Benchmarking RNA-Seq Aligners at Base-Level and Junction Base-Level Resolution Using the Arabidopsis thaliana Genome.

The utmost goal of selecting an RNA-Seq alignment software is to perform accurate alignments with a robust algorithm, which is capable of detecting the various intricacies underlying read-mapping procedures and beyond. Most alignment software tools are typically pre-tuned with human or prokaryotic data, and therefore may not be suitable for applications to other organisms, such as plants. The rapidly growing plant RNA-Seq databases call for the assessment of the alignment tools on curated plant data, which will aid the calibration of these tools for applications to plant transcriptomic data. We therefore focused here on benchmarking RNA-Seq read alignment tools, using simulated data derived from the model organism Arabidopsis thaliana. We assessed the performance of five popular RNA-Seq alignment tools that are currently available, based on their usage (citation count). By introducing annotated single nucleotide polymorphisms (SNPs) from The Arabidopsis Information Resource (TAIR), we recorded alignment accuracy at both base-level and junction base-level resolutions for each alignment tool. In addition to assessing the performance of the alignment tools at their default settings, accuracies were also recorded by varying the values of numerous parameters, including the confidence threshold and the level of SNP introduction. The performances of the aligners were found consistent under various testing conditions at the base-level accuracy; however, the junction base-level assessment produced varying results depending upon the applied algorithm. At the read base-level assessment, the overall performance of the aligner STAR was superior to other aligners, with the overall accuracy reaching over 90% under different test conditions. On the other hand, at the junction base-level assessment, SubRead emerged as the most promising aligner, with an overall accuracy over 80% under most test conditions.

The OsTIL1 lipocalin protects cell membranes from reactive oxygen species damage and maintains the 18:3-containing glycerolipid biosynthesis under cold stress in rice.

Lipocalins constitute a conserved protein family that binds to and transports a variety of lipids while fatty acid desaturases (FADs) are required for maintaining the cell membrane fluidity under cold stress. Nevertheless, it remains unclear whether plant lipocalins promote FADs for the cell membrane integrity under cold stress. Here, we identified the role of OsTIL1 lipocalin in FADs-mediated glycerolipid remodeling under cold stress. Overexpression and CRISPR/Cas9 mediated gene edition experiments demonstrated that OsTIL1 positively regulated cold stress tolerance by protecting the cell membrane integrity from reactive oxygen species damage and enhancing the activities of peroxidase and ascorbate peroxidase, which was confirmed by combined cold stress with a membrane rigidifier dimethyl sulfoxide or a H2 O2 scavenger dimethyl thiourea. OsTIL1 overexpression induced higher 18:3 content, and higher 18:3/18:2 and (18:2 + 18:3)/18:1 ratios than the wild type under cold stress whereas the gene edition mutant showed the opposite. Furthermore, the lipidomic analysis showed that OsTIL1 overexpression led to higher contents of 18:3-mediated glycerolipids, including galactolipids (monoglactosyldiacylglycerol and digalactosyldiacylglycerol) and phospholipids (phosphatidyl glycerol, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl serine and phosphatidyl inositol) under cold stress. RNA-seq and enzyme linked immunosorbent assay analyses indicated that OsTIL1 overexpression enhanced the transcription and enzyme abundance of four ω-3 FADs (OsFAD3-1/3-2, 7, and 8) under cold stress. These results reveal an important role of OsTIL1 in maintaining the cell membrane integrity from oxidative damage under cold stress, providing a good candidate gene for improving cold tolerance in rice.

POSMM: an efficient alignment-free metagenomic profiler that complements alignment-based profiling.

We present here POSMM (pronounced 'Possum'), Python-Optimized Standard Markov Model classifier, which is a new incarnation of the Markov model approach to metagenomic sequence analysis. Built on the top of a rapid Markov model based classification algorithm SMM, POSMM reintroduces high sensitivity associated with alignment-free taxonomic classifiers to probe whole genome or metagenome datasets of increasingly prohibitive sizes. Logistic regression models generated and optimized using the Python sklearn library, transform Markov model probabilities to scores suitable for thresholding. Featuring a dynamic database-free approach, models are generated directly from genome fasta files per run, making POSMM a valuable accompaniment to many other programs. By combining POSMM with ultrafast classifiers such as Kraken2, their complementary strengths can be leveraged to produce higher overall accuracy in metagenomic sequence classification than by either as a standalone classifier. POSMM is a user-friendly and highly adaptable tool designed for broad use by the metagenome scientific community.

Mapping Strengths and Weaknesses of Different Clustering Approaches to Deciphering Bacterial Chimerism.

Bacterial genomes are chimeras of DNA of different ancestries. Deconstructing chimeric genomes is central to understanding the evolutionary trajectories of their disparate components and thus the organisms as a whole in the light of their evolutionary contexts. Of specific interest is to delineate and quantify native (vertically inherited) and alien (horizontally acquired) components of bacterial genomes and also specify genomic fractions that represent different donor sources. An agglomerative clustering procedure that prioritizes grouping of proximal similar genomic segments has previously been invoked for this purpose in conjunction with a recursive segmentation procedure. Surprisingly, however, the relative strengths and weaknesses of different clustering approaches to deciphering bacterial chimerism have not yet been investigated, despite the need to robustly interpret tens of thousands of completely sequenced bacterial genomes and nearly complete genome assemblies available in the public databases. To bridge this knowledge gap and develop more robust approaches, we assessed different clustering methods, including segment order based (proximal) clustering, hierarchical clustering, affinity propagation clustering, and a novel network clustering approach on chimeric genomes modeled after bacterial genomes representing a broad spectrum of compositional complexity. Although segment order-based clustering and network clustering compared favorably with the other approaches in discriminating between native and alien DNA at genome optimized settings, network clustering did consistently better than other methods at parametric settings optimized on all test genomes together. Segment order-based clustering and hierarchical clustering outperformed other methods in alien DNA identification while preserving donor identity in the genomes. Our study highlights the strengths and weaknesses of different approaches and suggests how this can be leveraged to achieve a more robust deconstruction of bacterial chimerism.

The Arabidopsis gene co-expression network.

Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High-throughput technologies for assessment and quantification of genome-wide gene expression patterns have enabled systems-level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information-rich RNA-Seq datasets of the model plant Arabidopsis thaliana to construct a gene co-expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co-expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition in Arabidopsis onto the co-expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the first A. thaliana co-expression network constructed using the entire mRNA-Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for the Arabidopsis research community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.

RNA-Seq Data Analysis Pipeline for Plants: Transcriptome Assembly, Alignment, and Differential Expression Analysis.

In this chapter, we describe methods for analyzing RNA-Seq data, presented as a flow along a pipeline beginning with raw data from a sequencer and ending with an output of differentially expressed genes and their functional characterization. The first section covers de novo transcriptome assembly for organisms lacking reference genomes or for those interested in probing against the background of organism-specific transcriptomes assembled from RNA-Seq data. Section 2 covers both gene- and transcript-level quantifications, leading to the third and final section on differential expression analysis between two or more conditions. The pipeline starts with raw sequence reads, followed by quality assessment and preprocessing of the input data to ensure a robust estimate of the transcripts and their differential regulation. The preprocessed data can be inputted into the de novo transcriptome flow to assemble transcripts, functionally annotated using tools such as InterProScan or Blast2Go and then forwarded to differential expression analysis flow, or directly inputted into the differential expression analysis flow if a reference genome is available. An online repository containing sample data has also been made available, as well as custom Python scripts to modify the output of the programs within the pipeline for various downstream analyses.

Role of ribosomal RNA released from red cells in blood coagulation in zebrafish and humans.

Hemolytic disorders are characterized by hemolysis and are prone to thrombosis. It has previously been shown that the RNA released from damaged blood cells activates clotting. However, the nature of the RNA released from hemolysis is still elusive. We found that after hemolysis, red blood cells from both zebrafish and humans released RNA that contained mostly 5.8S ribosomal RNA (5.8S rRNA), This RNA activated coagulation in zebrafish and human plasmas. By using both natural and synthetic 5.8S rRNA and its truncated fragments, we found that the 3'-end 26-nucleotide-long RNA (3'-26 RNA) and its stem-loop secondary structure were necessary and sufficient for clotting activity. Corn trypsin inhibitor (CTI), a coagulation factor XII (FXII) inhibitor, blocked 3'-26 RNA-mediated coagulation activation in the plasma of both zebrafish and humans. CTI also inhibited zebrafish coagulation in vivo. 5.8S rRNA monoclonal antibody inhibited both 5.8S rRNA- and 3'-26 RNA-mediated zebrafish coagulation activity. Both 5.8S rRNA and 3'-26 RNA activated normal human plasma but did not activate FXII-deficient human plasma. Taken together, these results suggested that the activation of zebrafish plasma is via an FXII-like protein. Because zebrafish have no FXII and because hepatocyte growth factor activator (Hgfac) has sequence similarities to FXII, we knocked down the hgfac in adult zebrafish. We found that plasma from this knockdown fish does not respond to 3'-26 RNA. To summarize, we identified that an rRNA released in hemolysis activates clotting in human and zebrafish plasma. Furthermore, we showed that fish Hgfac plays a role in rRNA-mediated activation of coagulation.

Lipophilic signals lead to organ-specific gene expression changes in Arabidopsis seedlings.

In plants, N-acylethanolamines (NAEs) are most abundant in desiccated seeds and their levels decline during germination and early seedling establishment. However, endogenous NAE levels rise in seedlings when ABA or environmental stress is applied, and this results in an inhibition of further seedling development. When the most abundant, polyunsaturated NAEs of linoleic acid (18:2) and linolenic acid (18:3) were exogenously applied, seedling development was affected in an organ-specific manner. NAE 18:2 primarily affected primary root elongation and NAE 18:3 primarily affected cotyledon greening and expansion and overall seedling growth. The molecular components and signaling mechanisms involved in this pathway are not well understood. In addition, the bifurcating nature of this pathway provides a unique system in which to study the spatial aspects and interaction of these lipid-specific and organ-targeted signaling pathways. Using whole transcriptome sequencing (RNA-seq) and differential expression analysis, we identified early (1-3 hr) transcriptional changes induced by the exogenous treatment of NAE 18:2 and NAE 18:3 in cotyledons, roots, and seedlings. These two treatments led to a significant enrichment in ABA-response and chitin-response genes in organs where the treatments led to changes in development. In Arabidopsis seedlings, NAE 18:2 treatment led to the repression of genes involved in cell wall biogenesis and organization in roots and seedlings. In addition, cotyledons, roots, and seedlings treated with NAE 18:3 also showed a decrease in transcripts that encode proteins involved in growth processes. NAE 18:3 also led to changes in the abundance of transcripts involved in the modulation of chlorophyll biosynthesis and catabolism in cotyledons. Overall, NAE 18:2 and NAE 18:3 treatment led to lipid-type and organ-specific gene expression changes that include overlapping and non-overlapping gene sets. These data will provide future, rich opportunities to examine the genetic pathways involved in transducing early signals into downstream physiological changes in seedling growth.

Higher-order Markov models for metagenomic sequence classification.

MOTIVATION: Alignment-free, stochastic models derived from k-mer distributions representing reference genome sequences have a rich history in the classification of DNA sequences. In particular, the variants of Markov models have previously been used extensively. Higher-order Markov models have been used with caution, perhaps sparingly, primarily because of the lack of enough training data and computational power. Advances in sequencing technology and computation have enabled exploitation of the predictive power of higher-order models. We, therefore, revisited higher-order Markov models and assessed their performance in classifying metagenomic sequences. RESULTS: Comparative assessment of higher-order models (HOMs, 9th order or higher) with interpolated Markov model, interpolated context model and lower-order models (8th order or lower) was performed on metagenomic datasets constructed using sequenced prokaryotic genomes. Our results show that HOMs outperform other models in classifying metagenomic fragments as short as 100 nt at all taxonomic ranks, and at lower ranks when the fragment size was increased to 250 nt. HOMs were also found to be significantly more accurate than local alignment which is widely relied upon for taxonomic classification of metagenomic sequences. A novel software implementation written in C++ performs classification faster than the existing Markovian metagenomic classifiers and can therefore be used as a standalone classifier or in conjunction with existing taxonomic classifiers for more robust classification of metagenomic sequences. AVAILABILITY AND IMPLEMENTATION: The software has been made available at https://github.com/djburks/SMM. CONTACT: Rajeev.Azad@unt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The genome of jojoba (Simmondsia chinensis): A taxonomically isolated species that directs wax ester accumulation in its seeds.

Seeds of the desert shrub, jojoba (Simmondsia chinensis), are an abundant, renewable source of liquid wax esters, which are valued additives in cosmetic products and industrial lubricants. Jojoba is relegated to its own taxonomic family, and there is little genetic information available to elucidate its phylogeny. Here, we report the high-quality, 887-Mb genome of jojoba assembled into 26 chromosomes with 23,490 protein-coding genes. The jojoba genome has only the whole-genome triplication (γ) shared among eudicots and no recent duplications. These genomic resources coupled with extensive transcriptome, proteome, and lipidome data helped to define heterogeneous pathways and machinery for lipid synthesis and storage, provided missing evolutionary history information for this taxonomically segregated dioecious plant species, and will support efforts to improve the agronomic properties of jojoba.

Tissue-specific differences in metabolites and transcripts contribute to the heterogeneity of ricinoleic acid accumulation in Ricinus communis L. (castor) seeds.

INTRODUCTION: Castor (Ricinus communis L.) seeds are valued for their production of oils which can comprise up to 90% hydroxy-fatty acids (ricinoleic acid). Castor oil contains mono-, di- and tri- ricinoleic acid containing triacylglycerols (TAGs). Although the enzymatic synthesis of ricinoleic acid is well described, the differential compartmentalization of these TAG molecular species has remained undefined. OBJECTIVES: To examine the distribution of hydroxy fatty acid accumulation within the endosperm and embryo tissues of castor seeds. METHODS: Matrix assisted laser desorption/ionization mass spectrometry imaging was used to map the distribution of triacylglycerols in tissue sections of castor seeds. In addition, the endosperm and embryo (cotyledons and embryonic axis) tissues were dissected and extracted for quantitative lipidomics analysis and Illumina-based RNA deep sequencing. RESULTS: This study revealed an unexpected heterogeneous tissue distribution of mono-, di- and tri- hydroxy-triacylglycerols in the embryo and endosperm tissues of castor seeds. Pathway analysis based on transcript abundance suggested that distinct embryo- and endosperm-specific mechanisms may exist for the shuttling of ricinoleic acid away from phosphatidylcholine (PC) and into hydroxy TAG production. The embryo-biased mechanism appears to favor removal of ricinoleic acid from PC through phophatidylcholine: diacylglycerol acyltransferase while the endosperm pathway appears to remove ricinoleic acid from the PC pool by preferences of phospholipase A (PLA2α) and/or phosphatidylcholine: diacylglycerol cholinephosphotransferase. CONCLUSIONS: Collectively, a combination of lipidomics and transcriptomics analyses revealed previously undefined spatial aspects of hydroxy fatty acid metabolism in castor seeds. These studies underscore a need for tissue-specific studies as a means to better understand the regulation of triacylglycerol accumulation in oilseeds.

Environmental vibrios represent a source of antagonistic compounds that inhibit pathogenic Vibrio cholerae and Vibrio parahaemolyticus strains.

With the overuse of antibiotics, many pathogens including Vibrio cholerae and Vibrio parahaemolyticus have evolved multidrug resistance making treatment more difficult. While understanding the mechanisms that underlie pathogenesis is crucial, knowledge of bacterial interactions of V. cholerae and V. parahaemolyticus could provide insight to their susceptibility outside of the human host. Based on previous work showing competition among environmental strains, we predict that marine-derived bacteria should inhibit Vibrio pathogens and may be a source of unique antibiotic compounds. We tested a collection of 3,456 environmental Vibrio isolates from diverse habitats against a panel of V. cholerae and V. parahaemolyticus, and identified 102 strains that inhibited the growth of these pathogens. Phylogenetic analysis revealed that 40 pathogen-inhibiting strains were unique at the hsp60 gene sequence while 62 of the isolates were identical suggesting clonal groups. Genomic comparisons of ten strains revealed diversity even between clonal isolates and were identified as being closely related to known Vibrio crassostreae, Vibrio splendidus, and Vibrio tasmaniensis strains. Further analysis revealed multiple biosynthetic gene clusters within all sequenced genomes that encoded secondary metabolites with potential antagonistic activity. Thus, environmental vibrios represent a source of compounds that inhibit Vibrio pathogens.

Identification and Network-Enabled Characterization of Auxin Response Factor Genes in Medicago truncatula.

The Auxin Response Factor (ARF) family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF) genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses.

Glucose or Altered Ceramide Biosynthesis Mediate Oxygen Deprivation Sensitivity Through Novel Pathways Revealed by Transcriptome Analysis in Caenorhabditis elegans.

Individuals with type 2 diabetes display metabolic abnormalities, such as hyperglycemia, increased free fatty acids, insulin resistance, and altered ceramide levels, that contribute to vascular dysfunctions and compromised oxygen delivery. Caenorhabditis elegans fed a glucose-supplemented diet or with altered ceramide metabolism, due to a hyl-2 mutation, are sensitive to oxygen deprivation (anoxia). Our experiments showed that the combination of these factors further decreased the anoxia survival. RNA-sequencing analysis was performed to assess how a glucose-supplemented diet and/or a hyl-2 mutation altered the transcriptome. Comparison analysis of transcripts associated with anoxia-sensitive animals [hyl-2(tm2031) mutation or a glucose diet] revealed 199 common transcripts encoded by genes with known or predicted functions involving innate immunity, cuticle function (collagens), or xenobiotic and endobiotic phase I and II detoxification system. Use of RNA interference (RNAi) to target gene products of the xenobiotic and endobiotic phase I and II detoxification system (UDP-glycosyltransferase and Cytochrome p450 genes; ugt-15, ugt-18, ugt-19, ugt-41, ugt-63, cyp-13A12, cyp-25A1, and cyp-33C8) increased anoxia survival in wild-type animals fed a standard diet. Anoxia sensitivity of the hyl-2(tm2031) animals was suppressed by RNAi of cyp-25A1 or cyp-33C8 genes. A glucose diet fed to the P0 hermaphrodite decreased the anoxia survival of its F1 embryos; however, the RNAi of ugt-63 and cyp-33C8 suppressed anoxia sensitivity. These studies provide evidence that the detoxification system impacts oxygen deprivation responses and that C. elegans can be used to model the conserved detoxification system.