Zebrafish models for the functional genomics of neurogenetic disorders

In this review, we consider recent work using zebrafish to validate and study the functional consequences of mutations of human genes implicated in a broad range of degenerative and developmental disorders of the brain and spinal cord. Also we present technical considerations for those wishing to study their own genes of interest by taking advantage of this easily manipulated and clinically relevant model organism. Zebrafish permit mutational analyses of genetic function (gain or loss of function) and the rapid validation of human variants as pathological mutations. In particular, neural degeneration can be characterized at genetic, cellular, functional, and behavioral levels. Zebrafish have been used to knock down or express mutations in zebrafish homologs of human genes and to directly express human genes bearing mutations related to neurodegenerative disorders such as spinal muscular atrophy, ataxia, hereditary spastic paraplegia, amyotrophic lateral sclerosis (ALS), epilepsy, Huntington's disease, Parkinson's disease, fronto-temporal dementia, and Alzheimer's disease. More recently, we have been using zebrafish to validate mutations of synaptic genes discovered by large-scale genomic approaches in developmental disorders such as autism, schizophrenia, and non-syndromic mental retardation. Advances in zebrafish genetics such as multigenic analyses and chemical genetics now offer a unique potential for disease research. Thus, zebrafish hold much promise for advancing the functional genomics of human diseases, the understanding of the genetics and cell biology of degenerative and developmental disorders, and the discovery of therapeutics. This article is part of a Special Issue entitled Zebrafish Models of Neurological Diseases.     

Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradients

Fisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in several situations, for example adaptation to antibiotics, but comes with limitations, so that more mechanistic approaches are often preferred to interpret experimental data. It might be possible however to extend FGM assumptions to better account for mutational data. This is theoretically challenging in the context of antibiotic resistance because resistance mutations are assumed to be rare. In this article, we show with Escherichia coli how the fitness effects of resistance mutations screened at different doses of nalidixic acid vary across a dose‐gradient. We found experimental patterns qualitatively consistent with the basic FGM (rate of resistance across doses, gamma distributed costs) but also unexpected patterns such as a decreasing mean cost of resistance with increasing screen dose. We show how different extensions involving mutational modules and variations in trait covariance across environments, can be discriminated based on these data. Overall, simple extensions of the FGM accounted well for complex mutational effects of resistance mutations across antibiotic doses.     

Role of Immediate Early Protein ICP27 in the Differential Sensitivity of Herpes Simplex Viruses 1 and 2 to Leptomycin B

Leptomycin B (LMB) is a highly specific inhibitor of CRM1, a cellular karyopherin-β that transports nuclear export signal-containing proteins from the nucleus to the cytoplasm. Previous work has shown that LMB blocks herpes simplex virus 1 (HSV-1) replication in Vero cells and that certain mutations in viral immediate early protein ICP27 can confer LMB resistance. However, little is known of the molecular mechanisms involved. Here we report that HSV-2, a close relative of HSV-1, is naturally resistant to LMB. To see whether the ICP27 gene determines this phenotypic difference, we generated an HSV-1 mutant that expresses the HSV-2 ICP27 instead of the HSV-1 protein. This recombinant was fully sensitive to LMB, indicating that one or more other viral genes must be important in determining HSV-2''s LMB-resistant phenotype. In additional work, we report several findings that shed light on how HSV-1 ICP27 mutations can confer LMB resistance. First, we show that LMB treatment of HSV-1-infected cells leads to suppression of late viral protein synthesis and a block to progeny virion release. Second, we identify a novel type of ICP27 mutation that can confer LMB resistance, that being the addition of a 100-residue amino-terminal affinity purification tag. Third, by studying infections where both LMB-sensitive and LMB-resistant forms of ICP27 are present, we show that HSV-1''s sensitivity to LMB is dominant to its resistance. Together, our results suggest a model in which the N-terminal portion of ICP27 mediates a nonessential activity that interferes with HSV-1 replication when CRM1 is inactive. We suggest that LMB resistance mutations weaken or abrogate this activity.     

Assessing in vivo mutation frequencies and creating a high-resolution genome-wide map of fitness costs of Hepatitis C virus

Like many viruses, Hepatitis C Virus (HCV) has a high mutation rate, which helps the virus adapt quickly, but mutations come with fitness costs. Fitness costs can be studied by different approaches, such as experimental or frequency-based approaches. The frequency-based approach is particularly useful to estimate in vivo fitness costs, but this approach works best with deep sequencing data from many hosts are. In this study, we applied the frequency-based approach to a large dataset of 195 patients and estimated the fitness costs of mutations at 7957 sites along the HCV genome. We used beta regression and random forest models to better understand how different factors influenced fitness costs. Our results revealed that costs of nonsynonymous mutations were three times higher than those of synonymous mutations, and mutations at nucleotides A or T had higher costs than those at C or G. Genome location had a modest effect, with lower costs for mutations in HVR1 and higher costs for mutations in Core and NS5B. Resistance mutations were, on average, costlier than other mutations. Our results show that in vivo fitness costs of mutations can be site and virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes.     

Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must be maintained to avoid deleterious consequences ¹. External or internal factors that disrupt this balance can lead to protein aggregation, toxicity and cell death. In humans this is a major contributing factor to the symptoms associated with neurodegenerative disorders such as Huntington''s, Parkinson''s, and Alzheimer''s diseases ¹⁰. It is therefore essential that the proteins involved in maintenance of proteostasis be identified in order to develop treatments for these debilitating diseases. This article describes techniques for monitoring in vivo protein folding at near-real time resolution using the model protein firefly luciferase fused to green fluorescent protein (FFL-GFP). FFL-GFP is a unique model chimeric protein as the FFL moiety is extremely sensitive to stress-induced misfolding and aggregation, which inactivates the enzyme ¹². Luciferase activity is monitored using an enzymatic assay, and the GFP moiety provides a method of visualizing soluble or aggregated FFL using automated microscopy. These coupled methods incorporate two parallel and technically independent approaches to analyze both refolding and functional reactivation of an enzyme after stress. Activity recovery can be directly correlated with kinetics of disaggregation and re-solubilization to better understand how protein quality control factors such as protein chaperones collaborate to perform these functions. In addition, gene deletions or mutations can be used to test contributions of specific proteins or protein subunits to this process. In this article we examine the contributions of the protein disaggregase Hsp104 ¹³, known to partner with the Hsp40/70/nucleotide exchange factor (NEF) refolding system ⁵, to protein refolding to validate this approach.     

Stress-response balance drives the evolution of a network module and its host genome

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two-component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra-module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra- and extra-module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine-tune the module''s noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.     

Genetic Architecture of Intrinsic Antibiotic Susceptibility

Background

Antibiotic exposure rapidly selects for more resistant bacterial strains, and both a drug''s chemical structure and a bacterium''s cellular network affect the types of mutations acquired.

Methodology/Principal Findings

To better characterize the genetic determinants of antibiotic susceptibility, we exposed a transposon-mutagenized library of Escherichia coli to each of 17 antibiotics that encompass a wide range of drug classes and mechanisms of action. Propagating the library for multiple generations with drug concentrations that moderately inhibited the growth of the isogenic parental strain caused the abundance of strains with even minor fitness advantages or disadvantages to change measurably and reproducibly. Using a microarray-based genetic footprinting strategy, we then determined the quantitative contribution of each gene to E. coli''s intrinsic antibiotic susceptibility. We found both loci whose removal increased general antibiotic tolerance as well as pathways whose down-regulation increased tolerance to specific drugs and drug classes. The beneficial mutations identified span multiple pathways, and we identified pairs of mutations that individually provide only minor decreases in antibiotic susceptibility but that combine to provide higher tolerance.

Conclusions/Significance

Our results illustrate that a wide-range of mutations can modulate the activity of many cellular resistance processes and demonstrate that E. coli has a large mutational target size for increasing antibiotic tolerance. Furthermore, the work suggests that clinical levels of antibiotic resistance might develop through the sequential accumulation of chromosomal mutations of small individual effect.     

A Structural Systems Biology Approach for Quantifying the Systemic Consequences of Missense Mutations in Proteins

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell''s behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.     

Convergence in pigmentation at multiple levels: mutations,genes and function

Convergence—the independent evolution of the same trait by two or more taxa—has long been of interest to evolutionary biologists, but only recently has the molecular basis of phenotypic convergence been identified. Here, we highlight studies of rapid evolution of cryptic coloration in vertebrates to demonstrate that phenotypic convergence can occur at multiple levels: mutations, genes and gene function. We first show that different genes can be responsible for convergent phenotypes even among closely related populations, for example, in the pale beach mice inhabiting Florida''s Gulf and Atlantic coasts. By contrast, the exact same mutation can create similar phenotypes in distantly related species such as mice and mammoths. Next, we show that different mutations in the same gene need not be functionally equivalent to produce similar phenotypes. For example, separate mutations produce divergent protein function but convergent pale coloration in two lizard species. Similarly, mutations that alter the expression of a gene in different ways can, nevertheless, result in similar phenotypes, as demonstrated by sister species of deer mice. Together these studies underscore the importance of identifying not only the genes, but also the precise mutations and their effects on protein function, that contribute to adaptation and highlight how convergence can occur at different genetic levels.     

Regulation of LRRK2 Stability by the E3 Ubiquitin Ligase CHIP

Dominantly inherited mutations in the leucine-rich repeat kinase 2 gene (LRRK2) are the most common cause of familial Parkinson''s disease (PD) and have also been identified in individuals with sporadic PD. Although the exact cellular function of LRRK2 remains unknown, most PD-linked mutations appear to be toxic to cells in culture via mechanisms that depend on the kinase activity of LRRK2 or on the formation of cytoplasmic inclusions. Here we show that the E3 ubiquitin ligase CHIP physically associates with LRRK2 and regulates the cellular abundance of LRRK2. We further show that LRRK2 forms a complex with overexpressed and endogenous CHIP and Hsp90. Our data indicates that the destabilization of LRRK2 by CHIP is due to ubiquitination and proteasome-dependent degradation. Hsp90 can attenuate CHIP-mediated degradation and this can be blocked by the Hsp90 inhibitor geldanamycin. These findings provide important insight into the cellular regulation of LRRK2 stability and may lead to the development of therapeutics to treat PD based on controlling LRRK2 stability.     

Toward spatio‐temporal delineation of positive interactions in ecology

Given unprecedented rates of biodiversity loss, there is an urgency to better understand the ecological consequences of interactions among organisms that may lost or altered. Positive interactions among organisms of the same or different species that directly or indirectly improve performance of at least one participant can structure populations and communities and control ecosystem process. However, we are still in need of synthetic approaches to better understand how positive interactions scale spatio‐temporally across a range of taxa and ecosystems. Here, we synthesize two complementary approaches to more rigorously describe positive interactions and their consequences among organisms, across taxa, and over spatio‐temporal scales. In the first approach, which we call the mechanistic approach, we make a distinction between two principal mechanisms of facilitation—habitat modification and resource modification. Considering the differences in these two mechanisms is critical because it delineates the potential spatio‐temporal bounds over which a positive interaction can occur. We offer guidance on improved sampling regimes for quantification of these mechanistic interactions and their consequences. Second, we present a trait‐based approach in which traits of facilitators or traits of beneficiaries can modulate their magnitude of effect or how they respond to either of the positive interaction mechanisms, respectively. Therefore, both approaches can be integrated together by quantifying the degree to which a focal facilitator's or beneficiary's traits explain the magnitude of a positive effect in space and time. Furthermore, we demonstrate how field measurements and analytical techniques can be used to collect and analyze data to test the predictions presented herein. We conclude by discussing how these approaches can be applied to contemporary challenges in ecology, such as conservation and restoration and suggest avenues for future research.     

Disruptive membrane interactions of alpha-synuclein aggregates

Alpha synuclein (αS) is a ~14 kDa intrinsically disordered protein. Decades of research have increased our knowledge on αS yet its physiological function remains largely elusive. The conversion of monomeric αS into oligomers and amyloid fibrils is believed to play a central role of the pathology of Parkinson's disease (PD). It is becoming increasingly clear that the interactions of αS with cellular membranes are important for both αS's functional and pathogenic actions. Therefore, understanding interactions of αS with membranes seems critical to uncover functional or pathological mechanisms. This review summarizes our current knowledge of how physicochemical properties of phospholipid membranes affect the binding and aggregation of αS species and gives an overview of how post-translational modifications and point mutations in αS affect phospholipid membrane binding and protein aggregation. We discuss the disruptive effects resulting from the interaction of αS aggregate species with membranes and highlight current approaches and hypotheses that seek to understand the pathogenic and/or protective role of αS in PD.     

Harnessing the power of yeast to unravel the molecular basis of neurodegeneration

Several neurodegenerative diseases, such as Parkinson's disease (PD), Alzheimer's disease (AD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), or prion diseases, are known for their intimate association with protein misfolding and aggregation. These disorders are characterized by the loss of specific neuronal populations in the brain and are highly associated with aging, suggesting a decline in proteostasis capacity may contribute to pathogenesis. Nevertheless, the precise molecular mechanisms that lead to the selective demise of neurons remain poorly understood. As a consequence, appropriate therapeutic approaches and effective treatments are largely lacking. The development of cellular and animal models that faithfully reproduce central aspects of neurodegeneration has been crucial for advancing our understanding of these diseases. Approaches involving the sequential use of different model systems, starting with simpler cellular models and ending with validation in more complex animal models, resulted in the discovery of promising therapeutic targets and small molecules with therapeutic potential. Within this framework, the simple and well‐characterized eukaryote Saccharomyces cerevisiae, also known as budding yeast, is being increasingly used to study the molecular basis of several neurodegenerative disorders. Yeast provides an unprecedented toolbox for the dissection of complex biological processes and pathways. Here, we summarize how yeast models are adding to our current understanding of several neurodegenerative disorders.     

Determination of half-maximal inhibitory concentration using biosensor-based protein interaction analysis

Half-maximal inhibitory concentration (IC₅₀) is the most widely used and informative measure of a drug's efficacy. It indicates how much drug is needed to inhibit a biological process by half, thus providing a measure of potency of an antagonist drug in pharmacological research. Most approaches to determine IC₅₀ of a pharmacological compound are based on assays that utilize whole cell systems. While they generally provide outstanding potency information, results can depend on the experimental cell line used and may not differentiate a compound's ability to inhibit specific interactions. Here we show using the secreted Transforming Growth Factor-β (TGF-β) family ligand BMP-4 and its receptors as example that surface plasmon resonance can be used to accurately determine IC₅₀ values of individual ligand-receptor pairings. The molecular resolution achievable wih this approach can help distinguish inhibitors that specifically target individual complexes, or that can inhibit multiple functional interactions at the same time.     

Bacteriophage lambda overcomes a perturbation in its host–viral genetic network through mutualism and evolution of life history traits*

An important driver of evolution in viruses is natural selection to optimize the use of their hosts’ genetic network. To learn how viruses respond to this pressure, we disrupted the genetic network of Escherichia coli to inhibit replication of its virus, bacteriophage lambda, and then observed how λ evolved to compensate. We deleted E. coli's dnaJ gene, which lambda uses to initiate DNA replication. Lambda partially restored its ability to reproduce with just two adaptive mutations associated with genes J and S. The location of the mutations was unexpected because they were not in genes that directly interact with DnaJ, rather they affected seemingly unrelated life history traits. A nonsynonymous J mutation increased lambda's adsorption rate and an S regulatory mutation delayed lysis timing. Lambda also recovered some of its reproductive potential through intracellular mutualism. This study offers two important lessons: first, viruses can rapidly adapt to disruptive changes in their host's genetic network. Second, organisms can employ mechanisms thought to operate at the population scale, such as evolution of life history traits and social interactions, in order to overcome hurdles at the molecular level. As life science research progresses and new fields become increasingly specialized, these results remind us of the importance of multiscale and interdisciplinary approaches to understand adaptation.     

Multicolor Time-lapse Imaging of Transgenic Zebrafish: Visualizing Retinal Stem Cells Activated by Targeted Neuronal Cell Ablation

High-resolution time-lapse imaging of living zebrafish larvae can be utilized to visualize how biological processes unfold (for review see ¹). Compound transgenic fish which express different fluorescent reporters in neighboring cell types provide a means of following cellular interactions ² and/or tissue-level responses to experimental manipulations over time. In this video, we demonstrate methods that can be used for imaging multiple transgenically labeled cell types serially in individual fish over time courses that can span from minutes to several days. The techniques described are applicable to any study seeking to correlate the "behavior" of neighboring cells types over time, including: 1) serial ''catch and release'' methods for imaging a large number of fish over successive days, 2) simplified approaches for separating fluorophores with overlapping excitation/emission profiles (e.g., GFP and YFP), 3) use of hypopigmented mutant lines to extend the time window available for high-resolution imaging into late larval stages of development, 4) use of membrane targeted fluorescent reporters to reveal fine morphological detail of individual cells as well as cellular details in larger populations of cells, and 5) a previously described method for chemically-induced ablation of transgenically targeted cell types; i.e., nitroreductase (NTR) mediated conversion of prodrug substrates, such as metronidazole (MTZ), to cytotoxic derivatives ^3,5.As an example of these approaches, we will visualize the ablation and regeneration of a subtype of retinal bipolar neuron within individual fish over several days. Simultaneously we will monitor several other retinal cell types, including neighboring non-targeted bipolar cells and potential degeneration-stimulated retinal stem cells (i.e., Mϋller glia). This strategy is being applied in our lab to characterize cell- and tissue-level (e.g., stem cell niche) responses to the selective loss and regeneration of targeted neuronal cell types.     

Beyond isohydricity: The role of environmental variability in determining plant drought responses

Despite the appeal of the iso/anisohydric framework for classifying plant drought responses, recent studies have shown that such classifications can be strongly affected by a plant's environment. Here, we present measured in situ drought responses to demonstrate that apparent isohydricity can be conflated with environmental conditions that vary over space and time. In particular, we (a) use data from an oak species (Quercus douglasii) during the 2012–2015 extreme drought in California to demonstrate how temporal and spatial variability in the environment can influence plant water potential dynamics, masking the role of traits; (b) explain how these environmental variations might arise from climatic, topographic, and edaphic variability; (c) illustrate, through a “common garden” thought experiment, how existing trait‐based or response‐based isohydricity metrics can be confounded by these environmental variations, leading to Type‐1 (false positive) and Type‐2 (false negative) errors; and (d) advocate for the use of model‐based approaches for formulating alternate classification schemes. Building on recent insights from greenhouse and vineyard studies, we offer additional evidence across multiple field sites to demonstrate the importance of spatial and temporal drivers of plants' apparent isohydricity. This evidence challenges the use of isohydricity indices, per se, to characterize plant water relations at the global scale.     

Cooperative modulation by eIF4G of eIF4E-binding to the mRNA 5' cap in yeast involves a site partially shared by p20.

Interaction between the mRNA 5''-cap-binding protein eIF4E and the multiadaptor protein eIF4G has been demonstrated in all eukaryotic translation assemblies examined so far. This study uses immunological, genetic and biochemical methods to map the surface amino acids on eIF4E that contribute to eIF4G binding. Cap-analogue chromatography and surface plasmon resonance (SPR) analyses demonstrate that one class of mutations in these surface regions disrupts eIF4E-eIF4G association, and thereby polysome formation and growth. The residues at these positions in wild-type eIF4E mediate positive cooperativity between the binding of eIF4G to eIF4E and the latter''s cap-affinity. Moreover, two of the mutations confer temperature sensitivity in eIF4G binding to eIF4E which correlates with the formation of large numbers of inactive ribosome 80S couples in vivo and the loss of cellular protein synthesis activity. The yeast 4E-binding protein p20 is estimated by SPR to have a ten times lower binding affinity than eIF4G for eIF4E. Investigation of a second class of eIF4E mutations reveals that p20 shares only part of eIF4G''s binding site on the cap-binding protein. The results presented provide a basis for understanding how cycling of eIF4E and eIF4G occurs in yeast translation and explains how p20 can act as a fine, but not as a coarse, regulator of protein synthesis.