Concept: Model organism
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 5 years ago
A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
The common non-steroidal anti-inflammatory drug ibuprofen has been associated with a reduced risk of some age-related pathologies. However, a general pro-longevity role for ibuprofen and its mechanistic basis remains unclear. Here we show that ibuprofen increased the lifespan of Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster, indicative of conserved eukaryotic longevity effects. Studies in yeast indicate that ibuprofen destabilizes the Tat2p permease and inhibits tryptophan uptake. Loss of Tat2p increased replicative lifespan (RLS), but ibuprofen did not increase RLS when Tat2p was stabilized or in an already long-lived strain background impaired for aromatic amino acid uptake. Concomitant with lifespan extension, ibuprofen moderately reduced cell size at birth, leading to a delay in the G1 phase of the cell cycle. Similar changes in cell cycle progression were evident in a large dataset of replicatively long-lived yeast deletion strains. These results point to fundamental cell cycle signatures linked with longevity, implicate aromatic amino acid import in aging and identify a largely safe drug that extends lifespan across different kingdoms of life.
Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.
Cervical cancer is one of the leading causes of cancer death in women worldwide. The causative agents of cervical cancers, high-risk human papillomaviruses (HPVs), cause cancer through the action of two oncoproteins, E6 and E7. The E6 oncoprotein cooperates with an E3 ubiquitin ligase (UBE3A) to target the p53 tumour suppressor and important polarity and junctional PDZ proteins for proteasomal degradation, activities that are believed to contribute towards malignancy. However, the causative link between degradation of PDZ proteins and E6-mediated malignancy is largely unknown. We have developed an in vivo model of HPV E6-mediated cellular transformation using the genetic model organism, Drosophila melanogaster. Co-expression of E6 and human UBE3A in wing and eye epithelia results in severe morphological abnormalities. Furthermore, E6, via its PDZ-binding motif and in cooperation with UBE3A, targets a suite of PDZ proteins that are conserved in human and Drosophila, including Magi, Dlg and Scribble. Similar to human epithelia, Drosophila Magi is a major degradation target. Magi overexpression rescues the cellular abnormalities caused by E6+UBE3A coexpression and this activity of Magi is PDZ domain-dependent. Drosophila p53 was not targeted by E6+UBE3A, and E6+UBE3A activity alone is not sufficient to induce tumorigenesis, which only occurs when E6+UBE3A are expressed in conjunction with activated/oncogenic forms of Ras or Notch. Finally, through a genetic screen we have identified the insulin receptor signaling pathway as being required for E6+UBE3A induced hyperplasia. Our results suggest a highly conserved mechanism of HPV E6 mediated cellular transformation, and establish a powerful genetic model to identify and understand the cellular mechanisms that underlie HPV E6-induced malignancy.
Intracellular triacylglycerol (TAG) is a ubiquitous energy storage lipid also involved in lipid homeostasis and signaling. Comparatively, little is known about TAG’s role in other cellular functions. Here we show a pro-longevity function of TAG in the budding yeast Saccharomyces cerevisiae. In yeast strains derived from natural and laboratory environments a correlation between high levels of TAG and longer chronological lifespan was observed. Increased TAG abundance through the deletion of TAG lipases prolonged chronological lifespan of laboratory strains, while diminishing TAG biosynthesis shortened lifespan without apparently affecting vegetative growth. TAG-mediated lifespan extension was independent of several other known stress response factors involved in chronological aging. Because both lifespan regulation and TAG metabolism are conserved, this cellular pro-longevity function of TAG may extend to other organisms.
SUMMARY: InterMine is an open-source data warehouse system that facilitates the building of databases with complex data integration requirements and a need for a fast, customisable query facility. Using InterMine, large biological databases can be created from a range of heterogeneous data sources, and the extensible data model allows for easy integration of new data types. The analysis tools include a flexible query builder, genomic region search, and a library of “widgets” performing various statistical analyses. The results can be exported in many commonly used formats. InterMine is a fully extensible framework where developers can add new tools and functionality. Additionally, there is a comprehensive set of web services, for which client libraries are provided in five commonly used programming languages. AVAILABILITY: Freely available from http://www.intermine.org under the LGPL license. CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.
Red yeast rice (RYR) is made by fermenting the yeast Monascus purpureus over rice. It is a source of natural red food colorants, a food garnish and a traditional medication. Results of the current study demonstrated that polar fractions of the RYR preparations contained herbal-drug interaction activity, which if left unremoved, enhanced P-glycoprotein activity and inhibited the major drug metabolizing cytochromes P450, i,e, CYP 1A2, 2C9 and 3A4. The data from Caco-2 cell absorption and animal model studies further demonstrated that the pharmacokinetic modulation effect by RYR preparations containing the polar fractions (“untreated” preparation) was greater than that from RYR preparations with the polar fractions removed (“treated” preparation). The data indicates a potential for herb-drug interactions to be present in RYR commonly sold as nutritional supplements when the polar fractions are not removed and this should be taken into consideration when RYR is consumed with medications, including verapamil.
Cephalopoda are a class of Mollusca species found in all the world’s oceans. They are an important model organism in neurobiology. Unfortunately, the lack of neuronal molecular sequences, such as ESTs, transcriptomic or genomic information, has limited the development of molecular neurobiology research in this unique model organism.
Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.