Theoretically, the Lq type regularization with the lower value of q would lead to better solutions with more sparsity. Moreover, the L1/2 regularization can be taken as a representative of Lq (0 <q < 1) regularizations and has been demonstrated many attractive properties.Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples. In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in gene selection. The popular regularization technique is Lasso , compared to about 6 to 38 genes for ordinary L1 and elastic net regularization approaches.In this work, we investigate a sparse logistic regression with the L1/2 penalty achieves higher classification accuracy than those of ordinary L1 and elastic net regularization approaches, while fewer but informative genes are selected. This is an important consideration for screening and diagnostic applications, where the goal is often to develop an accurate test using as few features as possible in order to control cost. Therefore, the sparse logistic regression with the L1/2 penalty is effective technique for gene selection in real classification problems.From our evaluations, it is clear that the sparse logistic regression with the L With the development of DNA microarray technology, the biology researchers can analyze the expression levels of thousands of genes simultaneously. Many studies have demonstrated that microarray data are useful for classification of many cancers. However, from the biological perspective, only a small subset of genes is strongly indicative of a targeted disease, and most genes are irrelevant to cancer classification. The irrelevant genes may introduce noise and decrease classification accuracy. Moreover, from the machine learning perspective, too many genes may lead to overfitting and can negatively influence the classification performance. Due to the significance of these problems, effective gene selection methods are desirable to help to classify different cancer types and improve prediction accuracy.T-test, which examine each gene in isolation.In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Filter methods evaluate a gene based on discriminative power without considering its correlations with other genes -4. The dWrapper methods utilize a particular learning method as feature evaluation measurement to select the gene subsets in terms of the estimated classification errors and build the final classifier. Wrapper approaches can obtain a small subset of relevant genes and can significantly improve classification accuracy ,6. For eThe third group of gene selection procedures is embedded methods, which perform the variable selection as part of the statistical learning procedure. They are much more efficient computationally than wrapper methods with similar performance. Embedded methods have drawn much attention recently in the literature. The embedded methods are less computationally expensive and less prone to over fitting than the wrapper methods .1 type regularized (including Lasso and elastic net) linear model using a coordinate descent algorithm [1 regularization method, Gavin C. C. and Nicola L. C. [1/2 penalty, in particular for gene selection in cancer classification. The L1/2 penalty can be taken as a representative of Lq (0 <q < 1) penalty and has demonstrated many attractive properties, such as unbiasedness, sparsity and oracle properties [Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in the logistic regression models. Logistic regression is a powerful discriminative method and has a direct probabilistic interpretation which can obtain probabilities of classification apart from the class label information. In order to extract key features in classification problems, a series of regularized logistic regression methods have been proposed. For example, Shevade and Keerthi  proposedlgorithm ,12. Simila L. C.  investigoperties .1/2 regularization in the sparse logistic regression framework. The approach is applicable to biological data with high dimensions and low sample sizes. Empirical comparisons with sparse logistic regressions with the L1 penalty and the elastic net penalty demonstrate the effectiveness of the proposed L1/2 penalized logistic regression for gene selection in cancer classification problems.In this paper, we develop a coordinate descent algorithm to the Ln samples, D = {, , …, }, where Xi =  is ith input pattern with dimensionality p and yi is a corresponding variable that takes a value of 0 or 1; yi= 0 indicates the ith sample in Class 1 and yi= 1 indicates the ith sample is in Class 2. The vector Xi contains p features  for the ith sample and xij denotes the value of gene j for the ith sample. Define a classifier f(x) = ex /(1 + ex) such that for any input x with class label y, f(x) predicts y correctly. The logistic regression is expressed as:In this paper, we focus on a general binary classification problem. Suppose we have β =  are the coefficients to be estimated, note that β0 is the intercept. The log-likelihood is:Where β by minimizing the log-likelihood (2). In high dimensional application with p >>n, directly solving the logistic model (2) is ill-posed and may lead to overfitting. Therefore, the regularization approaches are applied to address the overfitting problem. When adding a regularization term to (2), the sparse logistic regression can be modelled as:We can obtain λ > 0 is a tuning parameter and P(B) is a regularization term. The popular regularization technique is Lasso (L1) [P(β) = ∑ |β|. Many L1 type regularization terms have been proposed in the recent years, such as SCAD [Where sso (L1) , which h as SCAD , elastic as SCAD , and MC+ as SCAD .q type regularization P(β) = ∑ |β|q with the lower value of q would lead to better solutions with more sparsity. However when q is very close to zero, difficulties with convergence arise. Therefore, Xu et al. [q (0 <q <1) regularization and revealed the extreme importance and special role of the L1/2 regularization. They proposed that when 1/2<q <1, the L1/2 regularization can yield most sparse results and its difficulty with convergence is not very high compared with that of the L1 regularization, while when 0<q <1/2, the performance of Lq penalties makes no significant difference and solving the L1/2 regularization is much simpler than solving the L0 regularization. Hence, the L1/2 regularization can be taken as a representative of Lq (0 <q < 1) regularizations. In this paper, we apply the L1/2 penalty to the logistic regression model. The sparse logistic regression model based on the L1/2 penalty has the form:Theoretically, the Lu et al.  further 1/2 regularization has been demonstrated many attractive properties, such as unbiasedness, sparsity and oracle properties. The theoretical and experimental analyses show that the L1/2 regularization is a competitive approach. Our work in this paper also reveals the effectiveness of the L1/2 regularization to solve the nonlinear logistic regression problems with a small number of predictive features (genes).The Lβj with the remaining elements of β fixed at their most recently updated values.The coordinate descent algorithm ,12 is a D has n samples, D = {, , …, }, where Xi =  is ith input variables with dimensionality p and yi is the corresponding response variable. The variables are standardized: Before introducing the coordinate descent algorithm for the nonlinear logistic regularization, we first consider a linear regularization case. Suppose the dataset P(B) is the regularization term. The coordinate descent algorithm solves βj and other βk ≠ j (k ≠ j represent the parameters remained after jth element is removed) are fixed. The equation (5) can be rewritten as:Where βj can be estimated as:The first order derivative at βj and 1 regularization (Lasso) can be defined as:Define lgorithm  for the 0 regularization, the thresholding operator of the coordinate descent algorithm can be defined as:Similarly, for the LI is the indicator function. This formula is equivalent to the hard thresholding operator [where operator .1/2 regularization for linear regression model. It is an iterative algorithm and can be seen as multivariate half thresholding approach. In this paper, we propose the univariate half thresholding operator of the coordinate descent algorithm for the L1/2 regularization. Based on equation (7), the gradient of the L1/2 regularization at βj can be expressed as:According to equations (8) and (9), we can know that the different penalties are associated with different thresholding operators. Therefore, Xu et al.  proposedβj > 0 statement, and let, βj = μ2. When βj > 0, the equation (10) can be redefined as:Firstly, we consider the ωj < 0, There are three cases of ωj < 0, the three roots of equation (11) can be expressed as follows:(i) If r > 0, none of the roots satisfices μ1 > 0. Thus, there is no solution to equation (11) when ωj < 0.where (ii) If  and There is still no solution to equation (11) in this case.(iii) If μ2 is a unique solution of equation (10). Thus, the equation (11) has non-zero roots only when In this case, the βj < 0 statement, we denoted βj = - μ2. The equation (10) can be transformed into the equation:On the other hand, in the The equation (12) also has a unique solution when  and In conclusion, the univariate half thresholding operator can be expressed as:φλ(ω) satisfies:where 1/2 regularization makes repeated use of the univariate half thresholding operator. The details of the algorithm will be described later. This coordinate descent algorithm for the regularization can be extended to the sparse logistic regression model. Based on the objective function (3) of the sparse logistic regression, one-term Taylor series expansion for l(B) has the form ofThe coordinate descent algorithm for the L1/2 penalty for sparse logistic regression and the details are given follows:Where Algorithm: The coordinate descent algorithm for sparse logistic with the L1/2 penalty1/2 penalized logistic regression works well in the sparsity problems, because the procedure does not need to change many irrelevant parameters and recalculate partial residuals for each update step.The coordinate descent algorithm for the L1/2 penalty in simulation study. We generate high-dimensional and low sample size data which contain many irrelevant features. Two methods are compared with our proposed approach: Sparse logistic regression with the Elastic Net penalty (LEN) and Sparse logistic regression with the Lasso penalty (L1).In this section, we evaluate the performance of the sparse logistic regression with the Lγi0,γi1,…,γip  independently from the standard normal distribution and the predictor vector is generated by j=1,…, p), where ρ is the correlation coefficient of the predictor vectors [We generated the vectors  vectors . The simϵ is the independent random error generated from N and σ is the parameter which controls the signal to noise. In every simulation, the dimension p of the predictor vector is 1000, and the first five true coefficients are nonzero: β1 = 1, β2 = 1, β3 = -1, β4 = -1, β5 = 1, and βj = 0(6 ≤ j ≤ 1000).Where λ in the sparse logistic regression models can be done in many ways and is often done by k-fold cross-validation (CV). Note that the choice of k will depend on the size of the training set. In our experiments, we use 10-fold cross-validation (k=10). The elastic net method has two tuning parameters, we need to cross-validate on a two-dimensional surface [The estimation of the optimal tuning parameter  surface .n = 50, 80, 100, the correlation coefficient ρ =0.1, 0.4 and the noise control parameter σ =0.2, 0.6 respectively. Each classifier was evaluated on a test data set including 100 samples. The experiments were repeated 30 times and we report the average test errors in Table n increases, the prediction performances of all the three methods are improved. For example when ρ =0.1, and σ =0.2, the average test errors of the L1/2 method are 28.2%, 10.7% and 8.1% with the sample sizes n=50, 80, and 100 respectively. When the correlation parameter ρ and the noise parameter σ increase, the prediction performances of all the three methods are decreased. For example, when ρ =0.4 and n =100, the average test errors from the L1/2 method increased from 9.1% to 15.1%, in which σ increased from 0.2 to 0.6. When σ =0.6 and n =80, the average test error from the L1/2 method increase from 18.4% to 20.5%, in which ρ increased from 0.1 to 0.4. Moreover, in our simulation, the influence of the noise may be larger than that of the variable correlation for the prediction performance of all the three methods. On the other hand, at the same parameter setting case, the prediction performance of the L1/2 method is consistent and better than the results of the LEN and L1 methods. For example, whenρ =0.1, σ =0.2 and n=100, the predictive error of the L1/2 method is 8.1% much better than 16.9% and 15.7% got by the LEN and L1 methods respectively.We consider the cases with the training sample size x1-x5 relevant features, the idealized average number of variables selected by each method is 5. In Table 1/2 penalized method are obviously closed to 5 and 3–10 times smaller than those of the LEN and L1 penalties at the same parameter setting. For example, when ρ =0.1, σ =0.2 and n=100, the average numbers from the LEN and L1 methods are 49.7 and 45.7 respectively, and the result of L1/2 method is 8.9. Moreover, when the sample size n, the correlation parameter ρ, and the noise parameter σ increase, the average numbers from all the three methods increase, but the values of the LEN and L1 methods increase faster than those of the L1/2 method. This means that the L1/2 penalized method consistently outperforms than other two methods in term of variable selection.Table 1/2 penalized method, we report the frequency with which each relevant variable was selected among 30 runs for each method in Table n=50), the L1/2 penalty selects the relevant variables slightly less frequently than the other two methods and all the three methods select true nonzero coefficients with difficulties, especially when ρ and σ are relatively large. For example, when ρ =0.4, σ =0.6, n=50, and for β5, the selected frequencies of the L1/2, LEN and L1 methods are 12, 14 and 13 respectively in 30 runs. As n increases, all the three methods tend to select the true nonzero coefficients more accurately and the L1/2 penalty method performs slightly better, in terms of variable frequencies, than the other two methods under the different parameter settings of ρ and σ. To sum up, Tables 1/2 method is winner among the competitors in terms of both prediction accuracy and variable selection in the different variable correlation and noise situations.To further evaluate the performance of the L1/2 penalized method with the LEN and L1 methods on 4 publicly available gene expression datasets: Leukaemia, Prostate, Colon and DLBCL. A brief description of these datasets is given below and summarized in Table In this section, we compare our proposed LThe original dataset was provided by Golub et al. , and conThis original dataset contains the expression profiles of 12,600 genes for 50 normal tissues and 52 prostate tumor tissues. For data preprocessing, we adopt the pretreatment method  to obtaihttp://microarray.princeton.edu/oncology.The colon microarray data set in Alon et al.  has 2000This dataset contains 77 microarray gene expression profiles of the 2 most prevalent adult lymphoid malignancies: 58 samples of diffuse large B-cell lymphomas (DLBCL) and 19 observations of follicular lymphoma (FL). Each sample contains 7,129 gene expression values. More information on these data can be found in Shipp MA et al. . For datλ, we employ the ten-fold cross validation scheme using the training set. We repeat this procedure 30 times and the averaged misclassification errors were reported in Table 1/2 penalty gives the average ten-fold cross validation error of 2/50 and the average test error of 1/22 with about 2 genes selected. The classifiers with LEN and L1 methods give the average ten-fold cross validation errors of 1/50 and the average test errors of 1/22 with about 9 and 6 genes selected respectively. This means that all three methods can be successfully applied to high-dimensional classification problems and classify the Leukaemia dataset with same accuracies. Note that, the L1/2 method achieved its success using only about 2 predictors (genes), compared to about 9 and 6 for the LEN and L1 methods. For Prostate and Colon datasets, it can be seen the L1/2 method achieves the best classification performances with the highest accuracy rates using much fewer genes compared with those of the LEN and L1 methods. For DLBCL dataset, the L1/2 logistic regression achieves better classification performance than that of the L1 method and worse than that of the LEN method. However, as well as other three datasets, the L1/2 method achieved its success using much less predictors (about 14 genes), compared to about 38 and 23 for the LEN and L1 methods. This is an important consideration for screening and diagnostic applications, where the goal is often to develop an accurate test using as few features as possible in order to control cost.We evaluate the prediction accuracy of the three penalized logistic regression models using random partition. This means that we divide the datasets at random such that approximate 70-80% of the datasets becomes training samples and the other 20-30% test samples. More information on these data is given in Table 1/2 method is 5. In contrast, Figures EN and L1 methods are 37 and 26 respectively. Generally speaking, the penalized logistic regression methods can be successfully applied to the cancer classification problems with high dimensional and low samples microarray data, and our proposed L1/2 method achieves better performance especially in gene selection.Figures 1/2 method, are experimentally proved to be correlated to leukemia of ALL or AML. The cystatin C gene is located at the extracellular region of the cell and has role in invasiveness of human glioblastoma cells. Decrease of cystatin C in the CSF might contribute to the process of metastasis and spread of the cancer cells in the leptomeningeal tissues [1/2 method and also selected by Ben-Dor et al. [The summaries of the 10 top-ranked informative genes found by the three sparse logistic regression methods for 4 gene expression datasets are shown in Tables  tissues . The mye tissues . For ther et al. , Yang anr et al.  and Li er et al. . On the r et al.  showed tr et al.  reportedEN and L1 methods. The evidence from the literatures showed that they are cancer related genes. For example, for the colon dataset, the genes cholinergic receptor, nicotinic, delta polypeptide (CHRND) and platelet/endothelial cell adhesion molecule-1 (PECAM1) were also selected by Maglietta R. et al. [In Tables . et al. , Wiese A. et al. , Wang S.. et al. , and Dai. et al. . These gOn the other hand, from Tables 1/2 penalty, we constructed KNN  classifiers using the relevant genes which were most frequently selected by the L1/2 penalized logistic regression method. In this experiment, we use the random leave-one-out cross validation (LOOCV) to evaluate the predictive ability and repeat 50 runs.In this section, to further evaluate the performance and prediction generality of the sparse logistic regression with L1/2 penalty can achieve high classification accuracy. The results indicate that the sparse logistic regression with the L1/2 penalty can select power discrimination genes.Table 1/2 penalty, and develop the corresponding coordinate descent algorithm as a novel gene selection approach. The proposed method utilizes a novel univariate half thresholding to update the estimated coefficients.In cancer classification application based on microarray data, only a small subset of genes is strongly indicative of a targeted disease. Thus, feature selection methods play an important role in cancer classification. In this paper, we propose and model sparse logistic regression with the L1/2 penalty achieve higher classification accuracy than those of ordinary L1 and elastic net regularization approaches, while fewer but informative genes are selected. Therefore, the sparse logistic regression with the L1/2 penalty is the effective technique for gene selection in real classification problem.Both simulation and microarray data studies show that the sparse logistic regression with the LIn this paper, we use the proposed method to solve binary cancer classification problem. However, many cancer classification problems involve multi-category microarray data. We plan to extend our proposed method to solve multinomial penalized logistic regression for multiclass cancer classification in our future work.All authors declare they have no competing interests.YL, CL and XZL developed the gene selection methodology, designed and carried out the comparative study, wrote the code, and drafted the manuscript. KSL, TMC, ZBX and HZ brought up the biological problem that prompted the methodological development and verified and provided discussion on the methodology, and co-authored the manuscript. The authors read and approved the manuscript.


TARDBP gene encoding TDP-43 cause dominantly inherited forms of this condition. To understand the relationship between TDP-43 misregulation and neurotoxicity, we and others have used Drosophila as a model system, in which overexpression of either wild-type TDP-43 or its ALS-associated mutants in neurons is sufficient to induce neurotoxicity, paralysis, and early death. Using microarrays, we have examined gene expression patterns that accompany TDP-43-induced neurotoxicity in the fly system. Constitutive expression of TDP-43 in the Drosophila compound eye elicited widespread gene expression changes, with strong upregulation of cell cycle regulatory genes and genes functioning in the Notch intercellular communication pathway. Inducible expression of TDP-43 specifically in neurons elicited significant expression differences in a more restricted set of genes. Genes that were upregulated in both paradigms included SpindleB and the Notch target Hey, which appeared to be a direct TDP-43 target. Mutations that diminished activity of Notch or disrupted the function of downstream Notch target genes extended the lifespan of TDP-43 transgenic flies, suggesting that Notch activation was deleterious in this model. Finally, we showed that mutation of the nucleoporin Nup50 increased the lifespan of TDP-43 transgenic flies, suggesting that nuclear events contribute to TDP-43-dependent neurotoxicity. The combined findings identified pathways whose deregulation might contribute to TDP-43-induced neurotoxicity in Drosophila.Cytosolic aggregation of the nuclear RNA-binding protein TDP-43 is a histopathologic signature of degenerating neurons in amyotrophic lateral sclerosis (ALS), and mutations in the  SOD1 gene, which was responsible for up to 20% of all fALS cases SOD1 have been identified. Biochemically, ALS mutations reduced SOD1 solubility and promote its aggregation, leading to numerous toxic effects Amyotrophic lateral sclerosis (ALS) is an age-dependent, fatal motor neuron disease with no effective treatment. Approximately 90% of ALS arises sporadically (sALS) without clear genetic etiology, whereas 10% of ALS cases are familial (fALS) and have a clear genetic component. The first established genetic cause of ALS was dominant mutations of the Recent advances in ALS genetics promise to illuminate ALS disease pathways and have renewed hopes for a therapeutic breakthrough. Prominent among these advances was the discovery that dominant mutations in two functionally and structurally-related RNA-binding proteins, TDP-43 and FUS/TLS, caused familial forms of ALS n repeats in target RNAs Originally identified as a binding factor of the HIV transactivation region, TDP-43 is an essential gene that regulates mRNA splicing by virtue of binding to (UG)Transgenic overexpression of wild-type or ALS mutants of TDP-43 induced robust neurotoxicity in transgenic animals, including mice, worms, and Drosophila , which is involved in uncoupling components of the electron transport chain to reduce oxidative phosphorylation, as well as cytochrome P450 homologs. Another group of genes are involved in regulation of the cell cycle. These include the mitotic kinases polo and Cdc2 and the cell cycle regulatory phosphatase, string/cdc25, as well as multiple variants of spindle proteins and cyclins. Finally, a large number of genes upregulated in our microarray are targets of the Notch intercellular communication pathway. Notch signaling regulates neuronal differentiation; this pathway allows a signal-sending cell, fated to become a neuron, to prevent neuronal differentiation in adjacent target cells We identified a number of functionally related groups of genes that were changed in our microarray . One groBobA (brother of bearded), Tom (Twin of M4), and Hey (Hairy/E(spl) related with YRPW motif) were strongly upregulated in whole heads of GMR-Gal4/UAS-TDP-43 flies versus GMR-Gal4/+ control  protein that is implicated in axon pruning Profilin1 gene were recently identified to cause familial ALS Because aberrant Notch activation has been previously implicated in prion-induced neurotoxicity, we further investigated the cause and significance of Notch pathway upregulation in TDP-43 transgenic flies  control . Quantit43 flies . FinallyBecause GMR-Gal4 drives TDP-43 transgene constitutively beginning early in development, it is likely that many of the gene expression changes observed in this system were late events in TDP-43 pathogenesis and/or might be due to non-specific neurotoxicity. We therefore sought to identify gene expression changes that occur more directly due to TDP-43 expression using the Geneswitch (GS) inducible transgene expression system. In this approach, a fusion protein of Gal4 and the progesterone receptor (PR) is expressed only in neurons via the pan-neuronal ELAV promoter spindle B, white, and the Notch target gene, Hey) were also upregulated in GMR-Gal4/UAS-TDP-43 flies. Overall, 63 genes were differentially expressed between ELAV-GS-Gal4/UAS-TDP-43 flies in the absence or presence RU-486 induction . Forty-seven of these genes were upregulated in the presence of TDP-43 induction; 16 were downregulated. However, of the 63 differentially expressed genes only 22 had clearly annotated functions  it is also possible that the limited overlap reflects cell-type selective gene expression patterns following TDP-43 misexpression. The finding that string and greatwall. string encodes the Drosophila homolog of mammalian Cdc25 family members, phosphatases that activate cyclin-dependent kinases during the cell cycle. greatwall encodes a kinase belonging to the protein kinase A, G, C (AGC) family that regulates chromatin condensation and G2/M cell cycle progression string and greatwall. While mutation of greatwall had no effect, mutation of string appears to extend the lifespan of a subset of D42-Gal4/UAS-TDP-43 flies  and 3/6 genes in the Brd complex . Additionally, the Notch target gene Hey, which is not found in either genetic complex, was identified in both the GMR-Gal4/UAS-TDP-43 constitutive and the ELAV-GS-Gal4/UAS-TDP-43 inducible array, suggesting that this gene could be a direct target of TDP-43-dependent regulation. Consistent with this idea, we found that TDP-43 bound to in vitro-transcribed Hey RNA  complex, E(spl)rv1Hey mutations did not rescue shortened lifespan in D42-Gal4/UAS-TDP-43 flies, overexpression of Hey specifically in motor neurons, independent of TDP-43, was embryonic lethal  that substantially increased the lifespan of D42-Gal4/UAS-TDP-43 flies, while an adjacent and partially overlapping deletion from the Exelixis collection (Exel6055) had no effect, even though both spanned the Hey locus , Coilin, Socs44a , CG42516, Pbp49 (PSEA-binding protein of 49 kDa), and Pabp2 (poly-A binding protein 2). Two of the genes have potential direct links to TDP-43. Nup50 encodes a nuclear pore protein, which may be involved in the nuclear import and export of TDP-43. The mammalian homolog of Pabp2 is known to be in a complex with TDP-43 where they potentially coregulate mRNA Nup50 line and two P-element Pabp2 lines for modification of D42-Gal4/UAS-TDP-43 fly lifespan. Additionally, we also tested a P-element line for the gene Rs1, which is within the deletion region of Exel7095 just adjacent to this overlap region and encodes an RNA helicase.Given these results, we focused on the Exel7094 and Exel7095 lines, which contain 17 and 18 genes, respectively. Interestingly, these deletions partially overlap, with six genes in common; the other strong rescuing line, ED1725, also contains this overlap region, further implicating these genes. These six candidate genes are: Pabp2 and Rs1 mutation had little or no effect on D42-Gal4/UAS-TDP-43 fly lifespan. However, Nup50 mutation significantly rescued D42-Gal4/UAS-TDP-43 flies with 23% increase in median survival  mitigated TDP-43-induced neuron dysfunction and caused TDP-43 to accumulate in the cytoplasm of Drosophila neurons t larvae . AlthougTBPH extends D42-Gal4/UAS-TDP-43 fly lifespan  locus, did significantly rescue the lifespan of TDP-43 flies, suggesting that aberrant activation of Notch is contributing to neuronal dysfunction in these flies. This idea is further supported by the fact that at least one Notch gene studied, Hey, is upregulated within 72 h of TDP-43 overexpression in the ELAV-GS-Gal4/UAS-TDP-43 system, though monoallelelic mutation of Hey was insufficient to rescue neurotoxicity in D42-Gal4/UAS-TDP-43 flies. Interestingly, Hey appears to be a direct RNA target of TDP-43 (cpo), which was overexpressed in ELAV-GS-Gal4/UAS-TDP-43 flies, harbors an exceptionally large intron with an extensive TG repeat that is a strong candidate for binding by TDP-43 Additionally, we found that TDP-43 upregulated a number of Notch target genes, implying activation of this cell differentiation pathway f TDP-43  and we pC. elegansThe mechanism by which Notch activation contributes to TDP-43 toxicity is unknown. We speculate that upregulation of Notch–occurring due to direct TDP-43 RNA-binding activity or in response to TDP-43-mediated neuronal injury–inhibits axonal regrowth at compromised neuromuscular junctions in TDP-43 transgenic flies. Although further work is needed to address whether Notch activation contributes to axonal defects in TDP-43 transgenic flies, this model is compatible with recent findings showing that Notch inhibits regrowth of laser-transected axons in Drosophila with overexpression and knockout of TBPH arrow expression ; greatwallEP515/Tm6b (#17174); BobAf03024(#18609); Heyf06656(#18997); DlRevF10SerRX82(#6300); E(Spl)rv1(#199); Pabp2KG02359/CyO(#13212); Pabp201/SM5 (#9838); Rs1K09514(#10885); Nup50KG09557(#15198); Exel6055(#7537); Exel17094(#7859); Exel17095(#7860); Exel16057(#7539); Exel16058(#7540); Exel17096(#7863); DrosDel1735(#9275); DrosDel1742(#9276); DrosDel1725(#8941).The Transgenic flies were homogenized in high salt lysis buffer. Proteins were resolved by SDS-PAGE and detected via Western blotting as previously described For survival analysis, flies were aged at 25°C with no more than 25 flies per vial. Vials were changed on a 3–5 day cycle. Death events were scored on a daily basis. Rescue in longevity was defined as greater than 5% increase in median lifespan in addition to the statistical threshold according to the Log-rank (Mantel-Cox) test, p<0.05. In the survival graphs shown, each set of experiments was done in the same time period with the corresponding control subjects in order to control longevity variation caused by environmental factors. Both genders were used in the survival assay unless otherwise specified.Statistical analysis was performed using GraphPad Software, San Diego California USA. For survival analysis, Log-rank (Mantel-Cox) test was performed by comparing testing subjects to the D42-Gal4/UAS-TDP-43 or D42-Gal4/+ control. For qPCR analysis, data were presented as mean ±SEM and analyzed by Student’s t-test. The significance of statistical difference was denoted by NS (p>0.05); *(p<0.05); **(p<0.001); ***(p<0.0001).Drosophila whole genome microarrays were used, and the data analyzed using Arraystar.To examine relative gene expression, transgenic fly heads were homogenized in Trizol reagent. RNA was then purified using chloroform extraction and ethanol precipitation. Labeling of RNA and hybridization was performed at the UW Gene Expression Center. Nimblegen 4-plex ATGCGGCGGACGCAGTACAC, TCGCTGATGCTGTCACGCACAT; Nek2: GCCCTACATCTGGCGGGTGC, TTTGGCATTCCCGGCCGCAT; arrow: TCGACAAGTCGCTGGTGTGCG, CGGCCGAGATGCAGAGCTTGTT; couch potato: ACAGGATCTGCAGCAGGGTGTA, TGGGCTGTGGTTTGGGTTTGCT; three rows: TGCCACGCGTAGTTCCCTGC, TGACAGGCTTCAATGGATCGCTGA; cannonball: TGGGACTCGAAATTGCTTGCTCCG, TCGGTGGGCGATCCATCTGA; BobA: ACTGAGGAGGAGCACCAGAA, ACCTCCAAGGCACACAAGTC; Tom: CAACTCTCAACCGAGTGCAA, GGGTTTGACCAGTTTCCTCA; Hey: TCGGATGAATCGATGACAGA, TAAGATGCAGCAGCAACCAC; Ucp4B: AAATGCAAATGGAGGGACAG, GAACTCGGCAATCAGAAAGC; chickadee: TCACGGCTTGTGTTGTCTTC, AACTGATCAGCGGCTTTGAC; polo: GCGTATGCCCCATTTACACT, GTGCGGAAGTTCTTCTCCTG; string: ATTCCGGTGCGCATTTATAC, GAATAGCGGCTTTCTCGTTG.Total RNA from 20 fly heads per genotype was extracted by Trizol reagent (Invitrogen) and reverse transcribed by iScript cDNA synthesis kit (BioRad). Quantitative PCR was performed by using iTaq SYBR green supermix (BioRad) according to the manufacturer’s protocol. Real-time PCR data were collected using BioRad CFX96 platform. GAPDH was used as reference gene. Relative mRNA level was calculated by normalizing to the control sample. The following primers were used in our qPCR analysis (forward primer listed first): nACRbeta: In vitro RNA-binding assays were performed according to Kim et al. with modifications Hey gene locus was subcloned into the NotI/BglII sites of pDS129.36 vector. In vitro transcription of the plasmid was carried out as described 2 of UV. Streptavidin beads (Novagen) were added and incubated overnight at 4°C. The beads were washed with an RNA binding buffer and eluted with an SDS loading buffer. Both elute and input fraction was analyzed by SDS-PAGE and Western blotting with anti-HA antibodies (Sigma).Fly 3rd instar larval brains were dissected and immunostained as previously described Table S1(XLS)Click here for additional data file.Table S2(XLS)Click here for additional data file.


Repetitive Transcranial Magnetic Stimulation (rTMS)/ Deep-brain Magnetic Stimulation (DMS) is an effective therapy for various neuropsychiatric disorders including major depression disorder. The molecular and cellular mechanisms underlying the impacts of rTMS/DMS on the brain are not yet fully understood.Here we studied the effects of deep-brain magnetic stimulation to brain on the molecular and cellular level. We examined the adult hippocampal neurogenesis and hippocampal synaptic plasticity of rodent under stress conditions with deep-brain magnetic stimulation treatment. We found that DMS promotes adult hippocampal neurogenesis significantly and facilitates the development of adult new-born neurons. Remarkably, DMS exerts anti-depression effects in the learned helplessness mouse model and rescues hippocampal long-term plasticity impaired by restraint stress in rats. Moreover, DMS alleviates the stress response in a mouse model for Rett syndrome and prolongs the life span of these animals dramatically.Deep-brain magnetic stimulation greatly facilitates adult hippocampal neurogenesis and maturation, also alleviates depression and stress-related responses in animal models. Transcranial magnetic stimulation (TMS) is a non-invasive approach of brain stimulation, which utilizes an insulated coil placed over the scalp and induces neural activity within the brain -3. TMS iDepression is a leading cause of psychiatric disability worldwide. During the past decade, the adult neurogenesis hypothesis of depression has been widely accepted, which postulated that the decline in adult hippocampal neurogenesis contributes to the pathophysiology of depression, while the biogenesis of adult new-born neurons in the dentate gyrus (DG) of hippocampus is required for the beneficial effects of antidepressant treatment -15. In nHere we show that DMS increases the proliferation of hippocampal neural progenitor cells in the adult brain, promotes the dendritic complexity of new-born neurons and enhances the neuronal activity in hippocampus, indicated by the up-regulation of activity-dependent genes. Moreover, administration of DMS not only alleviates the depression and anxiety-associated behaviors of a mouse model for Rett syndrome, but also strikingly prolongs the lifespan of these animals.To examine the effects of DMS on adult hippocampal neurogenesis in wild-type animals, we applied the DMS protocol to 6 weeks old wild-type mice 20 minutes daily. The schematic illustration of the DMS device is shown in  labeling strategy to monitor postnatal hippocampal neurogenesis in the mice subjected to DMS with P1 or P5, respectively. For the thymidine analog incorporation assay, BrdU was administered into adult mice 4 times with 2-hour interval by intraperitoneal injection after 4 or 7 days treatment of DMS  was dysregulated in the hippocampal tissues from patients with depression, suggesting that MKP-1 serves as a molecular marker for depression . ConsistNext we investigated whether increased adult hippocampal neurogenesis induced by DMS plays a critical role in alleviation of depressive behavior by elimination of neurogenesis using gamma irradiation (IR). After applying IR after foot shock, we found that IR abolished the effect of DMS on alleviation of depressive behaviors Figure D,E. Meanin vivo electrophysiological recording. Indeed, LTP was found to be impaired in rats undergoing restraint stress  in the neural circuitry of hippocampus -31. The s Figure F. Notabls Figure F,G. Thesloss-of-function mutations of gene MECP2 (Methyl-CpG-binding protein 2) [mecp2308/y) mimics severe phenotypes of Rett syndrome patients, including elevated anxiety and stress responses [mecp2308/y mutation.Finally we would like to further examine whether DMS could alleviate the anxiety-related behaviors in other disease models. Rett syndrome is a severe neural developmental disorder, primarily caused by otein 2) -35. The esponses . Thus wemecp2308/y mouse and its wild type littermates, using light–dark transition paradigm. We found that mice carrying mecp2308/y mutant appeared to spend much less time in light compartment and thus to have increased anxiety levels  received FDA clearance for the treatment of adult patients with intractable depression in 2008. It is assumed that magnetic stimulation makes use of electromagnetic principles to alter neural activity non-invasively, and induces focal as well as network effects in the brain. However, how rTMS changes the cellular behavior and functional connectivity remains enigmatic.Here we developed a novel rTMS paradigm, referred as DMS, to help the learned helpless animals recuperate from stress and depression. The short-term application of DMS to depressed mice not only improved their stress-related behavior but also amazingly reversed the pathophysiology of major depression, indicated by the alteration of molecular marker and neuronal plasticity. As the neurogenic hypothesis of depression gains momentum over the last decade, more and more evidence confirmed that the waning and waxing of neurogenesis in the hippocampus are important causal factors in the precipitation of and recovery from depression, respectively. Our study provided several lines of evidences to prove that DMS treatment increases adult neurogenesis and new-born neuron maturation in vivo. First, short-term DMS administration rapidly induces the proliferation of adult neural progenitors in the subgranular zone of DG. The enhanced neurogenesis by DMS treatment occurs in both adult and senescent animals. Second, the application of DMS to adult animals promotes the dendritic development new-born granule neurons, suggesting the elevated incorporation of new-born neurons into existing hippocampal circuitry might be achieved by magnetic stimulation. Finally, DMS treatment is shown to up-regulate the neural activity, which is beneficial to increase the production and release of neurogenic niche factors such as FGF1b. Thus, our results support the notion that DMS is an efficient therapeutic treatment for major depression disorders and the antidepressant effects of DMS may rely on the elevation of hippocampal adult neurogenesis and the modulation of hippocampal synaptic plasticity.Mecp2 seems to completely rescue various symptoms in animal models of disease, the efficacy and safety issues of gene delivery remain controversial. Here our results suggest a modified DMS paradigm which may relieve the stress-related symptoms, as well as improve the life quality of Rett syndrome patients. Further study of how DMS treatment may contribute the neural plasticity of the central nervous system under disorder conditions are very critical for deepen our understanding of therapeutic efforts towards the cure of neurodevelopmental disorders and neuropsychiatric disorders. In order to develop easier and more effective therapy for patients with neurodevelopmental disorders and neuropsychiatric disorders, we need to better understand how DMS treatment contributes to the neural plasticity of central nervous system under pathological conditions.Neural activity and experience, presumably acting on this local niche, regulate multiple stages of adult neurogenesis, from neural progenitor proliferation to newborn neuron maturation, synaptic integration and survival. Our study shows a new non-invasive way to enhance adult hippocampal neurogenesis probably by changing the network activity of hippocampus and inducing a beneficial neurogenic niche in the subgranular zone. Repetitive electrical stimulation in adult hippocampal slices induces NMDA receptor-dependent LTP and high frequency stimulation results in persistent increase in synaptic strength . StudiesTaken together, we report that a new DMS paradigm rapidly induces adult hippocampal neurogenesis and promotes development of DG new-born neurons. Our study provides a new non-invasive way to enhance adult hippocampal neurogenesis within a short period of time and make it possible to study the role of adult hippocampal neurogenesis in a gain-of-function manner. More importantly, we provided evidences that this new DMS paradigm efficiently rescues behavioral phenotypes and gene expression profiles in learned helplessness mouse model, as well as restoring hippocampal LTP impaired by restraint stress in rats. These results support the notion that DMS is an efficient therapeutic treatment for major depression disorders and strongly suggest that the antidepressant effect of DMS on depression disorders may rely on the improvement of hippocampal adult neurogenesis and the correction of hippocampal synaptic plasticity.mecp2308/y mice (005439) and nestin-cre mice (003771) were purchased from Jackson lab; floxed mecp2 (011918) were purchased from MMRRC at UC Davis.Animals were group-housed with free access to water and food in the established animal houses, with a 12 hours light/dark cycle and a thermo-regulated environment. The use and care of animals complied with the guideline of the Biomedical Research Ethics Committee at the Shanghai Institutes for Biological Science, CAS. Adult (6–7 weeks old) male C57BL/6 mice  were used in all experiments except electrophysiology trials which were performed on male Sprague Dawley rats , weighing 220–250 g. For DMS treatment, mice with their cage were placed in DMS machine. Metal parts of cages were removed prior to DMS treatment. The applied program was described in Supplementary Figures. Briefly, twenty minutes successive trains of DMS were administered daily for different consecutive days depending on the purpose of experiments.14 days DMS were administered on retrovirus injected mice and 4 or 7 days DMS on Brdu labeled mice while 5 days DMS on acute induced depression mice. The control group conditions were identical to their DMS group but received sham stimulation. For electrophysiology experiments, control group received no treatment, Restraint stress (RS) group were restrained in the fixing cage 20 min each day for 7 days, RS + DMS group received 20 min DMS treatment in the fixing cage for 7 days. Electrophysiology studies were carried out 0.5 h later after the last restraint stress or DMS treatment.Experiments were carried out on rats anesthetized with pentobarbital , and core temperature was maintained at 37 ± 0.5°C. Recordings of field excitatory postsynaptic potentials (EPSPs) were made from the CA1 stratum radiatum of the hippocampus in response to ipsilateral stimulation of the Schaffer collateral/commissural pathway using techniques described previously ,47. Reco8vg/ml was necessary to label new-born neurons in the adult DG.We thank Drs. Hongjun Song (Johns Hopkins University School of Medicine) and Zhengang Yang  for providing retroviral constructs and supernatant containing retrovirus. Cell debris were removed from supernatant through 0.22 μm filter and the filtered supernatant was centrifuged at 65000 g at 4°Cfor 2 h. Then supernatant was removed and 50 μl PBS was used to re-suspend virus. We seed 293 T to determine the titer of virus. High titer virus of 10For stereotaxic injection, it is critical to locate the exact hippocampus neurogenic area. The mouse was mounted onto the stereotaxic frame, orienting the head straight in terms of anterior-posterior axis and horizontally. Then we shaved a small area by a trimmer, cut the skin over the scull, cleaned up the blood in the wound.The needle was firstly moved to the bregma and then moved posteriorly 2.0 mm and laterally 1.5 mm to the injection position. A small hole was made on the scull using an electric drill and the needle tip was moved down by 2.25 mm from the water level of the hole. 1 μl retrovirus solution was injected to one side.http://rsbweb.nih.gov/ij/) was used to trace the dendrites of new-born neuron. Then the dendritic length and tips number were auto-analyzed.For both Brdu and retrovirus stereotaxic injection statistics, each group  has 4–5 mice. Each mouse was collected 9–12 slices from dorsal to ventral region of DG. And 1 or 2 intact neurons were chosen from each slice for analysis. ImageJ software (Mice received four or seven days DMS treatment and Bronodeoxyuridine (Brdu)  was intraperitoneally (i.p.) injected into the control or DMS group mice 12 hours after the last DMS treatment. Mice were killed two hours later after the last i.p. injection.Mice were transcardially perfused with 0.1 M cold PBS followed by 4% paraformaldehyde (PFA) fixation. The brains were post-fixed for 12 hours in 4% PFA and dehydrated in 30% sugar solution for another 12 hours. Coronal sections of 40-μm thickness were cut on the freezing microtome (LEICA CM1950) and stored in PBS.For Brdu immunostaining, free-floating sections were incubated in 2 N HCl for 15 min at 37 degree then neutralized in 0.1 M boric acid solution (pH 8.5) for 10 min and washed by PBS for 3 times (each time for 5 minutes). Sections were incubated in mouse anti-Brdu antibody  at 4 degree overnight and washed again then incubated in CF555 conjugated donkey anti-mouse secondary antibody  for 2.5 hours at room temperature.For GFP immunostaining, free-floating sections were incubated in mouse anti-GFP antibody  at 4 degree overnight and washed again then incubated in CF488 conjugated goat anti-mouse secondary antibody  for 2.5 hours (dark) at room temperature.+ new-born neurons.Immunostaining sections were photographed in Z-series stacks using a Nikon A1 confocal microscope. NIH Image J with NeuronJ plugin was used to count the Brdu-immunoreactive cell number in the dentate gyrus and to quantify the area of the dentate gyrus, also to analyze total dendritic length of GFPP < 0.05) was assessed using the two-tailed Student’s t-test.The results were expressed as Mean ± S.E.M. Statistical significance (Mice were placed into the inescapable shock chamber. 360 scrambled foot shocks (0.6 mA) with varying duration (1-3s) and interval-episodes (1-15s) were delivered over two consecutive days. Control group did not receive foot shocks but were placed to the shock chamber with equal time. On the first and second days, 8 hours after foot shocks, mice were administered DMS. Then mice were administered DMS for another 3 days.Mice were placed individually in the testing cylinder (33 cm high × 10 cm in diameter) containing water with 20 cm depth. The procedures were conducted as two minutes pretest followed by four minutes test and both sessions were videotaped. Observers scored the immobility (floating passively with slight movements) time in the test four minutes.Mice were anesthetized with 0.7% Pentobarbital sodium at 10ul/g, and exposed to cranial irradiation at a field of 3.5×11mm above the hippocampus but other body parts were protected with a 75 mm lead shield. Caesium-137 was used as radioactive source operated by MDS Nordion GC-3000Elan. The dose rate was approximately 4 Gy per min. The procedure lasted 5 min and 30 sec, delivering a total of 20 Gy in the center.mecp2308/y mutant mice were carried out light–dark transition test after daily DMS stimulation for 5 month at the age of 5–6 month. The chamber is divided into two unequal compartments by an opaque shelter with a hole (of height 4 cm) at the floor level. The smaller compartment (18cm×27cm×30cm) is painted black and covered by an opaque lid while the larger compartment (27cm×27cm×30cm) is uncovered and is illuminated by ceiling room lights. Firstly, mice were gently placed into the dark compartment. Transition between the two compartments and time spent in the light compartment were automatically recorded by the Ethovision videotracking system in a 10 min session.Total RNA was collected from fresh hippocampus tissue for reverse transcription . 2 × SYBR Green Master Mix  and QIAGEN Rotor-Gene Q machine were used in real time PCR experiments. Mouse MKP-1 real time primer: forward: CGCTTCTCGGAAGGATATGCT; reverse: GTCAATAGCCTCGTTGAACCAG. Fgf1b primers were used as reported .The authors declare that they have no competing interests.LX and ZQ conceived and supervised the project. YZ performed the majority of the work. RRM and MT performed electrophysiology work on rat. ZFC, MH, ZRG, XL, DLD, participated in early stage of this work including immunohistochemistry and daily DMS treatment for animals. JW designed the DMS machine. XX and WHZ and CYL participated in testing and data collection of DMS machine on animals. ZQ wrote the manuscript, with contributions from other authors. All authors read and approved the final manuscript.Illustration of DMS equipment and magnetic fields. (A) The positions of animal and DMS machine are showed. (B-C) Illustration of magnetic vector potential generated during Uniform and Linear phase by DMS equipment. (D-E) Illustration of Uniform and Linear magnetic fields generated by this equipment.Click here for fileThe effect of DMS to adult hippocampal neurogenesis. A) Hippocampal DG Ki67 and BrdU immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: Ki67; Red BrdU staining. (B) Quantification of (A). (C) Hippocampal DG Ki67 immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: Ki67 staining. Lower right panel: Amplification of boxed area of (A) panel. (E) Hippocampal DG GFAP and BrdU immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: GFAP; Red BrdU staining. (F) Quantification of (E). (G) Hippocampal DG GFAP immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: GFAP staining. Lower right panel: Amplification of boxed area of (G) panel. Scale bar = 100 μm.Click here for fileThe effect of DMS to adult hippocampal neurogenesis (Continued). (A) Hippocampal DG Tbr2 and BrdU immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: Tbr2; Red BrdU staining. (B) Quantification of (A). (C) Hippocampal DG Tbr2 immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: Ki67 staining. Lower right panel: Amplification of boxed area of (C) panel. (E) Hippocampal DG DCX and BrdU immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: DCX; Red BrdU staining. (F) Quantification of (E). (G) Hippocampal DG DCX immunostaining on mice with continuous DMS with P5 treatment for 7 days. Blue: DAPI; Green: DCX staining. Lower right panel: Amplification of boxed area of (G) panel. Scale bar = 100 μm.Click here for fileThe effect of DMS to development of mature hippocampal DG neurons. Example pictures of Golgi staining for hippocampal DG neuron in control (A) and DMS (B) condition. (C) Golgi staining for spines on hippocampal DG neurons. (D) Quantification of (C). (E) Immunohistochemistry for c-fos protein in laternal habenula region after DMS with P5 treatment for 4 days. (F) Quantification of (E).Click here for fileThe effect of DMS on stress-induced depressive phenotypes in rodent models. (A) Outline of foot-shock-induced depressive behavioral paradigm and DMS treatment experiments. (B-E) Behavioral effects of learned helplessness mouse model with DMS treatment during various time points. Values represent mean (±) SEM  Outline of gamma irradiation (IR) and BrdU injection experimentsHippocampal DG BrdU immunostaining on control mice (B), and with IR treatment (C). Red: BrdU; Blue: DAPI staining. (D) Quantification of . Values represent mean (±) SEM . Scale bar = 100 μm.Click here for file


A hybrid structure (HS) made of one-dimensional ZnO nanorods (NRs) and a two-dimensional synthesized graphene sheet was successfully constructed in this study. The uniform ZnO NRs were obtained by hydrothermal method and grown on a graphene surface that had been transferred to a polyethylene terephthalate substrate. The HS exhibited high transmittance (approximately 75%) over the visible wavelength range, even after cyclic bending with a small radius of curvature. Raman spectroscopy and Hall measurement were carried out to verify the chemical composition and electrical properties of the structure. Stable electrical conductance of the ZnO NR/graphene HS was achieved, and increase in carrier mobility decreased the resistance of the ZnO-with-graphene sheet in comparison with bare ZnO NRs. For ent years -6. Becaus [2/Vs) , a strets [2/Vs) . Moreoves [2/Vs) -13. Manys [2/Vs) , vapor–ls [2/Vs) , and puls [2/Vs) . Howevers [2/Vs) , light-es [2/Vs) , solar cs [2/Vs) , and pies [2/Vs) . In addis [2/Vs) ,22. To oIn this work, 1D ZnO NRs were synthesized by hydrothermal method onto a 2D graphene sheet to form an HS. High transmittance over the visible light region was obtained after synthesizing the ZnO NRs, and the sample displayed excellent mechanical properties after bending with a small radius. Notably, we essayed a Hall measurement of the HS, which consisted of ZnO NRs/graphene on a polyethylene terephthalate (PET) substrate.3/HCl solution for etching to strip the Cu foil. After etching, we retrieved the sample from the FeCl3/HCl solution, transferred it onto the PET substrate, and cleaned it with deionized water. Finally, the PMMA/graphene was soaked in an acetone solution to remove the PMMA and then washed with isopropyl alcohol [Each graphene sheet was prepared on a Cu foil by CVD and then spin-coated with a protective layer of poly(methyl methacrylate) (PMMA). The PMMA/graphene/Cu foil sample was immersed into an FeCl alcohol . A 100-nTo investigate the 3D hybrid nanostructure formed by combining 1D ZnO NRs with2D graphene, the ZnO seed layer was coated onto the graphene surface and annealed at a suitable temperature for the growth of ZnO NRs through hydrothermal method. The ZnO NRs presented here were obtained with a solution-based chemical synthesis. In a solution containing zinc nitrate hexahydrate and HMTA, hydroxyl ions were released through the thermal decomposition of the HMTA and reacted with zinc ions to form ZnO. The synthesis can be summarized in the following reactions:2− terminated surface, onto which the precursor molecules in the vicinity tend to be adsorbed [2+ cations usually form hydroxyl complexes as the precursors of ZnO.To observe the growth of the ZnO NRs on the graphene sheet, FE-SEM images were taken, as shown in Figure adsorbed . SimultaA concerning feature of the hybrid structure is that, although ZnO and graphene exhibit good optical transmittance in the visible spectral range, the scattering of light by ZnO NRs is suspected to lead to a decrease in transmittance to a certain extent. The optical transmittance of the ZnO NR/graphene hybrid structure was estimated by fabricating the structures on PET substrates. Figure −1 corresponds to the E2 mode of the ZnO hexagonal wurtzite structure [−1 is attributed to the E2g phonon of C sp2 atoms, and the D peak at approximately 1,350 cm−1 is accredited to local defects and disorder, such as the edges of graphene and graphite platelets [−1 has also been found that may be related to the formation and number of layers of the graphene [−1, the D peak at 1,353 cm−1, the G peak at 1,586 cm−1, and the 2D peak at 2,690 cm−1. The formation of few-layer graphene was verified by the intensity ratio of the G peak to that of the 2D peak, which was approximately 1 to 1.5, and by the position of the 2D peak [Raman spectroscopy is a promising method for inspecting the ordered/disordered crystal structures of carbonaceous materials and the different layer characteristics of graphene. It was also used to prove that ZnO nanostructures were grown on graphene surface. The usual peak at 437 to 439 cmtructure . The G platelets ,28. Moregraphene . Figure  2D peak ,31. In a2/Vs, 18 times higher than that of the ZnO film. It has been reported that there is an important relationship between mobility and sheet resistance because the carriers can be easily scattered by lattice defects [The device structure was fabricated as shown in Figure  defects . AccordiUniform ZnO NRs were obtained by hydrothermal method and grown on a graphene surface that had been transferred to a PET substrate. The ZnO NR/graphene HS exhibited high transmittance (approximately 75%) over the visible wavelength range, even after cyclic bending with a small radius of curvature. Stable electrical conductance of the ZnO NR/graphene HS was achieved, and the improvement of the ZnO sheet resistance by the incorporation of the graphene sheet can be attributed to the resultant increase in carrier mobility.The authors declare that they have no competing interests.RJC was the principal investigator and is also the corresponding author of this paper. ZCL and PKY were in charge of material preparation and characterization. KYL contributed to data analysis. SFJ and PWC contributed to graphene synthesis. All authors collaborated to complete this research and to compile this manuscript. All authors read and approved the final manuscript.


Myocardial differentiation is associated with the activation and expression of an array of cardiac specific genes. However, the transcriptional networks that control cardiac gene expression are not completely understood. Myocardin is a cardiac and smooth muscle-specific expressed transcriptional coactivator of Serum Response Factor (SRF) and is able to potently activate cardiac and smooth muscle gene expression during development. We hypothesize that myocardin discriminates between cardiac and smooth muscle specific genes by associating with distinct co-factors. Here, we show that myocardin directly interacts with Tbx5, a member of the T-box family of transcription factors involved in the Holt-Oram syndrome. Tbx5 synergizes with myocardin to activate expression of the cardiac specific genes atrial natriuretic factor (ANF) and alpha myosin heavy chain (α-MHC), but not that of smooth muscle specific genes SM22 or smooth muscle myosin heavy chain (SM-MHC). We found that this synergistic activation of shared target genes is dependent on the binding sites for Tbx5, T-box factor-Binding Elements (TBEs). Myocardin and Tbx5 physically interact and their interaction domains were mapped to the basic domain and the coil domain of myocardin and Tbx5, respectively. Our analysis demonstrates that the Tbx5G80R mutation, which leads to the Holt-Oram syndrome in humans, failed to synergize with myocardin to activate cardiac gene expression. These data uncover a key role for Tbx5 and myocardin in establishing the transcriptional foundation for cardiac gene activation and suggest that the interaction of myocardin and Tbx5 maybe involved in cardiac development and diseases. Heart formation is one of the earliest morphogenic events during animal development. The development of the heart is under the control of a complex transcriptional network, and dys-regulation of the expression or genetic mutation of cardiac genes often leads to abnormality in the morphology and the function of the heart Whereas haploinsufficiency of Tbx5 in transgenic mice mimics the HOS, homologous deletion of the Tbx5 gene led to early embryonic lethality Tbx5 cooperates with other transcriptional factors to regulate the expression of its target genes 6GG or CArG box Myocardin is a potent transcription cofactor for serum response factor (SRF) and is expressed in smooth and cardiac muscles Myocardin potently activates both cardiac and smooth muscle gene expression in a CArG-dependent manner To test whether Tbx5 and myocardin could synergistically activate the ANF promoter in cardiomyocytes, we transfected both myocardin and Tbx5 expression plasmids, together with the ANF luciferase reporter, into primarily cultured neonatal rat cardiomyocytes. As shown in The promoter/enhancer of the ANF gene contains multiple cis-elements for several transcription factors, including that of Nkx2.5-binding site (NKE), MEF2-binding site (MEF), GATA-element (GATA), T-box factor-Binding Element (TBE) as well as SRF-binding site (CArG) Next, we mutated the TBE site from the ANF115 minimal promoter and tested the activities of myocardin and Tbx5 to activate the reporter gene. Interestingly, we found that the TBE mutation abolished the synergy between myocardin and Tbx5 to activate this reporter . ConversTo test whether Tbx5 and myocardin interact directly, co-immunoprecipitation (Co-IP) assays were performed with cell extracts in which Myc- or Flag-tagged myocardin and Tbx5 were overexpressed in COS-7 cells. When Flag-Tbx5 was precipitated using anti-Flag antibodies, Tbx5-associated Myc-myocardin was readily detected by Western blotting . No suchWe used an alternative approach to confirm the interaction between myocardin and Tbx5. The full-length sequence of the myocardin gene was fused with the DNA-binding domain of the Gal4 gene to create a Gal4-myocardin fusion protein . The Gal4-myocardin was then transfected with the pL8G5-luciferase reporter in which the luciferase reporter is under the control of the Gal4 binding element UAS. Gal4-myocardin, but not pcDNA-Tbx5 or pcDNA-myocardin, potently activated the reporter gene . AdditioTo map the domains of myocardin that are responsible for its interaction with Tbx5, truncated myocardin mutants were generated and their potential interaction with Tbx5 was examined by co-immunoprecipitation (Co-IP) assays. The truncation mutants from the N-terminus up to the basic domain of myocardin can still interact with Tbx5, while further deletion past the basic domain abolished this capability . These rNext, we attempted to map the domains of the Tbx5 protein that mediate its interaction with myocardin. Series of Tbx5 deletion mutants were generated to test their interaction with myocardin using co-immunoprecipitation (Co-IP) assays. Our results showed that the N-terminal region (aa 1-100) was sufficient to interact with myocardin . SimilarMyocardin associates with the CArG boxes via the formation of a CArG/SRF/myocardin ternary complex Next, we performed EMSAs to test whether Tbx5 could affect the formation of myocardin/SRF/CArG ternary complex. When SRF was incubated with the CArG box probe, they form CArG/SRF complex lane 2, . InclusiTbx5 has been identified as the Holt-Oram syndrome (HOS) causative gene. Multiple missense mutations of the human Tbx5 gene have been identified from individuals with HOS In this study we demonstrated that Tbx5 and myocardin synergistically activate cardiac gene expression. This activity is specific to cardiac tissue as these two proteins failed to similarly regulate smooth muscle genes. Our results therefore reveal a transcriptional regulatory network for cardiac gene expression.How do Tbx5 and myocardin cooperate to activate cardiac gene expression? We found that myocardin and Tbx5 interact directly and we mapped their interaction domains to the N-termini of these two proteins. It was known that myocardin does not bind to DNA directly, but instead associates with SRF to form a myocardin/SRF/CArG ternary complex on the promoters/enhancers of its target genes Myocardin is expressed in both cardiac and smooth muscle cells and is capable of activating both cardiac and smooth muscle-specific gene expression. How is the specificity defined? We observed that the interaction between Tbx5 and myocardin helps mediate distinct smooth muscle and cardiac gene expression profiles. In differentiating smooth muscle cells, Tbx5 staining showed that it was present only in the cytoplasm, instead of the nucleus Tbx5 belongs to the family of T-box containing transcription factors. Several members of this family of transcription factors are also expressed in the heart. Interestingly, Tbx2 was shown to repress the expression of the ANF gene, in part, by impairing the recruitment of Nkx2.5 to the TBE or the NKE Multiple missense mutations of the human Tbx5 gene have been associated with the HOS. Interestingly, these mutations do not uniformly impair the function of Tbx5 in the same manner. For example, Tbx5 mutations G80R and R237Q have different effects on its synergistic transactivation of the cardiac-specific ANF gene, which will likely translate as subtle differences in the phenotype and severity of the HOS Myocardin and SRF-expression plasmids were described previously COS-7 cells were maintained in DMEM with 10% FBS as described Neonatal rat cardiomyocytes were prepared as described Co-IP and Western blot analyses were performed as described EMSAs were performed as described previously


Furthermore, L1-KD neurons showed longer and more undulated leading processes during translocation through the primitive cortical zone. The curvature index, a quantitative index for curvilinearity, as well as the length of the leading process, were increased, whereas the somal movement was decreased in L1-KD neurons during terminal translocation in the PCZ. These results suggest that L1 has a role in radial migration of cortical neurons.L1cam (L1) is a cell adhesion molecule associated with a spectrum of human neurological diseases, the most well-known being X-linked hydrocephalus. Although we recently demonstrated that L1 plays an important role in neuronal migration during cortical histogenesis, the mechanisms of delayed migration have still not been clarified. In this study, we found that cell locomotion in the intermediate zone and terminal translocation in the primitive cortical zone (PCZ) were affected by L1-knockdown (L1-KD). Time-lapse analyses revealed that L1-KD neurons produced by  The neural cell adhesion molecule L1cam (L1) is one of the adhesion molecules expressed in the developing central and peripheral nervous system in vitro was perturbed by the addition of anti-L1 antibody However, very few reports have supported the concept of neuronal migration in utero electroporation, we showed that in vivo L1-knockdown (KD) perturbed neuronal radial migration, accompanied with alterations in the expression of some transcription factors in the cortical plate Previously, using a shRNA strategy combined with All of the animal experiments conducted in this study were approved by the Institutional Review Board for Biomedical Research using Animals at Kyoto Prefectural University of Medicine, and the animals were handled according to the Institutional Guidelines and Regulations. Pregnant C57BL/6J mice were purchased from CLEA Co. Ltd.  or SHIMIZU Laboratory Supplies Co. Ltd., Japan SLC. . The day a vaginal plug was detected was designated as embryonic day 0.5 (E0.5).scrambled sequence) were cloned in linearized pGeneClipTM hMGFP Vector (Promega).We prepared shRNA expression plasmid as previously described The shRNA sequence is as follows.anti-L1 shRNA (shRNA2 and shRNA5):AGCCTTACCAGAAGGGAAAGT (location 3406–3426)shRNA2; GTGCTTCAGGATGAACGATTT (location 1652–1672)shRNA5; GGAATCTCATTCGATGCATACscramble shRNA (shNC); in utero electroporation . Pregnant mice were anesthetized by intraperitoneal injection of Ketamine , Xylazine  and saline cocktail (5/3/40 v/v/v 9.75 ml/kg bodyweight). After disinfection with 70% ethanol, a 2-cm midline laparotomy was performed, and the uterine horns were resected. In order to conduct the plasmid DNA microinjection, 0.85-mm internal diameter glass capillary tubes  were employed, using a micropipette puller PB-7 . After the glass capillary tubes were pulled out, the pipettes were broken at approximately 70–80 μm on the external diameter (at ca.1.0 cm from the shoulder of the pipette) by pinching with forceps, and then ground to make a 30° bevel. One μl of plasmid DNA solution (1.0 mg/ml) in endotoxin free TE buffer, with 0.005% trypan blue added to aid targeting, was injected into the lateral ventricles using a mouth-controlled pipette system  Embryos were harvested after 1 or 2 days, i.e., at E14.5 or E15.5. The brains were removed and transferred into a solution of 1.2% agarose  in phosphate-buffered saline  at 37°C and the agar was subsequently hardened on ice for 5 minutes. Each brain was coronally cut at 200 μm with a Vibratome .2 incubator at 37°C for 10 minutes, and kept in Neurobasal medium supplemented with B27 , 0.5 mM L-glutamine, and antibiotics.The following procedures were similar to those reported previously Z-section images were acquired, and all focal planes were merged using EZ-C1 software. The migrating neurons were traced and analyzed using NIH ImageJ software and the Manual Tracking plugin.The slices were observed with a laser confocal scanning microscope with epifluorescence, as well as differential interference contrast (DIC) optics . Fluorescent microscopic images were obtained sequentially at 10-minute intervals for 24–72 hours. Approximately 20 x and y values for each cell. The distances between the starting point  and the end point  were derived using the Pythagorean Theorem; the square root of the quantity of (x2 – x1)2 + (y2 – y1)2. The velocity was defined as the distance between starting and end points divided by time over the observation period The images acquired were analyzed to trace the neuronal migration and the velocity of the migration was measured with EZ-C1 software and the NIH ImageJ plugin . The position of the center of the soma was plotted in each image, and displacement of the soma was defined by the difference in location between the starting point and the end point. The locations were expressed as the Z-section images, were merged.The curvature index was defined as “the curvilinear length” in micrometers of a leading process divided by “the linear distance” between the ends of the leading process in utero electroporation, and then reached a point underneath the CP at E15.5. In contrast, L1-KD neurons mostly stayed in IZ at E15.5. Thus, in order to analyze the dynamic radial migration in IZ, we observed cultured slices from E14.5 to E17.5 (t-test)   shRNA:shNC, migrated into the IZ at E14.5, one day after the to E17.5 . Almost (t-test) . Interes t-test) movie S2in utero electroporation, when most of the electroporated cells were just migrating toward the pial surface in the PCZ .The L1-KD neurons in the upper CP showed an unusual morphology, although there were no significant differences in the morphology or migration speed in the deeper CP, compared with the control neurons (data not shown). In order to assess this phenomenon, cultured slices were prepared at E15.5, two days after the  the PCZ . Althoug the PCZ , the lea the PCZ . The meaP = 0.0431, unpaired Student's t-test). These observations indicate that the leading process of a L1-KD neuron tends to be longer and more undulated than that of a control neuron. It is likely, therefore, that the process of terminal translocation is affected by L1-KD.In order to analyze the aberrant morphology of the leading processes of the L1-KD neurons, we compared the extent of undulation by the curvature index. The mean curvature index of the leading processes in the L1-KD neurons was 1.073±0.026 (n = 11), whereas that of the leading processes in the control neurons was 1.019±0.006 (n = 13), showing a significantly higher curvature index in the L1-KD neurons  were cultured under time-lapse microscopy for up to 24 hours. The time-lapse observations showed that most of the control neurons demonstrated continuous translocation of the cell body toward the pial surface. The leading processes of the control neurons were straight and shortened in a smooth process along with somal translocation . HoweverP = 0.7870, paired Student's t-test) , and compared those leading processes to neurons 1 h after contact (1 h). In  t-test) . L1-KD n t-test) . In each t-test) , suggestx-axis value was the distance of each somal movement, while the y-axis value was the shortened length (Δ Leading process length) of its leading process, as shown in x-y distributions for a sample population of control and L1-KD neurons in the scatter plots were illustrated in We further evaluated the distance of the somal translocation in the PCZ. The in utero electroporation of L1-shRNA at E13.5.Recently, we demonstrated that L1cam-downregulation perturbed neuronal migration in developing murine cerebral cortex trans and perhaps in the cis, as well as heterophilic binding of L1 to its ligands would be lost in L1-KD neurons, because full-length L1 is deficient in the cell membrane of those neurons. It is thought that these homophilic and heterophilic interactions play critical roles in the developing brain by inducing sustained activation of ERK1/2, leading to an acceleration of cell migration and L1-dependent gene expression Our time-lapse analyses revealed that the locomotion of the L1-KD neurons was significantly decreased in the IZ, which would underlie our previous findings that significantly fewer L1-KD neurons migrated into the cortical plate at E16.5, compared with the control neurons The leading processes of the L1-KD neurons were significantly longer and undulated in the upper CP. It has been reported that integrins, particularly integrin alpha3-beta1 or alpha5-beta1, seemed to mediate the interaction between the tip of the leading process and the substrate, at least in the cerebral cortex in vivo, although the detailed molecular mechanisms remain unknown. We showed that L1-KD neurons can be classified into three groups according to the data on the curvature index and the process length. It is anticipated that these differences might reflect the extent of L1-KD in each cell, and/or the paracrine cue from heterophilic L1-integrin interactions.During the initial phase of terminal translocation, control neurons moved quickly into the PCZ associated with the smooth retraction of the leading process. However, in L1-KD neurons, the soma remained unmoved with a longer leading process. As previously reported Since studies have shown that knock-in mice in which the sixth Ig domain of L1 was deleted (L1-6D) The mechanism whereby abnormal radial migration results in hydrocephalus when L1cam is deficient is still not clear. In L1-6D mice, cerebral cortex showed a decreased number of neurons and a reduced amount of subcortical white matter associated with hydrocephalus. It is tempting to speculate that affected radial migration might subsequently induce cell loss in the cerebral cortex, which lost appropriate connections with subcortical or cortical projections due to the time lag between the migrating L1-KD neurons and the arrival of the incoming fibers. Further studies are required to evaluate this hypothesis.Finally, we propose the schema of a novel role of L1 in cortical migration during development . L1-KD iin utero electroporation of shRNA targeting L1 in murine corticogenesis. Time-lapse analyses revealed that L1-KD neurons showed decreased locomotion velocity and affected terminal translocation with aberrant leading processes and somal movement. Although the underlying molecular mechanisms remain unsolved, L1 plays an important role not only in axonal guidance, but also in neuronal migration during neocortical development.We analyzed the effect of L1-knockdown using Figure S1Neuro2a at 24h post-electroporation. Both shRNA2 and shRNA5 efficiently downregulated L1cam mRNA.(TIF)Click here for additional data file.Figure S2Both shRNAs downregulated L1cam at the protein level in Neuro2a. Western blot analyses revealed that both shRNA2 and shRNA5 efficiently downregulated L1cam protein in Neuro2a.(TIF)Click here for additional data file.Figure S3Cell surface L1cam was efficiently downregulated in Neuro2a. The expression of L1cam was significantly reduced on the cell membrane of Neuro2a cells transfected by shRNA2 or shRNA5.(TIF)Click here for additional data file.Figure S4Radial migration of cortical neurons was disrupted by in utero electroporation of shRNA5 as well as shRNA2.(TIF)Click here for additional data file.Text S1Materials and Methods of supporting experiments and Figure Legends of supporting figures and movies are described.(DOCX)Click here for additional data file.Movie S1This movie shows the time-lapse transition of migrating neurons expressing control-shRNA (shNC) from the intermediate zone (IZ) into the cortical plate (CP). The neurons with a single leading process migrate quickly in the IZ.(AVI)Click here for additional data file.Movie S2This movie shows the time-lapse transition of migrating neurons expressing L1cam-shRNA (shL1) from the intermediate zone (IZ) into the cortical plate (CP). The neurons migrate slowly and the somas often stay in the IZ.(AVI)Click here for additional data file.Movie S3This movie shows the time-lapse transition of migrating neurons expressing control-shRNA (shNC) in the cortical plate (CP). The leading processes of the control neurons are straight and shortened in a smooth process along with somal translocation.(AVI)Click here for additional data file.Movie S4This movie shows the time-lapse transition of migrating neurons expressing L1cam-shRNA (shL1) in the cortical plate (CP). The L1-KD neurons migrate with undulated long leading processes and some of the neurons transiently cease migration.(AVI)Click here for additional data file.


Terrestrial microbial decomposer communities thrive on a wide range of organic matter types that rarely ever meet their elemental demands. In this review we synthesize the current state-of-the-art of microbial adaptations to resource stoichiometry, in order to gain a deeper understanding of the interactions between heterotrophic microbial communities and their chemical environment. The stoichiometric imbalance between microbial communities and their organic substrates generally decreases from wood to leaf litter and further to topsoil and subsoil organic matter. Microbial communities can respond to these imbalances in four ways: first, they adapt their biomass composition toward their resource in a non-homeostatic behavior. Such changes are, however, only moderate, and occur mainly because of changes in microbial community structure and less so due to cellular storage of elements in excess. Second, microbial communities can mobilize resources that meet their elemental demand by producing specific extracellular enzymes, which, in turn, is restricted by the C and N requirement for enzyme production itself. Third, microbes can regulate their element use efficiencies , such that they release elements in excess depending on their demand . Fourth, diazotrophic bacteria and saprotrophic fungi may trigger the input of external N and P to decomposer communities. Theoretical considerations show that adjustments in element use efficiencies may be the most important mechanism by which microbes regulate their biomass stoichiometry. This review summarizes different views on how microbes cope with imbalanced supply of C, N and P, thereby providing a framework for integrating and linking microbial adaptation to resource imbalances to ecosystem scale fluxes across scales and ecosystems. Soil microbial communities are key players in global biogeochemical cycles, regulating core ecosystem processes such as organic matter decomposition, soil C sequestration and nutrient recycling. Microbial decomposers release extracellular enzymes (EEs), which deconstruct plant macromolecules and ultimately liberate soluble substrates for microbial uptake . In turnMicroorganisms can be linked to these ecosystem processes through the theory of ecological stoichiometry, which has emerged as a powerful tool for studying the functioning of both aquatic and terrestrial ecosystems . Soil isOn a global scale, the stoichiometry of soil microbial biomass has been shown to be more constrained in range and variance compared to its resource, which implies that microbes are largely homeostatic in terms of their biomass C:N:P . HomeostFigure 1), including (i) plasticity of microbial biomass C:N:P, (ii) production of EEs as C and nutrient acquisition strategy, (iii) adjustments in microbial element use efficiencies, and (iv) input of external nutrients by N-fixing prokaryotes or by saprotrophic fungi.The key questions regarding stoichiometric imbalances, i.e., how soil microbes regulate their C:N:P homeostasis and how this in turn affects the processing of organic matter, are still insufficiently understood. The aim of this review is to synthesize the current knowledge on the mechanisms that allow terrestrial microbial communities to thrive in a stoichiometrically imbalanced world. First, we review the spatial and temporal variability of resource stoichiometry and its effect on microbial biomass stoichiometry. Second, we present a mechanistic framework of how soil microbial communities cope with resource imbalances , exudates and soil organic matter (SOM). Decomposable plant products  exhibit much wider and more variable C:N:P ratios than microbes [see data compiled in le 1 and ; all C:Nle 1 and , or 3,05le 1 and . Leaf lile 1 and , and evele 1 and . Therefole 1 and . Neverthle 1 and . Woody dTable 1; . Wood ofTable 1; , and troAdding to this complexity, the availability and type of plant necromass varies seasonally e.g., . For insTable 2, dataset of Table 2). Significant differences in soil C:N:P along the SOM decomposition continuum therefore become evident, despite soils being stoichiometrically more constrained than plant detritus. As a result of the spatio-temporal variability in the availability and type of plant detritus and SOM, terrestrial microbial communities must cope with large spatio-temporal variability in resource C:N:P ratios.Soil organic matter, which is the result of microbial decomposition of plant necromass, is another major resource available for heterotrophic microorganisms. Microbial residues play an important role in the formation of SOM, as plant resources are converted to microbial biomass during decomposition, comprising a large proportion of SOM e.g., . ConsequThe range of biomass C, N, and P concentrations in soil microbial communities spans several orders of magnitude, although the linear and isometric relationships between these elements suggest a fixed, or at least a highly constrained, microbial C:N:P ratio . A meta-Table 2, shows that microbial biomass C:N and C:P, but not N:P, are positively correlated with SOC content. This points to indirect control of SOC content on microbial stoichiometry, i.e., soil C:N and C:P ratios increase with SOC content, and microbial C:N and C:P as well. Moreover, this pattern indicates that in soil with high SOC content and high C:N (C:P), N or P may become limiting while C availability is high, thereby increasing microbial biomass C:N:P. On a global scale, microbial C:N:P ratios were not (Table 2), indicating a large degree of stoichiometric homeostasis of soil microbial communities. Table 2), though the relationship between resource C:N and microbial C:N is strongly dampened in relation to the variability in resource elemental ratios. For example, a resource C:N ratio of 10 would predict a microbial biomass C:N of 8.1, but a 10-fold higher resource C:N of 100 would only lead to a 1.3-fold increase in microbial biomass C:N to 10.9.Biome- and ecosystem-level differences in microbial C:N:P ratios may be explained by co-variation with resource stoichiometry, or by soil type, soil pH, soil C content and/or latitude . were not , or onlywere not ; Table 2Table 1). We estimated the average global C:N imbalance – calculated as resource C:N divided by microbial biomass C:N – ranging from 2 (SOM) to values between 10 and 14 , and up to 50 (wood). The C:P imbalances are higher than the C:N imbalances, ranging from 7 (SOM), to 73 (litter), 100 (root litter) and 336 (wood). Smallest imbalances are found for N:P, ranging from 3 in SOM to 7 in plant detritus . These patterns in resource stoichiometry raise the question of how soil microbial communities cope with such large elemental imbalances.As a consequence of the relatively low variation in microbial biomass C:N:P, large C:N:P imbalances arise between different types of decomposable organic matter and decomposer communities : First, microbes can adjust their biomass C:N:P ratios to meet the elemental composition of their substrates; second, they change the elemental composition of their immediate substrates by producing EEs that preferentially deconstruct polymers that meet their demand for C and nutrients; third, after substrate uptake, they mineralize and excrete the elements in excess of their demand by regulating their element use efficiencies; and fourth, diazotrophic bacteria and saprotrophic fungi may trigger the input of external N and P to decomposer communities. Below, we review these four mechanisms in more detail:There are four main mechanisms for microbial decomposers to cope with elemental imbalances and thrive on substrates that do not meet their elemental demand for growth ((1)Non-homeostatic behavior of microbes reduces the stoichiometric imbalance to their resource. As demonstrated above, small adjustments of microbial biomass C:N:P ratios to resource C:N:P do indeed occur, and may help as an adaptive mechanism of microbial communities to stoichiometric imbalances. Adjustment of microbial biomass C:N:P to resource C:N:P ratios can occur due to two main mechanisms: first, microbial storage of elements in excess, leading to a convergence between the biomass and resource stoichiometries, and second, shifts in microbial community structure and concomitant shifts in biomass stoichiometry. Whereas the first mechanism would be a physiological adjustment of the stoichiometry of microorganisms, requiring that microbes are non-homeostatic, the latter is not a true adjustment, as a community change may have very different reasons.Despite the lack of information regarding changes in microbial C, N, or P storage, analyses of cultured organisms have shown that P storage in polyphosphates by bacteria and fungi is related to increased P availability , and C sin situ measurements of single-cell C:N:P ratios in soils by techniques such as X-ray microanalysis or NanoSIMS coupled with phylogenetic classification at broad scale  or fine scale Microbes adjust their EE production, to maximize the mobilization of substrates rich in the limiting element. Microbial regulation of EE production is complex, including constitutive secretion of low levels of EE (most likely to detect suitable substrates), induction of EE synthesis in response to increasing availability of complex substrates, and feedback inhibition of EE activity by their products  . In contIf microbes indeed reduce the elemental imbalance between bulk substrate (soil or litter) and their biomass composition through the release of EE, we would expect that the C:N:P ratio of dissolved organic matter (DOM) is closer to the stoichiometry of microbial biomass than the bulk resource. However, C:N ratios of DOM (DOC:DON) in soil water and leachates are often higher than C:N ratios of SOM and are only weakly, or not significantly, related across sites . Soil miin situ by soil microbes is to infer microbial C:N:P uptake from measured C:N:P ratios of mineralization fluxes, microbial biomass and bulk soil. However, this approach relies strongly on knowledge about microbial element use efficiencies Microbes excrete elements that are present in excess in their resource compared to their biomass composition by adjusting their element use efficiencies. If microbes cannot change the elemental composition of their immediate substrates or adjust their biomass composition accordingly, they could take up whatever substrate is available and release elements in excess of their requirement, while keeping those in short supply for growth. Microbes are able to achieve this balance by regulating their element use efficiencies, such as the carbon use efficiency . Microbial CUE is defined as the ratio of C invested in growth (new biomass production) over total C taken up . Progressively lower C:N and C:P ratios from litter to topsoil, and further to subsoil, correspond also to a decreasing C availability in relation to N and P (increasing C limitation). In order to cope with such differences in resource elemental composition, microbes are expected to adjust their element use efficiencies accordingly. For example, assuming strict homeostasis and negligible adjustment of EE, when microbes with a biomass C:N of 7  decompose plant litter with a relatively high C:N ratio of 70, they must release 63 units of C as CO2 per unit of N invested in growth, yielding a low CUE of about 0.1. By contrast, if SOM with a C:N close to that of the microbial biomass would be decomposed , CUE would converge toward the theoretical maximum of about 0.6 (The considerable differences in C:N:P stoichiometry between plant detritus (litter) and SOM result in distinct elemental limitations for the respective microbial communities , with broad implications for soil C sequestration and N losses from terrestrial ecosystems. This means that microbial homeostasis achieved through adaptations in microbial element use efficiencies is expected to cause a strong positive relationship between resource C:N:P and mineralization flux C:N:P , suggestive of increasing microbial NUE and PUE at high resource C:N and C:P ratios while microbial communities were homeostatic with respect to these element ratios . Moreove(4) Nitrogen-fixing prokaryotes and saprotrophic fungi increase the N and P availability by inputs from external sources. There are two major pathways for input of N or P to the decomposing material from external sources, (i) N fixation by prokaryotes and (ii) fungal transfer of N or P from nutrient-rich patches. Prokaryotic N fixers (diazotrophs) convert atmospheric N2 to ammonia, a highly energy demanding process that is under strict physiological control : (i) plasticity of microbial biomass C:N:P, (ii) compensatory regulation of EE production as C and nutrient acquisition strategy, (iii) adjustments in microbial element use efficiencies and (iv) input of external nutrients by diazotrophic bacteria or saprotrophic fungi. Although the four described mechanisms clearly operate in parallel, their contributions on spatial, temporal and ecosystem scales remain underexplored. The wealth of measurements of microbial biomass C:N:P ratios sets limits to the non-homeostatic behavior of soil microbial communities. Growth of terrestrial decomposer communities on many types of resources with either very wide C:nutrient ratios  or very low C:nutrient ratios  cannot be achieved solely through non-homeostatic behavior. Therefore, adaptation of microbial element use efficiencies and compensatory regulation of EE production are expected to contribute significantly to the adaptation of microorganisms to chemically diverse environments, together with external inputs of nutrients mediated by subgroups of the microbial community.In this review, we demonstrate that terrestrial microbial communities have to cope with a large spatio-temporal variability in resource stoichiometry, which can result in strong imbalances between resource composition and elemental demands by soil microbes. We highlight four major mechanisms that allow microbial communities to adapt to these environmental constraints (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.