Type Of Genetic Interaction Research Articles

PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers.

Synthetic lethality (SL) is a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect the cell viability. It can effectively expand the range of anti-cancer therapeutic targets. SL interactions are identified mainly by experimental screening and computational prediction. Recent machine-learning methods mostly learn the representation of each gene individually, ignoring the representation of the pairwise interaction between two genes. In addition, the mechanisms of SL, the key to translating SL into cancer therapeutics, are often unclear. To fill the gaps, we propose a pairwise interaction learning-based graph neural network (GNN) named PiLSL to learn the representation of pairwise interaction between two genes for SL prediction. First, we construct an enclosing graph for each pair of genes from a knowledge graph. Secondly, we design an attentive embedding propagation layer in a GNN to discriminate the importance among the edges in the enclosing graph and to learn the latent features of the pairwise interaction from the weighted enclosing graph. Finally, we further fuse the latent features with explicit features extracted from multi-omics data to obtain powerful gene representations for SL prediction. Extensive experimental results demonstrate that PiLSL outperforms the best baseline by a large margin and generalizes well under three realistic scenarios. Besides, PiLSL provides an explanation of SL mechanisms via the weighted paths in the enclosing graphs by attention mechanism. Our source code is available at https://github.com/JieZheng-ShanghaiTech/PiLSL.

A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism.

Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

Mapping Synthetic Dosage Lethal Genetic Interactions in Saccharomyces cerevisiae.

Synthetic dosage lethality (SDL) is a type of genetic interaction that occurs when increasing the expression of a gene causes a fitness defect, such as lethality, in a specific mutant background but has little effect on fitness in a wild-type background. SDL genetic interactions discovered in model organisms such as the budding yeast, Saccharomyces cerevisiae , represent candidate genetic interactions that may be conserved in human cells. In some cases, SDL genetic interactions can be applied to study the biological implications of genes overexpressed in cancer and to discover potential anticancer therapeutic drug targets. Here, we provide a protocol for screening a query overexpression gene against ordered arrays of yeast mutant strains to identify mutations that sensitize yeast to increased dosage of a specific gene product. We outline applications and procedures for screening with an inducibly overexpressed wild-type gene, a common feature of cancer cells, or with an inducibly overexpressed gene carrying a dominant-negative missense mutation as a model of protein-inhibitor interactions. This high-throughput screening platform is adapted from synthetic genetic array (SGA) technology and enables the generation of large-scale SDL genetic interaction networks that can be applied to study gene/pathway function and to identify cross-species cancer-relevant processes.

Abstract 36: Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity

Abstract The tissue-specificity of cancer and cancer risk is a fundamental open research question. Beyond advancing our understanding of carcinogenesis, elucidating the factors underlying cancer risk may also contribute to cancer prevention. Recent studies have shown that the variation in tissue cancer risk can be explained by the number of tissue stem cell divisions occurring during the lifetime and by abnormal levels of DNA methylation. While cancer risk is likely not determined by a single factor, no other factor has been reported since to account for this fundamental variation. Here we show that cancer synthetic lethality (SL) is another strong contributor of cancer risk in human tissues. SL is a well-known type of genetic interaction where cell death occurs under the combined inactivation of two paired SL genes but not either of them alone. Targeting SLs has been recognized as a highly valuable approach for cancer treatment. We hypothesized that since down-regulated cancer SL (cSL) gene pairs reduce the viability of cancer cells, they may impede the malignant transformation of normal cells, thus modulating cancer risk. Utilizing several recently published large-scale cancer SL networks, we systematically quantified the cancer SL load (defined as the fraction of down-regulated cancer SL gene pairs) in numerous normal and cancer tissues from the TCGA and GTEx datasets. Our key findings are: 1. The cSL loads are lower in cancers vs their matched normal tissues. Furthermore, we find that the cSL load decreases progressively as cancers develop from normal tissues through multiple stages of pre-malignant lesions. These results testify that high cSL load is detrimental to cancer cells, acting as a barrier to cancer development. That is, as normal cells undergo malignant transformation, they need to reactivate at least some of the down-regulated cSL genes for the emerging cancer cells to survive. 2. In accordance with these observations, we find that cSL load in normal tissues is strongly inversely correlated with their lifetime cancer risk. 3. We find that cSL load is the first identified predictor of cancer onset time across different normal tissues - higher cSL load in the younger population is associated with later onset of cancers in that tissue. 4. cSL are important contributors of the tissue/cancer-type specificity of numerous tumor suppressor genes (like BRCA1) - that is, the activity state of cSL partners of quite a few tumor suppressor genes predicts the specific tissues in which they are known to drive cancer. Taken together, these results show that synthetic lethality load in normal tissues is a novel important biological contributor of cancer risk in humans. While synthetic lethality has been attracting tremendous attention as a way to identify cancer vulnerabilities and target them, this is the first time that its role in mediating cancer development is uncovered. Citation Format: Nishanth Ulhas Nair, Kuoyuan Cheng, Joo Sang Lee, Eytan Ruppin. Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 36.

Curcumin functions as a MEK inhibitor to induce a synthetic lethal effect on KRAS mutant colorectal cancer cells receiving targeted drug regorafenib

Curcumin, a major yellow pigment and spice in turmeric and curry, has been demonstrated to have an anticancer effect in human clinical trials. Mutation of KRAS has been shown in 35%-45% of colorectal cancer, and regorafenib has been approved by the US FDA to treat patients with colorectal cancer. Synthetic lethality is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. Here, we reveal that curcumin significantly enhanced the growth inhibition of regorafenib in human colorectal cancer HCT 116 cells (KRAS mutant) to a greater extent than in human colorectal cancer HT-29 cells (KRAS wild-type), producing an additive or synergistic effect in HCT 116 cells and causing an antagonistic effect in HT-29 cells. Flow cytometric analysis showed that the addition of curcumin elevated apoptosis and greatly increased autophagy in HCT 116 cells but not in HT-29 cells. Mechanistically, curcumin behaved like MEK-specific inhibitor (U0126) to enhance regorafenib-induced growth inhibition, apoptosis and autophagy in HCT 116 cells. Our data suggest that curcumin may target one more gene other than mutant KRAS to enhance regorafenib-induced growth inhibition (synthetic lethality) in colorectal cancer HCT 116 cells, indicating a possible role of curcumin in regorafenib-treated KRAS mutant colorectal cancer.

Abstract SY45-02: Harnessing genetic interactions to advance whole-exome precision cancer treatment

Abstract Much of the current focus in cancer research is on studying cancer driver genes. In search for new and effective cancer drugs, this has been translated into searching for ‘actionable’ mutations in these genes, aiming at their therapeutic targeting. However, identifying novel genetic interactions occurring between cancer genes may open new drug treatment opportunities across the whole cancer genome. This talk will focus on studying the utility of two fundamental types of genetic interactions: The first are the well-known Synthetic Lethal interactions, describing the relationship between two genes whose combined inactivation is lethal to the cell. The second type are the much less studied Synthetic Rescues interactions, where a change in the activity of one gene is lethal to the cell but an alteration of its SR partner gene can rescue cell viability. I shall describe a new approach for the data-driven identification of these two types of genetic interactions by directly mining patients’ tumor data. Applying it to analyze the Cancer Genome Atlas (TCGA) data, we have identified the first pan-cancer genetic interaction networks shared across many types of cancer, which we then validated via existing and new experimental in vitro and in vivo screens. We find that: (a) Synthetic Lethal interactions offer an exciting venue for personalized selective anticancer treatments enabling the prediction of patients’ drug response and providing new selective drug target candidates, and (b) targeting Synthetic Rescue genes can mitigate resistance emerging to primary cancer therapy, including both targeted and immunotherapy. (c) Finally, I shall present recent unpublished results showing that we can quite accurately predict the context specificity of emerging synthetic-lethal based combination therapies, both in terms of the cancer types that are sensitive to them and re. the specific sensitivity of individual patients within a cancer type. Importantly, these results are obtained without the need for specific training on the datasets predicted and are derived directly from patients data, thus increasing the likelihood of their translational relevance. Citation Format: Eytan Ruppin. Harnessing genetic interactions to advance whole-exome precision cancer treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY45-02.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern.

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker’s yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Epistasis analysis of metabolic genes polymorphisms associated with ischemic heart disease in Yucatan

ObjectiveEpistasis is a type of genetic interaction that could explain much of the phenotypic variability of complex diseases. In this work, the effect of epistasis of metabolic genes and cardiovascular risk on the susceptibility to the development of ischaemic heart disease in Yucatan was determined. MethodsCase–control study in 79 Yucatecan patients with ischaemic heart disease and 101 healthy controls matched by age and origin with cases. The polymorphisms -108CT, Q192R, L55 M (paraoxonase 1; PON1), C677T, A1298C (5,10-methylenetetrahydrofolate reductase; MTHFR), and the presence/absence of the glutathione S-transferase T1 (GSTT1) gene were genotyped. Epistasis analysis was performed using the multifactorial dimensional reduction method. The best risk prediction model was selected based on precision (%), statistical significance (p<0.05) and cross-validation consistency. ResultsWe found an independent association of the null genotype GSTT1*0/0 (OR=3.39; CI: 1.29–8.87; p=0.017) and the null allele (OR=1.86; CI: 1.19–2.91; p=0.007) with ischaemic heart disease. The GSTT1*0 deletion and the 677TT genotype (MTHFR) were identified as being at a high cardiovascular risk, whereas the GSTT1*1 wild type genotype and the CC677 variant were at low risk. The gene–environment interaction identified the GSTT1 gene, C677T polymorphism (MTHFR) and hypertension as the factors that best explain ischaemic heart disease in the study population. ConclusionsThe interaction of the GSTT1 and MTHFR and hypertension may constitute a predictive model of risk for early onset ischaemic heart disease in the population of Yucatan.

Análisis de epistasia de polimorfismos de genes metabólicos asociados a cardiopatía isquémica en Yucatán

ObjectiveEpistasis is a type of genetic interaction that could explain much of the phenotypic variability of complex diseases. In this work, the effect of epistasis of metabolic genes and cardiovascular risk on the susceptibility to the development of ischemic heart disease in Yucatan was determined. MethodsCase-control study in 79 Yucatecan patients with ischemic heart disease and 101 healthy controls matched by age and origin with cases. The polymorphisms -108CT, Q192R, L55M (paraoxonase 1; PON1), C677T, A1298C (methylenetetrahydrofolate reductase; MTHFR), and the presence/absence of the glutathione S-transferase T1 (GSTT1) gene were genotyped. Epistasis analysis was performed using the multifactorial dimensional reduction method. The best risk prediction model was selected based on precision (%), statistical significance (P<0.05), and cross-validation consistency. ResultsWe found an independent association of the null genotype GSTT1*0/0 (OR=3.39, CI: 1.29-8.87, P=0.017) and the null allele (OR=1.86, CI: 1.19-2.91, P=0.007) with ischemic heart disease. The GSTT1*0 deletion and the 677TT genotype (MTHFR) were identified as being at a high cardiovascular risk, whereas the GSTT1*1 wild type genotype and the CC677 variant were at low risk. The gene-environment interaction identified the GSTT1 gene, C677T polymorphism (MTHFR), and hypertension as the factors that best explain ischemic heart disease in the study population. ConclusionsThe interaction of the MTHFR, GSTT1 and hypertension may constitute a predictive model of risk for early onset ischemic heart disease in the population of Yucatan.

Abstract 1558: Data-driven approach to detecting novel gene interactions in cancer with applications to drug response prediction and cancer stratification

Abstract The effect of a gene’s activity on a phenotype depends on the context of a complex network of functionally interacting genes. Several genetic interactions (GIs), such as Synthetic lethality and Synthetic Rescues, have been reported to have a significant functional role in cancer progression and provide potential candidates for selective cancer treatments. However, numerous other types of GIs with potential clinical significance are yet to be explored. In this work we generalize the concept of GI and detect ~70,000 GIs of different types with both molecular and clinical signature. We demonstrate their clinical predictive value as well as their ability to stratify breast cancer patients into refined clinical subtypes that might allow for better diagnosis and modified course of treatment. These results compare favorably with previous sequence based approaches and provide evidence for the importance of context specific genomic events and their effect on tumor progression. Additionally, the GI network accurately predicts patients’ drug response, where difference GI types are found to be predictive of distinct drugs in a complementary manner. This work provides the basis for future exploration of novel GI types as well as individual interactions with major impact on cancer progression. Citation Format: Assaf Magen, Avinash Das, Joo Lee, Sridhar Hannenhalli, Eytan Ruppin. Data-driven approach to detecting novel gene interactions in cancer with applications to drug response prediction and cancer stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1558. doi:10.1158/1538-7445.AM2017-1558

Synthetic Lethality Screens Using RNAi in Combination with CRISPR-based Knockout in Drosophila Cells.

A synthetic lethal interaction is a type of genetic interaction where the disruption of either of two genes individually has little effect but their combined disruption is lethal. Knowledge of synthetic lethal interactions can allow for elucidation of network structure and identification of candidate drug targets for human diseases such as cancer. In Drosophila, combinatorial gene disruption has been achieved previously by combining multiple RNAi reagents. Here we describe a protocol for high-throughput combinatorial gene disruption by combining CRISPR and RNAi. This approach previously resulted in the identification of highly reproducible and conserved synthetic lethal interactions (Housden et al., 2015).

SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets

Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.

Prediction of Genetic Interactions Using Machine Learning and Network Properties.

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

Abstract 129: RAD17 loss of function is synthetically lethal with the checkpoint kinase inhibitors AZD7762 or MK-1775

Abstract Synthetic lethal interactions are a type of genetic interaction in which the simultaneous loss of function of two genes in combination results in cell death. Recently, there has been much interest in the discovery of drugs that are selectively toxic to cancer cells by targeting proteins that form synthetic lethal interactions with tumor specific mutations. He we have identified that RAD17 loss of function is synthetically lethal with AZD7762, an inhibitor of Chk1 and Chk2, and with MK-1775, an inhibitor of Wee1. HeLa or LN428 glioblastoma cells were selectively responsive to AZD7762 or MK-1775 following shRNA-mediated depletion of RAD17. Cells with dual knockdown of CHEK1 and CHEK2 or knockdown of WEE1 were also sensitized to RAD17 knockdown. At baseline, cells with RAD17 knockdown had greater expression of the DNA damage marker H2AX, an effect that was magnified by either chemical inhibition or siRNA-mediated knockdown of CHEK1/2 or WEE1. Accumulation of H2AX was observed primarily in S and G2 phases, RAD17 loss did not have a significant effect on cell cycle progression. The combination of AZD7762 and MK-1775 resulted in synergistic toxicity in RAD17 knockdown but not control cells. Collectively, these results demonstrate that RAD17 loss of function sensitizes cells to inhibition of S and G2/M checkpoint kinases, resulting in greater accumulation of DNA damage and ultimately cell death. We suggest that AZD7762 and MK-1775 may have greater clinical efficacy in tumors with RAD17 mutation or deletion. Citation Format: John Paul Shen, Rohith Srivas, Ana Bojorquez-Gomez, Katherine Licon, Vignesh Sivaganesh, Jia L. Xu, Huwate Yeerna, Andrew Gross, Jian Feng Li, Robert Sobol, Trey Ideker. RAD17 loss of function is synthetically lethal with the checkpoint kinase inhibitors AZD7762 or MK-1775. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 129. doi:10.1158/1538-7445.AM2015-129

PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.

BackgroundSeveral types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.ResultsPhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.ConclusionsThis framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0375-1) contains supplementary material, which is available to authorized users.

Feature Identification of Compensatory Gene Pairs without Sequence Homology in Yeast

Genetic robustness refers to a compensatory mechanism for buffering deleterious mutations or environmental variations. Gene duplication has been shown to provide such functional backups. However, the overall contribution of duplication-based buffering for genetic robustness is rather small. In this study, we investigated whether transcriptional compensation also exists among genes that share similar functions without sequence homology. A set of nonhomologous synthetic-lethal gene pairs was assessed by using a coexpression network, protein-protein interactions, and other types of genetic interactions in yeast. Our results are notably different from those of previous studies on buffering paralogs. The low expression similarity and the conditional coexpression alone do not play roles in identifying the functionally compensatory genes. Additional properties such as synthetic-lethal interaction, the ratio of shared common interacting partners, and the degree of coregulation were, at least in part, necessary to extract functional compensatory genes. Our network-based approach is applicable to select several well-documented cases of compensatory gene pairs and a set of new pairs. The results suggest that transcriptional reprogramming plays a limited role in functional compensation among nonhomologous genes. Our study aids in understanding the mechanism and features of functional compensation more in detail.

Tie-dyed2Functions withTie-dyed1to Promote Carbohydrate Export from Maize Leaves

Regulation of carbon partitioning is essential for plant growth and development. To gain insight into genes controlling carbon allocation in leaves, we identified mutants that hyperaccumulate carbohydrates. tie-dyed2 (tdy2) is a recessive mutant of maize (Zea mays) with variegated, nonclonal, chlorotic leaf sectors containing excess starch and soluble sugars. Consistent with a defect in carbon export, we found that a by-product of functional chloroplasts, likely a sugar, induces tdy2 phenotypic expression. Based on the phenotypic similarities between tdy2 and two other maize mutants with leaf carbon accumulation defects, tdy1 and sucrose export defective1 (sxd1), we investigated whether Tdy2 functioned in the same pathway as Tdy1 or Sxd1. Cytological and genetic studies demonstrate that Tdy2 and Sxd1 function independently. However, in tdy1/+; tdy2/+ F(1) plants, we observed a moderate chlorotic sectored phenotype, suggesting that the two genes are dosage sensitive and have a related function. This type of genetic interaction is referred to as second site noncomplementation and has often, though not exclusively, been found in cases where the two encoded proteins physically interact. Moreover, tdy1; tdy2 double mutants display a synergistic interaction supporting this hypothesis. Additionally, we determined that cell walls of chlorotic leaf tissues in tdy mutants contain increased cellulose; thus, tdy mutants potentially represent enhanced feedstocks for biofuels production. From our phenotypic and genetic characterizations, we propose a model whereby TDY1 and TDY2 function together in a single genetic pathway, possibly in homo- and heteromeric complexes, to promote carbon export from leaves.

Sexual reproduction selects for robustness and negative epistasis in artificial gene networks

The mutational deterministic hypothesis for the origin and maintenance of sexual reproduction posits that sex enhances the ability of natural selection to purge deleterious mutations after recombination brings them together into single genomes. This explanation requires negative epistasis, a type of genetic interaction where mutations are more harmful in combination than expected from their separate effects. The conceptual appeal of the mutational deterministic hypothesis has been offset by our inability to identify the mechanistic and evolutionary bases of negative epistasis. Here we show that negative epistasis can evolve as a consequence of sexual reproduction itself. Using an artificial gene network model, we find that recombination between gene networks imposes selection for genetic robustness, and that negative epistasis evolves as a by-product of this selection. Our results suggest that sexual reproduction selects for conditions that favour its own maintenance, a case of evolution forging its own path.

Genetic interactions among Chlamydomonas reinhardtii mutations that confer resistance to anti-microtubule herbicides.

We previously described two types of genetic interactions among recessive mutations in the APM1 and APM2 loci of Chlamydomonas reinhardtii that may reflect a physical association of the gene products or their involvement in a common structure/process: (1) allele-specific synthetic lethality, and (2) unlinked noncomplementation, or dominant enhancement. To further investigate these interactions, we isolated revertants in which the heat sensitivity caused by the apm2-1 mutation is lost. The heat-insensitive revertants were either fully or partially suppressed for the drug-resistance caused by the apm2-1 allele. In recombination tests the revertants behaved as if the suppressing mutation mapped within the APM2 locus; the partial suppressors of apm2-1 herbicide resistance failed to complement apm2-1, leading to the conclusion that they were likely to be intragenic pseudorevertants. The apm2-1 partial suppressor mutations reversed apm1-apm2-1 synthetic lethality in an allele-specific manner with respect both to apm1- alleles and apm2-1 suppressor mutations. Those apm1- apm2-1rev strains that regained viability also regained heat sensitivity characteristic of the original apm2-1 mutation, even though the apm2-1 suppressor strains were fully heat-insensitive. The Hs+ phenotypes of apm2-1 partial suppressors were also reversed by treatment with the microtubule-stabilizing agent deuterium oxide (D2O). In addition to the above interactions, we observed interallelic complementation and phenotypic enhancement of temperature conditionality among apm1- alleles. Evidence of a role for the products of the two genes in microtubule-based processes was obtained from studying flagellar assembly in apm1- and apm2- mutants.