Hepatitis E virus (HEV) infection in pregnant women has a high incidence of developing fulminant hepatic failure (FHF) with significant mortality. Multiple amino acid changes in genotype 1 HEV ...(HEV-1) are reportedly linked to FHF clinical cases, but experimental confirmation of the roles of these changes in FHF is lacking. By utilizing the HEV-1 indicator replicon and infectious clone, we generated 11 HEV-1 single mutants, each with an individual mutation, and investigated the effect of these mutations on HEV replication and infection in human liver cells. We demonstrated that most of the mutations actually impaired HEV-1 replication efficiency compared with the wild type (WT), likely due to altered physicochemical properties and structural conformations. However, two mutations, A317T and V1120I, significantly increased HEV-1 replication. Notably, these two mutations simultaneously occurred in 100% of 21 HEV-1 variants from patients with FHF in Bangladesh. We further created an HEV-1 A317T/V1120I double mutant and found that it greatly enhanced HEV replication, which may explain the rapid viral replication and severe disease. Furthermore, we tested the effect of these FHF-associated mutations on genotype 3 HEV (HEV-3) replication and found that all the mutants had a reduced level of replication ability and infectivity, which is not unexpected due to distinct infection patterns between HEV-1 and HEV-3. Additionally, we demonstrated that these FHF-associated mutations do not appear to alter their sensitivity to ribavirin (RBV), suggesting that ribavirin remains a viable option for antiviral therapy for patients with FHF. The results have important implications for understanding the mechanism of HEV-1-associated FHF.
Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype–environment association (GEA) methods, which identify these loci based on correlations between ...genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyse many loci simultaneously, may be better suited to these data as they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods and five univariate and differentiation‐based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations, particularly redundancy analysis (RDA), showed a superior combination of low false‐positive and high true‐positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes and weak population structure tested here. The value of combining detections from different methods was variable and depended on the study goals and knowledge of the drivers of selection. Re‐analysis of genomic data from grey wolves highlighted the unique, covarying sets of adaptive loci that could be identified using RDA. Although additional testing is needed, this study indicates that RDA is an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.
Switchgrass is a promising energy crop has the potential to mitigate global warming and energy security, improve local ecology and generate profit. Its quantitative traits, such as biomass ...productivity and environmental adaptability, are determined by genotype‐by‐environment interaction (GEI) or response of genotypes grown across different target environments. To simulate the yield of switchgrass outside its original habitat, a genotype‐specific growth model, SwitchFor that captures GEI was developed by parameterising the MiscanFor model. Input parameters were used to describe genotype‐specific characteristics under different soil and climate conditions, which enables the model to predict the yield in a wide range of environmental and climate conditions. The model was validated using global field trail data and applied to estimate the switchgrass yield potentials on the marginal land of the Loess Plateau in China. The results suggest that upland and lowland switchgrass have significant differences in the spatial distribution of the adaptation zone and site‐specific biomass yield. The area of the adaption zone of upland switchgrass was 4.5 times of the lowland ecotype's. The yield difference between upland and lowland ecotypes ranges from 0 to 34 Mg ha−1. The weighted average yield of the lowland ecotype (20 Mg ha−1) is significantly higher than the upland type (5 Mg ha−1). The optimal yield map, generated by comparing the yield of upland and lowland ecotypes based on 1 km2 grid locations, illustrates that the total yield potential of the optimal switchgrass is 61.6–106.4 Tg on the marginal land of the Loess Plateau, which is approximately twice that of the individual ecotypes. Compared with the existing models, the accuracy of the yield prediction of switchgrass is significantly improved by using the SwitchFor model. This spatially explicit and cultivar‐specific model provides valuable information on land management and crop breeding and a robust and extendable framework for yield mapping of other cultivars.
A new cultivar‐specific switchgrass plant growth model, SwitchFor model, was developed and validated to give an accurate estimation of the switchgrass yield in a wide environment. The SwitchFor model was applied to the Loess Plateau region, which has about 12.8–20.8 Mha marginal land to estimate the yield potential. It was estimated that the Loess Plateau has a potential to produce up to 62–106 Tg switchgrass by considering the upland and lowland ecotype.
Plants have evolved intimate interactions with soil microbes for a range of beneficial functions including nutrient acquisition, pathogen resistance and stress tolerance. Further understanding of ...this system is a promising way to advance sustainable agriculture by exploiting the versatile benefits offered by the plant microbiome. The rhizosphere is the interface between plant and soil, and functions as the first step of plant defense and root microbiome recruitment. It features a specialized microbial community, intensive microbe-plant and microbe-microbe interactions, and complex signal communication. To decipher the rhizosphere microbiome assembly of soybean (Glycine max), we comprehensively characterized the soybean rhizosphere microbial community using 16S rRNA gene sequencing and evaluated the structuring influence from both host genotype and soil source.
Comparison of the soybean rhizosphere to bulk soil revealed significantly different microbiome composition, microbe-microbe interactions and metabolic capacity. Soil type and soybean genotype cooperatively modulated microbiome assembly with soil type predominantly shaping rhizosphere microbiome assembly while host genotype slightly tuned this recruitment process. The undomesticated progenitor species, Glycine soja, had higher rhizosphere diversity in both soil types tested in comparison to the domesticated soybean genotypes. Rhizobium, Novosphingobium, Phenylobacterium, Streptomyces, Nocardioides, etc. were robustly enriched in soybean rhizosphere irrespective of the soil tested. Co-occurrence network analysis revealed dominant soil type effects and genotype specific preferences for key microbe-microbe interactions. Functional prediction results demonstrated converged metabolic capacity in the soybean rhizosphere between soil types and among genotypes, with pathways related to xenobiotic degradation, plant-microbe interactions and nutrient transport being greatly enriched in the rhizosphere.
This comprehensive comparison of the soybean microbiome between soil types and genotypes expands our understanding of rhizosphere microbe assembly in general and provides foundational information for soybean as a legume crop for this assembly process. The cooperative modulating role of the soil type and host genotype emphasizes the importance of integrated consideration of soil condition and plant genetic variability for future development and application of synthetic microbiomes. Additionally, the detection of the tuning role by soybean genotype in rhizosphere microbiome assembly provides a promising way for future breeding programs to integrate host traits participating in beneficial microbiota assembly.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across ...donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.
Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive disorder resulting from pathogenic variants in the α‐L‐iduronidase (IDUA) gene. Clinical phenotypes range from severe (Hurler ...syndrome) to attenuated (Hurler‐Scheie and Scheie syndromes) and vary in age of onset, severity, and rate of progression. Defining the phenotype at diagnosis is essential for disease management. To date, no systematic analysis of genotype‐phenotype correlation in large MPS I cohorts have been performed. Understanding genotype‐phenotype is critical now that newborn screening for MPS I is being implemented. Data from 538 patients from the MPS I Registry (380 severe, 158 attenuated) who had 2 IDUA alleles identified were examined. In the 1076 alleles identified, 148 pathogenic variants were reported; of those, 75 were unique. Of the 538 genotypes, 147 (27%) were unique; 40% of patients with attenuated and 22% of patients with severe MPS I had unique genotypes. About 67.6% of severe patients had genotypes where both variants identified are predicted to severely disrupt protein/gene function and 96.1% of attenuated patients had at least one missense or intronic variant. This dataset illustrates a close genotype/phenotype correlation in MPS I but the presence of unique IDUA missense variants remains a challenge for disease prediction.
Mucopolysaccharidosis type I patient's variants and genotypes.
AIM To review Hepatitis C virus(HCV) prevalence and genotypes distribution worldwide.METHODS We conducted a systematic study which represents one of the most comprehensive effort to quantify global ...HCV epidemiology,using the best available published data between 2000 and 2015 from 138 countries(about 90% of the global population),grouped in 20 geographical areas(with the exclusion of Oceania),as defined by the Global Burden of Diseases project(GBD). Countries for which we were unable to obtain HCV genotype prevalence data were excluded from calculations of regional proportions,although their populations were included in the total population size of each region when generating regional genotype prevalence estimates.RESULTS Total global HCV prevalence is estimated at 2.5%(177.5 million of HCV infected adults),ranging from 2.9% in Africa and 1.3% in Americas,with a global viraemic rate of 67%(118.9 million of HCV RNA positive cases),varying from 64.4% in Asia to 74.8% in Australasia. HCV genotype 1 is the most prevalent worldwide(49.1%),followed by genotype 3(17.9%),4(16.8%) and 2(11.0%). Genotypes 5 and 6 are responsible for the remaining < 5%. While genotypes 1 and 3 are common worldwide,the largest proportion of genotypes 4 and 5 is in lower-income countries. Although HCV genotypes 1 and 3 infections are the most prevalent globally(67.0% if considered together),other genotypes are found more commonly in lowerincome countries where still account for a significant proportion of HCV cases.CONCLUSION A more precise knowledge of HCV genotype distribution will be helpful to best inform national healthcare models to improve access to new treatments.
Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression ...levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.
Bardet‐Biedl syndrome (BBS) is a recessive genetic disease causing multiple organ anomalies. Most patients carry mutations in genes encoding for the subunits of the BBSome, an octameric ciliary ...transport complex, or accessory proteins involved in the BBSome assembly or function. BBS proteins have been extensively studied using in vitro, cellular, and animal models. However, the molecular functions of particular BBS proteins and the etiology of the BBS symptoms are still largely elusive. In this study, we applied a meta‐analysis approach to study the genotype‐phenotype association in humans using our database of all reported BBS patients. The analysis revealed that the identity of the causative gene and the character of the mutation partially predict the clinical outcome of the disease. Besides their potential use for clinical prognosis, our analysis revealed functional differences of particular BBS genes in humans. Core BBSome subunits BBS2, BBS7, and BBS9 manifest as more critical for the function and development of kidneys than peripheral subunits BBS1, BBS4, and BBS8/TTC8, suggesting that incomplete BBSome retains residual function at least in the kidney.
Mutations in the BBSome genes, ARL6/BBS3, or BBS chaperonin genes lead to the development of Bardet‐Biedl syndrome (BBS). Renal anomalies are more frequent in patients with mutations in core BBSome subunits than in patients with mutations in peripheral BBSome subunits, suggesting that the BBSome core retains some residual function in kidneys.
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