172 research outputs found
Aplip1 - a potential scaffold protein of Drosophila melanogaster may be involved in facilitating signal transduction
No full textEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Integration of probabilistic functional networks without an external Gold Standard
No full textBackground: 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
Deep sequencing approaches for the analysis of prokaryotic transcriptional boundaries and dynamics
No full textThe 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
Preliminary investigation of the effects of long-term dietary intake of genistein and daidzein on hepatic histopathology and biochemistry in domestic cats (Felis catus)
No full textDietary isoflavones have been hypothesised to play a role in hepatic veno-occlusive disease in captive exotic felids, although empirical evidence is lacking. This study aimed to investigate the effect of long-term (>1 year) dietary genistein and daidzein exposure on the hepatic biochemistry and histology of domestic cats. Individual cats were assessed for hepatic enzyme and bile acid production before and after the removal of isoflavones from their diet in the treatment group (n=4), and at the same times in unexposed control animals (n=7). No significant differences were detectable in hepatic biochemistry between treatment and control groups, and all serum values were within the normal reference ranges for domestic cats. Additionally, treatment animals demonstrated slightly greater areas of fibrosis surrounding hepatic venules than control animals, but this difference was not statistically significant. On the basis of the results presented, dietary isoflavones, at the current dose and duration of exposure do not appear to modulate hepatic enzyme production or histological parameters
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Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking
No full textDue to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation.
A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered 'valid' by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described.
http://bioinformatics.leeds.ac.uk/~bmb4sjc.
Supplementary data are available at Bioinformatics online
Misincorporation by RNA polymerase is a major source of transcription pausingin vivo
No full textThe transcription error rate estimated from mistakes in end product RNAs is 10−3–10−5. We analyzed the fidelity of nascent RNAs from all actively transcribing elongation complexes (ECs) in Escherichia coli and Saccharomyces cerevisiae and found that 1–3% of all ECs in wild-type cells, and 5–7% 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
Structure-based evaluation of in silico predictions of protein–protein interactions using Comparative Docking
No full textDetection of biosignatures on Mars using Raman spectroscopy: expectations and limits
No full textThe well-established presence of liquid water on the surface of Mars during the Noachian and early Hesperian periods signifies that the planet was partly habitable [1,2]. Liquid water disappeared from the surface of Mars ca. 3.5 billion years ago [2]. Nevertheless, contrary to the Early Earth, which was covered by a global ocean, Mars was probably only patchily covered by lakes or seas, not necessary connected to each other. Consequently, life may have appeared and disappeared on one, or several, place(s) without colonizing the whole planet; hence the concept of punctuated spatial and temporal habitability proposed by Westall et al (2015) [2]. In addition to making landing site selection crucial, this specificity of Mars could have limited the evolution of life to very simple microorganisms.
Lack of oxygen in the atmosphere and, therefore, of an ozone layer, meant that its surface has been continuously exposed to high-energy UV radiation (down to 190 nm wavelength), deleterious for organics and responsible for the formation of hydrogen peroxide [1,3]. In the absence of a magnetic field, it is also exposed to the solar and galactic cosmic rays that reach the surface and the near subsurface [4]. In addition to making the surface of Mars presently highly inhospitable, this radiative environment may have altered inorganic and organic biosignatures with time.
Here we present the results of experiments to study the preservation of different types of representative biosignatures and their detectability using Raman spectroscopy under Mars conditions. This method, able to analyse both organic and mineral phases and compatible with in situ exploration, is particularly relevant to search for past or present traces of life and equipped the current NASA Mars 2020 and future ESA ExoMars rovers.
For ancient traces of life, we used terrestrial Precambrian microfossils as a reference. We showed that their biogenecity is difficult to demonstrate unambiguously using Raman spectroscopy alone and that such traces of life would be probably too subtle to be detected in situ on Mars [5,6].
For recent traces of life, where biomolecules have not been transformed into kerogen with time and metamorphism, we performed experiments in the laboratories where we studied the degradation of the Raman signal of different pigments and molecules during UV and cosmic rays irradiation. In particular, an original Raman system, called RAMSESS (RAMan SpEctroscopy for in Situ Studies), was developed at CNRS CEMHTI-Pelletron, Orléans, France, to study the changes in the Raman signal of different minerals and organic molecules in situ within the irradiation chamber (see Fig. 1) [7]. These experiments are very complementary to those obtained during the BIOMEX experiment on-board the ISS [8,9].
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[2] F. Westall, F. Foucher, N. Bost, M. Bertrand, D. Loizeau, J. L. Vago, G. Kminek, F. Gaboyer, K. A. Campbell, J.-G. Bréhéret, P. Gautret and C. S. Cockell, Astrobiology, 15, 998 (2015).
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[4] G. de Angelis, M. S. Clowdsley, R. C. Singleterry and J. W. Wilson, Advances in Space Research, 34, 1328 (2004).
[5] F. Foucher and F. Westall, Astrobiology, 13:1, 57 (2013).
[6] F. Foucher, M. R. Ammar and F. Westall, Journal of Raman Spectroscopy, 46, 873 (2015).
[7] A. Canizarès, F. Foucher, M. Baqué, J.-P. de Vera, T. Sauvage, O. Wendling, A. Bellamy, P. Sigot, T. Georgelin, P. Simon and F. Westall, Applied Spectroscopy, 76, 723 (2022).
[8] J.-P. de Vera, M. Alawi, T. Backhaus, M. Baqué, D. Billi, U. Böttger, T. Berger, M. Bohmeier, C. Cockell, R. Demets, R. de la Torre Noetzel, H. Edwards, A. Elsaesser, C. Fagliarone, A. Fiedler, B. Foing, F. Foucher, J. Fritz, F. Hanke, T. Herzog, G. Horneck, H.-W. Hübers, B. Huwe, J. Joshi, N. Kozyrovska, M. Kruchten, P. Lasch, N. Lee, S. Leuko, T. Leya, A. Lorek, J. Martinez-Frias, J. Meessen, S. Moritz, R. Moeller, K. Olsson-Francis, S. Onofri, S. Ott, C. Pacelli, O. Podolich, E. Rabbow, G. Reitz, P. Rettberg, O. Reva, L. Rothschild, L. G. Sancho, D. Schulze-Makuch, L. Selbmann, P. Serrano, U. Szewzyk, C. Verseux, J. Wadsworth, D. Wagner, F. Westall, D. Wolter and L., Zucconi, Astrobiology, 19:2, 145 (2019).
[9] M. Baqué, T. Backhaus, J. Meeßen, F. Hanke, U. Böttger, N. Ramkissoon, K. Olsson-Francis, M. Baumgärtner, D. Billi, A. Cassaro, R. de la Torre Noetzel, R. Demets, H. Edwards, P. Ehrenfreund, A. Elsaesser, B. Foing, F. Foucher, B. Huwe, J. Joshi, N. Kozyrovska, P. Lasch, N. Lee, S. Leuko, S. Onofri, S. Ott, C. Pacelli, E. Rabbow, L. Rothschild, D. Schulze-Makuch, L. Selbmann, P. Serrano, U. Szewzyk, C. Verseux, D. Wagner, F. Westall, L. Zucconi and J.-P. P. de Vera., Science Advances, 8, 7412 (2022
An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.
No full textDrug 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
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