimental data. Experimental data are invaluable for guiding sampling to the vicinity of the global energy minimum, but for larger proteins, the standard Rosetta fold-from-extended-chain protocol does not converge on the native minimum even with experimental data and the more powerful RASREC approach is necessary to converge to accurate solutions.Recent work has shown that NMR structures can be determined by integrating sparse NMR data with structure prediction methods such as Rosetta. The experimental data serve to guide the search for the lowest energy state towards the deep minimum at the native state which is frequently missed in Rosetta  NMR structure determination is challenging for proteins above 15 kDa. Spectral overlap and slow rotational tumbling lead to ambiguity in the NOESY-derived distance restraints. These problems can be partially overcome by deuteration, but then fewer proton spins are available to report on distances. Obtaining accurate structures from the resulting sparse datasets is an important current challenge.de-novo structure prediction sampling methodologies are also challenged as protein size and complexity increases.de-novo structure calculations run into sampling issues for proteins with more than 100 amino acids, and the success rate is also reduced for high-contact order structures.N-HN contacts—can guide sampling towards the native structure, and thus help ROSETTA to overcome some of the sampling issues.10Supplementing sparse restraints with the detailed physical-chemistry captured by structure prediction methods such as ROSETTA holds promise to achieve accurate structures for higher molecular weight proteins.et al. sought to overcome the difficulty in de novo structure prediction of nonlocal beta sheet topologies by resampling beta pairings using broken chain folding kinematics which held the desired pairings in place.et al. similarly used energy and occurrence frequency to identify, from an initial round of Rosetta models, strand pairings, and local structures likely to be present in the native structure and then generated models enriched in these features in a second round.et al. identified models present early in Rosetta trajectories which gave rise to low energy all atom structures, and restarted additional trajectories from these promising starting points.et al. developed an iterative approach to refining starting models with the correct topology by alternately rebuilding and refining regions of the structure that differed the most in the ensemble.15To overcome these size limitations we have developed an iterative sampling protocol that recombines structural features found in intermediate structures. The new resolution-adapted structural recombination (RASREC) protocol combines the strengths of previous approaches to optimization in Rosetta. Bradley As reported previously, using a beta-version of RASREC, we were able to push the size limit for CS-Rosetta structures well above the >15 kDa range using backbone NOE and RDC data.de-novo fragment assembly protocol of ROSETTA generally fails to converge on the native fold. Nevertheless, even in unsuccessful structure calculations native structural features tend to occur frequently. Experimental restraints further facilitate enrichment of these features. The RASREC protocol seeks to improve sampling close to the native structure by recombination of frequently occurring structural features. The structural features we focused on are beta-strand topologies, short contiguous stretches of backbone conformation (fragments), and noncontiguous collections of secondary structure elements, that is, a protein core.For larger proteins (>15 kDa), in particular those with a high-contact order, the proto-fold resampling. Such resampling allows changes on the fold level, but biases the search to  related folds. In the last stage we refine the protein to high-resolution and restrict aggressive sampling to the loops while the core regions can plastically adapt to accommodate changed loop-conformations. This stage, similar to the previous rebuild-and-refine protocolWe found it important to adapt feature-resampling to the resolution of the intermediate ensemble. At the beginning of the calculations when the precision  is still very low, a coarse-grained featurization of protein structure is required. We found a description that focuses on beta-strand topologies, that is, strand-pairings and their register, to be helpful. In the intermediate resolution range, we resample from early stage models which have given rise to low energy final models,The RASREC protocol has six Stages (I–VI), an initial exploration stage and five resampling stages. The first four stages use the ROSETTA low-resolution energy, the remaining two stages use ROSETTA all-atom energy. For both modes, low-resolution and all-atom, we maintain a pool of the 500 best scoring decoys. Additional energy terms based on prior knowledge or experimental data can be used. The acceptance rate into the pool is recorded and when below 10%, the respective resampling stage is terminated. The structure pool determines the features for the next generation of decoys during resampling Stages II–VI.To maintain sufficient diversity in the structural pool we require in Stages IV, V, and VI that newly generated structures are at least 2.0, 1.5, and 1.5 Å Ca-RMSD away from structures that existed in the pool at the start of a given trajectory. A freshly generated structure that is closer than this threshold to a structure in the pool will be swapped for this structure if it has a lower energy.The individual resampling stages—explained below—are (i) beta-topology sampling with the random pairs protocol, (ii) a combination of random pairs and topology resample, (iii) topology resampling (iv) fragment resampling and proto-fold resampling (v) loop-closing and all-atom refinement and finally (vi) core-resampling, (loop-)fragment resampling and all-atom refinement .As illustrated in de-novo fragment assembly to contain nonlocal fragments, and has been implemented in ROSETTA3.0.17To efficiently construct conformations that sample specific strand-topologies we represent the protein using a broken-chain “fold-tree”The fold-tree must not contain cycles, thus for each nonlocal strand–strand edge a peptide edge has to be removed somewhere else along the chain preferably in loop-regions. These chain breaks are closed in Stage V prior to all-atom refinement .At the beginning of each folding trajectory a specific fold-tree is constructed by choosing suitable strand–strand edges and chain break positions. The choices are governed by two different strategies, called RANDOM_PAIRS and TOPOLOGY_RESAMPLE.All residues with more than 70% sheet content in the (3mer) fragments are treated as putative beta residues. All stretches of 3 or more beta residues are considered a strand. A list of all possible pair-wise combinations of beta residues within different strands is generated with 4 entries per pair to cover all possible orientations and pleatings (see above). For each fragment-assembly trajectory up to two pairings are selected from this list with the only constraint that chosen pairings must not connect the same two strands twice.In this mode a topology is defined by specifying exactly which strand–strand pairs are in contact and in which orientation and pleating. For a given topology with N strand–strand pairs, we select up to N pairs for enforcement. For each enforced strand–strand pair exactly one residue pair is selected to become an edge in the fold-tree. The orientation and pleating of this pair defines the allowable rigid-body orientations for this edge.A topology is extracted from a decoy-conformation by using the DSSP definition of hydrogen bonds for the NH-CO hydrogen bondsSsp,I = Ni ), where Ni is the number of occurrence of this or equivalent strand–strand pairs, Li is the number of paired residues in the strand–strand pair, and COi is the maximum sequence separation within this strand pair. Low contact-order strand–strand pairs are more frequent, than high contact-order. Thus the score rewards high contact order (COi > 20). Moreover, we prefer longer stretches (Li) of consistent pairings, which are more likely to be found in a region where strand–strand pairings occur natively. The total score of a topology is taken to be the sum of the scores of its strand pairs. More complex schemes to predict beta topologies based on ROSETTA full-atom energy have been explored elsewhere.12The scoring rewards topologies whose individual strand–strand pairs are frequently occurring in the set of decoys. Strand–strand pairs from different decoys are considered equivalent if they have the same orientation and register, and if the pleating pattern matches. Thus, two equivalent strand–strand pairs may have different sets of hydrogen-bonded residues, as long as all other parameters match up . Each strand pair is assigned a score Fragment assembly in Rosetta is a Metropolis Monte-Carlo sampling of backbone torsion angles from a diverse library of protein structure fragments. The lowest scoring conformations generated in fragment assembly trajectories are selected, and new libraries of fragments harvested from the low-scoring decoys are used for resampling in Stages III–VI [Models with the roughly correct overall topology can often be discriminated by ROSETTA low-resolution energy and sparse NMR restraints. However, usually these emerging structures are still of low quality in the core-region, and thus inadequate for full-atom refinement that focuses on loop-rebuilding.et al. found generally precedes folding to the native structure.19As illustrated in Panels D1–D3 of For all proteins containing beta-sheets a nontrivial fold-tree is introduced in Stages I–III. For each enforced beta-pairing, also a nonphysical chain break somewhere in the peptide chain between the paired residues is required to avoid a cyclic fold-tree. These chain breaks must be closed to yield physically realistic models. Moreover, a chain break that cannot be closed is a strong criterion to reject a structure. Three mechanisms for closure exist which are combined. First, a chain break penalty is computed during the fragment assembly and its contribution to the overall energy increases each sampling cycle. Second, chain-breaks are removed by idealization of the chain-break residue, and third, by explicit loop-rebuilding. Two chain break penalty functions are used: the first depends only on distance, the second, also on the orientation of consecutive peptide planes; both penalties are linear (rather than quadratic) in the deviation from perfect closure.All RASREC trajectories apply the chain break energies, but explicit removal of the chain breaks is postponed until all-atom sampling commences. At this point, structures are often already well folded and the chain-break energy has had time to select for close to ideal loops. To remove the chain breaks entirely, an application of the ROSETTA idealization protocol is attempted; if unsuccessful loop-rebuilding is attempted instead. Chainbreak closure by idealization and loop rebuilding are described in detail in the following two sections.The idealization protocol generates strong positional restraints to bias towards the current conformation and then replaces the bond-lengths and angles of the chain-break residue with ideal values. The idealization introduces a perturbation of the backbone that can result in large amplitude motion downstream from the idealized chain break due to lever-effects along the peptide backbone. Subsequent minimization in torsion space under the influence of the positional restraints often succeeds in decreasing the downstream motion to <0.1 Å RMSD. However, if the chain-break is far from ideal, idealization will perturb the structure too much to recover the original conformation by minimization and the idealization-attempt is considered a failure.Loops that contain the chainbreak residue are rebuilt via fragment assembly until ideal closure without large-amplitude downstream effects can be achieved. Rebuild-windows are selected from the region between adjacent fold-tree nodes. First smaller windows are attempted and then progressively larger regions are rebuilt until the maximal allowable region (between fold-tree nodes) has been rebuild. For short windows (less than 10 residues) 100 monte-carlo steps of fragment assembly using only the chain break energy terms (unscored loops) are followed by cyclic coordinate descent (CCD) loop closure.Fragment assembly in the context of a set of distance restraints (such as NOEs) is difficult due to the ruggedness of the energy landscape and the large amplitude of the changes produced by individual moves.An “Archive”-framework was developed to facilitate development and implementation of iterative refinement protocols. The infrastructure allows distribution of RASREC-Rosetta as a fully automatic and platform independent application and thus significantly lowers the entry-barriers for new users.Iterative structure determination protocols have been explored in our lab and by others.de novo protocol this can be as much as 50–200% from the average and hence many CPUs may idle while waiting for completion of the slowest job . We first explored the use of the DAG-manager from the CONDOR queuing systemParallelization of the method without significant loss in performance is challenging. A large number of CPUs are used for conformational space exploration, whereas a single CPU is used for data collection and analysis between exploration stages. Processes in the exploration phase can vary drastically in length—for the Rosetta de-novo folding, loop-modeling, low- and high-resolution refinement. With 600–6000 s per trajectory on the BlueGene computer, 2048 processes generate up to four structures a second, and care had to be taken such that analysis and job-distribution do not cause bottlenecks.To overcome these issues we decided to implement job distribution using MPI within the ROSETTA3.0 frameworkThe software design is illustrated in MPIFileBuffer) exports virtual files to individual worker processes such that any existing rosetta protocol that uses the ROSETTA3.0 interface for structure IO (silent-IO)MPIFileBuffer process and written to a single common file while avoiding the scrambling of lines from different frames. No code changes on the protocol level were required thus allowing the use of any Rosetta protocol within this framework.The single-point File-IO (Jobdistributor maintains lists of batches (one per iteration) and jobs. Worker processes ask for a job, perform the calculation and write the result to file. Subsequently, they notify the job-distributor about the job's conclusion  and inquire about new jobs. If the job-queue is empty the Jobdistributor will prompt the archive to generate flags and options for a new batch.The ArchiveManager maintains the pool of structures. It is notified by the Jobdistributor if more than 100 new decoys are available for reading from one of the current batches. The structures are subsequently read and accepted into the pool based on predefined scoring criteria. The ArchiveManager increments to the next resampling stage when the acceptance ratio drops below 10%. When prompted by the Jobdistributor new batches are generated using the resampling strategy of the current stage.The More implementation details are given in Supporting Information Section 2.1.N–HN NOE restraints (NOE) were used as indicated in The RASREC protocol was run on 11 targets with 120–200 residues of distinct folds with all-alpha, alpha/beta, and all-beta topologies . For allAs a quality measure for the coordinate accuracy of a structure given a reference structure we compute RMSD and GDTMM on the CA atoms. The latter measure adopts a value between 0 and 1 for random and perfect coordinates, respectively. The GDT-MM  for 5pnt (2k1s) a near perfect beta-topology is achieved , row 1+2At iteration 11 a further significant improvement of accuracy and energy is achieved due to fragment resampling and proto-fold resampling (Stage IV). For 2k1s switching to all-atom mode in iteration 15 yields a further significant improvement in GDTMM and RMSD, with final structures around 1.5 Å RMSD. For 5pnt the all-atom mode yields no measurable improvement in backbone RMSD.In resampling Stage IV, fragment resampling is used to increase the frequency of near-native structures. To check the quality of fragments, RMSDs of 9mer fragments are computed against the corresponding stretches of backbone in the native structure. Although no new backbone torsion angles are introduced in the fragment assembly stages, new fragments are generated by combination of adjacent torsional angles from different source fragments. This effect can be observed in To overcome the size limitations of sparse data driven structure determination methods one can employ more sophisticated algorithms, like RASREC, or use additional experiments to obtain further restraint data. The results presented here suggest that our recent breakthrough in solving larger structureset al.et al.,et al.,et al.,RASREC uses discontinuous chain folding and the idea of resampling promising strand pairings and local structural features introduced by Bradley de-novo structure generation. Our results show that a strong synergy exists between sparse (NMR) restraint data and the ROSETTA methodology. For structures >15 kDa, the recombinant resampling method proves much more proficient in exploiting this synergy than the standard CS-Rosetta protocol. These results indicate the importance of continued development of enhanced sampling methods.By developing an iterative sampling protocol that enriches native-like features by resolution-adapted feature recombination (RASREC) we have overcome typical size limitations in Rosetta


We develop mathematical models for the role of hepatitis B e-antigen in creating immunological tolerance during hepatitis B virus infection and propose mechanisms for hepatitis B e-antigen clearance, subsequent emergence of a potent cellular immune response, and the effect of these on liver damage. We investigate the dynamics of virus-immune cells interactions, and derive parameter regimes that allow for viral persistence. We modify the model to account for mechanisms responsible for hepatitis B e-antigen loss, such as seroconversion and virus mutations that lead to emergence of cellular immune response to the mutant virus. Our models demonstrate that either seroconversion or mutations can induce immune activation and that instantaneous loss of e-antigen by either mechanism is associated with least liver damage and is therefore more beneficial for disease outcomes. Infection with Hepatitis B virus (HBV) leads to asymptomatic self-limiting infections in most immunocompetent adult infections and chronic infections in perinatal, early childhood, and immuno-compromised adult infections Spontaneous HBeAg loss marks transition from immune tolerance to immune clearance phase and is considered a beneficial event for disease prognosis, especially when it occurs at an early age The management of chronic HBV infection requires further understanding of the host-virus interactions leading to viral persistence. We aim to understand the role of HBeAg in creating immunological tolerance, the events leading to HBeAg clearance, subsequent emergence of potent cellular immune response, and the extent of liver damage. To provide insight into these mechanisms we develop mathematical models of cellular immune responses to a wild type HBeAg-positive virus and investigate interactions and parameter regimes that allow for viral persistence and eventual immune reactivation. Previous models have studied the dynamics of HBV clearance during acute infections In this study we use mathematical models to investigate the mechanisms leading to establishment of chronic hepatitis B infection and aim to understand how the presence of circulating HBeAg creates immunological tolerance. We derive and analyze a mathematical model of e-antigen induced T-cell anergy in an HBeAg-positive HBV infection. We then expand the model to investigate long-term virus dynamics when e-antigen is lost and T-cells become activated as a result of e-antigen seroconversion or HBeAg-positive virus mutation. In the seroconversion scenario, we determine the antibody levels required to successfully restore effector function to anergic T-cells. A prediction of our model is that sudden loss of e-antigen through induction of high antibody levels is beneficial to the host as it reduces overall liver cell death. While seroconversion is considered an important stage in HBV clearance Let System (1–3) has three steady states: a biologically irrelevant steady state, Several papers have documented the emergence of an anti-HBe antibody that binds e-antigen and enhances its removal Mutations in the hepatitis B virus core promoter region The mutation model has several steady states. The first one is biologically irrelevant, There are four steady states that represent immune activation. The first one represents immune activation against There are two steady states representing competent T-cell response to The last steady state corresponds to T-cells response to both viruses types, Under the tolerance condition (4) the only steady states that exist and can be stable are For model (1–3) we assume that the initial inoculum contains small concentrations of HBeAg-positive virus and circulating HBeAg (i.e. i.e. For model (6–10), we assume initial concentrations of The presence of circulatory HBeAg can induce clonal anergy characterized by the inability of HBeAg-specific T-cells to grow, maturate, and acquire effector function such as cytokine production An extreme case describing this scenario is represented by the failure of T-cells to differentiate, Since the loss of tolerance is marked by an increase in HBeAg-specific T-cell levels, we first explore the causes of this increase. In our model, immune recovery is represented by change in stability from the tolerance steady state Biologically, this means that when the maximal activation of HBeAg-specific T-cells is bigger than the combined effect of their natural death rate and the death rate at the peak of e-antigen inhibition then the tolerance is lost and the immune cells start removing virus. Condition (12) is independent of the size of the immune cells killing rate  also suggests that tolerance can be lost due to a net decrease in the HBeAg production. To place this on solid biological ground, we consider HBeAg seroconversion.i.e. Analytical investigation of model (1–3) with e-antigen equation modified to (5) states that when e  see  and S1. For both cases : immune tolerance to virus Analytical results show that the immune response to the mutant virus, city see .When the activation rate at the carrying capacity of the virus is larger than the T-cell death rate  and T-cells killing rate (pμ), as the size of An interesting implication of this model is that tolerance and loss of tolerance are not affected by the virus replication rate  may act as a tolerogen as well Figure S1Bifurcation diagrams showing the change in the HBeAg-positive virus steady state (top panel) and HBeAg-specific T cells steady state (lower panel) as a function of antibody levels Ig for  We used parameters from (TIF)Click here for additional data file.Figure S2Numerical curves showing the transition from an immune tolerant HBeAg-positive virus  The transition is marked by vertical lines. Parameters are (TIFF)Click here for additional data file.Supporting Information S1Here we perform asymptotic analysis for the models given by (1–3) and (6–10).(PDF)Click here for additional data file.