Factors contributing to false‐negative and potential false‐negative cytology reports in SurePath™ liquid‐based cervical cytology

N. Gupta, D. John, N. Dudding, J. Crossley and J. H. F. Smith
Factors contributing to false‐negative and potential false‐negative cytology reports in SurePath ? liquid‐based cervical cytology Objectives: The characteristics of false‐negative conventional cervical cytology smears have been well documented, but there is limited literature available for liquid‐based cytology (LBC), especially SurePath? samples. We aimed to assess the characteristics of false‐negative SurePath LBC samples. Methods: Over a period of 5 years, an audit of false‐negative reports in SurePath cervical cytology was undertaken. In a workload of 183, 112 samples, 481 (0.3%) false negatives were identified using two routes: those detected by routine laboratory internal quality control (rapid pre‐screening) (n = 463) and those reported as normal (true false negatives) with concurrent high‐grade cervical histology (n = 18). Ninety‐five false‐negative cases with a subsequent biopsy reported as at least cervical intraepithelial neoplasia grade 2 (CIN2+) were reviewed for a number of different cytomorphological features. Results: Of 95 samples with subsequent CIN2+, 30.5% predominately contained microbiopsies/hyperchromatic crowded cell groups (HCGs), 27.3% sparse dyskarytotic cells, 4.2% pale cell dyskaryosis, 6.3% small dyskaryotic cells; 3.2% were misinterpreted cells, 8.4% contained other distracting cells, 7.4% were low contrast, 5.3% were unexplained and 7.4% were true negatives. The mean number of microbiopsies/HCGs in that category was 4.6. The mean number of abnormal cells in the sparse dyskaryotic cell category was 13.8. Conclusions: Microbiopsies/HCGs were the commonest reason for false negatives. They were usually present in sufficient numbers to be detected but interpretation could be problematic. Dispersed single abnormal cells were usually not identified because of their scarcity or the presence of distracters.     

Genome‐wide analysis of Nilaparvata lugens nymphal responses to high‐density and low‐quality rice hosts

The brown planthopper (BPH) Nilaparvata lugens is an economically important pest on rice plants. In this study, the higher population density and yellow‐ripe stage of rice plants were used to construct adverse survival conditions (ASC) against BPH nymphs. Simultaneously, the low population density and tillering stage of rice plants were used to establish a suitable survival condition (SSC) as a control. Solexa/Illumina sequencing was used to identify genes of BPH nymphs responding to ASC. Significantly longer duration development of BPH nymphs and significantly lower brachypterous ratio of BPH adults were observed by ASC compared with SSC. A total of 2 544 differentially expressed genes (DEGs) were obtained and analyzed by BLASTx, Gene Ontology and KEGG Orthology. Gene ontology analysis revealed that the DEGs were mainly involved in categories of cell, cell part, cellular process, binding, catalytic, organelle and metabolic processes. 1 138 DEGs having enzyme commission numbers were assigned to different metabolic pathways. The largest clusters were neurodegenerative diseases (137, 12.0%), followed by carbohydrate metabolism (113, 9.9%), amino acid metabolism (94, 8.3%), nucleotide metabolism (76, 6.7%), energy metabolism (64, 5.6%), translation (60, 5.3%), lipid metabolism (58, 5.1%), and folding, sorting and degradation (52, 4.6%). Expressing profile of 11 DEGs during eight nymphal developmental stages of BPH were analyzed by quantitative real‐time polymerase chain reaction. The 11 genes exhibited differential expression between ASC and SSC during at least one developmental stage. The DEGs identified in this study provide molecular proof of how BPH reconfigures its gene expression profile to adapt to overcrowding and low‐quality hosts.     

A semi-parametric statistical model for integrating gene expression profiles across different platforms

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection. We propose a rank-based semi-parametric model to determine DEGs using information across different sources and apply it to the integration of RNA-seq and microarray data. By incorporating both the significance of differential expression and the consistency across platforms, our method effectively detects DEGs with moderate but consistent signals. We demonstrate the effectiveness of our method using simulation studies, MAQC/SEQC data and a synthetic microRNA dataset. Our integration method is not only robust to noise and heterogeneity in the data, but also adaptive to the structure of data. In our simulations and real data studies, our approach shows a higher discriminate power and identifies more biologically relevant DEGs than eBayes, DEseq and some commonly used meta-analysis methods.     

Elevated hsa‐miR‐590‐3p expression down‐regulates HMGB2 expression and contributes to the severity of IgA nephropathy

Peripheral blood mononuclear cells (PBMCs) play important roles in the pathogenesis of IgA nephropathy (IgAN). Our study aimed to provide a deep understanding of IgAN and focused on the dysregulation of hsa‐miR‐590‐3p and its target gene HMGB2 in PBMCs. Three gene expression profile datasets (GSE14795, GSE73953 and GSE25590) were downloaded from the GEO database. The DEGs (differentially expressed genes)‐miRNA network that was associated with IgAN was constructed by Cytoscape, and HMGB2 and hsa‐miR‐590‐3p were selected for further exploration. The dual‐luciferase reporter system was utilized to verify their interaction. Then, the expression levels of HMGB2 and hsa‐miR‐590‐3p in PBMCs were detected by qPCR in another cohort, and the correlation of their expression levels with the clinical pathological manifestations and serum Gd‐IgA1(galactose‐deficient IgA1) levels was also investigated. HMGB2 was identified as the target gene of hsa‐miR‐590‐3p. Furtherly, the elderly patients had higher HMGB2 expression levels than the expression levels of the younger patients. As the serum creatinine, serum BUN levels increased, the expression of HMGB2 decreased; Besides, the HMGB2 expression was positively correlated with serum complement 3(C3) levels, and it also had a negative correlation with the diastolic blood pressure, but not reach statistical significance. What is more, both hsa‐miR‐590‐3p and HMGB2 expression had a slight correlation tendency with serum Gd‐IgA1 levels in the whole population. In conclusion, HMGB2, the target gene of hsa‐miR‐590‐3p, was identified to correlate with the severity of IgAN, and this provides more clues for the pathogenesis of IgAN.     

Large‐scale image‐based profiling of single‐cell phenotypes in arrayed CRISPR‐Cas9 gene perturbation screens

High‐content imaging using automated microscopy and computer vision allows multivariate profiling of single‐cell phenotypes. Here, we present methods for the application of the CISPR‐Cas9 system in large‐scale, image‐based, gene perturbation experiments. We show that CRISPR‐Cas9‐mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image‐based phenotyping. We developed a pipeline to construct a large‐scale arrayed library of 2,281 sequence‐verified CRISPR‐Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine‐learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in‐depth characterization of gene perturbation effects. This approach enables genome‐scale image‐based multivariate gene perturbation profiling using CRISPR‐Cas9.     

High sensitivity of both sequencing and real‐time PCR analysis of KRAS mutations in colorectal cancer tissue

The KRAS mutation status predicts the outcome of treatment with epidermal growth factor receptor targeted agents, and therefore the testing for KRAS mutations has become an important diagnostic procedure. To optimize the quality of this test, we compared the results of the two most commonly used KRAS mutation tests, cycle sequencing and a real‐time PCR‐based assay, in DNA extracted from formalin‐fixed paraffin‐embedded (FFPE) colorectal cancer samples of 511 patients. The results were interpreted in the context of the tumour cell percentage and the assay parameters. In 510 samples KRAS mutation status assessment was successful. A KRAS mutation was detected in 201 tumours (39.4%). Sequencing and the real‐time PCR‐based assay generated the same result in 486 samples (95.3%). The sequencing result was considered false positive in one (0.2%) and false negative in nine samples (1.8%). The assay result was considered false positive in six (1.2%) and false negative in seven samples (1.4%). Explanations for discrepant test results were a higher sensitivity of the assay in samples with a low tumour cell percentage, occurrence of mutations that are not covered by the assay and δ Ct values approximating the cut‐off value of the assay. In conclusion, both sequencing and the real‐time PCR‐based assay are reliable tests for KRAS mutation analysis in FFPE colorectal cancer samples, with a sensitivity of 95.5% (95% confidence interval [CI] 91.7–97.9%) and 96.5% (95% CI 93.0–98.6%), respectively. The real‐time PCR based assay is the method of choice in samples with a tumour cell percentage below 30%.     

RNA‐sequencing profiles hippocampal gene expression in a validated model of cancer‐induced depression

To investigate the pathophysiology of cancer‐induced depression (CID), we have recently developed a validated CID mouse model. Given that the efficacy of antidepressants in cancer patients is controversial, it remains unclear whether CID is a biologically distinct form of depression. We used RNA‐sequencing (RNA‐seq) to investigate differentially expressed genes (DEGs) in hippocampi of animals from our CID model relative a positive control model of depressive‐like behavior induced with chronic corticosterone (CORT). To validate RNA‐seq results, we performed quantitative real‐time RT‐PCR (qRT‐PCR) on a subset of DEGs. Enrichment analysis using DAVID was performed on DEGs to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and biological process gene ontologies (GO:BP). qRT‐PCR results significantly predicted RNA‐seq results. RNA‐seq revealed that most DEGs identified in the CORT model overlapped with the CID model. Enrichment analyses identified KEGG pathways and GO:BP terms associated with ion homeostasis and neuronal communication for both the CORT and CID model. In addition, CID DEGs were enriched in pathways and terms relating to neuronal development, intracellular signaling, learning and memory. This study is the first to investigate CID at the mRNA level. We have shown that most hippocampal mRNA changes that are associated with a depressive‐like state are also associated with cancer. Several other changes occur at the mRNA level in cancer, suggesting that the CID model may represent a biologically distinct form of a depressive‐like state.