RNAi shows potential as an agricultural technology for insect control, yet, a relatively low number of robust lethal RNAi targets have been demonstrated to control insects of agricultural interest. In the current study, a selection of lethal RNAi target genes from the iBeetle (Tribolium castaneum) screen were used to demonstrate efficacy of orthologous targets in the economically important coleopteran pests Diabrotica virgifera virgifera and Meligethes aeneus. Transcript orthologs of 50 selected genes were analyzed in D. v. virgifera diet-based RNAi bioassays; 21 of these RNAi targets showed mortality and 36 showed growth inhibition. Low dose injection- and diet-based dsRNA assays in T. castaneum and D. v. virgifera, respectively, enabled the identification of the four highly potent RNAi target genes: Rop, dre4, ncm, and RpII140. Maize was genetically engineered to express dsRNA directed against these prioritized candidate target genes. T0 plants expressing Rop, dre4, or RpII140 RNA hairpins showed protection from D. v. virgifera larval feeding damage. dsRNA targeting Rop, dre4, ncm, and RpII140 in M. aeneus also caused high levels of mortality both by injection and feeding. In summary, high throughput systems for model organisms can be successfully used to identify potent RNA targets for difficult-to-work with agricultural insect pests.

"Parental RNA interference of genes involved in embryonic development of the western corn rootworm, Diabrotica virgifera virgifera LeConte" (2015). Faculty Publications: Department of Entomology. 420.

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Background RNA interference (RNAi) triggered by maize plants expressing RNA hairpins against specific western corn rootworm (WCR) transcripts have proven to be effective at controlling this pest. To provide robust crop protection, mRNA transcripts targeted by double‐stranded RNA must be sensitive to knockdown and encode essential proteins. Results Using WCR adult feeding assays, we identified Sec23 as a highly lethal RNAi target. Sec23 encodes a coatomer protein, a component of the coat protein (COPII) complex that mediates ER‐Golgi transport. The lethality detected in WCR adults was also observed in early instar larvae, the life stage causing most of the crop damage, suggesting that WCR adults can serve as an alternative to larvae for dsRNA screening. Surprisingly, over 85% transcript inhibition resulted in less than 40% protein knockdown, suggesting that complete protein knockdown is not necessary for Sec23 RNAi‐mediated mortality. The efficacy of Sec23 dsRNA for rootworm control was confirmed in planta; T0 maize events carrying rootworm Sec23 hairpin transgenes showed high levels of root protection in greenhouse assays. A reduction in larval survival and weight were observed in the offspring of WCR females exposed to Sec23 dsRNA LC25 in diet bioassays. Conclusion We describe Sec23 as RNAi target for in planta rootworm control. High mortality in exposed adult and larvae and moderate sublethal effects in the offspring of females exposed to Sec23 dsRNA LC25, suggest the potential for field application of this RNAi trait and the need to factor in responses to sublethal exposure into insect resistance management programs. © 2019 Society of Chemical Industry

Western corn rootworm, Diabrotica virgifera virgifera, is the major agronomically important pest of maize in the US Corn Belt. To augment the repertoire of the available dsRNA-based traits that control rootworm, we explored a potentially haplolethal gene target, wings up A (wupA), which encodes Troponin I. Troponin I, a component of the Troponin-Tropomyosin complex, is an inhibitory protein involved in muscle contraction. In situ hybridization showed that feeding on wupA-targeted dsRNAs caused systemic transcript knockdown in D. v. virgifera larvae. The knockdown of wupA transcript, and by extension Troponin I protein, led to deterioration of the striated banding pattern in larval body muscle and decreased muscle integrity. Additionally, the loss of function of the circular muscles surrounding the alimentary system led to significant accumulation of food material in the hind gut, which is consistent with a loss of peristaltic motion of the alimentary canal. In this study, we demonstrate that wupA dsRNA is lethal in D. v. virgifera larvae when fed via artificial diet, with growth inhibition of up to 50% within two days of application. Further, wupA hairpins can be stably expressed and detected in maize. Maize expressing wupA hairpins exhibit robust root protection in greenhouse bioassays, with several maize transgene integration events showing root protection equivalent to commercial insecticidal protein-expressing maize.

RNA interference (RNAi) is a promising next generation technology for the development of species-specific pest management. The key to successful RNAi based-plant protection is dependent in part on data-driven target gene selection, a challenging task due to the absence of laboratory strains and the seasonality of most pest species. In this study, we aimed to identify novel target genes by performing a knowledge-based approach in order to expand the spectrum of known potent RNAi targets. Recently, the protein-coding genes ncm, Rop, RPII-140, and dre4 have been identified as sensitive RNAi targets for pest control. Based on these potent RNAi targets, we constructed an interaction network and analyzed a selection of 30 genes in the model beetle Tribolium castaneum via injection of dsRNA synthesized by in vitro transcription. Nineteen of these targets induced significant mortality of over 70%, including six that caused 100% lethality. Orthologs of active T. castaneum RNAi targets were verified in the economically important coleopteran pests Diabrotica virgifera virgifera and Brassicogethes aeneus. Knockdown of D. v. virgifera genes coding for transcription factor Spt5, Spt6, and RNA polymerase II subunit RPII-33 caused over 90% mortality in larval feeding assays. Injection of dsRNA constructs targeting RPII-215 or the pre-mRNA-processing factor Prp19 into adult B. aeneus resulted in high lethality rates of 93 and 87%, respectively. In summary, the demonstrated knowledge-based approaches increased the probability of identifying novel lethal RNAi target genes from 2% (whole genome) to 36% (transcription- and splicing-related genes). In addition, performing RNAi pre-screening in a model insect increased also the probability of the identification essential genes in the difficult-to-work-with pest species D. v. virgifera and B. aeneus.

Ovarian cancer is the fifth leading cause of cancer-related deaths in women and is the deadliest gynecological malignancy in the United States. The standard treatment of ovarian cancer is based on debulking surgery followed by platinum- and taxane-based chemotherapy, and has remained the same over the past three decades. Over those years, molecular targeted and combination therapies have been developed and clinically approved, however the overall survival rate has not improved significantly due to chemo-resistance. Further, the majority of patients experience recurrence of treatment-resistant tumors. The genomic diversity within a tumor and the varying cell types within its microenvironment has placed significant importance on heterogeneity and its clinical implications. Intra-tumor heterogeneity has often been blamed for treatment failure in ovarian carcinoma. Consequently, intra-tumor heterogeneity is a key factor driving drug resistance, therapeutic failure, and poor outcomes and poses a significant challenge to personalized cancer medicine. Intra-tumor heterogeneity is a hallmark of cancer where the molecular and cellular interactions within the tumor microenvironment can dictate a cancer’s fate. Molecular profiling of bulk tissue specimens using methods such as whole-transcriptome sequencing are limited in their ability to resolve fine grain molecular signatures and hinder our utility to dissect the underlying biology of individual tumors. Although informatics approaches are available that attempt to disentangle tissue heterogeneity from bulk tumor data, emerging spatial whole transcriptome sequencing technologies allow a more precise delineation of cellular and molecular substructure in a comprehensive unbiased approach. Spatial Transcriptomics (ST) enables high-throughput whole transcriptomic sequencing within a single intact tissue by using a glass slide arrayed with barcoded cDNA primers at a resolution of 100um (3-30 cells). This workflow requires no tissue dissociation keeping fragile cell types intact. Resulting data from this workflow is overlaid on the tissue, displayed as a “cluster reference map”, providing comprehensive unbiased transcriptional substructure and unique possibilities for subsequent in situ analysis. The overall goal of this study is to utilize ST to unveil the unexplored landscape of intra-tumor heterogeneity in ovarian cancer and determine its translational relevance. Here, we applied ST to profile gene expression in fresh frozen OCT embedded sections from nine high grade ovarian patients. Three serial 5-micron frozen sections were placed on proprietary 10x Genomics Spatial Transcriptomics (ST) slides and processed using manufacturer specifications. Libraries were sequenced on the Illumina NovaSeq 6000 system and data was processed using 10X Genomics analytical tools. Next, we aggregated the transcriptional profiles of serial sections from each case increasing our power to cluster similar regions and identify differentially expressed genes within these tissues. Using serial sections of solid tumors from each subject we were not only able to profile each section at 100um resolution but also spatially resolve gene expression signatures and cluster regions of tissue based on these signatures. This ST workflow reliably quantitated an average spatial distribution of 19,285 genes per section in our ovarian cancer cohort. Interestingly, within our cohort we have extreme outliers that include primary tumors that did not have any response to standard adjuvant chemotherapy, paclitaxel and carboplatin, and patients that sustained a durable response to standard adjuvant chemotherapy and diagnosed as disease free ≥3 years and then presented with recurrent disease. We identified tumor heterogeneity as unique spatially-resolved gene expression clusters across each tissue section defined by individual gene sets associated with tumorigenic molecular processes, immune cell quantity and localization. These approaches highlight the power of spatial whole-transcriptomic sequencing in solid tumor studies to help unravel the complexity of heterogeneous cancers and provide a comprehensive characterization of transcriptional substructure within a single tissue section. Citation Format: Lee D. Gibbs, Lynda Roman, Michelle Webb, Rania Bassiouni, Stephen R Williams, Neil I. Weisenfeld, Nigel F. Delaney, Yifeng Yin, Solomon Rotimi, Jennifer Chew, Meghan Frey, Jing Qian, Heather Miller, Laila Murderspach, Diane Da Silva, Troy McEachron, David W. Craig, John D. Carpten. Novel Approaches for Accessing Molecular Heterogeneity [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr IA41.

Introduction: Differences in tumor heterogeneity, particularly the tumor microenvironment have not yet been explored as potential molecular features underlying cancer disparities. Molecular profiling of bulk tissue specimens using methods such as whole-transcriptome sequencing are limited in their ability to resolve fine grain molecular signatures and hinder our utility to dissect underlying biology of individual tumors. Although informatics approaches are available that attempt to disentangle tissue heterogeneity from bulk tumor data, emerging spatial whole transcriptome sequencing technologies allows a more precise delineation of cellular and molecular substructure in a comprehensive unbiased way. Here we present an investigation whole-transcriptome spatial sequencing in a diverse cohort of ovarian cancer cases. Methods: Three serial 5-micron frozen sections were placed on proprietary 10x Genomics Spatial Transcriptomics (ST) slides and processed using manufacturer specifications. Libraries were sequenced on the Illumina NovaSeq 6000 system and data was processed using the 10X Genomics analytical tools. ST data from each section was analyzed to create spatially defined cellular clusters at 100-micron resolution. Results: ST revealed cellular heterogeneity across the entirety of the tumor microenvironment in an anatomically resolved manner. Bioinformatic tools and molecular pathways to infer tumor purity were used to annotate tumor, immune, and stromal substructures. These data also revealed clear transcriptional substructure in some tumors, where different tumor regions were defined by unique gene sets associated with different molecular processes known to be related to tumorigenesis. Further, we observed clear transcriptional substructure in some tumors, where different tumor regions were enriched with molecular pathways that are associated with tumorigenesis (i.e., DNA Damage Response, and Protein Translation pathways), regions defined by immune infiltration (i.e. CD8+ T cells, T regulatory cells, Macrophages, and B cells), and molecular pathways that are associated with inflammatory signalling (i.e. complement, IL2, IL-10, and IL6 signaling pathways). Conclusion: These approaches highlight the power of spatial whole-transcriptomic approaches in solid tumor studies to help unravel the complexity of heterogeneous cancers and provide a comprehensive characterization of transcriptional substructure within a single tissue section. Citation Format: Lee D. Gibbs, Stephen R. Williams, Neil I. Weisenfeld, Rania Bassiouni, Nigel F. Delaney, Diane Da Silva, Yifeng Yin, Solomon Rotimi, Jennifer Chew, Meghan Frey, Jing Qian, Heather Miller, Laila Murderspach, Troy McEachron, David Craig, Lynda WOptional Roman, John D. Carpten. Spatial RNA-seq reveals intratumor heterogeneity and transcriptional substructure in a diverse cohort of high-grade ovarian cancers [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A087.