Leading Professional Society for Computational Biology and Bioinformatics
Connecting, Training, Empowering, Worldwide

banner

Genotype to Phenotype in Model and Non-Model Organisms

Schedule subject to change.
All times in Central Daylight Time (CDT)
Wednesday, May 12th
9:00-10:00
Keynote: Methodological advancements to improve metagenomics for surveillance of antimicrobial resistance
  • Noelle Noyes

Presentation Overview: Show

Antimicrobial resistance (AMR) is a global public health concern with complex microbial ecological and evolutionary underpinnings. Metagenomics enables comprehensive profiling of antimicrobial resistance genes (the resistome), allowing us to characterize the microbiome-wide processes that support development and persistence of AMR. Metagenomics also has the potential to support improved surveillance and tracking of AMR. However, these advanced applications rely on highly resolved and contextualized resistome data, which is not fully supported by current molecular and bioinformatic approaches. In this talk, I will detail methods that we are developing to generate more accurate and useful resistome data for AMR surveillance

10:00-10:45
Integrated investigation into the molecular pathophysiology of equine metabolic syndrome
  • Molly McCue, University of Minnesota, College of Veterinary Medicine, United States

Presentation Overview: Show

Equine metabolic syndrome (EMS) is a unique animal model if metabolic disturbances in humans and a significant health and welfare concern for horses. Despite the clinical manifestations of this disease being described nearly 25 years ago understanding of EMS is limited. Our rigorous assessment of EMS-associated clinical pathophysiologic traits in >900 horses has highlighted the need to unravel EMS pathophysiology at the molecular level through the exploration and integration of genomic, transcriptomic, metabolomic and environmental data.
Methods. Our cohort of >900 horses has been phenotyped clinically with neck- and girth-to-height ratios (measures of adiposity), fasting blood glucose, insulin, ACTH, triglyceride, NEFA, leptin and adiponectin concentrations, insulin and glucose 75 min post oral-sugar test, and for laminitis history an indicator of microvascular dysfunction and damage. Genomic data from 1.9 million genotypes from 286 Morgans and 264 Welsh ponies has been used for heritability estimation and genome-wide association. Transcriptomic data includes RNA sequencing of skeletal muscle and adipose tissue from 84 horses to asses differential gene expression and build co-expression networks. Metabolomic data including gas- and liquid-chromatography with mass spectrometry of pre- and post-OST serum from ~900 horses is being used to identify differences in metabolite abundance. Both transcriptomic and genomic data are being integrated with genomic data to narrow regions of association form GWAS and identify likely candidate genes. Environmental 2,3,7,8-TCDD toxic equivalents and 17ß-estradiol equivalents in plasma from 301 horses have been evaluated to determine the impact of persistent organic pollutants on EMS clinical phenotypes.
Key Findings and Implications. Heritability of EMS phenotypes ranged from ~0.3 (triglycerides) to ~0.9 (adiponectin) and GWAS identified ~2,000 positional candidate genes. >400 and >700 genes were differentially expressed in muscle and adipose tissue in hyperinsulinemic compared to normoinsulinemic animals. ≥150 metabolites had differing abundance in hyperinsulinemic, obese and/or laminitic horses. Integration of both transcriptomic and metabolomic data have narrowed GWAS regions of association and pointed to biologic candidate genes for further investigation. Plasma organic pollutants explained some of the variability in EMS phenotypes. Our integrated approach demonstrates that EMS impacts multiple tissues/metabolic processes, is a complex disorder influenced by genetics and environment and that integration across data types can guide allele discovery.

11:00-11:30
Physiological genomics of high-altitude adaptation in deer mice
  • Zac Cheviron, University of Montana, United States

Presentation Overview: Show

Evolutionary adaptation to novel environments often requires coordinated changes in multiple independent, but interacting, physiological systems. For example, reductions in barometric pressure and temperature at high elevation place severe constraints on the aerobic capacities of animals. Surmounting these challenges requires modifications to several physiological processes, and many high-elevation specialists have evolved convergent adaptations that improve oxygen delivery to and alter its consumption in respiring tissues. One of the best studied examples are deer mice (Peromyscus maniculatus), a widespread rodent with broadest elevation distribution of any North American mammal. High-elevation deer mice have evolved a suite of physiological adaptations that markedly improve whole-organism aerobic performance under hypoxia, a trait that is known to influence survival at high-elevation. While the physiological basis of high-altitude adaptation is well-characterized in this system, the genetic bases of these trait differences are not well-understood. Here, I will summarize the progress that my group has made on this front. I will highlight examples of studies that combine functional and evolutionary genomic approaches to understand both the regulatory basis of acclimatization responses, and test to whether genes associated with known adaptive phenotypes have experienced a history of natural selection at high-elevation. The results of this work not only shed light on the genetic basis of adaptive traits, but are also beginning to help form new hypotheses about the physiology of high-altitude adaptation, highlighting the reciprocal feedback and synergy of combined genomic and physiological perspectives.

11:30-11:45
A reference map for genetic interactions in a human cell
  • Chad Myers, Department of Computer Science and Engineering, University of Minnesota, United States
  • Mahfuzur Rahman, Department of Computer Science and Engineering, University of Minnesota, United States
  • Maximilian Billmann, Department of Computer Science and Engineering, University of Minnesota, United States
  • Michael Aregger, The Donnelly Centre, University of Toronto, Canada
  • Michael Costanzo, The Donnelly Centre, University of Toronto, Canada
  • Charles Boone, The Donnelly Centre, University of Toronto, Canada
  • Jason Moffat, The Donnelly Centre, University of Toronto, Canada
  • Catherine Ross, The Donnelly Centre, University of Toronto, Canada
  • Amy Tong, The Donnelly Centre, University of Toronto, Canada
  • Katherine Chan, The Donnelly Centre, University of Toronto, Canada
  • Henry Ward, Graduate Program in Bioinformatics and Computational Biology, University of Minnesota, United States
  • Matej Usaj, The Donnelly Centre, University of Toronto, Canada
  • Kevin Brown, The Donnelly Centre, University of Toronto, Canada
  • Brenda Andrews, The Donnelly Centre, University of Toronto, Canada

Presentation Overview: Show

A major focus of systems biology and genomic medicine is to link genotype to phenotype, yet accurately predicting disease states from genome sequence remains a major challenge. Genetic interaction networks in model organisms, principally yeast, have revealed how combinations of genome variants can impact phenotypes, and highlighted the importance of reference genetic networks for understanding gene function. We have used lessons learned from yeast to systematically map genome-wide genetic interactions using CRISPR/Cas9 in human cells. We performed 180 genome-wide screens using HAP1 query cell lines carrying loss-of-function mutations in genes in diverse bioprocesses, along with more than 30 screens in wildtype (wt) HAP1 cells to be used as a basis for robust scoring of genetic interactions. Overall, we screened more than 3 million unique gene pairs for interactions, representing the largest effort to date to study double mutant phenotypes in isogenic human cells. We developed a computational pipeline to identify quantitative genetic interactions (qGI) from these data. In total, we mapped approximately 43k negative (1.3% density) and 39k positive interactions (1.2% density) among the screened gene pairs. We identified several unexpected statistical artifacts in loss-of-function screens including frequent interactions caused by variation between wt HAP1 screens and potential clonal effects of HAP1 cells harboring a loss-of-function mutation. We describe statistical elements of the qGI scoring pipeline designed to normalize these effects and insights we gained about interpreting phenotypes from CRISPR/Cas9 screens in the process. We also describe what we have learned about the topology of negative and positive genetic interactions in human cells, the power of genetic interaction profiles to define gene function across the genome, and their connections to other types of functional relationships, many of which are conserved from yeast to human cells. In summary, we performed a large number of genome-wide CRISPR/Cas9 screens in specific genetic backgrounds and developed a computational pipeline that will guide the generation of a genome-wide reference genetic interaction network in human cells.

11:45-12:45
Lunch Break & Poster Hall
12:45-13:45
EDI Panel Discussion
13:45-14:10
Scalable Tensor Completion Algorithms for Learning Multi-way Associations across Biological Networks
  • Rui Kuang, University of Minnesota, United States

Presentation Overview: Show

nferring multi-way associations among the objects across multiple biological networks is a challenging high-order learning problem for bioinformatics applications such as multiple network alignment and multi-relational link prediction. Most existing methods rely on heuristic reconstruction of multi-way associations based on bipartite relations. In this talk, I will introduce our approach of modeling and predicting multi-way associations with new tensor completion algorithms. Our algorithms capture the high-order topological characteristics in the biological networks by manifold regularization with the graph Laplacian of a (Cartesian, tensor or strong) product of the networks, and then combine the product graph regularization with tensor completion to predict high-order structures. We first introduce Graph-Regularized Tensor Completion from Observed Pairwise Relations (GT-COPR) algorithm to predict disease-gene-chemical multi-relations based on protein-protein interactions network, chemical structural similarity network and phenotype-based human disease network, and the observed bipartite relations between them. Second, we also introduce Fast Imputation of Spatially-resolved transcriptomes with graph-regularized Tensor completion (FIST). FIST focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x,y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Experimental results on Comparative Toxicogenomics Database (CTD), Genomics of Drug Sensitivity in Cancer cell line dataset and ten 10x Genomics Visium spatial genomics datasets demonstrate the advantage of modeling high-order relations and data and the importance for the bioinformatics applications.

14:10-14:30
Using weakly electric fish to understand the evolution of convergently evolved, novel phenotypic traits
  • Jason Gallant, Montana State University, United States

Presentation Overview: Show

In Origin of Species, Darwin considered the convergent evolution of electric fishes as a special difficulty with his theory of Natural Selection, opining that ‘it is impossible to conceive the steps by which these wondrous organs have evolved’. Approximately 100 years later, biologists became aware of two independent radiations of electric fish species from South America and Africa that exhibit phenotypic convergence on nearly every level of biological organization. In this seminar, Dr. Gallant will give an overview of his laboratory’s research efforts to develop genomic resources and functional tools to understand the convergent evolution of electric fish and ongoing efforts to determine the proximate and ultimate causes of convergent electric signal evolution among African electric fish.

14:45-15:00
Mutations in bdcA and valS correlate with quinolone resistance in wastewater Escherichia Coli
  • Michael Schroeder, TU Dresden, Germany
  • Negin Malekian Boroujeni, TU Dresden, Germany

Presentation Overview: Show

To effectively combat antibiotic resistance, a detailed understanding of the processes driving the emergence of resistance is vital. When quinolones were introduced in the sixties, it took over a decade to identify their targets and mechanism of action and to unveil mutations in gyrA and parC as a cause for quinolone resistance.
Here, we show how a hypothesis-free, high-throughput approach based on deep sequencing and genome-wide association (GWAS) to resistance can pinpoint resistance-conferring mutations. We develop a tailored bacterial GWAS model, which takes population stratification into account. To maximise the diversity of genomes, we apply the model to E. coli from wastewater, which combines environmental, industrial, and human sources.
From around 100 wastewater E. coli genomes, we identified over 200,000 high-quality genomic variants. Among these variants, we found 13, which correlate very highly with quinolone resistance. Three of them are the known quinolone resistance-conferring mutations in gyrA and parC. The other ten variants appear in new candidate resistance genes including the biofilm dispersal gene bdcA and valS, which plays a key role in translation. Both processes can be connected to resistance formation. In bdcA, the mutation is in proximity to the active site and could hence impact the gene products' efficiency. The gene valS harbours synonymous mutations, which may have an indirect effect on valS function and resistance.
In summary, we demonstrate that GWAS effectively and comprehensively identifies resistance mutations without a priori knowledge of targets and mode of action and using data from a single site only. The approach recovers gyrA and parC mutations as main sources of quinolone resistance, which are complemented by novel candidate resistance mutations in bdcA and valS. More studies are needed to sustain the connection and to validate the mechanism of action.

15:00-15:20
Gene expression and physiological signatures of thermal stress, heterotrophy, and symbiosis in the facultatively symbiotic coral, Oculina arbuscula
  • Hanny Rivera, Woods Hole Oceanographic Institution, United States

Presentation Overview: Show

Understanding the molecular mechanisms that sustain a healthy symbiosis between corals and their algal endosymbiont is important given the impacts of temperature-driven coral bleaching events, where symbionts are lost and coral face widespread mortality. Here, we investigate the links between symbiotic state, heterotrophy, thermal stress, and immunity using the facultatively symbiotic and calcifying coral Oculina arbuscula. By using a facultatively symbiotic coral we can investigate molecular networks that regulate symbiosis in the absence of a stress response and then elucidate how these networks are modulated by heterotrophy and thermal stress. Using RNA-Seq, we first compared gene expression profiles between aposymbiotic and symbiotic branches of the same O. arbuscula colonies under baseline conditions. We find that many of the previously implicated pathways identified in studies using bleached corals, aposymbiotic larvae, or model systems, such as Aiptasia, are also differentially regulated in O. arbuscula tissues under non-stress conditions. We also compared conserved differences in regulation across symbiotic states in other taxa including sponges and salamanders. We further explored the potential mechanisms underlying the mitigation of bleaching under increased heterotrophy. We conducted a combined feeding and thermal stress experiment to examine how gene expression and host and symbiont physiology respond. Combined, our results provide new insights into the interactions between immunity, heterotrophy, and thermal stress, and symbiotic state.

15:20-15:40
Bridging the gap between metallomics & bioinformatics to study the nutritional economy of the coral holobiont
  • Hannah Reich, University of Rhode Island, United States

Presentation Overview: Show

The upkeep of the coral holobiont (host coral, dinoflagellate endosymbiont, endolithic algae, fungi, viruses, bacterial communities) is paramount to coral health and maintaining the stability of tropical marine ecosystems. Though the identity of the endosymbiotic dinoflagellate is critical to modulating the physiological capacity of these partnerships, they are also sensitive to environmental fluctuations, which further alter physiological performance. Trace metal (i.e., micronutrient) deficiency can result in marked differences in coral-symbiont growth and physiology but these responses vary substantially between and within species. In this talk, I integrate the study of micronutrient uptake (metallomics) and gene expression to better understand interspecific variation of endosymbiotic dinoflagellates. The identification of transcripts associated with micronutrient deficiency and transport allows for the creation of molecular micronutrient uptake estimations. The application of molecular micronutrient uptake estimations will allow for additional detection of micronutrient deficiency that may have been overlooked in previous studies.

15:40-16:30
Networking
Thursday, May 13th
10:00-10:25
Leveraging proteomics as a hypothesis generating, functional genomics tool in non-model organism biology
  • Michelle Heck, Cornell University, United States

Presentation Overview: Show

The vast majority of plant and animal viruses are transmitted by insect vectors with many crucial aspects of the transmission process being mediated by key protein-protein interactions. Yet, very few vector proteins interacting with virus have been identified and functionally characterized because insect vectors are non-model organisms and not tractable for molecular genetics research approaches. Potato leafroll virus (PLRV) is transmitted most effectively by Myzus persicae, the green peach aphid, in a circulative, non-propagative manner. A high-quality genome assembly of M. persicae is available for functional genomics studies to investigate the aphid proteins regulating virus transmission. Using an affinity purification strategy coupled to high-resolution mass spectrometry (AP-MS), we identified 11 proteins from M. persicae displaying high probability of interaction with PLRV and an additional 23 vector proteins with medium confidence interaction scores. Three of these aphid proteins were confirmed to directly interact with the structural proteins of PLRV and other luteovirid species via yeast two-hybrid. Immunolocalization of one of these direct PLRV-interacting proteins, an orthologue of the human innate immunity protein complement component 1 Q subcomponent-binding protein (C1QBP), shows that MpC1QBP partially co-localizes with PLRV within cytoplasmic puncta and along the periphery of aphid gut epithelial cells. Aphid feeding on a chemical inhibitor of C1QBP leads to increased PLRV acquisition and subsequently increased titer in inoculated plants, supporting a role for C1QBP in the acquisition and transmission efficiency of PLRV by M. persicae. We hypothesize that identifying vector proteins and the roles these proteins play in virus transmission will be crucial to developing novel, molecular-based strategies to control virus transmission by insects.

10:25-10:45
Model-based Identification of Conditionally-Essential Genes from Transposon Insertion Sequencing Data
  • Vishal Sarsani, University of Massachusetts Amherst, United States
  • Berent Aldikacti, University of Massachusetts Amherst, United States
  • Shai He, University of Massachusetts Amherst, United States
  • Rilee Zienert, National Institute of Child Health and Human Development, United States
  • Peter Chien, University of Massachusetts Amherst, United States
  • Patrick Flaherty, University of Massachusetts, Amherst, United States

Presentation Overview: Show

The understanding of bacterial gene function is greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertion attributable to gene-environment changes without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count change information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentususing both total and unique count data the model was able to identify a set of conditionally essential genes for each target condition that shed light on their functions and roles during various stress conditions.



International Society for Computational Biology
525-K East Market Street, RM 330
Leesburg, VA, USA 20176

ISCB On the Web

Twitter Facebook Linkedin
Flickr Youtube