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As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we develop and validate a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of ribonucleic acid sequencing (RNA-Seq) to whole genome and exome sequencing reveals that a standalone RNA-Seq assay offers the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data from 154 AML patients are used to develop a novel AML prognostic score, which is strongly associated with patient outcomes across 620 patients from three independent cohorts, and 42 patients from a prospective cohort. When combined with molecular risk guidelines, the risk score allows for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identify a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase, encoded by PTK2 , demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy. Several genomic features have been found for acute myeloid leukaemia (AML) but targeted clinical genetic testing fails to predict prognosis. Here, the authors generate an AML prognostic score from RNA-seq data of patients, which successfully stratifies AML patients and which may provide guidance for therapeutic strategies.

Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset ( n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset ( n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

Interstitial deletion of the long arm of chromosome 5 (del(5q)) is the most common structural genomic variant in myelodysplastic syndromes (MDS)1. Lenalidomide (LEN) is the treatment of choice for patients with del(5q) MDS, but half of the responding patients become resistant2 within 2 years. TP53 mutations are detected in ~20% of LEN-resistant patients3. Here we show that patients who become resistant to LEN harbour recurrent variants of TP53 or RUNX1. LEN upregulated RUNX1 protein and function in a CRBN- and TP53-dependent manner in del(5q) cells, and mutation or downregulation of RUNX1 rendered cells resistant to LEN. LEN induced megakaryocytic differentiation of del(5q) cells followed by cell death that was dependent on calpain activation and CSNK1A1 degradation4,5. We also identified GATA2 as a LEN-responsive gene that is required for LEN-induced megakaryocyte differentiation. Megakaryocytic gene-promoter analyses suggested that LEN-induced degradation of IKZF1 enables a RUNX1–GATA2 complex to drive megakaryocytic differentiation. Overexpression of GATA2 restored LEN sensitivity in the context of RUNX1 or TP53 mutations by enhancing LEN-induced megakaryocytic differentiation. Screening for mutations that block LEN-induced megakaryocytic differentiation should identify patients who are resistant to LEN.Martinez-Hoyer et al. identify recurrent TP53 or RUNX1 variants in patients with lenalidomide-resistant myelodysplastic syndromes that are associated with impaired RUNX1/GATA2-mediated megakaryocytic differentiation and cell death.

We recently described a 16-gene expression signature for improved risk stratification of acute myeloid leukemia (AML) patients called the AML Prognostic Score (APS). A subset of APS-high-risk AML patients showed increased levels of focal adhesion kinase (FAK), encoded by the Protein Tyrosine Kinase 2 (PTK2) gene, which was correlated with RUNX1 mutations. RUNX1 mutant cells are more sensitive to PTK2 inhibitors. As we were not able to detect RUNX1-binding sites in the PTK2 promoter, we hypothesized that RUNX1 might regulate micro(mi)RNAs that repress PTK2, such that loss-of-function RUNX1 mutations would result in reduced miRNA expression and derepression of PTK2. Examination of paired RNA-seq and miRNA-seq data from 301 AML cases revealed two miRNAs that positively correlated with RUNX1 expression, contained RUNX1-binding sites in their promoters and were predicted to target PTK2. We show that the hsa-let7a-2-3p and hsa-miR-135a-5p promoters are regulated by RUNX1, and that PTK2 is a direct target of both miRNAs. Even in the absence of RUNX1 mutations, hsa-let7a-2-3p and hsa-miR-135a-5p regulate PTK2 expression, and reduced expression of these two miRNAs sensitizes AML cells to PTK2 inhibition. These data explain how RUNX1 regulates PTK2, and identify potential miRNA biomarkers for targeting AML with PTK2 inhibitors.

Background: As scientists continue to pursue various ‘omics-based research, there is a need for high quality data for the most fundamental ‘omics of all: genomics. The bacterium Paenibacillus larvae is the causative agent of the honey bee disease American foulbrood. If untreated, it can lead to the demise of an entire hive; the highly social nature of bees also leads to easy disease spread, between both individuals and colonies. Biologists have studied this organism since the early 1900s, and a century later, the molecular mechanism of infection remains elusive. Transcriptomics and proteomics, because of their ability to analyze multiple genes and proteins in a highthroughput manner, may be very helpful to its study. However, the power of these methodologies is severely limited without a complete genome; we undertake to address that deficiency here. Results: We used the Illumina GAIIx platform and conventional Sanger sequencing to generate a 182-fold sequence coverage of the P. larvae genome, and assembled the data using ABySS into a total of 388 contigs spanning 4.5 Mbp. Comparative genomics analysis against fully-sequenced soil bacteria P. JDR2 and P. vortex showed that regions of poor conservation may contain putative virulence factors. We used GLIMMER to predict 3568 gene models, and named them based on homology revealed by BLAST searches; proteases, hemolytic factors, toxins, and antibiotic resistance enzymes were identified in this way. Finally, mass spectrometry was used to provide experimental evidence that at least 35% of the genes are expressed at the protein level. Conclusions: This update on the genome of P. larvae and annotation represents an immense advancement from what we had previously known about this species. We provide here a reliable resource that can be used to elucidate the mechanism of infection, and by extension, more effective methods to control and cure this widespread honey bee disease.

Acute myeloid leukemia (AML) is an aggressive cancer with very poor outcomes. To identify additional drivers of leukemogenesis, we analyzed sequencing data from 1,727 unique individual patients with AML, which revealed mutations in ubiquitin ligase family genes in 11.2% of samples from adult patients with AML with mutual exclusivity. The SKP1/CUL1/F-box (SCF) E3 ubiquitin ligase complex gene, FBXO11, was the most significantly downregulated gene of the SCF complex in AML. We found that FBXO11 interacts with and catalyzes K63-linked ubiquitination of LONP1 in the cytosol, to promote LONP1 entry into mitochondria. We show that depletion of FBXO11 or LONP1 reduced mitochondrial respiration through impaired LONP1 chaperone activity to assemble electron transport chain Complex IV. Reduced mitochondrial respiration secondary to FBXO11 or LONP1 depletion imparted myeloid-biased stem cell properties in primary CD34+ hematopoietic stem and progenitor cells (HSPCs) in vitro. In a human xenograft model, depletion of FBXO11 cooperated with AML1-ETO and mutant KRASG12D to generate serially transplantable AML. Our findings suggest that reduced FBXO11 cooperates to initiate AML by priming HSPC for myeloid-biased self renewal through attenuation of LONP1-mediated regulation of mitochondrial respiration.

Background Grosmannia clavigera is a bark beetle-vectored fungal pathogen of pines that causes wood discoloration and may kill trees by disrupting nutrient and water transport. Trees respond to attacks from beetles and associated fungi by releasing terpenoid and phenolic defense compounds. It is unclear which genes are important for G. clavigera 's ability to overcome antifungal pine terpenoids and phenolics. Results We constructed seven cDNA libraries from eight G. clavigera isolates grown under various culture conditions, and Sanger sequenced the 5' and 3' ends of 25,000 cDNA clones, resulting in 44,288 high quality ESTs. The assembled dataset of unique transcripts (unigenes) consists of 6,265 contigs and 2,459 singletons that mapped to 6,467 locations on the G. clavigera reference genome, representing ~70% of the predicted G. clavigera genes. Although only 54% of the unigenes matched characterized proteins at the NCBI database, this dataset extensively covers major metabolic pathways, cellular processes, and genes necessary for response to environmental stimuli and genetic information processing. Furthermore, we identified genes expressed in spores prior to germination, and genes involved in response to treatment with lodgepole pine phloem extract (LPPE). Conclusions We provide a comprehensively annotated EST dataset for G. clavigera that represents a rich resource for gene characterization in this and other ophiostomatoid fungi. Genes expressed in response to LPPE treatment are indicative of fungal oxidative stress response. We identified two clusters of potentially functionally related genes responsive to LPPE treatment. Furthermore, we report a simple method for identifying contig misassemblies in de novo assembled EST collections caused by gene overlap on the genome.

In western North America, the current outbreak of the mountain pine beetle (MPB) and its microbial associates has destroyed wide areas of lodgepole pine forest, including more than 16 million hectares in British Columbia. Grosmannia clavigera (Gc), a critical component of the outbreak, is a symbiont of the MPB and a pathogen of pine trees. To better understand the interactions between Gc, MPB, and lodgepole pine hosts, we sequenced the ∼30-Mb Gc genome and assembled it into 18 supercontigs. We predict 8,314 protein-coding genes, and support the gene models with proteome, expressed sequence tag, and RNA-seq data. We establish that Gc is heterothallic, and report evidence for repeat-induced point mutation. We report insights, from genome and transcriptome analyses, into how Gc tolerates conifer-defense chemicals, including oleoresin terpenoids, as they colonize a host tree. RNA-seq data indicate that terpenoids induce a substantial antimicrobial stress in Gc, and suggest that the fungus may detoxify these chemicals by using them as a carbon source. Terpenoid treatment strongly activated a ∼100-kb region of the Gc genome that contains a set of genes that may be important for detoxification of these host-defense chemicals. This work is a major step toward understanding the biological interactions between the tripartite MPB/fungus/forest system.

Background The mountain pine beetle (MPB, Dendroctonus ponderosae ) epidemic has affected lodgepole pine ( Pinus contorta ) across an area of more than 18 million hectares of pine forests in western Canada, and is a threat to the boreal jack pine ( Pinus banksiana ) forest. Defence of pines against MPB and associated fungal pathogens, as well as other pests, involves oleoresin monoterpenes, which are biosynthesized by families of terpene synthases (TPSs). Volatile monoterpenes also serve as host recognition cues for MPB and as precursors for MPB pheromones. The genes responsible for terpene biosynthesis in jack pine and lodgepole pine were previously unknown. Results We report the generation and quality assessment of assembled transcriptome resources for lodgepole pine and jack pine using Sanger, Roche 454, and Illumina sequencing technologies. Assemblies revealed transcripts for approximately 20,000 - 30,000 genes from each species and assembly analyses led to the identification of candidate full-length prenyl transferase, TPS, and P450 genes of oleoresin biosynthesis. We cloned and functionally characterized, via expression of recombinant proteins in E. coli , nine different jack pine and eight different lodgepole pine mono-TPSs. The newly identified lodgepole pine and jack pine mono-TPSs include (+)-α-pinene synthases, (-)-α-pinene synthases, (-)-β-pinene synthases, (+)-3-carene synthases, and (-)-β-phellandrene synthases from each of the two species. Conclusion In the absence of genome sequences, transcriptome assemblies are important for defence gene discovery in lodgepole pine and jack pine, as demonstrated here for the terpenoid pathway genes. The product profiles of the functionally annotated mono-TPSs described here can account for the major monoterpene metabolites identified in lodgepole pine and jack pine.

Background Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers. Results We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets. Conclusions Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases.