Anthony R. Colombo

Suppression of Transposable Elements in Leukemic Stem Cells

Genomic transposable elements (TEs) comprise nearly half of the human genome. The expression of TEs is considered potentially hazardous, as it can lead to insertional mutagenesis and genomic instability. However, recent studies have revealed that TEs are involved in immune-mediated cell clearance. Hypomethylating agents can increase the expression of TEs in cancer cells, inducing ‘viral mimicry’, causing interferon signalling and cancer cell killing. To investigate the role of TEs in the pathogenesis of acute myeloid leukaemia (AML), we studied TE expression in several cell fractions of AML while tracking its development (pre-leukemic haematopoietic stem cells, leukemic stem cells [LSCs], and leukemic blasts). LSCs, which are resistant to chemotherapy and serve as reservoirs for relapse, showed significant suppression of TEs and interferon pathways. Similarly, high-risk cases of myelodysplastic syndrome (MDS) showed far greater suppression of TEs than low-risk cases. We propose TE suppression as a mechanism for immune escape in AML and MDS. Repression of TEs co-occurred with the upregulation of several genes known to modulate TE expression, such as RNA helicases and autophagy genes. Thus, we have identified potential pathways that can be targeted to activate cancer immunogenicity via TEs in AML and MDS.

Author Correction: Suppression of Transposable Elements in Leukemic Stem Cells

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Understanding metabolic changes in aging bone marrow.

BackgroundAging is associated with complex molecular alterations at the cellular level. Bone marrow exhibits distinct phenotypic, genetic and epigenetic alterations with aging. Metabolic changes in the bone marrow related to aging have not been studied.MethodsIn this study, we characterized the metabolome and transcriptome of aging murine bone marrow and compared it with bone marrow from young healthy mice and chemotherapy treated mice; chemotherapy treatment is known to induce age-related changes in hematopoiesis.ResultsThe metabolome of the aging bone marrow exhibited a signature of suppressed fatty-acid oxidation: accumulation of free fatty acids, reduced acyl-carnitines and low β-hydroxy butyric acid. The aged bone marrow also exhibited a significant reduction in amino acid and nucleic acid pool. The transcriptome of the aging bone marrow revealed a signature of oxidative stress, known to be associated with mitochondrial dysfunction. Lastly, the metabolic and transcriptomic profiles of the bone marrow of chemotherapy treated mice did not show broad age-related changes but rather mostly resembled young healthy mice, suggestive of a lack of ‘metabolic aging’ with chemotherapy exposure.ConclusionOur results revealed broad changes in lipids, amino acids, and nucleotides in aging marrow tissue. Together, these data provide a rich resource for the study of metabolic changes associated with aging in bone marrow.

Senescence induction universally activates transposable element expression

ABSTRACT Senescent cells constitutively secrete inflammatory cytokines, known as the senescence-associated secretory phenotype (SASP). Previous work has implicated SASP in immune-mediated clearance of senescent cells; however, its regulation remains unknown. Our recent transcriptome profiling study has shown that human senescent human stem and progenitors (s-HSPCs) robustly express genomic transposable elements (TEs) and pathways of inflammation. Furthermore, hypomethylating agents have been previously shown to induce expression of TEs and activate the dsRNA recognition pathway and downstream interferon-stimulated genes, leading to immune mediated cell death. Therefore, to examine whether activation of TEs occurred universally, independent of their modality of senescence induction, we performed transcriptomic analysis in artificially-induced senescent cell-lines and observed a robust activation of TEs. Hence we propose that the expression of TEs might play a role in immune mediated clearance of senescent cells.

Transposable Element Expression Predicts Prognosis in Acute Myeloid Leukemia

Over half of the human genome is comprised of transposable elements (TE). TE have been implicated in cancer pathogenesis. Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression investigating its relationship to various mutations and its role in predicting prognosis has not been performed. We characterized TE expression in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas (TCGA). We identified significant dysregulation of TE, with distinct patterns of TE expression correlated to specific mutations and distinct coding gene networks. TP53 mutated AML had a unique TE expression signature and was associated with significantly suppressed expression of TE and various classes of non-coding RNA. We identified 17 candidate prognostic TE transcripts that can classify AML subtypes as either high or low risk. These 17 TE were able to further sub-stratify low risk AML (based on mutational profile and coding gene expression) into favorable and unfavorable prognostic categories. The expression signature of the 17 TE was able to predict prognosis in an independent cohort of 284 pediatric AML patients, and was also able to predict time to relapse in an independent dataset of relapsed adult cases. This first comprehensive study of TE expression in AML demonstrates that TE expression can be used as a biomarker for predicting prognosis in AML and also provides novel insights into the biology of TP53 mutated AML. Studies characterizing its role in other cancers are warranted.

Artemis: Rapid and Reproducible RNAseq Analysis for End Users

The recently introduced Kallisto[1] pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. However, as with all computational advances, reproducibility across experiments requires attention to detail. The elegant approach of Kallisto reduces dependencies, but we noted differences in quantification between versions of Kallisto, and both upstream preparation and downstream interpretation benefit from an environment that enforces a requirement for equivalent processing when comparing groups of samples. Therefore, we created the Arkas[3] and TxDbLite[4] R packages to meet these needs and to ease cloud-scale deployment of the above. TxDbLite extracts structured information directly from source FASTA files with per-contig metadata, while Arkas enforces versioning of the derived indices and annotations, to ensure tight coupling of inputs and outputs while minimizing external dependencies. The two packages are combined in Illuminas BaseSpace cloud computing environment to offer a massively parallel and distributed quantification step for power users, loosely coupled to biologically informative downstream analyses via gene set analysis (with special focus on Reactome annotations for ENSEMBL transcriptomes). Previous work (e.g. Soneson et al., 2016[34]) has revealed that filtering transcriptomes to exclude lowly-expressed isoforms can improve statistical power, while more-complete transcriptome assemblies improve sensitivity in detecting differential transcript usage. Based on earlier work by Bourgon et al., 2010[11], we included this type of filtering for both gene- and transcript-level analyses within Arkas. For reproducible and versioned downstream analysis of results, we focused our efforts on ENSEMBL and Reac-tome[2] integration within the qusage[19] framework, adapted to take advantage of the parallel and distributed environment in Illumina’s BaseSpace cloud platform. We show that quantification and interpretation of repetitive sequence element transcription is eased in both basic and clinical studies by just-in-time annotation and visualization. The option to retain pseudoBAM output for structural variant detection and annotation, while not insignificant in its demand for computation and storage, nonetheless provides a middle ground between de novo transcriptome assembly and routine quantification, while consuming a fraction of the resources used by popular fusion detection pipelines and providing options to quantify gene fusions with known breakpoints without reassembly. Finally, we describe common use cases where investigators are better served by cloud-based computing platforms such as BaseSpace due to inherent efficiencies of scale and enlightened common self-interest. Our experiences suggest a common reference point for methods development, evaluation, and experimental interpretation.The recently introduced Kallisto(dx.doi.org/10.1038/nbt.3519, Bray et al.,2015) pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. However, as with all computational advances, reproducibility across experiments requires attention to detail. The elegant approach of Kallisto reduces dependencies, but we noted differences in quantification between versions of Kallisto, and both upstream preparation and downstream interpretation benefit from an environment that enforces a requirement for equivalent processing when comparing groups of samples. Therefore, we created the Artemis and TxDbLite R packages to meet these needs and to ease cloud-scale deployment of the above. TxDbLite extracts structured information directly from source FASTA files with per-contig metadata, while Artemis enforces versioning of the derived indices and annotations, to ensure tight coupling of inputs and outputs while minimizing external dependencies. The two packages are combined in Illuminas BaseSpace cloud computing environment to offer a massively parallel and distributed quantification step for power users, loosely coupled to biologically informative downstream analyses via gene set analysis (with special focus on Reactome annotations for ENSEMBL transcriptomes). Previous work (e.g. Soneson, et al., 2016)(dx.doi.org/10.1186/s13059-015-0862-3) has revealed that filtering transcriptomes to exclude lowly-expressed isoforms can improve statistical power, while more-complete transcriptome assemblies improve sensitivity in detecting differential transcript usage. Based on earlier work by Bourgon et. al (http://www.pnas.org/content/107/21/9546.full), 2010, we included this type of filtering for both gene- and transcript-level analyses within Artemis. For reproducible and versioned downstream analysis of results, we focused our efforts on ENSEMBL and Reactome integration within the qusage (Yaari et. al, 2013) framework, adapted to take advantage of the parallel and distributed environment in Illuminas BaseSpace cloud platform. We show that quantification and interpretation of repetitive sequence element transcription is eased in both basic and clinical studies by just-in-time annotation and visualization. The option to retain pseudoBAM output for structural variant detection and annotation, while not insignificant in its demand for computation and storage, nonetheless provides a middle ground between de novo transcriptome assembly and routine quantification while consuming a fraction of the resources Finally, we describe common use cases where investigators are better served by cloud-based computing platforms such as BaseSpace due to inherent efficiencies of scale and enlightened common self-interest. Our experiences suggest a common reference point for methods development, evaluation, and experimental interpretation.