Determinants of health-related quality of life of patients with focal epilepsy: A systematic literature review.

Objective: Focal epilepsy can have significant negative impacts on a person's health-related quality of life (HRQoL). Although studies have been published on HRQoL in persons with focal epilepsy (PWFE), determinants of HRQoL have not been comprehensively examined. This systematic literature review (SLR) queried existing literature to identify aspects associated with HRQoL in PWFE without focus on resective epilepsy surgery, with an interest in identifying modifiable determinants for medical/nonmedical interventions.

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.

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.

The mA 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.

Mebendazole for Differentiation Therapy of Acute Myeloid Leukemia Identified by a Lineage Maturation Index.

Accurate assessment of changes in cellular differentiation status in response to drug treatments or genetic perturbations is crucial for understanding tumorigenesis and developing novel therapeutics for human cancer. We have developed a novel computational approach, the Lineage Maturation Index (LMI), to define the changes in differentiation state of hematopoietic malignancies based on their gene expression profiles. We have confirmed that the LMI approach can detect known changes of differentiation state in both normal and malignant hematopoietic cells.

Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data.

Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal. SL partners of cancer mutations are of great interest as pharmacological targets; however, identifying them by cell line-based methods is challenging. Here we develop MiSL (Mining Synthetic Lethals), an algorithm that mines pan-cancer human primary tumour data to identify mutation-specific SL partners for specific cancers.

A geographically-diverse collection of 418 human gut microbiome pathway genome databases.

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses.

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.

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.

A Pharmacogenetic Discovery: Cystamine Protects Against Haloperidol-Induced Toxicity and Ischemic Brain Injury.

Haloperidol is an effective antipsychotic agent, but it causes Parkinsonian-like extrapyramidal symptoms in the majority of treated subjects. To address this treatment-limiting toxicity, we analyzed a murine genetic model of haloperidol-induced toxicity (HIT). Analysis of a panel of consomic strains indicated that a genetic factor on chromosome 10 had a significant effect on susceptibility to HIT.

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.

Towards in vivo estimation of reaction kinetics using high-throughput metabolomics data: a maximum likelihood approach.

Background: High-throughput assays such as mass spectrometry have opened up the possibility for large-scale in vivo measurements of the metabolome. This data could potentially be used to estimate kinetic parameters for many metabolic reactions. However, high-throughput in vivo measurements have special properties that are not taken into account in existing methods for estimating kinetic parameters, including significant relative errors in measurements of metabolite concentrations and reaction rates, and reactions with multiple substrates and products, which are sometimes reversible.

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.

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.

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.

MYC through miR-17-92 suppresses specific target genes to maintain survival, autonomous proliferation, and a neoplastic state.

The MYC oncogene regulates gene expression through multiple mechanisms, and its overexpression culminates in tumorigenesis. MYC inactivation reverses turmorigenesis through the loss of distinguishing features of cancer, including autonomous proliferation and survival. Here we report that MYC via miR-17-92 maintains a neoplastic state through the suppression of chromatin regulatory genes Sin3b, Hbp1, Suv420h1, and Btg1, as well as the apoptosis regulator Bim.

Mining TCGA data using Boolean implications.

Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets.

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.

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.

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.

Gene Expression Commons: an open platform for absolute gene expression profiling.

Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments. However, the unknown and variable sensitivities of each probeset have rendered the absolute expression of any given gene nearly impossible to estimate. We have overcome this limitation by using a very large number (>10,000) of varied microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be reliably discovered through meta-analysis.

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.

Cd14 SNPs regulate the innate immune response.

CD14 is a monocytic differentiation antigen that regulates innate immune responses to pathogens. Here, we show that murine Cd14 SNPs regulate the length of Cd14 mRNA and CD14 protein translation efficiency, and consequently the basal level of soluble CD14 (sCD14) and type I IFN production by murine macrophages. This has substantial downstream consequences for the innate immune response; the level of expression of at least 40 IFN-responsive murine genes was altered by this mechanism.

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.