Statistical methods on detecting differentially expressed genes for RNA-seq data

Transcriptome mining and expression analysis of ABC transporter genes in a monophagous herbivore, Leucinodes orbonalis (Crambidae: Lepidoptera)

Insecticide resistance is a global concern and requires immediate attention to manage dreadful insect pests. One of the resistance mechanisms adopted by insects is through ATP-binding cassette (ABC) transporter proteins. These proteins rapidly transport and eliminate the insecticidal molecules across the lipid membranes (Phase III detoxification mechanism). In the present study, we investigated the potential role of ABC transporter genes in imparting insecticide resistance in field-collected insecticide resistant larvae of eggplant shoot and fruit borer (Leucinodes orbonalis; Crambidae: Lepidoptera). Dose-mortality bioassays against five insecticidal molecules revealed moderate to high levels of insecticide resistance (32.2. to 134.1-fold). Thirty-one genes encoding ABC transporter proteins were mined from the transcriptome resources of L. orbonalis. They were classified under eight sub-families (ABCA to ABCH). Phylogenetic analysis indicated ABCG is the most divergent, composed of nine genes as compared to many other insects. Transcriptome analysis of the insecticide resistant and susceptible strains of L. orbonalis revealed differential expression of 13 ABC transporter genes. The altered expression of these genes was further validated using qRT-PCR. The knockdown studies indicated the involvement of ABCD1 and ABCG2 genes in chlorantraniliprole resistance in the insecticide-resistant strain of L. orbonalis. This study unveils the additional mechanisms employed by L. orbonalis in resisting insecticide toxicity through accelerated excretion mode. These ABCD and ABCG family genes could be candidate targets in developing genome-assisted pest management strategies in the future.

Warmer temperature during asexual reproduction induce methylome, transcriptomic, and lasting phenotypic changes in Fragaria vesca ecotypes

Plants must adapt with increasing speed to global warming to maintain their fitness. One rapid adaptation mechanism is epigenetic memory, which may provide organisms sufficient time to adapt to climate change. We studied how the perennial Fragaria vesca adapted to warmer temperatures (28°C vs. 18°C) over three asexual generations. Differences in flowering time, stolon number, and petiole length were induced by warmer temperature in one or more ecotypes after three asexual generations and persisted in a common garden environment. Induced methylome changes differed between the four ecotypes from Norway, Iceland, Italy, and Spain, but shared methylome responses were also identified. Most differentially methylated regions (DMRs) occurred in the CHG context, and most CHG and CHH DMRs were hypermethylated at the warmer temperature. In eight CHG DMR peaks, a highly similar methylation pattern could be observed between ecotypes. On average, 13% of the differentially methylated genes between ecotypes also showed a temperature-induced change in gene expression. We observed ecotype-specific methylation and expression patterns for genes related to gibberellin metabolism, flowering time, and epigenetic mechanisms. Furthermore, we observed a negative correlation with gene expression when repetitive elements were found near (±2 kb) or inside genes. In conclusion, lasting phenotypic changes indicative of an epigenetic memory were induced by warmer temperature and were accompanied by changes in DNA methylation patterns. Both shared methylation patterns and transcriptome differences between F. vesca accessions were observed, indicating that DNA methylation may be involved in both general and ecotype-specific phenotypic variation.

Analysis of the difference between early-bolting and non-bolting roots of Angelica dahurica based on transcriptome sequencing

Angelica dahurica (Fisch. ex Hoffm.) Benth.et Hook.f.var.formosana (Boiss.) Shan et Yuan (A. dahurica) is a well-known medicinal plant that has a wide range of applications in the pharmaceutical, food, cosmetic, and other industries. However, the issue of early bolting has emerged as a major hindrance to its production. This problem not only reduces the yield of A. dahurica, but also has an impact on its active ingredients. To date, the molecular factors that contribute to early bolting and its impact on the growth of A. dahurica have not been thoroughly investigated. Therefore, we conducted a transcriptome study using the Illumina NovaSeq 6000 on two developmental types: early-bolting and non-bolting (normal) roots of A. dahurica. We obtained 2,185 up-regulated and 1,414 down-regulated genes in total. Many of the identified transcripts were related to genes involved in early bolting. The gene ontology analysis revealed several differentially expressed genes that are crucial in various pathways, primarily associated with cellular, molecular, and biological processes. Additionally, the morphological characteristics and coumarin content in the early bolting roots of A. dahurica were significantly altered. This study provides insight into the transcriptomic regulation of early bolting in A. dahurica, which can potentially be utilized to enhance its medicinal properties.

Riociguat attenuates left ventricular proteome and microRNA profile changes after experimental aortic stenosis in mice

BACKGROUND AND PURPOSE

Development and progression of heart failure (HF) involve endothelial and myocardial dysfunction as well as a dysregulation of the nitric oxide - soluble guanylyl cyclase - cyclic guanosine monophosphate (NO-sGC-cGMP) signalling pathway. Recently, we reported that the sGC stimulator riociguat (RIO) has beneficial effects on cardiac remodelling and progression of HF in response to chronic pressure overload. Here, we examined if these favourable RIO effects are also reflected in alterations of the myocardial proteome and microRNA profiles.

EXPERIMENTAL APPROACH

Male C57BL/6N mice underwent transverse aortic constriction (TAC) and sham operated mice served as controls. TAC and sham animals were randomised and treated with either RIO or vehicle for five weeks, starting three weeks post-surgery when cardiac hypertrophy was established. Afterwards we performed mass spectrometric proteome analyses and microRNA sequencing of proteins and RNAs, respectively, isolated from left ventricles (LV).

KEY RESULTS

TAC-induced changes of the LV proteome were significantly reduced by RIO treatment. Bioinformatics analyses revealed that RIO improved TAC-induced cardiovascular disease related pathways, metabolism and energy production, e.g. reversed alterations in the levels of myosin heavy chain 7 (MYH7), cardiac phospholamban (PLN), and ankyrin repeat domain-containing protein 1 (ANKRD1). RIO also attenuated TAC-induced changes of microRNA levels in the LV.

CONCLUSION AND IMPLICATIONS

The sGC stimulator RIO has beneficial effects on cardiac structure and function during pressure overload, which is accompanied by a reversal of TAC-induced changes of the cardiac proteome and microRNA profile. Our data support the potential of RIO as a novel HF therapeutic.

Transcriptome analyses reveal the expression profile of genes related to lignan biosynthesis in Anthriscus sylvestris L. Hoffm. Gen

Anthriscus sylvestris L. Hoffm. Gen (A. sylvestris) is a perennial herb widely used for antitussive and diuretic purposes in traditional Korean and Chinese medicine. Lignans are critical secondary metabolites with widely pharmacological activities in A. sylvestris. Using transcriptome data of A. sylvestris, we identified genes related to lignan biosynthesis. In all, 123,852 unigenes were obtained from the flowers, leaves, roots, and stems of A. sylvestris with the Illumina HiSeq 4000 platform. The average length of unigenes was 1,123 bp and 91,217 (73.65%) of them were annotated in public databases. Differentially expressed genes and root-specific genes were analyzed between roots and the other three tissue types by comparing gene expression profiles. Specifically, the key enzyme genes involved in lignan biosynthesis were identified and analyzed. The expression levels of some of these genes were highest in the roots, consistent with the accumulation of deoxypodophyllotoxin. These expression levels were experimentally verified via quantitative real-time polymerase chain reaction (qRT-PCR). This research provides valuable information on the transcriptome data of A. sylvestris and the identification of candidate genes associated with the biosynthesis of lignans, laying the foundation for further research on genomics in A. sylvestris and related species.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-022-01156-w.

An Oligomeric Sulfated Hyaluronan and Silk-Elastinlike Polymer Combination Protects against Murine Radiation Induced Proctitis

Semisynthetic glycosaminoglycan ethers (SAGEs) are short, sulfated hyaluronans which combine the natural properties of hyaluronan with chemical sulfation. In a murine model, SAGEs provide protection against radiation induced proctitis (RIP), a side effect of lower abdominal radiotherapy for cancer. The anti-inflammatory effects of SAGE have been studied in inflammatory diseases at mucosal barrier sites; however, few mechanisms have been uncovered necessitating high throughput methods. SAGEs were combined with silk-elastinlike polymers (SELPs) to enhance rectal accumulation in mice. After high radiation exposure to the lower abdominal area, mice were followed for 3 days or until they met humane endpoints, before evaluation of behavioral pain responses and histological assessment of rectal inflammation. RNA sequencing was conducted on tissues from the 3-day cohort to determine molecular mechanisms of SAGE–SELP. After 3 days, mice receiving the SAGE–SELP combination yielded significantly lowered pain responses and amelioration of radiation-induced rectal inflammation. Mice receiving the drug–polymer combination survived 60% longer than other irradiated mice, with a fraction exhibiting long term survival. Sequencing reveals varied regulation of toll like receptors, antioxidant activities, T-cell signaling, and pathways associated with pain. This investigation elucidates several molecular mechanisms of SAGEs and exhibits promising measures for prevention of RIP.

Complex basis of hybrid female sterility and Haldane's rule in Heliconius butterflies: Z-linkage and epistasis

Hybrids between species are often sterile or inviable. Hybrid unfitness usually evolves first in the heterogametic sex-a pattern known as Haldane's rule. The genetics of Haldane's rule have been extensively studied in species where the male is the heterogametic (XX/XY) sex, but its basis in taxa where the female is heterogametic (ZW/ZZ), such as Lepidoptera and birds, is largely unknown. Here, we analyse a new case of female hybrid sterility between geographic subspecies of Heliconius pardalinus. The two subspecies mate freely in captivity, but female F1 hybrids in both directions of cross are sterile. Sterility is due to arrested development of oocytes after they become differentiated from nurse cells, but before yolk deposition. We backcrossed fertile male F1 hybrids to parental females and mapped quantitative trait loci (QTLs) for female sterility. We also identified genes differentially expressed in the ovary as a function of oocyte development. The Z chromosome has a major effect, similar to the 'large X effect' in Drosophila, with strong epistatic interactions between loci at either end of the Z chromosome, and between the Z chromosome and autosomal loci on chromosomes 8 and 20. By intersecting the list of genes within these QTLs with those differentially expressed in sterile and fertile hybrids, we identified three candidate genes with relevant phenotypes. This study is the first to characterize hybrid sterility using genome mapping in the Lepidoptera and shows that it is produced by multiple complex epistatic interactions often involving the sex chromosome, as predicted by the dominance theory of Haldane's rule.

De novo transcriptome assembly of Angelica dahurica and characterization of coumarin biosynthesis pathway genes

Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav (A. dahurica) is a famous Chinese herb known for the production of coumarins, important secondary metabolites with wide-ranging pharmacological activities. In particular, the methoxylated coumarins like those produced by A. dahurica are known for their anti-inflammatory, anti-cancer, and anti-oxidant pharmacological effects. However, the molecular mechanism of coumarin biosynthesis in A. dahurica has not been studied. Such investigation could help scientists harness the biosynthesis potential of methoxylated coumarins. Here we present, three transcriptomes corresponding to leaf, root, and stem tissues of A. dahurica. A total of 114,310 unigenes with an average length of 1118 bp were de novo assembled, and 81,404 (71.21%) of those unigenes were annotated. Then, 181 unigenes encoding the seven key enzymes involved were identified, for which COMT (Caffeic acid 3-O-methyltransferase) was spatially used in a phylogenetic analysis, and some of these key enzyme genes were verified by qRT-PCR. Differentially expressed genes and root-specific-expressed genes were identified, by comparing genes' profile activity between roots and other tissues. Furthermore, multiple genes encoding key enzymes or transcription factors related to coumarin biosynthesis were identified and analyzed. This study is the first to report comprehensive gene information of A. dahurica at the transcriptional level, and to distinguish candidate genes related to its biosynthesis of coumarin, thus laying a foundation for this pathway's further exploration in A. dahurica.

A candidate gene identified in converting platycoside E to platycodin D from Platycodon grandiflorus by transcriptome and main metabolites analysis

Platycodin D and platycoside E are two triterpenoid saponins in Platycodon grandiflorus, differing only by two glycosyl groups structurally. Studies have shown β-Glucosidase from bacteria can convert platycoside E to platycodin D, indicating the potential existence of similar enzymes in P. grandiflorus. An L₉(3⁴) orthogonal experiment was performed to establish a protocol for calli induction as follows: the optimal explant is stems with nodes and the optimum medium formula is MS + NAA 1.0 mg/L + 6-BA 0.5 mg/L to obtain callus for experimental use. The platycodin D, platycoside E and total polysaccharides content between callus and plant organs varied wildly. Platycodin D and total polysaccharide content of calli was found higher than that of leaves. While, platycoside E and total polysaccharide content of calli was found lower than that of leaves. Associating platycodin D and platycoside E content with the expression level of genes involved in triterpenoid saponin biosynthesis between calli and leaves, three contigs were screened as putative sequences of β-Glucosidase gene converting platycoside E to platycodin D. Besides, we inferred that some transcription factors can regulate the expression of key enzymes involved in triterpernoid saponins and polysaccharides biosynthesis pathway of P. grandiflorus. Totally, a candidate gene encoding enzyme involved in converting platycoside E to platycodin D, and putative genes involved in polysaccharide synthesis in P. grandiflorus had been identified. This study will help uncover the molecular mechanism of triterpenoid saponins biosynthesis in P. grandiflorus.

Impact of Sequencing Depth and Library Preparation on Toxicological Interpretation of RNA-Seq Data in a "Three-Sample" Scenario

While RNA-sequencing (RNA-seq) has emerged as a standard approach in toxicogenomics, its full potential in gaining underlying toxicological mechanisms is still not clear when only three biological replicates are used. This "three-sample" study design is common in toxicological research, particularly in animal studies during preclinical drug development. Sequencing depth (the total number of reads in an experiment) and library preparation are critical to the resolution and integrity of RNA-seq data and biological interpretation. We used aflatoxin b1 (AFB1), a model toxicant, to investigate the effect of sequencing depth and library preparation in RNA-seq on toxicological interpretation in the "three-sample" scenario. We also compared different gene profiling platforms (RNA-seq, TempO-seq, microarray, and qPCR) using identical liver samples. Well-established mechanisms of AFB1 toxicity served as ground truth for our comparative analyses. We found that a minimum of 20 million reads was sufficient to elicit key toxicity functions and pathways underlying AFB1-induced liver toxicity using three replicates and that identification of differentially expressed genes was positively associated with sequencing depth to a certain extent. Further, our results showed that RNA-seq revealed toxicological insights from pathway enrichment with overall higher statistical power and overlap ratio, compared with TempO-seq and microarray. Moreover, library preparation using the same methods was important to reproducing the toxicological interpretation.

Stimulation of soluble guanylyl cyclase (sGC) by riociguat attenuates heart failure and pathological cardiac remodelling

BACKGROUND AND PURPOSE

Heart failure is associated with an impaired NO-soluble guanylyl cyclase (sGC)-cGMP pathway and its augmentation is thought to be beneficial for its therapy. We hypothesized that stimulation of sGC by the sGC stimulator riociguat prevents pathological cardiac remodelling and heart failure in response to chronic pressure overload.

EXPERIMENTAL APPROACH

Transverse aortic constriction or sham surgery was performed in C57BL/6N mice. After 3 weeks of transverse aortic constriction when heart failure was established, animals receive either riociguat or its vehicle for 5 additional weeks. Cardiac function was evaluated weekly by echocardiography. Eight weeks after surgery, histological analyses were performed to evaluate remodelling and the transcriptome of the left ventricles (LVs) was analysed by RNA sequencing. Cell culture experiments were used for mechanistically studies.

KEY RESULTS

Transverse aortic constriction resulted in a continuous decrease of LV ejection fraction and an increase in LV mass until week 3. Five weeks of riociguat treatment resulted in an improved LV ejection fraction and a decrease in the ratio of left ventricular mass to total body weight (LVM/BW), myocardial fibrosis and myocyte cross-sectional area. RNA sequencing revealed that riociguat reduced the expression of myocardial stress and remodelling genes (e.g. Nppa, Nppb, Myh7 and collagen) and attenuated the activation of biological pathways associated with cardiac hypertrophy and heart failure. Riociguat reversed pathological stress response in cultivated myocytes and fibroblasts.

CONCLUSION AND IMPLICATIONS

Stimulation of the sGC reverses transverse aortic constriction-induced heart failure and remodelling, which is associated with improved myocardial gene expression.

Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data

Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.

Cross-omics analysis revealed gut microbiome-related metabolic pathways underlying atherosclerosis development after antibiotics treatment

Objective

The metabolic influence of gut microbiota plays a pivotal role in the pathogenesis of cardiometabolic diseases. Antibiotics affect intestinal bacterial diversity, and long-term usage has been identified as an independent risk factor for atherosclerosis-driven events. The aim of this study was to explore the interaction between gut dysbiosis by antibiotics and metabolic pathways with the impact on atherosclerosis development.

Methods

We combined oral antibiotics with different diets in an Apolipoprotein E-knockout mouse model linking gut microbiota to atherosclerotic lesion development via an integrative cross-omics approach including serum metabolomics and cecal 16S rRNA targeted metagenomic sequencing. We further investigated patients with carotid atherosclerosis compared to control subjects with comparable cardiovascular risk.

Results

Here, we show that increased atherosclerosis by antibiotics was connected to a loss of intestinal diversity and alterations of microbial metabolic functional capacity with a major impact on the host serum metabolome. Pathways that were modulated by antibiotics and connected to atherosclerosis included diminished tryptophan and disturbed lipid metabolism. These pathways were related to the reduction of certain members of Bacteroidetes and Clostridia by antibiotics in the gut. Patients with atherosclerosis presented a similar metabolic signature as those induced by antibiotics in our mouse model.

Conclusion

Taken together, this work provides insights into the complex interaction between intestinal microbiota and host metabolism. Our data highlight that detrimental effects of antibiotics on the gut flora are connected to a pro-atherogenic metabolic phenotype beyond classical risk factors.

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Antibiotics exacerbate atherosclerosis independently of diet.

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Gut microbiota and metabolic alpha diversity are reduced by antibiotics.

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Pathways connected to atherogenesis are tryptophan and lipid metabolism.

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Metabolic changes are linked to reduced Clostridia and Bacteroidetes in the gut.

Genomic Profiling of BDE-47 Effects on Human Placental Cytotrophoblasts

Despite gradual legislative efforts to phase out flame retardants (FRs) from the marketplace, polybrominated diphenyl ethers (PBDEs) are still widely detected in human maternal and fetal tissues, eg, placenta, due to their continued global application in consumer goods and inherent biological persistence. Recent studies in rodents and human placental cell lines suggest that PBDEs directly cause placental toxicity. During pregnancy, trophoblasts play key roles in uterine invasion, vascular remodeling, and anchoring of the placenta-fetal unit to the mother. Thus, to study the potential consequences of PBDE exposures on human placental development, we used an in vitro model: primary villous cytotrophoblasts (CTBs). Following exposures, the endpoints that were evaluated included cytotoxicity, function (migration, invasion), the transcriptome, and the methylome. In a concentration-dependent manner, common PBDE congeners, BDE-47 and -99, significantly reduced cell viability and increased death. Upon exposures to sub-cytotoxic concentrations (≤ 5 µM), we observed BDE-47 accumulation in CTBs with limited evidence of metabolism. At a functional level, BDE-47 hindered the ability of CTBs to migrate and invade. Transcriptomic analyses of BDE-47 effects suggested concentration-dependent changes in gene expression, involving stress pathways, eg, inflammation and lipid/cholesterol metabolism as well as processes underlying trophoblast fate, eg, differentiation, migration, and vascular morphogenesis. In parallel assessments, BDE-47 induced low-level global increases in methylation of CpG islands, including a subset that were proximal to genes with roles in cell adhesion/migration. Thus, using a primary human CTB model, we showed that PBDEs induced alterations at cellular and molecular levels, which could adversely impact placental development.

Biallelic VPS35L pathogenic variants cause 3C/Ritscher-Schinzel-like syndrome through dysfunction of retriever complex

BACKGROUND

3C/Ritscher-Schinzel syndrome is characterised by congenital cranio-cerebello-cardiac dysplasia, where CCDC22 and WASHC5 are accepted as the causative genes. In combination with the retromer or retriever complex, these genes play a role in endosomal membrane protein recycling. We aimed to identify the gene abnormality responsible for the pathogenicity in siblings with a 3C/Ritscher-Schinzel-like syndrome, displaying cranio-cerebello-cardiac dysplasia, coloboma, microphthalmia, chondrodysplasia punctata and complicated skeletal malformation.

METHODS

Exome sequencing was performed to identify pathogenic variants. Cellular biological analyses and generation of knockout mice were carried out to elucidate the gene function and pathophysiological significance of the identified variants.

RESULTS

We identified compound heterozygous pathogenic variants (c.1097dup; p.Cys366Trpfs*28 and c.2755G>A; p.Ala919Thr) in the VPS35L gene, which encodes a core protein of the retriever complex. The identified missense variant lacked the ability to form the retriever complex, and the frameshift variant induced non-sense-mediated mRNA decay, thereby confirming biallelic loss of function of VPS35L. In addition, VPS35L knockout cells showed decreased autophagic function in nutrient-rich and starvation conditions, as well as following treatment with Torin 1. We also generated Vps35l^-/- mice and demonstrated that they were embryonic lethal at an early stage, between E7.5 and E10.5.

CONCLUSIONS

Our results suggest that biallelic loss-of-function variants in VPS35L underlies 3C/Ritscher-Schinzel-like syndrome. Furthermore, VPS35L is necessary for autophagic function and essential for early embryonic development. The data presented here provide a new insight into the critical role of the retriever complex in fetal development.

Alterations in the intestinal microbiota of patients with severe and active Graves' orbitopathy: a cross-sectional study

BACKGROUND

The intestinal microbiota was linked to autoimmune diseases. Graves' orbitopathy (GO) is an autoimmune disease that is usually associated with Graves' disease. However, information on the microbiome of GO patients is yet lacking.

OBJECTIVES

To investigate whether GO patients differ from healthy controls in the fecal microbiota.

DESIGN

A cross-sectional study.

SETTING

33 patients with severe and active GO and 32 healthy controls of Han nationality were enrolled between March 2017 and March 2018.

METHODS

The Gut microbial communities of the fecal samples of GO patients and healthy controls were analyzed and compared by 16S rRNA gene sequencing.

RESULTS

Community diversity (Simpson and Shannon) was significantly reduced in fecal samples from patients with GO as compared to controls (p < 0.05). The similarity observed while assessing the community diversity (PCoA) proposed that the microbiota of patients with GO differ significantly from those of controls (p < 0.05). At the phyla levels, the proportion of Bacteroidetes increased significantly in patients with GO (p < 0.05), while at the genus and species levels, significant differences were observed in the bacterial profiles between the two groups (p < 0.05).

LIMITATIONS

Single-centered study design and limited fecal samples.

CONCLUSIONS

The present study indicated distinctive features of the gut microbiota in GO patients. The study provided evidence for further exploration in the field of intestinal microbiota with respect to the diagnosis and treatment of GO patients by modifying the microbiota profile.

Transcriptome analysis of Polygonatum cyrtonema Hua: identification of genes involved in polysaccharide biosynthesis

BACKGROUND

Polygonatum cyrtonema Hua (P. cyrtonema) is one of the most important herbs in traditional Chinese medicine. Polysaccharides in P. cyrtonema plants comprise a class of important secondary metabolites and exhibit a broad range of pharmacological functions.

RESULTS

In order to identify genes involved in polysaccharide biosynthesis, we performed RNA sequencing analysis of leaf, root, and rhizome tissues of P. cyrtonema. A total of 164,573 unigenes were obtained by assembling transcripts from all three tissues and 86,063 of these were annotated in public databases. Differentially expressed genes (DEGs) were determined based on expression profile analysis, and DEG levels in rhizome tissues were then compared with their counterparts in leaf and root tissues. This analysis revealed numerous genes that were either up-regulated or uniquely expressed in the rhizome. Multiple genes encoding important enzymes, such as UDP glycosyltransferases (UGTs), or transcription factors involved in polysaccharide biosynthesis were identified and further analyzed, while a few genes encoding key enzymes were experimentally validated using quantitative real-time PCR.

CONCLUSION

Our results substantially expand the public transcriptome dataset of P. cyrtonema and provide valuable clues for the identification of candidate genes involved in metabolic pathways.

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data

RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at http://tanglab.hebtu.edu.cn/tanglab/Home/DREAMSeq.

Provitamin A biofortification of cassava enhances shelf life but reduces dry matter content of storage roots due to altered carbon partitioning into starch

Storage roots of cassava (Manihot esculenta Crantz), a major subsistence crop of sub-Saharan Africa, are calorie rich but deficient in essential micronutrients, including provitamin A β-carotene. In this study, β-carotene concentrations in cassava storage roots were enhanced by co-expression of transgenes for deoxy-d-xylulose-5-phosphate synthase (DXS) and bacterial phytoene synthase (crtB), mediated by the patatin-type 1 promoter. Storage roots harvested from field-grown plants accumulated carotenoids to ≤50 μg/g DW, 15- to 20-fold increases relative to roots from nontransgenic plants. Approximately 85%-90% of these carotenoids accumulated as all-trans-β-carotene, the most nutritionally efficacious carotenoid. β-Carotene-accumulating storage roots displayed delayed onset of postharvest physiological deterioration, a major constraint limiting utilization of cassava products. Large metabolite changes were detected in β-carotene-enhanced storage roots. Most significantly, an inverse correlation was observed between β-carotene and dry matter content, with reductions of 50%-60% of dry matter content in the highest carotenoid-accumulating storage roots of different cultivars. Further analysis confirmed a concomitant reduction in starch content and increased levels of total fatty acids, triacylglycerols, soluble sugars and abscisic acid. Potato engineered to co-express DXS and crtB displayed a similar correlation between β-carotene accumulation, reduced dry matter and starch content and elevated oil and soluble sugars in tubers. Transcriptome analyses revealed a reduced expression of genes involved in starch biosynthesis including ADP-glucose pyrophosphorylase genes in transgenic, carotene-accumulating cassava roots relative to nontransgenic roots. These findings highlight unintended metabolic consequences of provitamin A biofortification of starch-rich organs and point to strategies for redirecting metabolic flux to restore starch production.

RnaSeqSampleSize: real data based sample size estimation for RNA sequencing

Background

One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes.

Results

To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/.

Conclusions

RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2191-5) contains supplementary material, which is available to authorized users.

Transcriptome analysis of alcohol-treated microglia reveals downregulation of beta amyloid phagocytosis

BACKGROUND

Microglial activation contributes to the neuropathology associated with chronic alcohol exposure and withdrawal, including the expression of inflammatory and anti-inflammatory genes. In the current study, we examined the transcriptome of primary rat microglial cells following incubation with alcohol alone, or alcohol together with a robust inflammatory stimulus.

METHODS

Primary microglia were prepared from mixed rat glial cultures. Cells were incubated with 75 mM ethanol alone or with proinflammatory cytokines ("TII": IL1β, IFNγ, and TNFα). Isolated mRNA was used for RNAseq analysis and qPCR. Effects of alcohol on phagocytosis were determined by uptake of oligomeric amyloid beta.

RESULTS

Alcohol induced nitrite production in control cells and increased nitrite production in cells co-treated with TII. RNAseq analysis of microglia exposed for 24 h to alcohol identified 312 differentially expressed mRNAs ("Alc-DEs"), with changes confirmed by qPCR analysis. Gene ontology analysis identified phagosome as one of the highest-ranking KEGG pathways including transcripts regulating phagocytosis. Alcohol also increased several complement-related mRNAs that have roles in phagocytosis, including C1qa, b, and c; C3; and C3aR1. RNAseq analysis identified over 3000 differentially expressed mRNAs in microglia following overnight incubation with TII; and comparison to the group of Alc-DEs revealed 87 mRNAs modulated by alcohol but not by TII, including C1qa, b, and c. Consistent with observed changes in phagocytosis-related mRNAs, the uptake of amyloid beta1-42, by primary microglia, was reduced by alcohol.

CONCLUSIONS

Our results define alterations that occur to microglial gene expression following alcohol exposure and suggest that alcohol effects on phagocytosis could contribute to the development of Alzheimer's disease.

Transcriptome Sequencing Reveals Candidate NF-κB Target Genes Involved in Repeated Cocaine Administration

BACKGROUND

Drug-induced alterations in gene expression play an important role in the development of addictive behavior. Numerous transcription factors have been implicated in mediating the gene expression changes that occur in drug addiction. Nuclear factor kappa B is an inducible transcription factor complex that is rapidly activated by diverse stimuli.

METHODS

We performed next-generation high-throughput sequencing of the prefrontal cortex in a mouse model of repeated cocaine administration combined with pharmacological nuclear factor kappa B inhibition to identify nuclear factor kappa B target genes that participate in the cocaine addiction process.

RESULTS

We found that the nuclear factor kappa B antagonist sodium diethyldithiocarbamate trihydrate significantly reversed the cocaine-induced expression changes of the amphetamine addiction pathway. Genes that demonstrated differential expression in response to cocaine treatment that was also reversed by sodium diethyldithiocarbamate trihydrate were enriched for the axon guidance pathway. Furthermore, the nuclear factor kappa B homo-dimer motif could be mapped to 86 of these sodium diethyldithiocarbamate trihydrate-reversed genes, which were also enriched for axon guidance.

CONCLUSIONS

We suggest that nuclear factor kappa B directly modifies the expression of axon guidance pathway members, leading to cocaine sensitization. Our findings reveal the role of prefrontal cortex nuclear factor kappa B activity in addiction and uncover the molecular mechanisms by which nuclear factor kappa B drives changes in the addicted brain.

Network-based machine learning and graph theory algorithms for precision oncology

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Identification and phylogeny of putative PEPC genes in three toxin-producing Karenia (Dinophyta) species

Dense blooms of toxin-producing Karenia brevis increase local surface ocean pH through CO2 uptake. To identify genes that may contribute to bloom-related environmental pH and pCO2 changes, transcriptomes with RNA from K. brevis Wilson cultures that had been acclimated to low CO2 (250 ppm) or recent CO2 (350 ppm) pCO2 levels were assembled. Among the annotated transcripts were PEPC, PPDK, and PEPCK enzymes found in the model C4 carbon fixation pathway. Previous studies have demonstrated that the enzymatic activity of PEPC, PPDK, and/or PEPCK in some algae species, including marine diatoms, is influenced by variations in dissolved inorganic carbon. We found significantly similar PEPC, PPDK, and PEPCK enzymes in the transcriptomes of K. brevis and two sister species Karenia papilionacea, and Karenia mikimotoi. One or more isoforms of PEPC were also identified in the transcriptomes of thirty additional photosynthetic phytoplankton species from nine phyla. Phylogenetic trees were constructed with neighbor joining and maximum likelihood techniques to characterize the evolutionary relationship among phytoplankton, terrestrial plant C4, and terrestrial plant C3 PEPC sequences. Based on the nucleotide trees constructed during this study, the Karenia PEPC transcripts were more closely related to the terrestrial C4 genes than the terrestrial C3 genes. Furthermore, PEPC phylogeny among phytoplankton closely resembles phylogenetic trees constructed with ribosomal RNA. This study confirmed that the toxin-producing dinoflagellates K. brevis, K. mikimotoi, and K. papilionacea express putative PEPC, PEPCK, and PPDK transcripts.

Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments

Background

RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments.

Results

In this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method.

Conclusions

Our proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network (http://cran.r-project.org).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0994-9) contains supplementary material, which is available to authorized users.

Mining featured micro ribonucleic acids associated with lung cancer based on bioinformatics

Background

Few genetic markers useful for the screening of lung cancer risk exist. Although related research has shown that certain expression profiles of micro ribonucleic acids (miRNAs) are different in lung cancer versus the normal lung, such as miR-29a and miR-29s, the precise molecular mechanism of lung cancer remains obscure. In order to get a better understanding of the pathogenetic mechanism of lung cancer, we analyzed the differentially expressed genes (DEGs) and identified featured miRNAs in lung cancer tissues.

Methods

We used the gene expression profile GSE10072, including 49 gene chips of non-tumor tissues and 58 gene chips of lung tumor specimens. The DEGs between these two groups were identified by Limma package in R language. The TarBase database was used to construct the networks of miRNA regulating DEGs related to lung cancer. After ordering miRNAs regulating DEGs, we further screened featured miRNAs combined with the miR2Disease database.

Results

A total of 5572 DEGs were obtained between lung cancer and control specimens. After constructing a miRNA regulatory network, a total of 398 regulations between 57 miRNAs and 321 target genes existed. By intergrating the miR2Disease database and using a sorting algorithm, a total of six featured miRNAs related to lung cancer were identified, including miR-520h, miR-133a, miR-34, miR-103, miR-370, and miR-148. They might be involved in lung cancer progression by regulating ABCG2, PKM2, VAMP2, GPD1, MAP3K8, and DNMT3B, respectively.

Conclusion

The top 10 significant miRNAs, such as miR-520h, miR-133a, miR-34, and miR-103 may be potential therapeutic targets for lung cancer.

Power analysis and sample size estimation for RNA-Seq differential expression

It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F-measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool (http://www2.hawaii.edu/~lgarmire/RNASeqPowerCalculator.htm) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.

DAFS: a data-adaptive flag method for RNA-sequencing data to differentiate genes with low and high expression

BACKGROUND

Next-generation sequencing (NGS) has advanced the application of high-throughput sequencing technologies in genetic and genomic variation analysis. Due to the large dynamic range of expression levels, RNA-seq is more prone to detect transcripts with low expression. It is clear that genes with no mapped reads are not expressed; however, there is ongoing debate about the level of abundance that constitutes biologically meaningful expression. To date, there is no consensus on the definition of low expression. Since random variation is high in regions with low expression and distributions of transcript expression are affected by numerous experimental factors, methods to differentiate low and high expressed data in a sample are critical to interpreting classes of abundance levels in RNA-seq data.

RESULTS

A data-adaptive approach was developed to estimate the lower bound of high expression for RNA-seq data. The Kolmgorov-Smirnov statistic and multivariate adaptive regression splines were used to determine the optimal cutoff value for separating transcripts with high and low expression. Results from the proposed method were compared to results obtained by estimating the theoretical cutoff of a fitted two-component mixture distribution. The robustness of the proposed method was demonstrated by analyzing different RNA-seq datasets that varied by sequencing depth, species, scale of measurement, and empirical density shape.

CONCLUSIONS

The analysis of real and simulated data presented here illustrates the need to employ data-adaptive methodology in lieu of arbitrary cutoffs to distinguish low expressed RNA-seq data from high expression. Our results also present the drawbacks of characterizing the data by a two-component mixture distribution when classes of gene expression are not well separated. The ability to ascertain stably expressed RNA-seq data is essential in the filtering process of data analysis, and methodologies that consider the underlying data structure demonstrate superior performance in preserving most of the interpretable and meaningful data. The proposed algorithm for classifying low and high regions of transcript abundance promises wide-range application in the continuing development of RNA-seq analysis.

Improving transcriptome assembly through error correction of high-throughput sequence reads

The study of functional genomics, particularly in non-model organisms, has been dramatically improved over the last few years by the use of transcriptomes and RNAseq. While these studies are potentially extremely powerful, a computationally intensive procedure, the de novo construction of a reference transcriptome must be completed as a prerequisite to further analyses. The accurate reference is critically important as all downstream steps, including estimating transcript abundance are critically dependent on the construction of an accurate reference. Though a substantial amount of research has been done on assembly, only recently have the pre-assembly procedures been studied in detail. Specifically, several stand-alone error correction modules have been reported on and, while they have shown to be effective in reducing errors at the level of sequencing reads, how error correction impacts assembly accuracy is largely unknown. Here, we show via use of a simulated and empiric dataset, that applying error correction to sequencing reads has significant positive effects on assembly accuracy, and should be applied to all datasets. A complete collection of commands which will allow for the production of Reptile corrected reads is available at https://github.com/macmanes/error_correction/tree/master/scripts and as File S1.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

Age-adjusted nonparametric detection of differential DNA methylation with case-control designs

Background

DNA methylation profiles differ among disease types and, therefore, can be used in disease diagnosis. In addition, large-scale whole genome DNA methylation data offer tremendous potential in understanding the role of DNA methylation in normal development and function. However, due to the unique feature of the methylation data, powerful and robust statistical methods are very limited in this area.

Results

In this paper, we proposed and examined a new statistical method to detect differentially methylated loci for case control designs that is fully nonparametric and does not depend on any assumption for the underlying distribution of the data. Moreover, the proposed method adjusts for the age effect that has been shown to be highly correlated with DNA methylation profiles. Using simulation studies and a real data application, we have demonstrated the advantages of our method over existing commonly used methods.

Conclusions

Compared to existing methods, our method improved the detection power for differentially methylated loci for case control designs and controlled the type I error well. Its applications are not limited to methylation data; it can be extended to many other case–control studies.

Volcano plots in analyzing differential expressions with mRNA microarrays

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

From genes to networks: in systematic points of view

We present a report of the BIOCOMP'10 - The 2010 International Conference on Bioinformatics & Computational Biology and other related work in the area of systems biology.