Pharmacogene expression during progression of metabolic dysfunction-associated steatotic liver disease: Studies on mRNA and protein levels and their relevance to drug treatment.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common worldwide. Genes and proteins contributing to drug disposition may show altered expression as MASLD progresses. To assess this further, we undertook transcriptomic and proteomic analysis of 137 pharmacogenes in liver biopsies from a large MASLD cohort. We performed sequencing on RNA from 216 liver biopsies (206 MASLD and 10 controls). Untargeted mass spectrometry proteomics was performed on a 103 biopsy subgroup. Selected RNA sequencing signals were replicated with an additional 187 biopsies. Comparison of advanced MASLD (fibrosis score 3/4) with milder disease (fibrosis score 0-2) by RNA sequencing showed significant alterations in expression of certain phase I, phase II and ABC transporters. For cytochromes P450, CYP2C19 showed the most significant decreased expression (30 % of that in mild disease) but significant decreased expression of other CYPs (including CYP2C8 and CYP2E1) also occurred. CYP2C19 also showed a significant decrease comparing the inflammatory form of MASLD (MASH) with non-MASH biopsies. Findings for CYP2C19 were confirmed in the replication cohort. Proteomics on the original discovery cohort confirmed decreased levels of several CYPs as MASLD advanced but this decrease was greatest for CYP2C19 where levels fell to 40 % control. This decrease may result in decreased CYP2C19 activity that could be problematic for prescription of drugs activated or metabolized by CYP2C19 as MASLD advances. More limited decreases for other P450s suggest fewer issues with non-CYP2C19 drug substrates. Negative correlations at RNA level between CYP2C19 and several cytokine genes provided initial insights into the mechanism underlying decreased expression.

Mapping the transcriptome: Realizing the full potential of spatial data analysis.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Spatial transcriptomics reveals novel genes during the remodelling of the embryonic human arterial valves.

Abnormalities of the arterial valves, including bicuspid aortic valve (BAV) are amongst the most common congenital defects and are a significant cause of morbidity as well as predisposition to disease in later life. Despite this, and compounded by their small size and relative inaccessibility, there is still much to understand about how the arterial valves form and remodel during embryogenesis, both at the morphological and genetic level. Here we set out to address this in human embryos, using Spatial Transcriptomics (ST). We show that ST can be used to investigate the transcriptome of the developing arterial valves, circumventing the problems of accurately dissecting out these tiny structures from the developing embryo. We show that the transcriptome of CS16 and CS19 arterial valves overlap considerably, despite being several days apart in terms of human gestation, and that expression data confirm that the great majority of the most differentially expressed genes are valve-specific. Moreover, we show that the transcriptome of the human arterial valves overlaps with that of mouse atrioventricular valves from a range of gestations, validating our dataset but also highlighting novel genes, including four that are not found in the mouse genome and have not previously been linked to valve development. Importantly, our data suggests that valve transcriptomes are under-represented when using commonly used databases to filter for genes important in cardiac development; this means that causative variants in valve-related genes may be excluded during filtering for genomic data analyses for, for example, BAV. Finally, we highlight "novel" pathways that likely play important roles in arterial valve development, showing that mouse knockouts of RBP1 have arterial valve defects. Thus, this study has confirmed the utility of ST for studies of the developing heart valves and broadens our knowledge of the genes and signalling pathways important in human valve development.

Type I interferon receptor (IFNAR2) deficiency reveals Zika virus cytopathicity in human macrophages and microglia.

Macrophages are key target cells of Zika virus (ZIKV) infection, implicated as a viral reservoir seeding sanctuary sites such as the central nervous system and testes. This rests on the apparent ability of macrophages to sustain ZIKV replication without experiencing cytopathic effects. ZIKV infection of macrophages triggers an innate immune response involving type I interferons (IFN-I), key antiviral cytokines that play a complex role in ZIKV pathogenesis in animal models. To investigate the functional role of the IFN-I response we generated human induced pluripotent stem cell (iPSC)-derived macrophages from a patient with complete deficiency of IFNAR2, the high affinity IFN-I receptor subunit. Accompanying the profound defect of IFN-I signalling in IFNAR2 deficient iPS-macrophages we observed significantly enhanced ZIKV replication and cell death, revealing the inherent cytopathicity of ZIKV towards macrophages. These observations were recapitulated by genetic and pharmacological ablation of IFN-I signalling in control iPS-macrophages and extended to a model of iPS-microglia. Thus, the capacity of macrophages to support noncytolytic ZIKV replication depends on an equilibrium set by IFN-I, suggesting that innate antiviral responses might counterintuitively promote ZIKV persistence via the maintenance of tissue viral reservoirs relevant to pathogenesis.

Integration of probabilistic functional networks without an external Gold Standard.

BACKGROUND: Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Additionally, there typically are no Gold Standard data for non-model organisms. RESULTS: We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping. In addition ssNet results in less loss of data and produces a more complete network. CONCLUSIONS: The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source and with reduced data loss.

Expanding interactome analyses beyond model eukaryotes.

Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein-protein interaction data for these species. Recently, these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.

Mechanical-Stress-Related Epigenetic Regulation of ZIC1 Transcription Factor in the Etiology of Postmenopausal Osteoporosis.

Mechanical loading exerts a profound influence on bone density and architecture, but the exact mechanism is unknown. Our study shows that expression of the neurological transcriptional factor zinc finger of the cerebellum 1 (ZIC1) is markedly increased in trabecular bone biopsies in the lumbar spine compared with the iliac crest, skeletal sites of high and low mechanical stress, respectively. Human trabecular bone transcriptome analyses revealed a strong association between ZIC1 mRNA levels and gene transcripts characteristically associated with osteoblasts, osteocytes and osteoclasts. This supposition is supported by higher ZIC1 expression in iliac bone biopsies from postmenopausal women with osteoporosis compared with age-matched control subjects, as well as strongly significant inverse correlation between ZIC1 mRNA levels and BMI-adjusted bone mineral density (BMD) (Z-score). ZIC1 promoter methylation was decreased in mechanically loaded vertebral bone compared to unloaded normal iliac bone, and its mRNA levels correlated inversely with ZIC1 promoter methylation, thus linking mechanical stress to epigenetic control of gene expression. The findings were corroborated in cultures of rat osteoblast progenitors and osteoblast-like cells. This study demonstrates for the first time how skeletal epigenetic changes that are affected by mechanical forces give rise to marked alteration in bone cell transcriptional activity and translate to human bone pathophysiology.

Blood mRNA Expression in Alzheimer's Disease and Dementia With Lewy Bodies.

OBJECTIVES: The objective of this study was to investigate the expression of genes in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), both at the mild cognitive impairment (MCI) and dementia stages, to improve our understanding of disease pathophysiology and investigate the potential for diagnostic and prognostic biomarkers based on mRNA expression. DESIGN: Cross-sectional observational study. SETTING: University research center. PARTICIPANTS: People with MCI with Lewy bodies (MCI-LB, n=55), MCI-AD (n=19), DLB (n=38), AD (n=24) and a cognitively unimpaired comparison group (n=28). MEASUREMENTS: Ribonucleic acid sequencing of whole blood. Differentially expressed genes (DEGs) were identified and gene set enrichment analysis was carried out. RESULTS: Compared with the cognitively unimpaired group, there were 22 DEGs in MCI-LB/DLB and 61 DEGs in MCI-AD/AD. DEGS were also identified when comparing the two disease groups. Expression of ANP32A was associated with more rapid cognitive decline in MCI-AD/AD. Gene set enrichment analysis identified downregulation in gene sets including MYC targets and oxidative phosphorylation in MCI-LB/DLB; upregulation of immune and inflammatory responses in MCI-AD/AD; and upregulation of interferon-α and -γ responses in MCI-AD/AD compared with MCI-LB/DLB. CONCLUSION: This study identified multiple DEGs in MCI-LB/DLB and MCI-AD/AD. One of these DEGs, ANP32A, may be a prognostic marker in AD. Genes related to mitochondrial function were downregulated in MCI-LB/DLB. Previously reported upregulation of genes associated with inflammation and immune responses in MCI-AD/AD was confirmed in this cohort. Differences in interferon responses between MCI-AD/AD and MCI-LB/DLB suggest that there are key differences in peripheral immune responses between these diseases.

Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance.

BACKGROUND & AIMS: Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. METHODS: We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. RESULTS: Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2-4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5-8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2-4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p. CONCLUSIONS: Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD. LAY SUMMARY: MicroRNAs (miRNAs) are small pieces of nucleic acid that may turn expression of genes on or off. These molecules can be detected in the blood circulation, and their levels in blood may change in liver disease including non-alcoholic fatty liver disease (NAFLD). To see if we could detect specific miRNA associated with advanced stages of NAFLD, we carried out miRNA sequencing in a group of 183 patients with NAFLD of varying severity together with 10 population controls. We found that a number of miRNAs showed changes, mainly increases, in serum levels but that 1 particular miRNA miR-193a-5p consistently increased. We confirmed this increase in a second group of cases with NAFLD. Measuring this miRNA in a blood sample may be a useful way to determine whether a patient has advanced NAFLD without an invasive liver biopsy.

Therapeutic wavelengths of ultraviolet B radiation activate apoptotic, circadian rhythm, redox signalling and key canonical pathways in psoriatic epidermis.

Ultraviolet B radiation (UVB) exerts pleiotropic effects on human skin. DNA damage response and repair pathways are activated by UVB; if damage cannot be repaired, apoptosis ensues. Although cumulative UVB exposure predisposes to skin cancer, UVB phototherapy is widely used as an effective treatment for psoriasis. Previous studies defined the therapeutic action spectrum of UVB and showed that psoriasis is resistant to apoptosis. This study aimed to investigate early molecular responses within psoriasis plaques following irradiation with single equi-erythemogenic doses of clinically-effective (311 nm, narrow-band) compared to clinically-ineffective (290 nm) UVB. Forty-eight micro-dissected epidermal samples from 20 psoriatic patients were analyzed using microarrays. Our bioinformatic analysis compared gene expression between 311 nm irradiated, 290 nm irradiated and control psoriasis epidermis to specifically identify 311 nm UVB differentially expressed genes (DEGs) and their upstream regulatory pathways. Key DEGs and pathways were validated by immunohistochemical analysis. There was a dynamic induction and repression of 311 nm UVB DEGs between 6 h and 18 h, only a limited number of DEGs maintained their designated expression status between time-points. Key disease and function pathways included apoptosis, cell death, cell migration and leucocyte chemotaxis. DNA damage response pathways, NRF2-mediated oxidative stress response and P53 signalling were key nodes, interconnecting apoptosis and cell cycle arrest. Interferon signalling, dendritic cell maturation, granulocyte adhesion and atherosclerotic pathways were also differentially regulated. Consistent with these findings, top transcriptional regulators of 311 nm UVB DEGs related to: a) apoptosis, DNA damage response and cell cycle control; b) innate/acquired immune regulation and inflammation; c) hypoxia/redox response and angiogenesis; d) circadian rhythmicity; f) EGR/AP1 signalling and keratinocyte differentiation; and g) mitochondrial biogenesis. This research provides important insights into the molecular targets of 311 nm UVB, underscoring key roles for apoptosis and cell death. These and the other key pathways delineated may be central to the therapeutic effects of 311 nm in psoriasis.

Understanding the complexity of retina and pluripotent stem cell derived retinal organoids with single cell RNA sequencing: current progress, remaining challenges and future prospective.

Single-cell sequencing technologies have emerged as a revolutionary tool with transformative new methods to profile genetic, epigenetic, spatial, and lineage information in individual cells. Single-cell RNA sequencing (scRNA-Seq) allows researchers to collect large datasets detailing the transcriptomes of individual cells in space and time and is increasingly being applied to reveal cellular heterogeneity in retinal development, normal physiology, and disease, and provide new insights into cell-type specific markers and signaling pathways. In recent years, scRNA-Seq datasets have been generated from retinal tissue and pluripotent stem cell-derived retinal organoids. Their cross-comparison enables staging of retinal organoids, identification of specific cells in developing and adult human neural retina and provides deeper insights into cell-type sub-specification and geographical differences. In this article, we review the recent rapid progress in scRNA-Seq analyses of retina and retinal organoids, the questions that remain unanswered and the technical challenges that need to be overcome to achieve consistent results that reflect the complexity, functionality, and interactions of all retinal cell types.

Pax9 is required for cardiovascular development and interacts with Tbx1 in the pharyngeal endoderm to control 4th pharyngeal arch artery morphogenesis.

Developmental defects affecting the heart and aortic arch arteries are a significant phenotype observed in individuals with 22q11 deletion syndrome and are caused by a microdeletion on chromosome 22q11. TBX1, one of the deleted genes, is expressed throughout the pharyngeal arches and is considered a key gene, when mutated, for the arch artery defects. Pax9 is expressed in the pharyngeal endoderm and is downregulated in Tbx1 mutant mice. We show here that Pax9-deficient mice are born with complex cardiovascular malformations that affect the outflow tract and aortic arch arteries with failure of the 3rd and 4th pharyngeal arch arteries to form correctly. Transcriptome analysis indicated that Pax9 and Tbx1 may function together, and mice double heterozygous for Tbx1/Pax9 presented with a significantly increased incidence of interrupted aortic arch when compared with Tbx1 heterozygous mice. Using a novel Pax9Cre allele, we demonstrated that the site of this Tbx1-Pax9 genetic interaction is the pharyngeal endoderm, therefore revealing that a Tbx1-Pax9-controlled signalling mechanism emanating from the pharyngeal endoderm is required for crucial tissue interactions during normal morphogenesis of the pharyngeal arch artery system.

B-cell activity markers are associated with different disease activity domains in primary Sjögren's syndrome.

OBJECTIVES: B-cell activating factor (BAFF), ß-2 microglobulin (ß2M) and serum free light chains (FLCs) are elevated in primary SS (pSS) and associated with disease activity. We aimed to investigate their association with the individual disease activity domains of the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) in a large well-characterized pSS cohort. METHODS: Sera from pSS patients enrolled in the UK Primary Sjögren's Syndrome Registry (UKPSSR) (n = 553) and healthy controls (n = 286) were analysed for FLC (κ and λ), BAFF and ß2 M. Pearson correlation coefficients were calculated for patient clinical characteristics, including salivary flow, Schirmer's test, EULAR Sjögren's Syndrome Patient Reported Index and serum IgG levels. Poisson regression was performed to identify independent predictors of total ESSDAI and ClinESSDAI (validated ESSDAI minus the biological domain) scores and their domains. RESULTS: Levels of BAFF, ß2M and FLCs were higher in pSS patients compared to controls. All three biomarkers associated significantly with the ESSDAI and the ClinESSDAI. BAFF associated with the peripheral nervous system domain of the ESSDAI, whereas ß2M and FLCs associated with the cutaneous, biological and renal domains. Multivariate analysis showed BAFF, ß2M and their interaction to be independent predictors of ESSDAI/ClinESSDAI. FLCs were also shown to associate with the ESSDAI/ClinESSDAI but not independent of serum IgG. CONCLUSION: All biomarkers were associated with total ESSDAI scores but with differing domain associations. These findings should encourage further investigation of these biomarkers in longitudinal studies and against other disease activity measures.

Cross platform analysis of transcriptomic data identifies ageing has distinct and opposite effects on tendon in males and females.

The development of tendinopathy is influenced by a variety of factors including age, gender, sex hormones and diabetes status. Cross platform comparative analysis of transcriptomic data elucidated the connections between these entities in the context of ageing. Tissue-engineered tendons differentiated from bone marrow derived mesenchymal stem cells from young (20-24 years) and old (54-70 years) donors were assayed using ribonucleic acid sequencing (RNA-seq). Extension of the experiment to microarray and RNA-seq data from tendon identified gender specific gene expression changes highlighting disparity with existing literature and published pathways. Separation of RNA-seq data by sex revealed underlying negative binomial distributions which increased statistical power. Sex specific de novo transcriptome assemblies generated fewer larger transcripts that contained miRNAs, lincRNAs and snoRNAs. The results identify that in old males decreased expression of CRABP2 leads to cell proliferation, whereas in old females it leads to cellular senescence. In conjunction with existing literature the results explain gender disparity in the development and types of degenerative diseases as well as highlighting a wide range of considerations for the analysis of transcriptomic data. Wider implications are that degenerative diseases may need to be treated differently in males and females because alternative mechanisms may be involved.

Deep sequencing approaches for the analysis of prokaryotic transcriptional boundaries and dynamics.

The identification of the protein-coding regions of a genome is straightforward due to the universality of start and stop codons. However, the boundaries of the transcribed regions, conditional operon structures, non-coding RNAs and the dynamics of transcription, such as pausing of elongation, are non-trivial to identify, even in the comparatively simple genomes of prokaryotes. Traditional methods for the study of these areas, such as tiling arrays, are noisy, labour-intensive and lack the resolution required for densely-packed bacterial genomes. Recently, deep sequencing has become increasingly popular for the study of the transcriptome due to its lower costs, higher accuracy and single nucleotide resolution. These methods have revolutionised our understanding of prokaryotic transcriptional dynamics. Here, we review the deep sequencing and data analysis techniques that are available for the study of transcription in prokaryotes, and discuss the bioinformatic considerations of these analyses.

Misincorporation by RNA polymerase is a major source of transcription pausing in vivo.

The transcription error rate estimated from mistakes in end product RNAs is 10−3­10−5. We analyzed the fidelity of nascent RNAs from all actively transcribing elongation complexes (ECs) in Escherichia coli and Saccharomyces cerevisiae and found that 1­3% of all ECs in wild-type cells, and 5­7% of all ECs in cells lacking proofreading factors are, in fact, misincorporated complexes. With the exception of a number of sequence-dependent hotspots, most misincorporations are distributed relatively randomly. Misincorporation at hotspots does not appear to be stimulated by pausing. Since misincorporation leads to a strong pause of transcription due to backtracking, our findings indicate that misincorporation could be a major source of transcriptional pausing and lead to conflicts with other RNA polymerases and replication in bacteria and eukaryotes. This observation implies that physical resolution of misincorporated complexes may be the main function of the proofreading factors Gre and TFIIS. Although misincorporation mechanisms between bacteria and eukaryotes appear to be conserved, the results suggest the existence of a bacteria-specific mechanism(s) for reducing misincorporation in protein-coding regions. The links between transcription fidelity, human disease, and phenotypic variability in genetically-identical cells can be explained by the accumulation of misincorporated complexes, rather than mistakes in mature RNA.

Analysis of primary visual cortex in dementia with Lewy bodies indicates GABAergic involvement associated with recurrent complex visual hallucinations.

Dementia with Lewy bodies (DLB) patients frequently experience well formed recurrent complex visual hallucinations (RCVH). This is associated with reduced blood flow or hypometabolism on imaging of the primary visual cortex. To understand these associations in DLB we used pathological and biochemical analysis of the primary visual cortex to identify changes that could underpin RCVH. Alpha-synuclein or neurofibrillary tangle pathology in primary visual cortex was essentially absent. Neurone density or volume within the primary visual cortex in DLB was also unchanged using unbiased stereology. Microarray analysis, however, demonstrated changes in neuropeptide gene expression and other markers, indicating altered GABAergic neuronal function. Calcium binding protein and GAD65/67 immunohistochemistry showed preserved interneurone populations indicating possible interneurone dysfunction. This was demonstrated by loss of post synaptic GABA receptor markers including gephyrin, GABARAP, and Kif5A, indicating reduced GABAergic synaptic activity. Glutamatergic neuronal signalling was also altered with vesicular glutamate transporter protein and PSD-95 expression being reduced. Changes to the primary visual cortex in DLB indicate that reduced GABAergic transmission may contribute to RCVH in DLB and treatment using targeted GABAergic modulation or similar approaches using glutamatergic modification may be beneficial.

An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.

Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm.

Mining integrated semantic networks for drug repositioning opportunities.

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.