Using Transcriptomes as Mutant Phenotypes Reveals Functional Regions of a Mediator Subunit in Caenorhabditis elegans

Although transcriptomes have recently been used as phenotypes with which to perform epistasis analyses, they are not yet used to study intragenic function/structure relationships. We developed a theoretical framework to study allelic series using transcriptomic phenotypes. As a proof-of-concept, we apply our methods to an allelic series of dpy-22, a highly pleiotropic Caenorhabditis elegans gene orthologous to the human gene MED12, which encodes a subunit of the Mediator complex. Our methods identify functional units within dpy-22 that modulate Mediator activity upon various genetic programs, including the Wnt and Ras modules

A Theory of Genetic Analysis Using Transcriptomic Phenotypes

This thesis deals with the conceptual and computational framework required to use transcriptomes as effective phenotypes for genetic analysis. I demonstrate that there are powerful theoretical reasons why Batesonian epistasis should feature prominently in transcriptional phenotypes. I also show how to compute and interpret the aggregate statistics for transcriptome-wide epistasis and transcriptome-wide dominance using whole-organism transcriptomic profiles of C. elegans mutants. Finally, I developed the WormBase Enrichment Suite for enrichment analysis of genomic data. RNA-seq as a tool has enormous potential because it relies on protocols that are fast, simple and increasingly cheap. In spite of their potential, transcriptomes have seen their use largely limited to single-factor experiments. Even when many transcriptomes are collected, the main analytic approach is to apply clustering algorithms that correlate responses but do not have any power to identify causal mechanisms. I demonstrate that if a complete genetic experimental design is used (in the form of a full two-factor matrix), transcriptomes can establish genetic interactions between a pair of genes without the need for clustering algorithms. Surprisingly, when we performed epistasis analyses of hypoxia pathway mutants in C. elegans we did not simply observe a generalized epistatic interaction between the mutants. In fact, the transcriptomes recapitulated the same Batesonian epistatic relationship that had been observed using classical phenotypes. In other words, we observed that the transcriptomic phenotype of one gene can be masked by the transcriptomic phenotype of a second gene, such that a double mutant of these two genes has exactly the same phenotype as a single mutant of the epistatic gene. Motivated by this observation, we developed methods to recognize and interpret Batesonian epistasis at the transcriptomic level. This method relies on the calculation of a single aggregate coefficient that we named the transcriptome-wide epistasis coefficient. The observation that Batesonian epistasis could be reproduced on a transcriptomic level was surprising. To explain how transcriptome-wide epistasis can arise, I studied a simplified model of transcriptional regulation using statistical mechanics. These studies demonstrate that epistatic analysis is equivalent to a perturbative analysis of the partition function of a promoter. Moreover, these studies revealed that a sufficient condition for Batesonian epistasis to occur is if the two genes encode variables that are transformed and multiplied together to form an effective single compound variable. Finally, these studies clearly demonstrate the connection between statistical (or generalized) epistasis and Batesonian epistasis and establish a physical basis for genetic logic. Genetic analyses of gene functional units can also be carried out using allelic series in tandem with complementation (also known as dominance) tests. I developed a statistical coefficient known as transcriptome-wide dominance to enable analyses of allelic series using expression profiles. A crucial aspect of allelic series is the ability to enumerate the independent phenotypes associated with an arbitrary set of alleles. I developed the concept of phenotypic classes as a transcriptomic analogue of classical phenotypes for this purpose. Briefly, a phenotypic class is a set of transcripts that are differentially expressed in a specific set of genotypes. Thus, an allelic series consisting of two mutant alleles (and a wild-type) can at most result in 7 phenotypic classes. However, some of these phenotypic classes may be artifactual as a result of the significant false positive and false negative rates that are associated with RNA-seq. I developed a simple algorithm that tries to identify phenotypic classes that are artifactual, though often these classes may also be identified through a critical evaluation of their biological implications. I applied these concepts to a small allelic series of the dpy-22 gene, which encodes a Mediator subunit in C. elegans, and identified 3–4 functional units along with their sequence requirements. Finally, I developed the WormBase Enrichment Suite by implementing a hypergeometric test on the tissue, gene and phenotype ontology for C. elegans. The importance of this tool derives mainly from its integration to WormBase, the repository of all C. elegans knowledge, which means that the databases that are tested will undergo continuous improvement and curation, and thus will yield the most accurate results.</p

Using Transcriptomes as Mutant Phenotypes Reveals Functional Regions of a Mediator Subunit in Caenorhabditis elegans

Although transcriptomes have recently been used as phenotypes with which to perform epistasis analyses, they are not yet used to study intragenic function/structure relationships. We developed a theoretical framework to study allelic series using transcriptomic phenotypes. As a proof-of-concept, we apply our methods to an allelic series of dpy-22, a highly pleiotropic Caenorhabditis elegans gene orthologous to the human gene MED12, which encodes a subunit of the Mediator complex. Our methods identify functional units within dpy-22 that modulate Mediator activity upon various genetic programs, including the Wnt and Ras modules

Tissue enrichment analysis for C. elegans genomics

Background: Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these datasets and forming hypotheses based on them remains challenging. One way to analyze these datasets is to associate ontologies (hierarchical, descriptive vocabularies with controlled relations between terms) with genes and to look for enrichment of specific terms. Although Gene Ontology (GO) is available for Caenorhabditis elegans, it does not include anatomical information. Results: We have developed a tool for identifying enrichment of C. elegans tissues among gene sets and generated a website GUI where users can access this tool. Since a common drawback to ontology enrichment analyses is its verbosity, we developed a very simple filtering algorithm to reduce the ontology size by an order of magnitude. We adjusted these filters and validated our tool using a set of 30 gold standards from Expression Cluster data in WormBase. We show our tool can even discriminate between embryonic and larval tissues and can even identify tissues down to the single-cell level. We used our tool to identify multiple neuronal tissues that are down-regulated due to pathogen infection in C. elegans. Conclusions: Our Tissue Enrichment Analysis (TEA) can be found within WormBase, and can be downloaded using Python’s standard pip installer. It tests a slimmed-down C. elegans tissue ontology for enrichment of specific terms and provides users with a text and graphic representation of the results

Two new functions in the WormBase Enrichment Suite

Genome-wide experiments routinely generate large amounts of data that can be hard to interpret biologically. A common approach to interpreting these results is to employ enrichment analyses of controlled languages, known as ontologies, that describe various biological parameters such as gene molecular or biological function. In C. elegans, three distinct ontologies, the Gene Ontology (GO), Anatomy Ontology (AO), and the Worm Phenotype Ontology (WPO) are used to annotate gene function, expression and phenotype, respectively (Ashburner et al. 2000; Lee and Sternberg, 2003; Schindelman et al. 2011). Previously, we developed software to test datasets for enrichment of anatomical terms, called the Tissue Enrichment Analysis (TEA) tool (Angeles-Albores and Sternberg, 2016). Using the same hypergeometric statistical method, we extend enrichment testing to include WPO and GO, offering a unified approach to enrichment testing in C. elegans. The WormBase Enrichment Suite can be accessed via a user-friendly interface at http://www.wormbase.org/tools/enrichment/tea/tea.cgi. To validate the tools, we analyzed a previously published extracellular vesicle (EV)-releasing neuron (EVN) signature gene set derived from dissociated ciliated EV neurons (Wang et al. 2015) using WormBase Enrichment Suite based on the WS262 WormBase release. TEA correctly identified the CEM, hook sensillum and IL2 neuron as enriched tissues. The top phenotype associated with the EVN signature was chemosensory behavior. Gene Ontology enrichment analysis showed that cell projection and cell body were the most enriched cellular components in this gene set, followed by the biological processes neuropeptide signaling pathway and vesicle localization further down. The tutorial script used to generate the figure above can be viewed at: https://github.com/dangeles/TissueEnrichmentAnalysis/blob/master/tutorial/Tutorial.ipynb The addition of Gene Enrichment Analysis (GEA) and Phenotype Enrichment Analysis (PEA) to WormBase marks an important step towards a unified set of analyses that can help researchers to understand genomic datasets. These enrichment analyses will allow the community to fully benefit from the data curation ongoing at WormBase

Reconstructing a metazoan genetic pathway with transcriptome-wide epistasis measurements

RNA-sequencing (RNA-seq) is commonly used to identify genetic modules that respond to perturbations. In single cells, transcriptomes have been used as phenotypes, but this concept has not been applied to whole-organism RNA-seq. Also, quantifying and interpreting epistatic effects using expression profiles remains a challenge. We developed a single coefficient to quantify transcriptome-wide epistasis that reflects the underlying interactions and which can be interpreted intuitively. To demonstrate our approach, we sequenced four single and two double mutants of Caenorhabditis elegans. From these mutants, we reconstructed the known hypoxia pathway. In addition, we uncovered a class of 56 genes with HIF-1–dependent expression that have opposite changes in expression in mutants of two genes that cooperate to negatively regulate HIF-1 abundance; however, the double mutant of these genes exhibits suppression epistasis. This class violates the classical model of HIF-1 regulation but can be explained by postulating a role of hydroxylated HIF-1 in transcriptional control

The Caenorhabditis elegans Female State: Decoupling the Transcriptomic Effects of Aging and Sperm-Status

Understanding genome and gene function in a whole organism requires us to fully comprehend the life cycle and the physiology of the organism in question. Caenorhabditis elegans XX animals are hermaphrodites that exhaust their sperm after 3 d of egg-laying. Even though C. elegans can live for many days after cessation of egg-laying, the molecular physiology of this state has not been as intensely studied as other parts of the life cycle, despite documented changes in behavior and metabolism. To study the effects of sperm depletion and aging of C. elegans during the first 6 d of adulthood, we measured the transcriptomes of first-day adult hermaphrodites and sixth-day sperm-depleted adults, and, at the same time points, mutant fog-2(lf) worms that have a feminized germline phenotype. We found that we could separate the effects of biological aging from sperm depletion. For a large subset of genes, young adult fog-2(lf) animals had the same gene expression changes as sperm-depleted sixth-day wild-type hermaphrodites, and these genes did not change expression when fog-2(lf) females reached the sixth day of adulthood. Taken together, this indicates that changing sperm status causes a change in the internal state of the worm, which we call the female-like state. Our data provide a high-quality picture of the changes that happen in global gene expression throughout the period of early aging in the worm

Two new functions in the WormBase Enrichment Suite

Genome-wide experiments routinely generate large amounts of data that can be hard to interpret biologically. A common approach to interpreting these results is to employ enrichment analyses of controlled languages, known as ontologies, that describe various biological parameters such as gene molecular or biological function. In C. elegans, three distinct ontologies, the Gene Ontology (GO), Anatomy Ontology (AO), and the Worm Phenotype Ontology (WPO) are used to annotate gene function, expression and phenotype, respectively (Ashburner et al. 2000; Lee and Sternberg, 2003; Schindelman et al. 2011). Previously, we developed software to test datasets for enrichment of anatomical terms, called the Tissue Enrichment Analysis (TEA) tool (Angeles-Albores and Sternberg, 2016). Using the same hypergeometric statistical method, we extend enrichment testing to include WPO and GO, offering a unified approach to enrichment testing in C. elegans. The WormBase Enrichment Suite can be accessed via a user-friendly interface at http://www.wormbase.org/tools/enrichment/tea/tea.cgi. To validate the tools, we analyzed a previously published extracellular vesicle (EV)-releasing neuron (EVN) signature gene set derived from dissociated ciliated EV neurons (Wang et al. 2015) using WormBase Enrichment Suite based on the WS262 WormBase release. TEA correctly identified the CEM, hook sensillum and IL2 neuron as enriched tissues. The top phenotype associated with the EVN signature was chemosensory behavior. Gene Ontology enrichment analysis showed that cell projection and cell body were the most enriched cellular components in this gene set, followed by the biological processes neuropeptide signaling pathway and vesicle localization further down. The tutorial script used to generate the figure above can be viewed at: https://github.com/dangeles/TissueEnrichmentAnalysis/blob/master/tutorial/Tutorial.ipynb The addition of Gene Enrichment Analysis (GEA) and Phenotype Enrichment Analysis (PEA) to WormBase marks an important step towards a unified set of analyses that can help researchers to understand genomic datasets. These enrichment analyses will allow the community to fully benefit from the data curation ongoing at WormBase

The Caenorhabditis elegans Female State: Decoupling the Transcriptomic Effects of Aging and Sperm-Status

Understanding genome and gene function in a whole organism requires us to fully comprehend the life cycle and the physiology of the organism in question. Caenorhabditis elegans XX animals are hermaphrodites that exhaust their sperm after 3 d of egg-laying. Even though C. elegans can live for many days after cessation of egg-laying, the molecular physiology of this state has not been as intensely studied as other parts of the life cycle, despite documented changes in behavior and metabolism. To study the effects of sperm depletion and aging of C. elegans during the first 6 d of adulthood, we measured the transcriptomes of first-day adult hermaphrodites and sixth-day sperm-depleted adults, and, at the same time points, mutant fog-2(lf) worms that have a feminized germline phenotype. We found that we could separate the effects of biological aging from sperm depletion. For a large subset of genes, young adult fog-2(lf) animals had the same gene expression changes as sperm-depleted sixth-day wild-type hermaphrodites, and these genes did not change expression when fog-2(lf) females reached the sixth day of adulthood. Taken together, this indicates that changing sperm status causes a change in the internal state of the worm, which we call the female-like state. Our data provide a high-quality picture of the changes that happen in global gene expression throughout the period of early aging in the worm

Low-Level Resource Partitioning Supports Coexistence Among Functionally Redundant Bacteria During Successional Dynamics

Members of microbial communities can substantially overlap in substrate use. However, what enables functionally redundant microorganisms to coassemble or even stably coexist remains poorly understood. Here, we show that during unstable successional dynamics on complex, natural organic matter, functionally redundant bacteria can coexist by partitioning low-concentration substrates even though they compete for one simple, dominant substrate. We allowed ocean microbial communities to self-assemble on leachates of the brown seaweed Fucus vesiculosus and then analyzed the competition among 10 taxonomically diverse isolates representing two distinct stages of the succession. All, but two isolates, exhibited an average of 90% ± 6% pairwise overlap in resource use, and functional redundancy of isolates from the same assembly stage was higher than that from between assembly stages, leading us to construct a simpler four-isolate community with two isolates from each of the early and late stages. We found that, although the short-term dynamics of the four-isolate communities in F. vesiculosus leachate was dependent on initial isolate ratios, in the long term, the four isolates stably coexist in F. vesiculosus leachate, albeit with some strains at low abundance. We therefore explored the potential for nonredundant substrate use by genomic content analysis and RNA expression patterns. This analysis revealed that the four isolates mainly differed in peripheral metabolic pathways, such as the ability to degrade pyrimidine, leucine, and tyrosine, as well as aromatic substrates. These results highlight the importance of fine-scale differences in metabolic strategies for supporting the frequently observed coexistence of large numbers of rare organisms in natural microbiomes