A gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

Gene coevolution - which refers to gene pairs whose evolutionary rates covary across speciation events - is often observed among functionally related genes. We present a comprehensive gene coevolution network inferred from the examination of nearly three million gene pairs from 332 budding yeast species spanning ~400 million years of eukaryotic evolution. Modules within the network provide insight into cellular and genomic structure and function, such as genetic pleiotropy, genes functioning in distinct cellular compartments, vesicle transport, and DNA replication. Examination of the phenotypic impact of network perturbation across 14 environmental conditions using deletion mutant data from the baker's yeast Saccharomyces cerevisiae suggests that fitness in diverse environments is impacted by gene neighborhood and gene connectivity. By mapping the network onto the chromosomes of S. cerevisiae and the opportunistic human pathogen Candida albicans, which diverged ~235 million years ago, we discovered that coevolving gene pairs are not clustered in either species; rather, they are most often located on different chromosomes or far apart on the same chromosome. The budding yeast gene coevolution network captures the hierarchy of eukaryotic cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as an extensively linked ensemble of genes.

Bidirectional titration of yeast gene expression using a pooled CRISPR guide RNA approach

Most classic genetic approaches utilize binary modifications that preclude the identification of key knockdowns for essential genes or other targets that only require moderate modulation. As a complementary approach to these classic genetic methods, we describe a plasmid-based library methodology that affords bidirectional, graded modulation of gene expression enabled by tiling the promoter regions of all 969 genes that comprise the ito977 model ofSaccharomyces cerevisiae’s metabolic network. When coupled with a CRISPR-dCas9–based modulation and next-generation sequencing, this method affords a library-based, bidirection titration of gene expression across all major metabolic genes. We utilized this approach in two case studies: growth enrichment on alternative sugars, glycerol and galactose, and chemical overproduction of betaxanthins, leading to the identification of unique gene targets. In particular, we identify essential genes and other targets that were missed by classic genetic approaches.

Systematic Humanization of the Yeast Cytoskeleton Discerns Functionally Replaceable from Divergent Human Genes

Many gene families have been expanded by gene duplications along the human lineage, relative to ancestral opisthokonts, but the extent to which the duplicated genes function similarly is understudied. Here, we focused on structural cytoskeletal genes involved in critical cellular processes, including chromosome segregation, macromolecular transport, and cell shape maintenance. To determine functional redundancy and divergence of duplicated human genes, we systematically humanized the yeast actin, myosin, tubulin, and septin genes, testing ∼81% of human cytoskeletal genes across seven gene families for their ability to complement a growth defect induced by inactivation or deletion of the corresponding yeast ortholog. In five of seven families—all but α-tubulin and light myosin, we found at least one human gene capable of complementing loss of the yeast gene. Despite rescuing growth defects, we observed differential abilities of human genes to rescue cell morphology, meiosis, and mating defects. By comparing phenotypes of humanized strains with deletion phenotypes of their interaction partners, we identify instances of human genes in the actin and septin families capable of carrying out essential functions, but failing to fully complement the cytoskeletal roles of their yeast orthologs, thus leading to abnormal cell morphologies. Overall, we show that duplicated human cytoskeletal genes appear to have diverged such that only a few human genes within each family are capable of replacing the essential roles of their yeast orthologs. The resulting yeast strains with humanized cytoskeletal components now provide surrogate platforms to characterize human genes in simplified eukaryotic contexts.

Conserved +1 translational frameshifting in the S. cerevisiae gene encoding YPL034W

Living cells have developed exquisite mechanisms to ensure accurate translation of mRNA. Many of them are dedicated to preventing the change in reading frame during translation elongation. A minority of chromosomally encoded genes have evolved sequences that subvert standard decoding to program +1 translational frameshifting, either constitutively or in response to external stimuli. In the yeast Saccharomyces cerevisiae, three chromosomal genes are known to employ programmed +1 translational frameshifting for expression of full-length functional products. Here we identify a fourth yeast gene, YFS1, encompassing the existing predicted open reading frame YPL034W, with conserved programmed +1 frameshifting. Like the previously known examples, it appears to exploit peculiarities in tRNA abundance in S. cerevisiae.

A Sequence-Pair-Classification-Based Method for Detecting and Correcting Under-Clustered Gene Families

Gene families are groups of genes that have descended from a common ancestral gene present in the species under study. Current, widely used gene family building algorithms can produce family clusters that may be fragmented or missing true family sequences (under-clustering). Here we present a classification method based on sequence pairs that, first, inspects given families for under-clustering and then predicts the missing sequences for the families using family-specific alignment score cutoffs. We have tested this method on a set of curated, gold-standard (“true”) families from the Yeast Gene Order Browser (YGOB) database, including 20 yeast species, as well as a test set of intentionally under-clustered (“deficient”) families derived from the YGOB families. For 83% of the modified yeast families, our pair-classification method was able to reliably detect under-clustering in “deficient” families that were missing 20% of sequences relative to the full/” true” families. We also attempted to predict back the missing sequences using the family-specific alignment score cutoffs obtained during the detection phase. In the case of “pure” under-clustered families (under-clustered families with no “wrong”/unrelated sequences), for 78% of families the prediction precision and recall was ≥0.75, with mean precision = 0.928 and mean recall = 0.859. For “impure” under-clustered families, (under-clustered families containing closest sequences from outside the family, in addition to missing true family sequences), the prediction precision and recall was ≥0.75 for 63% of families with mean precision = 0.790 and mean recall = 0.869. To check if our method can detect and correct incomplete families obtained using existing family building methods, we attempted to correct 374 under-clustered yeast families produced using the OrthoFinder tool. We were able to predict missing sequences for at least 19 yeast families with mean precision of 0.9 and mean recall of 0.65. We also analyzed 14,663 legume families built using the OrthoFinder program, with 14 legume species. We were able to identify 1,665 OrthoFinder families that were missing one or more sequences - sequences which were previously un-clustered or clustered into unusually small families. Further, using a simple merging strategy, we were able to merge 2,216 small families into 933 under-clustered families using the predicted missing sequences. Out of the 933 merged families, we could confirm correct mergings in at least 534 families using the maximum-likelihood phylogenies of the merged families. We also provide recommendations on different types of family-specific alignment score cutoffs that can be used for predicting the missing sequences based on the “purity” of under-clustered families and the chosen precision and recall for prediction. Finally, we provide the containerized version of the pair-classification method that can be applied on any given set of gene families.

mRNA with Mammalian Codon Bias Accumulates in Yeast Mutants with Constitutive Stress Granules

Stress granules and P bodies are cytoplasmic structures assembled in response to various stress factors and represent sites of temporary storage or decay of mRNAs. Depending on the source of stress, the formation of these structures may be driven by distinct mechanisms, but several stresses have been shown to stabilize mRNAs via inhibition of deadenylation. A recent study identified yeast gene deletion mutants with constitutive stress granules and elevated P bodies; however, the mechanisms which trigger its formation remain poorly understood. Here, we investigate the possibility of accumulating mRNA with mammalian codon bias, which we termed the model RNA, in these mutants. We found that the model RNA accumulates in dcp2 and xrn1 mutants and in four mutants with constitutive stress granules overlapping with P bodies. However, in eight other mutants with constitutive stress granules, the model RNA is downregulated, or its steady state levels vary. We further suggest that the accumulation of the model RNA is linked to its protection from the main mRNA surveillance path. However, there is no obvious targeting of the model RNA to stress granules or P bodies. Thus, accumulation of the model RNA and formation of constitutive stress granules occur independently and only some paths inducing formation of constitutive stress granules will stabilize mRNA as well.

YQFC: a web tool to compare quantitative biological features between two yeast gene lists

Nowadays high-throughput omics technologies are routinely used in biological research. From the omics data, researchers can easily get two gene lists (e.g. stress-induced genes vs. stress-repressed genes) related to their biological question. The next step would be to apply enrichment analysis tools to identify distinct functional/regulatory features between these two gene lists for further investigation. Although various enrichment analysis tools are already available, two challenges remain to be addressed. First, most existing tools are designed to analyze only one gene list, so they cannot directly compare two gene lists. Second, almost all existing tools focus on identifying the enriched qualitative features (e.g. gene ontology [GO] terms, pathways, domains, etc.). Many quantitative features (e.g. number of mRNA isoforms of a gene, mRNA half-life, protein half-life, transcriptional plasticity, translational efficiency, etc.) are available in the yeast, but no existing tools provide analyses on these quantitative features. To address these two challenges, here we present Yeast Quantitative Features Comparator (YQFC) that can directly compare various quantitative features between two yeast gene lists. In YQFC, we comprehensively collected and processed 85 quantitative features from the yeast literature and yeast databases. For each quantitative feature, YQFC provides three statistical tests (t-test, U test and KS test) to test whether this quantitative feature is statistically different between the two input yeast gene lists. The distinct quantitative features identified by YQFC may help researchers to study the underlying molecular mechanisms that differentiate the two input yeast gene lists. We believe that YQFC is a useful tool to expedite the biological research that uses high-throughput omics technologies. Database URL http://cosbi2.ee.ncku.edu.tw/YQFC/

mRNA with mammalian codon bias accumulates in yeast mutants with constitutive stress granules

Stress granules and P bodies are cytoplasmic structures assembled in response to various stress factors and represent sites of temporary storage or decay of mRNAs. Depending on the source of stress, the formation of these structures may be driven by distinct mechanisms, but several stresses were shown to stabilize mRNAs via inhibition of deadenylation. A recent study identified yeast gene deletion mutants with constitutive stress granules and elevated P bodies; however, the mechanisms which trigger its formation remain poorly understood. Here, we investigate the possibility of accumulating mRNA with mammalian codon bias, which we termed the model RNA, in these mutants. We found that the model RNA accumulates in dcp2 and xrn1 mutants and in four mutants with constitutive stress granules overlapping with P bodies. However, in eight other mutants with constitutive stress granules the model RNA is downregulated, or its steady state levels vary. We further suggest that the accumulation of the model RNA is linked to its protection from the main mRNA surveillance path. However, there is no obvious targeting of the model RNA to stress granules or P bodies. Thus, accumulation of the model RNA and formation of constitutive stress granules occur independently and only some paths inducing formation of constitutive stress granules will stabilize mRNA as well.

Systematic humanization of the yeast cytoskeleton discerns functionally replaceable from divergent human genes

Many gene families have been expanded by gene duplications along the human lineage, relative to ancestral opisthokonts, but the extent to which the duplicated genes function similarly is understudied. Here, we focused on structural cytoskeletal genes involved in critical cellular processes including chromosome segregation, macromolecular transport, and cell shape maintenance. To determine functional redundancy and divergence of duplicated human genes, we systematically humanized the yeast actin, myosin, tubulin, and septin genes, testing ∼85% of human cytoskeletal genes across 7 gene families for their ability to complement a growth defect induced by deletion of the corresponding yeast ortholog. In 5 of 7 families—all but α-tubulin and light myosin, we found at least one human gene capable of complementing loss of the yeast gene. Despite rescuing growth defects, we observed differential abilities of human genes to rescue cell morphology, meiosis, and mating defects. By comparing phenotypes of humanized strains with deletion phenotypes of their interaction partners, we identify instances of human genes in the actin and septin families capable of carrying out essential functions, but apparently failing to interact with components of the yeast cytoskeleton, thus leading to abnormal cell morphologies. Overall, we show that duplicated human cytoskeletal genes appear to have diverged such that only a few human genes within each family are capable of replacing the essential roles of their yeast orthologs. The resulting yeast strains with humanized cytoskeletal components now provide surrogate platforms to characterize human genes in simplified eukaryotic contexts.