JoVSA: Editorial

Vincentian Universities are engaged in service at so many levels and in so many ways, yet it is easy to move through our day unaware of the herculean efforts that our students and colleagues are engaged in. The Vincentian Universities seem rooted in the idea of service. For us, service is not another trend that we adopted, but rather it has always been part of our constitution. The work presented in this issue provides two direct examples of how we can better serve

Online Training of New Curators

The basic information in Reactome is provided by bench biologists who are experts on a particular pathway, the Reactome Team is always working hard to drive engagement. This engagement between experts, curators, editors and reviewers requires maintenance and improvement, and in this sense Reactome is itself a model for large biocuration projects that are driven by community engagement. 
 
This tutorial will highlight issues from the perspective of online training participants, the trainer's and the audience's. From the audience perspective the tutorial will introduce the concepts that drive the Reactome data model, cover the basic steps that a researcher would have to follow in order to breakdown a biological pathway into its "reaction-based" Reactome representation. Introduce the user to the tools that are used by authors, the "authortool" and the tools used by curators, the "curatortool" to move that data into the Reactome database. 
 
From the trainer perspective the tutorial will focus on the essential role that a clear explanation of a resource's data model plays in priming the audience for the technical aspects of biocuration. Technical challenges and online delivery methods will be discussed and examples of systems used will be presented with discussion of the negative and positive aspects. Pedagogical models for enhancing audience participation will be briefly presented. 
 
The Reactome project is a collaboration among Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and The Gene Ontology Consortium to develop a curated resource of core pathways and reactions in human biology. The information in this database is authored by biological researchers with expertise in their fields, maintained by the Reactome editorial staff, and cross referenced with the sequence databases at NCBI, Ensembl and UniProt, the UCSC Genome Browser , KEGG (Gene and Compound ), ChEBI, PubMed and GO. 
 
The information is then managed by groups of curators at CSHL and EBI, peer-reviewed by other researchers and published on the web. While Reactome is targeted at human pathways, it also includes many individual biochemical reactions from non-human systems such as rat, mouse, pufferfish and zebrafish. This makes the database relevant to the many researchers who work on model organisms. All the information in Reactome is backed up by its provenance: either a literature citation or an electronic inference based on sequence similarity. 
 
Reactome is a free on-line resource, and Reactome software is open-source

Guidelines for the functional annotation of microRNAs using the Gene Ontology.

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual).R.P.H. and R.C.L are supported by funding from a British Heart Foundation grant (RG/13/5/30112) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.M. is a Senior Research Fellow of the British Heart Foundation (FS/13/2/29892). A.Z. is an Intermediate Fellow of the British Heart Foundation (FS/13/18/30207). D.S. is supported by a grant awarded to the Mouse Genome Database from the National Human Genome Research Institue at the US National Institutes of Health (HG-00330). P.D’E., M.G., M.O-M. are supported by grants from the US National Institutes of Health (P41 HG003751 and U54 GM114833), Ontario Research Fund, and the European Molecular Biology Laboratory. D.H. is supported by a grant awarded to the Zebrafish Information Network fromthe National Human Genome Research Institute at the US National Institutes of Health (HG002659). A.Z.K. is funded by a NIHR University College London Hospitals Biomedical Research Centre, Research Capability Funding award (RCF) (RCF123). L.M. is a Ragnar Söderberg fellow in Medicine (M-14/55), and received funding from Swedish Heart-Lung-Foundation (20120615, 20130664, 20140186). Huntley, RP 22 R.B. and D.O-S. are supported by R.B. and D.O-S. are supported by a grant awarded to The Gene Ontology Consortium (Principal Investigators: JA Blake, JM Cherry, S Lewis, PW Sternberg and P Thomas) by the National Human Genome Research Institute (NHGRI) (#U41 HG22073). V.P. and J.R.S. are supported by a grant from the National Heart, Lung, and Blood Institute on behalf of the National Institutes of Health (HL64541). K.V.A. is supported by a grant awarded to the Gene Ontology Consortium from the National Human Genome Research Institute at the US National Institutes of Health (HG002273). V.W. is supported by a Wellcome Trust grant (104967/Z/14/Z). We would like to thank Leonore Reiser and Tanya Berardini who provided guidance on the plant miRNA processing pathway. Also thanks to David Hill, Harold Drabkin, Judith Blake, Karen Christie, Donghui Li and Pascale Gaudet who contributed to discussions regarding GO curation procedures and to Lisa Matthews and Bruce May who provided helpful feedback on the manuscript. We are very grateful to Tony Sawford and Maria Martin from the European Bioinformatics Institute for access to the online GO curation tool, which is an essential component of this annotation project. Many thanks to members of the GO Editorial Office for useful discussions about the placement and definition of new GO terms. We also thank Alex Bateman and Anton Petrov for being responsive to our feedback regarding RNAcentral functionality. Author contributions: R.C.L. initiated discussions in the GO Consortium regarding miRNA curation guidelines and supervised the project, R.P.H. researched and constructed the guidelines and wrote the manuscript, R.P.H., R.C.L., D.S., R.B., P.D’E., M.G., M.O-M., D.H., V.P., J.R.S., K.V.A. and V.W. contributed to discussions regarding GO curation procedures and provided feedback on the manuscript. D.O-S. provided the expertise on definitions and placements of miRNA-related GO terms and performed the necessary updates and additions to both the GO and to the annotation extension relations used herein. M.M., A.Z., L.M. and A.Z.K. provided guidance with the scientific aspect of the guidelines and provided feedback on the manuscript.This is the final version of the article. It first appeared from Cold Spring Harbor Press via http://dx.doi.org/10.1261/rna.055301.11

Spot sputum samples are at least as good as early morning samples for identifying Mycobacterium tuberculosis

Supported by the Global Alliance for TB Drug Development with support from the Bill and Melinda Gates Foundation, the European and Developing Countries Clinical Trials Partnership (Grant IP.2007.32011.011), US Agency for International Development, UK Department for International Development, Directorate General for International Cooperation of the Netherlands, Irish Aid, Australia Department of Foreign Affairs and Trade, National Institutes of Health, AIDS Clinical Trials Group. The study was also supported by grants from the National Institute of Allergy and Infectious Diseases (NIAID) (UM1AI068634, UM1 AI068636, and UM1AI106701) and by NIAID grants to the University of KwaZulu Natal, South Africa, AIDS Clinical Trials Group (ACTG) site 31422 (1U01AI069469); to the Perinatal HIV Research Unit, Chris Hani Baragwanath Hospital, South Africa, ACTG site 12301 (1U01AI069453); and to the Durban International Clinical Trials Unit, South Africa, ACTG site 11201 (1U01AI069426). Bayer Healthcare for donated moxifloxacin and Sanofi donated rifampin.Background:  The use of early morning sputum samples (EMS) to diagnose tuberculosis (TB) can result in treatment delay given the need for the patient to return to the clinic with the EMS, increasing the chance of patients being lost during their diagnostic workup. However, there is little evidence to support the superiority of EMS over spot sputum samples. In this new analysis of the REMoxTB study, we compare the diagnostic accuracy of EMS with spot samples for identifying Mycobacterium tuberculosis pre- and post-treatment. Methods:  Patients who were smear positive at screening were enrolled into the study. Paired sputum samples (one EMS and one spot) were collected at each trial visit pre- and post-treatment. Microscopy and culture on solid LJ and liquid MGIT media were performed on all samples; those missing corresponding paired results were excluded from the analyses. Results:  Data from 1115 pre- and 2995 post-treatment paired samples from 1931 patients enrolled in the REMoxTB study were analysed. Patients were recruited from South Africa (47%), East Africa (21%), India (20%), Asia (11%), and North America (1%); 70% were male, median age 31 years (IQR 24–41), 139 (7%) co-infected with HIV with a median CD4 cell count of 399 cells/μL (IQR 318–535). Pre-treatment spot samples had a higher yield of positive Ziehl–Neelsen smears (98% vs. 97%, P = 0.02) and LJ cultures (87% vs. 82%, P = 0.006) than EMS, but there was no difference for positivity by MGIT (93% vs. 95%, P = 0.18). Contaminated and false-positive MGIT were found more often with EMS rather than spot samples. Surprisingly, pre-treatment EMS had a higher smear grading and shorter time-to-positivity, by 1 day, than spot samples in MGIT culture (4.5 vs. 5.5 days, P < 0.001). There were no differences in time to positivity in pre-treatment LJ culture, or in post-treatment MGIT or LJ cultures. Comparing EMS and spot samples in those with unfavourable outcomes, there were no differences in smear or culture results, and positive results were not detected earlier in Kaplan–Meier analyses in either EMS or spot samples. Conclusions:  Our data do not support the hypothesis that EMS samples are superior to spot sputum samples in a clinical trial of patients with smear positive pulmonary TB. Observed small differences in mycobacterial burden are of uncertain significance and EMS samples do not detect post-treatment positives any sooner than spot samples.Publisher PDFPeer reviewe

Exocrine Proteins Including Trypsin(ogen) as a Key Biomarker in Type 1 Diabetes

   Objective Proteomic profiling can identify useful biomarkers. Monozygotic(MZ) twins, discordant for a condition represent an ideal test population. We aimed to investigate and validate proteomic profiling in twins with type 1 diabetes and in other well characterised cohorts. Research Design and Methods A broad, multiplex analysis of 4068 proteins in sera from MZ twins concordant (n=43) and discordant for type 1 diabetes (n=27) identified major differences which were subsequently validated by a trypsin(ogen) assay in MZ pairs concordant (n=39) and discordant (n=42) for type 1 diabetes, individuals at-risk (n=195) and with type 1 diabetes (n=990), as well as with non-insulin requiring adult-onset diabetes diagnosed as either autoimmune (n=96) or type 2 (n=291). Results Proteomic analysis identified major differences between exocrine enzyme levels in discordant MZ twin pairs despite strong correlation between twins, whether concordant or discordant for type 1 diabetes (p Conclusions Type 1 diabetes is associated with altered exocrine function, even before onset. Twin data suggest roles for genetic and non-genetically determined factors. Exocrine/endocrine interactions are important under-investigated factors in type 1 diabetes.</p

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr

Emergent Oscillations in Networks of Stochastic Spiking Neurons

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework