Genomic Expression Responses to DNA-damaging Agents and the Regulatory Role of the Yeast ATR Homolog Mec1p

Gasch, Audrey P.; Huang, Mingxia; Metzner, Sandra I.; Botstein, David; Elledge, Stephen J.; Brown, Patrick O.

doi:10.1091/mbc.12.10.2987

Cited by 504 publications

(602 citation statements)

References 68 publications

Supporting

Mentioning

542

Contrasting

Unclassified

Order By: Relevance

“…A published microarray dataset, investigating the transcriptomic response of yeast to DNA damage [5] was chosen as the basis of the workshops, since the conservation of DNA repair machinery across phyla allows the data analysis to be easily extended to look at orthologous sequences in other organisms, and to link to human diseases, ensuring a broad appeal across the class. The raw data is freely available via web download, and was provided to the students to allow them to carry out their own analyses on real research data.…”

Section: Workhop Content and Structurementioning

confidence: 99%

“…Downstream of the phospho-inositol kinase-related kinases are two classes of checkpoint kinases, including CHK1 and CHK2 in mammals and Chk1p and Rad53p in yeast. An additional kinase in yeast, named Dun1p, acts downstream of Rad53p and is involved in both cell-cycle arrest and transcriptional regulation in the DNA damage response [5]. Mutations in components of the ATR/Mec1 pathways result in hypersensitivity to DNA-damaging agents and, in higher organisms, predisposition to cancer [7,8].…”

Section: Biological Backgroundmentioning

confidence: 99%

“…However, little is known of the transcriptional control exerted by the Mec1 kinase. In the study underpinning the computer workshops [5], DNA microarrays were used to characterize the genomic expression programs in wildtype and mec1 mutant cells responding to two different DNA-damaging agents: the methylating agent methylmethane sulfonate (MMS) and ionizing (g) radiation. MMS and ionizing radiation inflict different types of DNA damage by distinct mechanisms, so the study investigated whether each type of DNA damage agent triggered a specific transcriptional response, and, using mec1 knockout mutants, whether those responses were Mec1-dependent [5].…”

Section: Biological Backgroundmentioning

confidence: 99%

“…Without being provided with the Gasch et al publication, [5] the students were then given the broad framework of three central research questions: (i) Does gamma radiation cause a similar transcriptional response to another DNA damaging agent, MMS? (ii) Which genes are induced, which repressed, and are DNA damage response genes induced?…”

Section: Biological Backgroundmentioning

confidence: 99%

“…During the functional genomics lecture series students were introduced to the yeast Saccharomyces cerevisiae as a model organism for functional genomics. The follow-on practical classes comprise 4 two-hour workshops and derive their starting material from a published S. cerevisiae microarray study investigating transcriptomic responses to DNA damage agents [5]. The workshops, described below, are carried out in computer teaching labs over 4 consecutive weeks, with a final report submitted two weeks later.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Self‐directed student research through analysis of microarray datasets: A computer‐based functional genomics practical class for masters‐level students

Grenville‐Briggs

Stansfield

2011

Biochem Molecular Bio Educ

View full text Add to dashboard Cite

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate active learning through experience of current research methods in bioinformatics and functional genomics. They seek to closely mimic a realistic research environment, and require the students first to propose research hypotheses, then test those hypotheses using specific sections of the microarray dataset. The complexity of the microarray data provides students with the freedom to propose their own unique hypotheses, tested using appropriate sections of the microarray data. This research latitude was highly regarded by students and is a strength of this practical. In addition, the focus on DNA damage by radiation and mutagenic chemicals allows them to place their results in a human medical context, and successfully sparks broad interest in the subject material. In evaluation, 79% of students scored the practical workshops on a five-point scale as 4 or 5 (totally effective) for student learning. More broadly, the general use of microarray data as a ''student research playground'' is also discussed.Keywords: Saccharomyces cerevisiae, DNA damage response, transcript profiling, microarray, bioinformatics.It is generally accepted that students learn best through inquiry-based approaches, (active learning) and this is particularly true in science courses. Actively constructing knowledge from a combination of experiences, interactions and interpretations optimizes the acquisition of an expert understanding of scientific concepts [1]. Therefore, it is not surprising that social constructivism is the dominant learning theory in science education [2]; the investigative nature of science lends itself well to such effective teaching and learning strategies. Science research now uses a vast array of technology and it is both desirable, and increasingly possible, to incorporate the use of such technology into a University classroom setting to provide methodological training. This allows students to interact with information in new ways, to create their own visualizations and understanding of scientific data. We have been guided by these core principles in the design of a new series of computer workshops centred on functional genomics and systems biology. The workshops, which analyse microarray data, crucially have a strong research theme running through them, taking advantage of the vast information resource represented by transcriptomic datasets to allow students to propose, then test, their own hypotheses. Students thus actively construct knowledge from a self-directed investigation, applying a range of research tools to their own experiments.Systems biology is a relatively new discipline, in which mathematical modeling of biological systems is used as a tool to understand the structure...

show abstract

Section: Workhop Content and Structurementioning

confidence: 99%