Search

A total of 16 OsS40 genes of Oryza sativa were identified in our previous work, but their functions remain unclear. In this study, 13 OsS40 members were knocked out using the CRISPR/cas9 gene-editing technology. After screening phenotype characterization of CRISPR/Cas9 mutants compared to WT, five oss40s mutants exhibited a stay-green phenotype at 30 days after heading. Moreover, increased grain size and grain weight occurred in the oss40-1, oss40-12 , and oss40-14 lines, while declined grain weight appeared in the oss40-7 and oss40-13 mutants. The transcript levels of several senescence-associated genes (SAGs), chlorophyll degradation-related genes (CDGs), as well as WRKY members were differentially decreased in the five stay-green oss40s mutants compared to WT. Five oss40 mutants also exhibited a stay-green phenotype when the detached leaves were incubated under darkness for 4 days. OsSWEET4 and OsSWEET1b were significantly upregulated, while OsSWEET1a and OsSWEET13 were significantly downregulated in both oss40-7 and oss40-14 compared to WT. Furthermore, these five OsS40 displayed strong transcriptional activation activity and were located in the nucleus. Most of the OsS40 genes were downregulated in the oss40-1, oss40-7 , and oss40-12 mutants, but upregulated in the oss40-13 and oss40-14 mutants, indicating coordinated regulation among OsS40 members. These results suggest that OsS40-1, OsS40-7, OsS40-12, OsS40-13 , and OsS40-14 are senescence-associated genes, involved in the senescence and carbon allocation network by modulating other OsS40 members, SWEET member genes, and senescence-related gene expression. [ABSTRACT FROM AUTHOR]

Background: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). Methods: We first used histopathological image features and machine‐learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA‐HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. Results: Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5‐year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5‐year AUC = 0.860) and test set (5‐year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high‐risk and low‐risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. Conclusions: Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients. Method: We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients. Results: A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models. Conclusion: In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients. [ABSTRACT FROM AUTHOR]

Cervical lesion grading is critical for effective patient management. A three-tier classification (cervical intraepithelial neoplasia [CIN] grade 1, 2 or 3) based on H&E slide review is widely used. However, for reasons of considerable inter-observer variation in CIN grade assignment and for want of a biomarker validating a three-fold stratification, CAP-ASCCP LAST consensus guidelines recommend a two-tier system: low- or high-grade squamous intraepithelial lesions (LSIL or HSIL). In this study, high-risk HPV E6/E7 and p16 mRNA expression patterns in eighty-six CIN lesions were investigated by RNAscope chromogenic in situ hybridization (CISH). Specimens were also screened by immunohistochemistry for p16INK4a (clone E6H4), and by tyramide-based CISH for HPV DNA. HPV genotyping was performed by GP5+/6+ PCR combined with cycle-sequencing. Abundant high-risk HPV RNA CISH signals were detected in 26/32 (81.3%) CIN 1, 22/22 (100%) CIN 2 and in 32/32 (100%) CIN 3 lesions. CIN 1 staining patterns were typified (67.7% specimens) by abundant diffusely staining nuclei in the upper epithelial layers; CIN 2 lesions mostly (66.7%) showed a combination of superficial diffuse-stained nuclei and multiple dot-like nuclear and cytoplasmic signals throughout the epithelium; CIN 3 lesions were characterized (87.5%) by multiple dot-like nuclear and cytoplasmic signals throughout the epithelial thickness and absence/scarcity of diffusely staining nuclei (trend across CIN grades: P<0.0001). These data are consistent with productive phase HPV infections exemplifying CIN 1, transformative phase infections CIN 3, whereas CIN 2 shows both productive and transformative phase elements. Three-tier data correlation was not found for the other assays examined. The dual discernment of diffuse and/or dot-like signals together with the assay’s high sensitivity for HPV support the use of HPV E6/E7 RNA CISH as an adjunct test for deciding lesion grade when CIN 2 grading may be beneficial (e.g. among young women) or when ‘LSIL vs. HSIL’ assignment is equivocal. [ABSTRACT FROM AUTHOR]

Formalin fixed, paraffin embedded (FFPE) tissue is the most common specimen available for application of diagnostic assays on tissue after microscopic examination. However this approach is not molecular biology friendly, with significant damage to both nucleic acids and proteins by degradation and cross-linking. Extraction of sufficient, quality RNA from FFPE tissue is a barrier to the development of new diagnostic assays. We have optimized a reliable RNA extraction method based on deparaffinization at high temperature coupled with 3-day lysis at 650C. The average total RNA yield is 4.5 to 5.5 pg per 1 µm³ of archival FFPE tissue, and 260/280 ratios are between 1.80 and 1.95. With a new optimized RNA isolation protocol, we are investigated the fundamental factors which influence on RNA integrity within FFPE tissue. Using mouse kidneys as a standardized tissue, we profiled the pattern of RNA degradation based on fixation time, tissue processing time and fixative buffer. Quality was measured by bioanalyzer tracings, RT-PCR and branched DNA assays of specific genes. Fixation time and tissue processing time have significant impact on RNA quality. Our data suggested that the middle region of gene could be a promising target for RNA analysis. Overall, these date of this study provide fundamental knowledge in studies of gene expression analysis using archival FFPE tissues. [ABSTRACT FROM AUTHOR]

Abstract: We describe a novel method to quantitatively measure messenger RNA (mRNA) expression of multiple genes directly from crude cell lysates and tissue homogenates without the need for RNA purification or target amplification. The multiplex branched DNA (bDNA) assay adapts the bDNA technology to the Luminex fluorescent bead-based platform through the use of cooperative hybridization, which ensures an exceptionally high degree of assay specificity. Using in vitro transcribed RNA as reference standards, we demonstrated that the assay is highly specific, with cross-reactivity less than 0.2%. We also determined that the assay detection sensitivity is 25,000 RNA transcripts with intra- and interplate coefficients of variance of less than 10% and less than 15%, respectively. Using three 10-gene panels designed to measure proinflammatory and apoptosis responses, we demonstrated sensitive and specific multiplex gene expression profiling directly from cell lysates. The gene expression change data demonstrate a high correlation coefficient (R 2 =0.94) compared with measurements obtained using the single-plex bDNA assay. Thus, the multiplex bDNA assay provides a powerful means to quantify the gene expression profile of a defined set of target genes in large sample populations. [Copyright &y& Elsevier]