The ISMB 2016 (Intelligent Systems for Molecular Biology) will take place July 8-12 in Orlando, Florida. The poster session chair is Iddo Friedberg, BCB faculty member in the VMPM Department at Iowa State.
Both of the Conference Chairs for this year's conference have taken part in BCB sponsored symposia at Iowa State. Teresa Przytycka, Conference Co-chair, Head, Algorithmic Methods in Computational and Systems Biology Section NCBI /NLM/ NIH, Bethesda, and Pierre Baldi, Conference Co-chair, Department of Computer Science, University of California, Irvine.
Six BCB students will be presenting posters and one of them, Zeb Arendsee in Eve Wurtele's lab will present a talk as well. Here are their poster abstracts:
Lab of Eve Wurtele
Genetics, Development and Cell Biology Department
B11 - The evolutionary origin of orphan genes
- Zebulun Arendsee, Iowa State University, United States of America
- Eve Syrkin Wurtele, Iowa State University, United States of America
Short Abstract: Many of the most powerful tools in biology rely on inference of homologs via sequence-based algorithms. However, many loci are invisible to such methods. Those that are short or rapidly evolving, such as orphan genes and small non-coding RNAs, may yield no significant hits. Whereas low-complexity or high-copy number loci may hide in a crowd of false positives. Searching by context bypasses this problem. We present an algorithm for tracing loci between genomes using a synteny map, and test its efficacy by mapping all Arabidopsis thaliana-specific genes to the genomes of eight related species. By reducing the search space and winnowing false positives, we were able to assess the origin of the individual orphan genes with unprecedented resolution. We traced many to their non-genic cousins, identifying the non-genic footprint from which they arose. We linked others to putative genes in related species from which they diverged beyond recognition. Knowing the approximate location of each gene across species also provides a starting point for future studies. Our pipeline can easily be adapted to contextualize elusive elements such as small RNAs and lineage-specific genes in any species for which reliable synteny maps can be built.
Lab of Julie Dickerson
Electrical and Computer Engineering Department
G17 - Integration of Genomic and Transcriptomic Data for Metabolic Evolution Experiments in E. coli
- Erin Boggess, Iowa State University, United States
- Yingxi Chen, Iowa State University, United States
- Laura Jarboe, Iowa State University, United States
- Julie Dickerson, Iowa State University, United States
Short Abstract: In microbial engineering, metabolic evolution is an essential method for developing organisms with a desired phenotype such as tolerance or product yield. In an evolution experiment, organisms with advantageous phenotypes emerge under strong selective pressure and displace the parent strain in a population. Mutations in the evolved strains are credited with improved fitness. This method generates a strain with a desired phenotype, but understanding how genomic variations relate to fitness requires further investigation.
Evolved strains can contain numerous mutations of which only some demonstrate phenotypic changes and may be relevant to fitness. Genomic sequencing identifies mutations, but interpreting variations in the context of larger biological systems remains a challenge. Our previous work produced a pipeline for mutation analysis that leverages public E. coli databases and computational tools such as structure prediction software. Mutations in coding regions and extragenic regulatory sites are analyzed and then visualized on an integrated gene regulatory and metabolic network to investigate their relationships and relevance to metabolic pathways.
Mutations affecting regulators can be difficult to interpret without additional information. Incorporating their entire regulons without into the network can introduce numerous nodes and impede analysis. To better interpret such cases, associated transcriptomic experiments can provide insight into the implications of genomic variations.
Here, we demonstrate the integration of genomic data from an E. coli evolution study for improved octanoic acid tolerance and associated RNA-seq experiments for the parent and evolved strains. The additional transcriptomic data reveals the impact mutations in regulators have on genes in associated regulons.
Lab of Robert Jernigan
Biochemistry, Biophysics and Molecular Biology Department
Title : Studying Phenotypic impact of non-synonymous single nucleotide variants in LOC_ Os05g26040 and LOC_ Os05g27960 in Oryza sativa.
The prime objective of the study is finding phenotypic consequences of
single nucleotide variants in the coding region of LOC_ Os05g26040 and
LOC_ Os05g27960 with Oryza sativa japonica as species of interest.
LOC_ Os05g27960 encodes for Endo Beta N-Acetylglucosaminidase, enzyme
exhibiting hydrolase activity, whereas Pumilio-family RNA binding
protein is predicted as a conserved domain in coding regions of
LOC_Os05g26040. Pumilio-family RNA-binding proteins play a role in
controlling gene expression at the post-transcriptional level by
promoting RNA decay and repressing translation in other organisms
including humans, yeasts and plants like Arabidopsis.
We implemented SIFT sequence, PANTHER and I-mutant tools to classify
the single nucleotide variants lying in the coding regions of both the
loci. Further, the impact of detrimental non-synonymous variants was
studied on translated protein product of LOC_Os05g26040.The
theoretical model of Pumilio-family RNA-binding protein in
LOC_Os05g26040 was modelled via homology modelling. Further, single
point mutations were induced in modelled protein product and
comparative docking studies were carried out in native as well as
mutation-induced protein structures.
In LOC_ Os05g27960, we determined two nonsense variants in the nucleotide regions coding for hydrolase domain, which suggests their presence leads to truncated protein product, finally leading todysfunctional hydrolase . The detrimental non-synonymous SNPs (Singlenucleotide Polymorphism) located in the Pumilio protein encoded by LOC_Os05g26040 lead to changes in binding energies with RNA at the binding site. This can be considered as one of the key consequences of the presence of the variants in the RNA-binding domain. Further insights into how the mutations affect were determined based on structural features. Previous studies suggest Pumilio protein participate in cell development and differentiation in other organisms including lower vertebrates and invertebrates. Hence, the variants affecting its RNA binding activity pose a potential to hinder cell development. Studying the impact of these variants by experimental studies will be useful to study to find out if their presence is harmful to the crops. In addition, the computational pipeline devised during the study can be utilised to determine phenotypic consequences of SNPs in organisms where tools have yet not been developed for phenotypic characterization of single nucleotide variants.
Lab of Susan Lamont
Animal Science Department
- John Hsieh, Iowa State University, United States of America
- James Cornette, Iowa State University, United States of America
- Carolyn Lawrence-Dill, Iowa State University, United States of America
Short Abstract: The Moore Method was originally developed by R.L. Moore to teach advanced mathematics in the college setting. There have been many adaptations of the Moore Method, under the broad term Modified Moore Method (M3), which are now classified as a variant of inquiry based learning (IBL). Despite the growing popularity of M3, it is rarely applied beyond mathematics. At Iowa State University, we designed and taught an “Introduction to Bioinformatics” undergraduate course using M3 for the first time during Fall semester 2015. The class size was small (n=12), and students all had a background in the natural sciences, most in the biological sciences. Students had little to no formal training in computational sciences. During the 16-week course, students learned to: 1) work on a remote Linux server, 2) read and write Python code, 3) tackle classic bioinformatics problems, and 4) solve current bioinformatics problems with available tools. As with all M3 courses, learning objectives were met through carefully designed questions given to students prior to each class session. Class sessions were completely led by students (i.e., reversed classroom) presenting solution to the assigned questions. The application of M3 to our course has led to several desirable student outcomes: 1) engagement and ownership of the course material, 2) development of a strong sense of community, and 3) uniform learning outcomes. One of the difficulties we experienced with applying M3 was the creation of the course material. It was tough to create questions that were challenging enough without overwhelming the students.
Lab of Robert Jernigan
Biochemistry, Biophysics and Molecular Biology Department
Title: Changes to Dynamics upon Oligomerization Identify Key Functional Protein Sites
Summary: Oligomerization is the assembly of protein subunits to form a complex functional biological macromolecule, an oligomer. It is one of the fundamental means through which nature equips proteins with the ability to perform complex functions and attain greater stability. Oligomers can exist either as an assemblage of identical blocks of proteins, homooligomers or can form a mosaic of heterogenous subunits termed heterooligomers. In this study, we investigate the dynamic effect of oligomerization and its functional significance on a set of 145 diverse homooligomeric proteins.
We employ Elastic Network Model to inspect the change in residue fluctuations upon oligomerization and then couple it with residue conservation score to understand the functional significance of regions with altered dynamics. The study here reveals the importance of sites with dampened fluctuations post oligomerization. These sites can be located either in the interface or in the non-interface regions of the oligomeric assembly and can harbor key functional residues. A case study on the bovine glutamate dehydrogenase further confirms that these residues can serve as orthosteric ligand binding sites. This study introduces a novel approach for identifying functional residues in homooligomeric proteins which can further be investigated as potential drug targets.
Lab of Roger Wise
Plant Pathology and Microbiology
Title: Differential expression of powdery mildew candidate effectors on barley loss-of-function mutant hosts
Summary: Obligate fungal pathogens (e.g., rusts and mildews) are a major threat to grain production worldwide. Because they are unable to survive autonomously, obligate parasites represent an ideal class of associations to explore interdependent signaling between disease agents and their hosts. This project focuses on the well-characterized barley-powdery mildew pathosystem, where the outcome is determined largely by the plant’s response to secreted fungal effectors. Using next generation sequencing, we have identified candidate effectors that promote pathogenesis by manipulating host metabolism and immunity.
Here is information on the ISMB 2016 conference organizers:
Teresa Przytycka, Conference Co-chair, Head, Algorithmic Methods in Computational and Systems Biology Section NCBI /NLM/ NIH, Bethesda, United States
Teresa Przytycka is a Senior Investigator at the National Center for Biotechnology Information, National Institutes of Health where she heads Algorithmic Methods in Computational and Systems Biology research group. Her research focuses on dynamical properties of biological systems including spatial, temporal and/or contextual variation and exploring how such variations are impacting gene expression, how they affect network topology, function, and the phenotype of the organism. In particular, her group strives to understand the emergence of complex disease phenotypes such as cancer, to identify molecular pathways dys-regulated in cancer, and to develop computational methods for detecting causal genetic mutations, and their interactions. She is also working on understanding the role of alternative DNA structures for gene regulation and developing methods for analysis HT SELEX data.
Dr. Przytycka is the bioinformatics section head of Nature Molecular Therapy Nucleic Acids and an associated editor of several journals including PloS Computational Biology, BMC Bioinformatics, and IEEE Transactions on Computational Biology and Bioinformatics.
Pierre Baldi, Conference Co-chair, Department of Computer Science, University of California, Irvine, United States
Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Chancellor's Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has pioneered the development and application of deep learning methods to problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the prediction of protein structures and gene regulatory mechanisms in bioinformatics. He is and Elected Fellow of the ISCB.