scholarly journals Evolutionary Highways to Persistent Bacterial Infection

2018 ◽  
Author(s):  
Jennifer A Bartell ◽  
Lea M Sommer ◽  
Janus A J Haagensen ◽  
Anne Loch ◽  
Rocio Espinosa ◽  
...  
Keyword(s):  
Complex Trait ◽  
Host Finding ◽  
Patient Specific ◽  
Rapid Adaptation ◽  
Multiple Traits ◽  
New Associations ◽  
Persistent Infections ◽  
Evolutionary Trajectories ◽  
Pathoadaptive Mutations ◽  
Trait Correlations

ABSTRACTPersistent infections require bacteria to evolve from their naïve colonization state by optimizing fitness in the host. This optimization involves coordinated adaptation of multiple traits, obscuring evolutionary trends and complicating infection management. Accordingly, we screen 8 infection-relevant phenotypes of 443 longitudinal Pseudomonas aeruginosa isolates from 39 young cystic fibrosis patients over 10 years. Using statistical modeling, we map evolutionary trajectories and identify trait correlations accounting for patient-specific influences. By integrating previous genetic analyses of 474 isolates, we provide a window into early adaptation to the host, finding: 1) a 2-3 year timeline of rapid adaptation after colonization, 2) variant “naïve” and “adapted” states reflecting discordance between phenotypic and genetic adaptation, 3) adaptive trajectories leading to persistent infection via 3 distinct evolutionary modes, and 4) new associations between phenotypes and pathoadaptive mutations. Ultimately, we effectively deconvolute complex trait adaptation, offering a framework for evolutionary studies and precision medicine in clinical microbiology.

scholarly journals The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga

2020 ◽  
Author(s):  
Nathan G. Walworth ◽  
Jana Hinners ◽  
Phoebe A. Argyle ◽  
Suzana G. Leles ◽  
Martina A. Doblin ◽  
...  
Keyword(s):  
De Novo ◽  
Principal Component ◽  
Biogeochemical Cycling ◽  
Microbial Evolution ◽  
Interactive Effects ◽  
Ecosystem Models ◽  
Evolutionary Trajectories ◽  
Adaptive Walks ◽  
Sheer Number ◽  
Trait Correlations

AbstractMicrobes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here we present an approach for modeling the interactive effects of de novo biological change and multivariate trait correlation evolution using principal component axes. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO2. We found that only a limited number of phenotypes emerged. Applying adaptive trait correlations to the starting population (historical bias) accelerated adaptation while highly convergent, nonrandom phenotypic solutions emerged irrespective of bias. These findings are consistent with a limited set of evolutionary trajectories underlying the vast amount of possible trait combinations (phenotypes). Critically, we demonstrate that these dynamics emerge in an empirically defined multidimensional trait space and show that trait correlations, in addition to trait values, must evolve to explain multi-trait adaptation. Identifying high probability high-fitness outcomes based on trait correlations is necessary in order to connect microbial evolutionary responses to biogeochemical cycling, thereby enabling the incorporation of these dynamics into global ecosystem models.

scholarly journals CancerTracer: a curated database for intrapatient tumor heterogeneity

2019 ◽  
Author(s):  
Chen Wang ◽  
Jian Yang ◽  
Hong Luo ◽  
Kun Wang ◽  
Yu Wang ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Predictive Biomarkers ◽  
Patient Specific ◽  
Intratumor Heterogeneity ◽  
Cancer Therapies ◽  
Specific Tumor ◽  
Multiple Biopsies ◽  
Evolutionary Trajectories ◽  
Personalized Cancer ◽  
Clonal Composition

Abstract Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we’ve named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies.

Time spent together and time spent apart affect song, feather colour and range overlap in tinkerbirds

2020 ◽  
Vol 129 (2) ◽  
pp. 439-458 ◽  
Author(s):  
Alexander N G Kirschel ◽  
Emmanuel C Nwankwo ◽  
Nadya Seal ◽  
Gregory F Grether
Keyword(s):  
Complex Trait ◽  
Character Displacement ◽  
Sub Saharan Africa ◽  
Multiple Traits ◽  
Phenotypic Differences ◽  
Interacting Species ◽  
Evolutionary Diversification ◽  
Sub Saharan ◽  
Range Overlap ◽  
Reproductive Processes

Abstract Most studies on the processes driving evolutionary diversification highlight the importance of genetic drift in geographical isolation and natural selection across ecological gradients. Direct interactions among related species have received much less attention, but they can lead to character displacement, with recent research identifying patterns of displacement attributed to either ecological or reproductive processes. Together, these processes could explain complex, trait-specific patterns of diversification. Few studies, however, have examined the possible effects of these processes together or compared the divergence in multiple traits between interacting species among contact zones. Here, we show how traits of two Pogoniulus tinkerbird species vary among regions across sub-Saharan Africa. However, in addition to variation between regions consistent with divergence in refugial isolation, both song and morphology diverge between the species where they coexist. In West Africa, where the species are more similar in plumage, there is possible competitive or reproductive exclusion. In Central and East Africa, patterns of variation are consistent with agonistic character displacement. Molecular analyses support the hypothesis that differences in the age of interaction among regions can explain why species have evolved phenotypic differences and coexist in some regions but not others. Our findings suggest that competitive interactions between species and the time spent interacting, in addition to the time spent in refugial isolation, play important roles in explaining patterns of species diversification.

scholarly journals A systematic genetic analysis and visualization of phenotypic heterogeneity among orofacial cleft GWAS signals

2018 ◽  
Author(s):  
Jenna C. Carlson ◽  
Deepti Anand ◽  
Azeez Butali ◽  
Carmen J. Buxo ◽  
Kaare Christensen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Expression Patterns ◽  
Complex Trait ◽  
Genetic Effects ◽  
Phenotypic Heterogeneity ◽  
New Associations ◽  
Orofacial Clefting ◽  
Shared Genetic

AbstractPhenotypic heterogeneity is a hallmark of complex traits, and genetic studies of such traits may focus on them as a single diagnostic entity or by analyzing specific components. For example, in orofacial clefting (OFC), three subtypes – cleft lip (CL), cleft lip and palate (CLP), and cleft palate (CP) have been studied separately and in combination. To further dissect the genetic architecture of OFCs and how a given associated locus may be contributing to distinct subtypes of a trait we developed a framework for quantifying and interpreting evidence of subtype-specific or shared genetic effects in complex traits. We applied this technique to create a “cleft map” of the association of 30 genetic loci with three OFC subtypes. In addition to new associations, we found loci with subtype-specific effects (e.g., GRHL3 (CP), WNT5A (CLP)), as well as loci associated with two or all three subtypes. We cross-referenced these results with mouse craniofacial gene expression datasets, which identified additional promising candidate genes. However, we found no strong correlation between OFC subtypes and expression patterns. In aggregate, the cleft map revealed that neither subtype-specific nor shared genetic effects operate in isolation in OFC architecture. Our approach can be easily applied to any complex trait with distinct phenotypic subgroups.

scholarly journals A simple genetic basis of adaptation to a novel thermal environment results in complex metabolic rewiring in Drosophila

2017 ◽  
Author(s):  
François Mallard ◽  
Viola Nolte ◽  
Ray Tobler ◽  
Martin Kapun ◽  
Christian Schlötterer
Keyword(s):  
Complex Traits ◽  
Thermal Environment ◽  
Natural Populations ◽  
Thermal Adaptation ◽  
Complex Trait ◽  
Natural Environments ◽  
Clinal Variation ◽  
Rapid Adaptation ◽  
Metabolic Switch ◽  
Intermediate Allele

AbstractPopulation genetic theory predicts that rapid adaptation is largely driven by complex traits encoded by many loci of small effect. Because large effect loci are quickly fixed in natural populations, they should not contribute much to rapid adaptation. To investigate the genetic architecture of thermal adaptation - a highly complex trait - we performed experimental evolution on a natural Drosophila simulans population. Transcriptome and respiration measurements revealed extensive metabolic rewiring after only ∼60 generations in a hot environment. Analysis of genome-wide polymorphisms identified two interacting selection targets, Sestrin and SNF4Aγ, pointing to AMPK, a central metabolic switch, as a key factor for thermal adaptation. Our results demonstrate that large-effect loci segregating at intermediate allele frequencies can allow natural populations to rapidly respond to selection. Because SNF4Aγ also exhibits clinal variation in various Drosophila species, we suggest that this large effect polymorphism is maintained by temporal and spatial temperature variation in natural environments.

scholarly journals Bayesian multivariate reanalysis of large genetic studies identifies many new associations

2019 ◽  
Author(s):  
Michael C. Turchin ◽  
Matthew Stephens
Keyword(s):  
Genetic Variation ◽  
Multivariate Analyses ◽  
Association Studies ◽  
Plasma Lipid ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Association Analyses ◽  
New Associations ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.1Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

scholarly journals Integration of rare large-effect expression variants improves polygenic risk prediction

2020 ◽  
Author(s):  
Craig Smail ◽  
Nicole M. Ferraro ◽  
Matthew G. Durrant ◽  
Abhiram S. Rao ◽  
Matthew Aguirre ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Rare Variants ◽  
Complex Trait ◽  
Risk Scores ◽  
Multiple Traits ◽  
Polygenic Risk ◽  
Common Genetic Variants ◽  
Using Data ◽  
The Uk ◽  
The Impact

SummaryPolygenic risk scores (PRS) aim to quantify the contribution of multiple genetic loci to an individual’s likelihood of a complex trait or disease. However, existing PRS estimate genetic liability using common genetic variants, excluding the impact of rare variants. We identified rare, large-effect variants in individuals with outlier gene expression from the GTEx project and then assessed their impact on PRS predictions in the UK Biobank (UKB). We observed large deviations from the PRS-predicted phenotypes for carriers of multiple outlier rare variants; for example, individuals classified as “low-risk” but in the top 1% of outlier rare variant burden had a 6-fold higher rate of severe obesity. We replicated these findings using data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) biobank and the Million Veteran Program, and demonstrated that PRS across multiple traits will significantly benefit from the inclusion of rare genetic variants.

scholarly journals Leaf anatomy is not correlated to CAM function in a C3+CAM hybrid species, Yucca gloriosa

2020 ◽  
Author(s):  
Karolina Heyduk ◽  
Jeremy N Ray ◽  
Jim Leebens-Mack
Keyword(s):  
Leaf Anatomy ◽  
Time Course ◽  
Intraspecific Variability ◽  
Complex Trait ◽  
Hybrid Origin ◽  
Cam Plants ◽  
Hybrid Species ◽  
Anatomy And Physiology ◽  
Cam Species ◽  
Trait Correlations

Abstract Background and Aims Crassulacean acid metabolism (CAM) is often considered to be a complex trait, requiring orchestration of leaf anatomy and physiology for optimal performance. However, the observation of trait correlations is based largely on comparisons between C3 and strong CAM species, resulting in a lack of understanding as to how such traits evolve and the level of intraspecific variability for CAM and associated traits. Methods To understand intraspecific variation for traits underlying CAM and how these traits might assemble over evolutionary time, we conducted detailed time course physiological screens and measured aspects of leaf anatomy in 24 genotypes of a C3+CAM hybrid species, Yucca gloriosa (Asparagaceae). Comparisons were made to Y. gloriosa’s progenitor species, Y. filamentosa (C3) and Y. aloifolia (CAM). Key Results Based on gas exchange and measurement of leaf acids, Y. gloriosa appears to use both C3 and CAM, and varies across genotypes in the degree to which CAM can be upregulated under drought stress. While correlations between leaf anatomy and physiology exist when testing across all three Yucca species, such correlations break down at the species level in Y. gloriosa. Conclusions The variation in CAM upregulation in Y. gloriosa is a result of its relatively recent hybrid origin. The lack of trait correlations between anatomy and physiology within Y. gloriosa indicate that the evolution of CAM, at least initially, can proceed through a wide combination of anatomical traits, and more favourable combinations are eventually selected for in strong CAM plants.

EPCO-23. A SINGLE-CELL BASED PRECISION MEDICINE APPROACH USING GLIOBLASTOMA PATIENT-SPECIFIC MODELS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi6-vi7
Author(s):  
Abdullah Feroze ◽  
James Park ◽  
Samuel Emerson ◽  
Anca Mihalas ◽  
Dirk Keene ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
Standard Of Care ◽  
In Vivo Studies ◽  
Patient Specific ◽  
Recurrent Tumor ◽  
Care Treatment ◽  
Standard Of Care Treatment ◽  
Evolutionary Trajectories

Abstract Glioblastoma is a heterogeneous tumor made up of cell states that evolve over time. We modeled tumor evolutionary trajectories during standard-of-care treatment using multimodal single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multimodal data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that regulate subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, neo-adjuvant, or recurrent setting. Our proof-of-concept work herein provides the basis for the development of a modeling and analytical system that enables single-cell characterization of an individual patient’s tumor and inferred therapeutic vulnerabilities. Although further validation is required, in the form of in vivo studies of these putative druggable targets, our preliminary analysis and results suggest that systems biology techniques can be used to infer and predict therapeutic vulnerabilities that are either selected or induced during standard-of-care treatment. Ultimately, the information gathered from such systematic modeling and analysis of individual tumors may inform clinical treatment in a more targeted manner and enable a rational, tailored precision medicine that accounts for intratumoral cell heterogeneity.

Multiple traits associated with salt tolerance in lucerne: revealing the underlying cellular mechanisms

2008 ◽  
Vol 35 (7) ◽  
pp. 640 ◽  
Author(s):  
Christiane F. Smethurst ◽  
Kieren Rix ◽  
Trevor Garnett ◽  
Geoff Auricht ◽  
Antoine Bayart ◽  
...  
Keyword(s):  
Salt Tolerance ◽  
Field Experiments ◽  
Leaf Tissue ◽  
Complex Trait ◽  
Multiple Traits ◽  
Cellular Mechanisms ◽  
Biochemical Processes ◽  
Psii Photochemistry ◽  
Salinity Levels ◽  
Leaf Tissues

Salinity tolerance is a complex trait inferring the orchestrated regulation of a large number of physiological and biochemical processes at various levels of plant structural organisation. It remains to be answered which mechanisms and processes are crucial for salt tolerance in lucerne (Medicago sativa L.). In this study, salinity effects on plant growth characteristics, pigment and nutrient composition, PSII photochemistry, leaf sap osmolality, changes in anatomical and electrophysiological characteristics of leaf mesophyll, and net ion fluxes in roots of several lucerne genotypes were analysed. Salinity levels ranged from 40 to ~200 mm NaCl, and were applied to either 2-month-old plants or to germinating seedlings for a period of between 4 and 12 weeks in a series of hydroponic, pot and field experiments. Overall, the results suggest that different lucerne genotypes employ at least two different mechanisms for salt tolerance. Sodium exclusion appeared to be the mechanism employed by at least one of the tolerant genotypes (Ameristand 801S). This cultivar had the lowest leaf thickness, as well as the lowest concentration of Na+ in the leaf tissue. The other tolerant genotype, L33, had much thicker leaves and almost twice the leaf Na+ concentration of Ameristand. Both cultivars showed much less depolarisation of leaf membrane potential than the sensitive cultivars and, thus, had better K+ retention ability in both root and leaf tissues. The implications of the above measurements for screening lucerne germplasm for salt tolerance are discussed.

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