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Article ; Online: Deep mutational scanning

Huijin Wei / Xianghua Li

Frontiers in Genetics, Vol

A versatile tool in systematically mapping genotypes to phenotypes

2023 Volume 14

Abstract: Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic ... ...

Abstract	Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers’ projects.
Keywords	deep mutational scanning ; genotype-phenotype mapping ; massively parallel mutagenesis ; high-throughput analysis ; systems biology ; biotechnology ; Genetics ; QH426-470
Subject code	501
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Fractional crop-planting area projection by integrating geographic grid data and agricultural statistics based on random forest regression

Chunlin Huang / Jinliang Hou / Xianghua Li / Ying Zhang / Jifu Guo

International Journal of Digital Earth, Vol 16, Iss 2, Pp 4446-

2023 Volume 4470

Abstract: Accurate fractional crop-planting area (FCPA) mapping is a challenging task as it requires leveraging the advantages of geographic data in detailed spatial expression and agricultural statistics in the description of crop types and quantitative ... ...

Abstract	Accurate fractional crop-planting area (FCPA) mapping is a challenging task as it requires leveraging the advantages of geographic data in detailed spatial expression and agricultural statistics in the description of crop types and quantitative characteristics. We present a robust method to disaggregate the agricultural statistics within each county unit to 1-km scale grid by combining particle swarm optimization (PSO)-based feature selection with Random Forest (RF) regression, and an iterative area-gapallocation(IAGA) method is implemented to reconcile the discrepancies between the disaggregating results and statistics. The agriculture in Gansu Province, China, is characterized by complex heterogeneous smallholder farming landscapes. We tested this methodology in Gansu and explored the synergistic estimation of FCPA for six types of crops (i.e. wheat, maize, oil-bearing, vegetable,orchards, and other crops) in 2010. The results showed that the derived FCPA maps matched well with the statistics in terms of quantity, while also providing spatial details. The quantitative evaluation results indicated that the derived FCPA had good accuracy,with a higher R2 above 0.97, a lower RMSE below 1%, and an absolute error between 1.53-5.24%. The proposed methodology provides valuable insights for practical large-scale FCPA mapping at a high spatial resolution in a cost-effective manner.
Keywords	fractional crop-planting area ; geographic grid data ; agricultural statistics ; rf ; pso ; Mathematical geography. Cartography ; GA1-1776
Subject code	518
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biophysical ambiguities prevent accurate genetic prediction

Xianghua Li / Ben Lehner

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 11

Abstract: In quantitative genetics, it is widely assumed that mutations combine additively or epistasis can be predicted with statistical or mechanistic models. Here, the authors use the phage lambda repressor model to show how biophysical ambiguity and non- ... ...

Abstract	In quantitative genetics, it is widely assumed that mutations combine additively or epistasis can be predicted with statistical or mechanistic models. Here, the authors use the phage lambda repressor model to show how biophysical ambiguity and non-monotonic functions confound phenotypic prediction.
Keywords	Science ; Q
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biophysical ambiguities prevent accurate genetic prediction

Xianghua Li / Ben Lehner

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 11

Abstract	In quantitative genetics, it is widely assumed that mutations combine additively or epistasis can be predicted with statistical or mechanistic models. Here, the authors use the phage lambda repressor model to show how biophysical ambiguity and non-monotonic functions confound phenotypic prediction.
Keywords	Science ; Q
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Cellular response to β‐amyloid neurotoxicity in Alzheimer's disease and implications in new therapeutics

Haolin Zhang / Xianghua Li / Xiaoli Wang / Jiayu Xu / Felice Elefant / Juan Wang

Animal Models and Experimental Medicine, Vol 6, Iss 1, Pp 3-

2023 Volume 9

Abstract: Abstract β‐Amyloid (Aβ) is a specific pathological hallmark of Alzheimer's disease (AD). Because of its neurotoxicity, AD patients exhibit multiple brain dysfunctions. Disease‐modifying therapy (DMT) is the central concept in the development of AD ... ...

Abstract	Abstract β‐Amyloid (Aβ) is a specific pathological hallmark of Alzheimer's disease (AD). Because of its neurotoxicity, AD patients exhibit multiple brain dysfunctions. Disease‐modifying therapy (DMT) is the central concept in the development of AD therapeutics today, and most DMT drugs that are currently in clinical trials are anti‐Aβ drugs, such as aducanumab and lecanemab. Therefore, understanding Aβ's neurotoxic mechanism is crucial for Aβ‐targeted drug development. Despite its total length of only a few dozen amino acids, Aβ is incredibly diverse. In addition to the well‐known Aβ1‐42, N‐terminally truncated, glutaminyl cyclase (QC) catalyzed, and pyroglutamate‐modified Aβ (pEAβ) is also highly amyloidogenic and far more cytotoxic. The extracellular monomeric Aβx‐42 (x = 1–11) initiates the aggregation to form fibrils and plaques and causes many abnormal cellular responses through cell membrane receptors and receptor‐coupled signal pathways. These signal cascades further influence many cellular metabolism‐related processes, such as gene expression, cell cycle, and cell fate, and ultimately cause severe neural cell damage. However, endogenous cellular anti‐Aβ defense processes always accompany the Aβ‐induced microenvironment alterations. Aβ‐cleaving endopeptidases, Aβ‐degrading ubiquitin‐proteasome system (UPS), and Aβ‐engulfing glial cell immune responses are all essential self‐defense mechanisms that we can leverage to develop new drugs. This review discusses some of the most recent advances in understanding Aβ‐centric AD mechanisms and suggests prospects for promising anti‐Aβ strategies.
Keywords	Alzheimer's disease (AD) ; astrocytes ; endopeptidase ; glutaminyl cyclase (QC) ; microglia ; p75 neurotrophin receptor (p75NTR) ; Medicine (General) ; R5-920
Subject code	612
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning

Xianghua Li / Jinliang Hou / Chunlin Huang

Remote Sensing, Vol 13, Iss 5038, p

2021 Volume 5038

Abstract: Accurate high-resolution gridded livestock distribution data are of great significance for the rational utilization of grassland resources, environmental impact assessment, and the sustainable development of animal husbandry. Traditional livestock ... ...

Abstract	Accurate high-resolution gridded livestock distribution data are of great significance for the rational utilization of grassland resources, environmental impact assessment, and the sustainable development of animal husbandry. Traditional livestock distribution data are collected at the administrative unit level, which does not provide a sufficiently detailed geographical description of livestock distribution. In this study, we proposed a scheme by integrating high-resolution gridded geographic data and livestock statistics through machine learning regression models to spatially disaggregate the livestock statistics data into 1 km × 1 km spatial resolution. Three machine learning models, including support vector machine (SVM), random forest (RF), and deep neural network (DNN), were constructed to represent the complex nonlinear relationship between various environmental factors (e.g., land use practice, topography, climate, and socioeconomic factors) and livestock density. By applying the proposed method, we generated a set of 1 km × 1 km spatial distribution maps of cattle and sheep for western China from 2000 to 2015 at five-year intervals. Our projected cattle and sheep distribution maps reveal the spatial heterogeneity structures and change trend of livestock distribution at the grid level from 2000 to 2015. Compared with the traditional census livestock density, the gridded livestock distribution based on DNN has the highest accuracy, with the determinant coefficient ( R 2 ) of 0.75, root mean square error ( RMSE ) of 9.82 heads/km 2 for cattle, and the R 2 of 0.73, RMSE of 31.38 heads/km 2 for sheep. The accuracy of the RF is slightly lower than the DNN but higher than the SVM. The projection accuracy of the three machine learning models is superior to those of the published Gridded Livestock of the World (GLW) datasets. Consequently, deep learning has the potential to be an effective tool for high-resolution gridded livestock projection by combining geographic and census data.
Keywords	machine learning ; livestock ; spatialization ; western China ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest

Yansi Chen / Jinliang Hou / Chunlin Huang / Ying Zhang / Xianghua Li

Remote Sensing, Vol 13, Iss 2988, p

2021 Volume 2988

Abstract: Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ...

Abstract	Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping.
Keywords	maize area ; multi-temporal image ; Sentinel-1 ; Sentinel-2 ; random forest ; Science ; Q
Subject code	910
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Unsupervised community detection in attributed networks based on mutual information maximization

Junyou Zhu / Xianghua Li / Chao Gao / Zhen Wang / Jurgen Kurths

New Journal of Physics, Vol 23, Iss 11, p

2021 Volume 113016

Abstract: Community detection is of great significance for understanding network functions and behaviors. With the successful application of deep learning in network-based analyses, recent studies have turned to utilizing graph convolutional networks (GCNs) to ... ...

Abstract	Community detection is of great significance for understanding network functions and behaviors. With the successful application of deep learning in network-based analyses, recent studies have turned to utilizing graph convolutional networks (GCNs) to this problem due to their capability in capturing network attributes. Nevertheless, most existing GCN-based community detection approaches are semi-supervised and local structure-aware, even though community detection is an unsupervised learning problem essentially. In this paper, we develop a novel GCN method for unsupervised community detection under the framework of mutual information (MI) maximization, called UCDMI. Specifically, a novel MI maximization mechanism is developed to capture more fine-grained information of the global network structure in an unsupervised manner. Moreover, a new aggregation function is proposed for GCN to distinguish the importance between different neighboring nodes, which enables our method to identify more high-quality node representations and improve the community detection performance. Our extensive experiments demonstrate the effectiveness of our proposed UCDMI compared with several state-of-the-art community detection methods.
Keywords	community detection ; attributed networks ; graph convolutional networks ; mutual information ; Science ; Q ; Physics ; QC1-999
Subject code	006
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	IOP Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Different Cell Wall-Degradation Ability Leads to Tissue-Specificity between Xanthomonas oryzae pv . oryzae and Xanthomonas oryzae pv . oryzicola

Jianbo Cao / Chuanliang Chu / Meng Zhang / Limin He / Lihong Qin / Xianghua Li / Meng Yuan

Pathogens, Vol 9, Iss 3, p

2020 Volume 187

Abstract: Xanthomonas oryzae pv. oryzae ( Xoo ) and Xanthomonas oryzae pv. oryzicola ( Xoc ) lead to the devastating rice bacterial diseases and have a very close genetic relationship. There are tissue-specificity differences between Xoo and Xoc , i.e., Xoo only ... ...

Abstract	Xanthomonas oryzae pv. oryzae ( Xoo ) and Xanthomonas oryzae pv. oryzicola ( Xoc ) lead to the devastating rice bacterial diseases and have a very close genetic relationship. There are tissue-specificity differences between Xoo and Xoc , i.e., Xoo only proliferating in xylem vessels and Xoc spreading in intercellular space of mesophyll cell. But there is little known about the determinants of tissue-specificity between Xoo and Xoc . Here we show that Xoc can spread in the intercellular spaces of mesophyll cells to form streak lesions. But Xoo is restricted to growth in the intercellular spaces of mesophyll cells on the inoculation sites. In vivo, Xoc largely breaks the surface and inner structures of cell wall in mesophyll cells in comparison with Xoo . In vitro, Xoc strongly damages the cellulose filter paper in comparison with Xoo . These results suggest that the stronger cell wall-degradation ability of Xoc than that of Xoo may be directly determining the tissue-specificity.
Keywords	xanthomonas oryzae pv. oryzae ; xanthomonas oryzae pv. oryzicola ; rice ; cell wall-degradation ; tissue-specificity ; Medicine ; R
Language	English
Publishing date	2020-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Allelic differentiation at the Sg-1 locus for the terminal sugar of the C-22 position of group A saponin in Chinese wild soybean (Glycine soja Sieb. & Zucc.)

Takahashi, Yuya / Chigen Tsukamoto / Kejing Wang / Xianghua Li

Molecular breeding. 2018 July, v. 38, no. 7

2018

Abstract: Group A saponins are thought to be the cause of bitter and astringent tastes in processed foods of soybean (Glycine max), and the elimination of group A saponins is an important breeding objective. The group A saponins include two main Aa and Ab types, ... ...

Abstract	Group A saponins are thought to be the cause of bitter and astringent tastes in processed foods of soybean (Glycine max), and the elimination of group A saponins is an important breeding objective. The group A saponins include two main Aa and Ab types, controlled by codominant alleles at the Sg-1 locus that is one of several key loci responsible for saponin biosynthesis in the subgenus Glycine soja. However, A0 mutant lacking group A saponin is a useful gene resource for soybean quality breeding. Here, eight Chinese wild soybean A0 accessions were sequenced to reveal the mutational mechanisms, and the results showed that these mutants were caused by at least three kinds of mechanisms involving four allelic variants (sg-10−b2, sg-10−b3, Sg-1b−0, and Sg-1b−01). The sg-10−b2 had two nucleotide deletions at positions + 72 and + 73 involving in the 24th and 25th amino acids. The sg-10−b3 contained a stop codon (TGA) at the 254th residue. The Sg-1b−0 and Sg-1b−01 were two novel A0-type mutants, which likely carried normal structural alleles, and nevertheless did not encode group A saponin due to unknown mutations beyond the normal coding regions. In addition, to reveal the structural features, allelic polymorphism, and mechanisms of the abiogenetic absence of group A (i.e., A0 phenotype), nucleotide sequence analysis was performed for the Sg-1 locus in wild soybean (Glycine soja). The results showed that Sg-1 alleles had a lower conservatism in the coding region; as high as 18 sequences were found in Chinese wild soybeans in addition to the Sg-1a (Aa) and Sg-1b (Ab) alleles. Sg-1a and Sg-1b alleles were characterized by eight synonymous codons and nine amino acid substitutions. Two evolutionarily transitional allelic sequences (Sg-1a7 and Sg-1b2) from Sg-1a toward Sg-1b were detected.
Keywords	alleles ; amino acid substitution ; amino acids ; biosynthesis ; breeding ; Glycine max ; Glycine soja ; loci ; mutants ; phenotype ; processed foods ; saponins ; sequence analysis ; sequence deletion ; soybeans ; stop codon ; sugars
Language	English
Dates of publication	2018-07
Size	p. 93.
Publishing place	Springer Netherlands
Document type	Article
ZDB-ID	1230924-2
ISSN	1572-9788 ; 1380-3743
ISSN (online)	1572-9788
ISSN	1380-3743
DOI	10.1007/s11032-018-0851-9
Database	NAL-Catalogue (AGRICOLA)

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