Monika Mehta

DNA topoisomerase II (TOP2) generates transient DNA double-strand breaks that are trapped as TOP2-DNA covalent complexes (TOP2cc) by antibiotic and chemotherapy drugs. Here, we characterize tools for study of cellular responses to TOP2cc, exploiting a Saccharomyces cerevisiae TOP2 mutant (TOP2-F1025Y,R1128G) that generates spontaneous and inhibitor-induced covalent complexes at elevated frequencies. This Top2-hc (for "hypercleavage") mutant protein inhibits yeast cell growth when expressed alone or with endogenous Top2, and growth defects are exacerbated in DNA-repair-deficient genetic backgrounds and/or in the presence of low doses of the Top2 poison mAMSA. We generated analogous mutations in human and mouse TOP2A and TOP2B that gave increased TOP2cc, hypersensitization to topoisomerase poisons, increased DNA damage, and decreased cell survival in cultured cells. We further established knock-in mouse models with inducible, tissue-specific expression of each TOP2-hc isoform, demonstrating overt organismal toxicity and cellular markers of DNA damage responses. To illustrate the potential of these genetic tools, we carried out proof-of-principle screens in yeast and cultured human cells for sensitivity to TOP2-hc. The yeast screen revealed strong requirements for homologous recombination, moderate roles for sister chromatid cohesion and kinetochore function, and dependencies on vesicle and vacuolar functions. The pilot shRNA screen in human cells revealed shared requirements for resistance to expression of either TOP2A-hc or TOP2B-hc as well as examples of isoform specificity. These findings establish hypercleavage mutant proteins as effective tools for studying topoisomerase isoform-specific DNA damage and offer a foundation for exploring TOP2cc toxicity and tolerance in vivo.

Lassa virus (LASV) is a rodent-borne mammarenavirus that causes tens to hundreds of thousands of human infections annually in Western Africa. Approximately 20% of these infections progress to Lassa fever (LF), an acute disease with case–fatality rates from ≈20–70%. Currently, there are no approved vaccines or specific therapeutics to prevent or treat LF. The LASV genome consists of a small (S) segment that has two genes, GP and NP, and a large (L) segment that has two genes, L and Z. In both segments, the two genes are separated by non-coding intergenic regions (IGRs). Recombinant LASVs (rLASVs), in which the L segment IGR was replaced with the S segment IGR or in which the GP gene was codon-deoptimized, lost fitness in vitro, were highly attenuated in vivo, and, when used as vaccines, protected domesticated guinea pigs from otherwise lethal LASV exposure. Here, we report the generation of rLASV/IGR-CD, which includes both determinants of attenuation and further enhances the safety of the vaccine compared with its predecessors. rLASV/IGR-CD grew to high titers in Vero cells, which are approved for human vaccine production, but did not cause signs of disease or pathology in guinea pigs. Importantly, guinea pigs vaccinated with rLASV/IGR-CD were completely protected from disease and death after a typically lethal exposure to wild-type LASV. Our data support the development of rLASV/IGR-CD as a live-attenuated LF vaccine with stringent safety features.

Patients with autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED) develop chronic mucocutaneous candidiasis during childhood and about 10% of senior patients develop upper GI tract squamous cell carcinoma (SCC), suggesting that C. albicans infection may be relevant to the cause of SCC development in APECED patients. In this study, we investigated the role of C. albicans infection in upper GI tract SCC development using an APECED-like IKKα kinase-dead (KA/KA) mouse model. We found that KA/KA mice developed upper GI tract SCC after oral infection of WT C. albicans weekly for three months. Interestingly, while upper GI tract SCC development in C. albicans - infected KA/KA mice is IL-17a-independent, EGFR pathway plays an important role in C. albicans infection-mediated tumorigenesis in KA/KA mice, as evidenced by significantly lower SCC incidence in the upper GI tract of KA/KA mice infected with Als3^Δ/Δ C. albicans. Moreover, upper GI tract SCCs from WT C. albicans-infected KA/KA mice exhibited robust p-EGFR and Ki67 signals. However, both p-EGFR and Ki67 signals were only mildly increased in upper GI tract of Als3^Δ/Δ C. albicans-infected KA/KA mice compared to WT control mice. Meanwhile, EGFR inhibition dramatically reduced C. albicans colonization in upper GI tract and the weight of stomach in KA/KA mice. Furthermore, we found that aberrant infiltrations of neutrophils and macrophages with significant inflammatory signatures were present in the upper GI tract SCCs in KA/KA mice. Interestingly, KA/KA neutrophils exhibited a defect in C. albicans killing capacity due to functional defects in ROS generation, degranulation, NETs formation, and apoptosis. We reasoned that KA/KA neutrophils’ inability in C. albicans killing capacity may further enhance the tumor-promoting microenvironment. Finally, whole genome sequencing data suggested that C. albicans infection may lead to genome instability. Currently we are investigating the role of neutrophil infiltration in C. albicans infection-mediated upper GI tract tumorigenesis. Citation Format: Feng Zhu, Quynh T. Phan, Elise M. Ferre, Jami Willette-Brown, Rosalba Salcedo, Tsai-Wei Shen, Yu Fan, Yongmei Zhao, Monika Mehta, Daoud Meerzaman, Bao Tran, Giorgio Trinchieri, Michail S. Lionakis, Scott G. Filler, Yinling Hu. C. albicans infection mediates upper gastrointestinal tract malignancy independently of Il17a in an APECED-like mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3044.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor–normal genomic DNA (gDNA) samples derived from a breast cancer cell line—which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations—and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking ‘tumor-only’ or ‘matched tumor–normal’ analyses.

Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

Single-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithm’s logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

Innate defense regulator RP-182 targets CD206 ^high M2-like macrophages in malignant and noncancerous diseases.