Coordinated analysis of exon and intron data reveals novel differential gene expression changes

Whole blood RNA extraction efficiency contributes to variability in RNA sequencing data sets

Numerous methodologies are used for blood RNA extraction, and large quantitative differences in recovered RNA content are reported. We evaluated three archived data sets to determine how extraction methodologies might influence mRNA and lncRNA sequencing results. The total quantity of RNA recovered /ml of blood affects RNA sequencing by impacting the recovery of weakly expressed mRNA, and lncRNA transcripts. Transcript expression (TPM counts) plotted in relation to transcript size (base pairs, bp) revealed a 30% loss of short to midsized transcripts in some data sets. Quantitative recovery of RNA is of considerable importance, and it should be viewed more judiciously. Transcripts common to the three data sets were subsequently normalized and transcript mean TPM counts and TPM count coefficient of variation (CV) were plotted in relation to increasing transcript size. Regression analysis of mean TPM counts versus transcript size revealed negative slopes in two of the three data sets suggesting a reduction of TPM transcript counts with increasing transcript size. In the third data set, the regression slope line of mRNA transcript TPM counts approximates zero and TPM counts increased in proportion to transcript size over a range of 200 to 30,000 bp. Similarly, transcript TPM count CV values also were uniformly distributed over the range of transcript sizes. In the other data sets, the regression CV slopes increased in relation to transcript size. The recovery of weakly expressed and /or short to midsized mRNA and lncRNA transcripts varies with different RNA extraction methodologies thereby altering the fundamental sequencing relationship between transcript size and TPM counts. Our analysis identifies differences in RNA sequencing results that are dependent upon the quantity of total RNA recovery from whole blood. We propose that incomplete RNA extraction directly impacts the recovery of mRNA and lncRNA transcripts from human blood and speculate these differences contribute to the "batch" effects commonly identified between sequencing results from different archived data sets.

Inferring Time-Lagged Causality Using the Derivative of Single-Cell Expression

Many computational methods have been developed to infer causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing the conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships. Furthermore, we showed that the number of regulatory relationships identified by CITL was significantly more than that expected by chance. We provide an R package and a command-line tool of CITL for different usage scenarios.

Genome-Wide Identification and Expression Profiling of KCS Gene Family in Passion Fruit (Passiflora edulis) Under Fusarium kyushuense and Drought Stress Conditions

Plant and fruit surfaces are covered with cuticle wax and provide a protective barrier against biotic and abiotic stresses. Cuticle wax consists of very-long-chain fatty acids (VLCFAs) and their derivatives. β-Ketoacyl-CoA synthase (KCS) is a key enzyme in the synthesis of VLCFAs and provides a precursor for the synthesis of cuticle wax, but the KCS gene family was yet to be reported in the passion fruit (Passiflora edulis). In this study, thirty-two KCS genes were identified in the passion fruit genome and phylogenetically grouped as KCS1-like, FAE1-like, FDH-like, and CER6-like. Furthermore, thirty-one PeKCS genes were positioned on seven chromosomes, while one PeKCS was localized to the unassembled genomic scaffold. The cis-element analysis provides insight into the possible role of PeKCS genes in phytohormones and stress responses. Syntenic analysis revealed that gene duplication played a crucial role in the expansion of the PeKCS gene family and underwent a strong purifying selection. All PeKCS proteins shared similar 3D structures, and a protein-protein interaction network was predicted with known Arabidopsis proteins. There were twenty putative ped-miRNAs which were also predicted that belong to nine families targeting thirteen PeKCS genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation results were highly associated with fatty acid synthase and elongase activity, lipid metabolism, stress responses, and plant-pathogen interaction. The highly enriched transcription factors (TFs) including ERF, MYB, Dof, C2H2, TCP, LBD, NAC, and bHLH were predicted in PeKCS genes. qRT-PCR expression analysis revealed that most PeKCS genes were highly upregulated in leaves including PeKCS2, PeKCS4, PeKCS8, PeKCS13, and PeKCS9 but not in stem and roots tissues under drought stress conditions compared with controls. Notably, most PeKCS genes were upregulated at 9th dpi under Fusarium kyushuense biotic stress condition compared to controls. This study provides a basis for further understanding the functions of KCS genes, improving wax and VLCFA biosynthesis, and improvement of passion fruit resistance.

Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

BACKGROUND

RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis.

RESULTS

A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent "trendlines" for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the "trendline" profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65-70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11-20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15.

CONCLUSIONS

This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis.

The evolution of hemocyanin genes in Tectipleura: a multitude of conserved introns in highly diverse gastropods

Background

Hemocyanin is the oxygen transporter of most molluscs. Since the oxygen affinity of hemocyanin is strongly temperature-dependent, this essential protein needs to be well-adapted to the environment. In Tectipleura, a very diverse group of gastropods with > 27,000 species living in all kinds of habitats, several hemocyanin genes have already been analyzed. Multiple independent duplications of this gene have been identified and may represent potential adaptations to different environments and lifestyles. The aim of this study is to further explore the evolution of these genes by analyzing their exon–intron architectures.

Results

We have reconstructed the gene architectures of ten hemocyanin genes from four Tectipleura species: Aplysia californica, Lymnaea stagnalis, Cornu aspersum and Helix pomatia. Their hemocyanin genes each contain 53 introns, significantly more than in the hemocyanin genes of Cephalopoda (9–11), Vetigastropoda (15) and Caenogastropoda (28–33). The gene structures of Tectipleura hemocyanins are identical in terms of intron number and location, with the exception of one out of two hemocyanin genes of L. stagnalis that comprises one additional intron. We found that gene structures that differ between molluscan lineages most probably evolved more recently through independent intron gains.

Conclusions

The strict conservation of the large number of introns in Tectipleura hemocyanin genes over 200 million years suggests the influence of a selective pressure on this gene structure. While we could not identify conserved sequence motifs within these introns, it may be simply the great number of introns that offers increased possibilities of gene regulation relative to hemocyanin genes with less introns and thus may have facilitated habitat shifts and speciation events. This hypothesis is supported by the relatively high number of introns within the hemocyanin genes of Pomacea canaliculata that has evolved independently of the Tectipleura. Pomacea canaliculata belongs to the Caenogastropoda, the sister group of Heterobranchia (that encompass Tectipleura) which is also very diverse and comprises species living in different habitats. Our findings provide a hint to some of the molecular mechanisms that may have supported the spectacular radiation of one of Metazoa’s most species rich groups.