The m6A RNA demethylase FTO is a HIF-independent synthetic lethal partner with the VHL tumor suppressor.

Loss of the von Hippel-Lindau (VHL) tumor suppressor is a hallmark feature of renal clear cell carcinoma. VHL inactivation results in the constitutive activation of the hypoxia-inducible factors (HIFs) HIF-1 and HIF-2 and their downstream targets, including the proangiogenic factors VEGF and PDGF. However, antiangiogenic agents and HIF-2 inhibitors have limited efficacy in cancer therapy due to the development of resistance. Here we employed an innovative computational platform, Mining of Synthetic Lethals (MiSL), to identify synthetic lethal interactions with the loss of VHL through analysis of primary tumor genomic and transcriptomic data. Using this approach, we identified a synthetic lethal interaction between VHL and the m6A RNA demethylase FTO in renal cell carcinoma. MiSL identified FTO as a synthetic lethal partner of VHL because deletions of FTO are mutually exclusive with VHL loss in pan cancer datasets. Moreover, FTO expression is increased in VHL-deficient ccRCC tumors compared to normal adjacent tissue. Genetic inactivation of FTO using multiple orthogonal approaches revealed that FTO inhibition selectively reduces the growth and survival of VHL-deficient cells in vitro and in vivo. Notably, FTO inhibition reduced the survival of both HIF wild type and HIF-deficient tumors, identifying FTO as an HIF-independent vulnerability of VHL-deficient cancers. Integrated analysis of transcriptome-wide m6A-seq and mRNA-seq analysis identified the glutamine transporter SLC1A5 as an FTO target that promotes metabolic reprogramming and survival of VHL-deficient ccRCC cells. These findings identify FTO as a potential HIF-independent therapeutic target for the treatment of VHL-deficient renal cell carcinoma.

Cell cycle progression in Caulobacter requires a nucleoid-associated protein with high AT sequence recognition.

Faithful cell cycle progression in the dimorphic bacterium Caulobacter crescentus requires spatiotemporal regulation of gene expression and cell pole differentiation. We discovered an essential DNA-associated protein, GapR, that is required for Caulobacter growth and asymmetric division. GapR interacts with adenine and thymine (AT)-rich chromosomal loci, associates with the promoter regions of cell cycle-regulated genes, and shares hundreds of recognition sites in common with known master regulators of cell cycle-dependent gene expression. GapR target loci are especially enriched in binding sites for the transcription factors GcrA and CtrA and overlap with nearly all of the binding sites for MucR1, a regulator that controls the establishment of swarmer cell fate. Despite constitutive synthesis, GapR accumulates preferentially in the swarmer compartment of the predivisional cell. Homologs of GapR, which are ubiquitous among the α-proteobacteria and are encoded on multiple bacteriophage genomes, also accumulate in the predivisional cell swarmer compartment when expressed in Caulobacter The Escherichia coli nucleoid-associated protein H-NS, like GapR, selectively associates with AT-rich DNA, yet it does not localize preferentially to the swarmer compartment when expressed exogenously in Caulobacter, suggesting that recognition of AT-rich DNA is not sufficient for the asymmetric accumulation of GapR. Further, GapR does not silence the expression of H-NS target genes when expressed in E. coli, suggesting that GapR and H-NS have distinct functions. We propose that Caulobacter has co-opted a nucleoid-associated protein with high AT recognition to serve as a mediator of cell cycle progression.

CauloBrowser: A systems biology resource for Caulobacter crescentus.

Caulobacter crescentus is a premier model organism for studying the molecular basis of cellular asymmetry. The Caulobacter community has generated a wealth of high-throughput spatiotemporal databases including data from gene expression profiling experiments (microarrays, RNA-seq, ChIP-seq, ribosome profiling, LC-ms proteomics), gene essentiality studies (Tn-seq), genome wide protein localization studies, and global chromosome methylation analyses (SMRT sequencing). A major challenge involves the integration of these diverse data sets into one comprehensive community resource. To address this need, we have generated CauloBrowser (www.caulobrowser.org), an online resource for Caulobacter studies. This site provides a user-friendly interface for quickly searching genes of interest and downloading genome-wide results. Search results about individual genes are displayed as tables, graphs of time resolved expression profiles, and schematics of protein localization throughout the cell cycle. In addition, the site provides a genome viewer that enables customizable visualization of all published high-throughput genomic data. The depth and diversity of data sets collected by the Caulobacter community makes CauloBrowser a unique and valuable systems biology resource.

The global regulatory architecture of transcription during the Caulobacter cell cycle.

Each Caulobacter cell cycle involves differentiation and an asymmetric cell division driven by a cyclical regulatory circuit comprised of four transcription factors (TFs) and a DNA methyltransferase. Using a modified global 5' RACE protocol, we globally mapped transcription start sites (TSSs) at base-pair resolution, measured their transcription levels at multiple times in the cell cycle, and identified their transcription factor binding sites. Out of 2726 TSSs, 586 were shown to be cell cycle-regulated and we identified 529 binding sites for the cell cycle master regulators. Twenty-three percent of the cell cycle-regulated promoters were found to be under the combinatorial control of two or more of the global regulators. Previously unknown features of the core cell cycle circuit were identified, including 107 antisense TSSs which exhibit cell cycle-control, and 241 genes with multiple TSSs whose transcription levels often exhibited different cell cycle timing. Cumulatively, this study uncovered novel new layers of transcriptional regulation mediating the bacterial cell cycle.

Mutant WT1 is associated with DNA hypermethylation of PRC2 targets in AML and responds to EZH2 inhibition.

Acute myeloid leukemia (AML) is associated with deregulation of DNA methylation; however, many cases do not bear mutations in known regulators of cytosine guanine dinucleotide (CpG) methylation. We found that mutations in WT1, IDH2, and CEBPA were strongly linked to DNA hypermethylation in AML using a novel integrative analysis of The Cancer Genome Atlas data based on Boolean implications, if-then rules that identify all individual CpG sites that are hypermethylated in the presence of a mutation. Introduction of mutant WT1 (WT1mut) into wild-type AML cells induced DNA hypermethylation, confirming mutant WT1 to be causally associated with DNA hypermethylation. Methylated genes in WT1mut primary patient samples were highly enriched for polycomb repressor complex 2 (PRC2) targets, implicating PRC2 dysregulation in WT1mut leukemogenesis. We found that PRC2 target genes were aberrantly repressed in WT1mut AML, and that expression of mutant WT1 in CD34(+) cord blood cells induced myeloid differentiation block. Treatment of WT1mut AML cells with short hairpin RNA or pharmacologic PRC2/enhancer of zeste homolog 2 (EZH2) inhibitors promoted myeloid differentiation, suggesting EZH2 inhibitors may be active in this AML subtype. Our results highlight a strong association between mutant WT1 and DNA hypermethylation in AML and demonstrate that Boolean implications can be used to decipher mutation-specific methylation patterns that may lead to therapeutic insights.

Automated identification of stratifying signatures in cellular subpopulations.

Elucidation and examination of cellular subpopulations that display condition-specific behavior can play a critical contributory role in understanding disease mechanism, as well as provide a focal point for development of diagnostic criteria linking such a mechanism to clinical prognosis. Despite recent advancements in single-cell measurement technologies, the identification of relevant cell subsets through manual efforts remains standard practice. As new technologies such as mass cytometry increase the parameterization of single-cell measurements, the scalability and subjectivity inherent in manual analyses slows both analysis and progress. We therefore developed Citrus (cluster identification, characterization, and regression), a data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. The methodology of Citrus is demonstrated through the identification of known and unexpected pathway responses in a dataset of stimulated peripheral blood mononuclear cells measured by mass cytometry. Additionally, the performance of Citrus is compared with that of existing methods through the analysis of several publicly available datasets. As the complexity of flow cytometry datasets continues to increase, methods such as Citrus will be needed to aid investigators in the performance of unbiased--and potentially more thorough--correlation-based mining and inspection of cell subsets nested within high-dimensional datasets.

Ly6d marks the earliest stage of B-cell specification and identifies the branchpoint between B-cell and T-cell development.

Common lymphoid progenitors (CLPs) clonally produce both B- and T-cell lineages, but have little myeloid potential in vivo. However, some studies claim that the upstream lymphoid-primed multipotent progenitor (LMPP) is the thymic seeding population, and suggest that CLPs are primarily B-cell-restricted. To identify surface proteins that distinguish functional CLPs from B-cell progenitors, we used a new computational method of Mining Developmentally Regulated Genes (MiDReG). We identified Ly6d, which divides CLPs into two distinct populations: one that retains full in vivo lymphoid potential and produces more thymocytes at early timepoints than LMPP, and another that behaves essentially as a B-cell progenitor.

The multiple PDZ domain protein Mpdz/MUPP1 regulates opioid tolerance and opioid-induced hyperalgesia.

BACKGROUND: Opioids are a mainstay for the treatment of chronic pain. Unfortunately, therapy-limiting maladaptations such as loss of treatment effect (tolerance), and paradoxical opioid-induced hyperalgesia (OIH) can occur. The objective of this study was to identify genes responsible for opioid tolerance and OIH. RESULTS: These studies used a well-established model of ascending morphine administration to induce tolerance, OIH and other opioid maladaptations in 23 strains of inbred mice. Genome-wide computational genetic mapping was then applied to the data in combination with a false discovery rate filter. Transgenic mice, gene expression experiments and immunoprecipitation assays were used to confirm the functional roles of the most strongly linked gene. The behavioral data processed using computational genetic mapping and false discovery rate filtering provided several strongly linked biologically plausible gene associations. The strongest of these was the highly polymorphic Mpdz gene coding for the post-synaptic scaffolding protein Mpdz/MUPP1. Heterozygous Mpdz +/- mice displayed reduced opioid tolerance and OIH. Mpdz gene expression and Mpdz/MUPP1 protein levels were lower in the spinal cords of low-adapting 129S1/Svlm mice than in high-adapting C57BL/6 mice. Morphine did not alter Mpdz expression levels. In addition, association of Mpdz/MUPP1 with its known binding partner CaMKII did not differ between these high- and low-adapting strains. CONCLUSIONS: The degrees of maladaptive changes in response to repeated administration of morphine vary greatly across inbred strains of mice. Variants of the multiple PDZ domain gene Mpdz may contribute to the observed inter-strain variability in tolerance and OIH by virtue of changes in the level of their expression.

The role of Abcb5 alleles in susceptibility to haloperidol-induced toxicity in mice and humans.

BACKGROUND: We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study. METHODS AND FINDINGS: A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered. CONCLUSIONS: ABCB5 alleles alter susceptibility to HIT in mouse and humans. This discovery leads to a new model that (at least in part) explains inter-individual differences in susceptibility to a drug-induced CNS toxicity.

A better prognosis for genetic association studies in mice.

Although inbred mouse strains have been the premier model organism used in biomedical research, multiple studies and analyses have indicated that genome-wide association studies (GWAS) cannot be productively performed using inbred mouse strains. However, there is one type of GWAS in mice that has successfully identified the genetic basis for many biomedical traits of interest: haplotype-based computational genetic mapping (HBCGM). Here, we describe how the methodological basis for a HBCGM study significantly differs from that of a conventional murine GWAS, and how an integrative analysis of its output within the context of other 'omic' information can enable genetic discovery. Consideration of these factors will substantially improve the prognosis for the utility of murine genetic association studies for biomedical discovery.

Liquid chromatography/mass spectrometry methods for measuring dipeptide abundance in non-small-cell lung cancer.

RATIONALE: Metabolomic profiling is a promising methodology of identifying candidate biomarkers for disease detection and monitoring. Although lung cancer is among the leading causes of cancer-related mortality worldwide, the lung tumor metabolome has not been fully characterized. METHODS: We utilized a targeted metabolomic approach to analyze discrete groups of related metabolites. We adopted a dansyl [5-(dimethylamino)-1-naphthalene sulfonamide] derivatization with liquid chromatography/mass spectrometry (LC/MS) to analyze changes of metabolites from paired tumor and normal lung tissues. Identification of dansylated dipeptides was confirmed with synthetic standards. A systematic analysis of retention times was required to reliably identify isobaric dipeptides. We validated our findings in a separate sample cohort. RESULTS: We produced a database of the LC retention times and MS/MS spectra of 361 dansyl dipeptides. Interpretation of the spectra is presented. Using this standard data, we identified a total of 279 dipeptides in lung tumor tissue. The abundance of 90 dipeptides was selectively increased in lung tumor tissue compared to normal tissue. In a second set of validation tissues, 12 dipeptides were selectively increased. CONCLUSIONS: A systematic evaluation of certain metabolite classes in lung tumors may identify promising disease-specific metabolites. Our database of all possible dipeptides will facilitate ongoing translational applications of metabolomic profiling as it relates to lung cancer.

MiDReG: a method of mining developmentally regulated genes using Boolean implications.

We present a method termed mining developmentally regulated genes (MiDReG) to predict genes whose expression is either activated or repressed as precursor cells differentiate. MiDReG does not require gene expression data from intermediate stages of development. MiDReG is based on the gene expression patterns between the initial and terminal stages of the differentiation pathway, coupled with "if-then" rules (Boolean implications) mined from large-scale microarray databases. MiDReG uses two gene expression-based seed conditions that mark the initial and the terminal stages of a given differentiation pathway and combines the statistically inferred Boolean implications from these seed conditions to identify the relevant genes. The method was validated by applying it to B-cell development. The algorithm predicted 62 genes that are expressed after the KIT+ progenitor cell stage and remain expressed through CD19+ and AICDA+ germinal center B cells. qRT-PCR of 14 of these genes on sorted B-cell progenitors confirmed that the expression of 10 genes is indeed stably established during B-cell differentiation. Review of the published literature of knockout mice revealed that of the predicted genes, 63.4% have defects in B-cell differentiation and function and 22% have a role in the B cell according to other experiments, and the remaining 14.6% are not characterized. Therefore, our method identified novel gene candidates for future examination of their role in B-cell development. These data demonstrate the power of MiDReG in predicting functionally important intermediate genes in a given developmental pathway that is defined by a mutually exclusive gene expression pattern.

Identification of drug targets by chemogenomic and metabolomic profiling in yeast.

OBJECTIVE: To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all of its genes, chemogenomic profiling in yeast has been used to identify drug targets. As measurement of drug-induced changes in cellular metabolites can yield considerable information about the effects of a drug, we investigated whether combining chemogenomic and metabolomic profiling in yeast could improve the characterization of drug targets. BASIC METHODS: We used chemogenomic and metabolomic profiling in yeast to characterize the target for five drugs acting on two biologically important pathways. A novel computational method that uses a curated metabolic network was also developed, and it was used to identify the genes that are likely to be responsible for the metabolomic differences found. RESULTS AND CONCLUSION: The combination of metabolomic and chemogenomic profiling, along with data analyses carried out using a novel computational method, could robustly identify the enzymes targeted by five drugs. Moreover, this novel computational method has the potential to identify genes that are causative of metabolomic differences or drug targets.

Computational genetic discoveries that could improve perioperative medicine.

PURPOSE OF REVIEW: The review examines the rationale and translational utility of computational genetic studies using murine models of biomedical traits. RECENT FINDINGS: Computational genetic mapping studies have identified the genetic basis for biomedical trait differences in 16 different murine models, including several that are of importance to perioperative medicine. SUMMARY: The results have generated new treatments for alleviating incisional pain and narcotic drug withdrawal symptoms, which are now in clinical trials. A recent study identified allelic differences affecting chronic pain responses in mice and humans, which may enable a new 'personalized' approach to treating chronic pain.

Next-generation computational genetic analysis: multiple complement alleles control survival after Candida albicans infection.

Candida albicans is a fungal pathogen that causes severe disseminated infections that can be lethal in immunocompromised patients. Genetic factors are known to alter the initial susceptibility to and severity of C. albicans infection. We developed a next-generation computational genetic mapping program with advanced features to identify genetic factors affecting survival in a murine genetic model of hematogenous C. albicans infection. This computational tool was used to analyze the median survival data after inbred mouse strains were infected with C. albicans, which provides a useful experimental model for identification of host susceptibility factors. The computational analysis indicated that genetic variation within early classical complement pathway components (C1q, C1r, and C1s) could affect survival. Consistent with the computational results, serum C1 binding to this pathogen was strongly affected by C1rs alleles, as was survival of chromosome substitution strains. These results led to a combinatorial, conditional genetic model, involving an interaction between C5 and C1r/s alleles, which accurately predicted survival after infection. Beyond applicability to infectious disease, this information could increase our understanding of the genetic factors affecting susceptibility to autoimmune and neurodegenerative diseases.

Architecture and inherent robustness of a bacterial cell-cycle control system.

A closed-loop control system drives progression of the coupled stalked and swarmer cell cycles of the bacterium Caulobacter crescentus in a near-mechanical step-like fashion. The cell-cycle control has a cyclical genetic circuit composed of four regulatory proteins with tight coupling to processive chromosome replication and cell division subsystems. We report a hybrid simulation of the coupled cell-cycle control system, including asymmetric cell division and responses to external starvation signals, that replicates mRNA and protein concentration patterns and is consistent with observed mutant phenotypes. An asynchronous sequential digital circuit model equivalent to the validated simulation model was created. Formal model-checking analysis of the digital circuit showed that the cell-cycle control is robust to intrinsic stochastic variations in reaction rates and nutrient supply, and that it reliably stops and restarts to accommodate nutrient starvation. Model checking also showed that mechanisms involving methylation-state changes in regulatory promoter regions during DNA replication increase the robustness of the cell-cycle control. The hybrid cell-cycle simulation implementation is inherently extensible and provides a promising approach for development of whole-cell behavioral models that can replicate the observed functionality of the cell and its responses to changing environmental conditions.

Combined analysis of murine and human microarrays and ChIP analysis reveals genes associated with the ability of MYC to maintain tumorigenesis.

The MYC oncogene has been implicated in the regulation of up to thousands of genes involved in many cellular programs including proliferation, growth, differentiation, self-renewal, and apoptosis. MYC is thought to induce cancer through an exaggerated effect on these physiologic programs. Which of these genes are responsible for the ability of MYC to initiate and/or maintain tumorigenesis is not clear. Previously, we have shown that upon brief MYC inactivation, some tumors undergo sustained regression. Here we demonstrate that upon MYC inactivation there are global permanent changes in gene expression detected by microarray analysis. By applying StepMiner analysis, we identified genes whose expression most strongly correlated with the ability of MYC to induce a neoplastic state. Notably, genes were identified that exhibited permanent changes in mRNA expression upon MYC inactivation. Importantly, permanent changes in gene expression could be shown by chromatin immunoprecipitation (ChIP) to be associated with permanent changes in the ability of MYC to bind to the promoter regions. Our list of candidate genes associated with tumor maintenance was further refined by comparing our analysis with other published results to generate a gene signature associated with MYC-induced tumorigenesis in mice. To validate the role of gene signatures associated with MYC in human tumorigenesis, we examined the expression of human homologs in 273 published human lymphoma microarray datasets in Affymetrix U133A format. One large functional group of these genes included the ribosomal structural proteins. In addition, we identified a group of genes involved in a diverse array of cellular functions including: BZW2, H2AFY, SFRS3, NAP1L1, NOLA2, UBE2D2, CCNG1, LIFR, FABP3, and EDG1. Hence, through our analysis of gene expression in murine tumor models and human lymphomas, we have identified a novel gene signature correlated with the ability of MYC to maintain tumorigenesis.

Aquila enables reference-assisted diploid personal genome assembly and comprehensive variant detection based on linked reads.

We introduce Aquila, a new approach to variant discovery in personal genomes, which is critical for uncovering the genetic contributions to health and disease. Aquila uses a reference sequence and linked-read data to generate a high quality diploid genome assembly, from which it then comprehensively detects and phases personal genetic variation. The contigs of the assemblies from our libraries cover >95% of the human reference genome, with over 98% of that in a diploid state. Thus, the assemblies support detection and accurate genotyping of the most prevalent types of human genetic variation, including single nucleotide polymorphisms (SNPs), small insertions and deletions (small indels), and structural variants (SVs), in all but the most difficult regions. All heterozygous variants are phased in blocks that can approach arm-level length. The final output of Aquila is a diploid and phased personal genome sequence, and a phased Variant Call Format (VCF) file that also contains homozygous and a few unphased heterozygous variants. Aquila represents a cost-effective approach that can be applied to cohorts for variation discovery or association studies, or to single individuals with rare phenotypes that could be caused by SVs or compound heterozygosity.

Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads.

Motivation: Identifying structural variants (SVs) is critical in health and disease, however, detecting them remains a challenge. Several linked-read sequencing technologies, including 10X Genomics, TELL-Seq and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads. Results: Aquila_stLFR first partitions long fragment reads into two haplotype-specific blocks with the assistance of the high-quality reference genome, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size deletions (50 bp-10 kb) with high sensitivity and medium-size insertions (50 bp-1 kb) with high specificity. Availability and implementation: Source code and documentation are available on https://github.com/maiziex/Aquila_stLFR. Supplementary information: Supplementary data are available at Bioinformatics Advances online.