Unifying the known and unknown microbial coding sequence space
- Max Planck Institute for Marine Microbiology, Germany
- University of Chicago, United States
- Institut de Ciències del Mar-CMIMA (CSIC), Spain
- University of Arizona, United States
- Alfred Wegener Institute, Germany
- Spanish Council for Research, Spain
- Genoscope, Institut François Jacob, CEA, CNRS, France
- King Abdullah University of Science and Technology, Saudi Arabia
- European Molecular Biology Laboratory, United Kingdom
- University of Copenhagen, Denmark
- Seoul National University, Republic of Korea
- University of Bremen, Germany
Abstract
Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40%-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.
Data availability
We used public data as described in the Methods section and Appendix 1-table 5.The code used for the analyses in the manuscript is available at https://github.com/functional-dark-side/functional-dark-side.github.io/tree/master/scripts. A list with the program versions can be found in https://github.com/functional-dark-side/functional-dark-side.github.io/blob/master/programs_and_versions.txt.The code to create the figures is available at https://github.com/functional-dark-side/vanni_et_al-figures, and the data for the figure can be downloaded from https://doi.org/10.6084/m9.figshare.12738476.v2. A reproducible version of the workflow is available at https://github.com/functional-dark-side/agnostos-wf.The data is publicly available at https://doi.org/10.6084/m9.figshare.12459056.
Article and author information
Author details
Matthew S Schechter
Emilio O Casamayor
Antonio Fernàndez-Guerra
Funding
Max Planck Society
- Chiara Vanni
European Union's Horizon 2020 (INMARE)
- Antonio Fernàndez-Guerra
Biotechnology and Biological Sciences Research Council
- Alex Mitchell
European Molecular Biology Laboratory
- Robert D Finn
Spanish Agency of Science MICIU/AEI (INTERACTOMA RTI2018-101205-B-I00)
- Emilio O Casamayor
Spanish Ministry of Economy and Competitiveness (MAGGY (CTM2017-87736-R))
- Silvia G Acinas
- Pablo Sánchez
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Vanni et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 6,575
- views
-
- 972
- downloads
-
- 61
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Unifying the known and unknown microbial coding sequence space
eLife 11:e67667.
https://doi.org/10.7554/eLife.67667
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.
-
- Computational and Systems Biology
Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.
