Rogan Magee

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types.

Abstract Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on ‘binarized’ isomiR profiles: each isomiR is simply labeled as ‘present’ or ‘absent’. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15× reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting ‘Pan-cancer Atlas’ of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers.

Assessment of isomiR Discrimination Using Commercial qPCR Methods

We sought to determine whether commercial quantitative polymerase chain reaction (qPCR) methods are capable of distinguishing isomiRs: variants of mature microRNAs (miRNAs) with sequence endpoint differences. We used two commercially available miRNA qPCR methods to quantify miR-21-5p in both synthetic and real cell contexts. We find that although these miRNA qPCR methods possess high sensitivity for specific sequences, they also pick up background signals from closely related isomiRs, which influences the reliable quantification of individual isomiRs. We conclude that these methods do not possess the requisite specificity for reliable isomiR quantification.

Threshold-seq: a tool for determining the threshold in short RNA-seq datasets

Summary: We present ‘Threshold‐seq,’ a new approach for determining thresholds in deep‐sequencing datasets of short RNA transcripts. Threshold‐seq addresses the critical question of how many reads need to support a short RNA molecule in a given dataset before it can be considered different from ‘background.’ The proposed scheme is easy to implement and incorporate into existing pipelines. Availability and Implementation: Source code of Threshold‐seq is freely available as an R package at: http://cm.jefferson.edu/threshold‐seq/ Contact: isidore.rigoutsos@jefferson.edu Supplementary information: Supplementary data are available at Bioinformatics online.

MINTbase v2.0: a comprehensive database for tRNA-derived fragments that includes nuclear and mitochondrial fragments from all The Cancer Genome Atlas projects

Abstract MINTbase is a repository that comprises nuclear and mitochondrial tRNA-derived fragments (‘tRFs’) found in multiple human tissues. The original version of MINTbase comprised tRFs obtained from 768 transcriptomic datasets. We used our deterministic and exhaustive tRF mining pipeline to process all of The Cancer Genome Atlas datasets (TCGA). We identified 23 413 tRFs with abundance of ≥ 1.0 reads-per-million (RPM). To facilitate further studies of tRFs by the community, we just released version 2.0 of MINTbase that contains information about 26 531 distinct human tRFs from 11 719 human datasets as of October 2017. Key new elements include: the ability to filter tRFs on-the-fly by minimum abundance thresholding; the ability to filter tRFs by tissue keywords; easy access to information about a tRF’s maximum abundance and the datasets that contain it; the ability to generate relative abundance plots for tRFs across cancer types and convert them into embeddable figures; MODOMICS information about modifications of the parental tRNA, etc. Version 2.0 of MINTbase contains 15x more datasets and nearly 4x more distinct tRFs than the original version, yet continues to offer fast, interactive access to its contents. Version 2.0 is available freely at http://cm.jefferson.edu/MINTbase/.

Energy Recycling by Co-Combustion of Coal and Recovered Paint Solids from Automobile Paint Operations

Abstract During the past decade, there has been substantial interest in recovering energy from many unwanted byproducts from industries and municipalities. Co-combustion of these products with coal seems to be the most cost-effective approach. The combustion process typically results in emissions of pollutants, especially fine particles and trace elements. This paper presents the results of an experimental study of particulate emission and the fate of 13 trace elements (arsenic [As], barium [Ba], cadmium [Cd], chromium [Cr], copper [Cu], cobalt [Co], manganese [Mn], molybdenum [Mo], nickel [Ni], lead [Pb], mercury [Hg], vanadium [V], and zinc [Zn]) during combustion tests of recovered paint solids (RPS) and coal. The emissions from combustions of coal or RPS alone were compared with those of co-combustion of RPS with subbituminous coal. The distribution/partitioning of these toxic elements between a coarse-mode ash (particle diameter [dp] > 0.5 µm), a submicrometer-mode ash (dp < 0.5 µm), and flue gases was also evaluated. Submicrometer particles generated by combustion of RPS alone were lower in concentration and smaller in size than that from combustion of coal. However, co-combustion of RPS and coal increased the formation of submicrometer-sized particles because of the higher reducing environment in the vicinity of burning particles and the higher volatile chlorine species. Hg was completely volatilized in all cases; however, the fraction in the oxidized state increased with co-combustion. Most trace elements, except Zn, were retained in ash during combustion of RPS alone. Mo was mostly retained in all samples. The behavior of elements, except Mn and Mo, varied depending on the fuel samples. As, Ba, Cr, Co, Cu, and Pb were vaporized to a greater extent from co-combustion of RPS and coal than from combustion of either fuel. Evidence of the enrichment of certain toxic elements in submicrometer particles has also been observed for As, Cd, Cr, Cu, and Ni during co-combustion.

The presence or absence alone of miRNA isoforms (isomiRs) successfully discriminate amongst the 32 TCGA cancer types

Previously, we demonstrated that miRNA isoforms (isomiRs) are constitutive and their expression profiles depend on tissue, tissue state, and disease subtype. We have now extended our isomiR studies to The Cancer Genome Atlas (TCGA) repository. Specifically, we studied whether isomiR profiles can distinguish amongst the 32 cancers. We analyzed 10,271 datasets from 32 cancers and found 7,466 isomiRs from 807 miRNA hairpin-arms to be expressed above threshold. Using the top 20% most abundant isomiRs, we built a classifier that relied on “binary” isomiR profiles: isomiRs were simply represented as ‘present’ or ‘absent’ and, unlike previous methods, all knowledge about their expression levels was ignored. The classifier could label tumor samples with an average sensitivity of 93% and a False Discovery Rate of 3%. Notably, its ability to classify well persisted even when we reduced the set of used features (=isomiRs) by a factor of 10. A counterintuitive finding of our analysis is that the isomiRs and miRNA loci with the highest ability to classify tumors are not the ones that have been attracting the most research attention in the miRNA field. Our results provide a framework in which to study cancer-type-specific isomiRs and explore their potential uses as cancer biomarkers

Systems-Level Analysis Of 32 TCGA Cancers Reveals Disease-Dependent tRNA Fragmentation Patterns And Very Selective Associations With Messenger RNAs And Repeat Elements

We mined 10,274 datasets from The Cancer Genome Atlas (TCGA) for tRNA fragments (tRFs) that overlap nuclear and mitochondrial (MT) mature tRNAs. Across 32 cancer types, we identified 20,722 distinct tRFs, a third of which arise from MT tRNAs. Most of the fragments belong to the novel category of i-tRFs, i.e. they are wholly internal to the mature tRNAs. The abundances and cleavage patterns of the identified tRFs depend strongly on cancer type. Of note, in all 32 cancer types, we find that tRNAHisGTG produces multiple and abundant 5´-tRFs with a uracil at the -1 position, instead of the expected post-transcriptionally-added guanosine. Strikingly, these -1U His 5´tRFs are produced in ratios that remain constant across all analyzed normal and cancer samples, a property that makes tRNAHisGTG unique among all tRNAs. We also found numerous tRFs to be negatively correlated with many messenger RNAs (mRNAs) that belong primarily to four universal biological processes: transcription, cell adhesion, chromatin organization and development/morphogenesis. However, the identities of the mRNAs that belong to these processes and are negatively correlated with tRFs differ from cancer to cancer. Notably, the protein products of these mRNAs localize to specific cellular compartments, and do so in a cancer-dependent manner. Moreover, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in multiple categories of repeat elements. Conversely, the genomic span of mRNAs that are positively correlated with tRFs are depleted in repeat elements. These findings suggest novel and far-reaching roles for tRFs and indicate their involvement in system-wide interconnections in the cell. All discovered tRFs from TCGA can be downloaded from https://cm.jefferson.edu/tcga-mintmap-profiles or studied interactively through the newly-designed version 2.0 of MINTbase at https://cm.jefferson.edu/MINTbase. NOTE: while the manuscript is under review, the content on the page https://cm.jefferson.edu/tcgamintmap-profiles is password protected and available only to Reviewers. Key Points Complexity: tRNAs exhibit a complex fragmentation pattern into a multitude of tRFs that are conserved within the samples of a given cancer but differ across cancers. Very extensive mitochondrial contributions: the 22 tRNAs of the mitochondrion (MT) contribute 1/3rd of all tRFs found across cancers, a disproportionately high number compared to the tRFs from the 610 nuclear tRNAs. Uridylated (not guanylated) 5´-His tRFs: in all human tissues analyzed, tRNAHisGTG produces many abundant modified 5´-tRFs with a U at their “-1” position (-1U 5´-tRFs), instead of a G. Likely central roles for tRNAHisGTG: the relative abundances of the -1U 5´-tRFs from tRNAHisGTG remain strikingly conserved across the 32 cancers, a property that makes tRNAHisGTG unique among all tRNAs and isoacceptors. Selective tRF-mRNA networks: tRFs are negatively correlated with mRNAs that differ characteristically from cancer to cancer. Mitochondrion-encoded tRFs are associated with nuclear proteins: in nearly all cancers, and in a cancer-specific manner, tRFs produced by the 22 mitochondrial tRNAs are negatively correlated with mRNAs whose protein products localize to the nucleus. tRFs are associated with membrane proteins: in all cancers, and in a cancer-specific manner, nucleus-encoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products localize to the cell’s membrane. tRFs are associated with secreted proteins: in all cancers, and in a cancer-specific manner, nucleusencoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products are secreted from the cell. tRFs are associated with numerous mRNAs through repeat elements: in all cancers, and in a cancerspecific manner, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in specific categories of repeat elements. intra-cancer tRF networks can depend on sex and population origin: within a cancer, positive and negative tRF-tRF correlations can be modulated by patient attributes such as sex and population origin. web-enabled exploration of an “Atlas for tRFs”: we released a new version of MINTbase to provide users with the ability to study 26,531 tRFs compiled by mining 11,719 public datasets (TCGA and other sources).

Abstract B43: Transcriptomic Heterogeneity of microRNA Isoforms and tRNA Fragments contributes to Race-based Differences in Breast and Prostate Cancers

We define transcriptomic heterogeneity (TH) as the phenomenon wherein “the same exact segment of DNA produces different RNA products either in different tissues of the same individual, or in the same tissue of individuals who differ in one or more variables such as sex, population origin, race, age, etc.” In TH, the disease state is associated with differences in the RNA molecules that are produced from a given segment of DNA. This differs from genomic heterogeneity wherein one associates a disease state with variations and polymorphisms in the DNA template itself. We have shown that TH is relevant to the study of race disparities in breast and prostate cancers, and implicated two classes of non-coding RNAs (ncRNAs): microRNAs and transfer RNAs. Next, we describe our findings separately for each class. MicroRNAs (miRNAs) are a well-known class of powerful regulators that control the abundance of messenger RNAs (mRNAs), and, thus, of proteins, in animals and plants. MiRNA studies long assumed that each arm of the miRNA precursor produced at most one consequential mature miRNA. Rapidly emerging data have now revealed a complex picture whereby a given miRNA precursor arm simultaneously produces a cloud of isoforms, the isomiRs, with 5´ and 3´ endpoints that differ slightly from one another9s. Emerging findings suggest that isomiRs represent a fundamental paradigm shift in how to study the roles of miRNAs in cancer and force us to reconsider the conventional view of “one-miRNA-precursor-arm-one-product.” Firstly, isomiRs are known to enter the RNA interference (RNAi) pathway and thus have functional roles in regulating transcript and protein abundance. Secondly, we showed that in healthy individuals and cancer patients, the identities and abundances of the isomiRs produced by a miRNA genomic locus depend on a person9s race, sex, and population origin and also on tissue type, disease subtype, and possibly other variables. Thirdly, using BT-20 and MDA-MB-468, two cell lines modeling triple negative breast cancer (TNBC) in White (Wh) and Black or African American (B/Aa) patients respectively, we demonstrated that distinct isomiRs from the same miRNA locus can target largely non-overlapping groups of mRNAs. Fourthly, using the same two cell lines (BT-20 and MDA-MB-468) we showed that the impact on proliferation by a given isomiR differs by race. Transfer RNAs (tRNAs) were discovered sixty years ago. tRNAs are present in all three kingdoms of life. The conventional understanding had been that the genomic loci encoding tRNAs produce a precursor transcript which is processed to give rise to the mature tRNA used in codon translation. As was the case with miRNAs, the analysis of deep sequencing data revealed that tRNA fragments, known as tRFs, are produced from the full-length premature or mature tRNAs. We carried out parallel investigations of the profiles of tRFs across hundreds of healthy individuals and cancer patients and were able to generate several key results. Firstly, we showed that tRFs are produced constitutively in healthy people and in cancer patients. Secondly, we showed that the identities and abundances of the tRFs produced by a tRNA genomic locus depend on a person9s race, sex, and population origin and also on tissue type, disease subtype, and possibly other variables. Thirdly, we showed that tRFs from the same tRNA alter the expression of largely non-overlapping groups of mRNAs. Fourthly, we showed that the impact on proliferation by a given tRF differs by race. Our findings show that isomiRs and tRFs are newly discovered important regulators whose roles depend on a patient9s race. These currently uncharacterized molecules need to be taken into account in studies of race-based cancer disparities. Citation Format: Isidore Rigoutsos, Aristeidis G. Telonis, Phillipe Loher, Rogan Magee, Yi Jing, Eric Londin. Transcriptomic Heterogeneity of microRNA Isoforms and tRNA Fragments contributes to Race-based Differences in Breast and Prostate Cancers. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr B43.

Comments on: "A comprehensive repertoire of tRNA-derived fragments in prostate cancer"

We evaluated the deep-sequencing (RNA-seq) data from human prostate tissue that were reported in [1] and the tRNA-derived fragments described in the original analysis. Our study of the same RNA-seq datasets reveals a considerably different pool of tRNA fragments, many of them with higher abundances than the fragments reported in [1]. We also evaluated the q-PCR approach proposed in [1]. As the approach lacks 5’-endpoint specificity, it will not estimate correctly the abundance of many of the tRFs that are present in the sampled RNA populations from human prostate tissue.