Automation of high-throughput mRNA-seq library preparation: a robust, hands-free and time efficient methodology.

Over the last decade, whole transcriptome profiling, also known as RNA-sequencing (RNA-seq), has quickly gained traction as a reliable method for unbiased assessment of gene expression. Integration of RNA-seq expression data into other omics datasets (e.g., proteomics, metabolomics, or epigenetics) solidifies our understanding of cell-specific regulatory patterns, yielding pathways to investigate the key rules of gene regulation. A limitation to efficient, at-scale utilization of RNA-seq is the time-demanding library preparation workflows, which is a 2-day or longer endeavor per cohort/sample size. To tackle this bottleneck, we designed an automated workflow that increases throughput capacity, while minimizing human error to enhance reproducibility. To this end, we converted the manual protocol of the NEBNext Directional Ultra II RNA Library Prep Kit for Illumina on the Beckman Coulter liquid handler, Biomek i7 Hybrid workstation. A total of 84 RNA samples were isolated from two human cell lines and subjected to comparative manual and automated library preparation methods. Qualitative and quantitative results indicated a high degree of similarity between libraries generated manually or through automation. Yet, there was a significant reduction in both hands-on and assay time from a 2-day manual to a 9-hour automated workflow. Using linear regression analysis, we found the Pearson correlation coefficient between libraries generated manually or by automation to be almost identical to a sample being sequenced twice (R²= 0.985 vs 0.983). This demonstrates that high-throughput automated workflows can be of great benefit to genomic laboratories by enhancing efficiency of library preparation, reducing hands-on time and increasing throughput potential.

Differentiation therapy and the mechanisms that terminate cancer cell proliferation without harming normal cells.

Chemotherapeutic drugs have a common intent to activate apoptosis in tumor cells. However, master regulators of apoptosis (e.g., p53, p16/CDKN2A) are frequently genetically inactivated in cancers, resulting in multidrug resistance. An alternative, p53-independent method for terminating malignant proliferation is to engage terminal-differentiation. Normally, the exponential proliferation of lineage-committed progenitors, coordinated by the master transcription factor (TF) MYC, is self-limited by forward-differentiation to terminal lineage-fates. In cancers, however, this exponential proliferation is disengaged from terminal-differentiation. The mechanisms underlying this decoupling are mostly unknown. We performed a systematic review of published literature (January 2007-June 2018) to identify gene pathways linked to differentiation-failure in three treatment-recalcitrant cancers: hepatocellular carcinoma (HCC), ovarian cancer (OVC), and pancreatic ductal adenocarcinoma (PDAC). We analyzed key gene alterations in various apoptosis, proliferation and differentiation pathways to determine whether it is possible to predict treatment outcomes and suggest novel therapies. Poorly differentiated tumors were linked to poorer survival across histologies. Our analyses suggested loss-of-function events to master TF drivers of lineage-fates and their cofactors as being linked to differentiation-failure: genomic data in TCGA and ICGC databases demonstrated frequent haploinsufficiency of lineage master TFs (e.g., GATA4/6) in poorly differentiated tumors; the coactivators that these TFs use to activate genes (e.g. ARID1A, PBRM1) were also frequently inactivated by genetic mutation and/or deletion. By contrast, corepressor components (e.g., DNMT1, EED, UHRF1, and BAZ1A/B), that oppose coactivators to repress or turn off genes, were frequently amplified instead, and the level of amplification was highest in poorly differentiated lesions. This selection by neoplastic evolution towards unbalanced activity of transcriptional corepressors suggests these enzymes as candidate targets for inhibition aiming to re-engage forward-differentiation. This notion is supported by both pre-clinical and clinical trial literature.

GATA4 loss of function in liver cancer impedes precursor to hepatocyte transition.

The most frequent chromosomal structural loss in hepatocellular carcinoma (HCC) is of the short arm of chromosome 8 (8p). Genes on the remaining homologous chromosome, however, are not recurrently mutated, and the identity of key 8p tumor-suppressor genes (TSG) is unknown. In this work, analysis of minimal commonly deleted 8p segments to identify candidate TSG implicated GATA4, a master transcription factor driver of hepatocyte epithelial lineage fate. In a murine model, liver-conditional deletion of 1 Gata4 allele to model the haploinsufficiency seen in HCC produced enlarged livers with a gene expression profile of persistent precursor proliferation and failed hepatocyte epithelial differentiation. HCC mimicked this gene expression profile, even in cases that were morphologically classified as well differentiated. HCC with intact chromosome 8p also featured GATA4 loss of function via GATA4 germline mutations that abrogated GATA4 interactions with a coactivator, MED12, or by inactivating mutations directly in GATA4 coactivators, including ARID1A. GATA4 reintroduction into GATA4-haploinsufficient HCC cells or ARID1A reintroduction into ARID1A-mutant/GATA4-intact HCC cells activated hundreds of hepatocyte genes and quenched the proliferative precursor program. Thus, disruption of GATA4-mediated transactivation in HCC suppresses hepatocyte epithelial differentiation to sustain replicative precursor phenotype.