Predicting genome-wide redundancy using machine learning

Understanding redundancy and resilience: Redundancy in life is provided by distributing functions across networks rather than back-up systems: Redundancy in life is provided by distributing functions across networks rather than back-up systems

Understanding how evolution generates and maintains redundancy to cope with damage and loss of function in living systems could inspire applications from new therapies to resilient computer networks.

Degree of Functional Divergence in Duplicates Is Associated with Distinct Roles in Plant Evolution

Gene duplication is a major mechanism to create new genes. After gene duplication, some duplicated genes undergo functionalization, whereas others largely maintain redundant functions. Duplicated genes comprise various degrees of functional diversification in plants. However, the evolutionary fate of high and low diversified duplicates is unclear at genomic scale. To infer high and low diversified duplicates in Arabidopsis thaliana genome, we generated a prediction method for predicting whether a pair of duplicate genes was subjected to high or low diversification based on the phenotypes of knock-out mutants. Among 4,017 pairs of recently duplicated A. thaliana genes, 1,052 and 600 are high and low diversified duplicate pairs, respectively. The predictions were validated based on the phenotypes of generated knock-down transgenic plants. We determined that the high diversified duplicates resulting from tandem duplications tend to have lineage-specific functions, whereas the low diversified duplicates produced by whole-genome duplications are related to essential signaling pathways. To assess the evolutionary impact of high and low diversified duplicates in closely related species, we compared the retention rates and selection pressures on the orthologs of A. thaliana duplicates in two closely related species. Interestingly, high diversified duplicates resulting from tandem duplications tend to be retained in multiple lineages under positive selection. Low diversified duplicates by whole-genome duplications tend to be retained in multiple lineages under purifying selection. Taken together, the functional diversities determined by different duplication mechanisms had distinct effects on plant evolution.

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

Highly efficient multiplex editing: one-shot generation of 8× Nicotiana benthamiana and 12× Arabidopsis mutants

Genome editing by RNA-guided nucleases, such as SpCas9, has been used in numerous different plant species. However, to what extent multiple independent loci can be targeted simultaneously by multiplexing has not been well documented. Here, we developed a toolkit, based on a highly intron-optimized zCas9i gene, which allows assembly of nuclease constructs expressing up to 32 single guide RNAs (sgRNAs). We used this toolkit to explore the limits of multiplexing in two major model species, and report on the isolation of transgene-free octuple (8×) Nicotiana benthamiana and duodecuple (12×) Arabidopsis thaliana mutant lines in a single generation (T1 and T2 , respectively). We developed novel counter-selection markers for N. benthamiana, most importantly Sl-FAST2, comparable to the well-established Arabidopsis seed fluorescence marker, and FCY-UPP, based on the production of toxic 5-fluorouracil in the presence of a precursor. Targeting eight genes with an array of nine different sgRNAs and relying on FCY-UPP for selection of non-transgenic T1 , we identified N. benthamiana mutant lines with astonishingly high efficiencies: All analyzed plants carried mutations in all genes (approximately 112/116 target sites edited). Furthermore, we targeted 12 genes by an array of 24 sgRNAs in A. thaliana. Efficiency was significantly lower in A. thaliana, and our results indicate Cas9 availability is the limiting factor in such higher-order multiplexing applications. We identified a duodecuple mutant line by a combination of phenotypic screening and amplicon sequencing. The resources and results presented provide new perspectives for how multiplexing can be used to generate complex genotypes or to functionally interrogate groups of candidate genes.

Characteristics of Plant Essential Genes Allow for within- and between-Species Prediction of Lethal Mutant Phenotypes

Essential genes represent critical cellular components whose disruption results in lethality. Characteristics shared among essential genes have been uncovered in fungal and metazoan model systems. However, features associated with plant essential genes are largely unknown and the full set of essential genes remains to be discovered in any plant species. Here, we show that essential genes in Arabidopsis thaliana have distinct features useful for constructing within- and cross-species prediction models. Essential genes in A. thaliana are often single copy or derived from older duplications, highly and broadly expressed, slow evolving, and highly connected within molecular networks compared with genes with nonlethal mutant phenotypes. These gene features allowed the application of machine learning methods that predicted known lethal genes as well as an additional 1970 likely essential genes without documented phenotypes. Prediction models from A. thaliana could also be applied to predict Oryza sativa and Saccharomyces cerevisiae essential genes. Importantly, successful predictions drew upon many features, while any single feature was not sufficient. Our findings show that essential genes can be distinguished from genes with nonlethal phenotypes using features that are similar across kingdoms and indicate the possibility for translational application of our approach to species without extensive functional genomic and phenomic resources.

Analysis of functional redundancies within the Arabidopsis TCP transcription factor family

Analyses of the functions of TEOSINTE-LIKE1, CYCLOIDEA, and PROLIFERATING CELL FACTOR1 (TCP) transcription factors have been hampered by functional redundancy between its individual members. In general, putative functionally redundant genes are predicted based on sequence similarity and confirmed by genetic analysis. In the TCP family, however, identification is impeded by relatively low overall sequence similarity. In a search for functionally redundant TCP pairs that control Arabidopsis leaf development, this work performed an integrative bioinformatics analysis, combining protein sequence similarities, gene expression data, and results of pair-wise protein-protein interaction studies for the 24 members of the Arabidopsis TCP transcription factor family. For this, the work completed any lacking gene expression and protein-protein interaction data experimentally and then performed a comprehensive prediction of potential functional redundant TCP pairs. Subsequently, redundant functions could be confirmed for selected predicted TCP pairs by genetic and molecular analyses. It is demonstrated that the previously uncharacterized class I TCP19 gene plays a role in the control of leaf senescence in a redundant fashion with TCP20. Altogether, this work shows the power of combining classical genetic and molecular approaches with bioinformatics predictions to unravel functional redundancies in the TCP transcription factor family.

Tissue-specific profiling reveals transcriptome alterations in Arabidopsis mutants lacking morphological phenotypes

Traditional genetic analysis relies on mutants with observable phenotypes. Mutants lacking visible abnormalities may nevertheless exhibit molecular differences useful for defining gene function. To examine this, we analyzed tissue-specific transcript profiles from Arabidopsis thaliana transcription factor gene mutants with known roles in root epidermis development, but lacking a single-gene mutant phenotype due to genetic redundancy. We discovered substantial transcriptional changes in each mutant, preferentially affecting root epidermal genes in a manner consistent with the known double mutant effects. Furthermore, comparing transcript profiles of single and double mutants, we observed remarkable variation in the sensitivity of target genes to the loss of one or both paralogous genes, including preferential effects on specific branches of the epidermal gene network, likely reflecting the pathways of paralog subfunctionalization during evolution. In addition, we analyzed the root epidermal transcriptome of the transparent testa glabra2 mutant to clarify its role in the network. These findings provide insight into the molecular basis of genetic redundancy and duplicate gene diversification at the level of a specific gene regulatory network, and they demonstrate the usefulness of tissue-specific transcript profiling to define gene function in mutants lacking informative visible changes in phenotype.