Quest for orthologs 2024

Fiona Brinkman is a Distinguished Professor in Bioinformatics and Genomics at Simon Fraser University, interested in developing more preventative, sustainable, and holistic approaches for infectious disease control and supporting health. She is most known for R&D of software and databases aiding analysis of microbial and human omics data, including PSORT, IslandViewer, Pseudomonas.com, and InnateDB.com. She leads data integration for the CHILD Cohort Study - the largest multidisciplinary, longitudinal, population-based birth cohort study in Canada, including diverse omics data. She has co-led development of the IRIDA.ca platform, which is now the primary platform for Canada’s Public Health Agency to analyze infectious disease outbreaks using combined epidemiological/lab/genomics data. She contributed to the pandemic response, co-leading Data Analytics for the Canadian COVID-19 Genomics Network and more recently CoVaRR-Net. She has a strong interest in bioinformatics education and mentoring young scientists. She is on several committees/Boards, including the ELIXIR and European Nucleotide Archive Scientific Advisory Boards. Her awards include a TR100 award from MIT, Thompson Reuters “World’s Most Influential Scientific Minds”, and most recently she received a University of Waterloo Distinguished Alumni Award and became a Fellow of the Royal Society of Canada.

Talk title:

Dealing with the Data Deluge as we get Back to Basics

Talk summary:

One of the biggest challenges faced in biology is enabling accurate ortholog prediction in the face of an incredible amount of genomic data - data that includes genomic rearragements and lateral transfers which invalidate many computational approaches. I will present some thoughts on these challenges, noting tools/approaches some may not be familiar with that we have developed to aid invesigations that can still be capitalized on. These include (1) an analysis of a duplication within a gene in all life to aid base characterizations of organismal Domains of Archaea, Bacteria, and Eukarya, (2) our old OrtholugeDB and associated method, and (3) a more recent tool that aims to integrate phylogeny with prediction of regions of lateral transfer for bacterial outbreak analysis. Challenges around dealing with the growing datasets, while maintaining bioinformatics resources, will be discussed, along with the issue of rampant errors in ortholog inference being made in the larger research community. However, there is optimism. More robust analyses are possible in our larger research community through the coordinated efforts and leasdership of this Quest for Orthologs consortium, coupled with more communication (to researchers, bioinformatics resource developers, and within student training programs), about key basics for robust ortholog prediction - customized to address specific use cases.

Markus Hecker is a Professor and a Tier 1 Canada Research Chair in Predictive Toxicology and Chemical Safety with 26 years of experience in conducting research in environmental toxicology and chemical safety assessment. He is considered a global expert in environmental risk assessment, ecotoxicogenomics, hazard characterization of contaminants in native fishes and amphibians, and development of alternatives to live animal testing. Dr. Hecker is a member of the College of the Royal Society of Canada, an endowed guest professor at Goethe University in Frankfurt (Germany) and a visiting/guest professor at Xiamen University (China). He served as the co-Editor-in-Chief of Aquatic Toxicology and is/was an advisor/expert to several national and international organizations including Environment and Climate Change Canada, Health Canada, the U.S. Environmental Protection Agency, the European Food Security Agency and the Organization for Economic Cooperation and Development. Markus has authored or co-authored over 230 peer-reviewed papers, review articles, editorials and book chapters over his career (H-Index: 61).

Keywords: Adverse outcome pathways, alternatives to animal testing, aquatic toxicology, cross-species extrapolation, ecological risk assessment, endocrine disruption, environmental DNA, fish biology, in vitro assays, new approach methods, one health, test validation, toxicogenomics.

Talk title: Using ‘omics to assess the risks of environmental contamination across aquatic vertebrates - Current challenges and opportunities

Talk summary: Chemical contamination poses one of the greatest threats to biodiversity, particularly for freshwater vertebrates like fish, which have seen an over 80% decline since the 1970s. With over 350,000 chemicals in use and numerous pollutants from various sources entering the environment, understanding their impact on fish diversity is crucial. Traditional chemical risk assessment strategies rely on costly and time-consuming animal testing of a few model species, failing to represent the over 30,000 fish species worldwide. Recent advancements in high-content and high-throughput 'omics' technologies now enable comprehensive analysis across diverse species, including those with limited genetic information. These technologies provide unprecedented opportunities to characterize the genetic design of organisms, identify molecular perturbations due to contaminants, and explore the conservation of molecular targets and pathways. Ortholog research plays a crucial role in these efforts, offering insights into the genetic basis for conserved and differential susceptibility and resistance of organisms to contaminants. For example, the conservation of genes involved with detoxification processes (e.g. the arylhydrocarbon receptor (AhR) in oviparous vertebrates) can help predict species sensitivity to certain groups of pollutants (e.g. dioxin-like chemicals). Despite these advancements, significant challenges remain due to evolutionary distances among fishes, genome duplications, and limited availability of annotated genomes particularly for non-model species. Many fish genes with unknown functions are highly affected by contaminants and likely play crucial roles in mediating toxicity or compensatory responses. Ortholog research can address these challenges and advance ecological risk assessments and management strategies. Ortholog information can support the development of comprehensive cross-species toxicity databases and facilitate the identification of common molecular pathways affected by pollutants, which is particularly important in enhancing risk predictions in non-model species. It can also help identify keystone species (critical components of ecosystems) to focus conservation efforts, as well as enable predictive modelling of evolutionary adaptation to pollutants. Finally, high-throughput screening for pollutant resistance genes across various species can lead to genetic or biotechnological interventions to enhance species resilience. This presentation will explore the current state of mechanistic 'omics' research and approaches to support chemical and ecological risk assessments, aiming to spark discussions on engaging the ortholog community to address existing limitations and opportunities in the field.

I received an undergraduate degree in human biology and zoology at the University of Toronto, M.Sc. in laboratory medicine and pathobiology at the SickKids Research Institute, followed by a Ph.D. in molecular genetics with Profs. Charlie Boone and Brenda J. Andrews at the Donnelly Centre, University of Toronto. I then conducted postdoctoral work with Prof. Morag Park at the Goodman Cancer Research Centre, McGill University. Currently, I am an Assistant Professor in the Department of Biology at Concordia University and affiliated with the Centre for Applied Synthetic Biology and Centre for Structural & Functional Genomics, as well as Canada Research Chair Tier 2 in Synthetic and Functional Genomics. I am cross-appointed to the Goodman Cancer Institute and the Department of Human Genetics at McGill University. My main research area is integrative synthetic and functional genomics, with a focus on complex genetic interaction networks.

Talk title: Genetic Network Rewiring Between Distantly Related Eukaryotic Species

Talk summary: Synthetic lethality represents an extreme example of a genetic interaction that occurs when a combination of mutations in different genes results in lethality, which would not be expected from the combined effects of individual viable single mutants. The extent of genetic interaction network conservation differs from genome sequence conservation between species. Two distantly yeast species, S. cerevisiae and S. pombe, diverged ~500 Mya and despite 75% genome conservation, they display 29% genetic interaction network conservation. Other distantly related eukaryotes such as C. elegans and H. sapiens diverged ~600 Mya. Here, we investigate genetic network rewiring by studying the genetic interactions that underlie conditional essentiality of single mutants between S. cerevisiae, S. pombe, C. elegans, and H. sapiens, whereby a gene is essential (ES) in one species but nonessential (NES) in another. We have extensively studied the 2853 S. cerevisiae - S. pombe orthologs, where ~15% are conditional ES. From 269 conditional NES S. cerevisiae genes (S. pombe ES, S. cerevisiae NES), we identified 124 cases which are rewired by synthetic lethal digenic interactions that modify conditional NES single mutants to synthetic lethal double mutants. Single mutant fitness, phenotype rate and genetic interaction degree differentiate conditional NES genes that were rewired by synthetic lethal interactions suggesting that they are functionally important in S. cerevisiae. To understand the functional relationship between conditional NES genes and their rewiring synthetic lethal interactions, we overlapped them with common functional standards and found that they were co-expressed, co-localized, co-annotated, shared protein-protein interactions and showed similar phenotypic profiles suggesting that genetic rewiring of ES genes is local. When extending these findings to C. elegans and H. sapiens, 14-23% of orthologs are conditional ES between species. Preliminary results reveal that 14-17% of conditional NES genes in C. elegans are rewired by synthetic lethal interactions where the rewiring interactions and conditional NES genes are co-annotated, co-localized and share protein-protein interactions, indicating a functional connection similar to yeast. Understanding the rewiring of gene essentiality and how it is modulated by genetic interactions in distantly related eukaryotes may reveal principles of genetic network conservation and shed light on synthetic lethal therapeutic strategies for human disease.

The Orengo Group's research focuses on how proteins function and evolve, how relatives in a family acquire new functions and how they evolve to operate in different biological contexts. Over twenty years ago, the group established the CATH evolutionary classification of protein domains which is a partner resource in InterPro and widely accessed. The Orengo Group develops methods for predicting functions for proteins and the networks they participate in. They are interested in integrating heterogenous functional genomics data to learn how protein networks rewire during evolution and under different biological conditions. To optimise and validate these methods the group collaborate with experimental groups characterising the signalling processes involved in development, neuropathic pain, ageing and cancer. Orengo has collaborated with over 30 groups worldwide and several consortia including the EU funded InteGr8, Biosapiens, EMBRACE, ENFIN, IMPACT, Europain consortia exploiting CATH tools and data, the NIH funded MCSG, SGCID consortia researching structural genomics and the CRUK funded LPC and DDIP consortia researching biological networks. The original CATH resource publication has been cited ~2600 times. Christine Orengo became a Fellow of the Royal Society of Biology in 2014, an elected member of EMBO in 2014, and a Fellow of the International Society of Computational Biology in 2016. She has been on the board of ISCB since 2011 and a Vice president of ISCB since 2013.

Talk title: AlphaFold structures expand our understanding of functional divergence in protein families

Talk summary: The AlphaFold Database provides more than 200 million predicted structures. Using a number of AI/deep learning based tools we have identified the globular domain regions within these structures and classified them in our protein domain structure classification of evolutionary superfamilies (CATH). This allowed us to identify nearly 8000 new structural superfamilies, more than doubling the number of CATH superfamilies. It also expanded the structural data in CATH by 400-fold. The good quality AlphaFold structural models are helping us to understand more about the changes in functional sites and mechanisms in different functional families within CATH superfamilies. We are also applying novel AI/ML methods to extend our functional family classification to improve functional annotations of uncharacterised proteins.