Translation deregulation in human disease.

Advances in sequencing and high-throughput techniques have provided an unprecedented opportunity to interrogate human diseases on a genome-wide scale. The list of disease-causing mutations is expanding rapidly, and mutations affecting mRNA translation are no exception. Translation (protein synthesis) is one of the most complex processes in the cell. The orchestrated action of ribosomes, tRNAs and numerous translation factors decodes the information contained in mRNA into a polypeptide chain. The intricate nature of this process renders it susceptible to deregulation at multiple levels. In this Review, we summarize current evidence of translation deregulation in human diseases other than cancer. We discuss translation-related diseases on the basis of the molecular aberration that underpins their pathogenesis (including tRNA dysfunction, ribosomopathies, deregulation of the integrated stress response and deregulation of the mTOR pathway) and describe how deregulation of translation generates the phenotypic variability observed in these disorders.

Modeling of African population history using f-statistics is biased when applying all previously proposed SNP ascertainment schemes.

f-statistics have emerged as a first line of analysis for making inferences about demographic history from genome-wide data. Not only are they guaranteed to allow robust tests of the fits of proposed models of population history to data when analyzing full genome sequencing data-that is, all single nucleotide polymorphisms (SNPs) in the individuals being analyzed-but they are also guaranteed to allow robust tests of models for SNPs ascertained as polymorphic in a population that is an outgroup in a phylogenetic sense to all groups being analyzed. True "outgroup ascertainment" is in practice impossible in humans because our species has arisen from a substructured ancestral population that does not descend from a homogeneous ancestral population going back many hundreds of thousands of years into the past. However, initial studies suggested that non-outgroup-ascertainment schemes might produce robust enough results using f-statistics, and that motivated widespread fitting of models to data using non-outgroup-ascertained SNP panels such as the "Affymetrix Human Origins array" which has been genotyped on thousands of modern individuals from hundreds of populations, or the "1240k" in-solution enrichment reagent which has been the source of about 70% of published genome-wide data for ancient humans. In this study, we show that while analyses of population history using such panels work well for studies of relationships among non-African populations and one African outgroup, when co-modeling more than one sub-Saharan African and/or archaic human groups (Neanderthals and Denisovans), fitting of f-statistics to such SNP sets is expected to frequently lead to false rejection of true demographic histories, and failure to reject incorrect models. Analyzing panels of SNPs polymorphic in archaic humans, which has been suggested as a solution for the ascertainment problem, has limited statistical power and retains important biases. However, by carrying out simulations of diverse demographic histories, we show that bias in inferences based on f-statistics can be minimized by ascertaining on variants common in a union of diverse African groups; such ascertainment retains high statistical power while allowing co-analysis of archaic and modern groups.

A quantile integral linear model to quantify genetic effects on phenotypic variability.

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

Leveraging phenotypic variability to identify genetic interactions in human phenotypes.

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

YAP/TAZ deficiency reprograms macrophage phenotype and improves infarct healing and cardiac function after myocardial infarction.

Adverse cardiac remodeling after myocardial infarction (MI) causes structural and functional changes in the heart leading to heart failure. The initial post-MI pro-inflammatory response followed by reparative or anti-inflammatory response is essential for minimizing the myocardial damage, healing, and scar formation. Bone marrow-derived macrophages (BMDMs) are recruited to the injured myocardium and are essential for cardiac repair as they can adopt both pro-inflammatory or reparative phenotypes to modulate inflammatory and reparative responses, respectively. Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are the key mediators of the Hippo signaling pathway and are essential for cardiac regeneration and repair. However, their functions in macrophage polarization and post-MI inflammation, remodeling, and healing are not well established. Here, we demonstrate that expression of YAP and TAZ is increased in macrophages undergoing pro-inflammatory or reparative phenotype changes. Genetic deletion of YAP/TAZ leads to impaired pro-inflammatory and enhanced reparative response. Consistently, YAP activation enhanced pro-inflammatory and impaired reparative response. We show that YAP/TAZ promote pro-inflammatory response by increasing interleukin 6 (IL6) expression and impede reparative response by decreasing Arginase-I (Arg1) expression through interaction with the histone deacetylase 3 (HDAC3)-nuclear receptor corepressor 1 (NCoR1) repressor complex. These changes in macrophages polarization due to YAP/TAZ deletion results in reduced fibrosis, hypertrophy, and increased angiogenesis, leading to improved cardiac function after MI. Also, YAP activation augmented MI-induced cardiac fibrosis and remodeling. In summary, we identify YAP/TAZ as important regulators of macrophage-mediated pro-inflammatory or reparative responses post-MI.

Maternal age generates phenotypic variation in Caenorhabditis elegans.

Genetically identical individuals that grow in the same environment often show substantial phenotypic variation within populations of organisms as diverse as bacteria, nematodes, rodents and humans. With some exceptions, the causes are poorly understood. Here we show that isogenic Caenorhabditis elegans nematodes vary in their size at hatching, speed of development, growth rate, starvation resistance, fecundity, and also in the rate of development of their germline relative to that of somatic tissues. We show that the primary cause of this variation is the age of an individual's mother, with the progeny of young mothers exhibiting several phenotypic impairments. We identify age-dependent changes in the maternal provisioning of the lipoprotein complex vitellogenin to embryos as the molecular mechanism that underlies the variation in multiple traits throughout the life of an animal. The production of sub-optimal progeny by young mothers may reflect a trade-off between the competing fitness traits of a short generation time and the survival and fecundity of the progeny.

Pheno-RNA, a method to associate genes with a specific phenotype, identifies genes linked to cellular transformation.

Cellular transformation is associated with dramatic changes in gene expression, but it is difficult to determine which regulated genes are oncogenically relevant. Here we describe Pheno-RNA, a general approach to identifying candidate genes associated with a specific phenotype. Specifically, we generate a "phenotypic series" by treating a nontransformed breast cell line with a wide variety of molecules that induce cellular transformation to various extents. By performing transcriptional profiling across this phenotypic series, the expression profile of every gene can be correlated with the strength of the transformed phenotype. We identify ∼200 genes whose expression profiles are very highly correlated with the transformation phenotype, strongly suggesting their importance in transformation. Within biological categories linked to cancer, some genes show high correlations with the transformed phenotype, but others do not. Many genes whose expression profiles are highly correlated with transformation have never been associated with cancer, suggesting the involvement of heretofore unknown genes in cancer.

A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

Data integration uncovers the metabolic bases of phenotypic variation in yeast.

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results. This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

Survey on phenotypic resistance in Enterococcus faecalis: comparison between the expression of biofilm-associated genes in Enterococcus faecalis persister and non-persister cells.

BACKGROUND: Phenotypic resistance is considered as a serious therapeutic challenge for which a definitive remedy has not been discovered yet. Biofilm and persister cell formation are two well-studied phenotypic resistance phenomena, leading to the recalcitrance and relapse of different types of chronic infections. The presence of persister cells in biofilm structures seems to be one of the main factors contributing to the relapse of infections and treatment failure. Given the dormant and inert nature of persister cells, they can be easy targets for the immune system factors. Biofilm formation can be a survival strategy for the defenseless persister cells. Thus, this study was aimed to evaluate the expression of biofilm-associated genes in Enterococcus faecalis persister and non-persister cells. METHODS: Vancomycin susceptibility and biofilm formation ability were investigated among 95 E. faecalis clinical isolates using microtiter broth dilution and microtiter plate assays, respectively. PCR was used to determine the presence of biofilm-related genes (gelE, esp, and agg) among the vancomycin-susceptible, biofilm producer E. faecalis isolates (91 isolates). Minimum bactericidal concentration for biofilms (MBCB) were determined for vancomycin using the MTP assay. Bacterial persister assay was performed using an enzymatic lysis assay. Finally, the expression of biofilm-related genes was compared between the persister and non-persister isolates of E. faecalis using real-time qPCR. RESULTS: E. faecalis isolates showed a high level of susceptibility (95.8%) to vancomycin (MIC < 1 µg/mL). The gelE, esp, and agg genes were found in 91 (100%), 72 (79.12), and 74 (81.32) of the isolates, respectively. All the E. faecalis isolates were tolerant to vancomycin in the biofilm condition, showing a MBCB of > 2500 µg/mL. Based on the enzymatic lysis assay, only 3 isolates, out of the 91, had the ability to form persister cells. The expression of biofilm-associated genes was higher among the persister compared to non-persister E. faecalis isolates. CONCLUSIONS: Biofilm-associated persister cells indicated a high vancomycin tolerance compared to non-persister cells. Moreover, persister isolates showed a higher tendency for biofilm formation and a higher expression level of the biofilm-associated genes, compared to non-persister isolates.

An estimator of first coalescent time reveals selection on young variants and large heterogeneity in rare allele ages among human populations.

Allele age has long been a focus of population genetic research, primarily because it can be an important clue to the fitness effects of an allele. By virtue of their effects on fitness, alleles under directional selection are expected to be younger than neutral alleles of the same frequency. We developed a new coalescent-based estimator of a close proxy for allele age, the time when a copy of an allele first shares common ancestry with other chromosomes in a sample not carrying that allele. The estimator performs well, including for the very rarest of alleles that occur just once in a sample, with a bias that is typically negative. The estimator is mostly insensitive to population demography and to factors that can arise in population genomic pipelines, including the statistical phasing of chromosomes. Applications to 1000 Genomes Data and UK10K genome data confirm predictions that singleton alleles that alter proteins are significantly younger than those that do not, with a greater difference in the larger UK10K dataset, as expected. The 1000 Genomes populations varied markedly in their distributions for singleton allele ages, suggesting that these distributions can be used to inform models of demographic history, including recent events that are only revealed by their impacts on the ages of very rare alleles.

New insight into bacterial social communication in natural host: Evidence for interplay of heterogeneous and unison quorum response.

Many microbes exhibit quorum sensing (QS) to cooperate, share and perform a social task in unison. Recent studies have shown the emergence of reversible phenotypic heterogeneity in the QS-responding pathogenic microbial population under laboratory conditions as a possible bet-hedging survival strategy. However, very little is known about the dynamics of QS-response and the nature of phenotypic heterogeneity in an actual host-pathogen interaction environment. Here, we investigated the dynamics of QS-response of a Gram-negative phytopathogen Xanthomonas pv. campestris (Xcc) inside its natural host cabbage, that communicate through a fatty acid signal molecule called DSF (diffusible signal factor) for coordination of several social traits including virulence functions. In this study, we engineered a novel DSF responsive whole-cell QS dual-bioreporter to measure the DSF mediated QS-response in Xcc at the single cell level inside its natural host plant in vivo. Employing the dual-bioreporter strain of Xcc, we show that QS non-responsive cells coexist with responsive cells in microcolonies at the early stage of the disease; whereas in the late stages, the QS-response is more homogeneous as the QS non-responders exhibit reduced fitness and are out competed by the wild-type. Furthermore, using the wild-type Xcc and its QS mutants in single and mixed infection studies, we show that QS mutants get benefit to some extend at the early stage of disease and contribute to localized colonization. However, the QS-responding cells contribute to spread along xylem vessel. These results contrast with the earlier studies describing that expected cross-induction and cooperative sharing at high cell density in vivo may lead to synchronize QS-response. Our findings suggest that the transition from heterogeneity to homogeneity in QS-response within a bacterial population contributes to its overall virulence efficiency to cause disease in the host plant under natural environment.

Genetic determinants of gut microbiota composition and bile acid profiles in mice.

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions.

Phenotypic Effects of Substitutions within the Receptor Binding Site of Highly Pathogenic Avian Influenza H5N1 Virus Observed during Human Infection.

Highly pathogenic avian influenza (HPAI) viruses are enzootic in wild birds and poultry and continue to cause human infections with high mortality. To date, more than 850 confirmed human cases of H5N1 virus infection have been reported, of which ∼60% were fatal. Global concern persists that these or similar avian influenza viruses will evolve into viruses that can transmit efficiently between humans, causing a severe influenza pandemic. It was shown previously that a change in receptor specificity is a hallmark for adaptation to humans and evolution toward a transmittable virus. Substantial genetic diversity was detected within the receptor binding site of hemagglutinin of HPAI A/H5N1 viruses, evolved during human infection, as detected by next-generation sequencing. Here, we investigated the functional impact of substitutions that were detected during these human infections. Upon rescue of 21 mutant viruses, most substitutions in the receptor binding site (RBS) resulted in viable virus, but virus replication, entry, and stability were often impeded. None of the tested substitutions individually resulted in a clear switch in receptor preference as measured with modified red blood cells and glycan arrays. Although several combinations of the substitutions can lead to human-type receptor specificity, accumulation of multiple amino acid substitutions within a single hemagglutinin during human infection is rare, thus reducing the risk of virus adaptation to humans.IMPORTANCE H5 viruses continue to be a threat for public health. Because these viruses are immunologically novel to humans, they could spark a pandemic when adapted to transmit between humans. Avian influenza viruses need several adaptive mutations to bind to human-type receptors, increase hemagglutinin (HA) stability, and replicate in human cells. However, knowledge on adaptive mutations during human infections is limited. A previous study showed substantial diversity within the receptor binding site of H5N1 during human infection. We therefore analyzed the observed amino acid changes phenotypically in a diverse set of assays, including virus replication, stability, and receptor specificity. None of the tested substitutions resulted in a clear step toward a human-adapted virus capable of aerosol transmission. It is notable that acquiring human-type receptor specificity needs multiple amino acid mutations, and that variability at key position 226 is not tolerated, reducing the risk of them being acquired naturally.

Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor.

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.

Craniofacial features of 3q29 deletion syndrome: Application of next-generation phenotyping technology.

3q29 deletion syndrome (3q29del) is a recurrent deletion syndrome associated with neuropsychiatric disorders and congenital anomalies. Dysmorphic facial features have been described but not systematically characterized. This study aims to detail the 3q29del craniofacial phenotype and use a machine learning approach to categorize individuals with 3q29del through analysis of 2D photos. Detailed dysmorphology exam and 2D facial photos were ascertained from 31 individuals with 3q29del. Photos were used to train the next-generation phenotyping algorithm DeepGestalt (Face2Gene by FDNA, Inc, Boston, MA) to distinguish 3q29del cases from controls and all other recognized syndromes. Area under the curve of receiver operating characteristic curves (AUC-ROC) was used to determine the capacity of Face2Gene to identify 3q29del cases against controls. In this cohort, the most common observed craniofacial features were prominent forehead (48.4%), prominent nose tip (35.5%), and thin upper lip vermillion (25.8%). The FDNA technology showed an ability to distinguish cases from controls with an AUC-ROC value of 0.873 (p = 0.006) and led to the inclusion of 3q29del as one of the supported syndromes. This study found a recognizable facial pattern in 3q29del, as observed by trained clinical geneticists and next-generation phenotyping technology. These results expand the potential application of automated technology such as FDNA in identifying rare genetic syndromes, even when facial dysmorphology is subtle.

An intrafamilial phenotypic variability in Ellis-Van Creveld syndrome due to a novel 27 bps deletion mutation.

Ellis-van Creveld (EvC) syndrome is an autosomal recessive disease, characterized by ectodermal, skeletal, and cardiac anomalies. We report intrafamilial phenotypic variability in three new EvC syndrome cases. Affected males in this study showed only ectodermal abnormalities, whereas an affected female showed the classical presentation of EvC Syndrome, including bilateral postaxial polydactyly of hands and feet, and congenital heart defects. Whole exome sequencing was performed to identify the causative variant, followed by validation and segregation analysis using Sanger sequencing. A homozygous deletion variant (c.731_757del) was identified in exon 6 of the EVC gene (NM_153717.2). The identified variant is considered to be the most likely candidate variant for the EvC syndrome in the family based on previous reports validating the role of EVC variants in the EvC syndrome. The disease correctly segregated in the family members, as all affected members were homozygous, and obligate carriers were heterozygous. Our family is remarkable in highlighting the variable expressivity of the EvC phenotype within the same family, due to a homozygous deletion mutation in the EVC gene. The variable expressivity might be due to the hypomorphic nature of mutation, or the presence of additional variants in modifier genes or in the regulatory regions of the EVC/EVC2 genes.

A high-throughput and open-source platform for embryo phenomics.

Phenomics has the potential to facilitate significant advances in biology but requires the development of high-throughput technologies capable of generating and analysing high-dimensional data. There are significant challenges associated with building such technologies, not least those required for investigating dynamic processes such as embryonic development, during which high rates of temporal, spatial, and functional change are inherently difficult to capture. Here, we present EmbryoPhenomics, an accessible high-throughput platform for phenomics in aquatic embryos comprising an Open-source Video Microscope (OpenVIM) that produces high-resolution videos of multiple embryos under tightly controlled environmental conditions. These videos are then analysed by the Python package Embryo Computer Vision (EmbryoCV), which extracts phenomic data for morphological, physiological, behavioural, and proxy traits during the process of embryonic development. We demonstrate the broad-scale applicability of EmbryoPhenomics in a series of experiments assessing chronic, acute, and multistressor responses to environmental change (temperature and salinity) in >30 million images of >600 embryos of two species with markedly different patterns of development-the pond snail Radix balthica and the marine amphipod Orchestia gammarellus. The challenge of phenomics is significant but so too are the rewards, and it is particularly relevant to the urgent task of assessing complex organismal responses to current rates of environmental change. EmbryoPhenomics can acquire and process data capturing functional, temporal, and spatial responses in the earliest, most dynamic life stages and is potentially game changing for those interested in studying development and phenomics more widely.

Heterologous Hsp90 promotes phenotypic diversity through network evolution.

Biological processes in living cells are often carried out by gene networks in which signals and reactions are integrated through network hubs. Despite their functional importance, it remains unclear to what extent network hubs are evolvable and how alterations impact long-term evolution. We investigated these issues using heat shock protein 90 (Hsp90), a central hub of proteostasis networks. When native Hsp90 in Saccharomyces cerevisiae cells was replaced by the ortholog from hypersaline-tolerant Yarrowia lipolytica that diverged from S. cerevisiae about 270 million years ago, the cells exhibited improved growth in hypersaline environments but compromised growth in others, indicating functional divergence in Hsp90 between the two yeasts. Laboratory evolution shows that evolved Y. lipolytica-HSP90-carrying S. cerevisiae cells exhibit a wider range of phenotypic variation than cells carrying native Hsp90. Identified beneficial mutations are involved in multiple pathways and are often pleiotropic. Our results show that cells adapt to a heterologous Hsp90 by modifying different subnetworks, facilitating the evolution of phenotypic diversity inaccessible to wild-type cells.

Pharmacometrics-Based Considerations for the Design of a Pharmacogenomic Clinical Trial Assessing Irinotecan Safety.

PURPOSE: Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity. METHODS: Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing. RESULTS: The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar's test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power. CONCLUSIONS: The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.