Correspondingly, treatment with exogenous BDNF increased cell proliferation. In in vivo studies in athymic nude mice bearing multidrug-resistant uterine sarcoma cell tumors, we demonstrate suppression of tumor growth by treatment with K252a, but not K252b, as reflected by decreased cell proliferation and increased levels of apoptosis and caspase-3/7 activities without obvious side effects. Our findings indicated that endogenous signaling of the TrkB pathway contributed to uterine sarcoma cell growth, and inhibition of TrkB signaling in these tumors could provide a novel medical therapy for patients with uterine sarcomas.Uterine leiomyosarcoma is an aggressive tumor typically found at advanced stages due to difficulties with early diagnosis. Because uterine leiomyosarcoma is resistant to conventional radiation and chemotherapy, the development of more potent medical therapeutics is anticipated. Using quantitative real-time RT-PCR and immunostaining, we found the expression of brain-derived neurotrophic factor (BDNF) and neurotropin-4/5, together with their receptor, tyrosine kinase B (TrkB), in different uterine sarcoma cell lines and primary tumor samples from uterine leiomyosarcoma patients. We noted that levels of BDNF were more abundant than those of neurotropin-4/5. Moreover, the expression of TrkB and its ligands was elevated in a multidrug-resistant cell line and samples obtained from patients with leiomyosarcoma. In cultured uterine sarcoma cells, inhibition of endogenous TrkB signaling by treatment with either the soluble TrkB ectodomain or the Trk receptor inhibitor, K252a, suppressed cell proliferation and increased apoptosis based on cell viability and proliferation,  Leiomyosarcoma is most common subtype among uterine sarcomas. The most effective treatment for this disease is a complete resection of the primary lesion at an early stage. However, a differential diagnosis between early stage uterine leiomyosarcomas and myomas is difficult. In fact, a uterine leiomyosarcoma diagnosis is often made after surgery for benign uterine myomas Brain-derived neurotrophic factor (BDNF) belongs to the neurotrophin family, and binds to the receptor tyrosine kinase B (TrkB) and the pan-neurotrophin receptor p75 (p75NTR) with high and low affinities, respectively in vitro cell growth and survival using the soluble ectodomain of TrkB and a pan-Trk receptor inhibitor. We also demonstrated that the levels of TrkB and BDNF transcripts were elevated in samples obtained from patients with leiomyosarcoma as compared with those of uterine myometrium and leiomyoma. Furthermore, we showed that a Trk inhibitor suppressed tumor growth in athymic nude mice bearing uterine sarcoma cell tumors.The placenta is a fast-growing organ that displays some tumor-like properties, e.g., high rates of trophoblast cell proliferation and invasion. We demonstrated that BDNF promotes proliferation and survival of trophectoderm cells before implantation et al. 2.The human uterine sarcoma cell lines, MES-SA and MES-SA/Dx5, and human uterine leiomyosarcoma cell line, SKN were purchased from the American Type Culture Collection  and the Japan Health Science Foundation , respectively. MES-SA/Dx5 is a multidrug-resistant variant of MES-SA developed by Harker To study the expression of tyrosine receptor kinase (Trk) family and thier ligands, conventional RT-PCR was performed. Total RNA was extracted from cells and tissues using an RNeasy Mini kit . Uterine leiomyosarcoma tissue sample was obtained from patients at the Department of Obstetrics and Gynecology, Akita University, during surgical resection with ethical committee approval and informed consent from patients. PCR primers for TrkB, p75NTR, BDNF, NT4/5 and β-actin were used as described Immunofluorescent detection of TrkB and their ligands in MES-SA/Dx5 cells was performed as described Total RNA was extracted from cell lines and frozen surgical specimens of human myometrium  and uterine leiomyoma (uterine benign tumor) using RNeasy Mini kit (Qiagen). Due to insufficient number of frozen samples of uterine leiomyosarcoma, total RNA was extracted from formalin-fixed parafine-embeded (FFPE) samples using RNeasy FFPE kit (Qiagen). After reverse transcription of 1 µg tRNA of each samples by Prime Script RT reagent kit , real-time PCR was performed using LightCycler 480 system with SYBR Green I Master mix . Primers for β-actin, TrkB, BDNF, NT-4/5, and galectin-1 were as follows:5′-CCACACTGTGCCCATCTACG-3′; antisense, 5′-AGGATCTTCATGAGGTAGTCAGTCAG-3′; TrkB: sense, 5′-CTGAAGGATGCCAGTGACAA-3′; antisense, 5′-ATGTGCTCATGCTGGAGGTT-3′; BDNF: sense, 5′-GCCAGGACAGAGCTGACAA-3′; antisense, 5′-TCCATGAACAGACAGGATGG-3′; NT4/5: sense, 5′-TGGATTCGAATTGACACTGC-3′; antisense, 5′-CCTGAGGTCTCTCAGCATCC-3′; galectin-1: sense, 5′-TGCAACAGCAAGGACGGC-3′; antisense, 5′-CACCTCTGCAACACTTCCA-5′. In all reactions, HotStartTaq DNA polymerase was activated by an initial denaturation at 95°C for 5 min, followed by 45 cycles of denaturation at 95°C for 10 sec and annealing at/elongation at β-actin, 64°C; TrkB, BDNF and NT4/5, 60°C; Galectin-1, 61°C for 10 sec and elongation at 72°C for 10 sec. Fluorescent signals were monitored at the end of the elongation phase for each cycle. Data were normalized based on β-actin transcript levels. Human tissue samples were obtained from patients at the Department of Obstetrics and Gynecology, Akita University with ethical committee approval and informed consent from patients.β-actin: sense, To examine roles of endogenous Trk ligands in cell proliferation and apoptosis, MES-SA, MES-SA/Dx5 and SKN cells were seeded in 96-well culture plates at a density of 300 cells/well. Cells were incubated overnight before beginning a 48 h culture in serum-free medium with or without different doses of the following compounds: the TrkB soluble ectodomain ; the pan-specific Trk inhibitor, K252a 2 ×0.5. The tumors were resected after 15 days of treatment, weighed, embedded and sectioned. In addition to hematoxylin and eosin (H&E) staining to assess morphology, in vivo cell proliferation and apoptosis were evaluated by Ki-67 immunostaining and the TUNEL assay, respectively, as described 2, 1 mM EDTA, and a protease inhibitor cocktail (Roche). Data were standardized by protein concentration and represented as fold increases relative to controls.To explore the roles of endogenous Trk ligands during t-test. One-way ANOVA, followed by Fisher’s protected least significant difference test, was used to evaluate other differences. Data are expressed as the mean ± SEM.The Mann−Whitney U test was performed to compare the levels of TrkB and its ligands in human uterine sarcoma cell lines. The effects of drug treatment on tumor volume were analyzed using a Students’ The expression of Trk receptors and their ligands in uterine sarcoma cells was examined by PCR and immunostaining. Transcripts encoding TrkB, p75NTR, BDNF and NT4/5 were detected in both MES-SA and MES-SA/Dx5 cells . Other TIn human uterine samples, the expression levels of TrkB and its ligands were compared among uterine myometrium (n = 8), leiomyoma (n = 8) and leiomyosarcoma (n = 7) by quantitative real-time RT-PCR. The levels of TrkB and BDNF transcripts were significantly increased in uterine leiomyosarcoma as compared to those in myometrium and leiomyoma , whereasTo determine the roles of endogenous TrkB ligands in cell proliferation and apoptosis of sarcoma cells, MES-SA and MES-SA/Dx5 cells, were incubated with K252a or the TrkB ectodomain. Cell proliferation was inhibited by treatment with either the K252a or TrkB ectodomain, but not with the K252b . In leioTo examine the effect of BDNF on cell proliferation, cells were cultured with or without different doses of BDNF. As shown in in vitro inhibition of sarcoma cell growth by Trk receptor inhibitors could be translated into in vivo antitumor activity, K252a was administered to athymic nude mice bearing tumor xenografts of MES-SA/Dx5 cells, which were chosen for these studies due to their high BDNF and TrkB expression levels and multidrug-resistant character that could be more severe model of uterine sarcoma for in vivo analyses. When the tumor volume reached >80 mm3, animals were given vehicle alone, K252a, or K252b at 1 mg/kg every 3 days for 15 days. Tumors in animals treated with K252a were significantly smaller than those that received vehicle or K252b  and uterine leiomyosarcoma cell lines (SKN) . Due to In uterine leiomyosarcoma samples obtained from patients, the levels of BDNF and TrkB were significantly high as compared with uterine myometrium and leiomyoma. Interestingly, the leiomyosarcoma patients with high BDNF expression also showed significantly high levels of TrkB, but not NT4/5. Due to poor outcomes exhibiting overexpression of BDNF and TrkB in neuroblastomas The expression of galectin-1 is shown to regulate by the activation of TrkB in neuroblastoma cells Although NT4/5 is another TrkB ligand, its expression levels in uterine sarcoma cells are low, suggesting that BDNF acts as the predominant ligand in the context of uterine sarcoma cell growth. Similar expression patterns were demonstrated in choriocarcinoma cells in vitro, K252a is likely to be specific for TrkB, although other members of the Trk family, including TrkA and TrkC, were expressed in these sarcoma cells , easily delivered to tumor xenografts via i.p. administration with no overt side effects. By contrast, the TrkB ectodomain is a large glycosylated protein (MW = 75–100 kDa), which is unstable in the body. Thus, we used K252a for in vivo studies. Although uterine sarcoma is a highly invasive and metastatic cancer, MES-SA tumor cells xenografted into mouse uterine muscle failed to metastasize after 42 days The expression of both TrkB and TrkB ligands in uterine sarcoma cells suggested the presence of autocrine loop of TrkB signaling. However, Several molecular targeted agents have been developed and introduced into clinical trials for the treatment of uterine leiomyosarcoma in vitro and in vivo studies. Moreover, we demonstrated the expression level of TrkB and BDNF in uterine leiomyosarcoma associated with clinical malignancy in patients. Further elucidation of the mechanisms of BDNF/TrkB-induced leiomyosarcoma growth could lead to novel therapeutic approaches for the treatment of patients with this disease.In conclusion, our findings demonstrated important roles for TrkB signaling in uterine leiomyosarcomas based on Figure S1Expression of TrkA, TrkC, NGF and NT3 in uterine sarcoma cell lines. Expression of TrkA, TrkC, NGF and NT3 mRNA was detected by RT-PCR in the uterine sarcoma cell lines, MES-SA and MES-SA/Dx5. As loading controls, β-actin mRNA levels were assessed. The negative controls lacked template DNA.(TIF)Click here for additional data file.Figure S2Expression of TrkB, p75NTR and their ligands in uterine leiomyosarcoma cell line, SKN and roles of endogenous TrkB signaling in in vitro cell proliferation and survival (A) Expression of TrkB, p75NTR and their ligands mRNA was detected by RT-PCR. The levels of β-actin mRNA were assessed as loading internal controls. The negative controls lacked template DNA. nc, negative control. (B) Effects of suppression of endogenous TrkB signaling on cell proliferation. SKN cells were cultured in medium alone , or with different doses of the TrkB ectodomain (TrkB EC), the pan-Trk inhibitor, K252a, or its inactive analogue, K252b. Cell proliferation activity was determined using the cell proliferation reagent WST1 (n = 6). Columns, mean; bars, SE. *, P<0.05 vs. control. (C) Effects of suppression of endogenous TrkB signaling on cell survival. SKN cells were treated without or with K252a (1 µM). Apoptosis was determined using the caspase-3/7 assay (n = 6). Data were represented as fold increases relative to controls at individual culture times. Columns, mean; bars, SE. *, P<0.05 vs. control.(TIF)Click here for additional data file.


Idiopathic pulmonary fibrosis (IPF) and pulmonary sarcoidosis are typical interstitial lung diseases with unknown etiology that cause lethal lung damages. There are notable differences between these two pulmonary disorders, although they do share some similarities. Gene expression profiles have been reported independently, but differences on the transcriptional level between these two entities have not been investigated.All expression data of lung tissue samples for IPF and sarcoidosis were from published datasets in the Gene Expression Omnibus (GEO) repository. After cross platform normalization, the merged sample data were grouped together and were subjected to statistical analysis for finding discriminate genes. Gene enrichments with their corresponding functions were analyzed by the online analysis engine “Database for Annotation, Visualization and Integrated Discovery” (DAVID) 6.7, and genes interactions and functional networks were further analyzed by STRING 9.0 and Cytoscape 3.0.0 Beta1. One hundred and thirty signature genes could potentially differentiate one disease state from another. Compared with normal lung tissue, tissue affected by IPF and sarcoidosis displayed similar signatures that concentrated on proliferation and differentiation. Distinctly expressed genes that could distinguish IPF from sarcoidosis are more enriched in processes of cilium biogenesis or degradation and regulating T cell activations. Key discriminative network modules involve aspects of bone morphogenetic protein receptor two (BMPR2) related and v-myb myeloblastosis viral oncogene (MYB) related proliferation.This study is the first attempt to examine the transcriptional regulation of IPF and sarcoidosis across different studies based on different working platforms. Groups of significant genes were found to clearly distinguish one condition from the other. While IPF and sarcoidosis share notable similarities in cell proliferation, differentiation and migration, remarkable differences between the diseases were found at the transcription level, suggesting that the two diseases are regulated by overlapping yet distinctive transcriptional networks. Idiopathic pulmonary fibrosis (IPF), the most common type of idiopathic interstitial pneumonia, is a chronic, progressive, irreversible and lethal lung disease of unknown etiology Sarcoidosis, a multisystemic disorder, is defined as the manifestation of immune granulomas found in many organs. The incidence varies depending on age, sex, race and geographic factors, but the predominantly affected systems are the lungs and lymphatic system The extensive use of microarray technology has had a profound impact on characterizing the transcriptional changes of lung diseases. Microarray studies based on different platforms using different sample sources  have provided a huge amount of information about lung disease processes, and have been successfully used in determining gene expression patterns or finding potential biomarkers This study was designed to identify genetic programs that differentially regulate IPF and sarcoidosis based on previously published microarray data from the Gene Expression Omnibus (GEO) datasets. We found similarities and diversities in gene expression between IPF and sarcoidosis. Our results also provided novel signature genes for pathogenic analysis and diagnostic or therapeutic purposes.http://www.ncbi.nlm.nih.gov/projects/geo/). In this study, datasets of all human lung tissue were used for further analysis and, to make comparisons across platforms, datasets by commonly used platforms like Affymetrix and Agilent were retained.Expression profile data related to either idiopathic pulmonary fibrosis or sarcoidosis were acquired from the GEO repository Selected datasets downloaded from the GEO repository in different forms contain different forms of expression measurements and probe annotation files. In particular, expression intensities of Affymetrix HGU133 Plus 2.0 were saved in.CEL files, and can be extracted by robust multi-array average (RMA) using software Affymetrix® Expression Console™ version 1.1.Probes and their measurements of each expression profile from different platforms were all mapped to a common gene list as previously described 2 transformed and then merged by cross platform normalization (xpn) which were performed using R 2.14.1 and Bioconductor package CONOR All expression estimates were logP<0.05, the same criterion for discovering differently expressed genes P<0.01were used by Lockstone et al. P<0.005 by Crouser et al. In order to find different expressed genes and make comparisons over normal, IPF and sarcoidosis lung tissues, we carried out unpaired student’s t-tests on the integrated super array data. Significantly expressed genes are defined as those with Significantly expressed genes were analyzed by hierarchical clustering algorithms using the software Gene Cluster 3.0. Expression estimates were first adjusted by centering genes and arrays, then followed by two-way clustering (TWC) using the Euclidean distance similarity metric and the complete linkage clustering method, and clustering results were visualized by Java TreeView. Unsupervised TWC analysis can not only reveal the expression trend of genes but also isolate the outlier samples that possess unique expression features. We carried out our data mining by alternately using unpaired student’s t-tests and TWC until one circumstance can be differentiated from another completely.http://www-stat.stanford.edu/~tibs/SAM/) To get significantly expressed genes for each comparison, , the supervised learning method was carried out on the final unsupervised TWC clustering results using the software Significance Analysis of Microarrays (SAM) 4.0 (http://david.abcc.ncifcrf.gov/) for inquiring functional annotations and gene enrichments http://string.embl.de/) http://www.cytoscape.org/) Signature genes that can be used to discriminate each condition were subsequently submitted to the online software “Database for Annotation, Visualization and Integrated Discovery” (DAVID) 6.7 . After filtering datasets by organism of Homo sapiens and sample source of lung tissue, ten datasets remained . In this2 transformed expression estimates of the selected four datasets. Cross-platform normalization (xpn) method based on a simple block-linear model is the procedure of measurements normalization of data from two or more studies. Gene expression estimates in each study are represented as specific matrix. Data normalizing procedure is an iterative clustering process until convergence to a local minimum of the squared Euclidean distance sum. Xpn is not gene-wise affine compared with other methods in the literature, which can successfully remove systematic differences between platforms while preserving biological information 2 transformation is used to make our data more symmetric that will be helpful for plotting, and the transformation can also make the random variation more constant 2 transformed then xpn normalized. Here, 1,897 genes were filtered out as they were not shared by the four studies.To make comparable estimates for the four datasets, xpn method was carried out on the logP<0.05). In order to get perfect clustering results, outliers with different clustering patterns were filtered out  in each comparison can be used as signature genes to separate the sample from the opposite condition more clearly . In bothTo illustrate the gene network of biological systems for each circumstance, signature genes were submitted to the online analysis software DAVID for gene enrichment and functional analysis. Gene-gene interactions were further retrieved and displayed for validating gene behaviors using STRING software.P<0.001) (P<0.001)  . SimilarP<0.001) . TherefoP<0.001) and T cell activation (P<0.01)  . This maFor comparison between normal and IPF, significantly expressed genes strongly enriched in “glycoprotein” process show strong interactions on the protein level . Eleven Significantly expressed genes of sarcoidosis compared with normal that also show some protein to protein interactions  include Surprisingly, within the above significantly expressed genes that show strong interactive relationships, seven genes POSTN, MMP1, COL3A1, COMP, SOO1, COL15A1 and FIGF show consistency by IPF and sarcoidosis. This suggests that extracellular matrix forming or degradation and the accumulation of collagen could be the key similar biological pathways shared by IPF and sarcoidosis based on our research. By comparing with IPF, sarcoidosis shows significantly up regulated expression of CD274, IL7R and PAG1, which show strong protein to protein interactions . Down-reMolecular interaction networks and functional modules were visualized by the open source bioinformatics software Cytoscape and the related plugin jActive Modules. Signature genes that could be used to distinct IPF from normal, to distinct sarcoidosis from normal, and to distinct IPF from sarcoidosis, were all submitted to Cytoscape, respectively, and that gave three gene network complexes as shown in + T cell activation followed by formation of granulomas. Non self healing pulmonary sarcoidosis will develop to pulmonary fibrosis due to genetic variations In-depth learning of critical genes that could draw interactive networks opened insight into pathogenesis. Critical gene networks provided descriptions of disease development in aspects of angiogenesis, neurotrophy and immune activations, which in turn reflect disease morphology. Our results suggest that IPF mainly results from epithelial injury/activation followed by inflammation in response to fibrosis. Excessive inflammation then leads to CD4Although a number of genes have been filtered out for analysis, we can pry out main genomic changes and provide information about pathogenic mechanism. Outlier samples that do not contribute to unsupervised clustering analysis are likely due to other risks of diseases. For instance, smoking has been confirmed to be an environmental factor of IPF Although no complete signaling pathway has been described in this study, and no exact pathogenic mechanisms have been clearly illustrated for IPF and sarcoidosis, our study opened up new insights in genetic change analysis based on multiple databases, and our investigations may be more validated than single studies. Significantly expressed genes, especially those in critical modules used for discriminating different conditions, can be used to draw a regulatory map in disease development. Further studies with the aim of verifying gene expressions and investigating biological functions can at least in theory give a guide for new discoveries in pathogenic research.In this study, we performed meta-analysis with published datasets with IPF and sarcoidosis and found there were significant differences at the transcriptional level between them, while they did share some similarities. Compared with healthy lung tissue, IPF and sarcoidosis tissue are similar due to formations of extracellular matrixes and collagens. However, transcription networks containing significant expressed genes when comparing those two disorders further present discriminative signaling regulations by MYB and BMPR2, which suggest different prognoses. Furthermore, we believe that our research can promote illustrations of disorder mechanisms, and our method could be used as an essential tool for studying disorders on a transcriptional level.Table S1GEO Datasets Summary of IPF and Sarcoidosis. Summary of datasets that fit all criteria of what we used, filtered datasets were summarized by accession number, title, summary, organism, platform, total transcripts, total samples (n = 182), sample source, control  sample number (n = 97), control  sample sources, data format, data contributor and reference which the datasets were published.(XLS)Click here for additional data file.Table S2Integrated data. In order to get comparable data for further analysis, microarray estimates of selected datasets were merged by xpn method. The integrated data contains 96 lung tissue samples  of four different studies and 10,213 probes, known as common genes were included. For convenience, sample symbols were renamed according to the original studies.(RAR)Click here for additional data file.Table S3Sample participations during clustering analysis. To get perfect TWC results, outlier samples that overlap between two conditions were filtered out. When compare normal and IPF samples, 7 out of 27 normal samples and 10 out of 48 IPF samples were filtered out. In comparison between normal and sarcoidosis samples, 10 out of 27 normal samples and 5 out of 21 sarcoidosis samples were filtered out. In addition, 32 out of 48 IPF samples and 9 out of 21 sarcoidosis samples were filtered out. Key: positive signs = samples were used; negative signs = samples were filtered out; backslashes = samples were absent.(XLS)Click here for additional data file.Table S4Expression patterns for each comparison. Unsupervised clustering analysis (TWC method) was carried out to figure out general expression pattern for each comparison. Between normal and IPF, 20 out of 27 normal and 38 out of 48 IPF can be generally separated by 2,946 up regulated and 466 down regulated genes. Between normal and sarcoidosis, 17 out of 27 normal and 16 out of 21 sarcoidosis samples can be described by 920 up regulated and 176 down regulated genes. By comparing with sarcoidosis, IPF has 2,051 up regulated and 961 down regulated discriminative genes. (XLS)Click here for additional data file.Table S5Significant expressed genes for each comparison. Supervised classifications based on TWC results (SAM method) further extracted significant expressed genes that could be used to distinct one condition from another. Comparison of normal with IPF generated 197 up regulated and 275 down regulated significant expressed genes. Between normal and sarcoidosis, there are 96 up regulated and 174 down regulated significant expressed genes. To sarcoidosis, IPF has 382 up regulated and 826 down regulated significant expressed genes. Complete gene lists were summarized in (XLS)Click here for additional data file.Table S6Signature genes. 130 significant expressed genes (65 up regulated and 65 down regulated) was selected as the key signatures for each comparisons, TWC analysis shown that they could be used to strongly discriminate normal and disease conditions, and for the comparison of normal versus IPF or the comparison of normal versus sarcoidosis, stronger or weaker sample cluster can well forms according to the expression measurements. The complete gene lists were in (XLS)Click here for additional data file.Table S7Functional network complexes and modules. Signature genes of each comparison gave functional network complexes. The highly connected network regions are so called modules that could play vital role in diseases development. (XLS)Click here for additional data file.


TARDBP gene that encode TDP-43 as causative for cases of sporadic and familial ALS http://www.molgen.ua.ac.be/FTDMutations/. Certain mutant forms of TDP-43, such as M337V, are associated with increased low molecular weight (LMW) fragments compared to wild-type (WT) TDP-43 and cause neuronal apoptosis and developmental delay in chick embryos. Such findings support a direct link between altered TDP-43 function and neurodegeneration.Abnormal distribution, modification and aggregation of transactivation response DNA-binding protein 43 (TDP-43) are the hallmarks of multiple neurodegenerative diseases, especially frontotemporal lobar degeneration with ubiquitin-positive inclusions (FTLD-U) and amyotrophic lateral sclerosis (ALS). Researchers have identified 44 mutations in the M337V) carrying this mutation. hTDP-43M337V was expressed primarily in the nuclei of neurons in the brain and spinal cord, and intranuclear and cytoplasmic phosphorylated TDP-43 aggregates were frequently detected. The levels of TDP-43 LMW products of ~25 kDa and ~35 kDa species were also increased in the transgenic mice. Moreover, overexpression of hTDP-43M337V dramatically down regulated the levels of mouse TDP-43 (mTDP-43) protein and RNA, indicating TDP-43 levels are tightly controlled in mammalian systems. TDP-43M337V mice displayed reactive gliosis, widespread ubiquitination, chromatolysis, gait abnormalities, and early lethality. Abnormal cytoplasmic mitochondrial aggregates and abnormal phosphorylated tau were also detected in the mice.To explore the pathogenic properties of the M337V mutation, we generated and characterized two mouse lines expressing human TDP-43 (hTDP-43M337V mouse model indicates that overexpression of hTDP-43M337V alone is toxic in vivo. Because overexpression of hTDP-43 in wild-type TDP-43 and TDP-43M337V mouse models produces similar phenotypes, the mechanisms causing pathogenesis in the mutant model remain unknown. However, our results suggest that overexpression of the hTDP-43M337V can cause neuronal dysfunction due to its effect on a number of cell organelles and proteins, such as mitochondria and TDP-43, that are critical for neuronal activity. The mutant model will serve as a valuable tool in the development of future studies designed to uncover pathways associated with TDP-43 neurotoxicity and the precise roles TDP-43 RNA targets play in neurodegeneration.Our novel TDP-43 TARDBP, the gene encoding TDP-43, are associated with sporadic and familial ALS [n motif and is involved in gene transcription, pre-mRNA splicing, mRNA stability, and mRNA transport [M337V under control of the mouse prion (PrP) promoter [TDP-43 is the major component of ubiquitinated inclusions in most cases of ALS and FTLD-U ,2, and tlial ALS . TDP-43 ransport ,9. Underransport -16. Thatpromoter .M337V mice, hTDP-43M337V is mainly expressed in the brain and spinal cord, a finding that is consistent with transgenic mice overexpressing wild-type TDP-43 that we previously reported [M337V mice exhibited certain features similar to those seen in ALS, such as TDP-43 cleavage, phosphorylation, aggregation, increased ubiquitination, gliosis, gait disturbances, and early lethality; however, the mice also exhibited other features not yet reported in humans, which may be due to the effect of hTDP-43M337V overexpression. Such features include: down regulation of mTDP-43, abnormal mitochondrial aggregation and abnormal tau phosphorylation.In the TDP-43reported . TDP-43MWT line 3c hereafter termed TDP-43WT), which we previously generated with the same promoter [M337V showed that protein expression was highest in the brain and spinal cord, with low levels in other tissues  using real time-PCR analyses of hemizygous and homozygous brains  TDP-43, we generated transgenic mice using the mouse prion promoter to constitutively drive expression of full-length hTDP-43 carrying the M337V mutation. Three  of eight independent founder lines showed germline transmission. The expression level of human TDP-43 was low in line 1 (~20% of endogenous levels), while lines 4 and line 6 had similar hTDP-43 expression levels to that of wild-type TDP-43 mice . They failed to show proper escape extension by splaying their hindlimbs upon elevation, as shown by their NT counterparts  was similar to that described for line 4.At approximately post-natal day 21, homozygous TDP-43s Figure . They dis Figure . By ~ 1 s Figure . Due in s Figure . We had M337V and NT mice. The results showed that TDP-43 was mostly in nuclei in the brain and spinal cord of TDP-43M337V and NT mice and that the TDP-43M337V mice specifically expressed high levels of hTDP-43 that were not seen in the controls ; however, increased ubiquitination and reactive gliosis were observed in TDP-43M337V mice and not in NT mice  showed similar pathological changes.A striking histological feature in TDP-43s Figure  located s Figure . Neuronae Figure . Ubiquite Figure . Ubiquite Figure , and co-e Figure . Reactive Figure ; Table 1WT model . HomozygM337V mice were immunoreactive for the mitochondrial marker, COX-IV  results confirmed that the majority of neuronal cytoplasmic inclusions in the spinal motor neurons of TDP-43M337V mice contained aggregates of mitochondria .The eosinophilic aggregates in spinal motor neurons of TDP-43V Figure , which iV Figure  did not a Figure  that wera Figure . The agga Figure . The dege Figure  were smae Figure . In NT me Figure ; howevere Figure . The mitns Table . HomozygWT mice is accompanied by changes in protein levels and/or phosphorylation of proteins that regulate mitochondrial fission and fusion [M337V mice and our previously described TDPWT mice, we sought to determine if mitochondrial fission and fusion proteins were altered in the TDP-43M337V mice. Surprisingly, the alterations to pDLP1 (Ser616), Fis1, and MFN1 that were observed in the TDP-43WT mice, regardless of line, were not present in the TDP-43M337V mice, despite the comparable mitochondrial aggregation in each transgenic model . Abnormally phosphorylated tau was rarely found in the spinal cord (data not shown). The regional distribution of hyperphosphorylated tau is summarized in Table Abnormal, phosphorylated tau was found in the brains of both TDP-43V Figure  and TDP-e Figure  showed se Figure . Additioe Figure . Westerne Figure , while te Figure . To expln Figure . The levM337V mice) develop phenotypic and pathologic features including gait disturbances, gliosis, increased ubiquitination, TDP-43 truncation, phosphorylation, and both nuclear and cytoplasmic phosphorylated inclusions.In the current study, we generated and characterized a novel transgenic mouse model that overexpresses hTDP-43 carrying the M337V mutation under the mouse prion promoter. Homozygous TDP-43 M337V mice  were also noted in a second independent line of TDP-43M337V mice (line 6), suggesting that these features were not simply due to transgene insertional effects.One limitation of our model is that although many of the features are consistent with those observed in ALS, there are several features of this model that are incongruous with the human disease. Phenotypically, our mice show retarded growth from early life; whereas, humans with ALS typically show progressive disease after initially normal development into adulthood. While our TDP-43se model ; howevergression . TDP-43Mpairment ,31, whicWe observed some regional differences in our TDP-43 transgenic models, despite the relatively uniform expression of the TDP-43 proteins throughout the gray matter of the brain and spinal cord. For example, nuclear pTDP-43 inclusions, abnormal mitochondrial aggregation and chromatolysis were mainly observed in spinal cord, while cytoplasm pTDP-43 inclusions and tau phosphorylation were primarily seen in the brain. This indicates that the mechanisms regulating TDP-43 and being regulated by TDP-43 are likely to be different in the brain and spinal cord and warrant further investigation.M337V mouse model indicates that overexpression of hTDP-43M337V alone is toxic in vivo. TDP-43M337V mice recapitulate certain pathologic features seen in neurodegenerative diseases, including TDP-43 fragmentation, phosphorylation, aggregation, increased ubiquitination and gliosis. While these features could also indicate general neuronal dysfunction, the mice also exhibit down regulation of mouse TDP-43, abnormal mitochondria aggregation and abnormal tau phosphorylation. Because overexpression of mutant hTDP-43 produces phenotypes similar to the wild-type TDP-43 model, the mechanisms causing pathogenesis in the mutant model remain unknown. However, these results should not be surprising, given the mutant TDP-43 and wild-type TDP-43 biochemically are similar in human disease. As such, the TDP-43M337V mice may serve as a valuable tool for future studies of yet-to-be-examined disease pathways and the precise roles TDP-43 RNA targets play in neurodegeneration.In summary, this novel TDP-43XhoI site of the pcDNA 3.1. To generate the M337V mutation, site directed mutagenesis was performed using Quikchange kit (Strategene). The primers used for the mutation were: 5'-CAGTTGGGGTATGGTGGGCATGTTAGC-3' and 5'-GCTAACATGCCCACCATACCCCAACTG-3'. After confirming the mutation by sequencing, the M337V TDP-43 cDNA was inserted into the XhoI site of MoPrP vector [NotI, gel purified and digested with β-agarose. DNA was filtered, concentrated and diluted to 3 ng/μl in microinjection buffer. The transgene was microinjected into fertilized C57BL/6 (B6) mouse eggs and re-implanted into pseudopregnant females. Eight founders were mated with B6 mice to determine germline transmission and establish expression levels. TDP-43 founder lines (line 4 and 6) that have similar transgene levels to the TDP-43WT mice we previously described [The human wild-type TDP-43 cDNA was generated as previously described  and was P vector . Followiescribed , were usTransgenic mice were identified by PCR using hTDP-43-specific primers: 5'- TGGAGAAGTTCTTATGGTGCAGGTC-3' and 5'-GGTATTAGCCTATGGGGGACAC-3' against control actin-specific primers (5'-CGGAACCGCTCATTGCC-3' and 5'-ACCCACACTGTGCCCATCTA-3'). Homozygous mice were identified by Quantitative real-time PCR .® Gene Expression Assays . Total RNA was isolated either from tail samples for identification of homozygous mice or from brain or spinal cord tissue to determine the levels of human and mouse TDP-43 transcripts. TRIzol  and Pure Link™ RNA Mini Kit  were used for RNA extraction. 3 μg RNA were used to synthesize cDNA using the High Capacity cDNA Reverse Transcription Kit . The qPCR assay used the following: hTDP-43 Hs00606522_m1, mTDP-43 Mm00523866_m and 18S rRNA Hs99999901_s1. The PCR was run on the ABI 7900 and data analyzed using Software RQ Manager 1.2 .Levels of human and mouse TDP-43 transcripts were determined via TaqManSagittal half brain and spinal column were immersion fixed in 10% formalin for immunohistochemistry, and the other half brain was frozen on dry ice for biochemistry. After 24 hours, the spinal cord was removed from the vertebral column and fixed overnight.Tissues were homogenized at 10 ml/g (volume/weight) in lysis buffer . Following centrifugation, supernatant was assessed by BCA assay . Following western blotting, membranes were incubated with mouse monoclonal TDP-43 antibody, which was generated using amino acids 1 to 261 of hTDP-43 as the immunogen and was found to recognize amino acids 205-222 of hTDP-43 by epitope mapping ; rabbit 20. Antigen retrieval was performed in a dH2O steam bath for 30 min. Tissues were immunostained with monoclonal TDP-43 antibody or antibodies toward pS403/S404-phosphorylated TDP-43, ubiquitin, glial fibrillary acidic protein (GFAP), ionized calcium-binding adaptor molecule 1 (IBA-1), cytochrome oxidase subunit IV (COX-IV), or phospho-tau  using the DAKO Autostainer (Dako Auto Machine Corporation) and the DAKO EnVision+ HRP system. DAKO Liquid DAB Substrate-Chromogen system was the chromogen. See Additional file Tissues were embedded in paraffin, sectioned (5 μm thick) and mounted on glass slides. Sections were deparaffinized in xylene and rehydrated in a graded series of alcohol, followed by dH4, dehydrated in alcohol and propylene oxide, and finally infiltrated and embedded in Epon 812. Ultrathin sections mounted on copper grids were stained with uranyl acetate and lead citrate. Images were obtained with a Gatan CCD camera using a Philips 208S electron microscope.Spinal cords from 4%-paraformaldehyde-perfused mice were immersed in 2.5% glutaraldehyde-0.1 M cacodylate buffer, post-fixed in 1% OsOFront paws were painted red, and hind feet were painted blue with nontoxic paint. The mice were placed in a plastic tunnel with a strip of white paper covering the floor. At the end of the tunnel was an enclosed black box to encourage mice to cross the paper and into the box. The test was repeated until mice left at least five pairs of adjacent footprints.p < 0.05 were considered statistically significant.One-way ANOVA with Tukey's posthoc analysis were used to compare measures among 3 groups. Student's t-test analysis was used to compare measures between 2 groups. Kaplan-Meier methods were used for survival analysis. For data presentation, normalized values were averaged and presented as mean ± standard error of means (SEM). Values of ALS: amyotrophic lateral sclerosis; FTLD-U: frontotemporal lobar degeneration with ubiquitin-positive inclusions; TDP-43: TAR DNA binding protein-43; hTDP-43: human TDP-43; mTDP-43: mouse TDP-43. pTDP-43: phospho-TDP-43.YX, YZ, JL, and LP are inventors of this and related mouse models; however, no royalties have been generated from this invention.YX and YZ performed experiments, data analysis and co-wrote the manuscript. XC performed experiments, WL performed Electron Microscopy study. CS edited the manuscript. JL, DWD and LP conceived of the study, participated in its design and coordination and edited the manuscript. All authors read and approved the final manuscript.M337V mice but hTDP-43 itself is not ubiquitinatedFigure A1: Increased ubiquitin levels in TDP-43. Human TDP-43 was immunoprecipitated from brain homogenates derived from nontransgenic and homozygous TDP-43m337v mice. Briefly, brain homogenates containing 500 μg protein were incubated with 1.5 μg mouse monoclonal TDP-43 antibody overnight at 4°C with gentle shaking. Protein G agarose was added for 4 h at 4°C then pelleted by centrifugation. Protein thus captured was eluted using sample loading buffer and resolved by SDS/PAGE for Western blot analysis. Shown are immunoblots of the inputs and the immunoprecipitated proteins, probed using an antibody to ubiquitin or to total TDP-43. Note that a marked increase in ubiquitin levels is observed in the homogenates derived from homozygous mice prior to immunoprecipitation. Nonetheless, the immunoprecipitated human TDP-43 is not immunopositive for ubiquitin. Arrow = IgG Heavy Chain.Click here for fileM337V miceFigure A2: No mitochondrial fission and fusion protein changes in TDP-43. Immunoblot analysis of Ser616-phosphorylated DLP1, DLP1, Fis1, and mitofusin 1 (MFN1) expression level in brain lysates of nontransgenic(NT), hemizygous(Hemi), and homozygous(Homo) TDP-43M337V mice of both line 4 and line 6. There are no significant protein changes among different mice groups.Click here for fileAdditional Table: Primary Antibody List. Full list of the primary antibodies used in this study.Click here for file


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