Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Cancer Lett. 2016 Nov 1;382(1):95-109

Authors: Herskind C, Talbot CJ, Kerns SL, Veldwijk MR, Rosenstein BS, West CM

Abstract
Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

PMID: 26944314 [PubMed - in process]

Large scale gene regulatory network inference with a multi-level strategy.

Mol Biosyst. 2016 Feb;12(2):588-97

Authors: Wu J, Zhao X, Lin Z, Shao Z

Abstract
Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology. Inspired by the Dialogue for Reverse Engineering Assessments and Methods (DREAM) projects, many excellent gene regulatory network inference algorithms have been proposed. However, it is still a challenging problem to infer a gene regulatory network from gene expression data on a large scale. In this paper, we propose a gene regulatory network inference method based on a multi-level strategy (GENIMS), which can give results that are more accurate and robust than the state-of-the-art methods. The proposed method mainly consists of three levels, which are an original feature selection step based on guided regularized random forest, normalization of individual feature selection and the final refinement step according to the topological property of the gene regulatory network. To prove the accuracy and robustness of our method, we compare our method with the state-of-the-art methods on the DREAM4 and DREAM5 benchmark networks and the results indicate that the proposed method can significantly improve the performance of gene regulatory network inference. Additionally, we also discuss the influence of the selection of different parameters in our method.

PMID: 26687446 [PubMed - indexed for MEDLINE]

Application of dynamic topic models to toxicogenomics data.

BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):368

Authors: Lee M, Liu Z, Huang R, Tong W

Abstract
BACKGROUND: All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data.
RESULTS: A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms.
CONCLUSIONS: We demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.

PMID: 27766956 [PubMed - in process]

Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts.

BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):350

Authors: Roy S, Curry BC, Madahian B, Homayouni R

Abstract
BACKGROUND: The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs.
RESULTS: For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms.
CONCLUSIONS: LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin .

PMID: 27766940 [PubMed - in process]

Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams.

BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):335

Authors: Eshleman R, Singh R

Abstract
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse.
METHODS: We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning.
RESULTS: We examine both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method leads to improvements ranging between 5 and 8 % in terms of the aforementioned measures. Additionally, we employ our method to discover several ADEs which, though present in medical literature and Twitter-streams, are not represented in the SIDER databases.
CONCLUSIONS: We present a DEG integration model as a powerful formalism for the analysis of drug-effect relationships that is general enough to accommodate diverse data sources, yet rigorous enough to provide a strong mechanism for ADE identification.

PMID: 27766937 [PubMed - in process]

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Identification of Potential Therapeutics to Conquer Drug Resistance in Salmonella typhimurium: Drug Repurposing Strategy.

BioDrugs. 2016 Oct 19;

Authors: Preethi B, Shanthi V, Ramanathan K

Abstract
BACKGROUND: Salmonella typhimurium is the main cause of gastrointestinal illness in humans, and treatment options are decreasing because drug-resistant strains have emerged.
OBJECTIVE: The objective of this study was to use computational drug repurposing to identify a novel candidate with an effective mechanism of action to circumvent the drug resistance.
METHODS: We used the Mantra 2.0 database to initially screen drug candidates that share similar gene expression profiles to those of quinolones. Data were further reduced using pharmacophore mapping theory. Finally, we employed molecular-simulation studies to calculate the binding affinity of the screened candidates with DNA gyrase, alongside an analysis of side effects.
RESULTS: A total of 16 drug candidates from the Mantra 2.0 database were screened. The pharmacophoric features of the screened candidates were examined and nalidixic acid features compared using the PharamGist program. A total of 11 compounds with the highest pharmacophore score were considered for binding energy calculation. Finally, we analysed the side effects of the eight drug candidates that showed significant binding affinity in the simulation study.
CONCLUSION: Overall, flufenamic acid and sulconazole may be potential drug candidates that could be studied in vitro to assess their resistance profile against Salmonella enterica Typhimurium.

PMID: 27761807 [PubMed - as supplied by publisher]

Identifying and Tackling Emergent Vulnerability in Drug-Resistant Mycobacteria.

ACS Infect Dis. 2016 Sep 9;2(9):592-607

Authors: Padiadpu J, Baloni P, Anand K, Munshi M, Thakur C, Mohan A, Singh A, Chandra N

Abstract
The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

PMID: 27759382 [PubMed - in process]

Identification of genes expressed in the migrating primitive myeloid lineage of Xenopus laevis.

Dev Dyn. 2016 Jan;245(1):47-55

Authors: Agricola ZN, Jagpal AK, Allbee AW, Prewitt AR, Shifley ET, Rankin SA, Zorn AM, Kenny AP

Abstract
BACKGROUND: During primitive hematopoiesis in Xenopus, cebpa and spib expressing myeloid cells emerge from the anterior ventral blood island. Primitive myeloid cells migrate throughout the embryo and are critical for immunity, healing, and development. Although definitive hematopoiesis has been studied extensively, molecular mechanisms leading to the migration of primitive myelocytes remain poorly understood. We hypothesized these cells have specific extracellular matrix modifying and cell motility gene expression.
RESULTS: In situ hybridization screens of transcripts expressed in Xenopus foregut mesendoderm at stage 23 identified seven genes with restricted expression in primitive myeloid cells: destrin; coronin actin binding protein, 1a; formin-like 1; ADAM metallopeptidase domain 28; cathepsin S; tissue inhibitor of metalloproteinase-1; and protein tyrosine phosphatase nonreceptor 6. A detailed in situ hybridization analysis revealed these genes are initially expressed in the aVBI but become dispersed throughout the embryo as the primitive myeloid cells become migratory, similar to known myeloid markers. Morpholino-mediated loss-of-function and mRNA-mediated gain-of-function studies revealed the identified genes are downstream of Spib.a and Cebpa, key transcriptional regulators of the myeloid lineage.
CONCLUSIONS: We have identified genes specifically expressed in migratory primitive myeloid progenitors, providing tools to study how different gene networks operate in these primitive myelocytes during development and immunity.

PMID: 26264370 [PubMed - indexed for MEDLINE]

Theory based PKPD of S- and R-warfarin and effects on INR: influence of body size, composition and genotype in cardiac surgery patients.

Br J Clin Pharmacol. 2016 Oct 20;:

Authors: Xue L, Holford N, Ding XL, Shen ZY, Huang CR, Zhang H, Zhang JJ, Guo ZN, Xie C, Zhou L, Chen ZY, Liu LS, Miao LY

Abstract
AIMS: The aims of this study are to apply a theory based mechanistic model to describe the pharmacokinetics (PK) and pharmacodynamics (PD) of S- and R-warfarin.
METHODS: Clinical data were obtained from 264 patients. Total concentrations for S- and R-warfarin were measured by ultra-high performance liquid tandem mass spectrometry. Genotypes were measured using pyrosequencing. A sequential population pharmacokinetic parameter with data method was used to describe the international normalized ratio (INR) time course. Data were analyzed with NONMEM. Model evaluation was based on parameter plausibility and prediction-corrected visual predictive checks.
RESULTS: The warfarin PK had been described using one-compartment model. CYP2C9 *1/*3 genotype had reduced clearance (CL) for S-warfarin, but increased CL for R-warfarin. The in vitro parameters for the relationship between prothrombin complex activity (PCA) and INR were markedly different (A = 0.560, B = 0.386) from the theory based values (A = 1, B = 0). There was a small difference between healthy subjects and patients. A sigmoid Emax pharmacodynamic model inhibiting PCA synthesis as a function of S-warfarin concentration predicted INR. Small R-warfarin effects was described by competitive antagonism of S-warfarin inhibition. Patients with VKORC1 AA and CYP4F2 CC or CT genotypes had lower C50 for S-warfarin.
CONCLUSION: A theory based PKPD model describes warfarin concentrations and clinical response. Expected PK and PD genotype effects were confirmed. The role of predicted fat free mass with theory based allometric scaling of PK parameters was identified. R-warfarin had a minor effect compared with S-warfarin on PCA synthesis. INR is predictable from 1/PCA in vivo.

PMID: 27763679 [PubMed - as supplied by publisher]

Pharmacogenomics of genes involved in antifolate drug response and toxicity in osteosarcoma.

Expert Opin Drug Metab Toxicol. 2016 Oct 19;:1-13

Authors: Hattinger CM, Tavanti E, Fanelli M, Vella S, Picci P, Serra M

Abstract
INTRODUCTION: Antifolates are structural analogs of folates, which have been used as antitumor drugs for more than 60 years. The antifolate drug most commonly used for treating human tumors is methotrexate (MTX), which is utilized widely in first-line treatment protocols of high-grade osteosarcoma (HGOS). In addition to MTX, two other antifolates, trimetrexate and pemetrexed, have been tested in clinical settings for second-line treatment of recurrent HGOS with patients unfortunately showing modest activity. Areas covered: There is clinical evidence which suggsest that, like other chemotherapeutic agents, not all HGOS patients are equally responsive to antifolates and do not have the same susceptibility to experience adverse drug-related toxicities. Here, we summarize the pharmacogenomic information reported so far for genes involved in antifolate metabolism and transport and in MTX-related toxicity in HGOS patients. Expert opinion: Identification and validation of genetic biomarkers that significantly impact clinical antifolate treatment response and related toxicity may provide the basis for a future treatment modulation based on the pharmacogenetic and pharmacogenomic features of HGOS patients.

PMID: 27758143 [PubMed - as supplied by publisher]

Pharmacogenomics of statins: understanding susceptibility to adverse effects.

Pharmgenomics Pers Med. 2016;9:97-106

Authors: Kitzmiller JP, Mikulik EB, Dauki AM, Murkherjee C, Luzum JA

Abstract
Statins are a cornerstone of the pharmacologic treatment and prevention of atherosclerotic cardiovascular disease. Atherosclerotic disease is a predominant cause of mortality and morbidity worldwide. Statins are among the most commonly prescribed classes of medications, and their prescribing indications and target patient populations have been significantly expanded in the official guidelines recently published by the American and European expert panels. Adverse effects of statin pharmacotherapy, however, result in significant cost and morbidity and can lead to nonadherence and discontinuation of therapy. Statin-associated muscle symptoms occur in ~10% of patients on statins and constitute the most commonly reported adverse effect associated with statin pharmacotherapy. Substantial clinical and nonclinical research effort has been dedicated to determining whether genetics can provide meaningful insight regarding an individual patient's risk of statin adverse effects. This contemporary review of the relevant clinical research on polymorphisms in several key genes that affect statin pharmacokinetics (eg, transporters and metabolizing enzymes), statin efficacy (eg, drug targets and pathways), and end-organ toxicity (eg, myopathy pathways) highlights several promising pharmacogenomic candidates. However, SLCO1B1 521C is currently the only clinically relevant pharmacogenetic test regarding statin toxicity, and its relevance is limited to simvastatin myopathy.

PMID: 27757045 [PubMed - in process]

Effects of Using Personal Genotype Data on Student Learning and Attitudes in a Pharmacogenomics Course.

Am J Pharm Educ. 2016 Sep 25;80(7):122

Authors: Weitzel KW, McDonough CW, Elsey AR, Burkley B, Cavallari LH, Johnson JA

Abstract
Objective. To evaluate the impact of personal genotyping and a novel educational approach on student attitudes, knowledge, and beliefs regarding pharmacogenomics and genomic medicine. Methods. Two online elective courses (pharmacogenomics and genomic medicine) were offered to student pharmacists at the University of Florida using a flipped-classroom, patient-centered teaching approach. In the pharmacogenomics course, students could be genotyped and apply results to patient cases. Results. Thirty-four and 19 student pharmacists completed the pharmacogenomics and genomic medicine courses, respectively, and 100% of eligible students (n=34) underwent genotyping. Student knowledge improved after the courses. Seventy-four percent (n=25) of students reported better understanding of pharmacogenomics based on having undergone genotyping. Conclusions. Completion of a novel pharmacogenomics elective course sequence that incorporated personal genotyping and genomic medicine was associated with increased student pharmacist knowledge and improved clinical confidence with pharmacogenomics.

PMID: 27756930 [PubMed - in process]

Epigenetics of Aging and Alzheimer's Disease: Implications for Pharmacogenomics and Drug Response.

Int J Mol Sci. 2015 Dec 21;16(12):30483-543

Authors: Cacabelos R, Torrellas C

Abstract
Epigenetic variability (DNA methylation/demethylation, histone modifications, microRNA regulation) is common in physiological and pathological conditions. Epigenetic alterations are present in different tissues along the aging process and in neurodegenerative disorders, such as Alzheimer's disease (AD). Epigenetics affect life span and longevity. AD-related genes exhibit epigenetic changes, indicating that epigenetics might exert a pathogenic role in dementia. Epigenetic modifications are reversible and can potentially be targeted by pharmacological intervention. Epigenetic drugs may be useful for the treatment of major problems of health (e.g., cancer, cardiovascular disorders, brain disorders). The efficacy and safety of these and other medications depend upon the efficiency of the pharmacogenetic process in which different clusters of genes (pathogenic, mechanistic, metabolic, transporter, pleiotropic) are involved. Most of these genes are also under the influence of the epigenetic machinery. The information available on the pharmacoepigenomics of most drugs is very limited; however, growing evidence indicates that epigenetic changes are determinant in the pathogenesis of many medical conditions and in drug response and drug resistance. Consequently, pharmacoepigenetic studies should be incorporated in drug development and personalized treatments.

PMID: 26703582 [PubMed - indexed for MEDLINE]

The promises of quantitative systems pharmacology modelling for drug development.

Comput Struct Biotechnol J. 2016;14:363-370

Authors: Knight-Schrijver VR, Chelliah V, Cucurull-Sanchez L, Le Novère N

Abstract
Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

PMID: 27761201 [PubMed - in process]

Identifying and Tackling Emergent Vulnerability in Drug-Resistant Mycobacteria.

ACS Infect Dis. 2016 Sep 9;2(9):592-607

Authors: Padiadpu J, Baloni P, Anand K, Munshi M, Thakur C, Mohan A, Singh A, Chandra N

Abstract
The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

PMID: 27759382 [PubMed - in process]

Inference of gene regulation functions from dynamic transcriptome data.

Elife. 2016 Sep 21;5:

Authors: Hillenbrand P, Maier KC, Cramer P, Gerland U

Abstract
To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a 'gene regulation function' (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

PMID: 27652904 [PubMed - in process]

Mammalian Reverse Genetics without Crossing Reveals Nr3a as a Short-Sleeper Gene.

Cell Rep. 2016 Jan 26;14(3):662-77

Authors: Sunagawa GA, Sumiyama K, Ukai-Tadenuma M, Perrin D, Fujishima H, Ukai H, Nishimura O, Shi S, Ohno R, Narumi R, Shimizu Y, Tone D, Ode KL, Kuraku S, Ueda HR

Abstract
The identification of molecular networks at the system level in mammals is accelerated by next-generation mammalian genetics without crossing, which requires both the efficient production of whole-body biallelic knockout (KO) mice in a single generation and high-performance phenotype analyses. Here, we show that the triple targeting of a single gene using the CRISPR/Cas9 system achieves almost perfect KO efficiency (96%-100%). In addition, we developed a respiration-based fully automated non-invasive sleep phenotyping system, the Snappy Sleep Stager (SSS), for high-performance (95.3% accuracy) sleep/wake staging. Using the triple-target CRISPR and SSS in tandem, we reliably obtained sleep/wake phenotypes, even in double-KO mice. By using this system to comprehensively analyze all of the N-methyl-D-aspartate (NMDA) receptor family members, we found Nr3a as a short-sleeper gene, which is verified by an independent set of triple-target CRISPR. These results demonstrate the application of mammalian reverse genetics without crossing to organism-level systems biology in sleep research.

PMID: 26774482 [PubMed - indexed for MEDLINE]

Gene co-expression networks shed light into diseases of brain iron accumulation.

Neurobiol Dis. 2016 Mar;87:59-68

Authors: Bettencourt C, Forabosco P, Wiethoff S, Heidari M, Johnstone DM, Botía JA, Collingwood JF, Hardy J, UK Brain Expression Consortium (UKBEC), Milward EA, Ryten M, Houlden H

Abstract
Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention.

PMID: 26707700 [PubMed - indexed for MEDLINE]