An Integrated Expression Profiling Reveals Target Genes of TGF-β and TNF-α Possibly Mediated by MicroRNAs in Lung Cancer Cells

<div><p>EMT (epithelial-mesenchymal transition) is crucial for cancer cells to acquire invasive phenotypes. In A549 lung adenocarcinoma cells, TGF-β elicited EMT in Smad-dependent manner and TNF-α accelerated this process, as confirmed by cell morphology, expression of EMT markers, capacity of gelatin lysis and cell invasion. TNF-α stimulated the phosphorylation of Smad2 linker region, and this effect was attenuated by inhibiting MEK or JNK pathway. Comprehensive expression analysis unraveled genes differentially regulated by TGF-β and TNF-α, such as cytokines, chemokines, growth factors and ECM (extracellular matrices), suggesting the drastic change in autocrine/paracrine signals as well as cell-to-ECM interactions. Integrated analysis of microRNA signature enabled us to identify a subset of genes, potentially regulated by microRNAs. Among them, we confirmed TGF-β-mediated induction of miR-23a in lung epithelial cell lines, target genes of which were further identified by gene expression profiling. Combined with in silico approaches, we determined HMGN2 as a downstream target of miR-23a. These findings provide a line of evidence that the effects of TGF-β and TNF-α were partially mediated by microRNAs, and shed light on the complexity of molecular events elicited by TGF-β and TNF-α.</p> </div

Putative targets of TGF-β-induced miR-23a.

<p>(A) Schematic description of integrated analysis of mRNA and miRNA arrays. Gene expression signature in the cells treated with TGF-β1 and TNF-α revealed 759 annotated genes downregulated <0.67-fold. Out of them, the target filter program listed 76 genes as putative miR-23a targets. Transfection of synthetic pre-miR-23a in A549 cells identified 408 genes downregulated <0.67-fold. There were 15 genes shared in these two groups. (B) Scatter plot representation of the transcripts in A549 cells transfected with control (X-axis) or synthetic pre-miR-23a (Y-axis) for 48 h. The transcripts are plotted using log2 normalized data. The threshold of transcript levels was set as induced >2.0-fold or repressed <0.5-fold, and is indicated in each scattergram. Plots of total transcripts (right), and those selected by the target filter program (right), are presented. (C) The list of 15 genes identified by integrated analyses.</p

Upregulated genes by TGF-β and/or TNF-α, 24 h after stimulation.

<p>Upregulated genes by TGF-β and/or TNF-α, 24 h after stimulation.</p

HMGN2 is regulated by miR-23a.

<p>(A) Putative miR-23a binding sequence in the 3′UTR of human HMGN2 mRNA (WT: wild-type). The underlined sequence shows the nucleotides generated by mutagenesis to abolish the binding of miR-23a (Mut: mutant). (B) Immunoblotting for HMGN2 in A549, H441 and BEAS2B cells transfected with control or synthetic pre-miR-23a for 48 h, followed by TGF-β1 stimulation for additional 48 h. α-tubulin was used as loading control. (C) HEK293T cells were transfected with luciferase reporters containing the HMGN2 3′UTR with wild-type or mutated target site as shown in Fig. 6A, along with empty or pri-miR-23a expression vector. Luciferase assay was performed 48 h after transfection.</p

Upregulated genes by TGF-β1 and/or TNF-α, 2 h after stimulation.

<p>Upregulated genes by TGF-β1 and/or TNF-α, 2 h after stimulation.</p

TNF-α enhances TGF-β-mediated EMT in A549 lung cancer cells.

<p>(A) A549 cells were pretreated with LY-364947 (TβR-I inhibitor) or control DMSO for 60 min, further cultured with 5 ng/ml TGF-β1 and/or 10 ng/ml TNF-α for 48 h, and analyzed by phase-contrast microscopy. Bar: 50 µm. (B) Cell circularity was measured using Image J software to quantify cell morphological change following the described treatment. (C) Immunoblotting analyses of E-cadherin and N-cadherin in A549 cells stimulated with TGF-β1 and/or TNF-α for 48 h in the presence or absence of LY-364947. α-tubulin was used as loading control. (D) Gelatin zymography. A549 cells treated as described were cultured with serum free media for additional 48 h. The conditioned media were collected and the same amount of protein was electrophoresed. Gelatin digestion by activated MMP-2 and MMP-9 was visualized by Coomassie blue staining. (E) Cell invasion assay. The migrated cells through the culture inserts coated with Matrigel were trypsinized and counted. Each experiment was performed in triplicate and the averaged relative ratios from 3 independent experiments were presented. Error bars: SD. *<i>P</i><0.05 (Student’s t-test).</p

TNF-α phosphorylates Smad2 linker region via MEK-Erk and JNK pathways.

<p>(A) A549 cells were stimulated with TGF-β1 and/or TNF-α for 60 min and immunoblotting was performed for total Smad2, phosphorylated Smad2 (linker region: Ser 245/250/255), phosphorylated Smad2 (C-terminal region: Ser 465/467), total Smad3, phosphorylated Smad3 (C-terminal region: Ser 423/425). (B) A549 cells were pretreated with DMSO or chemical inhibitors (LY-364947, U0126, SP600125 and SB203580) for 60 min, followed by TNF-α stimulation. The cell lysates were collected at the indicated time points, and immunoblotting was performed for Smad2, phosphorylated Smad2 (linker region: Ser 245/250/255), Erk, phosphorylated Erk, p38, phosphorylated p38 and phosphorylated c-Jun. α-tubulin was used as loading control.</p

TGF-β-mediated EMT is Smad-dependent.

<p>(A–B) A549 cells were transfected with siRNAs for Smad4 (si Smad4), or negative control siRNAs (si NTC) and cultured for 48 h. The cells were further cultured with 5 ng/ml TGF-β1 and/or 10 ng/ml TNF-α for 2 h (B) or 24 h (A), and RNA was collected. Quantitative PCR was performed for Smad4, E-cadherin, Smad7 and PAI-1 at the indicated time. Expression was normalized to that of GAPDH. Error bars: SD. (C) Cell lysates were collected 48 h after TGF-β1 and/or TNF-α treatment. Immunoblotting was performed for E-cadherin, N-cadherin and Smad4. α-tubulin was used as loading control.</p

Collision detection on transmission lines with optical interferometer

V diplomski nalogi skušamo ugotoviti, v kolikšni meri je možno zaznavati in klasificirati trke na jeklenicah daljnovodov z optičnim interferometrom. Na začetku predstavimo osnovne pojme interferometrije in opišemo uporabljen optični interferometer. V jedru diplomske naloge natančneje opišemo eksperimentalni protokol in obdelavo signalov. Nadaljujemo z implementacijo algoritmov za segmentacijo in klasifikacijo zajetih signalov ter predstavimo dobljene rezultate. Segmentacijo izvedemo v domeni števila prehodov signala skozi ničlo, za klasifikacijo pa uporabimo večplastno nevronsko mrežo z algoritmom vzvratnega učenja. Rezultati študije nakazujejo, da sta implementirani segmentacija in klasifikacija uspešni v 77 % izvedenih trkov različnih predmetov.We analyse feasibility of collision detection on transmission lines with optical interferometer. We first provide a brief introduction into interferometry, along with a description of the optical interferometer used for measurements in this study. Afterwards, we describe the conducted experimental protocol and signal processing methodology. The focus is on implementation of algorithms for signal segmentation and collision classification. We used zero-crossing algorithm to transform signals into segmentation domain. Classification of collisions is done with a multilayer neural network trained by the backpropagation algorithm. The results demonstrate an average success rate of 77% for segmentation and classification of collision with five different objects

Sistematización de la experiencia de un ambiente de aprendizaje enriquecido por TIC durante la práctica clínica en fisioterapia cardiopulmonar en un hospital de nivel II de la ciudad de Cali

Esta investigación se centra en la caracterización de la experiencia de 4 estudiantes de fisioterapia de IX semestre de la Institución Universitaria Escuela Nacional del Deporte (IUEND) durante la implementación de un ambiente de aprendizaje enriquecido con Tecnologías de la Información y la Comunicación (TIC) en la práctica clínico – asistencial en Salud Cardiopulmonar; la cual se fundamenta en el hacer y pone a prueba las bases conceptuales del ciclo de fundamentación; todo esto con el fin de identificar las experiencias significativas que facilitan el aprendizaje y desarrollo de competencias clínicas, además analizar si este tipo de estrategias de enseñanza -aprendizaje permite al estudiante y al docente asesor superar inconvenientes propios de la práctica clínica como: optimizar tiempos de atención a pacientes, estudio independiente y trabajo colaborativo, retomar e integrar gran cantidad de conceptos y procedimientos aprendidos en IV semestre con las nuevas experiencias y la realidad del paciente; y a la vez cumplir con funciones administrativas propios del rol del fisioterapeuta asistencial (estadística, indicadores, desarrollo de guías, etc.) que dificultan el proceso de aprendizaje; concluyendo que los ambientes mediados por TIC pueden lograr superar estas dificultades y favorecer finalmente el aprendizaje significativo (juicio clínico), en el que se fundamenta el ciclo de práctica profesional