Root Hair Single Cell Type Specific Profiles of Gene Expression and Alternative Polyadenylation Under Cadmium Stress

Transcriptional networks are tightly controlled in plant development and stress responses. Alternative polyadenylation (APA) has been found to regulate gene expression under abiotic stress by increasing the heterogeneity at mRNA 3′-ends. Heavy metals like cadmium pollute water and soil due to mining and industry applications. Understanding how plants cope with heavy metal stress remains an interesting question. The Arabidopsis root hair was chosen as a single cell model to investigate the functional role of APA in cadmium stress response. Primary root growth inhibition and defective root hair morphotypes were observed. Poly(A) tag (PAT) libraries from single cell types, i.e., root hair cells, non-hair epidermal cells, and whole root tip under cadmium stress were prepared and sequenced. Interestingly, a root hair cell type-specific gene expression under short term cadmium exposure, but not related to the prolonged treatment, was detected. Differentially expressed poly(A) sites were identified, which largely contributed to altered gene expression, and enriched in pentose and glucuronate interconversion pathways as well as phenylpropanoid biosynthesis pathways. Numerous genes with poly(A) site switching were found, particularly for functions in cell wall modification, root epidermal differentiation, and root hair tip growth. Our findings suggest that APA plays a functional role as a potential stress modulator in root hair cells under cadmium treatment

scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Motivation: Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 30 enriched strategy in library construction, the most commonly used scRNA-seq protocol—10 Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results: Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data.This research was supported in part by the Fundamental Research Funds for the Central Universities in China [Xiamen University: 20720170076 and 20720190106], and the National Natural Science Foundation of China [61802323, 31801268 and 61573296]

Root Hair Single Cell Type Specific Profiles of Gene Expression and Alternative Polyadenylation Under Cadmium Stress

Transcriptional networks are tightly controlled in plant development and stress responses. Alternative polyadenylation (APA) has been found to regulate gene expression under abiotic stress by increasing the heterogeneity at mRNA 3′-ends. Heavy metals like cadmium pollute water and soil due to mining and industry applications. Understanding how plants cope with heavy metal stress remains an interesting question. The Arabidopsis root hair was chosen as a single cell model to investigate the functional role of APA in cadmium stress response. Primary root growth inhibition and defective root hair morphotypes were observed. Poly(A) tag (PAT) libraries from single cell types, i.e., root hair cells, non-hair epidermal cells, and whole root tip under cadmium stress were prepared and sequenced. Interestingly, a root hair cell type-specific gene expression under short term cadmium exposure, but not related to the prolonged treatment, was detected. Differentially expressed poly(A) sites were identified, which largely contributed to altered gene expression, and enriched in pentose and glucuronate interconversion pathways as well as phenylpropanoid biosynthesis pathways. Numerous genes with poly(A) site switching were found, particularly for functions in cell wall modification, root epidermal differentiation, and root hair tip growth. Our findings suggest that APA plays a functional role as a potential stress modulator in root hair cells under cadmium treatment

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq

Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3′ untranslated region, tissue-specific, cross-species, and single-cell pA prediction

LemK_MSA: A multiple sequence alignment method with sequence vectorization based on Lempel-Ziv

Conference Name:2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012. Conference Address: Kaohsiung, Taiwan. Time:November 2, 2012 - November 6, 2012.AandF; Tailift Co., Ltd; SPINTECH; Smart Motion Control Co.,Ltd.; FXB Flexible Motion; et alIn this paper, we propose a method for multiple sequence alignment, LemK_MSA, which integrates Lempel-Ziv based sequence vectorization and k-means clustering analysis. LemK_MSA converts multiple sequence alignment into corresponding 10-dimensional vector alignment by 10 types of copy modes. Then it uses k-means algorithm and NJ algorithm to divide the sequences into several groups and calculate guide tree of each part with the vectors of the sequences. A complete guide tree for multiple sequence alignment could be constructed by merging guide tree of every group. Thus, the time efficiency of processing multiple sequence alignment, especially for large-scale sequences, can be improved. The high-throughput mouse antibody sequences are used to validate the proposed method. Compared to ClustalW, MAFFT and Mbed, LemK_MSA is more than ten times efficient while ensuring the alignment accuracy at the same time. LemK_MSA also provides an effective method to analyze the evolutionary relationship and structural features among high-throughput sequences. ? (2013) Trans Tech Publications, Switzerland

detectIR: A Novel Program for Detecting Perfect and Imperfect Inverted Repeats Using Complex Numbers and Vector Calculation

<div><p>Inverted repeats are present in abundance in both prokaryotic and eukaryotic genomes and can form DNA secondary structures – hairpins and cruciforms that are involved in many important biological processes. Bioinformatics tools for efficient and accurate detection of inverted repeats are desirable, because existing tools are often less accurate and time consuming, sometimes incapable of dealing with genome-scale input data. Here, we present a MATLAB-based program called <i>detectIR</i> for the perfect and imperfect inverted repeat detection that utilizes complex numbers and vector calculation and allows genome-scale data inputs. A novel algorithm is adopted in <i>detectIR</i> to convert the conventional sequence string comparison in inverted repeat detection into vector calculation of complex numbers, allowing non-complementary pairs (mismatches) in the pairing stem and a non-palindromic spacer (loop or gaps) in the middle of inverted repeats. Compared with existing popular tools, our program performs with significantly higher accuracy and efficiency. Using genome sequence data from HIV-1, <i>Arabidopsis thaliana</i>, <i>Homo sapiens</i> and <i>Zea mays</i> for comparison, <i>detectIR</i> can find lots of inverted repeats missed by existing tools whose outputs often contain many invalid cases. <i>detectIR</i> is open source and its source code is freely available at: <a href="https://sourceforge.net/projects/detectir" target="_blank">https://sourceforge.net/projects/detectir</a>.</p></div

The comparison of <i>detectImperfectIR</i> with EMBOSS and MATLAB for imperfect inverted repeat detection.

<p>The comparison of <i>detectImperfectIR</i> with EMBOSS and MATLAB for imperfect inverted repeat detection.</p

Determinants of public T cell responses

National Basic Research Program of China (973 Program) [2009CB522200]; National Natural Science Foundation of China [30830092, 30921005, 91029304, 81061160512, 61174161]; Sino-Swiss International Collaboration Grant [2009DFA32760]; Science Planning Program of Fujian Province [2009J1010]; Specialized Research Fund for the Doctoral Program of Higher Education of China [20090121110022]; Xiamen University [2011121047, 201112G018, CXB2011035]Historically, sharing T cell receptors (TCRs) between individuals has been speculated to be impossible, considering the dramatic discrepancy between the potential enormity of the TCR repertoire and the limited number of T cells generated in each individual. However, public T cell response, in which multiple individuals share identical TCRs in responding to a same antigenic epitope, has been extensively observed in a variety of immune responses across many species. Public T cell responses enable individuals within a population to generate similar antigen-specific TCRs against certain ubiquitous pathogens, leading to favorable biological outcomes. However, the relatively concentrated feature of TCR repertoire may limit T cell response in a population to some other pathogens. It could be a great benefit for human health if public T cell responses can be manipulated. Therefore, the mechanistic insight of public TCR generation is important to know. Recently, high-throughput DNA sequencing has revolutionized the study of immune receptor repertoires, which allows a much better understanding of the factors that determine the overlap of TCR repertoire among individuals. Here, we summarize the current knowledge on public T-cell response and discuss future challenges in this field