96 research outputs found
Digitalomics – digital transformation leading to omics insights
No full textMolecular Oncology at McGill UniversitySegal Cancer Proteomics Centre at the Jewish General HospitalGenome Canada and Genome Quebec via the MutaQuant GAPPTerry Fox Research Institute and the Alvin Segal Family FoundationWarren Y. Soper Clinical Proteomics Centre at the Jewish General Hospita
Reverse engineering gene regulatory networks related to Quorum sensing in the plant pathogen Pectobacterium Atrosepticum
No full textThe objective of the project reported in the present chapter was the reverse engineering of gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum from micorarray gene expression profiles, obtained from the wild-type and eight knockout strains. To this end, we have applied various recent methods from multivariate statistics and machine learning: graphical Gaussian models, sparse Bayesian regression, LASSO (least absolute shrinkage and selection operator), Bayesian networks, and nested effects models. We have investigated the degree of similarity between the predictions obtained with the different approaches, and we have assessed the consistency of the reconstructed networks in terms of global topological network properties, based on the node degree distribution. The chapter concludes with a biological evaluation of the predicted network structures
Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes
No full textGenotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation
Concert schedules and artist itineraries from the Symphony Australia tours of 1979.
No full textTypescript travel arrangements for individual performers including dates, mode of transport, concert programmes and handwritten ammendments.; Condition: brittle, fragile paper.; Also available online http://nla.gov.au/nla.mus-vn5759839. Touring artists include: Neville Amadio (flute) -- Maire-Claire Alain (organ) -- Australian youth orchestra -- Trevor Barnard (piano) -- Hermann Baumann (horn) -- Robert Bickerstaff (baritone) -- Maria Bieshu (soprano) -- David Bollard (piano) -- June Bronhill (soprano) -- Katherine Capewell (contralto) -- Vanco Cavdarski (chief conductor) -- Romola Costantino (piano) -- David Cubbin (flute) -- John Curro (conductor) -- Ivan Davis (piano) -- Robert Dawe (bass-baritone) -- Richard Divall (conductor) -- Leonard Dommett (conductor) -- Ronald Dowd (tenor) -- Winifred Durie (viola) -- Lauris Elms (contralto) -- David Miller (piano) -- Thomas Edmonds (tenor) -- Margareta Elkins (mezzo-soprano) -- Gerald English (tenor) -- Peter Eros (conductor) -- Gustave Fenyo (piano) -- Glenys Fowles (soprano) -- Louis Fremaux (chief conductor) -- Erick Friedman (violin) -- Lamberto Gardelli (conductor) -- Rhondda Gillespie (piano) -- Isador Goodman (piano) -- Nance Grant (soprano) -- Erich Gruenberg (violin) -- Ronda Vickers (piano)
A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes
No full textMuch Ado about Zero
No full textLINE retrotransposons actively shape mammalian genomes. Denli et al. reveal a new open reading frame, ORF0, on the antisense strand of human LINE-1 encoding a small regulatory protein. This finding may represent the birth of an emerging retrotransposon gene that can adopt various fates, as it can be fused to adjacent host sequences
A panoptic approach to studying microRNA expression in breast tumors
No full textBreast cancer is the second leading cause of cancer related death amongst women and has a major impact on the lives of those affected by it. Previous research has uncovered that miRNAs, a type non-coding RNA, play a key role in the onset and development of breast tumors. Genomics, transcriptomics and proteomics studies have shed light on the intricate involvement of miRNA in mediating breast cancer progression. These small molecules are involved in many biological processes and we have yet to understand all the levels of complexity. Studying miRNA can lead to more efficient methods of screening and treatment for breast tumors while providing us with other key information from the molecular level. As confirmed by previous studies, miRNAs are good candidates for diagnostic as well as prognostic markers. This project takes a panoptic approach to studying miRNA in breast cancer genomics and proteomics and will strive to accomplish the following goals: Visualize miRNA expression data through heatmaps and identify sub-types. - We hypothesize that we will see a difference in expression of miRNAs across subtypes. Use heatmaps to examine miRNA expression across race. - We hypothesize that miRNA expression will vary across race. Perform PCA to deduce potential miRNA biomarkers for each subtype of breast cancer. - We hypothesize that we will find at least one unique biomarker for each subtype of breast cancer. Find the top 20 miRNA pairs with statistically significant correlation in expression. - We hypothesize that these pairs will have miRNAs from the same family. Study networks and pathways. - We hypothesize that we will find networks within subtypes of breast cancer. We also feel that miRNAs will be involved in more than one disease pathway. Data from The Cancer Genome Atlas (TCGA) data portal, which is an open source data portal open to the public, was utilized for the purposes of this project. The data is generated through miRNA-sequencing techniques with the aid of an Illumina Genome Analyzer. We used a combination of self-generated scripts in Perl and R and web based databases to filter, sort and analyze our data [1, 2]. On visual interpretation of our heatmaps we found different patterns of miRNA expression in the various sub-types of breast cancer. Patterns across race were not as significantly different upon visual interpretation but this may be attributed to data from a small cohort. Our PCA revealed potential biomarkers for each subtype of breast cancer. We suggest hsa-mir-127 and hsa-mir-379 as potential biomarkers of the basal subtype, hsa-mir-19a, hsa-mir126, hsa-mir-20a and hsa-mir-30a as potential biomarkers of the HER2 subtype, hsa-mir-222 as a potential biomarker of the Luminal A subtype and hsa-mir-152, hsa-mir-26b and hsa-mir-200c as potential biomarkers of the Luminal B subtype. By graphing these potential biomarkers across all subtypes we have visually confirmed our findings. We found 20 pairs of miRNAs with statistically significant correlation in expression and of those 6 were pairs that with miRNAs that are not related. We found miRNA-gene networks within two subtypes of breast cancer and generated a schematic to show the first layer of interaction. We used an online tool to generate another schematic that illuminates miRNA involvement in various pathways. We feel that our study has led to some interesting findings. We encourage future studies to further validate our proposed biomarkers as well as improve our methods. Our study lacked data from normal tissue samples; the inclusion of which we feel would have yielded more thorough results. In conclusion, we believe that computational methods of data analysis in studying miRNA expression data are truly powerful and the results of these methods will only get more accurate as more data is made available.Ph.D.Includes bibliographical referencesby Priyanka Bhat
International Network for Comparison of HIV Neutralization Assays: The NeutNet Report II
No full textAUTHORS: Leo Heyndrickx5*, Alan Heath4, Enas Sheik-Khalil3, Jose Alcami1, Vera Bongertz2, Marianne Jansson6,
Mauro Malnati7, David Montefiori8, Christiane Moog9, Lynn Morris10, Saladin Osmanov11,
Victoria Polonis12, Meghna Ramaswamy4, Quentin Sattentau13, Monica Tolazzi14,
Hanneke Schuitemaker15, Betty Willems5, Terri Wrin16, Eva Maria Fenyo¨ 3, Gabriella Scarlatti14 --- AFFILIATIONS - Unidad de Immunopatologia del SIDA, Instituto de Salud Carlos III, Madrid, Spain, 2 Laboratory of AIDS and Molecular Immunology, Fundac¸a˜o Oswaldo Cruz, Rio de
Janeiro, Brazil, 3 Department of Laboratory Medicine, University of Lund, Lund, Sweden, 4 National Institute for Biological Standards and Control, Potters Bar,
Hertfordshire, United Kingdom, 5 Virology Unit, Biomedical Department, Institute of Tropical Medicine, Antwerp, Belgium, 6 Department of Microbiology, Tumor and Cell
Biology, Karolinska Institutet, Stockholm, Sweden, 7 Unit of Human Virology, San Raffaele Scientific Institute, Milan, Italy, 8 Duke University Medical Center, Durham, North
Carolina, United States of America, 9 Pathoge´nie des infections persistantes, University Louis Pasteur, Strasbourg, France, 10 National Institute for Communicable
Diseases, Johannesburg, South Africa, 11 WHO-UNAIDS HIV Vaccine Initiative, World Health Organization, Geneva, Switzerland, 12 Department of Vaccine Research, Henry
Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland, United States of America, 13 The Sir William Dunn School of Pathology, The University
of Oxford, Oxford, United Kingdom, 14 Viral Evolution and Transmission Unit, San Raffaele Scientific Institute, Milan, Italy, 15 Department of Experimental Immunology,
Academic Medical Center at the University of Amsterdam, Amsterdam, The Netherlands, 16 Monogram Biosciences, San Francisco, California, United States of America.Background: Neutralizing antibodies provide markers for vaccine-induced protective immunity in many viral infections. By
analogy, HIV-1 neutralizing antibodies induced by immunization may well predict vaccine effectiveness. Assessment of
neutralizing antibodies is therefore of primary importance, but is hampered by the fact that we do not know which assay(s)
can provide measures of protective immunity. An international collaboration (NeutNet) involving 18 different laboratories
previously compared different assays using monoclonal antibodies (mAbs) and soluble CD4 (Phase I study)
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