Bioinformatics Specialist, Biostatistician, Microbiomics Team Lead
McGill University, Quebec, Canada
Bioinformatics specialist, biostatistician, and expert in gene markers and microbiome metagenomics, Dr. Emmanuel Gonzalez is the multi-omics team leader at the Canadian Center for Computational Genomics (C3G) at McGill University. His scientific interests center around the intimate relationship between multicellular hosts and their associated microbiome, focusing on the resolution at which meta-omics can be analyzed.
In 2019, while publishing an open-source pipeline for multiple advanced genomics applications for single organism genetics involving a team of 23 bioinformaticians (GenePipes), he published with two biologists a high-resolution microbiota analysis pipeline (ANCHOR) and applied it to the microbial world of the International Space Station. He won a Canadian Space Agency grant the following year and applied it to study the impact of two years of extreme isolation on the microbiome of six astronauts during MARS500 mission. In 2020, he was invited by a NASA Ames director to be part of two international teams of experts in microbiome (GeneLab).
His high-resolution approaches have led to clear novel discoveries of biological significance across the life sciences research: human health (colorectal cancer, antibiotics, HIV, chronic rhinosinusitis, and chronic pain), environment (wastewater, phytoremediation, compost) and animals (polar bears, mice, crustaceans, coral). In 2022, following his work on cancer research, he was awarded an honorary affiliation with the Department of Oncology, at McGill University's Faculty of Medicine.
His involvement in milk microbiome started in 2021 with a collaboration with nutritionist Dr. Kristine Koski to study the milk microbiome of underrepresented communities using the technology developed for Space Science to characterize microbial communities at high resolution. Their ongoing research aims at identifying several maternal factors that modify the human milk microbiome in the Mam-Mayan indigenous community in Guatemala including maternal age, parity, BMI and stage of lactation.
Disclosure(s): No relationship to disclose, including relevant financial relationships