Aromatase inhibitor (AI) resistance is a major obstacle in the treatment of estrogen receptor-positive breast cancer. Everolimus (EVE) ameliorates AI-resistant breast cancer and is therefore used in cancer treatment. However, some patients show resistance to EVE. Here, we used 30 clones of long-term estrogen-deprived (LTED) MCF-7 cells as a model of AI-resistant breast cancer. We examined changes in protein phosphatase type 2A (PP2A) and cancerous inhibitor of PP2A (CIP2A), a negative regulator of PP2A, in LTED cells treated with EVE. In LTED cells with high sensitivity to EVE, CIP2A expression decreased at low EVE concentrations; however, in LTED cells poorly sensitive to EVE, CIP2A and PP2A did not change upon exposure to EVE. Therefore, we hypothesized that there is a relation between expression of CIP2A and sensitivity to EVE. Knockdown of CIP2A increased the sensitivity to EVE in three clones poorly sensitive to EVE. Additionally, we found that treatment with FSK, which activates PP2A, increased the sensitivity of the cells to EVE. Our data point to CIP2A and PP2A as novel therapeutic targets for AI-resistant breast cancer. Estrogen plays a crucial role in the development and progression of estrogen receptor alpha (ERα)-positive breast cancer , 2. Arom2)-independent MCF-7 cell proliferation ), S6 kinase phosphorylated on S371 (pS6K [S371]) and pER (S167) and decreased levels of PP2A compared to cells grown in medium containing E2.Next, we investigated the effect of El Figure . In cellWe have previously shown that that LTED cells have lower levels of PP2A compared with MCF-7 . Here, w2-independent proliferation of breast cancer cells (AI-resistant breast cancers). We hypothesized that the sensitivity to EVE and the expression of PP2A and CIP2A were closely related and decided to investigate this effect in cell clones. This endeavor required the creation of several clones: several studies indicate indeed that, to investigate the mechanism of drug resistance it is necessary to compare a large number of cell lines , 5% sucrose, 0.01% Bromophenol Blue . The protein content was determined using a RC DC™ Protein Assay kit  with bovine serum albumin  as standard. For western blot analysis, the samples (5 μg of protein/lane) were separated by 10% SDS-polyacrylamide gel electrophoresis (PAGE) and transferred to a polyvinylidene difluoride (PVDF) membrane . The membrane was pre-incubated with ImmunoBlock  at 20–25° C for 30 min, and then incubated at 4° C overnight with a primary antibody. The membrane was subsequently washed with TBS-T buffer  and incubated with a horseradish peroxidase (HRP)-labeled secondary anti-rabbit  or anti-mouse  antibody for one hour. After the membrane was washed with TBS-T buffer, immunoreactive bands were visualized using the Immobilon Western Chemiluminescent HRP Substrate . The intensity of the chemiluminescence of specific bands was digitized using the Cool Saver software  and quantified. Rabbit polyclonal antibodies against, ERα (1:2000), pER S167 (1:500), and GAPDH (1:2000) were purchased from Santa Cruz Biotechnology . Rabbit poly and monoclonal antibodies against Akt (1:2000), phosphorylated Akt Ser473 (1:1000), S6K (1:1000), phosphorylated S6K (1:1000) and CIP2A (1:1000) were purchased from Cell Signaling Technology, Inc., . Anti-PP2A alpha + beta antibody (1:5000) was purchased from Abcam  All antibodies were diluted in the Can Get SignalTotal RNA was extracted from treated cells using the TRIzol reagent  and reverse transcribed using the PrimeScript RT reagent kit . Quantitative PCR (qPCR) was performed in triplicate using the ABI Perkin-Elmer Prism 7300HT Sequence detection system . Taqman gene expression assays (Applied Biosystems) were used to detect expression of CIP2A (Taqman Accession ID Hs00405413_m1) and PP2A (Hs00988483_m1); GAPDH (Hs999999905_m1) was used as a housekeeping gene. Relative quantities were determined using the ΔΔCt method, according to the manufacturer›s instructions.t-test. Data are represented as the mean ± the standard error of the mean (SEM). Data were considered significant when P < 0.05.All experimental data comparing more than two groups were analyzed by one-way ANOVA followed by Fisher's protected least significant difference (PLSD) test. When differences were significant, subsequent analysis with the Post Hoc test with the Bonferroni correction was performed. Other statistical comparisons were conducted by a two-tailed unpaired


As a part of the C4 Rice Consortium’s efforts to identify genes needed to support C4 photosynthesis, EMS mutagenized sorghum populations were generated and screened to identify genes that cause a loss of C4 function. Stable carbon isotope ratio (δ13C) of leaf dry matter has been used to distinguishspecies with C3 and C4 photosynthetic pathways. Here, we report the identification of a sorghum (Sorghum bicolor) mutant with a low δ13C characteristic. A mutant (named Mut33) with a pale phenotype and stunted growth was identified from an EMS treated sorghum M2 population. The stable carbon isotope analysis of the mutants showed a decrease of 13C uptake capacity. The noise of random mutation was reduced by crossing the mutant and its wildtype (WT). The back-cross (BC1F1) progenies were like the WT parent in terms of 13C values and plant phenotypes. All the BC1F2 plants with low δ13C died before they produced their 6th leaf. Gas exchange measurements of the low δ13C sorghum mutants showed a higher CO2 compensation point (25.24 μmol CO2.mol-1air) and the maximum rate of photosynthesis was less than 5μmol.m-2.s-1. To identify the genetic determinant of this trait, four DNA pools were isolated; two each from normal and low δ13C BC1F2 mutant plants. These were sequenced using an Illumina platform. Comparison of allele frequency of the single nucleotide polymorphisms (SNPs) between the pools with contrasting phenotype showed that a locus in Chromosome 10 between 57,941,104 and 59,985,708 bps had an allele frequency of 1. There were 211 mutations and 37 genes in the locus, out of which mutations in 9 genes showed non-synonymous changes. This finding is expected to contribute to future research on the identification of the causal factor differentiating C4 from C3 species that can be used in the transformation of C3 to C4 plants.Recent efforts to engineer C Discrimination against 13C during carbon fixation is greatly dependent on the photosynthetic type, mainly due to the characteristics of the enzyme catalyzing the first step in carbon fixation. Carbon isotope discrimination is defined as Δ = Rair/Rp-1 where Rair and Rp stand for the 13C/12C ratio in the air and the photosynthetic product, respectively ) BTx623 seeds were obtained from Professor William L. Rooney of the Sorghum Breeding and Genetics Division . Hence, the accession was named BTx623-Rooney or WT-R .Semi-quantitative PCR with SYBR Green I Master mix  in a final reaction volume of 20 μl was performed with primers specific to the gene of interest . The EF1S1 FigThe X-axis shows the position in Mb units in respective chromosome whereas Y-axis shows the AF values.(TIF)Click here for additional data file.S2 FigThe X-axis shows the position in Mb units in respective chromosome whereas Y-axis shows the AF values.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 FigTissue name: FP = foliar primordial plastochron 1; FP34 = foliar primordial plastochron 3 or 4; FP5 = foliar primordial plastochron 5; FI = foliar immature; FE = foliar expanded.(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 FigThe Sobic.010G239700, Sobic.010G249000, Sobic.010G250100, Sobic.010G264000, and Sobic.010G241900 showed no apparent changes in the transcript expression compared to the wild type. EF1α is the housekeeping gene.(TIF)Click here for additional data file.S1 TableThe annotations of the genes are according to phytozome v.02.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


The thalamus or “inner chamber” of the brain is divided into ~30 discrete nuclei, with highly specific patterns of afferent and efferent connectivity. To identify genes that may direct these patterns of connectivity, we used two strategies. First, we used a bioinformatics pipeline to survey the predicted proteomes of nematode, fruitfly, mouse and human for extracellular proteins containing any of a list of motifs found in known guidance or connectivity molecules. Second, we performed clustering analyses on the Allen Developing Mouse Brain Atlas data to identify genes encoding surface proteins expressed with temporal profiles similar to known guidance or connectivity molecules. In both cases, we then screened the resultant genes for selective expression patterns in the developing thalamus. These approaches identified 82 candidate connectivity labels in the developing thalamus. These molecules include many members of the Ephrin, Eph-receptor, cadherin, protocadherin, semaphorin, plexin, Odz/teneurin, Neto, cerebellin, calsyntenin and Netrin-G families, as well as diverse members of the immunoglobulin (Ig) and leucine-rich receptor (LRR) superfamilies, receptor tyrosine kinases and phosphatases, a variety of growth factors and receptors, and a large number of miscellaneous membrane-associated or secreted proteins not previously implicated in axonal guidance or neuronal connectivity. The diversity of their expression patterns indicates that thalamic nuclei are highly differentiated from each other, with each one displaying a unique repertoire of these molecules, consistent with a combinatorial logic to the specification of thalamic connectivity. The thalamus, or “inner chamber” of the brain, is a crucial nexus in the brain’s circuitry. It is not only a relay station that conveys sensory information from the periphery to the cerebral cortex, it is also a conduit for cortico-cortical communication , as wellA striking characteristic of the thalamus is its subdivision into ~30 discrete nuclei, which subserve distinct functions and which have highly selective connectivity patterns with the structures mentioned above, most obviously with specific cortical areas. Some nuclei, such as those conveying primary sensory information, project quite selectively to one or a small number of cortical areas, while others, which integrate signals from multiple sources, project in a more diffuse manner across larger areas of cortex. Within each nucleus there are also varying proportions of distinct cell types that either project to specific areas, driving receptive field properties in input layers of cortex, such as layer 4, or that project more widely across cortex and provide modulatory inputs, for example to layer 1 , 7.Different thalamic nuclei thus act as discrete targets for innervation from a wide array of sources, and, in turn, project their axons to numerous other structures with areal, laminar and cell-type specificity. In order to establish these connections, growing axons must be guided to appropriate regions, must recognise the appropriate target and avoid inappropriate ones, and must make the right kinds of synapses on the right kinds of cells. These processes are mediated in general by surface and secreted proteins, which act as signals and receptors, enabling cellular recognition for pathfinding and target selection.Numerous protein families have been identified as playing important roles in thalamic axon guidance and connectivity. Many of these, including Ephrins/Eph-receptors, Netrins/DCC/Unc5s, Slits/Robos, Neuregulin-1/Erbb4, secreted semaphorins/ neuropilins and L1 cell adhesion molecules, mediate general processes such as avoidance of the hypothalamus, projection into the internal capsule and topographic organisation of thalamocortical and/or corticothalamic projections through this intermediate target region . By At earlier stages, a number of studies have described the combinatorial expression patterns of patterning molecules or transcription factors across the thalamus, many of which are expressed selectively in some nuclei and not others –18. Somein situ hybridization to find those expressed in selective or differential patterns across the neonatal thalamus. Second, we analysed the Allen Developing Mouse Brain Atlas (devABA) database for genes encoding surface or secreted proteins, which showed temporal expression profiles similar to known genes for axon guidance or synaptic connectivity. The expression patterns of these genes were then examined to identify selectively or differentially expressed genes at mid or late embryonic stages. Together, these approaches have identified 82 genes encoding candidate connectivity labels in the developing thalamus. The expression patterns of these genes are highly diverse, such that individual thalamic nuclei express distinct repertoires of these surface molecules, consistent with a combinatorial logic to the specification of connectivity.Here, we describe two parallel approaches to identify genes encoding surface or secreted proteins expressed in discrete patterns across the developing thalamus. First, we screened the predicted proteomes of human, mouse, fly and worm for conserved genes encoding predicted transmembrane proteins with any of a number of protein motifs commonly found in axon guidance molecules. These genes were screened by Drosophila melanogaster and/or in the nematode Caenorhabditis elegans.The proteomes of mammals contain many predicted proteins of unknown function. We were interested to discover genes encoding predicted transmembrane proteins that contain any of a number of protein motifs found in known axon guidance molecules. As many known axon guidance molecules are highly conserved from vertebrates to invertebrates, we further concentrated on proteins that had predicted orthologues in the fruitfly DCC and Neogenin, Robos, Slits, L1CAMs, Contactins, Eph-receptors, Ephrins, FGFs and FGFRs, receptor protein tyrosine phosphatases, Cadherins, Protocadherins, Semaphorins, Plexins and Integrins. . Using in situ hybridization for these genes on P0 mouse brain sections, as previously described for genes encoding LRR proteins . Nt. NtNtng1Astn2, Cntnap4, Dlk1, Dner, Fzd7, Gpc3, Lrp8, Lypd1 , Odz3 and Trp53i11 . These display varying degrees of specificity., For example, Fzd7 is highly restricted to what may become a single thalamic nucleus, while Gpc3, is expressed in a small number of scattered cells throughout the thalamus . Astn2, Cntnap4, Dlk1, Lypd1 all show quite selective expression in some developing nuclei and not others, while Dner, Lrp8 and Trp53i11 are expressed more widely, but still differentially. The Odz3 expression pattern documented in the devABA mirrored that seen in our own in situ hybridization experiments extremely faithfully  has been previously directly implicated in specifying thalamic connectivity. Mutation of Lrp8, along with the related gene Vldlr (very low-density lipoprotein receptor), alters retinogeniculate innervation, through mechanisms that appear independent of the function of these proteins as Reelin receptors [Cntnap4 affects synaptic outputs from GABAergic interneurons and ventral tegmental area dopaminergic neurons [Fzd5 is extremely selectively expressed in the parafascicular nucleus of thalamus [eceptors . Mutatio neurons . Gpc3  [Dlk1 (Delta-like-1 homolog) [Dner (Delta/Notch-Like EGF Repeat Containing) [Lypd1/Lynx2 (LY6/PLAUR Domain Containing 1) [Trp53i11 remain largely unknown.A variety of other functions in neural development or function have been shown for actin-2) , Dlk1 (Dhomolog) , Dner (Dtaining)  and Lypdining 1)  but thesBmp3 (Bone morphogenetic protein-3), Igfbp5 (Insulin-like growth factor binding protein 5), Inhba , Gfra1 and Gfra2 (GDNF Family Receptor Alpha 1 and 2), Nrn (Neuritin or Neuritin-1/Nrn1), Tgfb2 , Vgf (VGF nerve growth factor inducible) and Wif1 (WNT inhibitory factor 1).A variety of growth factors and receptors show selective or differential expression across the late embryonic thalamus . These iDifferential expression of the GDNF receptors Gfra1, Gfra2 and Ret has been reported previously in postnatal rat thalamus, with notable overlap in the reticular nucleus , also seOur dual screening approach has identified 82 genes encoding candidate connectivity labels in the developing thalamus. Several lines of evidence make these molecules highly plausible candidates to be involved in specifying some aspects of thalamic connectivity: (i) they encode proteins with motifs that are common in known connectivity molecules; (ii) some of them are members of gene families with already known functions in axon guidance or synaptic connectivity; (iii) they cluster with known axon guidance or synaptogenesis molecules based on their temporal pattern of expression in the embryonic thalamus; and (iv) they are selectively or differentially expressed across dorsal thalamic nuclei at stages when connectivity is being established.The genes we have identified include multiple members of large families where some genes have been previously implicated, such as Ephrins and Eph receptors, cadherins and protocadherins, semaphorins and plexins, and Odz/teneurin genes, where we have expanded the number of candidates potentially involved. Our findings also highlight the selective expression in thalamus of a number of genes or gene families recently implicated in synaptic connectivity in other regions, including several Ig and LRR family members, calsyntenins, cerebellins and Netrin-Gs. In addition, we have found strikingly selective expression of many genes from diverse families not previously implicated in either thalamocortical development or neuronal connectivity.The systematic nature of the devABA data provides the opportunity to collate and compare these expression patterns across gene families, and to draw some general inferences about the molecular logic specifying thalamic connectivity. The most striking aspect of these patterns is how unique they all are. Across these 82 genes, even when considering only one or two anatomical sections, we did not observe any two patterns that we would say are identical. This clearly did not have to be the case. There might well have been trends or consistent sub-patterns that many genes fell into. Most of the differentiation of expression levels (at E15.5 or E18.5) reflects discrete protonuclear divisions, rather than broad gradients across the whole dorsal thalamus, which, a priori, was just as likely an outcome. This indicates that thalamic nuclei are highly differentiated from each other, with each one displaying a unique repertoire of these molecules. There is, in addition, clear evidence of non-uniform gene expression within nuclei, reflecting the known sub-differentiation of these fields. Thus, while initial guidance of thalamic axons may be defined by general topography (reviewed in ), later There is indeed strong evidence for combinatorial interactions, both within and between the gene families identified. For example, co-expression of Ephrins and Eph-receptors in cis can alter responses to these proteins in trans . SimilarWhether the genes we have identified actually encode connectivity labels will, of course, require functional experiments, but the list here is at least likely highly enriched for such molecules. It is by no means comprehensive, however, as both of our strategies had important limitations. The first approach concentrated on genes that are conserved from vertebrates to invertebrates, based on the simple rationale that many of the major families of axon guidance molecules are conserved. Such molecules may be expected to play more important and thus more obvious roles in nervous system development than newly evolved ones, with concomitantly stronger phenotypes when mutated. In addition, functional analyses of such gene families may be further simplified in invertebrate model systems, which often only have one gene copy, whereas mammals often have multiple genes with possibly redundant functions. Thus, while this strategy identified plausible genes that are highly amenable to functional analyses, it excluded the large fraction of the mammalian proteome that is not obviously conserved in invertebrates.Our second strategy took advantage of the Allen Brain Atlas database of expression in the developing mouse brain . This daIn summary, the extensive set of genes identified here as candidate connectivity labels provides a strong starting point and context for functional analyses of single molecules and of their potential combinatorial interactions, which may coordinately specify the complex connectivity patterns of thalamic nuclei.All animal work was approved by the Ethics Committee of Trinity College Dublin and the animal licence to KJM (Ref. B100/3527) from the Department of Health and Children. Newborn mice were sacrificed by decapitation.in situ hybridization were designed to encompass a section of coding sequence and 3’UTR >500bp in length. Briefly this involved TA cloning of PCR products into the TOPO vector (Invitrogen) and simultaneous synthesis and DIG-labeling of RNA transcripts from linearised vector using T7- or Sp6- RNA polymerase. Some cRNA probes were transcribed directly from the PCR products amplified with a sense primer and an antisense primer with T7 promoter sequence at its 5’-end. Primers used were:Digoxigenin (DIG)-labeled antisense cRNA probes for TGACTATGGCACAGAGCCTAKirrel1_for GAAGGAGAGGAAAGGGCAKirrel1_rev CAAAAGAACTTGGTCCGGATKirrel2_for CACTTGGAGAGATGGATTCACKirrel2_rev TACGTGCAGTTTGACAAGGCKirrel3_for AGGCTGCAAGGAATACAGACKirrel3_rev AGACTCACCTCCTGCAAATCIgsf9_for GCGGTAATACGACTCACTATAGGGCATTCCTGTTCAGCTCCCAIgsf9_T7Rev TGACACATTCCACAACGGIgsf9b_for GCGGTAATACGACTCACTATAGGGCCCTTATTCCACTTCACCACAGIgsf9b_T7Rev CATCATACACTGTGGACCTGSdk1_for GCGGTAATACGACTCACTATAGGGAGAAGTTCTGCACCCGTCSdk1_T7rev AATGGTGGTTCTTAGTGGTCASdk2_for GCGGTAATACGACTCACTATAGGGCAAACGAAGGATGAGAACCSdk2_T7rev CATTCAGTGTGAGCTCCAGAOdz1_for GCGGTAATACGACTCACTATAGGGCTGACGCAAAGGCAGAGATOdz1_T7rev TGAGGTCCAATATGAGATCCOdz1_2_for GCGGTAATACGACTCACTATAGGGCAACATCATAATCTCTTTGCCCOdz1_2_T7rev AGAGTCAAGCAAGCGAGAAOdz2_for GCGGTAATACGACTCACTATAGGGCGAGGTGGCAGCAGGTTATOdz2_T7rev GTGCAATATGAGATGTTCCGOdz2_2_for GCGGTAATACGACTCACTATAGGGCAGTAAAGTGGACGAGCTTGGOdz2_2_T7rev GAAGAGTCAACAGTGGGAAGAOdz3_for GCGGTAATACGACTCACTATAGGGCTGCTACAGGAGAATCTGCACOdz3_T7rev CAACCAAATCATTTCCACGOdz3_2_for GCGGTAATACGACTCACTATAGGGCTGGATTAGTTTGGTGAGCGOdz3_2_T7rev TTCAGAAGCAACTCAAGGCOdz4_for GCGGTAATACGACTCACTATAGGGCCAACACAGTCAGGAATACGGOdz4_T7rev AGAAGGAGCTGAAGGTGGOdz4_2_for GCGGTAATACGACTCACTATAGGGCCACGATGCTGTTGCTACTCOdz4_2_T7rev GGCTCCAGAACTGTGTATATCCNeto1_for GCGGTAATACGACTCACTATAGGGCGAGTCTTGCCGAAGGAATANeto1_T7rev AGAAGTCAGTGCAGTGTGGNeto1_2_for GCGGTAATACGACTCACTATAGGGCGAGCTGCTTCCGTCATAGNeto1_2_T7rev CATCAGGAATTGTCTTGGTCNeto2_for GCGGTAATACGACTCACTATAGGGCCTGCTCTGCCTGACTTAACANeto2_T7rev GAATCAAGCACATTCCTGCNeto2_2_for GCGGTAATACGACTCACTATAGGGCGGATCACTCCGACTCCTGNeto2_2_T7rev CACCCTGAACATCGATCCClstn1_for GCGGTAATACGACTCACTATAGGGCTCACCCTCTGTCGACAAGClstn1_T7rev GCCACTGTCGTCATCATTATCClstn2_for GCGGTAATACGACTCACTATAGGGCATGGTGACATCATCTCCAGAClstn2_T7rev TCCCAGTCATGTGCTCAGTClstn3_for GCGGTAATACGACTCACTATAGGGCAGTAGAGAAGGACACAGCAGCClstn3_T7rev In situ hybridization (ISH) was carried out on vibratome-sectioned C57BL6 mouse brains (Jackson Laboratories) as described in [2, 1% Tween-20) prior to colourimetric detection using 2μl/ml NBT/BCIP (Roche) in NTMT. Sections were mounted on Superfrost glass slides  and analysed with an Olympus IX51 microscope.ribed in  with modThe database pipeline was used as described in . In summwww.brain-map.org) comprises expression values of 2002 genes obtained from ISH images in the sagittal plane across four embryonic  and three early postnatal ages  [http://chechiklab.biu.ac.il/~lior/cerebellum.html. The data is expressed as expression density. For each brain region R, the expression density is defined as the sum of expressing pixels in R divided by the total number of pixels that intersect R . Each set of values is dependent on specific gene expression. We normalized the data to reflect variations of expression for each gene. A value of 1 indicates average expression levels whereas a value below or above 1 indicates decreased or increased expression levels compared to average, respectively. With this normalised data, we generated the developmental matrix of 1996 genes x 7 time-points as 6 genes had no expression values throughout. These genes are Chrna1, Hoxd9, Lef1, Nrp2, Pbx1 and Rnd2.The Allen Brain Atlas (ABA) provides a survey of gene expression in the brain from mid-gestation through aging in different species. Of interest to us is the data on the mouse brain. The detailed process of mouse brain data acquisition is explained in Lein, Hawrylycz . Brieflyand P28) . These tand P28)  extractehttp://developingmouse.brain-map.org/docs/Overview.pdf):The genes of the developmental dataset fall into one of these five categories  to subsequently find their protein counterparts. Where multiple isoforms exist, the longest sequence was used for further analyses. We screened Fasta format sequences through PSORTII to find cellular localisation of the protein products and the number of predicted helices of transmembrane proteins (http://psort.hgc.jp) [http://www.cbs.dtu.dk/services/SignalP/, version 4.1)[http://mendel.imp.ac.at/sat/gpi/gpi_server.html, version last modified June 17th 2005) [http://www.cbs.dtu.dk/services/TMHMM/, version 2.0)[http://pfam.xfam.org)[http://smart.embl-heidelberg.de, version 2.12.1 released on August 6th 2012)[http://www.ebi.ac.uk/interpro/search/sequence-search, version 49.0 released on November 20th 2014)[We screened protein products for cellular localisation with an emphasis on the extracellular compartment. More specifically, we looked for signal peptides, transmembrane domains and glycophosphatidylinositol (GPI)-anchors. Since genes were listed with their Allen gene name in the dataset, we looked for the MGI symbol and Ensembl gene identifiers (through .hgc.jp) , 123. PSsion 4.1), 125. A th 2005) –129. Whesion 2.0), 131. Whxfam.org) and SMAR6th 2012), 134, mo0th 2014). For ide0th 2014).http://www.geneontology.org)[Finally, we updated the gene annotations of the dataset from Thompson, Ng (117). Thompson and colleagues had 1385 genes out of the 2002 falling into one or more of these categories: axon guidance pathway, cell adhesion, receptor tyrosine kinases (RTKs) and their ligands, Notch signaling, Wnt signaling, transcription factor activity, basic helix-loop-helix transcription factor, forkhead transcription factor, homeobox transcription factor, nuclear receptor, POU domain genes, neurotransmitter pathway, ion channel, and G-protein coupled receptors (GPCRs). We screened genes for the following gene ontology (GO) term annotations (logy.org) through Axon Guidance GO:0007411;Synapse GO:0045202;- Pattern Specification Process GO:0007389;- Neural Tube Patterning GO:0021532;- Rostrocaudal Neural Tube Patterning GO:0021903;Patterning:—Regionalisation GO:0003002;- Protein Binding Transcription Factor Activity GO:0000988;- Nucleic Acid Binding Transcription Factor Activity GO:0001071;- Sequence-Specific DNA Binding Transcription Factor Activity GO:0003700.Transcription Factor Activity:Since many genes had more than one annotation, we grouped them as follows:- Group 1: axon guidance pathway and cell adhesion;- Group 2: synapse;- Group 3: receptor tyrosine kinases and their ligands, and patterning;- Group 4: neurotransmission pathway ;- Group 5: chromatin and transcription factor activity- Group 6: other  and unannotated.We only had 206 unannotated genes. Each gene belonged to only one group. When a gene fell into more than one group, we prioritised the groups as presented.k-means clustering with the Euclidean distance method on the developmental dataset using the clustering module integrated in ArrayPipe (http://www.pathogenomics.ca/arraypipe—version 1.7)[We performed sion 1.7). Each clk-means clustering analyses or enriched in particular clusters. Statistical significance was determined at p-value < 0.05. Where multiple tests were performed on the same dataset, the Bonferroni correction was taken into account.We performed chi square statistics to determine whether our developmental dataset was normally distributed in regards to cellular localisation and groups across the 10 clusters obtained during S1 TableGenes are organised in clusters defined by TRIBE-MCL, as described . Genes f(XLS)Click here for additional data file.S2 TableAbsolute values of expression densities for 2002 genes from the devABA database per gene per time-point are shown. NaN stands for Not a Number. Blank cells indicate absence of data in the devABA.(XLSX)Click here for additional data file.S3 Tablek-means Euclidean distance 6–18, with 1000 iterations (columns I-U). Clusters are sorted at k = 10, followed by k = 11, followed by k = 12 and so on until k = 18. Three colour scale of cluster number: red < yellow < green, for better visualisation.Normalised expression densities per gene per time-point (columns B-H). Thalamic expression densities were obtained for 1996 genes. Heatmap’s 3 colour scale of gene expression data: 0.2, red; 1, white; blue, 5. Normalised gene expression was clustered by similar patterns of expression using (XLSX)Click here for additional data file.S4 Tablek-nearest neighbor (k-NN) algorithm with k1 = 9 and k2 = 23 [Each gene was matched to its MGI symbol and Ensembl gene identifier. From these, protein sequences were obtained—where multiple isoforms were found, the longest sequence was used for further analyses. Fasta format sequences were screened through PSORT II for cellular localisation . PSORT II uses two-fold  k2 = 23 . Screens k2 = 23 –129. GPI k2 = 23 , 131. Co(XLSX)Click here for additional data file.S5 Tablek = 10 followed by gene name (Column I). Each gene was matched to its MGI symbol (Column J). Protein localisation was assessed after combining the data from the different screens performed as either in the Golgi (gol), cytoplasmic (cyt), cytoskeletal (csk), in the endoplasmic reticulum (end), extracellular including cell wall (ext), mitochondrial (mit), nuclear (nuc), peroxisomal (per), on the plasma membrane (pla) or in vesicles of secretory system . Moreover, when the protein was deemed in the extracellular compartment, it was found to be either GPI-achored (anchored), secreted, single-pass transmembrane (single-pass) or multi-pass transmembrane . Functional assessment was grouped in six different categories: 1) axon guidance pathway and cell adhesion; 2) synapse; 3) receptor tyrosine kinases and their ligands, and patterning; 4) neurotransmission pathway ; 5) chromatin and transcription factor activity; and 6) other  and unannotated (Column M). Three colour scale of cluster number and group number: red < yellow < green, for better visualisation (Columns I and M). See text for details.Normalised expression densities per gene per time-point (Columns B-H). Heatmap’s 3 colour scale of gene expression data: 0.2, red; 1, white; 5, blue. Data is sorted by cluster number at (XLSX)Click here for additional data file.S6 TableGenes are listed by cluster and grouped into those with known roles in thalamocortical connectivity, known functions in axon guidance of synaptogenesis more generally or no such known functions (Other).(XLSX)Click here for additional data file.S1 FigKirrel3, Igsf9b and Sdk2, one rostral and one more caudal. Scale bar: 1 mm.Two coronal sections are shown for (TIFF)Click here for additional data file.S2 FigOdz1, Odz2, Odz3 and Odz4, one rostral and one more caudal. In addition to restricted expression in dorsal thalamus, there is also graded expression of Odz genes across cortex (cx) and striatum (str) in differing patterns. Scale bar: 1 mm.Two coronal sections are shown for (TIFF)Click here for additional data file.S3 FigOdz1, Odz2, Odz3 and Odz4, one rostral and one more caudal. Differential expression across the dorsal thalamus is already evident at this stage. The corresponding entire brain sections are shown in Figure S4. Scale bar: 500 μm.Two coronal sections are shown for (TIFF)Click here for additional data file.S4 FigOdz1, Odz2, Odz3 and Odz4, one rostral and one more caudal. Scale bar: 1 mm.Two coronal sections are shown for (TIFF)Click here for additional data file.S5 FigNeto1 and Neto2, one rostral, one at an intermediate level (mid) and one more caudal. Scale bar: 1 mm.Three coronal sections are shown for (TIFF)Click here for additional data file.S6 FigNeto1 and Neto2, one rostral and one more caudal. Differential expression across the dorsal thalamus is already evident at this stage. Scale bar: 1 mm.Two coronal sections are shown for (TIFF)Click here for additional data file.S7 Figk = 6 to 18 (k indicated at top left corner of each table). Clusters were organised chronologically with early peaks of expression at the top and later peaks of expression at the bottom. Heatmap’s 3 colour scale of gene expression data: 0.2, red; 1, white; 5, blue. k = 10 was used for further analyses; the corresponding tableframe is in bold.Normalised expression densities were averaged per cluster to see the trends of expression for the results of all clustering analyses from (TIF)Click here for additional data file.S8 FigIn situ hybridization data from a sagittal section of the thalamus at E18.5 obtained from the devABA (A and E). Corresponding section from the anatomic reference atlas . Higher magnifications of the thalamus from squared regions in A, B, E and F, respectively . Scale bar in A, B, E and F, 880 μm; C, D, G and H, 214 μm. PT (pretectum) and prethalamus (PTh) are labeled within their region bordering the thalamus. p1, prosomere 1 ; p1A, alar plate of prosomere 1; p1B, basal plate of prosomere 1; p2, prosomere 2 ; p2A, alar plate of prosomere 2; p2B, basal plate of prosomere 2; p3, prosomere 3 ; p3A, alar plate of prosomere 3; p3B, basal plate of prosomere 3. Note that voxels were assigned regional labels for thalamus and thalamic tegmentum separately.(TIFF)Click here for additional data file.S9 Figin situ hybridizations are on sections from P0 nenonates, while the devABA sections are from E18.5 embryos. Despite this difference of about a day, there is strikingly good correspondence in expression patterns across the dorsal thalamus, striatum (str), cortex (cx) and other brain regions. Scale bar: 1 mm.Two sagittal sections are shown, one lateral and one more medial. Our (TIFF)Click here for additional data file.


We report reoccurrence of highly pathogenic avian influenza A(H5N2) virus clade 2.3.4.4 in a wild mallard in Alaska, USA, in August 2016. Identification of this virus in a migratory species confirms low-frequency persistence in North America and the potential for re-dissemination of the virus during the 2016 fall migration. Historically, apparently effective geographic barriers (Bering and Chukchi Seas of the North Pacific Ocean) appeared to limit dissemination of Asian-origin, highly pathogenic avian influenza virus (HPAIV), such as influenza A(H5N1) virus A/goose/Guangdong/1/1996 (Gs/GD), between the Old and New Worlds  fly to Alaska, USA, to breed. Thus, Alaska is a potentially major location for intercontinental virus transmission  virus, hereafter known as 8887/2016(H5N2) virus, to trace its origin and understand its genetic relationship to HPAIV H5N2 isolated in 2014–2015 .http://www.offlu.net/fileadmin/home/en/resource-centre/pdf/Influenza_A_Cleavage_Sites.pdf). Homology BLAST searches showed that all genes had >99.2% nucleotide similarity with genes of H5N2 virus outbreak strains collected during late February–March 2015 (We considered 8887/2016(H5N2) virus an HPAIV on the basis of amino acid sequence at the hemagglutinin proteolytic cleavage site (PLRERRRKR/G), as shown for other Gs/GD HPAIV H5Nx subtypes in subclade 2.3.4  virus formed a cluster with viruses from initial detections in the midwestern United States, including a snow goose in Missouri, a backyard poultry farm in Kansas, and a turkey farm in Minnesota . Our epi–3  substitutions/site/year. In the root-to-tip regression plot of maximum-likelihood phylogeny, we found that 8887/2016(H5N2) virus fell below the regression line, which indicated sequences that are slightly less divergent than average of 2014–2015 H5N2 outbreak viruses . Consistent clustering of 8887/2016(H5N2) virus with other H5N2 outbreak viruses in phylogenies for each gene suggests that the 8887/2016(H5N2) virus probably evolved through genetic drift from common ancestors of outbreak viruses in the absence of further reassortment  Figure 2 viruses  Figure 3The last reported detection during the influenza outbreak in the United States in 2015 was from a Canada goose in Michigan on June 17. There were 2 detections by PCR  from mallards in July (bird banding effort in Utah) and November (hunter harvest in Oregon) during surveillance in 2015–2016. Sequence of the HPAIV H5N2 from a wild mallard during surveillance in 2016–2017, evidence for continued evolution of this virus lineage, widespread detections of HPAIV H5N2 in healthy wild birds  virus clade 2.3.4.4 in wild birds, Alaska, USA, 2016.


In vitro, M1 binding to large unilamellar vesicles (LUVs), which contain negatively charged lipids, decreased significantly when the M1 R76/77/78 motif was mutated. In cells, M1 alone was mainly observed in the nucleus (47%) and in the cytosol (42%). Conversely, when co-expressed with the viral proteins NS1/NEP and M2, M1 was relocated to the cell membranes (55%), as shown by subcellular fractionation experiments. This minimal system allowed the production of M1 containing-VLPs. However, M1 with mutations in the arginine triplet accumulated in intracellular clusters and its incorporation in VLPs was strongly diminished. M2 over-expression was essential for M1 membrane localization and VLP production, whereas the viral trans-membrane proteins HA and NA seemed dispensable. These results suggest that the M1 arginine triplet participates in M1 interaction with membranes. This R76/77/78 motif is essential for M1 incorporation in virus particles and the importance of this motif was confirmed by reverse genetic demonstrating that its mutation is lethal for the virus. These results highlight the molecular mechanism of M1-membrane interaction during the formation of influenza A(H1N1)pdm09 virus particles which is essential for infectivity.The influenza A(H1N1)pdm09 virus caused the first influenza pandemic of the 21st century. In this study, we wanted to decipher the role of conserved basic residues of the viral M1 matrix protein in virus assembly and release. M1 plays many roles in the influenza virus replication cycle. Specifically, it participates in viral particle assembly, can associate with the viral ribonucleoprotein complexes and can bind to the cell plasma membrane and/or the cytoplasmic tail of viral transmembrane proteins. M1 contains an N-terminal domain of 164 amino acids with two basic domains: the nuclear localization signal on helix 6 and an arginine triplet (R76/77/78) on helix 5. To investigate the role of these two M1 basic domains in influenza A(H1N1)pdm09 virus molecular assembly, we analyzed M1 attachment to membranes, virus-like particle (VLP) production and virus infectivity.  The influenza A(H1N1)pdm virus represents a public health threat and is still circulating in humans. A better understanding of its replication cycle and viral transmission is crucial for developping new antiviral strategies which might help to control the next pandemics. Influenza viruses belong to the Orthomyxoviridae family of negative-sense, single-stranded and segmented RNA genome viruses. The influenza A virus is composed of eight viral RNA segments  that encode ten major proteins. The production of new infectious virions requires their simultaneous incorporation during virus assembly. Assembly and budding of influenza virions is a multi-step process that occurs at the cell plasma membrane of infected cells . M1-. M1-via  pathway  9]. Alt. Altvia  pathway . However pathway . The ass pathway . It has  pathway , before  pathway . Thus, M pathway  or/and o pathway  or M2 [1 pathway  16]. As. Asvia t pathway . The M1  pathway . However pathway . M1 has in vitro  and is ein vitro . In the  . Th. Th28]. in vitro  [33 [3332] [in vitro  [32]. VPin vitro , but notet al. , an, anet alembranes . The quiembranes  15]    . Th. Th16]. S1 FigExpression of influenza A(H1N1)pdm09 HA viral envelope proteins was checked in the Post-Nuclear Supernatant  after transfection of HEK 293T cells with the indicated plasmids using western blotting with a human serum obtained using an influenza A(H1N1)pdm09 strain isolated from a vaccinated patient. HA-1 of ~63KDa is indicated.(TIF)Click here for additional data file.S2 FigA) Immunofluorescence confocal microscopy images of HEK 293T cells transfected with pcDNA-M1 R76/77/78A), pcDNA-M2, pHW2000-NS1/NEP and M* containing the R76/77/78 mutation, and Rab11-mRFP if any (a). M1 was detected using a primary anti-M1 antibody  and vesicular markers using primary anti-EEA1, CD63, LC3 or Lamp2 antibodies, as indicated (in green). Transmission images are in grey. Scale bars, 5 μm. B) Quantification of co-localization of the M1 R76/77/78 signal with the indicated vesicle markers (Mander’s overlap coefficients).(TIF)Click here for additional data file.


U2AF1S34F is expressed from one of the two endogenous U2AF1 loci. By altering levels of mutant or wild-type U2AF1 in this cell line and by analyzing published data on human lung adenocarcinomas, we show that S34F-associated changes in alternative splicing are proportional to the ratio of S34F:wild-type gene products and not to absolute levels of either the mutant or wild-type factor. Preferential recognition of specific 3′ splice sites in S34F-expressing cells is largely explained by differential in vitro RNA-binding affinities of mutant versus wild-type U2AF1 for those same 3′ splice sites. Finally, we show that lung adenocarcinoma cell lines bearing U2AF1 mutations do not require the mutant protein for growth in vitro or in vivo. In contrast, wild-type U2AF1 is required for survival, regardless of whether cells carry the U2AF1S34F allele. Our results provide mechanistic explanations of the magnitude of splicing changes observed in U2AF1-mutant cells and why tumors harboring U2AF1 mutations always retain an expressed copy of the wild-type allele.We have asked how the common S34F mutation in the splicing factor U2AF1 regulates alternative splicing in lung cancer, and why wild-type U2AF1 is retained in cancers with this mutation. A human lung epithelial cell line was genetically modified so that  U2AF1S34F mutation.Large-scale genomics studies have identified recurrent mutations in many genes that fall outside the conventional domain of proto-oncogenes. They include genes encoding factors that mediate RNA splicing; mutations affecting four of these genes are present in up to half of proliferative myeloid disorders and in a significant number of solid tumors, including lung adenocarcinoma. Here we have characterized several properties of a common mutant version of the U2AF1 splicing factor, a component of the U2 auxiliary factor complex, in lung cells. We have found that mutant-associated changes in splice site selection are primarily influenced by the ratio of mutant and wild-type U2AF1 gene products; thus increasing wild-type U2AF1 levels represses the mutant-induced splicing program. We show that the altered splice site preferences of mutant U2AF1 can be attributed to changes in its binding to relevant 3′ splice sites. We also show that mutant U2AF1 is different from some oncogenes: the growth properties of lung cancer cell lines carrying the mutant allele are unaffected by loss of the mutant gene, while the wild-type allele is absolutely required for survival. These results advance our understanding of the molecular determinants of the mutant-associated splicing program, and they highlight previously unappreciated roles of wild-type U2AF1 in the presence of the recurrent  U2AF1, SF3B1, SRSF2 and ZRSR2) have recently been reported in up to 50% of myelodysplastic syndromes (MDS) and related neoplasms and at lower frequencies in a variety of solid tumors [U2AF1 is known to be recurrently mutated in lung adenocarcinomas (LUADs) [U2AF1 in LUAD affects codon 34 and always changes the conserved serine in a zinc knuckle motif to phenylalanine . This striking mutational consistency suggests a critical, yet unknown, role for U2AF1S34F during lung carcinogenesis. In addition, the wild-type (WT) U2AF1 allele is always retained in cancers with common U2AF1 mutations, including U2AF1S34F [U2AF1 is not known.Somatic mutations in genes encoding four splicing factors (d tumors . Among t (LUADs) . The onlIn vitro crosslinking assays showed that U2AF1 contacts the AG dinucleotide at the intron-exon boundary and flanking sequences [U2AF1 is a component of the U2 small nuclear ribonucleoprotein auxiliary factor complex (U2AF) ,11. Duriequences –16.U2AF1 mutations are known to cause specific alterations in RNA splicing, most notably affecting the inclusion of cassette exons in mRNA [Consistent with the critical role that U2AF1 plays in RNA splicing,  in mRNA –20. Howe in mRNA . Another in mRNA . Finally in mRNA . HoweverU2AF1S34F is critically dependent on expression of the wild-type, but not the mutant, allele of U2AF1.Here, we combine genetic and biochemical approaches to show that wild-type U2AF1 antagonizes the S34F-associated splicing program in lung epithelial cells. Analyses of the transcriptomes of primary LUAD samples as well as isogenic lung cells in culture indicate that the ratio of mutant to wild-type U2AF1 gene products is a critical determinant of the magnitude of S34F-associated changes in alternative splicing. S34F-associated splicing alterations can be largely explained by differences in the relative affinities of U2AF-SF1 complexes containing mutant versus wild-type U2AF1 for RNA containing the relevant 3′ splice sites. Moreover, we show that proliferation of cancer cells with U2AF1 mutation, S34F.  We identified cassette exons whose inclusion was increased or decreased by ten percent or more in each tumor with the S34F mutation relative to the median inclusion of each cassette exon across all tumors without a U2AF1 mutation. We then identified consensus sequence motifs for the 3′ splice sites lying immediately upstream of the cassette exons that were promoted or repressed in association with U2AF1S34F, represented by “sequence logos” as shown in Figs U2AF1 mutation as controls.Before undertaking experiments to study the effects of mutant U2AF1 in cultured cells, we first examined 512 transcriptomes from primary human LUADs published by The Cancer Genome Atlas (TCGA) . ThirteeU2AF1-mutant tumor. Notably, as illustrated by the sequence logos, the nucleotide distribution at the -3 position (boxed) of promoted and repressed exons was different from that observed upstream of the much larger number of unaffected exons: A replaced T as the second most common nucleotide preceding the promoted exons, while T was more common than C in the sequence preceding the repressed exons. These patterns were observed in nine of the thirteen tumors with the U2AF1S34F allele  mRNA. Tumors with quasi-WT patterns had low S34F:WT mRNA ratios (ranging from 0.27 to 0.31), whereas all but one tumor with the S34F-associated pattern had higher ratios (0.43 or more)  . In contU2AF1 DNA copy number correlated with the estimated levels of total U2AF1 mRNA  might account, at least in part, for the varying S34F:WT mRNA ratios in LUAD samples . We correlated S34F:WT ratios with the quantitative inclusion of specific S34F-responsive cassette exons and 5' extensions of exons resulting from competing 3′ splice sites that were reported previously [ASUN and STRAP) or more (ATR) inclusion, and two 5' extensions of exons (FMR1 and CASP8) that were used less frequently in the presence of U2AF1S34F. Tumors with the highest S34F:WT mRNA ratios showed the lowest inclusion levels of the cassette exon in STRAP mRNA, whereas tumors without a U2AF1 mutation had the highest level of inclusion  and cyclin-dependent kinase-4 (CDK4) [U2AF1S34F allele at an endogenous locus in HBEC3kt cells, we adopted a published genomic DNA editing approach [PiggyBac transposon that leaves no traces of exogenous DNA at the locus (PiggyBac elements), we chose one subclone from each of the two wild-type intermediate clones (referred to as WT1 and WT2 cells) and two subclones from the sole mutant intermediate clone (referred to as MUT1a and MUT1b cells) for all subsequent experiments with isogenic HBEC3kt cells  was previously derived from normal human bronchial tissue and immortalized by introduction of expression vectors encoding human telomerase reverse transcriptase (4 (CDK4) . To knocapproach , using the locus . Wild-tyhe locus . From thkt cells . The MUT protein . Using hlls Figs  and S5C,U2AF1 S34F allele affects mRNA splicing, we first assayed the inclusion levels of 20 cassette exons that were previously reported to be associated with mutant U2AF1 in both LUAD and AML (acute myeloid leukemia) [ASUN and STRAP cassette exons, were indeed S34F-dependent in our engineered cells using RT-qPCR with isoform-specific primers  . We confers Figs  and S6.U2AF1S34F and that the MUT cells exhibited similar alterations in splicing relative to their WT counterparts as do primary LUAD transcriptomes.We next evaluated the global difference in cassette exon recognition in MUT versus WT cells using RNA-seq . MUT andU2AF1S34F mutation to test the hypothesis that the S34F:WT ratio, rather than absolute levels of the mutant or wild-type gene products, controls S34F-associated splicing. We tested two specific predictions. First, changing the levels of U2AF1S34F while keeping the S34F:WT ratio constant should not affect S34F-associated splicing. Second, changing the level of wild-type U2AF1 while keeping the level of U2AF1S34F constant  should alter the inclusion of S34F-dependent cassette exons. We tested these predictions in the isogenic HBEC3kt cells by manipulating levels of wild-type or mutant U2AF1 gene products and measuring the subsequent changes in S34F-associated splicing.We next used our isogenic cell model with the U2AF1 mRNA concordantly in MUT1a cells, keeping the S34F:WT mRNA ratio constant. This was achieved by transducing MUT1a cells with short hairpin RNAs (shRNAs) that target regions of the U2AF1 transcripts distant from the S34F missense mutation. The same shRNAs were also introduced in WT1 cells as a control. Allele-sensitive RT-qPCR confirmed that the mRNA ratio of the mutant and wild-type U2AF1 remained constant  designed to match either the WT or mutant U2AF1 sequences were shown to disrupt either reading frame selectively, generating indels (insertions and deletions) at the U2AF1 locus and thereby changing the S34F:WT ratios (U2AF1 is required for the growth of these cells (as shown below), we extracted RNA and protein from cells six days after transduction with Cas9 and sgRNA-WT, when depletion of wild-type U2AF1 was incomplete , two involve competition between different 3′ splice sites (in CASP8 and SRP19), and one involves a choice between two mutually exclusive exons (in H2AFY). We conclude that the importance of the S34F:WT ratio for S34F-dependent splicing changes extends from cassette exon recognition to other types of alternative splicing.We observed similar results when we extended our analyses to include other types of alternative splicing beyond cassette exon recognition . We used2AF1S34F . Two of ZFAND1, FXR1, ATR, MED15) and decrease (CEP164) in exon inclusion, as well as competing 3′ splice site recognition (FMR1). These splicing events exhibited S34F-associated alterations in both our isogenic cell lines , which is consistent with the greater prevalence of -3A than -3T preceding S34F-promoted exons.The trends among S34F-altered RNA binding affinities of U2AF1 complexes  for the 34F Figs  and S1A.CEP164, FMR1, ZFAND1, FXR1)  . SimilarR1)  were either similar to wild-type or decreased . As such, a “net gain” in affinity for the proximal relative to distal 3′ splice site could explain the observed S34F-associated exon inclusion in ATR mRNAs. For the MED15 pre-mRNA, deviation of the S34F-associated splicing changes and a simple RNA affinity model suggested that additional mechanisms control selection of the MED15 3′ splice sites. In total, our measurements of in vitro RNA-binding affinities are sufficient to explain six of seven tested alterations in splice site recognition driven by U2AF1S34F  do not exhibit obvious phenotypic properties of neoplastic transformation—such as a growth advantage over wild-type cells  the mutant allele, we searched the COSMIC database for LUAD cell lines with the U2AF1S34F allele [U2AF1 locus  and one copy of a variant allele. We confirmed that U2AF1S34F was the minor allele in these cells by Sanger sequencing and allele-specific RT-qPCR  and the HBEC3kt-derived MUT1a cell line.Another attribute of some well-known oncogenes, such as F allele . Two celU2AF1S3F allele was confirmed by restoration of a normal RNA splicing profile in subclonal cells derived from the clonogenic assay colonies  exhibited altered growth, in the form of a two-fold increase in clonogenicity . SuccessAF1 cDNA  that is AF1 cDNA . OverallU2AF1S34F on tumor growth in vivo, we derived H441 and HCC78 cells transduced with Cas9 and sgRNA-S34F or sgRNA-GFP as polyclonal pools or as clones , U2AF1 can support in vitro splicing reactions more efficiently than nuclear extracts derived from cells overexpressing wild-type U2AF1 for a minigene with a specific 3′ splice site sequence [Several lines of evidence support the idea that U2AF1S34F is capable of initiating pre-mRNA splicing once it binds to RNA. Nuclear extracts of cells overexpressing mutant sequence . In addisequence . Our obssequence ).In light of our findings and existing evidence from the literature, we propose a model wherein mutant U2AF1 drives differential splicing by favoring the recognition of one of two competing 3′ splice sites . This moOur proposed model is not exclusive of other possible contributing effects, such as competitive binding of mutant and wild-type U2AF1 to a factor with low stoichiometry , or effeU2AF1S34F is recurrently found in LUAD, other solid tumors, and myeloid disorders, suggesting that the mutant allele confers a physiological property that provides a selective advantage during neoplasia. A gene ontology (GO) analysis for genes that show S34F-associated alterative splicing in HBEC3kt-derived isogenic cells shows significant alterations in many biological processes such as mRNA processing, RNA splicing, G2/M transition of mitotic cell cycle, double-strand break repair and organelle assembly . However, we did not observe signs of neoplastic transformation or changes in cell proliferation after introducing U2AF1S34F into the endogenous U2AF1 locus in HBEC3kt cells , H441, A549 (ATCC) and HCC78 cells (DMSZ) were cultured as previously described  or accorTotal RNA was extracted and reverse transcribed as previously described . SplicinClonogenic assay was performed by infecting cells with lentiviruses two days before seeding them into 100 mm dishes (1000 live cells per dish) to grow colonies. Growth media were supplemented with puromycin (1 μlg/ml) for selecting infected cells and were changed once a week for up to three weeks. Cell colonies were stained with 0.03% methylene blue (in 20% methanol) for 5 min. Clonogenicity is defined as colony numbers formed as a percent of those in control cells.U2AF1S34F allele and allelic-specific disruption of U2AF1 are described in the Supplemental Materials and Methods.Details of all DNA constructs used in the study and the genome editing approaches for creating the High throughput mRNA sequencing (RNA-seq) was conducted in the Sequencing Facility of the National Cancer Institute. RNA quality was assessed by 2100 Bioanalyzer (Agilent). Total RNA with good integrity values (RIN > 9.0) was used for poly A selection and library preparation using the Illumina TruSeq RNA library prep kit. Two or four samples were pooled per lane and ran on the HiSeq2000 or HiSeq2500 instrument using TruSeq V3 chemistry. All samples were sequenced to the depth of 100 million pair-end 101 bp reads per sample.Splicing analysis of RNA-seq data from the TCGA LUAD cohort as well as engineered HBEC3kt, H441, and HCC78 cell lines was performed as previously described . A briefPurification of U2AF1 complexes, as illustrated in t test or otherwise stated. In all analyses, p values ≤ 0.05 are considered statistically significant.All experiments were independently performed at least three times unless otherwise stated. Statistical significance was determined by two-tailed Student's S1 Text(DOCX)Click here for additional data file.S1 FigA: Logos from tumors carrying a U2AF1S34F mutation and showing “typical S34F” consensus 3′ splice sites. Panel B: Logos from tumors carrying the U2AF1S34F mutation but with compromised S34F-associated features at the -3 position (“quasi-WT” pattern). Panel C: Logos from tumors without a U2AF1 mutation. Sequence logos deduced from tumor transcriptomes #7903 and #7727 are also displayed in As in (PDF)Click here for additional data file.S2 FigU2AF1S34F mRNA (panel A), the level of total U2AF1 mRNA (panel B), or the percentage of tumor nuclei in the tissue samples (panel C). The definitions of the shape and color of each dot are the same as in B—D. (D), The amount of U2AF1 mRNA is displayed in relation to gains and losses of U2AF1 DNA copy number. Box indicates U2AF1 mRNA levels from the 25th to 75th percentiles within the group. The median value is represented as a line in box. Error bars represent the range of values. TPM, transcripts per million. ns, not significant as determined by student’s t test .Tumors with transcriptomes that display “typical S34F” or “quasi-WT” consensus 3′ splice sites Figs  are plot(PDF)Click here for additional data file.S3 FigU2AF1S34F mRNA, total U2AF1 mRNA, or percent tumor nuclei. Panels A–D, cassette exon in STRAP mRNA that is preferentially skipped in the presence of the U2AF1S34F mutation. Panels E–H, cassette exon in ASUN mRNA that is preferentially skipped in the presence of the U2AF1 S34F mutation; Panels I–L, cassette exon in ATR mRNA that is preferentially included in the presence of the U2AF1S34F mutation; Panels M–P, the 5' extension of the FMR1 exon that is less frequently included in the presence of the U2AF1S34F mutation; Panel Q–T, the 5' extension of the CASP8 exon that is less frequently included in the presence of the U2AF1S34F mutation. U2AF1-mutant LUAD transcriptomes containing sufficient number of informative reads to calculate the inclusion frequency were included in the analysis. The median usage of the indicated isoform for all transcriptomes from tumors without the mutation is shown as a triangle. The definitions of the shape and color of each dot are the same as in B—D. r, Pearson’s association coefficient. Panel A is also shown in Pearson’s correlation was calculated to seek relationships between the inclusion levels of five representative splicing events and four factors in several LUAD tumors in the TCGA data set: the S34F:WT mRNA ratio, the level of (PDF)Click here for additional data file.S4 Fig(A). Strategy for identifying intermediate cell clones by Southern blot. Left Panel: Southern blot probes and the expected fragment sizes after restriction enzyme digestion of genomic DNA. Right Panel: Details of the Southern blot strategy. E, EcoR I; X, Xho I; E1, E2, E8, exons 1, 2, and 8 (black boxes); E2/mE2, wild-type or S34F-mutant exon 2; yellow box, drug selection cassette (PGK-Hygro∆TK) flanked by inverted terminal repeat sequences for recognition by the Piggybac transposase (green lines); thick black lines, Southern blot probes. (B). Drug-resistant intermediate clones were identified by Southern blot. Cell clones with the expected pattern of restriction fragments are marked with a star. (C). Top panel: Strategy for identifying mutant and wild-type intermediate clones by PCR amplicon sequencing. The PCR primer P1-F is upstream of the 5' homology arm sequence from the donor vector, while P1-R is within the drug selection cassette. Therefore, the PCR amplicon is specific for the intermediate allele. Bottom Panel: Sanger sequencing results show that one of the three intermediate clones, #21, carried the S34F mutation, while clones #12 and #16 were wild-type for U2AF1. The codons (on the reverse strand) for Ser34 or Phe34 are underlined. (D). The relationship of the intermediate and final cell clones. (E). U2AF1 mRNA and protein levels were similar in the final cell clones. Top Panel: U2AF1 mRNA was measured by RT-qPCR and normalized to levels of GAPDH mRNA. The relative U2AF1 mRNA level in WT1 cells was set to 1.0. (Bottom) Immunoblots for U2AF1 and ACTB using total cell lysates from the indicated cell lines.(PDF)Click here for additional data file.S5 Fig(A). Upper Panel: Sequences of the primers and probes used in the allele-sensitive S34F/WT SNP Taqman assay. The nucleotide corresponding to the S34F missense mutation (reverse strand) is underlined. Bottom Panel: Diagram to show that all the primers (arrows) and probes (bar) are located within exon 2, allowing detection of the S34F:WT U2AF1 ratio for both genomic DNA and mRNA. (B). The S34F and wild-type (WT) probes are specific for their targets. Characterization of probe specificity was performed using plasmid DNA carrying either the WT or S34F mutant exon 2 of U2AF1. Eight 10-fold serial dilutions of the plasmid DNA templates were used (starting concentration: 2 x 10^8 molecules per 10 μl reaction), and the assay was performed in triplicate. According to the qPCR amplification curves (delta Rn vs. cycle), the WT probe is specific for wild-type U2AF1, while the S34F probe can detect wild-type U2AF1 at a lower efficiency  for DNA templates at the same concentration). (C). The mRNA levels of S34F and wild-type U2AF1 are similar in MUT1a and MUT1b cells, as revealed by the allele-sensitive S34F/WT SNP Taqman Assay (the S34F:WT mRNA ratio approximated 1). This result is consistent with the RNA-seq result in (PDF)Click here for additional data file.S6 FigU2AF1S34F mutation as judged by RNA-seq of LUAD and AML tumor samples from TCGA [ASUN and STRAP cassette exons are also shown in The relative amounts of short and long isoforms of the designated genes were measured by RT-qPCR to estimate changes in the inclusion of cassette exons, that were previously reported to be affected by the rom TCGA . The spl(PDF)Click here for additional data file.S7 Fig(A). Knockdown of total U2AF1 mRNA with the indicated shRNAs does not affect S34F:WT mRNA ratios in MUT1a cells, as measured by the allele-sensitive Taqman S34F/WT SNP assay. (B). The indicated U2AF1 shRNAs reduced total U2AF1 mRNA levels. . Reduction of U2AF1 mRNA does not affect the inclusion of cassette exons in USP25 and AXL mRNAs. Both of the cassette exons showed increased inclusion in the presence of U2AF1S34F ((E). Reduction of total U2AF1 mRNA favors the selection of the isoform without the 5' extended exon (“short” isoform) in a competing 3′ splice site event in SLC25C2 mRNA in both WT1 and MUT1a cells. Selection of the isoform containing the 5' extended exon (“long” isoform) was shown to be dependent on U2AF1 in cells expressing only wild-type U2AF1 [Additional assays used in the studies shown in 2AF1S34F . (E). Repe U2AF1 . The car(PDF)Click here for additional data file.S8 FigU2AF1 genomic DNA except at the middle position of the S34 codon (underlined) in the mutant sequence. The CGG sequence (in red) served as the protospacer adjacent motif (PAM) for CRISPR-Cas9. The corresponding mouse U2af1 genomic sequence, which was used to construct the U2AF1 cDNA for overexpression Click here for additional data file.S9 FigA) quantifying the S34F:WT mRNA ratios by the allele-sensitive S34F/WT SNP Taqman assay or (panel B) RNAseq for counting the number of reads supporting either wild-type or mutant U2AF1. Error bars represent standard deviation (s.d.) from a representative experiment in panel A.MUT1a cells were transduced with lentiviruses expressing Cas9 and either sgRNA-GFP, sgRNA-WT, or sgRNA-S34F. Cells were harvested 6 days later for (panel (PDF)Click here for additional data file.S10 FigA or B. Cassette exon events Click here for additional data file.S11 FigA. ATR; Panel B. MED15; Panel C. H2AFY; Panel D. CASP8; Panel E. SRP19), using RT-qPCR with isoform-specific primers in a droplet digital PCR instrument. The cartoon above each panel depicts the type of alternative splicing being measured. The relative isoform ratio in control WT1 cells (transduced with sgRNA-WT and Cas9) is set to one. Student’s t test was used for comparing the difference in the indicated comparisons. Asterisks represent statistically significant changes. Error bar represents s.e.m. (n = 3).Samples from the experiment shown in (PDF)Click here for additional data file.S12 FigD) that resulted from three replicates are inset.The wild-type or mutant (S34F) U2AF1 protein complexes with U2AF2 and SF1 were titrated into 5' fluorescein-tagged RNA oligonucleotides over the indicated range of concentrations. The average fluorescence anisotropy data points and error bars of three independent titrations are overlaid with the nonlinear fits as described in the Supplemental Materials and Methods. The RNA sequences are indicated below each graph title. The average values and standard deviations of the apparent equilibrium dissociation constants (K(PDF)Click here for additional data file.S13 FigA, B). Equal numbers of isogenic HBEC cells  were plated and the numbers of live and dead cells were quantified over the course of three days. Cell viability was determined by Trypan Blue staining. (C). Cell viability was calculated as the ratio of dead versus live cells. Error bars represent s.d. from a representative experiment.((PDF)Click here for additional data file.S14 Fig(A). U2AF1S34F is the minor allele in H441 and HCC78 lung adenocarcinoma cell lines. Genomic PCR was performed for H441 and HCC78 cells, using a PCR primer pair that flanks the codon Serine 34 . The PCR amplicon was subject to Sanger sequencing. A region containing the codon Serine 34 (in grey) is shown. “TCT” (encoding Serine) is the wild-type sequence, while “TTT” encodes Phenylalanine. (B). Polyclonal cell lines were established for H441, HCC78 and A549 cells transduced with Cas9 and either sgRNA-GFP or sgRNA-S34F, followed by RNA extraction and RT-qPCR to determine the ratio of mutant and wild-type U2AF1 mRNA by the allele-sensitive S34F/WT SNP Taqman assay. (C). The relative short/long isoform ratios of ASUN and STRAP mRNAs were measured as in (PDF)Click here for additional data file.S15 FigA). The S34F:WT DNA ratios was examined in clonal cell lines derived from H441 and HCC78 LUAD lines and MUT1a cells after transduction with Cas9 and sgRNA-GFP (boxed lines) or sgRNA-S34F. The clones were expanded and the genomic DNA was harvested to measure the ratio of mutant and WT U2AF1 DNA by the allele-sensitive S34F/WT SNP Taqman assay. (B). Levels of U2AF1 and ACTB protein were measured by immunoblots in extracts from selected MUT1a-derived cell clones from Panel A, as well as from two clones derived from WT1 cells. The WT1-derived cell clones lack one intact WT U2AF1 allele and were generated by transiently transfecting WT1 cells with Cas9 and sgRNA-GFP or sgRNA-WT. (C). As in ASUN and STRAP mRNAs were measured in MUT1a- or WT1-derived cell clones. Error bars represent s.d. from a representative experiment.((PDF)Click here for additional data file.S16 FigA). Heat map depicting the inclusion levels of altered cassette exons. Dendrograms were constructed from an unsupervised cluster analysis based on all cassette exons that showed at least a 10% change in use between the indicated cell lines. (B). Sequence logos from 3′ splice sites preceding cassette exons with altered use in control H441 cells (a polyclone line transduced with Cas9 and sgRNA-GFP), comparing to H441 cells with the U2AF1S34F allele disrupted (a polyclone line transduced with Cas9 and sgRNA-S34F), display typical S34F-associated features. Logos were constructed as in Whole transcriptome of selected H441 cell clones were measured by RNA-seq. The changes in cassette exon inclusion levels were compared as (PDF)Click here for additional data file.S17 FigSame as (PDF)Click here for additional data file.S18 FigA. Competition occurs between the proximal and distal 3′ splice sites in the presence of wild-type U2AF1 and its co-factors (yellow ellipse), resulting in alternative usage of the cassette exon (black box) under steady state. Mutant U2AF1 and its co-factors (red ellipse) have different affinities for the proximal and/or distal 3′ splice sites, altering the usage of the cassette exon. The brackets indicate regions surrounding the proximal and distal 3′ splice sites. B. In this alternative mode, wild-type U2AF1 binds to a co-factor  that is presented at a lower stoichiometry. The wild-type complex recognizes the 3′ splice sites to mediate the recognition of the cassette exon. Mutant U2AF1 competes with wild-type U2AF1 for this rating-limiting co-factor. Mutant U2AF1-containing complex has different affinities for the splice sites, resulting in alteration in the inclusion of the cassette exon. In these cartoons, blank boxes represent constitutive exons flanking the cassette exon. Introns are the black lines connecting the exons. Arrows with solid or dotted lines represent affinities of the splicing factors to RNA. Grey lines represent possible splices and the weight of the lines represents altered splicing efficiency.(PDF)Click here for additional data file.S1 TableU2AF1S34F. In each case, the isoform for which information is given is as follows: the inclusion isoform for cassette exons (type “se”), the intron-proximal isoform for competing 5' or 3′ splice site events (types “a5ss” or “a3ss”), inclusion of the upstream exon for mutually exclusive exon events (type “mxe”), splicing of retained introns annotated as alternative (type “ri”) or constitutive (type “ci”), or canonical splicing of an annotated constitutive junction (type “cj”). The columns specify relevant annotations for each event as follows: “Coordinates” specifies the genomic coordinates of the event; “Gene” specifies the Ensembl gene ID; “Gene Name” specifies the gene name; “Gene Description” is a description of the gene as given by Ensembl; “Type” specifies the type of splicing event; “NMD Target” specifies whether the isoform is a predicted substrate for degradation by nonsense mediated decay (NMD) . The columns prefixed by “Psi_” indicate the inclusion frequency of the above-mentioned isoforms in a given sample. The columns prefixed by “deltaPsi_” give the absolute difference in isoform ratio  for the indicated comparisons. Table is restricted to events that are differentially spliced in at least one comparison.Each row of the table corresponds to an isoform of a splicing event that is differentially spliced in at least one of the indicated comparisons between cell lines that do or do not express (XLS)Click here for additional data file.S2 TableU2AF1S34F mutation versus all WT samples by group comparison. “p-value”, p-value computed with the Mann-Whitney U test for group comparisons.Same as (XLS)Click here for additional data file.S3 TableSummary of RNA binding affinity changes Figs  and S12 (XLS)Click here for additional data file.S4 TableSame as (XLS)Click here for additional data file.S5 TableSame as (XLS)Click here for additional data file.S6 Table3 or more (per flank) was considered successful tumor engraftment. The numbers of mice with successful vs. unsuccessful tumor engraftment (Tumor / No Tumor) were shown.The indicated cell lines were derived after transducing them with Cas9 and sgRNA-GFP or sgRNA-S34F (see (XLS)Click here for additional data file.S7 TableThe columns “Gene Name”, “Coordinates” and “Type” are defined the same way as those in (XLS)Click here for additional data file.


Induction of Nuclear factor erythroid 2 (NF-E2)-related factor 2 (Nrf2) has been demonstrated to be involved in cisplatin resistance in ovarian cancer. Solute carrier family 40 member 1 (SLC40A1) is an iron exporter, which possesses many putative Nrf2 binding sites. Here we hypothesize that it may be a possible downstream gene of Nrf2. Elevated level of Nrf2 and reduced level of SLC40A1 were found in cisplatin–resistant ovarian cancer cells as compared with cisplatin-sensitive ovarian cancer cells. Exogenous knockdown of Nrf2 leaded to increased expression of SLC40A1. While overexpression of Nrf2 resulted in decreased expression of SLC40A1. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assay revealed that Nrf2 inhibited the transcription of SLC40A1. Overexpression of SLC40A1 was able to reverse cisplatin resistance induced by Nrf2, while knockdown of SLC40A1 restored cisplatin resistance and increased iron concentration. Desferal, an iron chelator, was found to overcome cisplatin resistance through iron deprivation. Its function was boosted when combined with brusatol, an Nrf2 inhibitor. Taken together, this study first demonstrated that Nrf2 could transcriptionally suppress the expression of SLC40A1. Iron overload induced by SLC40A1 resulted in cisplatin resistance in ovarian cancer. Targeting iron metabolism may be a new therapeutic strategy to reverse drug resistance in ovarian cancer treatment. Ovarian cancer is one of the deadliest gynecological malignancies. Cytoreductive surgery and platinum-based chemotherapy are the first-line treatment , 2. HoweNuclear factor erythroid 2 (NF-E2)-related factor 2 (Nrf2) is a key transcriptional factor involved in protecting cells against oxidative stresses. Nrf2 has been found to promote cell survival though activating many ARE-bearing genes, such as autophagy signaling associated genes , 5, ABC http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3). So we hypothesize that iron metabolism signaling pathway mediated by SLC40A1 is involved in cisplatin resistance induced by Nrf2.Furthermore, abnormal iron metabolism is also associated with cancer development . PersistIn this study, we try to confirm Nrf2’s regulation on SLC40A1, and explore the role of iron metabolism in chemoresistance in ovarian cancer.To study whether there is a relationship between Nrf2 and SLC40A1, we analyzed their mRNA expression in three cisplatin-sensitive ovarian cancer cells  and their derived cisplatin-resistant ones . Elevated mRNA level of Nrf2 and reduced mRNA level of SLC40A1 were found in three cisplatin–resistant ovarian cancer cells as compared with their corresponding cisplatin-sensitive ovarian cancer cells Figure . MoreoveBecause bioinformatics analysis indicated that there are many putative Nrf2 binding sites in the promotor of SLC40A1, we hypothesize that SLC40A1 is one of down-stream genes of Nrf2, which is negatively regulated by the latter. To test this hypothesis, we adopted two methods to alter the expression of Nrf2 in order to observe the expression change of SLC40A1. Interestingly, it revealed that overexpression of Nrf2 by pCDH-CMV-MCS-EF1-copGFP-Nrf2 or tert-Butylhydroquinone (TBHQ) both inhibited the expression of SLC40A1 in A2780 Figure , while kTo test whether SLC40A1 is a novel downstream gene of Nrf2, standard regression test was performed. The result revealed that co-transfected with SLC40A1 and Nrf2 plasmid significantly reduced cell viability compared to Nrf2 plasmid alone in A2780 and COC1 Figure . While cin vivo. Through designing different primes aimed at the putative binding sites, it was found that there was an interaction between Nrf2 and SLC40A1 . But no obvious dose-dependent relation was observed when it was given different quantity of pCDH-CMV-MCS-EF1-copGFP-Nrf2 plasmid Figure . This re1 Figure . Further1 Figure .To figure out whether there was any feedback reaction of SLC40A1 on Nrf2, the protein level of Nrf2 was determined by western blot after SLC40A1 was upregulated or knockdown. Interestingly, when SLC40A1 was knockdown in A2780, the expression of Nrf2 and its downstream gene NQO1 decreased Figure . Inversehttp://string-db.org/)  Figure . To test) Figure . This su) Figure . However) Figure . Similar) Figure . This da3 leadedto increased intracellular iron and resulted in cisplatin resistance in A2780 and COC1 . Interestingly, most of the interactive proteins were found to be associated with iron metabolism. So we suppose that abnormal iron metabolism induced by SLC40A1 may be a potential reason for overexpression of Nrf2 associated genes. However, this hypothesis still needs further experiments to confirm in the future.In this study, we also found that SLC40A1 could promote the expression of Nrf2 in return. This results tell us that there is an interaction loop between Nrf2 and SLC40A1, but not a simple linear relationship. To keep the iron homeostasis, SLC40A1 positively regulates the expression of Nrf2 to maintain its stability. But as a membrane protein, how SLC40A1 regulates Nrf2 is still unknown. To answer this question, we analyzed the interactive protein of SLC40A1 by online software . The SYBR premix ExTaq II system was purchased from Takara . Small interfering RNA of SLC40A1 and its nonspecific control were purchased from Riobobio , and the siRNA sequences were shown in Table Desferal was purchased from Novartis Pharma . Cisplatin and tert-Butylhydroquinone (TBHQ), an activator of Nrf2 , were obHuman ovarian carcinoma cell line A2780 and its derived cisplatin-resistant cell line A2780CP, PEO1 and its derived cisplatin-resistant cell line PEO4 ovarian cancer were cultured as following. Cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum  and penicillin (100 U/mL)/streptomycin (100 μg/mL), which were obtained as preciously described . OvarianSLC40A1 CDS fragment was amplified with primer sequence as follows: Forward, AGAAGATTCTAGAGCTAGCGAATTCATGACCAGGGCGGGAGATCACAA and reverse, GCAGATCCTTCGCGGCCGCGGATCCTCAAACAACAGATGTATTTGCTTGA. The purified PCR products were cloned into the pCDH-CMV-MCS-EF1-puro plasmid. While, the luciferase reporter plasmid containing the SLC40A1 promotor was obtained from Genewiz , which was used as a template to amplify different length of truncated bodies of SLC40A1 promoter. The primers used were shown in Table *105 Cells per well in 6-well plate. After 18–24 h, cells were transiently transfected using Lipofectamine 2000 according to the manufacturer’s recommendations  with 2ug plasmid DNA or 5ul siRNA. The transfection efficiency was determined by Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot.Cells were seeded at 2–ΔΔCt value.The TRIzol reagent  was used for the extraction of total RNA from ovarian cancer cells. RT reactions were performed by RT kit according to the manufacturer’s recommendations . Then, The SYBR premix ExTaq II system was used to perform PCR analysis on the PRISM 7500 . All qRT-PCR primers involved in this study were listed in Table Western blot was performed as preciously described . Twenty-4 different ovarian cancer cells were seeded in 96-well plates and treated with different concentrations of compounds  for 24-48h. Then, the CCK8 reagent was added to the medium and incubated for 2h for absorbance measurement with the Bio-Rad microplate reader at the wavelength of 450 nm . All treatments were performed in triplicate.Cell viability was determined by Cell Counting Kit 8 (CCK8) assay according to the manufacturer’s recommendations . Briefly, 1× 10*103/well in 96-well plate. After 24h, co-transfection was performed with pGL3-SLC40A1, pCDH-CMV-MCS-EF1-copGFP-Nrf2, pCDH-CMV-MCS-EF1-copGFP and Renilla plasmid. All plasmids were gently added to each well with Lipofectamine 2000 . After 48h transfection, all cells were rinsed thoroughly by PBS. Then, 20ul cell lysis buffer was added to each well using the Dual Luciferase Assay System  according to the manufacturer’s recommendations. When all cells were splitted thoroughly, 50ul firefly luciferase reagent was added to each well. Firefly luciferase levels were analyzed with PerkinElmer 2030 Multilabel Reader (PerkinElmer). Finally, 50ul renilla luciferase reagent was added into each well, and renilla luciferase levels were analyzed. The relative luciferase activity of each sample was obtained with dividing firefly luciferase by renilla luciferase. All treatments were performed in triplicate.HEK293-T cells were seeded at a density of 5ChIP assays were performed according the EZ-ChIP™ Kit . When cell confluence of A2780CP reached 80%, 30ug/ml cisplatin was added to culture medium to promote protein and chromatin crosslinking. After 4 hours. 1% formaldehyde was given to each 10 cm culture plate to fix the protein-chromatin complex for 10 min and the residual formaldehyde was neutralized with glycine for 5 min at room temperature. The following process of ChIP was performed as previously described . Soluble*105 Cells per well in 6-well plate. Treatment with different reagents for 24-72h, cells were washed and splitted as preciously described [Cells were seeded at 2escribed . Iron coAll statistical analyses were performed using SPSS software (version 18.0). Two independent samples student t-test was used to compare differences between two groups. The differences were considered significant when a two-sided P value < 0.05.


However, higher plasma 25(OH)D concentrations correlated with lower phosphorylation of glycogen synthase kinase-3 (GSK-3) αSer21 and βSer9 in skeletal muscle  and higher GSK-3 αSer21 and βSer9 phosphorylation in adipose tissue . Furthermore, higher plasma 25(OH)D concentrations were associated with greater phosphorylation of both protein kinase-B (AktSer473)  and insulin receptor substrate-1 (IRS-1Ser312)  in adipose tissue. No associations were found between plasma 25(OH)D concentration and IRS-1Tyr612 phosphorylation in skeletal muscle and adipose tissue. The divergent findings between muscle and adipose tissue with regard to the association between 25(OH)D and insulin signaling proteins may suggest a tissue-specific interaction with varying effects on glucose homeostasis. Further research is required to elucidate the physiological relevance of 25(OH)D in each tissue.Vitamin D has been suggested to play a role in glucose metabolism. However, previous findings are contradictory and mechanistic pathways remain unclear. We examined the relationship between plasma 25-hydroxyvitamin D (25(OH)D), insulin sensitivity, and insulin signaling in skeletal muscle and adipose tissue. Seventeen healthy adults  underwent a hyperinsulinemic-euglycemic clamp, and resting skeletal muscle and adipose tissue biopsies. In this cohort, the plasma 25(OH)D concentration was not associated with insulin sensitivity ( Vitaminulations . Vitaminulations . In addiulations ,9 which ulations ,9,10; houlations ,12,13. Pulations ,15,16,17ulations . Likewisrylation . Further pathway  that is  pathway . HoweverThis study is a subset of a larger study of 111 healthy normoglycemic adults, where we reported a positive association between plasma 25(OH)D concentration and insulin sensitivity measured by hyperinsulinemic-euglycemic clamp . Due to 2/min and the glucose infusion rate (GIR) was averaged over the last 40 min of the insulin clamp and expressed relative to serum insulin levels . Fasting blood samples for 25(OH)D analysis were drawn prior to the start of the insulin clamp. Primary antibodies specific for phosphorylation of glycogen synthase kinase (GSK-3) α/βSer21/9 , protein kinase-B , serine phosphorylation of human insulin receptor substrate 1 , and tyrosine phosphorylation of IRS-1Tyr612 (Biosource), were used to quantify muscle and adipose tissue protein as per methods previously reported [Insulin sensitivity was measured via a 2 h hyperinsulinemic-euglycemic clamp. Insulin was infused at a constant rate of 40 mU/mreported .Vitamin D assays were performed using a DiaSorin Liaison Analyser , which uses the direct competitive chemiluminescent immunoassay method (CLIA) to quantitatively determine the total 25(OH)D in plasma. During the first incubation, 25(OH)D is dissociated from its binding proteins and binds to the specific antibody on the solid phase. After 10 min, the tracer (vitamin D linked to an isoluminol derivative) is added. Following a second incubation, the unbound material is removed with a wash cycle and the starter reagent is added to initiate a flash chemiluminescent reaction. The light signal emitted is measured by a photomultiplier as relative light units (RLU) and is inversely proportional to the concentration of 25(OH)D present in the sample. Inter- and intra-assay coefficients of variation (CV) using the Diasorin method were <10% and <4%, respectively.t-tests assuming equal variance were conducted to compare mean insulin sensitivity values between groups of different vitamin D status. Correlations of combined data, and data stratified by deficient/insufficient (25(OH)D < 50 nmol/L) and sufficient (25(OH)D ≥ 50 nmol/L) vitamin D status, were analyzed using linear regression analysis. Due to difficulty in adipose tissue and skeletal muscle sampling in humans, samples were not available for some protein analysis for all subjects (n = 11–16). Cook’s Distance was used as a measure of influence where observations greater than 4/N Cook’s D were excluded from correlation analysis [p < 0.05).All data were log transformed to approximate normal distribution prior to analysis. Unpaired analysis . All datEthical approval was obtained from the Alfred Hospital Ethics Committee and the Monash University Human Research Ethics Committee and complied with the Declaration of Helsinki (2013)  All partParticipant characteristics are reported in 2), there was a positive association between 25(OH)D and insulin sensitivity . In the subgroup of 17 individuals where skeletal muscle and adipose tissue was available, the M-value was not significantly different between vitamin D–deficient/–insufficient (30 ± 10 nmol/L) and vitamin D-sufficient (82 ± 28 nmol/L) individuals , even after adjustment for age, BMI, fat mass, sex, and vitamin D status .As previously reported, in the larger sample of 111 individuals  = 30.2 ± 5.1 kg/mp = 0.9; . The plaSer21 and βSer9, but the latter did not reach significance (p = 0.06), whereas in adipose tissue 25(OH)D was associated with a higher phosphorylation of GSK-3 αSer21 and βSer9 . Tyrosine phosphorylation of IRS-1Tyr612 in adipose tissue and skeletal muscle did not correlate with 25(OH)D concentrations . GSK-3 αSer21 and βSer9 phosphorylation was associated with higher phosphorylated AktSer473 in adipose tissue .In skeletal muscle, a higher plasma 25(OH)D concentration was associated with lower phosphorylation of GSK-3 αnd βSer9 . In adip-1Ser312 , wherease tissue  but not Ser21 and βSer9 in skeletal muscle  or adipose tissue . On the other hand, insulin sensitivity was associated with higher phosphorylation of AktSer473 and lower phosphorylation of IRS-1Ser312 in skeletal muscle .Insulin sensitivity was not associated with phosphorylation of GSK-3 αl muscle , but notSer312, GSK-3 α/βSer21/9, and AktSer473, in skeletal muscle and adipose tissue.We report that the association between 25(OH)D and insulin signaling proteins appears to be tissue specific. Differential associations were detected between 25(OH)D concentration and key glucose homeostasis signaling proteins IRS-1Considering the well-known role of calcium in glycemic control  and giveAn important finding of the current study was the strong correlation between the plasma 25(OH)D concentration and key insulin signaling proteins involved in glucose homeostasis, which appear to occur in a tissue-specific manner. Previous reports suggest that vitamin D deficiency may influence glucose homeostasis through increased intracellular calcium leading to aberrant insulin signaling and attenuation of downstream insulin signaling enzymes such as glycogen synthase and the glucose transporter 4 (GLUT 4) ,17,34,35Ser312 is implicated with impaired insulin signaling and glucose uptake in part through IRS-1 degradation, impaired IRS-1 tyrosine phosphorylation, and attenuation of insulin-stimulated glucose uptake [Ser312 in adipose tissue but not in skeletal muscle tissue. In addition, 25(OH)D concentration was not associated with basal tyrosine phosphorylation of IRS-1Tyr612, a positive regulator of insulin-stimulated glucose uptake [2D in the enhancement of insulin signaling and insulin sensitivity in non-human studies [Ser312 phosphorylation in skeletal muscle likely plays a greater role in insulin sensitivity than in adipose tissue. Indeed, we report that greater phosphorylation of IRS-1Ser312 in skeletal muscle only was related to lower whole body insulin sensitivity. Thus, in the context of the current findings, the plasma 25(OH)D concentration does not appear to be related to basal IRS-1Tyr612 phosphorylation or IRS-1Ser312 phosphorylation in skeletal muscle; however, future research is required to elucidate the physiological relevance of 25(OH)D and IRS-1Ser312 signaling in adipose tissue.Increased phosphorylation of IRS-1e uptake . We repoe uptake , in eith studies ,19, alth studies  reportedThis study has potential limitations which include a relatively small sample size, and the measurement of insulin signaling proteins under basal fasted conditions and not during or after insulin stimulation. The inclusion of populations who are insulin resistant, have type 2 diabetes, and/or exhibit a greater range of vitamin D status would allow for greater exploration of the relationship between vitamin D status, and insulin sensitivity and protein signaling. Future studies are required to confirm these findings with larger and more diverse populations. Interventional studies in vitamin D–deficient and insulin-resistant individuals would allow for greater exploration of the physiological role and relevance of 25(OH)D and insulin signaling proteins. It is possible that vitamin D status is subsidiary to insulin resistance and an expression of ill health rather than a primary moderator of glycemic control . HoweverIn conclusion, plasma 25(OH)D was strongly associated with phosphorylation of insulin signaling proteins in a tissue-specific manner. The clinical implications of these findings need to be examined with adequately powered randomized controlled trials.