scholarly journals Autophagy as a mechanism for adaptive prediction-mediated emergence of drug resistance

2021 ◽  
Author(s):  
Nivedita Nivedita ◽  
John D. Aitchison ◽  
Nitin S. Baliga
Keyword(s):  
Drug Resistance ◽  
Stressful Environment ◽  
Adaptive Prediction ◽  
Genome Wide ◽  
Microbial Infections ◽  
Effective Therapeutic Strategy ◽  
Subsequent Selection ◽  
Tolerant State ◽  
Core Genes ◽  
Selection Of

ABSTRACTDrug resistance is a major problem in treatment of microbial infections and cancers. There is growing evidence that a transient drug tolerant state may precede and potentiate the emergence of drug resistance. Therefore, understanding the mechanisms leading to tolerance is critical for combating drug resistance and for the development of effective therapeutic strategy. Through laboratory evolution of yeast, we recently demonstrated that adaptive prediction (AP), a strategy employed by organisms to anticipate and prepare for a future stressful environment, can emerge within 100 generations by linking the response triggered by a neutral cue (caffeine) to a mechanism of protection against a lethal agent (5-FOA). Here, we demonstrate that mutations selected across multiple laboratory evolved lines had linked the neutral cue response to core genes of autophagy. Across these evolved lines, conditional activation of autophagy through AP conferred tolerance, and potentiated subsequent selection of mutations in genes specific to overcoming the toxicity of 5-FOA. We propose a model to explain how extensive genome-wide genetic interactions of autophagy facilitates emergence of AP over short evolutionary timescales to potentiate selection of resistance-conferring mutations.

scholarly journals Autophagy as a Mechanism for Adaptive Prediction-Mediated Emergence of Drug Resistance

2021 ◽  
Vol 12 ◽  
Author(s):  
Nivedita Nivedita ◽  
John D. Aitchison ◽  
Nitin S. Baliga
Keyword(s):  
Drug Resistance ◽  
Stressful Environment ◽  
Adaptive Prediction ◽  
Genome Wide ◽  
Microbial Infections ◽  
Effective Therapeutic Strategy ◽  
New Perspective ◽  
Tolerant State ◽  
Core Genes ◽  
Selection Of

Drug resistance is a major problem in treatment of microbial infections and cancers. There is growing evidence that a transient drug tolerant state may precede and potentiate the emergence of drug resistance. Therefore, understanding the mechanisms leading to tolerance is critical for combating drug resistance and for the development of effective therapeutic strategy. Through laboratory evolution of yeast, we recently demonstrated that adaptive prediction (AP), a strategy employed by organisms to anticipate and prepare for a future stressful environment, can emerge within 100 generations by linking the response triggered by a neutral cue (caffeine) to a mechanism of protection against a lethal agent (5-fluoroorotic acid, 5-FOA). Here, we demonstrate that mutations selected across multiple laboratory-evolved lines had linked the neutral cue response to core genes of autophagy. Across these evolved lines, conditional activation of autophagy through AP conferred tolerance, and potentiated subsequent selection of mutations in genes specific to overcoming the toxicity of 5-FOA. These results offer a new perspective on how extensive genome-wide genetic interactions of autophagy could have facilitated the emergence of AP over short evolutionary timescales to potentiate selection of 5-FOA resistance-conferring mutations.

scholarly journals Bayesian models with dominance effects for genomic evaluation of quantitative traits

2012 ◽  
Vol 94 (1) ◽  
pp. 21-37 ◽  
Author(s):  
ROBIN WELLMANN ◽  
JÖRN BENNEWITZ
Keyword(s):  
Genomic Selection ◽  
Bayesian Methods ◽  
Mate Selection ◽  
Quantitative Traits ◽  
Main Research ◽  
Genome Wide ◽  
Standard Tool ◽  
Additive Genetic Effects ◽  
Subsequent Selection ◽  
Selection Of

SummaryGenomic selection refers to the use of dense, genome-wide markers for the prediction of breeding values (BV) and subsequent selection of breeding individuals. It has become a standard tool in livestock and plant breeding for accelerating genetic gain. The core of genomic selection is the prediction of a large number of marker effects from a limited number of observations. Various Bayesian methods that successfully cope with this challenge are known. Until now, the main research emphasis has been on additive genetic effects. Dominance coefficients of quantitative trait loci (QTLs), however, can also be large, even if dominance variance and inbreeding depression are relatively small. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. A general hierarchical Bayesian model for genomic selection that can realistically account for dominance is introduced. Several submodels are proposed and compared with respect to their ability to predict genomic BV, dominance deviations and genotypic values (GV) by stochastic simulation. These submodels differ in the way the dependency between additive and dominance effects is modelled. Depending on the marker panel, the inclusion of dominance effects increased the accuracy of GV by about 17% and the accuracy of genomic BV by 2% in the offspring. Furthermore, it slowed down the decrease of the accuracies in subsequent generations. It was possible to obtain accurate estimates of GV, which enables mate selection programmes.

scholarly journals A microfluidic device enabling drug resistance analysis of leukemia cells via coupled dielectrophoretic detection and impedimetric counting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yağmur Demircan Yalçın ◽  
Taylan Berkin Töral ◽  
Sertan Sukas ◽  
Ender Yıldırım ◽  
Özge Zorlu ◽  
...  
Keyword(s):  
Drug Resistance ◽  
K562 Cells ◽  
Leukemia Cells ◽  
Drug Resistant ◽  
Wild Type ◽  
Gene Expressions ◽  
Before And After ◽  
The Difference ◽  
Selection Of ◽  
Resistant Cells

AbstractWe report the development of a lab-on-a-chip system, that facilitates coupled dielectrophoretic detection (DEP-D) and impedimetric counting (IM-C), for investigating drug resistance in K562 and CCRF-CEM leukemia cells without (immuno) labeling. Two IM-C units were placed upstream and downstream of the DEP-D unit for enumeration, respectively, before and after the cells were treated in DEP-D unit, where the difference in cell count gave the total number of trapped cells based on their DEP characteristics. Conductivity of the running buffer was matched the conductivity of cytoplasm of wild type K562 and CCRF-CEM cells. Results showed that DEP responses of drug resistant and wild type K562 cells were statistically discriminative (at p = 0.05 level) at 200 mS/m buffer conductivity and at 8.6 MHz working frequency of DEP-D unit. For CCRF-CEM cells, conductivity and frequency values were 160 mS/m and 6.2 MHz, respectively. Our approach enabled discrimination of resistant cells in a group by setting up a threshold provided by the conductivity of running buffer. Subsequent selection of drug resistant cells can be applied to investigate variations in gene expressions and occurrence of mutations related to drug resistance.

Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysisThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽  
2010 ◽  
Vol 53 (11) ◽  
pp. 1002-1016 ◽  
Author(s):  
B.R. Cullis ◽  
A.B. Smith ◽  
C.P. Beeck ◽  
W.A. Cowling
Keyword(s):  
Pedigree Information ◽  
Environment Interaction ◽  
Correlation Matrices ◽  
New Approach ◽  
Genome Wide ◽  
Selection Indices ◽  
New Varieties ◽  
Oilseed Brassicas ◽  
Analytic Models ◽  
Selection Of

Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I of this series, we presented an approach to modelling V × E interaction in the analysis of complex multi-environment trials using factor analytic models. In this paper, we develop a range of statistical tools which explore V × E interaction in this context. These tools include graphical displays such as heat-maps of genetic correlation matrices as well as so-called E-scaled uniplots that are a more informative alternative to the classical biplot for large plant breeding multi-environment trials. We also present a new approach to prediction for multi-environment trials that include pedigree information. This approach allows meaningful selection indices to be formed either for potential new varieties or potential parents.

scholarly journals A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jia-Rou Liu ◽  
Po-Hsiu Kuo ◽  
Hung Hung
Keyword(s):  
Characteristic Curve ◽  
Real Data ◽  
Tuning Parameter ◽  
Practical Implementation ◽  
Tuning Parameters ◽  
Genome Wide ◽  
Feature Based ◽  
The Difference ◽  
Gwa Studies ◽  
Selection Of

Large-p-small-ndatasets are commonly encountered in modern biomedical studies. To detect the difference between two groups, conventional methods would fail to apply due to the instability in estimating variances int-test and a high proportion of tied values in AUC (area under the receiver operating characteristic curve) estimates. The significance analysis of microarrays (SAM) may also not be satisfactory, since its performance is sensitive to the tuning parameter, and its selection is not straightforward. In this work, we propose a robust rerank approach to overcome the above-mentioned diffculties. In particular, we obtain a rank-based statistic for each feature based on the concept of “rank-over-variable.” Techniques of “random subset” and “rerank” are then iteratively applied to rank features, and the leading features will be selected for further studies. The proposed re-rank approach is especially applicable for large-p-small-ndatasets. Moreover, it is insensitive to the selection of tuning parameters, which is an appealing property for practical implementation. Simulation studies and real data analysis of pooling-based genome wide association (GWA) studies demonstrate the usefulness of our method.

Pleiotropic drug-resistance attenuated genomic library improves elucidation of drug mechanisms

2015 ◽  
Vol 11 (11) ◽  
pp. 3129-3136 ◽  
Author(s):  
Namal V. C. Coorey ◽  
James H. Matthews ◽  
David S. Bellows ◽  
Paul H. Atkinson
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Drug Resistance ◽  
Mechanism Of Action ◽  
Gene Deletion ◽  
Genomic Library ◽  
Deletion Mutants ◽  
Pleiotropic Drug Resistance ◽  
Genome Wide

Identifying Saccharomyces cerevisiae genome-wide gene deletion mutants that confer hypersensitivity to a xenobiotic aids the elucidation of its mechanism of action (MoA).

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Glycine max (L.) Merr.]

2021 ◽  
Vol 11 ◽  
Author(s):  
Mu-Chien Lai ◽  
Zheng-Yuan Lai ◽  
Li-Hsin Jhan ◽  
Ya-Syuan Lai ◽  
Chung-Feng Kao
Keyword(s):  
Glycine Max ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Soybean Seeds ◽  
Flooding Tolerance ◽  
Big Data Mining ◽  
Genome Wide ◽  
Glycine Max L ◽  
Mapping Gene ◽  
Selection Of

Soybean [Glycine max (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. In recent years there has been increasingly more drastic weather caused by climate change, with flooding, drought, and unevenly distributed rainfall gradually increasing in terms of the frequency and intensity worldwide. Severe flooding has caused extensive losses to soybean production and there is an urgent need to breed strong soybean seeds with high flooding tolerance. The present study demonstrates bioinformatics big data mining and integration, meta-analysis, gene mapping, gene prioritization, and systems biology for identifying prioritized genes of flooding tolerance in soybean. A total of 83 flooding tolerance genes (FTgenes), according to the appropriate cut-off point, were prioritized from 36,705 test genes collected from multidimensional genomic features linking to soybean flooding tolerance. Several validation results using independent samples from SoyNet, genome-wide association study, SoyBase, GO database, and transcriptome databases all exhibited excellent agreement, suggesting these 83 FTgenes were significantly superior to others. These results provide valuable information and contribution to research on the varieties selection of soybean.

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