Harm reduction strategies in acute care for people who use alcohol and/or drugs: A scoping review.

BACKGROUND: People who use alcohol and/or drugs (PWUAD) are at higher risk of infectious disease, experiencing stigma, and recurrent hospitalization. Further, they have a higher likelihood of death once hospitalized when compared to people who do not use drugs and/or alcohol. The use of harm reduction strategies within acute care settings has shown promise in alleviating some of the harms experienced by PWUAD. This review aimed to identify and synthesize evidence related to the implementation of harm reduction strategies in acute care settings. METHODS: A scoping review investigating harm reduction strategies implemented in acute care settings for PWUAD was conducted. A search strategy developed by a JBI-trained specialist was used to search five databases (Medline, Embase, CINAHL, PsychInfo and Scopus). Screening of titles, abstracts and full texts, and data extraction was done in duplicate by two independent reviewers. Discrepancies were resolved by consensus or with a third reviewer. Results were reported narratively and in tables. Both patients and healthcare decision makers contributing to the development of the protocol, article screening, synthesis and feedback of results, and the identification of gaps in the literature. FINDINGS: The database search identified 14,580 titles, with 59 studies included in this review. A variety of intervention modalities including pharmacological, decision support, safer consumption, early overdose detection and turning a blind eye were identified. Reported outcome measures related to safer use, managed use, and conditions of use. Reported barriers and enablers to implementation related to system and organizational factors, patient-provider communication, and patient and provider perspectives. CONCLUSION: This review outlines the types of alcohol and/or drug harm reduction strategies, which have been evaluated and/or implemented in acute care settings, the type of outcome measures used in these evaluations and summarizes key barriers and enablers to implementation. This review has the potential to serve as a resource for future harm reduction evaluation and implementation efforts in the context of acute care settings.

A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets.

Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein-protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Children, young people and parent engagement in health intervention design and implementation: A scoping review.

INTRODUCTION: Engaging children and young people (CYP) with and without their parents in health research has the potential to improve the development and implementation of health interventions. However, to our knowledge, the scope of engagement activities used with this population and barriers to their engagement is unknown. The objective of this review was to identify and describe CYP engagement with and without their parents in the development and/or implementation of health interventions. METHODS: This scoping review included any primary research studies reporting on engaging CYP, with or without parents, in the design and/or implementation of health interventions. Healthcare professionals had to be involved over the course of the study and the study had to take place in either community, primary or tertiary care settings. The following databases were searched in May 2017, May 2020 and June 2021: Medline (OVID), CINAHL (EBSCO) and Embase (Elsevier). Two independent reviewers screened titles, abstracts and full-text articles and used a previously piloted extraction form to extract and summarize information from the included articles. RESULTS: Twenty-eight articles discussing twenty-four studies were included. CYP engagement throughout the research cycle was limited. There were no observed differences in the reported presence of engagement, types of interventions or outcomes of engagement between studies engaging CYP or CYP and parents. Studies engaging CYP and parents contained limited information on how these relationships affected outcomes of engagement. Engagement was enabled primarily by the maintenance of resources and relationships among stakeholders. CONCLUSIONS: Although CYP engagement often influenced health intervention and implementation design, they are inconsistently engaged across the research cycle. It is unclear whether parental involvement enhances CYP engagement. Future research should consider reporting guidelines to clarify the level of CYP and/or parent engagement, and enhance CYP engagement by fostering synergistic and sustainable partnerships with key stakeholders. PATIENT OR PUBLIC CONTRIBUTION: A parent partner with codesign experience contributed to the creation of the research questions, screened titles, abstracts and full texts, helped with data extraction and provided feedback on the manuscript.

Using a learning health system framework to examine COVID-19 pandemic planning and response at a Canadian Health Centre.

BACKGROUND: The COVID-19 pandemic has presented a unique opportunity to explore how health systems adapt under rapid and constant change and develop a better understanding of health system transformation. Learning health systems (LHS) have been proposed as an ideal structure to inform a data-driven response to a public health emergency like COVID-19. The aim of this study was to use a LHS framework to identify assets and gaps in health system pandemic planning and response during the initial stages of the COVID-19 pandemic at a single Canadian Health Centre. METHODS: This paper reports the data triangulation stage of a concurrent triangulation mixed methods study which aims to map study findings onto the LHS framework. We used a triangulation matrix to map quantitative (textual and administrative sources) and qualitative (semi-structured interviews) data onto the seven characteristics of a LHS and identify assets and gaps related to health-system receptors and research-system supports. RESULTS: We identified several health system assets within the LHS characteristics, including appropriate decision supports and aligned governance. Gaps were identified in the LHS characteristics of engaged patients and timely production and use of research evidence. CONCLUSION: The LHS provided a useful framework to examine COVID-19 pandemic response measures. We highlighted opportunities to strengthen the LHS infrastructure for rapid integration of evidence and patient experience data into future practice and policy changes.

Health system impacts of SARS-CoV - 2 variants of concern: a rapid review.

BACKGROUND: As of November 25th 2021, four SARS-CoV - 2 variants of concern (VOC: Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2)) have been detected. Variable degrees of increased transmissibility of the VOC have been documented, with potential implications for hospital and health system capacity and control measures. This rapid review aimed to provide a synthesis of evidence related to health system responses to the emergence of VOC worldwide. METHODS: Seven databases were searched up to September 27, 2021, for terms related to VOC. Titles, abstracts, and full-text documents were screened independently by two reviewers. Data were extracted independently by two reviewers using a standardized form. Studies were included if they reported on at least one of the VOC and health system outcomes. RESULTS: Of the 4877 articles retrieved, 59 studies were included, which used a wide range of designs and methods. Most of the studies reported on Alpha, and all except two reported on impacts for capacity planning related to hospitalization, intensive care admissions, and mortality. Most studies (73.4%) observed an increase in hospitalization, but findings on increased admission to intensive care units were mixed (50%). Most studies (63.4%) that reported mortality data found an increased risk of death due to VOC, although health system capacity may influence this. No studies reported on screening staff and visitors or cohorting patients based on VOC. CONCLUSION: While the findings should be interpreted with caution as most of the sources identified were preprints, evidence is trending towards an increased risk of hospitalization and, potentially, mortality due to VOC compared to wild-type SARS-CoV - 2. There is little evidence on the need for, and the effect of, changes to health system arrangements in response to VOC transmission.

Public health implications of SARS-CoV-2 variants of concern: a rapid scoping review.

OBJECTIVES: The four SARS-CoV-2 variants of concern (VOC; Alpha, Beta, Gamma and Delta) identified by May 2021 are highly transmissible, yet little is known about their impact on public health measures. We aimed to synthesise evidence related to public health measures and VOC. DESIGN: A rapid scoping review. DATA SOURCES: On 11 May 2021, seven databases (MEDLINE, Embase, the Cochrane Database of Systematic Reviews, Central Register of Controlled Trials, Epistemonikos' L-OVE on COVID-19, medRxiv, bioRxiv) were searched for terms related to VOC, public health measures, transmission and health systems. No limit was placed on date of publication. ELIGIBILITY CRITERIA: Studies were included if they reported on any of the four VOCs and public health measures, and were available in English. Only studies reporting on data collected after October 2020, when the first VOC was reported, were included. DATA EXTRACTION AND SYNTHESIS: Titles, abstracts and full-text articles were screened by two independent reviewers. Data extraction was completed by two independent reviewers using a standardised form. Data synthesis and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. RESULTS: Of the 37 included studies, the majority assessed the impact of Alpha (n=32) and were conducted in Europe (n=12) or the UK (n=9). Most were modelling studies (n=28) and preprints (n=28). The majority of studies reported on infection control measures (n=17), followed by modifying approaches to vaccines (n=13), physical distancing (n=6) and either mask wearing, testing or hand washing (n=2). Findings suggest an accelerated vaccine rollout is needed to mitigate the spread of VOC. CONCLUSIONS: The increased severity of VOC requires proactive public health measures to control their spread. Further research is needed to strengthen the evidence for continued implementation of public health measures in conjunction with vaccine rollout. With no studies reporting on Delta, there is a need for further research on this and other emerging VOC on public health measures.

Identifying harm reduction strategies for alcohol and drug-use in inpatient care settings and emergency departments: a scoping review protocol.

INTRODUCTION: People who use alcohol and/or drugs (PWUAD) are at high risk of medical complications, frequent hospitalisation and drug-related death following discharge from inpatient settings and emergency departments (EDs). Harm reduction strategies implemented in these settings may mitigate negative health outcomes for PWUAD. However, the scope of harm reduction strategies used globally within inpatient settings and EDs is unknown. The objective of this review is to identify and synthesise reported harm reduction strategies that have been implemented across inpatient settings and EDs for PWUAD. METHODS AND ANALYSIS: This review will include studies from any country and health service reporting on harm reduction strategies implemented in inpatient settings or EDs. The population of interest includes people of any race, gender and age identifying as PWUAD, or individuals who provided care to PWUAD. Studies which describe implementation strategies and barriers and enablers to implementation will be included. Studies published in English, or those available for English translation will be included. The following databases will be searched: MEDLINE All (Ovid), Embase (Elsevier Embase.com), CINAHL with Full Text (EBSCOhost), PsycINFO (EBSCOhost) and SCOPUS (Elsevier Scopus.com). A grey literature search will be conducted. There will be no date restrictions on the search. Titles, abstracts and full texts will be screened in duplicate. Data will be extracted using a standardised form. The results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews. ETHICS AND DISSEMINATION: Scoping reviews do not require ethical approval. Patient partners with lived experience and relevant knowledge users will be engaged as research team members throughout all phases of the research process. A report detailing context, methodology and findings from this review will be disseminated to knowledge users and relevant community stakeholders. This review will be submitted for publication to a relevant peer-reviewed journal.

TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.

Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.

Type-2 diabetes-associated variants with cross-trait relevance: Post-GWAs strategies for biological function interpretation.

Genome-wide association studies (GWAs) for type 2 diabetes (T2D) have been successful in identifying many loci with robust association signals. Nevertheless, there is a clear need for post-GWAs strategies to understand mechanism of action and clinical relevance of these variants. The association of several comorbidities with T2D suggests a common etiology for these phenotypes and complicates the management of the disease. In this study, we focused on the genetics underlying these relationships, using systems genomics to identify genetic variation associated with T2D and 12 other traits. GWAs studies summary statistics for pairwise comparisons were obtained for glycemic traits, obesity, coronary artery disease, and lipids from large consortia GWAs meta-analyses. We used a network medicine approach to leverage experimental information about the identified genes and variants with cross traits effects for biological function interpretation. We identified a set of 38 genetic variants with cross traits effects that point to a main network of genes that should be relevant for T2D and its comorbidities. We prioritized the T2D associated genes based on the number of traits they showed association with and the experimental evidence showing their relation to the disease etiology. In this study, we demonstrated how systems genomics and network medicine approaches can shed light into GWAs discoveries, translating findings into a more therapeutically relevant context.

Mechanistic interplay between ceramide and insulin resistance.

Recent research adds to a growing body of literature on the essential role of ceramides in glucose homeostasis and insulin signaling, while the mechanistic interplay between various components of ceramide metabolism remains to be quantified. We present an extended model of C16:0 ceramide production through both the de novo synthesis and the salvage pathways. We verify our model with a combination of published models and independent experimental data. In silico experiments of the behavior of ceramide and related bioactive lipids in accordance with the observed transcriptomic changes in obese/diabetic murine macrophages at 5 and 16 weeks support the observation of insulin resistance only at the later phase. Our analysis suggests the pivotal role of ceramide synthase, serine palmitoyltransferase and dihydroceramide desaturase involved in the de novo synthesis and the salvage pathways in influencing insulin resistance versus its regulation.

IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data.

MOTIVATION: Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite. RESULTS: We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signalling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points are estimated and 49 new kinase-target links are derived. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git. CONTACT: marcel.mischnik@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sample Size Calculations for Detecting Disease-Modifying Osteoarthritis Drug Effects on Knee Replacement Incidence in Clinical Trials: Data From the Osteoarthritis Initiative.

OBJECTIVE: To evaluate the extent to which the current designs of clinical trials in knee osteoarthritis (OA) permit detection of a therapeutic effect of disease-modifying OA drugs (DMOADs) on the incidence of knee replacement, and to provide estimates of the required sample sizes. METHODS: We selected distinct subcohorts of the Osteoarthritis Initiative (OAI), based on available information on eligibility criteria for clinical knee OA trials (ClinicalTrials.gov) and additional subcohorts stratified for age, sex, and the severity of radiographic OA. The observed incidence of knee replacement in these OAI subcohorts was used to estimate the expected incidence of knee replacement in the control group of a clinical trial. Based on this estimate, the sample sizes required to detect hypothetical treatment effects on the incidence of knee replacement were calculated, assuming observation periods of 2, 5, or 7 years. RESULTS: The cumulative knee replacement incidence rates in the OAI subcohorts ranged from 0.9% to 12.9%. The corresponding sample sizes required to detect 50% improvement by the DMOAD, with a power of 80% and 95% confidence, were 5,459 and 362, respectively. Including only women with advanced age and radiographic OA increased the incidence of knee replacement and decreased the required sample size. CONCLUSION: The sample sizes that are commonly used in clinical trials do not enable the effects of a DMOAD on incident knee replacement to be detected with sufficient power and confidence. The estimated incidence rates of knee replacement and the corresponding sample sizes are important for informing the design of trials for disease course-modifying effects as well as for socioeconomic evaluation of a DMOAD in terms of preventing knee replacement.

Comparative gene expression profiling identifies common molecular signatures of NF-κB activation in canine and human diffuse large B cell lymphoma (DLBCL).

We present the first comparison of global transcriptional changes in canine and human diffuse large B-cell lymphoma (DLBCL), with particular reference to the nuclear factor-kappa B (NF-κB) pathway. Microarray data generated from canine DLBCL and normal lymph nodes were used for differential expression, co-expression and pathway analyses, and compared with analysis of microarray data from human healthy and DLBCL lymph nodes. The comparisons at gene level were performed by mapping the probesets in canine microarrays to orthologous genes in humans and vice versa. A considerable number of differentially expressed genes between canine lymphoma and healthy lymph node samples were also found differentially expressed between human DLBCL and healthy lymph node samples. Principal component analysis using a literature-derived NF-κB target gene set mapped to orthologous canine array probesets and human array probesets clearly separated the healthy and cancer samples in both datasets. The analysis demonstrated that for both human and canine DLBCL there is activation of the NF-κB/p65 canonical pathway, indicating that canine lymphoma could be used as a model to study NF-κB-targeted therapeutics for human lymphoma. To validate this, tissue arrays were generated for canine and human NHL and immunohistochemistry was employed to assess NF-κB activation status. In addition, human and canine B-cell lymphoma lines were assessed for NF-κB activity and the effects of NF-κB inhibition.

Cardioprotective effects of lixisenatide in rat myocardial ischemia-reperfusion injury studies.

BACKGROUND: Lixisenatide is a glucagon-like peptide-1 analog which stimulates insulin secretion and inhibits glucagon secretion and gastric emptying. We investigated cardioprotective effects of lixisenatide in rodent models reflecting the clinical situation. METHODS: The acute cardiac effects of lixisenatide were investigated in isolated rat hearts subjected to brief ischemia and reperfusion. Effects of chronic treatment with lixisenatide on cardiac function were assessed in a modified rat heart failure model after only transient coronary occlusion followed by long-term reperfusion. Freshly isolated cardiomyocytes were used to investigate cell-type specific mechanisms of lixisenatide action. RESULTS: In the acute setting of ischemia-reperfusion, lixisenatide reduced the infarct-size/area at risk by 36% ratio without changes on coronary flow, left-ventricular pressure and heart rate. Treatment with lixisenatide for 10 weeks, starting after cardiac ischemia and reperfusion, improved left ventricular end-diastolic pressure and relaxation time and prevented lung congestion in comparison to placebo. No anti-fibrotic effect was observed. Gene expression analysis revealed a change in remodeling genes comparable to the ACE inhibitor ramipril. In isolated cardiomyocytes lixisenatide reduced apoptosis and increased fractional shortening. Glucagon-like peptide-1 receptor (GLP1R) mRNA expression could not be detected in rat heart samples or isolated cardiomyocytes. Surprisingly, cardiomyocytes isolated from GLP-1 receptor knockout mice still responded to lixisenatide. CONCLUSIONS: In rodent models, lixisenatide reduced in an acute setting infarct-size and improved cardiac function when administered long-term after ischemia-reperfusion injury. GLP-1 receptor independent mechanisms contribute to the described cardioprotective effect of lixisenatide. Based in part on these preclinical findings patients with cardiac dysfunction are currently being recruited for a randomized, double-blind, placebo-controlled, multicenter study with lixisenatide. TRIAL REGISTRATION: (ELIXA, ClinicalTrials.gov Identifier: NCT01147250).

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology.

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process.

PChopper: high throughput peptide prediction for MRM/SRM transition design.

BACKGROUND: The use of selective reaction monitoring (SRM) based LC-MS/MS analysis for the quantification of phosphorylation stoichiometry has been rapidly increasing. At the same time, the number of sites that can be monitored in a single LC-MS/MS experiment is also increasing. The manual processes associated with running these experiments have highlighted the need for computational assistance to quickly design MRM/SRM candidates. RESULTS: PChopper has been developed to predict peptides that can be produced via enzymatic protein digest; this includes single enzyme digests, and combinations of enzymes. It also allows digests to be simulated in 'batch' mode and can combine information from these simulated digests to suggest the most appropriate enzyme(s) to use. PChopper also allows users to define the characteristic of their target peptides, and can automatically identify phosphorylation sites that may be of interest. Two application end points are available for interacting with the system; the first is a web based graphical tool, and the second is an API endpoint based on HTTP REST. CONCLUSIONS: Service oriented architecture was used to rapidly develop a system that can consume and expose several services. A graphical tool was built to provide an easy to follow workflow that allows scientists to quickly and easily identify the enzymes required to produce multiple peptides in parallel via enzymatic digests in a high throughput manner.

LimsPortal and BonsaiLIMS: development of a lab information management system for translational medicine.

BACKGROUND: Laboratory Information Management Systems (LIMS) are an increasingly important part of modern laboratory infrastructure. As typically very sophisticated software products, LIMS often require considerable resources to select, deploy and maintain. Larger organisations may have access to specialist IT support to assist with requirements elicitation and software customisation, however smaller groups will often have limited IT support to perform the kind of iterative development that can resolve the difficulties that biologists often have when specifying requirements. Translational medicine aims to accelerate the process of treatment discovery by bringing together multiple disciplines to discover new approaches to treating disease, or novel applications of existing treatments. The diverse set of disciplines and complexity of processing procedures involved, especially with the use of high throughput technologies, bring difficulties in customizing a generic LIMS to provide a single system for managing sample related data within a translational medicine research setting, especially where limited IT support is available. RESULTS: We have designed and developed a LIMS, BonsaiLIMS, around a very simple data model that can be easily implemented using a variety of technologies, and can be easily extended as specific requirements dictate. A reference implementation using Oracle 11 g database and the Python framework, Django is presented. CONCLUSIONS: By focusing on a minimal feature set and a modular design we have been able to deploy the BonsaiLIMS system very quickly. The benefits to our institute have been the avoidance of the prolonged implementation timescales, budget overruns, scope creep, off-specifications and user fatigue issues that typify many enterprise software implementations. The transition away from using local, uncontrolled records in spreadsheet and paper formats to a centrally held, secured and backed-up database brings the immediate benefits of improved data visibility, audit and overall data quality. The open-source availability of this software allows others to rapidly implement a LIMS which in itself might sufficiently address user requirements. In situations where this software does not meet requirements, it can serve to elicit more accurate specifications from end-users for a more heavyweight LIMS by acting as a demonstrable prototype.

Discretization provides a conceptually simple tool to build expression networks.

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes "microarray" data using known relationships which are successfully identified by our method.We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS.The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic--identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs.

Global array-based transcriptomics from minimal input RNA utilising an optimal RNA isolation process combined with SPIA cDNA probes.

BACKGROUND: Technical advances in the collection of clinical material, such as laser capture microdissection and cell sorting, provide the advantage of yielding more refined and homogenous populations of cells. However, these attractive advantages are counter balanced by the significant difficulty in obtaining adequate nucleic acid yields to allow transcriptomic analyses. Established technologies are available to carry out global transcriptomics using nanograms of input RNA, however, many clinical samples of low cell content would be expected to yield RNA within the picogram range. To fully exploit these clinical samples the challenge of isolating adequate RNA yield directly and generating sufficient microarray probes for global transcriptional profiling from this low level RNA input has been addressed in the current report. We have established an optimised RNA isolation workflow specifically designed to yield maximal RNA from minimal cell numbers. This procedure obtained RNA yield sufficient for carrying out global transcriptional profiling from vascular endothelial cell biopsies, clinical material not previously amenable to global transcriptomic approaches. In addition, by assessing the performance of two linear isothermal probe generation methods at decreasing input levels of good quality RNA we demonstrated robust detection of a class of low abundance transcripts (GPCRs) at input levels within the picogram range, a lower level of RNA input (50 pg) than previously reported for global transcriptional profiling and report the ability to interrogate the transcriptome from only 10 pg of input RNA. By exploiting an optimal RNA isolation workflow specifically for samples of low cell content, and linear isothermal RNA amplification methods for low level RNA input we were able to perform global transcriptomics on valuable and potentially informative clinically derived vascular endothelial biopsies here for the first time. These workflows provide the ability to robustly exploit ever more common clinical samples yielding extremely low cell numbers and RNA yields for global transcriptomics.

Ablation of the locus coeruleus increases oxidative stress in tg-2576 transgenic but not wild-type mice.

Mice transgenic for production of excessive or mutant forms of beta-amyloid differ from patients with Alzheimer's disease in the degree of inflammation, oxidative damage, and alteration of intermediary metabolism, as well as the paucity or absence of neuronal atrophy and cognitive impairment. Previous observers have suggested that differences in inflammatory response reflect a discrepancy in the state of the locus coeruleus (LC), loss of which is an early change in Alzheimer's disease but which is preserved in the transgenic mice. In this paper, we extend these observations by examining the effects of the LC on markers of oxidative stress and intermediary metabolism. We compare four groups: wild-type or Tg2576 Aß transgenic mice injected with DSP4 or vehicle. Of greatest interest were metabolites different between ablated and intact transgenics, but not between ablated and intact wild-type animals. The Tg2576_DSP4 mice were distinguished from the other three groups by oxidative stress and altered energy metabolism. These observations provide further support for the hypothesis that Tg2576 Aß transgenic mice with this ablation may be a more congruent model of Alzheimer's disease than are transgenics with an intact LC.