Fig. 1. Blood exosomal miRNA changes in patients with SCZ

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Fig. 1. Blood exosomal miRNA changes in patients with SCZ Fig. 1. Blood exosomal miRNA changes in patients with SCZ. (A) Serum exosome observation by electron microscopy and ... Fig. 1. Blood exosomal miRNA changes in patients with SCZ. (A) Serum exosome observation by electron microscopy and ×7000 magnification. (B) Western blots show the exosomal marker Alix and Flotillin-1 were present in the exosomes extracted from blood. (C) Western blots show that endoplasmic reticulum marker CNX and Golgi marker GP-73 were not present in the peripheral blood exosomes. (D) Serum exosome validation by nanoparticle tracking device—ZetaView. Note that the particles peak around at 100 nm. (E) MA-plot displaying miRNA differences between cases and controls for the training set of participants. (F) MA-plot for the testing set of participants. (G) MA-plot for pooled set of participants. (H) Significantly changed blood exosomal miRNA expression in the 49 cases as compared with 46 controls. CNX, Calnexin; GP-73, Golgi protein-73. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Schizophr Bull, sby191, https://doi.org/10.1093/schbul/sby191 The content of this slide may be subject to copyright: please see the slide notes for details.

Fig. 2. Blood exosomal miRNAs as biomarkers to differentiate between SCZ patients and controls. (A) ROC curves were ... Fig. 2. Blood exosomal miRNAs as biomarkers to differentiate between SCZ patients and controls. (A) ROC curves were utilized to evaluate the accuracy of a cluster of 11 miRNAs for the diagnosis of SCZ in a training set of participants. Boxplot (B) and scatter plot (C) of probability of participants belonging to cases by the 11 miRNAs in the training set. (D) ROC curves were utilized to evaluate the accuracy of a cluster of 11 miRNAs for the diagnosis of SCZ in a testing set of participants. Boxplot (E) and scatter plot (F) of probability of participants belonging to case in the testing set. AUC, area under curve; ROC, receiver operating characteristic. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Schizophr Bull, sby191, https://doi.org/10.1093/schbul/sby191 The content of this slide may be subject to copyright: please see the slide notes for details.

Fig. 3. miRNA coexpression modules dysregulated in blood exosomes of SCZ patients. (A) Dendrogram showing miRNA ... Fig. 3. miRNA coexpression modules dysregulated in blood exosomes of SCZ patients. (A) Dendrogram showing miRNA coexpression modules defined in 95 samples. Color bars indicate dynamic module assignments (Dynamic Tree Cut) and merged module assignments (Merged dynamic). (B) Pearson’s correlation coefficient (and P value in parentheses) between disease status, age, gender, disease severity, and module eigengene. (C) Log2 transformed fold change distribution of miRNAs in the module black, blue, brown, green greenyellow, purple, turquoise, and yellow. (D–F) Coexpression network plots for greenyellow, black, and yellow modules. Node size is proportional to node connectivity, and edge indicates coexpression of connected nodes with intramodular connectivity >0.5. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Schizophr Bull, sby191, https://doi.org/10.1093/schbul/sby191 The content of this slide may be subject to copyright: please see the slide notes for details.

Fig. 4. Quantitative reverse transcription-polymerase chain reaction validation of differentially expressed miRNAs in ... Fig. 4. Quantitative reverse transcription-polymerase chain reaction validation of differentially expressed miRNAs in the blood exosomes of SCZ patients. hsa-miR-206 (A), hsa-miR-144-3p (C), and hsa-miR-619-5p (E) expression in the blood exosomes of 100 cases and 100 controls. hsa-miR-206 (B), hsa-miR-144-3p (D), and hsa-miR-619-5p (F) in the blood exosomes of 57 FEDF SCZ patients, 43 CT SCZ patients, and 100 controls. FEDF, first-episode, drug-free; CT, chronically treated. **P < .01, ***P < .001. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Schizophr Bull, sby191, https://doi.org/10.1093/schbul/sby191 The content of this slide may be subject to copyright: please see the slide notes for details.

Fig. 5. Validation of bioinformatic prediction of miRNA target genes Fig. 5. Validation of bioinformatic prediction of miRNA target genes. (A) Whole-blood BDNF mRNA levels between 45 ... Fig. 5. Validation of bioinformatic prediction of miRNA target genes. (A) Whole-blood BDNF mRNA levels between 45 FEDF SCZ patients and 45 controls. (B) Serum BDNF protein levels between 53 FEDF SCZ patients, 51 CT SCZ patients, and 47 controls. (C) KEGG pathway enrichment result for the SCZ-affected upregulated yellowgreen module. (D) GALNT8, (E) GLANT15, and (F) GLANT18 whole-blood mRNA levels between 45 FEDF SCZ patients and 45 controls. CT, chronically treated; FEDF, first-episode, drug-free. *P < .05, ***P < .001. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Schizophr Bull, sby191, https://doi.org/10.1093/schbul/sby191 The content of this slide may be subject to copyright: please see the slide notes for details.