A genome wide dosage suppressor network reveals genomic robustness

Unraveling radiation resistance strategies in two bacterial strains from the high background radiation area of Chavara-Neendakara: A comprehensive whole genome analysis

This paper reports the results of gamma irradiation experiments and whole genome sequencing (WGS) performed on vegetative cells of two radiation resistant bacterial strains, Metabacillus halosaccharovorans (VITHBRA001) and Bacillus paralicheniformis (VITHBRA024) (D10 values 2.32 kGy and 1.42 kGy, respectively), inhabiting the top-ranking high background radiation area (HBRA) of Chavara-Neendakara placer deposit (Kerala, India). The present investigation has been carried out in the context that information on strategies of bacteria having mid-range resistance for gamma radiation is inadequate. WGS, annotation, COG and KEGG analyses and manual curation of genes helped us address the possible pathways involved in the major domains of radiation resistance, involving recombination repair, base excision repair, nucleotide excision repair and mismatch repair, and the antioxidant genes, which the candidate could activate to survive under ionizing radiation. Additionally, with the help of these data, we could compare the candidate strains with that of the extremely radiation resistant model bacterium Deinococccus radiodurans, so as to find the commonalities existing in their strategies of resistance on the one hand, and also the rationale behind the difference in D10, on the other. Genomic analysis of VITHBRA001 and VITHBRA024 has further helped us ascertain the difference in capability of radiation resistance between the two strains. Significantly, the genes such as uvsE (NER), frnE (protein protection), ppk1 and ppx (non-enzymatic metabolite production) and those for carotenoid biosynthesis, are endogenous to VITHBRA001, but absent in VITHBRA024, which could explain the former's better radiation resistance. Further, this is the first-time study performed on any bacterial population inhabiting an HBRA. This study also brings forward the two species whose radiation resistance has not been reported thus far, and add to the knowledge on radiation resistant capabilities of the phylum Firmicutes which are abundantly observed in extreme environment.

Radiation impacts gene redundancy and biofilm regulation of cryoconite microbiomes in Northern Hemisphere glaciers

BACKGROUND

Glaciers harbor diverse microorganisms adapted to extreme conditions with high radiation, fluctuating temperature, and low nutrient availability. In glacial ecosystems, cryoconite granules are hotspots of microbial metabolic activity and could influences the biogeochemical cycle on glacier surface. Climate change could influence glacier dynamics by changing regional meteorological factors (e.g., radiation, precipitation, temperature, wind, and evaporation). Moreover, meteorological factors not only influence glacier dynamics but also directly or indirectly influence cryoconite microbiomes. However, the relationship of the meteorological factors and cryoconite microbiome are poorly understood.

RESULTS

Here, we collected 88 metagenomes from 26 glaciers distributed in the Northern Hemisphere with corresponding public meteorological data to reveal the relationship between meteorological factors and variation of cryoconite microbiome. Our results showed significant differences in taxonomic and genomic characteristics between cryoconite generalists and specialists. Additionally, we found that the biogeography of both generalists and specialists was influenced by solar radiation. Specialists with smaller genome size and lower gene redundancy were more abundant under high radiation stress, implying that streamlined genomes are more adapted to high radiation conditions. Network analysis revealed that biofilm regulation is a ubiquitous function in response to radiation stress, and hub genes were associated with the formation and dispersion of biofilms.

CONCLUSION

These findings enhance our understanding of glacier cryoconite microbiome variation on a hemispheric scale and indicate the response mechanisms to radiation stress, which will support forecasts of the ecological consequences of future climate change. Video Abstract.

Resurrection from lethal knockouts: Bypass of gene essentiality

Understanding genotype-phenotype relationships is a central pursuit in biology. Gene knockout generates a complete loss-of-function genotype and is a commonly used approach for probing gene functions. The most severe phenotypic consequence of gene knockout is lethality. Genes with a lethal knockout phenotype are called essential genes. Based on genome-wide knockout analyses in yeasts, up to approximately a quarter of genes in a genome can be essential. Like other genotype-phenotype relationships, gene essentiality is subject to background effects and can vary due to gene-gene interactions. In particular, for some essential genes, lethality caused by knockout can be rescued by extragenic suppressors. Such "bypass of essentiality" (BOE) gene-gene interactions have been an understudied type of genetic suppression. A recent systematic analysis revealed that, remarkably, the essentiality of nearly 30% of essential genes in the fission yeast Schizosaccharomyces pombe can be bypassed by BOE interactions. Here, I review the history and recent progress on uncovering and understanding the bypass of gene essentiality.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Yeast genetic interaction screens in the age of CRISPR/Cas

The ease of performing both forward and reverse genetics in Saccharomyces cerevisiae, along with its stable haploid state and short generation times, has made this budding yeast the consummate model eukaryote for genetics. The major advantage of using budding yeast for reverse genetics is this organism's highly efficient homology-directed repair, allowing for precise genome editing simply by introducing DNA with homology to the chromosomal target. Although plasmid- and PCR-based genome editing tools are quite efficient, they depend on rare spontaneous DNA breaks near the target sequence. Consequently, they can generate only one genomic edit at a time, and the edit must be associated with a selectable marker. However, CRISPR/Cas technology is efficient enough to permit markerless and multiplexed edits in a single step. These features have made CRISPR/Cas popular for yeast strain engineering in synthetic biology and metabolic engineering applications, but it has not been widely employed for genetic screens. In this review, we critically examine different methods to generate multi-mutant strains in systematic genetic interaction screens and discuss the potential of CRISPR/Cas to supplement or improve on these methods.

Modular epistasis and the compensatory evolution of gene deletion mutants

Screens for epistatic interactions have long been used to characterize functional relationships corresponding to protein complexes, metabolic pathways, and other functional modules. Although epistasis between adaptive mutations is also common in laboratory evolution experiments, the functional basis for these interactions is less well characterized. Here, we quantify the extent to which gene function (as determined by a genome-wide screen for epistasis among deletion mutants) influences the rate and genetic basis of compensatory adaptation in a set of 37 gene deletion mutants nested within 16 functional modules. We find that functional module has predictive power: mutants with deletions in the same module tend to adapt more similarly, on average, than those with deletions in different modules. At the same time, initial fitness also plays a role: independent of the specific functional modules involved, adaptive mutations tend to be systematically more beneficial in less-fit genetic backgrounds, consistent with a general pattern of diminishing returns epistasis. We measured epistatic interactions between initial gene deletion mutations and the mutations that accumulate during compensatory adaptation and found a general trend towards positive epistasis (i.e. mutations tend to be more beneficial in the background in which they arose). In two functional modules, epistatic interactions between the initial gene deletions and the mutations in their descendant lines caused evolutionary entrenchment, indicating an intimate functional relationship. Our results suggest that genotypes with similar epistatic interactions with gene deletion mutations will also have similar epistatic interactions with adaptive mutations, meaning that genome scale maps of epistasis between gene deletion mutations can be predictive of evolutionary dynamics.

The effects of mutations often depend on the presence or absence of other mutations. This phenomenon, known as epistasis, has been used extensively to infer functional associations between genes. For example, genes that participate in the same functional module will often show a characteristic pattern of positive epistasis where the knockout of one gene will mask the deleterious effects of knockouts in the other genes. In the context of adaptation, epistasis can cause the outcomes of evolution to depend strongly on the initial genotype. Although studies have found that epistasis is common in laboratory populations, we do not know the extent to which the patterns of epistasis that reveal functional associations overlap with the patterns of epistasis that are important in evolution. Here, by comparing evolution in strains with gene deletions in different functional modules, we quantify the effect of functional epistasis on evolutionary outcomes. We find that mutants with deletions in the same module have more similar evolutionary outcomes, on average, than mutants with deletions in different modules. This suggests that screens for epistasis between gene deletion mutations will not only reveal functional interactions between those genes but may also predict evolutionary dynamics.

Adapting to stress - chaperome networks in cancer

In this Opinion article, we aim to address how cells adapt to stress and the repercussions chronic stress has on cellular function. We consider acute and chronic stress-induced changes at the cellular level, with a focus on a regulator of cellular stress, the chaperome, which is a protein assembly that encompasses molecular chaperones, co-chaperones and other co-factors. We discuss how the chaperome takes on distinct functions under conditions of stress that are executed in ways that differ from the one-on-one cyclic, dynamic functions exhibited by distinct molecular chaperones. We argue that through the formation of multimeric stable chaperome complexes, a state of chaperome hyperconnectivity, or networking, is gained. The role of these chaperome networks is to act as multimolecular scaffolds, a particularly important function in cancer, where they increase the efficacy and functional diversity of several cellular processes. We predict that these concepts will change how we develop and implement drugs targeting the chaperome to treat cancer.

Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression

The phenotypic consequences of a given mutation can vary across individuals. This so-called background effect is widely observed, from mutant fitness of loss-of-function variants in model organisms to variable disease penetrance and expressivity in humans; however, the underlying genetic basis often remains unclear. Taking insights gained from recent large-scale surveys of genetic interaction and suppression analyses in yeast, we propose that the genetic network context for a given mutation may shape its propensity of exhibiting background-dependent phenotypes. We argue that further efforts in systematically mapping the genetic interaction networks beyond yeast will provide not only key insights into the functional properties of genes, but also a better understanding of the background effects and the (un)predictability of traits in a broader context.

Introns provide a platform for intergenic regulatory feedback of RPL22 paralogs in yeast

Ribosomal protein genes (RPGs) in Saccharomyces cerevisiae are a remarkable regulatory group that may serve as a model for understanding genetic redundancy in evolutionary adaptations. Most RPGs exist as pairs of highly conserved functional paralogs with divergent untranslated regions and introns. We examined the roles of introns in strains with various combinations of intron and gene deletions in RPL22, RPL2, RPL16, RPL37, RPL17, RPS0, and RPS18 paralog pairs. We found that introns inhibited the expression of their genes in the RPL22 pair, with the RPL22B intron conferring a much stronger effect. While the WT RPL22A/RPL22B mRNA ratio was 93/7, the rpl22aΔi/RPL22B and RPL22A/rpl22bΔi ratios were >99/<1 and 60/40, respectively. The intron in RPL2A stimulated the expression of its own gene, but the removal of the other introns had little effect on expression of the corresponding gene pair. Rpl22 protein abundances corresponded to changes in mRNAs.

Using splicing reporters containing endogenous intron sequences, we demonstrated that these effects were due to the inhibition of splicing by Rpl22 proteins but not by their RNA-binding mutant versions. Indeed, only WT Rpl22A/Rpl22B proteins (but not the mutants) interacted in a yeast three-hybrid system with an RPL22B intronic region between bp 165 and 236. Transcriptome analysis showed that both the total level of Rpl22 and the A/B ratio were important for maintaining the WT phenotype. The data presented here support the contention that the Rpl22B protein has a paralog-specific role.

The RPL22 singleton of Kluyveromyces lactis, which did not undergo whole genome duplication, also responded to Rpl22-mediated inhibition in K. lactis cells. Vice versa, the overproduction of the K. lactis protein reduced the expression of RPL22A/B in S. cerevisiae. The extraribosomal function of of the K. lactis Rpl22 suggests that the loop regulating RPL22 paralogs of S. cerevisiae evolved from autoregulation.

Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.

Mapping a diversity of genetic interactions in yeast

Genetic interactions occur when the combination of multiple mutations yields an unexpected phenotype, and they may confound our ability to fully understand the genetic mechanisms underlying complex diseases. Genetic interactions are challenging to study because there are millions of possible different variant combinations within a given genome. Consequently, they have primarily been systematically explored in unicellular model organisms, such as yeast, with a focus on pairwise genetic interactions between loss-of-function alleles. However, there are many different types of genetic interactions, such as those occurring between gain-of-function or heterozygous mutations. Here, we review recent advances made in the systematic analysis of such diverse genetic interactions in yeast, and briefly discuss how similar studies could be undertaken in human cells.

Genetic analysis of the Candida albicans biofilm transcription factor network using simple and complex haploinsufficiency

Biofilm formation by Candida albicans is a key aspect of its pathobiology and is regulated by an integrated network of transcription factors (Bcr1, Brg1, Efg1, Ndt80, Rob1, and Tec1). To understand the details of how the transcription factors function together to regulate biofilm formation, we used a systematic genetic interaction approach based on generating all possible double heterozygous mutants of the network genes and quantitatively analyzing the genetic interactions between them. Overall, the network is highly susceptible to genetic perturbation with the six network heterozygous mutants all showing alterations in biofilm formation (haploinsufficiency). In addition, many double heterozygous mutants are as severely affected as homozygous deletions. As a result, the network shows properties of a highly interdependent ‘small-world’ network that is highly efficient but not robust. In addition, these genetic interaction data indicate that TEC1 represents a network component whose expression is highly sensitive to small perturbations in the function of other networks TFs. We have also found that expression of ROB1 is dependent on both auto-regulation and cooperative interactions with other network TFs. Finally, the heterozygous NDT80 deletion mutant is hyperfilamentous under both biofilm and hyphae-inducing conditions in a TEC1-dependent manner. Taken together, genetic interaction analysis of this network has provided new insights into the functions of individual TFs as well as into the role of the overall network topology in its function.

Biofilm formation is part and parcel of the ability of Candida albicans, a normal component of the human gastrointestinal microbial community, to cause disease. Recent work by many investigators has provided detailed information regarding how C. albicans converts to the biofilm stage of growth. The vast majority of these studies have involved the study of strains lacking a single gene that affects biofilm formation. Using this genetic approach in combination with other genome-wide techniques, a network of transcription factors was identified that play a crucial role in regulating biofilm-related processes. Here, we have systematically generated and characterized strains with mutations in two biofilm network transcription factors in order to determine how these genes interact. This represents the first systematic quantitative genetic interaction study in C. albicans and, by combining our results with that of previous labs, provides a new level of detail regarding the integrated functions of these transcription factors. In addition, our data indicate that the biofilm transcription factor network is very sensitive to genetic perturbation and propose that this fragility is a function of its small-world topography which, in turn, promotes network efficiency at the expense of robustness.

Mechanisms of suppression: The wiring of genetic resilience

Recent analysis of genome sequences has identified individuals that are healthy despite carrying severe disease-associated mutations. A possible explanation is that these individuals carry a second genomic perturbation that can compensate for the detrimental effects of the disease allele, a phenomenon referred to as suppression. In model organisms, suppression interactions are generally divided into two classes: genomic suppressors which are secondary mutations in the genome that bypass a mutant phenotype, and dosage suppression interactions in which overexpression of a suppressor gene rescues a mutant phenotype. Here, we describe the general properties of genomic and dosage suppression, with an emphasis on the budding yeast. We propose that suppression interactions between genetic variants are likely relevant for determining the penetrance of human traits. Consequently, an understanding of suppression mechanisms may guide the discovery of protective variants in healthy individuals that carry disease alleles, which could direct the rational design of new therapeutics.