Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures.A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis.The two independent microarray studies were found to be comparable, although differing in their experimental methodologies . The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results.The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development. Microarray studies aiming to identify differentially expressed as well as co-regulated gene sets and signaling pathways involved in different cellular states have greatly improved our understanding of breast cancer at the molecular level. The power of expression profiling using cDNA or DNA microarrays for distinguishing subgroups of breast cancers has been demonstrated by several groups -4.The identification of an intrinsic gene-set exhibiting high variability among different tumor clusters has been informative in describing different subtypes of breast cancer samples. However, only a few papers have been published on gene expression profiles of normal cell populations in breast tissue -9. Thereet al. identified differentially expressed genes between ER+ and ER- breast tumors by gathering 9 independent breast cancer microarray studies [et al. were able to identify a new intrinsic gene-set for breast cancer subtype prediction by combining multiple microarray datasets to assess prognosis [Meta-analysis of microarray datasets has the potential to lead to more comprehensive measures of the existing differential gene expression data and can therefore provide gene sets with a high diagnostic value. Meta-analysis of independent microarray datasets generated with the common objective of identifying differentially expressed genes in a certain type of cancer has also been performed for breast cancer. In a very recent meta-analysis study, Smith  studies . Another studies . Similarrognosis .delete-d-jacknife procedure in which a subset of data is excluded to find out the frequency of selecting a particular gene as differentially expressed [leave-one-out resampling [Several different meta-analysis approaches exist in the literature. In some, each individual study contributes rather independently to the meta-analysis -15 wherexpressed . The numsampling .Existing meta-analytic approaches applied to different types of cancer show the power of a combined study for identifying novel genes not present in the existing literature  ,21. Invadelete-d-jacknife, a series of d sample size values have been tested to assess the extent of variability across the tumor samples and the stability of differential expression. Comparison of probability value distributions obtained for the test and randomized samples has led to determination of the degree of differential expression between groups tested. Accordingly, we report that the Sorlie et al. [et al. [In the present study, we primarily aimed to develop a novel methodology for the meta-analysis of independent microarray datasets. Using this methodology, we provide gene lists that (a) are discriminative of breast cancer types  and normal breast cell populations, (b) may yield breast tumor markers that are invariably expressed across independent experiments, and (c) provide a set of consistently differentially expressed gene candidates with potential discriminative ability for tumor subtypes. Using a method similar to e et al.  and Zhao [et al.  datasets [et al. ,7,8 usin [et al. . A subseet al. [et al. [[Two independent microarray gene expression data sets, Sorlie et al.  and Zhao [et al. , were do[et al. [. Gene fi®) between the Sorlie and Zhao datasets for the IDC and ILC samples separately. Those clones with probability values less than 0.05 (after Bonferroni correction) were excluded from further analysis. This two-step data filtering resulted in a common set of 1726 IMAGE clones for the analysis of ductal and normal samples, and 2029 IMAGE clones for the analysis of lobular and normal samples. Upon taking the intersection of the ductal-normal and lobular-normal clone sets, 1522 IMAGE clones were available for the ductal-lobular analysis. The resulting clone subsets were further filtered by removing IMAGE clones with more than 40% missing data for the two groups in comparison  in the combined data before application of the resampling steps. In addition, if an IMAGE clone had a sample size  less than the resampling sample size, data on this IMAGE clone was also removed.Data were filtered separately for ductal and lobular samples. IMAGE clones with more than 50% missing data in either of the Sorlie or Zhao datasets were excluded from the common clone set. Data filtering was further improved by performing two-tailed Student's t-tests with equal variance (Matlab® using Statistics Toolbox is available upon request). Accordingly, a preset number of samples was selected from each group  of the original dataset, referred herein as the test. The p-value was calculated indicating the significance of the difference between the group medians based on the Wilcoxon Rank Sum Test. This test was repeated for a series of i number of iterations; at the end of each iteration scheme, a set of p-values (pt) per IMAGE clone was obtained. The above procedure was also applied to each of the shuffled datasets yielding pr1 and pr2. P-value distributions were then tested in a pair-wise fashion  using the two-sample Kolmogorov-Smirnov test for each clone in the dataset , ksr values were sorted in the ascending order and the kth value from the top  was determined as FDRobserved, where k equals the expected value of FDR  multiplied by the number of IMAGE clones tested. FDRobserved was set as the threshold according to which IMAGE clones were assigned as significant or not. If kst of a particular gene had a value that was smaller than the FDRobserved, the gene was accepted to be significant.We have used a resampling method for meta-analysis of microarray data in which the significance of the difference between group medians  could be tested upon a series of resampling schemes from the original and multiple randomly shuffled datasets , repetitively for i number of times where i = 10, 20, 30, ..., 100 and 150. For each particular i, three parameters were recorded, namely, kst values, the mean expression value of each of the two groups compared, and the significance of the differential expression based on kst and ksr. These above steps were then repeated with different sample sizes: For ductal vs. lobular comparison, n was set to be 3, 4, 5, 6, 10, 15 and 20. On the other hand, since the total number of normal samples was 7, the highest sampling value could be set to 6 for ductal vs. normal and lobular vs. normal comparisons, and n equaled 3, 4, 5 and 6. These sample size-iteration combinations led to 77 runs for ductal vs. lobular analysis, and 44 runs for ductal vs. normal and lobular vs. normal analyses. At the end, a final differentially expressed gene set was determined for each of the three comparisons  by gathering the IMAGE clones that were assigned as significant in 90% or more of these 44 or 77 runs. The mean values of each of the two groups in comparison obtained at n = 20  and i = 150 were used as an estimate of the measure of expression.The above tasks were performed for a particular sample size et al. performed an analysis of tumors from 49 breast cancer patients that were successfully classified into luminal and basal classes, and a novel molecular apocrine class. Apocrine tumors were estrogen receptor negative ER(-) and androgen receptor positive AR(+), while luminal tumors were ER(+) and AR(+), and basal tumors were ER(-) and AR(-). Details of the breast specimens /ER (-)) available from GEO database were used in the supervised class prediction with a binary tree algorithm [The ".cel files" of the three publicly available independent microarray gene expression data sets, GDS2635  and GDS1Primary tumor samples and matched non-tumor breast tissues were obtained from patients (n = 10) during surgery and immediately snap-frozen in liquid nitrogen and stored at -80°C until RNA extraction. The frozen tissue samples were sectioned and mounted on glass slides. The slides were stained with hematoxylin and eosin for histopathological examinations. Only those tumor samples with more than 90% of tumor cells and matched tissue pairs with normal histological examination were included in this study. These frozen tissues were cut into 5-μm-thick sections and used for RNA isolation and cDNA synthesis. All the tumor samples had been classified as infiltrating ductal carcinoma. The use of the tissue material in this project was approved by the Research Ethics Committee of Ankara Numune Research and Teaching Hospital and consents were obtained in accordance with the Helsinki Declaration.The frozen breast specimens were put into Trizol reagent , disrupted with a homogenizer and total RNA was isolated according to the manufacturer's instructions. Genomic DNA contaminations were removed by on-column DNaseI treatment . The concentration of the isolated RNA and the ratio of absorbance at 260 nm to 280 nm were measured with the NanoDrop ND-1000 spectrophotometer  in triplicate.First-strand cDNA was synthesized from 1 μg total RNA using oligo(dT) primers using Revert Aid First strand cDNA synthesis kit according to the manufacturer's instructions . The cDNA was diluted at a ratio of 1:5 before being used as a PCR template and stored at -20°C until further use.FN1, ID4, EGFR, ADAMTS1, ATF3, IGFBP6, and PRNP. The geometrical mean of ACTB, TBP and SDHA1 gene expression values were used as internal control for relative gene expression quantitation [®).Real-time qRT-PCR analysis was performed using gene-specific primer pairs  of a gene in the meta-data. For example, the GSN gene had a highly significant differential expression between ductal and normal samples as evidenced by the highly skewed distribution towards lower p-values whereas the RAP2A gene exhibited a uniform distribution of p-values  as well as the number of differentially expressed IMAGE clones were plotted with respect to the increasing number of iterations Figure . As the It is reasonable to assume that use of a single sample size and iteration number may not be adequate to understand the variability among the tumor samples Figure . It mighDifferentially-expressed gene lists for DN and LN contained 298 (282 genes) and 216 (202 genes) IMAGE clones, respectively , each of which corresponds to a novel contribution by our meta-analysis . All of the above genes except FN1 were found to be significantly associated with the tumor ER status . Protein expression levels of these four genes were investigated using the Human Protein Atlas, a public resource for immunohistochemistry (IH) of normal and pathological human tissues . IH data were available for CDH1, TFAP2A, and RBP4 proteins; and only data from antibodies exhibiting differential expression among breast tumors were reported herein. Accordingly, 2 out of 3 ILC samples exhibited moderate to strong signals for RBP4 (Antibody CAB00455) whereas 7 out of 9 IDC samples were either negative or had weak staining. CDH1 data in the Protein Atlas database was not very informative since the number of ILC samples were limited, but a moderate signal was detected for the ILC sample whereas 5 out of 6 IDC samples expressed CDH1 strongly (Antibody CAB000087). Similarly, TFAP2A was weakly or moderately expressed in the two ILC samples examined whereas a moderate to strong staining was observed in 5 of the 9 IDC samples. Although sample size in the ILC samples in the Human Protein Atlas database was limited, there was a corresponding trend between the mRNA levels reported by the present study and the protein level assessment obtained from the Human Protein Atlas. Future studies should include testing of the genes extracted by meta-analysis using protein level studies such as Western blotting or immunohistochemistry on a large set of IDC and ILC samples to confirm their predictive power.Comparison of fold-change values of the DL meta-gene list consisting of 65 genes with that of the Turashvili's DL list (GDS2635) resulted in a high degree of correlation , suggesting that the direction and magnitude of expression change between the IDC and ILC samples were largely consistent between data from different microarray experiments. Furthermore, we combined published expression data from IDC and ILC samples from experiments performed by Bertucci et al  with theMAF) from the meta-gene list for validation of the meta-analysis. Expression profiles of these genes were tested in independent paired IDC breast tumor and non-tumor tissue samples through real time qRT-PCR. Our results were consistent with those of the meta-analysis such that GSN, SPTBN1, SFRP1 and MAF were down-regulated in most tumor samples with respect to their matched non-tumor samples whereas COX6C, RAD21, GSPT1, NME1 and PTTG1 were up-regulated .We first selected nine genes that were found to be differentially expressed in both the DN and LN lists  also varied considerably between the two studies. Median rank scores  or quantDue to the large number of comparisons involved in microarray data analysis, it is important to take into account the false positive error rate and control it for the number of tests performed. FDR is a well-known methodology for multiple-test correction; its estimation relies on calculation of the number of false positives in a randomly permuted set of experiments . Therefoet al. [CDH1 mutation and/or underexpression was common but not universal to ILCs in general [Invasive breast tumors comprise 18 different histological types , most ofet al.  reported general . Low-graet al. [et al. [et al. [et al. [The other three studies presenting data on ILC and IDC, Turashvili et al. , Sorlie  [et al.  and Zhao [et al.  have use [et al. , meta-an [et al.  has alsoCHD1, TFAP2A, RBP4, and ALDH1A1 genes as commonly modulated. Indeed, CDH1 is one of the best-studied discriminators for ductal/lobular breast cancer specimens in the literature by immunohistochemistry and at the genomic level. In breast cancer, reduced CDH1 expression has been found in 50% of invasive ductal carcinomas, whereas CDH1 expression was almost always absent in infiltrating lobular carcinoma (ILC) [TFAP2A was shown to be highly expressed in ductal tumor cells while normal cells expressed TFAP2A in the inner glandular cell layer [RBP4 in the literature in connection with ductal vs. lobular breast cancer distinction while ALDH1A1 protein levels were shown to exhibit differences among the ductal carcinoma patients [Comparisons between the meta-analysis and the Turashvili and Bertucci studies pointed out to ma (ILC) ,37,38. Tll layer . On the ll layer . There ipatients . The canOn the other hand, analyses of Sorlie, Zhao, and Turashvili data showed that tumor cells were remarkably distinct from their respective normals in their transcription profiles implicating that whatever the subtype structure underneath, most of the variability among samples was due to changes during tumorigenesis. Accordingly, the idea that genes discriminating tumor from normal in a stable manner also may have information on the state of the tumorigenesis is a valid one.et al. [Breast tumor subtype classification remains a complicated issue due to difficulties associated with the presence of multiple interacting factors such as the presence or absence of node-filtration, ER-positivity, metastatic potential, different degrees of genomic instability, and tumor cell origin. For example, basal like cancers have distinct molecular expression profiles and histological differences when compared with the luminal type . Nielsenet al.  have catet al. . Howeveret al. . It is aet al. ,46. In get al. ,47. ThusATF3, ADAMTS1, EGFR, PRNP, IGFBP6, ID4, SFRP1, SPTBN1, and FN1 with ability to classify tumors into basal and luminal subclasses. Additionally most of them accurately differentiated ER(+) and ER(-) tumors  also keep information about intrinsic subtypes. Accordingly, meta-analysis identified ID4 was found to be a novel tumor suppressor gene in normal human breast tissues and epigenetically silenced in breast cancer cell lines and primary breast tumors [ID4 in the mammary duct epithelium may be regulated by estrogen depending on the differential expression pattern of ID4 in ER(+) and ER(-) breast tumors [SFRP1 on the other hand is a frizzled-related protein that plays a role in a variety of cellular processes, including control of cell polarity, cell fate determination, and malignant transformation. In previous studies, loss of SFRP1 was found to be associated with cancer progression and poor prognosis in breast cancer [ATF3) is a member of the ATF/cyclic AMP response element-binding family of transcription factors. It was shown to enhance apoptosis in the untransformed mammary epithelial cells while protecting the aggressive cells and enhancing cell motility. Array analyses indicated that ATF3 upregulated the expression of several genes in the tumor necrosis factor pathway in the untransformed mammary epithelial cells. However, the expression of several genes implicated in tumor metastasis including fibronectin (FN1) was upregulated in aggressive cells. ATF3 was also shown to regulate the transcription of FN1, one of the genes obtained in the present study. ATF3 gene copy number was at least doubled in 80% of the breast tumors examined; protein levels also were elevated in close to 50% of these tumors [Among those genes, t tumors ,49. As st tumors . SFRP1 ot cancer ,52. EGFRt cancer ,54. Actie tumors .Since the normal vs. tumor classification was strikingly distinct based on meta-analysis, and a gene-set with the capacity for breast cancer subtype classification, we further analyzed a set of normal matched tumors for selected genes from the meta-gene list using real-time qRT-PCR. The selected 16 significant genes were shown to have expression profiles similar to those found from the meta-analysis. Our findings also suggested that these genes could be used as predictors of tumor status regardless of the origin of the reference samples, i.e., a matched or pooled reference tissue. Since the number of samples used in qRT-PCR was relatively small, increasing the sample size may help generalize our results to a wider range of breast tumor samples.There was a high level of correlation between fold changes obtained from the DL meta-genes and those from the Turashvili dataset, regardless of the different sample extraction methods used in each study . The meta-gene list discriminating between ductal and lobular breast tumor samples at the mRNA level requires further confirmation at the protein level to better assess discriminatory power. Future validation studies might concentrate on whether meta-analysis specific genes also participate in prediction of level of prognosis and/or time to disease-free survival.In this study, meta-analysis of two independent comparable microarray data sets allowed us to provide genes that are able to discriminate IDC and ILC and normal mammary cells from the tumors that either study by itself was not able to identify. We also provided highly generalized and stable gene lists that could be used for prediction of tumor or normal status. The meta-gene list for tumor/normal comparison had a striking predictive ability based on comparisons made with three independent microarray datasets. The resampling approach proposed herein has the ability to detect a set of differentially expressed genes, with the least amount of within-group variability. This meta-analytic approach thus provides a method to combine two or more independent cancer data sets leading to the identification of differentially expressed gene sets for better understanding of cancer development and progression.DL: Ductal and Lobular; DN: Ductal and Normal; FDR: False Discovery Rate; IDC: Invasive Ductal Carcinoma; ILC: Invasive Lobular Carcinoma; LN: Lobular and Normal; qRT-PCR: Quantitative Reverse Transcriptase Polymerase Chain Reaction; SMD: Stanford Microarray Database.The authors declare that they have no competing interests.IGY, BGD and OK originally conceptualized the meta-analysis method and application to breast cancer datasets, and wrote the manuscript. OK and SK developed the meta-analysis algorithm; SK contributed to manuscript writing. BGD and SK applied the algorithm to breast cancer datasets and generated figures. BDG performed all BRB-tool analyses and carried out expression studies. ARO and OK wrote algorithms for data extraction and compilation. BB carried out the surgical removal of the sample tissues and GE, the pathological assessment of the surgical tissue materials. IGY coordinated the study and participated in its design. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Complete list of common IMAGE clones. The data represents 4769 common IMAGE clones from combined Sorlie and Zhao datasets from the study of Zhao et al. [o et al. .Click here for fileComplete list of common IMAGE clones. The data represents 4769 common IMAGE clones from combined Sorlie and Zhao datasets from the study of Sorlie et al. [e et al. .Click here for fileGenes used for real-time qRT-PCR analysis. Gene names, accession numbers and gene specific primer pairs used for real-time qRT-PCR analysis of the selected genes.Click here for fileCorrelation of global median and quantile normalized data. The figure shows the correlation of global median and quantile normalized data of ductal (D) and normal (N) tissue samples.Click here for fileGene set differentially expressed between ductal (D) and normal (N) breast tissue samples. The data provided represents the list of 298 IMAGE clones differentially expressed between ductal (D) and normal (N) samples with 90% significance.Click here for fileGene set differentially expressed between lobular (L) and normal (N) breast tissue samples. The data provided represents the list of 216 IMAGE clones differentially expressed between lobular (L) and normal (N) samples with 90% significance.Click here for fileGene set differentially expressed between ductal (D) and lobular (L) breast tissue samples. The data provided represents the list of 66 IMAGE clones differentially expressed between ductal (D) and lobular (L) samples with 80% significance.Click here for fileList of meta-analysis specific genes. The file includes the list of genes differentially expressed between DN, LN, and DL samples.Click here for fileValidation of meta-analysis gene lists by three independent microarray datasets. The file represents the gene lists obtained by comparing the DN and LN meta-gene list to the gene expression profiles of normal and breast tumor tissues from three independent microarray datasets.Click here for file


To improve design of robotic lower limb exoskeletons for gait rehabilitation, it is critical to identify neural mechanisms that govern locomotor adaptation to robotic assistance. Previously, we demonstrated soleus muscle recruitment decreased by ~35% when walking with a pneumatically-powered ankle exoskeleton providing plantar flexor torque under soleus proportional myoelectric control. Since a substantial portion of soleus activation during walking results from the stretch reflex, increased reflex inhibition is one potential mechanism for reducing soleus recruitment when walking with exoskeleton assistance. This is clinically relevant because many neurologically impaired populations have hyperactive stretch reflexes and training to reduce the reflexes could lead to substantial improvements in their motor ability. The purpose of this study was to quantify soleus Hoffmann (H-) reflex responses during powered versus unpowered walking.We tested soleus H-reflex responses in neurologically intact subjects (n=8) that had trained walking with the soleus controlled robotic ankle exoskeleton. Soleus H-reflex was tested at the mid and late stance while subjects walked with the exoskeleton on the treadmill at 1.25 m/s, first without power (first unpowered), then with power (powered), and finally without power again (second unpowered). We also collected joint kinematics and electromyography.When the robotic plantar flexor torque was provided, subjects walked with lower soleus electromyographic (EMG) activation (27-48%) and had concomitant reductions in H-reflex amplitude (12-24%) compared to the first unpowered condition. The H-reflex amplitude in proportion to the background soleus EMG during powered walking was not significantly different from the two unpowered conditions.These findings suggest that the nervous system does not inhibit the soleus H-reflex in response to short-term adaption to exoskeleton assistance. Future studies should determine if the findings also apply to long-term adaption to the exoskeleton. Many research groups are developing robotic lower limb exoskeletons to assist in locomotion training after neurological injury -6. The eIn recent studies from our laboratory, we examined how healthy young subjects adapted to a robotic ankle exoskeleton during walking ,15. The Increased stretch reflex inhibition with robotic exoskeleton training would be particularly relevant to gait rehabilitation for individuals after neurological injuries. Individuals who had stroke, spinal cord injury, cerebral palsy, and traumatic brain injury often demonstrate abnormally high stretch reflexes that substantially affect their movement capabilities -34. A nuThe purpose of this study was to quantify soleus reflex responses in neurologically intact subjects trained to walk with the robotic ankle exoskeleton. By identifying how devices modify musculoskeletal and neural systems with use in neurologically intact subjects, researchers and clinicians have a much better chance of determining which patient populations might benefit from practice with the robotic devices. We used the Hoffmann (H-) reflex, an electrical analogue of the stretch reflex, to examine soleus reflex responses during walking both with the exoskeleton powered and with the exoskeleton unpowered. The H-reflex is elicited by stimulating the afferent nerve (Ia sensory) directly and bypassing the muscle spindle. H-reflex measurements have been extensively used to study how the stretch reflex is modulated centrally -45. The Eight healthy, neurologically intact subjects  gave written informed consent and participated in the study. The University of Michigan Medical School Institutional Review Board approved the protocol, and the study conformed to the standards set by the Declaration of Helsinki.We constructed a custom-made orthosis Figure  for the Soleus H-reflex was tested while subjects walked with the exoskeleton on the treadmill at 1.25 m/s, first without power (first unpowered), then with power (powered), and finally without power again (second unpowered). Before the testing of soleus H-reflex, subjects had completed two 30-minute treadmill training sessions for walking with the powered ankle exoskeleton controlled by soleus EMG ,15. In aWe collected ankle kinematics, artificial muscle force, electromyography (EMG) and ground reaction forces while subjects walked on a custom-constructed force-measuring split-belt treadmill. The three-dimensional kinematic data were collected by using 8-camera video system . Artificial muscle force data were collected with force transducers  mounted on the bracket of orthosis. We placed bipolar surface electrodes on the left shank to record EMGs  from tibialis anterior (TA), soleus (SOL), medial gastrocnemius (MG), lateral gastrocnemius (LG).We elicited the soleus H-reflex by stimulating  the tibial nerve with a cathode placed in the popliteal fossa and an anode (7-cm diameter) on the patella Figure . The eleFor the walking trials, we tested the soleus H-reflex in the 3 conditions . We used a footswitch (B&L engineering) to detect heel strikes in real time and estimated the duration of a gait cycle from at least 90 strides in each condition. We divided the gait cycle into 16 equal epochs (10 epochs in the stance). The majority of powered assistance occurred at the middle to late stance, and this was the time period of the largest reductions in the soleus muscle activation ,15. BecaMmax, maximal evoked muscle response) has been used regularly to control constant effective stimulus intensity to the afferent nerve [Mmax over a stride [Mmax, we first collected Mmax data (3 Mmax measurements) of each epoch by delivering a larger stimulus than the one evoked Mmax during quiet standing (at least 1.2 times of stimulation intensity for evoking Mmax during quiet standing).The size of the M-wave as a percentage of the maximal M-wave , background EMG amplitude (EMG/Mmax), and the ratio of H-wave and background EMG (H/EMG). To reduce the inter-subject variability, we then normalized the H-reflex, mean EMG amplitudes and the ratio between H-reflex and background EMG in each condition to the values of the first unpowered condition.For background soleus EMG amplitudes, we calculated the mean of rectified averaged soleus EMG of each time epoch. We normalized the H-reflex amplitudes and mean EMG measurements to the ectrodes . Since tectrodes , we calcp < 0.05. If a main effect  was detected, we used Wilcoxon signed ranks tests to discriminate differences between the powered condition and each of the two unpowered conditions  with Bonferroni's correction (adjusted α = 0.025). All statistical analyses were performed in SPSS statistics version 17.0 .We performed Friedman tests to test for differences in normalized H-reflex amplitudes, soleus EMG amplitudes and the ratio between H-reflex and background EMG at the three epochs among the three conditions . For the small sample size, we chose the nonparametric methods because the validity of this approach does not depend crucially on normality assumption. We set the significance level at p = 0.002; both Wilcoxon signed ranks tests, p < 0.025), epoch 6  and epoch 7  but not for epoch 8  and the rest of the epochs in stance compared to the two unpowered conditions  but not at epoch 6  and epoch 8  during powered walking  but not in the epoch 6  and epoch 8 . For further comparisons at epoch 5, the ratio of H-wave and soleus EMG in the powered condition was significantly different from the ratio in the first unpowered condition  but not the second unpowered condition .The reduction in soleus EMG activation was much more than the reduction in H-wave amplitude during powered walking. Subjects had significantly lower H-wave amplitudes at epoch 5  as well as overall excitability of the motoneuron pool . The unaltered H-reflex modulation in this study indicates that stretch reflex inhibition  is likely not one of the mechanisms for reducing soleus EMG when adapting to robotic assistance with short term training. Instead, our results suggest that mechanisms for this short-term adaptation to the robotic assistance could be decreased excitability of the soleus motoneuron pool, resulting from increased inhibition of the motor neurons or a reduction in supra-spinal drive .Adaptation to the robotic exoskeleton assistance during walking may occur in two phases, a quick adaptation that occurs in the first few hours or days and a much longer adaptation that continues for weeks -62. The During the initial learning of a motor task, increased attention may also enhance the reflex responses. Previous studies have shown greater H-reflex responses during the initial training on a novel locomotion task such as obstacle avoidance during walking  and backOur findings suggest that the nervous system does not inhibit the soleus H-reflex in response to short-term adaption to exoskeleton assistance as a mechanism for reducing soleus muscle recruitment. Likely mechanisms for the decrease in soleus EMG include spinal or supraspinal post-synaptic inhibition of the soleus motor neurons. Previous results that found H-reflex inhibition in a subject with long term exoskeleton training experience  suggest The authors declare that they have no competing interests.PCK recruited subjects, managed data collections, completed data analysis and drafted the manuscript. CLL developed a custom-written program to control the timing of electrical stimuli, assisted with data analysis and helped edit the manuscript. DPF conceived of the study, provided guidance on experimental design, and helped draft and edit the manuscript. All authors read and approved the final manuscript.


Recent studies have begun to unravel the molecular basis of tiling and self-avoidance, two important cellular mechanisms that shape neuronal circuitry during development in both invertebrates and vertebrates. Dscams and Turtle (Tutl), two Ig superfamily proteins, have been shown to mediate contact-dependent homotypic interactions in tiling and self-avoidance. By contrast, the Activin pathway regulates axonal tiling in a contact-independent manner. These cell surface signals may directly or indirectly regulate the activity of the Tricornered kinase pathway and/or other intracellular signaling pathways to prevent the overlap between same-type neuronal arbors in the sensory or synaptic input field. Neuronal circuit formation requires proper interactions between neurites of developing neurons during embryonic development. A neurite can interact with another neurite in a homotypic (i.e. interaction between neurites from same-type neurons or sister branches from the same neuron) or heterotypic manner (i.e. interaction between neurites from different types of neurons). These interactions have been shown to play important roles in regulating a variety of processes such as axonal guidance, dendrite morphogenesis and synaptogenesis -3. This Tiling involves the recognition between certain same-type or functionally equivalent neurons, which allows the neurites from same-type neurons to completely cover the sensory or synaptic input field with no or minimal overlap. Tiling is likely required for providing an anatomical basis for parallel detection of same-type sensory information in the receptive field and thus allows the spatial discrimination of sensory information. The phenomenon of tiling was first discovered in the cat retina by Boycott and colleagues in 1981  Fig. 1A1A. TheirHomotypic interactions can also regulate self-avoidance where sister neurites from the same neuron repel each other to ensure the uniform coverage of receptive fields for effectively processing sensory information. The concept of self-avoidance was first proposed from studies in the giant Amazon leech in the early 1980s ,15 Fig.. Those sIt has long been proposed that specific contact-dependent cell surface recognition mechanisms must exist to allow a neurite to repel a like neurite but not a unlike neurite in tiling and self-avoidance. For instance, it was shown that removal of a ganglion neuron in the rat retina led to the invasion of dendrites from its neighboring same-type neuron into the vacant area . SimilarIn vitro studies demonstrate that each isoform binds to itself but shows little binding to a different isoform [The first clue for a cell surface recognition mechanism underlying tiling and self-avoidance came from recent studies on the Down's syndrome cell adhesion molecule Dscam . Dscam b isoform ,32  protein is the second cell surface determinant shown to regulate homotypic interaction in tiling and self-avoidance ,45. Tutltutl mutants, R7 terminals frequently extended laterally and fused with other R7 terminals in neighboring columns. Single cell mosaic analysis indicates that tutl is required both cell-autonomously and cell non-autonomously for preventing the fusion between neighboring R7 terminals. This result, together with that Tutl mediates homophilic cell-cell aggregation in cultured cells [tutl mutant R7 axon to invade its neighbors. We propose two alternative models for the action of Tutl in R7 tiling (Fig. Our recent study demonstrates a role for Tutl in tiling R7 photoreceptor axons in Drosophila . In the ed cells , supporting Fig. . In the tutl significantly increased the overlap between sister branches in class IV da neurons. However, unlike Dscam1 that is required in all four classes of da neurons [tutl mutations did not affect self-avoidance in class I, II and III da neurons [Tutl has also been implicated a role in regulating dendritic self-avoidance . Loss of neurons -39, tutl neurons . One pos neurons -39, Tutl neurons .While it is well established that contact-dependent homotypic repulsion plays a key role in axonal/dendritic tiling, several studies indicate that tiling also involves a contact-independent mechanism ,19. For tutl could significantly enhanced the tiling phenotype in mutants defective in Activin signaling [Genetic studies show that mutations in the genes encoding components of the Activin pathway caused specific defects in R7 tiling as R7 terminals frequently extended laterally to invade their neighboring columns . Those cignaling , suggestRecent genetic dissection of dendritic patterning in Drosophila da neurons has led to the identification of several components of the Tricornerd kinase pathway in dendritic tiling and self-avoidance Fig. , includiTrc belongs to the NDR family of Ser/Thr kinases . In DrosDscam1 and Trc, or between tutl and Trc. In addition, unlike Dscam1 and Tutl, Trc is also required for tiling class IV da neurons [The mechanism by which the Trc signaling pathway is activated by cell surface signals in tiling and self-avoidance remains elusive. While a similar self-avoidance phenotype in class IV da neurons was observed in mutants defective in Dscam1 or Tutl ,37-39,45 neurons , indicatRecent studies have significantly advanced our understanding of the molecular mechanism of tiling and self-avoidance in the developing nervous system. The identification of Dscams and Turtle sheds light on the molecular basis of contact-dependent homotypic interactions underlying tiling and self-avoidance. While the Drosophila Dscam1 functions as a self-recognition code to mediate homotypic repulsion, mouse Dscams appear to act as a general de-adhesion factor that may antagonize the function of certain cell-type-specific adhesion molecules. Whether Turtle mediates homotypic repulsion or de-adhesion remains to be determined. Unlike Dscams and Turtle, the Activin pathway regulates the tiling of Drosophila R7 photoreceptor axons in a contact-independent manner. These cell surface signals may directly or indirectly regulate the activity of the Tricornered kinase pathway and/or other intracellular signaling pathways. Future studies will be necessary to determine the exact mechanism by which the signals from the activation of these cell surface receptors are converted into the changes in the motility of axonal/dendritic terminals in tiling and self-avoidance.The authors declare that they have no competing interests.SC and YR wrote the manuscript. All authors read and approve the manuscript.


Early clinical results are encouraging, but the variable response may partly depend on local pharmacokinetics. This study evaluated the in vitro cytotoxic effects of 131I-Lipiodol on human hepatocellular carcinoma (Hep-G2), human colorectal metastatic cancer (SW620), human colorectal hepatic cancer (LoVo) and human umbilical vein endothelial cells (HUVEC) cell lines. The cell cultures were exposed to 131I-Lipiodol for 48 h, following which cell counts and viability were assessed by haemocytometer, S-Rhodamine uptake and radioactivity assay. The effect of exposure to control Lipiodol, 131I-Lipiodol and 131I alone was evaluated. 131I-Lipiodol was cytotoxic against all the cancer cell lines but not against the non-malignant (HUVEC) cell line. The cytotoxicity effects were very similar in all the cancer cell lines. There were no cytotoxic effects following exposure to plain 131I in any of the cell lines . A similar trend was seen with radioactivity counts using a gamma counter. The cytotoxic effect of 131I-Lipiodol had a graded effect with an increase in cytotoxicity following the increase in the radioactive dose. This study showed that there was a marked cytotoxic effect by 131I-Lipiodol on all the cancer cell lines. There was no difference between the controls and the 131Iodine. This suggests that effective 131I-Lipiodol targeted therapy is dependent on the uptake and retention of Lipiodol by malignant cells. © 1999 Cancer Research CampaignIntra-arterial Lipiodol has been used to deliver targeted therapies to primary, and some metastatic, liver cancers. Targeted radiotherapy has been used by substituting the iodine in Lipiodol with


Glycan biosynthesis occurs though a multi-step process that requires a variety of enzymes ranging from glycosyltransferases to those involved in cytosolic sugar metabolism. In many cases, glycan biosynthesis follows a glycan-specific, linear pathway. As glycosyltransferases are generally regulated at the level of transcription, assessing the overall transcriptional profile for glycan biosynthesis genes seems warranted. However, a systematic approach for assessing the correlation between glycan expression and glycan-related gene expression has not been reported previously.To facilitate genetic analysis of glycan biosynthesis, we sought to correlate the expression of genes involved in cell-surface glycan formation with the expression of the glycans, as detected by glycan-recognizing probes. We performed cross-sample comparisons of gene expression profiles using a newly developed, glycan-focused cDNA microarray. Cell-surface glycan expression profiles were obtained using flow cytometry of cells stained with plant lectins. Pearson's correlation coefficients were calculated for these profiles and were used to identify enzyme genes correlated with glycan biosynthesis.This method, designated correlation index-based responsible-enzyme gene screening (CIRES), successfully identified genes already known to be involved in the biosynthesis of certain glycans. Our evaluation of CIRES indicates that it is useful for identifying genes involved in the biosynthesis of glycan chains that can be probed with lectins using flow cytometry. The biosynthesis of glycan chains is a multi-step process. First, free sugars are biosynthesized by sugar-specific metabolic pathways. Then, these sugar molecules are further metabolized to nucleotide sugars, which serve as donors for glycosyltransferases Thus far, more than 300 enzymes and transporter genes have been reported to be involved in the metabolism and biosynthesis of different glycans in diverse cell types and at various stages. Each glycan structure has its own specific biosynthetic pathway. The introduction of cloning expression methodology DNA microarray technology is very powerful because it can simultaneously detect changes in the expression levels of a large number of genes. In the field of glycobiology, extensive efforts have been made to identify the genes involved in glycan biosynthesis, and many have been shown to encode glycosyltransferases of the ER or Golgi apparatus. Many of these genes have been cloned, including those encoding large enzyme families In the present study, we analyzed the glycan-related gene expression profiles for possible correlations with cellular glycan expression profiles in a cross-sample manner, using Pearson's correlation coefficient. This analysis successfully identified specific genes encoding regulatory enzymes for the biosynthesis of specific glycans, from among the candidate genes of the glycan biosynthesis pathways. We designated this method correlation index-based responsible-enzyme gene screening, or CIRES .2–6-Galβ1–4-GlcNAc-R and that the sialyltransferase gene ST6GAL1 is responsible for the biosynthesis of the glycan epitope recognized by GL7 The rat monoclonal antibody GL7 specifically stains germinal center B cells upon T cell-dependent antigen immunization. We recently demonstrated that GL7 recognizes the glycan Neu5AcαThe glycan-related gene expression profiles were obtained using total RNA isolated from six human B-cell lines cultured under optimal conditions, and cross-sample comparisons of these profiles were made in relation to a commercially available universal reference RNA consisting of a mixture of polyA(+) RNA from various organs. The gene expression profiles were determined as a ratio of the gene expression level to the universal reference cDNA expression level on the glycan-focused microarray . Thus, tAfter staining the cells with various anti-glycan probes (lectins) of known specificity, we determined the glycan expression profiles using flow cytometry. For each cell line, the transcriptional profile of glycan-related genes was used for cross-sample correlation analysis with the glycan expression profile.Cell-surface glycan expression has been extensively studied using plant lectins that recognize specific glycan epitopes. To evaluate whether correlation analyses of lectin staining and glycan-related gene expression might provide useful information, we first performed lectin staining of a set of human B cells  to obtain their cross-sample profiles of lectin epitope expression. We analyzed the strength of the correlations using Pearson's correlation coefficient, which is a standard, well-established method for assessing correlation. To prevent possible bias in the lectin choice, we used 15 plant lectins supplied in two commercially available sets.To evaluate the efficacy of the calculations, the lectins were first divided into two groups based on the presence or absence of previous reports asserting a correlation between cell surface expression of a specific lectin epitope and expression of a certain glycosyltransferase gene. The lectins that lacked a reported correlation were divided into highly specific (or narrow) and broadly specific groups. The highly specific (narrow) lectins were further assigned to one of two subgroups according to the position of the epitope  on the glycan chain.1–6 branching of N-acetylglucosamine (GlcNAc), which often correlates with tumor progression MGAT5 (GnT-V) gene 1–6-branched N-glycans. PHA-L4 epitope expression is diminished in Mgat5-null mice MGAT5 expression was strongly correlated with the PHA-L4 staining profile, as shown in MGAT5 expression (CI = 0.93) represents a highly significant correlation. Other correlated glycan-related genes were judged to be irrelevant to the biosynthesis of this epitope and are listed in PHA-L4 recognizes tri- or tetraantennary N-glycans with βMGAT5 gene was responsible for expression of the PHA-L4 epitope. This prediction was confirmed by retrovirus-mediated gene expression in Namalwa B cells ((MGAT5-IRES-EGFP) was introduced into Namalwa cells, the level of PHA-L4 epitope expression was higher in the EGFP-positive population than in the EGFP-negative population. To rule out the possibility that viral infection somehow altered the cell surface glycan independently of glycosyltransferase expression, the vector carrying only IRES-EGFP was used as a negative control. In Namalwa cells expressing only EGFP, the EGFP-positive and EGFP-negative populations expressed identical levels of the PHA-L4 determinant  or N-acetylgalactosamine . We previously showed that SSA epitope expression is induced in CHO cells by stable transfection with the rat ST6GAL1 gene St6gal1 in mice eliminated the expression of the Sambucus nigra agglutinin (SNA) epitope ST6GAL1 gene expression correlated with SSA epitope expression, as determined by flow cytometry, in six B-cell lines. Although SSA staining in the six B-cell lines varied in intensity  units on glycan chains. These findings indicate that the SSA epitope detected by flow cytometry might be located at the terminus of poly-LacNAc units, which are often found on the β1–6 branch, and might extend beyond the glycocalyx of the cell surface.SSA recognizes αntensity , the sta1–3GalNAc-Thr/Ser), and the capping of this epitope by sialylation severely reduces the affinity of the interaction 2–3 sialyltransferase activity, which sialylates the Gal residue St3gal1 caused a deficiency in the derepression of PNA reactivity during thymocyte development and eventually resulted in deficient CD8+ T cell maturation PNA recognizes the Gal-exposed core-1 structure  attached to the chitobiose FUT8 has been shown to be responsible for the biosynthesis of a core Fuc on N-glycans FUT8 gene expression and cell surface LCA staining has not been demonstrated in flow cytometry experiments.We assessed the staining profiles of lectins that recognize terminal structures of glycan chains. LCA recognizes the biantennary N-glycan chain with core αFUT8 expression profile revealed a correlation .In theory, UEA-I binding should be affected by the expression of i.e., sialyltransferase for SSA), this result was not surprising. It indicates that the abundant expression of LacNAc structures ensures the detection of a correlation between a terminal enzyme expression profile and the expression of a terminal glycan detected by flow cytometry. Moreover, capping of LacNAc should have a negative effect on its recognition by RCA120, which would make the detection of epitope expression more complex. It was clear that this procedure is not universally effective for lectin epitopes but that the effectiveness of the procedure depends on the type of glycan recognized by a lectin.RCA120 preferentially recognizes terminal LacNAc structures found in various classes of glycans. These LacNAc structures are biosynthesized by a large group of B4GalT 1–4-Man α1–3-branched triantennary N-glycan MGAT5 , 1986), compared with control   compared with control expression , possibly due to the competitive relationship between MGAT3 and MGAT5 B3GNT2.DSA recognizes tri- and tetraantennary N-glycans. It is specific for GlcNAc βan MGAT5 . This reI, 1247) . InteresMGAT5 (MGAT3 (GnT-III), which is the GlcNAc transferase gene expected to correlate by virtue of its known epitope specificity  on the cell-surface presentation of a lectin epitope.MAM is a mixture of two lectin subunits, MAL and MAH. MAL binds to Sia αll lines . This ispression . Thus, t1–6 GlcNAc-containing glycans WGA is thought to preferentially recognize clustered N-acetyl groups found in N-acetylneuraminic acid (Neu5Ac), GlcNAc, and GalNAc. Neu5Ac is often a major WGA ligand because the Sia density on the termini of glycan chains tends to increase for the highly branched N-linked glycans ST6GAL1 gene and weakly with that of the ST3GAL3 gene  and the α2,3-linked Sia-specific Salmonella typhimurium sialidase . Finding negative relationships is difficult in normal biological experimental setups but may have been possible using CIRES because of the unbiased correlation coefficient calculation resulting from the large set of cross-sample comparisons.Interestingly, CIRES also found negative correlations for some epitopes, which is consistent with the fact that CIRES results are highly dependent on the regulatory mechanisms of glycan epitope expression, some of which are negative . All cells were cultured under commonly used conditions and were used in logarithmic growth phase. Under these conditions, the glycan expression phenotype obtained by lectin staining tended to be uniform within a cell line and unique for each cell line. Thus, the data obtained by lectin staining with excess probe were suitable for determining the mean fluorescence intensity (MFI) of lectin staining.This method compared the relative glycan expression profiles of cell lines whose cellular size and character might not be uniform; therefore, we normalized the staining signal by using the ratio of the stained sample MFI to the control MFI. This eliminates the absolute glycan expression signals and normalizes the relative expression results to the staining and data acquisition conditions.The statistical analysis is the core of the CIRES method. Since glycosylation follows a defined pathway, we could have set up an algorithm suitable for the correlation analyses. However, we chose to use the well-established Pearson's correlation coefficient for the analyses. Regardless of the calculation method, correlations were detected even when the profiles did not completely match. Based on these calculations, a list of genes can be ordered according to the strength of their correlation. This list might be useful in designing further experiments to confirm biological functions.In glycan biosynthesis, not all relationships are positive in nature, and some lectins used in this study yielded negative correlations. Thus, it is possible to detect dominant inhibitors of specific glycan biosynthesis steps using CIRES. Moreover, in theory, CIRES is not limited to glycan biosynthesis but could be used in any system for which numeric phenotypic expression results (such as glycan expression) can be obtained for a set of cultured cells. The correlation of non-glycosylation phenotypes with gene expression in the same system should be possible.One limitation of the standard microarray technique is that it can only detect the relative cDNA expression levels of two samples. This was actually useful when we took the ratio of the signal for each gene relative to that for reference RNA, thus circumventing the microarray problems associated with spot-to-spot variation and hybridization variation for different nucleotide sequences. Almost all of the spots hybridized with universal reference RNA (data not shown). Normal, uniform cell culture conditions were used in this experiment to maximize reproducibility.In order to calculate the gene expression ratios in a cross-sample manner, we wanted to avoid negative signals in the microarray scans. To do this, we directly used the raw data, instead of applying a cut-off value to the microarray signal by deducting the local background signal from the raw spot signals. We consider this to be a valid option, because we confirmed that the scanner readout after hybridization was reproducible, even for spots yielding weaker signals, and that both options yielded a similar order of relative strength (data not shown).Normalization was also important because the array results were compared in a cross-sample manner. Sum-based normalization was applied to obtain better correlation coefficients for proven lectins (data not shown). The validity of the calculations was dependent on the use of a consistent standard, making a universal reference essential for the cross-sample comparison. In general, the microarray results tended to be more consistent for stronger hybridization signals than for weaker signals.For an epitope whose expression is regulated by a single gene product in its biosynthetic pathway and for which the supply of substrate for that biosynthetic reaction is abundant, CIRES will identify the gene involved in the biosynthesis of the epitope. The finding that the RCA120staining profile did not correlate with a specific glycosyltransferase was therefore not surprising, because it binds LacNAc. Generally, we obtained better correlation coefficients for epitopes created by terminal modifications of LacNAc. CIRES was also useful in identifying major genes responsible for epitope capping, which is negatively correlated with glycan expression, as in the case of PNA.Our CIRES results tended to show correlations with the GlcNAc transferases. This may result from the use of flow cytometry to detect lectin binding, and thus glycan expression, because lectin staining preferentially detects glycans that extend beyond the glycocalyx. Moreover, the level of epitope expression may not linearly correlate with the lectin signal, because lectins tend to form multimers. As poly-LacNAcs are the usual core units for the presentation of some functional glycan epitopes, GlcNAc transferases found to correlate could also be important for the epitope presentation suitable for lectin-based recognition.Thus far, we have not identified any enzymes other than glycosyltransferases that strongly correlate with epitope expression levels. However, a subtle negative correlation was noted between the expression of the Con-A epitope and mannosidase expression. Our failure to identify additional enzymes might have resulted from the limited number of lectins available to us. In theory, other factors involved in biosynthetic pathways, such as factors involved in sugar-nucleotide biosynthesis or transport, could also regulate glycan expression on the cell surface.Specific probes are unquestionably important for the successful application of CIRES. The most important aspect of lectin specificity is not affinity for the glycan ligand but rather the exclusivity of the enzyme. To calculate statistically significant correlations, a set of cell lines expressing different amounts of the target glycan should be used. The uniform expression of the target glycan in the cell set is undesirable.To evaluate this methodology, we used the plant lectins that have been used extensively in other glycan studies. Since the correlations are identified by statistical calculations alone, we first assessed those lectins that had already been correlated with specific enzyme genes, to test for the identification of genes that matched glycan expression only by chance. Therefore, when CIRES is applied using uncharacterized glycan-binding probes, the biological relevance of the correlated genes must be confirmed by altering their expression in the cells. This requirement could be viewed as a limitation of the system. However, the alteration of target glycan expression has been an enduring and major objective of glycobiological experiments concerning lectins. As indicated in CIRES alone cannot identify the specific glycan structure to which an anti-glycan probe binds; at the same time, knowledge of the structure of interest does not necessarily mean that modifying its expression on cells is possible. Using the CIRES method will likely result in the production of cells with modified glycan expression, but the actual structure bound by the probe may be unknown. This situation is somewhat similar to that of glycan-array binding studies, in which the probe that binds best on the glycan array does not necessarily bind the same glycan on the array as on cells and the identification of the glycan structure does not always result in the identification of the relevant biosynthetic enzyme. Thus, CIRES could be combined with conventional glycan-binding assays Two commercially available sets of biotin-conjugated plant lectins (Plant Lectin Set I and II) were obtained from Seikagaku . R-Phycoerythrin-conjugated streptavidin was obtained from Caltag (USA). The B-cell lines Daudi, Namalwa, Raji, Ramos, KMS-BM, and KMS-PE were obtained from the Japanese Collection of Research Bioresources and were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum, sodium pyruvate, non-essential amino acids, and 2-mercaptoethanol.Gene expression profiling of the six B-cell lines was performed using a glycan-focused cDNA microarray  and a GEO Platform (GPL #3465) and compared with reference RNA  to create GEO Series GSE 4407, as reported elsewhere 5 B cells in 100 µl FACS buffer (1% BSA and NaN3 in PBS(–)) was incubated with excess biotinylated plant lectin probes at room temperature for 30 min. R-Phycoerythrin- or FITC-conjugated streptavidin was used to detect lectin binding. Data were obtained using FACScan or FACSCaliber  and analyzed using FlowJo  or Cellquest software . To cross-compare staining signals between cell lines, the mean fluorescence intensity (MFI) of the background staining was adjusted to around 10, and the relative staining signal was expressed as the ratio of sample MFI divided by the control MFI.A total of 2.5 × 10Fluorochrome signals on the microarray were acquired by an array scanner (Affymetrix 428) without background subtraction and were then background-corrected using a smoothing function P) were calculated using the correlation coefficient test. For a sample size of six, a correlation coefficient of 0.81 indicates a statistical significance level of 5%. The genes that correlated with lectin staining by this method were ranked according to correlation strength , 1 mM DTT, 1 mM EDTA, 0.25 M sucrose, and protease inhibitor cocktail (Roche)]. Postnuclear supernatants were further ultracentrifuged, and the pellets (membrane fraction) were resuspended in lysis buffer. The suspensions were subjected to lectin blotting with HRP-conjugated PHA-E4. The signal intensity was measured by exposure of the membrane to LAS300 .Arthrobacter ureafaciens  and Salmonella typhimurium  were used.Sialidase treatment was carried out as reported elsewhere Table S1Complete gene expression profiles obtained from cDNA microarray: Fluorescent Cy3  and Cy5 (each Bcell line) readout data were acquired from the array scanner and following statistical calculations were applied using the software program R. First, background was corrected using “Edward method”. Each data was then normalized using Linear Models for Microarray Data (LIMMA) to correct bias between fluorescent dyes. Finally, the Cy5 signal from the B cell lines was divided by the Cy3 signal to obtain the relative expression profile for each spot and resultant relative values were shown in columns I through N for each cells. Column A shows serial number of all spots on microarray and column B through E show physical location of all spots on glass slide-based cDNA microarray.(0.31 MB XLS)Click here for additional data file.Table S2Lists of correlated genes with lectin staining: This is an Exel file consists of 14 worksheets. First sheet shows full list of genes spotted on the Glycan-focuesed microarray and their gene ID numbers. Following sheets are the lists of genes that exhibited stronger correlation with indicated lectin staining profiles. See “(0.40 MB XLS)Click here for additional data file.


Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment. Figure-ground segmentation is achieved by assigning visual elements to either objects or background as a primary step in visual perception. Two main processes in organizing figure-ground segmentation are boundary assignment and surface segregation . BoundarIntrinsic horizontal connections that connect surrounding neurons convey information from beyond the classical receptive field and can provide contextual information of the target stimulus. However, it has been shown that contextual suppressive effects come from large regions (4–7mm), while the horizontal spread of axons is limited (up to 3.5–4.5 mm radius in V1 monkey). Together with the slow conductance velocities  of these fibers, these observations cast doubt on a role for horizontal connections in perceptual integration. Feedback projections from higher visual areas to lower areas are more suitable to provide the contextual information necessary for figure-ground segmentation. Feedback projections have high conductance velocity (∼3–10 m/sec), have large spread in V1, and influence surround mediated responses in V1. Finally, theoretical and most, if not all, computer models explain figure-ground segmentation by recurrent processing through horizontal and/or feedback connections.Yet several arguments are inconsistent with a leading role of feedback projections in producing contextual effects and figure-ground segmentation. For instance, V2 is the main contributor of feedback to the primary visual cortex, though inactivation of V2 has no effect on centre-surround interactions of neurons in the primary visual cortex In contrasts, apart from carrying the sensory information, a role of feed-forward projections in producing surround effects related to figure-ground segmentation is unknown. Many findings, however, point out that contextual effects may modify the feedforward signal and extra-classical surround suppression is present at the first stages of sensory processing in the retina, LGN and V1. The aim of this study is therefore to have a better understanding of the role of feedforward connections in figure-ground organization, in particular in surface segmentation and border-ownership coding. For that reason we constructed a purely feedforward spiking model omitting horizontal and feedback connections  and testThe figure-ground image is accurately represented  because not essential for segregating figure from ground; important here is that background neurons are silent.Whereas neurons in the first layer received continuous input from the figure-ground image, neurons in the second layer received spiking input from the first layer. Each neuron received retinotopic excitatory input and global inhibitory input from all spiking neurons in the first layer. For feature map 1  the spatial pattern of spiking activity in the second layer mirrored the excitatory input pattern ,6. In coBesides figure-ground signals, many (18% in V1 and 59% in V2) neurons in early visual cortex show selective responses to contour borders There are 3 types of edge detection cells described: edge contrast polarity, border-ownership and a combination of them. In principle our model can reproduce all the different types of neurons that signal contrast borders by applying different combinations of sub-regions from the second layer and/or from the first layer and from the two feature maps. A further product of such combinations of sub-regions is that neurons coding border-ownership are orientation selective. This has also been described in the visual cortex where edge detection is mainly observed for neurons that have an orientation preference In the visual cortex, contextual interactions are complex and heterogeneous and are observed for stimuli far outside the classical receptive field. For textures stimuli, Zipser We also tested the model for more than one figure. In We tested the model for different figure-ground contrasts. To do so, we decreased the input pixels values from 1 to 0, in steps of 0.1. Lowering the figure-ground contrast causes a gradual weakening of figure-ground signal; an effect produced by weaker figure responses and higher background responses . Such a The goal of the present study was to examine the role of feedforward connections in figure-ground operations. We found that our three layered model of spiking neurons could perform figure-ground segregation and one-sided border-ownership assignment in a purely feedforward manner. The feedforward segregation of figure from ground was robust. A decrease of the input contrast by 80% still yielded figure-ground segregation. Figure-ground segregation occurred for very small figures (even for the size of 1×1 pixel) and for large figures. Since the surround inhibition depended on stimulus size, figure-ground segregation failed when the figure size approximated the background size. This agrees with human figure-ground perception, where small stimuli are interpreted as figures and larger ones as background. When figure and background have the same size the assignment of figure and ground becomes ambiguous see e.g. .The first layer transformed the figure-ground texture input into a spike map, which was send to the layer 2 neurons. In the second layer, neurons received retinotopic excitatory input and global inhibitory connections from all the spiking neurons in the preceding layer of the corresponding feature map. In the first feature map only a minority of the total number of neurons, those at the figure location, contributed to the global inhibitory effect. Consequently each neuron in the second layer received relatively weak inhibition, which was not sufficiently strong to cancel out the excitatory activation of the neurons at the figure location. Neurons outside the central figure region only received global inhibition and remained silent . In the Feedback projections from higher visual areas to lower areas are believed to provide the contextual information necessary for figure-ground segmentation. Yet several studies indicate that feedback projections may not be the sole component for producing contextual effects and figure-ground segmentation. For instance, V2 is the main contributor of feedback to the primary visual cortex, though inactivation of V2 has no effect on centre-surround interactions of neurons in the primary visual cortex Moreover, removing feedback  to V1 impairs figure-ground perception, but does not affect visual detection of textured figure-ground stimuli Visual information entering the retina produces graded potentials which are converted by ganglion cells into action potentials. Our model neurons in the first layer convert continuous texture input into spikes, and therefore the first layer can correspond to the ganglion cell layer of the retina. The second layer of the model may represent the LGN as the main recipient of ganglion connections. The retinotopic excitatory connections mimic the powerful synaptic excitatory contacts that each LGN neuron receives from one to three retinal ganglion cells Alternatively, the second layer of our model may correspond to V1. In this case, LGN present just a relay of retinal information. In a previous version we successfully tested this by adding an extra layer representing the LGN. The thalamocortical connections are highly convergent maintaining the retinotopic mapping in V1. In V1, they synchronously activate layer 4 spiny cells, which in turn activate directly the upper layer neurons. Furthermore, thalamocortical synapse specifically and strongly excite the fast spiking network The last layer may represent V2, which receives its main feedforward input from V1. Neurons in V2 aggregate V1 receptive fields at similar but not identical topographical locations. In such a design V2 neurons show spatial in-homogeneity in the two-dimensional receptive field structure. V2 receptive fields contain sub-regions that are tuned to similar or dissimilar orientations A notable outcome of our model is that the figure-ground signal pops-out immediately after receiving the first spikes, both at the border and at the centre of the figure. Also border-ownership assignment occurred at similar time as figure-ground segregation. At first glance this may seem odd compared to the often reported late onset of figure-ground segregation in the visual cortex. General, non-specific surround suppression is one of the earliest contextual effects, which takes about 7 ms to develop after response onset Boundary detections and surface filling-in are other issues related to the onset of figure-ground segregation. Neurophysiological observations show that figure-ground modulation occurs first at the border of the figure followed by modulation for the center region of the textured figure Many computational models exists explaining figure-ground segregation and border-ownership. Most, if not all, computer models One model Most of the models are conductance based models excluding the rich and complex response behavior of neurons. Some models rely on lateral connections for figure ground organization Our intention was to test feedforward segregation of textures that previously had been studied in primates and computer models, and which are believed to depend on recurrent processing. The model was not designed for complex or natural images, neither was the intention to obtain state of the art figure-ground segmentation. On the contrary, to understand the role of feedforward connections in figure-ground segregation we constructed a minimalistic feedforward architecture. Therefore, we deliberately omitted recurrent processing, thereby severely constraining the possible outcomes of the model. For example, feature interactions are not possible with the current network because the lack of horizontal connections. Nevertheless, our simple network advocates a feedforward organization of figure-ground. According to our model data, one-sided border-ownership coding does not depend on local feature contrast but is based on surface segregation of the figure. Thus, our model predicts that local border detection and border ownership coding employ different neural mechanisms. Furthermore, in our model we modeled global inhibition by adding a negative weight to the feedforward connections and not by introducing local inhibitory cells at layer 2. In this way the combination in time of excitatory and strong inhibitory inputs mimic the synchronous activation and the strong and global inhibition described in the early visual system. Further studies should reveal how figure-ground segregation occurs by including inhibitory cells. Finally, considering the simplicity of our model figure-ground segregation may occur already at the earliest stages of visual processing.In the visual system it is not possible to separate axonal circuits and to analyze their function in isolation. Computational modeling of neural networks offers a complementary role to allow dissecting axonal circuits. Using biophysical realistic spiking neurons, we tested to what extent feed-forward connections contribute to the neural mechanisms underlying figure-ground organization. Our simple, 3 layered feed-forward spiking model performs figure-ground segmentation and one-sided border-ownership coding. It turns out that global inhibition and rebound spiking are important ingredients for figure-ground organization. We conclude that figure-ground organization includes besides feedback also a feedforward component.The model is composed of three layers, each containing two arrays of N×N units or neurons of the Izhikevich type . For allij in one layer solely connects to neuron Nij in the next layer. Thus the excitatory part of a neuron's receptive field has size one. The pattern of inhibitory connections differs between layers. Neurons in the first layer do not receive inhibitory signals from the texture input. In the second layer all neurons of a feature map (see below Inputs) receive inhibition from all neurons located in the same feature map of the first layer. In the third layer, a neuron Nij receives feature specific inhibition from a neighbor of neuron Nij located in the second layer. In principle, there are eight neighbors; for simplicity we chose only one  is in fact applied to I in (1) is the result of summing different matrix contributions of the formF is either the two dimensional figure itself or the binary array defined by the presence of spikes, i.e., with ones where condition (2) is satisfied and zeros elsewhere. The F itself, multiplied by the corresponding weight. The inhibitory part, whose associate receptive field has the same size as F, produces a spatially constant term –hence the F coefficients times the inhibitory weight. Thus, all layer 2 units within a feature map received the same inhibitory input. In our design, the employed weights are Cell dynamics is described by the ‘simple’ spiking model of Izhikevich The path from layer 2 to layer 3, where border-ownership assignments take place, may be described in terms of two receptive sub fields, inhibitory and excitatory, both of size one and next to each other. Their working is more easily expressed by means of the convolution


The brain is a major site of microRNA (miRNA) gene expression, but the spatial expression patterns of miRNAs within the brain have not yet been fully covered.We have characterized the regional expression profiles of miRNAs in five distinct regions of the adult rat brain: amygdala, cerebellum, hippocampus, hypothalamus and substantia nigra. Microarray profiling uncovered 48 miRNAs displaying more than three-fold enrichment between two or more brain regions. Notably, we found reciprocal expression profiles for a subset of the miRNAs predominantly found (> ten times) in either the cerebellum (miR-206 and miR-497) or the forebrain regions .The results indicate that some miRNAs could be important for area-specific functions in the brain. Our data, combined with previous studies in mice, provides additional guidance for future investigations of miRNA functions in the brain. MicroRNAs (miRNAs) are a group of small 19–24 nt non-coding RNAs that control protein levels through inhibition of mRNA translation or stability A relatively large number of the known miRNAs are expressed in the mammalian brain Interestingly, several miRNAs have been shown to be involved in neurological and psychiatric diseases. We have previously shown associations between schizophrenia and SNPs located in the vicinity of the mir-206/mir-133b cluster and mir-198 The brain is the most complex tissue in the mammalian organism, and it shows both regional and left-right subdivision in function and anatomy in many structures. This is well-known in humans The functions of brain-expressed miRNA have been investigated in recent years, but the detailed mapping of miRNA expression patterns in the brain is sparse and the functions of most of the miRNAs in normal brain tissue still remain to be elucidated. Their regulatory properties, the pleiotrophic effects they are assumed to have, and their high abundance in the brain indicate that miRNAs may be involved in area-specific functions of the adult rat brain. So we investigated whether brain-expressed miRNAs are equally abundant in various brain regions in unstimulated male rats.Adult male Sprague-Dawley rats  were sacrificed by decapitation. The brain was quickly removed and samples were immediately dissected on a cold surface, snap-frozen in pulverized dry ice, and stored at −80°C until further processing. For each animal, 3–4 brain regions were dissected from the right and left hemispheres separately. In total, samples from 6 different brain regions were obtained and each brain region was sampled from a total of 4 animals.The rat brain stereotaxic atlas was used for identification and delineation of brain regions (Paxinos and Watson) All animals were handled in strict accordance with good animal practice under licence from the Danish Animal Experimentation Inspectorate and following the guidelines of the European Communities Council Directive of 24 November 1986 (86/609/EEC).Frozen samples were quickly homogenized in QIAzol Lysis Reagent  and the <200 nt RNA fraction (including miRNAs) was immediately extracted using the RNeasy® Lipid Tissue Mini Kit  following the modified protocol which enables purification of miRNA and total RNA from the same sample in subsequent steps.The NCode™ Multi-Species miRNA Microarray dual-colour system V2  was used for miRNA expression analysis. These miRNA microarrays can detect all known mature miRNAs in mirBase 9.0 and include specific probes for 236 known rat miRNAs. Each probe is spotted in triplicate on each array. All samples were hybridized against a human brain universal reference RNA sample . This design allowed for comparison between hemispheres as well as regions. Each miRNA sample (600 ng) was poly-A tailed and tagged with the sequence tag for Alexa Fluor® 5 fluorophor using the NCode™ miRNA Labeling System , while the universal reference RNA was labelled using Alexa Fluor® 3. Samples were hybridized against the reference to NCode™ Multi-Species miRNA Microarrays  overnight using the NCode™ Multi-Species miRNA Microarrays Kit  and following the instructions given by the manufacturer. Arrays were then hybridized with Alexa Fluor® 3 and Alexa Fluor® 5 capture reagents and washed. Hybridization was performed on a MAUI hybridization station . Each array was subsequently scanned using an Agilent DNA microarray scanner, and images were processed using the GenePix Pro software .http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16725. The within-region variability of selected miRNAs were calculated as the standard deviation (sd) of the fully normalized data and the F-test was used to test for equal variances between individual miRNA and the mean sd of all miRNA within a region.Data analyses were conducted using the freely available statistical program “R” and several packages from the Bioconductor project. The LIMMA package To examine the quality of the expression data we performed unsupervised hierarchical clustering on the fully normalized expression data using the “R” package's “cluster”, “bioDist” and “Mfuzz” For some microRNAs (specified below), a TaqMan® MicroRNA Assay from Applied Biosystems  was used for qRT-PCR evaluation of the results of the microarray analyses. The qRT-PCR reactions were performed on the same RNA samples that were applied to the microarrays. Normalization was done with miR-103 (part # 4373158) as an endogenous control and the abundance of each miRNA was determined using the relative standard curve method. The selected miRNAs were rno-let-7a (part # 4373169), rno-miR-132 (part # 4373143), rno-miR-206 (part # 4373092) and rno-miR-320 (part # 4395388). Reverse transcription was carried out in triplicate with the TaqMan® MicroRNA RT Kit  using the manufacturer's recommended protocol on each of the 4 biological replicates from the relevant regions and hemispheres see . Real-tiTissue-specific expression patterns can provide important clues to the physiological function of a miRNA. Comprehensive studies on miRNA expression throughout the mouse brain have been published We used hierarchical clustering to examine the quality of the data from all six brain regions. Samples obtained from the individual regions were grouped together into clusters that reflected their biological relationship . The varMost of the miRNA on the array was detected in all samples across all regions (mean = 181 miRNAs) and in a majority of cases only subtle differences were seen between the different regions. Only 15.3% (36 miRNAs) of the miRNAs had very low abundances in all regions . In line with studies in mice and zebra fish, we found that especially miR-124a and miR-29a were highly abundant in all regions, whereas the expression levels for miR-9 were more moderate (data not shown) miR-206 has previously been classified as a muscle-specific miRNA To investigate whether miRNA expression levels differed between the two hemispheres, we compared miRNA abundances of the right and left side of the brain within each region. As shown in To examine whether the findings from the array data represented true right/left differences, we used qRT–PCR to measure the relative abundance of let-7a, miR-132 and miR-320. We found no evidence for hemispheric differences in gene expression levels for either of the miRNAs investigated , which sWe have characterized the miRNA expression profiles in both hemispheres of five anatomically distinct brain regions, thus providing an overview of the miRNA present in the rat brain. Overall the brain tissues were characterized by having measurable levels of the majority of the known rat miRNAs. This is consistent with data on mouse brain tissues published previously Our data provides a detailed map of miRNA brain expression in rats and shows that there are some differences in the expression in the cerebellum of a subset of the detectable transcripts, which are either highly enriched (miR-206 and miR-497) or nearly depleted . Accumulations of miR-497 in the cerebellum, of miR-7 in the hypothalamus, and of miR-221 and miR-222 in the hippocampus have also been described in mice In the light of the anatomical and functional differences between brain regions, miRNA region-specific expression may not be surprising. However, it is intriguing that the expression of a subset of the miRNAs appears to be mutually exclusive between different parts of the brain. The cerebellum is mainly involved in controlling movement and coordination and the neuronal circuits in the cerebellar cortex are conserved across most of the vertebrate lineages Some clues to the function of the individual miRNAs may be deduced from information on their target genes. Most of the known validated targets for the nine miRNAs that show large regional differences in their abundances in the rat brain  and miR-7 . It has recently been shown that the insulin-like growth factor 1 (IGF-1) is targeted by miR-206 In the brain, the actions of IGF-1 are modulated by estrogen receptor alpha (ESR1), which directly interacts with several components of the IGF-1 transduction pathway including IGF-1R and IRS1 Strikingly, two of the downstream targets of IGF-1 signalling, namely connexin 43 and zona occludens 1, are also validated targets for miR-206 The examples outlined indicate that several of the miRNAs that show mutually exclusive expression patterns in the brain have a potential functional overlap that converge onto IGF-1 signalling pathways and downstream targets. Normal brain development and function depend on IGF-1 signalling in spatial and temporal-specific patterns, and IGF-1 acts as an autocrine or paracrine factor to promote proliferation of neuronal progenitors, neuronal and oligodendrocyte differentiation and survival Multiple levels of regulation may be necessary to shift the action of IGF-1 toward the required function. The required functions may depend on the micro-environmental conditions, cell type, or subcellular localization, and the differential expression of regulatory miRNAs may facilitate the process for obtaining strict tissue-specific and timely control of such functions.Figure S1Cluster analysis demonstrating miRNAs grouped according to their regional expression profiles in adult male rat brain including samples from the prefrontal cortex. The dendrogram shows that the samples from six brain regions are grouped according to their biological relatedness except samples from prefrontal cortex (pfc). The branch lengths of the dendrogram are measures of the difference between samples and show that the within-region variability in miRNA abundances is low compared to the between-region variability. am: amygdala; cb: cerebellum; hip: hippocampus; hyp: hypothalamus; sn: substantia nigra.(2.39 MB TIF)Click here for additional data file.Figure S2Heat map highlighting genes showing more than threefold difference in miRNA abundances between regions in adult male rat brain. Genes that showed statistical significant differences (p = 0.01) in expression between two or more regions after correction for multiple testing by BH and had an absolute log2-fold-change greater than 3 are represented. am: amygdala; cb: cerebellum; hip: hippocampus; hyp: hypothalamus and sn: substantia nigra.(2.39 MB TIF)Click here for additional data file.


An Arabidopsis thaliana transcriptional network reveals regulatory mechanisms for the control of genes related to stress adaptation. Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes.Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures.We show that the A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote.These data suggest that  Escherichia coli . Values of ulations .F: absolute efficiency; FFL: feed-forward loop; GO: Gene Ontology; P: precision; S: sensitivity; TAIR: The Arabidopsis Information Resource; TF: transcription factor.SFE conceived the study. JC, GR and AJ performed all the computations. All authors analyzed the data and contributed to writing the manuscript.The following additional data are available with the online version of this paper: Figures S1 to S8  of the transcriptional model with respect to the reference set. The vertical dashed line indicates the optimum value for the z-score threshold (= 5) according to the F value. Figure S4: gene distribution in the pathways (clusters) found in the transcriptional network. Figure S5: stress distribution of the transcriptional network. Figure S6: absolute and relative gene expression errors versus the regression coefficient between the experimental and predicted gene expressions for all conditions from the training set. Figure S7: regression coefficient between the experimental and predicted gene expressions for all conditions versus the number of TFs regulating that gene. Figure S8: predictive power for gene expression of the effective model . We show the regression coefficient (R2) between the model and experimental profiles across the 1,436 conditions for the best (top) and worst (bottom) predicted genes.Figure S1: Click here for fileTable S1: fit of the distributions of outgoing and incoming connectivities for the transcriptional and non-transcriptional models to different statistical distributions. Table S2: three-gene motifs for the transcriptional model showing abundance and statistical significance. Table S3: four-gene motifs for the transcriptional model showing abundance and statistical significance. Table S4: three-gene motifs for the non-transcriptional model showing abundance and statistical significance. Table S5: four-gene motifs for the non-transcriptional model showing abundance and statistical significance.Click here for fileThe effective (text file) and transcriptional models (SBML file).Click here for file