Advanced Applications of RNA Sequencing and Challenges

Advancements in genetic analysis: Insights from a case study and review of next-generation sequencing techniques for veterinary oncology applications

Acute myeloid leukemia (AML) poses significant challenges in veterinary medicine, with limited treatment options and poor survival rates. While substantial progress has been made in characterizing human AML, translating these advancements to veterinary practice has been hindered by limited molecular understanding and diagnostic tools. The case study presented illustrates the application of whole genome sequencing in diagnosing AML in a dog, showcasing its potential in veterinary oncology. Our approach facilitated comprehensive genomic analysis, identifying mutations in genes that may be associated with AML pathogenesis in dogs, such as KRAS, IKZF1, and RUNX1. However, without supportive evidence of its clinical utility (eg, association with response to treatment or prognosis), the information is limited to exploration. This article reviews the comparative features of canine AML with human AML and discusses strategies to shrink the knowledge gap between human and veterinary medicine with cost-effective next-generation sequencing (NGS) techniques. By utilizing these approaches, the unique and shared molecular features with human AML can be identified, aiding in molecular classification and therapeutic development for both species. Despite the promise of NGS, challenges exist in implementing it into routine veterinary diagnostics. Cost considerations, turnaround times, and the need for robust bioinformatics pipelines and quality control measures must be addressed. Most importantly, analytical and clinical validation processes are essential to ensure the reliability and clinical utility of NGS-based assays. Overall, integrating NGS technologies into veterinary oncology holds great potential for advancing our understanding of AML and improving disease stratification, in hopes of improving clinical outcomes.

A comprehensive workflow for optimizing RNA-seq data analysis

BACKGROUND

Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge.

RESULTS

By utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz.

CONCLUSION

The experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.

Unveiling the orchestration: mycobacterial small RNAs as key mediators in host-pathogen interactions

Small RNA (sRNA) molecules, a class of non-coding RNAs, have emerged as pivotal players in the regulation of gene expression and cellular processes. Mycobacterium tuberculosis and other pathogenic mycobacteria produce diverse small RNA species that modulate bacterial physiology and pathogenesis. Recent advances in RNA sequencing have enabled identification of novel small RNAs and characterization of their regulatory functions. This review discusses the multifaceted roles of bacterial small RNAs, covering their biogenesis, classification, and functional diversity. Small RNAs (sRNAs) play pivotal roles in orchestrating diverse cellular processes, ranging from gene silencing to epigenetic modifications, across a broad spectrum of organisms. While traditionally associated with eukaryotic systems, recent research has unveiled their presence and significance within bacterial domains as well. Unlike their eukaryotic counterparts, which primarily function within the context of RNA interference (RNAi) pathways, bacterial sRNAs predominantly act through base-pairing interactions with target mRNAs, leading to post-transcriptional regulation. This fundamental distinction underscores the necessity of elucidating the unique roles and regulatory mechanisms of bacterial sRNAs in bacterial adaptation and survival. By doing these myriad functions, they regulate bacterial growth, metabolism, virulence, and drug resistance. In Mycobacterium tuberculosis, apart from having various roles in the bacillus itself, small RNA molecules have emerged as key regulators of gene expression and mediators of host-pathogen interactions. Understanding sRNA regulatory networks in mycobacteria can drive our understanding of significant role they play in regulating virulence and adaptation to the host environment. Detailed functional characterization of Mtb sRNAs at the host-pathogen interface is required to fully elucidate the complex sRNA-mediated gene regulatory networks deployed by Mtb, to manipulate the host. A deeper understanding of this aspect could pave the development of novel diagnostic and therapeutic strategies for tuberculosis.

SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

High-throughput mRNA-seq atlas of human placenta shows vast transcriptome remodeling from first to third trimester†

The placenta, composed of chorionic villi, changes dramatically across gestation. Understanding differences in ongoing pregnancies are essential to identify the role of chorionic villi at specific times in gestation and develop biomarkers and prognostic indicators of maternal-fetal health. The normative mRNA profile is established using next-generation sequencing of 124 first trimester and 43 third trimester human placentas from ongoing healthy pregnancies. Stably expressed genes (SEGs) not different between trimesters and with low variability are identified. Differential expression analysis of first versus third trimester adjusted for fetal sex is performed, followed by a subanalysis with 23 matched pregnancies to control for subject variability using the same genetic and environmental background. Placenta expresses 14,979 polyadenylated genes above sequencing noise (transcripts per million > 0.66), with 10.7% SEGs across gestation. Differentially expressed genes (DEGs) account for 86.7% of genes in the full cohort [false discovery rate (FDR) < 0.05]. Fold changes highly correlate between the full cohort and subanalysis (Pearson = 0.98). At stricter thresholds (FDR < 0.001, fold change > 1.5), there remains 50.1% DEGs (3353 upregulated in first and 4155 upregulated in third trimester). This is the largest mRNA atlas of healthy human placenta across gestation, controlling for genetic and environmental factors, demonstrating substantial changes from first to third trimester in chorionic villi. Specific differences and SEGs may be used to understand the specific role of the chorionic villi throughout gestation and develop first trimester biomarkers of placental health that transpire across gestation, which can be used for future development of biomarkers for maternal-fetal health.

Transcriptome analysis of Citrus sinensis reveals potential responsive events triggered by Candidatus Liberibacter asiaticus

Citrus Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas), is a devastating immune-mediated disorder that has a detrimental effect on the citrus industry, with the distinguishing feature being an eruption of reactive oxygen species (ROS). This study explored the alterations in antioxidant enzyme activity, transcriptome, and RNA editing events of organelles in C. sinensis during CLas infection. Results indicated that there were fluctuations in the performance of antioxidant enzymes, such as ascorbate peroxidase (APX), catalase (CAT), glutathione reductase (GR), peroxidase (POD), and superoxide dismutase (SOD), in plants affected by HLB. Transcriptome analysis revealed 3604 genes with altered expression patterns between CLas-infected and healthy samples, including those associated with photosynthesis, biotic interactions, and phytohormones. Samples infected with CLas showed a decrease in the expression of most genes associated with photosynthesis and gibberellin metabolism. It was discovered that RNA editing frequency and the expression level of various genes in the chloroplast and mitochondrion genomes were affected by CLas infection. Our findings provide insights into the inhibition of photosynthesis, gibberellin metabolism, and antioxidant enzymes during CLas infection in C. sinensis.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions

OBJECTIVES

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome.

METHODS

Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions.

RESULTS

Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players.

CONCLUSION

This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Transcriptomics of Human Brain Tissue in Parkinson's Disease: a Comparison of Bulk and Single-cell RNA Sequencing

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease leading to motor dysfunction and, in some cases, dementia. Transcriptome analysis is one promising approach for characterizing PD and other neurodegenerative disorders by informing how specific disease events influence gene expression and contribute to pathogenesis. With the emergence of single-cell and single-nucleus RNA sequencing (scnRNA-seq) technologies, the transcriptional landscape of neurodegenerative diseases can now be described at the cellular level. As the application of scnRNA-seq is becoming routine, it calls to question how results at a single-cell resolution compare to those obtained from RNA sequencing of whole tissues (bulk RNA-seq), whether the findings are compatible, and how the assays are complimentary for unraveling the elusive transcriptional changes that drive neurodegenerative disease. Herein, we review the studies that have leveraged RNA-seq technologies to investigate PD. Through the integration of bulk and scnRNA-seq findings from human, post-mortem brain tissue, we use the PD literature as a case study to evaluate the compatibility of the results generated from each assay and demonstrate the complementarity of the sequencing technologies. Finally, through the lens of the PD transcriptomic literature, we evaluate the current feasibility of bulk and scnRNA-seq technologies to illustrate the necessity of both technologies for achieving a comprehensive insight into the mechanism by which gene expression promotes neurodegenerative disease. We conclude that the continued application of both assays will provide the greatest insight into neurodegenerative disease pathology, providing both cell-specific and whole-tissue level information.

Anciently duplicated genes continuously recruited to heart expression in vertebrate evolution are associated with heart chamber increase

Although gene/genome duplications in the early stage of vertebrates have been thought to provide major resources of raw genetic materials for evolutionary innovations, it is unclear whether they continuously contribute to the evolution of morphological complexity during the course of vertebrate evolution, such as the evolution from two heart chambers (fishes) to four heart chambers (mammals and birds). We addressed this issue by our heart RNA-Seq experiments combined with published data, using 13 vertebrates and one invertebrate (sea squirt, as an outgroup). Our evolutionary transcriptome analysis showed that number of ancient paralogous genes expressed in heart tends to increase with the increase of heart chamber number along the vertebrate phylogeny, in spite that most of them were duplicated at the time near to the origin of vertebrates or even more ancient. Moreover, those paralogs expressed in heart exert considerably different functions from heart-expressed singletons: the former are functionally enriched in cardiac muscle and muscle contraction-related categories, whereas the latter play more basic functions of energy generation like aerobic respiration. These findings together support the notion that recruiting anciently paralogous genes that are expressed in heart is associated with the increase of chamber number in vertebrate evolution.

Transcriptomic landscape based on annotated clinical features reveals PLPP2 involvement in lipid raft-mediated proliferation signature of early-stage lung adenocarcinoma

BACKGROUND

Image-based screening improves the detection of early-stage lung adenocarcinoma (LUAD)but also highlights the issue of high false-positive diagnoses, which puts patients at a risk of unnecessary over-treatment. Therefore, more precise discrimination criteria are required to ensure that patients with early-stage LUAD receive appropriate treatments.

METHODS

We integrated 158 early-stage LUAD cases from 2 independent cohorts, including 30 matched resected specimens with complete radiological and pathological information, and 128 retrospective pathological pair-samples with partial follow-up data. This integration allowed us to conduct a correlation analysis between clinical phenotype and transcriptome landscape. Immunohistochemistry was performed using tissue microarrays to examine the expression of phospholipid phosphatase 2 (PLPP2) and lipid-raft markers. Lipidomics analysis was used to determine the changes of lipid components in PLPP2-overexpressed cells. To assess the effects of PLPP2 on the malignant phenotypes of LUAD cells, we conducted mice tumor-bearing experiments and in vitro cellular experiments by knocking down PLPP2 and inhibiting lipid raft synthesis with MβCD, respectively.

RESULTS

Bioinformatics analysis indicated that the co-occurrence of lipid raft formation and rapid cell proliferation might exhibit synergistic effects in driving oncogenesis from lung preneoplasia to adenocarcinoma. The enhanced activation of the cell cycle promoted the transition from non-invasive to invasive status in early-stage LUAD, which was related to an increase in lipid rafts within LUAD cells. PLPP2 participated in lipid raft formation by altering the component contents of lipid rafts, such as esters, sphingomyelin, and sphingosine. Furthermore, elevated PLPP2 levels were identified as an independent prognostic risk factor for LUAD patients. Further results from in vivo and in vitro experiments confirmed that PLPP2 could induce excessive cell proliferation by enhancing lipid raft formation in LUAD cells.

CONCLUSIONS

Our study has revealed the characteristics of gene expression profiles in early-stage LUAD patients with the different radiological and pathological subtypes, as well as deciphered transcriptomic evolution trajectory from preneoplasia to invasive LUAD. Furthermore, it suggests that PLPP2-mediated lipid raft synthesis may be a significant biological event in the initiation of early-stage LUAD, offering a potential target for more precise diagnosis and therapy in clinical settings.

PRANA: an R package for differential co-expression network analysis with the presence of additional covariates

BACKGROUND

Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis.

RESULTS

We develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p-values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output.

CONCLUSION

Herewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html .

Transcriptomic and Physiological Analysis Reveals Genes Associated with Drought Stress Responses in Populus alba × Populus glandulosa

Drought stress affects plant productivity by altering plant responses at the morphological, physiological, and molecular levels. In this study, we identified physiological and genetic responses in Populus alba × Populus glandulosa hybrid clones 72-30 and 72-31 after 12 days of exposure to drought treatment. After 12 days of drought treatment, glucose, fructose, and sucrose levels were significantly increased in clone 72-30 under drought stress. The Fv/Fo and Fv/Fm values in both clones also decreased under drought stress. The changes in proline, malondialdehyde, and H2O2 levels were significant and more pronounced in clone 72-30 than in clone 72-31. The activities of antioxidant-related enzymes, such as catalase and ascorbate peroxidase, were significantly higher in the 72-31 clone. To identify drought-related genes, we conducted a transcriptomic analysis in P. alba × P. glandulosa leaves exposed to drought stress. We found 883 up-regulated and 305 down-regulated genes in the 72-30 clone and 279 and 303 in the 72-31 clone, respectively. These differentially expressed genes were mainly in synthetic pathways related to proline, abscisic acid, and antioxidants. Overall, clone 72-31 showed better drought tolerance than clone 72-30 under drought stress, and genetic changes also showed different patterns.

High-throughput mRNA-seq atlas of human placenta shows vast transcriptome remodeling from first to third trimester

Background

The placenta, composed of chorionic villi, changes dramatically across gestation. Understanding differences in ongoing pregnancies are essential to identify the role of chorionic villi at specific times in gestation and develop biomarkers and prognostic indicators of maternal- fetal health.

Methods

The normative mRNA profile is established using next-generation sequencing of 124 first trimester and 43 third trimester human placentas from ongoing healthy pregnancies. Stably expressed genes not different between trimesters and with low variability are identified. Differential expression analysis of first versus third trimester adjusted for fetal sex is performed, followed by a subanalysis with 23 matched pregnancies to control for subject variability using the same genetic and environmental background.

Results

Placenta expresses 14,979 mRNAs above sequencing noise (TPM>0.66), with 1,545 stably expressed genes across gestation. Differentially expressed genes account for 86.7% of genes in the full cohort (FDR<0.05). Fold changes highly correlate between the full cohort and subanalysis (Pearson = 0.98). At stricter thresholds (FDR<0.001, fold change>1.5), there are 6,941 differentially expressed protein coding genes (3,206 upregulated in first and 3,735 upregulated in third trimester).

Conclusion

This is the largest mRNA atlas of healthy human placenta across gestation, controlling for genetic and environmental factors, demonstrating substantial changes from first to third trimester in chorionic villi. Specific differences and stably expressed genes may be used to understand the specific role of the chorionic villi throughout gestation and develop first trimester biomarkers of placental health that transpire across gestation, which can be used for future development of biomarkers in maternal-fetal disease.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

RNA sequencing least shrew (Cryptotis parva) brainstem and gut transcripts following administration of a selective substance P neurokinin NK1 receptor agonist and antagonist expands genomics resources for emesis research

The least shrew is among the subset of animals that are capable of vomiting and therefore serves as a valuable research model for investigating the biochemistry, molecular biology, pharmacology, and genomics of emesis. Both nausea and vomiting are associated with a variety of illnesses (bacterial/viral infections, bulimia, exposure to toxins, gall bladder disease), conditions (pregnancy, motion sickness, emotional stress, overeating) and reactions to drugs (chemotherapeutics, opiates). The severe discomfort and intense fear associated with the stressful symptoms of nausea and emesis are the major reason for patient non-compliance when being treated with cancer chemotherapeutics. Increased understanding of the physiology, pharmacology and pathophysiology underlying vomiting and nausea can accelerate progress for developing new antiemetics. As a major animal model for emesis, expanding genomic knowledge associated with emesis in the least shrew will further enhance the laboratory utility of this model. A key question is which genes mediate emesis, and are they expressed in response to emetics/antiemetics. To elucidate the mediators of emesis, in particular emetic receptors, their downstream signaling pathways, as well as the shared emetic signals, we carried out an RNA sequencing study focused on the central and peripheral emetic loci, the brainstem and gut. Thus, we sequenced RNA extracted from brainstem and gut tissues from different groups of least shrews treated with either a neurokinin NK1 receptor selective emetic agonist, GR73632 (5 mg/kg, i.p.), its corresponding selective antagonist netupitant (5 mg/kg, i.p.), a combination of these two agents, versus their corresponding vehicle-pretreated controls and drug naïve animals. The resulting sequences were processed using a de novo transcriptome assembly and used it to identify orthologs within human, dog, mouse, and ferret gene sets. We compared the least shrew to human and a veterinary species (dog) that may be treated with vomit-inducing chemotherapeutics, and the ferret, another well-established model organism for emesis research. The mouse was included because it does not vomit. In total, we identified a final set of 16,720 least shrew orthologs. We employed comparative genomics analyses as well as gene ontology enrichment, KEGG pathway enrichment and phenotype enrichment to better understand the molecular biology of genes implicated in vomiting.

De novo Transcriptome Analysis and Gene Expression Profiling of Corylus Species

Hazelnut (Corylus), which has high commercial and nutritional benefits, is an important tree for producing nuts and nut oil consumed as ingredient especially in chocolate. While Corylus avellana L. (Euro-pean hazelnut, Betulaceae) and Corylus colurna L. (Turkish hazelnut, Betulaceae) are the two common hazelnut species in Europe, C. avellana L. (Tombul hazelnut) is grown as the most widespread hazelnut species in Turkey, and C. colurna L., which is the most important genetic resource for hazelnut breeding, exists naturally in Anatolia. We generated the transcriptome data of these two Corylus species and used these data for gene discovery and gene expression profiling. Total RNA from young leaves, flowers (male and female), buds, and husk shoots of C. avellana and C. colurna were used for two different libraries and were sequenced using Illumina HiSeq4000 with 100 bp paired-end reads. The transcriptome data 10.48 and 10.30 Gb of C. avellana and C. colurna, respectively, were assembled into 70,265 and 88,343 unigenes, respectively. These unigenes were functionally annotated using the TRAPID platform. We identified 25,312 and 27,051 simple sequen-ce repeats (SSRs) for C. avellana and C. colurna, respectively. TL1, GMPM1, N, 2MMP, At1g29670, CHIB1 unigenes were selected for validation with qPCR. The first de novo transcriptome data of C. co-lurna were used to compare data of C. avellana of commercial importance. These data constitute a valuable extension of the publicly available transcriptomic resource aimed at breeding, medicinal, and industrial research studies.

A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data have multiple and diverse applications. They are often aimed at benchmarking bioinformatics algorithms for purposes such as sample classification, differential expression analysis, correlation and network studies and the optimization of data integration and normalization techniques. Here, we propose a general framework to compare synthetically generated RNA-seq data and select a data-generating tool that is suitable for a set of specific study goals. As there are multiple methods for synthetic RNA-seq data generation, researchers can use the proposed framework to make an informed choice of an RNA-seq data simulation algorithm and software that are best suited for their specific scientific questions of interest.

Transcriptome Analysis of Goat Mammary Gland Tissue Reveals the Adaptive Strategies and Molecular Mechanisms of Lactation and Involution

To understand how genes precisely regulate lactation physiological activity and the molecular genetic mechanisms underlying mammary gland involution, this study investigated the transcriptome characteristics of goat mammary gland tissues at the late gestation (LG), early lactation (EL), peak lactation (PL), late lactation (LL), dry period (DP), and involution (IN) stages. A total of 13,083 differentially expressed transcripts were identified by mutual comparison of mammary gland tissues at six developmental stages. Genes related to cell growth, apoptosis, immunity, nutrient transport, synthesis, and metabolism make adaptive transcriptional changes to meet the needs of mammary lactation. Notably, platelet derived growth factor receptor beta (PDGFRB) was screened as a hub gene of the mammary gland developmental network, which is highly expressed during the DP and IN. Overexpression of PDGFRB in vitro could slow down the G1/S phase arrest of goat mammary epithelial cell cycle and promote cell proliferation by regulating the PI3K/Akt signaling pathway. In addition, PDGFRB overexpression can also affect the expression of genes related to apoptosis, matrix metalloproteinase family, and vascular development, which is beneficial to the remodeling of mammary gland tissue during involution. These findings provide new insights into the molecular mechanisms involved in lactation and mammary gland involution.

Hypothesis-driven dragging of transcriptomic data to analyze proven targeted pathways in Rhinella arenarum larvae exposed to organophosphorus pesticides

Transcriptional analysis of the network of transcription regulators and target pathways in exposed organisms may be a hard task when their genome remains unknown. The development of hundreds of qPCR assays, including primer design and normalization of the results with the appropriate housekeeping genes, seems an unreachable task. Alternatively, we took advantage of a whole transcriptome study on Rhinella arenarum larvae exposed to the organophosphorus pesticides azinphos-methyl and chlorpyrifos to evaluate the transcriptional effects on a priori selected groups of genes. This approach allowed us to evaluate the effects on hypothesis-selected pathways such as target esterases, detoxifying enzymes, polyamine metabolism and signaling, and regulatory pathways modulating them. We could then compare the responses at the transcriptional level with previously described effects at the enzymatic or metabolic levels to obtain global insight into toxicity-response mechanisms. The effects of both pesticides on the transcript levels of these pathways could be considered moderate, while chlorpyrifos-induced responses were more potent and earlier than those elicited by azinphos-methyl. Finally, we inferred a prevailing downregulation effect of pesticides on signaling pathways and transcription factor transcripts encoding products that modulate/control the polyamine and antioxidant response pathways. We also tested and selected potential housekeeping genes based on those reported for other species. These results allow us to conduct future confirmatory studies on pesticide modulation of gene expression in toad larvae.

Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge

The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians’ diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles.

Effects of Salt Stress on Transcriptional and Physiological Responses in Barley Leaves with Contrasting Salt Tolerance

Salt stress negatively impacts crop production worldwide. Genetic diversity among barley (Hordeum vulgare) landraces adapted to adverse conditions should provide a valuable reservoir of tolerance genes for breeding programs. To identify molecular and biochemical differences between barley genotypes, transcriptomic and antioxidant enzyme profiles along with several morpho-physiological features were compared between salt-tolerant (Boulifa) and salt-sensitive (Testour) genotypes subjected to salt stress. Decreases in biomass, photosynthetic parameters, and relative water content were low in Boulifa compared to Testour. Boulifa had better antioxidant protection against salt stress than Testour, with greater antioxidant enzymes activities including catalase, superoxide dismutase, and guaiacol peroxidase. Transcriptome assembly for both genotypes revealed greater accumulation of differentially expressed transcripts in Testour compared to Boulifa, emphasizing the elevated transcriptional response in Testour following salt exposure. Various salt-responsive genes, including the antioxidant catalase 3, the osmoprotectant betaine aldehyde dehydrogenase 2, and the transcription factors MYB20 and MYB41, were induced only in Boulifa. By contrast, several genes associated with photosystems I and II, and light receptor chlorophylls A and B, were more repressed in Testour. Co-expression network analysis identified specific gene modules correlating with differences in genotypes and morpho-physiological traits. Overall, salinity-induced differential transcript accumulation underlies the differential morpho-physiological response in both genotypes and could be important for breeding salt tolerance in barley.

A comparison of mRNA sequencing (RNA-Seq) library preparation methods for transcriptome analysis

BACKGROUND

mRNA sequencing is a powerful technique, which is used to investigate the transcriptome status of a gene of interest, such as its transcription level and splicing variants. Presently, several RNA sequencing (RNA-Seq) methods have been developed; however, the relative advantage of each method has remained unknown. Here we used three commercially available RNA-Seq library preparation kits; the traditional method (TruSeq), in addition to full-length double-stranded cDNA methods (SMARTer and TeloPrime) to investigate the advantages and disadvantages of these three approaches in transcriptome analysis.

RESULTS

We observed that the number of expressed genes detected from the TeloPrime sequencing method was fewer than that obtained using the TruSeq and SMARTer. We also observed that the expression patterns between TruSeq and SMARTer correlated strongly. Alternatively, SMARTer and TeloPrime methods underestimated the expression of relatively long transcripts. Moreover, genes having low expression levels were undetected stochastically regardless of any three methods used. Furthermore, although TeloPrime detected a significantly higher proportion at the transcription start site (TSS), its coverage of the gene body was not uniform. SMARTer is proposed to be yielded for nonspecific genomic DNA amplification. In contrast, the detected splicing event number was highest in the TruSeq. The percent spliced in index (PSI) of the three methods was highly correlated.

CONCLUSIONS

TruSeq detected transcripts and splicing events better than the other methods and measured expression levels of genes, in addition to splicing events accurately. However, although detected transcripts and splicing events in TeloPrime were fewer, the coverage at TSS was highest. Additionally, SMARTer was better than TeloPrime with regards to the detected number of transcripts and splicing events among the understudied full-length double-stranded cDNA methods. In conclusion, for short-read sequencing, TruSeq has relative advantages for use in transcriptome analysis.

Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE

The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the investigation of alternative splicing and others, have created novel challenges for data management, exploration, analysis, and visualization. Although a large variety of RNA-Seq data analysis software packages has emerged, both open-source and commercial, most fail to simultaneously address the above challenges, while they lack obvious functionalities, such as estimating RNA abundance over non-annotated genomic regions of interest in real time. We have developed SeqCVIBE, an R Shiny web application for the interactive exploration, analysis, visualization, and genome browsing of large RNA-Seq datasets. SeqCVIBE allows for multiple on-the-fly visualizations and calculations, such as differential expression analysis, averaging genomic signals over specific regions of the genome, and calculating RNA abundances over custom, potentially non-annotated regions, such as novel long non-coding RNAs. In addition, SeqCVIBE comprises a database for pre-analyzed data, where users can navigate and explore results, as well as perform a variety of basic on-the-fly analyses and export the outcomes. Finally, we demonstrate the value of SeqCVIBE in the elucidation of the interplay of a novel lincRNA, WiNTRLINC1, and Wnt signaling in colon cancer.

InterTADs: integration of multi-omics data on topologically associated domains, application to chronic lymphocytic leukemia

The integration of multi-omics data can greatly facilitate the advancement of research in Life Sciences by highlighting new interactions. However, there is currently no widespread procedure for meaningful multi-omics data integration. Here, we present a robust framework, called InterTADs, for integrating multi-omics data derived from the same sample, and considering the chromatin configuration of the genome, i.e. the topologically associating domains (TADs). Following the integration process, statistical analysis highlights the differences between the groups of interest (normal versus cancer cells) relating to (i) independent and (ii) integrated events through TADs. Finally, enrichment analysis using KEGG database, Gene Ontology and transcription factor binding sites and visualization approaches are available. We applied InterTADs to multi-omics datasets from 135 patients with chronic lymphocytic leukemia (CLL) and found that the integration through TADs resulted in a dramatic reduction of heterogeneity compared to individual events. Significant differences for individual events and on TADs level were identified between patients differing in the somatic hypermutation status of the clonotypic immunoglobulin genes, the core biological stratifier in CLL, attesting to the biomedical relevance of InterTADs. In conclusion, our approach suggests a new perspective towards analyzing multi-omics data, by offering reasonable execution time, biological benchmarking and potentially contributing to pattern discovery through TADs.

Quercetin Regulates Calcium and Phosphorus Metabolism Through the Wnt Signaling Pathway in Broilers

This study intended to explore the effect and mechanism of different doses of dietary quercetin on calcium and phosphorus metabolism to provide an experimental basis for preventing leg disease in broilers. A total of 480 1-day-old healthy Arbor Acre broilers were randomly allotted into four groups (0, 0.02, 0.04, 0.06%) for 42 days. Compared with control, 0.06% quercetin significantly increased the unit weight and the relative weight of tibia in broilers (P < 0.05). Meanwhile, phosphorus content and bone mineral density (BMD) were significantly increased by 0.06% dietary quercetin supplementation in tibia (P < 0.05). Ash of tibia was significantly increased by 0.04 and 0.06% quercetin in broilers (P < 0.05). In addition, 0.06% quercetin significantly increased the content of serum calcium-binding protein (CB), estradiol (E₂), osteocalcin (OC), alkaline phosphatase (ALP), and calcitonin (CT) (P < 0.05); 0.04% quercetin significantly increased 1,25-dihydroxyvitamin D₃ (1,25-(OH)₂D₃) (P < 0.05) content in serum of broilers. The content of serum parathyroid (PTH) was significantly decreased by 0.02 and 0.06% quercetin (P < 0.05) in broilers. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the Wnt signaling pathway was a key signaling pathway of calcium and phosphorus metabolism in broilers which was significantly regulated by quercetin. The differentially expressed genes (DEGs) from transcriptome sequencing were validated with real-time quantitative PCR (RT-qPCR). In conclusion, 0.06% dietary quercetin supplementation improved calcium and phosphorus metabolism by regulating the Wnt signaling pathway in broilers.

Single-Cell and Bulk RNASeq Profiling of COVID-19 Patients Reveal Immune and Inflammatory Mechanisms of Infection-Induced Organ Damage

The SARS-CoV-2 virus’s ability to induce hypercytokinemia and cause multiple organ failure makes it imperative to find effective treatments. To understand the mechanism of viral infection and its effects on organ tissues, we analyzed multiple single-cell and bulk RNAseq data from COVID-19 patients’ organ samples. Various levels of severity of infection were accounted for, with comparative analyses between mild, moderate, and severely infected patients. Our analysis uncovered an upregulation of the innate immune response via several inflammatory genes, IL-2, IL-6, IL-8, IL-17A, and NF-κB. Consequently, we found that the upregulation of these downstream effects can lead to organ injury. The downregulated pathways such as eukaryotic initiation factor 2 (eIF2) and eIF4-mediated host translation, were found to lead to an increased viral translation. We also found that the loss of inhibitory peptides can suppress an overactive innate immune response via NF-κB and interleukin-mediated pathways. Investigation of viral-host protein mapping showed that the interaction of viral proteins with host proteins correlated with the down- and upregulation of host pathways such as decreased eIF2-mediated host translation and increased hypertrophy and fibrosis. Inflammation was increased via the stimulation of pro-inflammatory cytokines and suppression of host translation pathways that led to reduced inflammatory inhibitors. Cardiac hypertrophy and organ fibrosis were the results of increased inflammation in organs of severe and critical patients. Finally, we identified potential therapeutic targets for the treatment of COVID-19 and its deleterious effects on organs. Further experimental investigation would conclusively determine the effects of COVID-19 infection on organs other than the lungs and the effectiveness of the proposed therapeutic targets.

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Background

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.

Results

The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (P_DEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.

Conclusions

MBCdeg with DEGES normalization can be used in the identification of DEGs when the P_DEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04438-4.

Performance of a scalable RNA extraction-free transcriptome profiling method for adherent cultured human cells

RNA sequencing enables high-content/high-complexity measurements in small molecule screens. Whereas the costs of DNA sequencing and RNA-seq library preparation have decreased consistently, RNA extraction remains a significant bottleneck to scalability. We evaluate the performance of a bulk RNA-seq library prep protocol optimized for analysis of many samples of adherent cultured cells in parallel. We combined a low-cost direct lysis buffer compatible with cDNA synthesis (in-lysate cDNA synthesis) with Smart-3SEQ and examine the effects of calmidazolium and fludrocortisone-induced perturbation of primary human dermal fibroblasts. We compared this method to normalized purified RNA inputs from matching samples followed by Smart-3SEQ or Illumina TruSeq library prep. Our results show the minimal effect of RNA loading normalization on data quality, measurement of gene expression patterns, and generation of differentially expressed gene lists. We found that in-lysate cDNA synthesis combined with Smart-3SEQ RNA-seq library prep generated high-quality data with similar ranked DEG lists when compared to library prep with extracted RNA or with Illumina TruSeq. Our data show that small molecule screens or experiments based on many perturbations quantified with RNA-seq are feasible at low reagent and time costs.

Can functional genomic diversity provide novel insights into mechanisms of community assembly? A pilot study from an invaded alpine streambed

An important focus of community ecology, including invasion biology, is to investigate functional trait diversity patterns to disentangle the effects of environmental and biotic interactions. However, a notable limitation is that studies usually rely on a small and easy-to-measure set of functional traits, which might not immediately reflect ongoing ecological responses to changing abiotic or biotic conditions, including those that occur at a molecular or physiological level. We explored the potential of using the diversity of expressed genes-functional genomic diversity (FGD)-to understand ecological dynamics of a recent and ongoing alpine invasion. We quantified FGD based on transcriptomic data measured for 26 plant species occurring along adjacent invaded and pristine streambeds. We used an RNA-seq approach to summarize the overall number of expressed transcripts and their annotations to functional categories, and contrasted this with functional trait diversity (FTD) measured from a suite of characters that have been traditionally considered in plant ecology. We found greater FGD and FTD in the invaded community, independent of differences in species richness. However, the magnitude of functional dispersion was greater from the perspective of FGD than from FTD. Comparing FGD between congeneric alien-native species pairs, we did not find many significant differences in the proportion of genes whose annotations matched functional categories. Still, native species with a greater relative abundance in the invaded community compared with the pristine tended to express a greater fraction of genes at significant levels in the invaded community, suggesting that changes in FGD may relate to shifts in community composition. Comparisons of diversity patterns from the community to the species level offer complementary insights into processes and mechanisms driving invasion dynamics. FGD has the potential to illuminate cryptic changes in ecological diversity, and we foresee promising avenues for future extensions across taxonomic levels and macro-ecosystems.

Transcriptomic Analysis of Salt-Stress-Responsive Genes in Barley Roots and Leaves

Barley is characterized by a rich genetic diversity, making it an important model for studies of salinity response with great potential for crop improvement. Moreover, salt stress severely affects barley growth and development, leading to substantial yield loss. Leaf and root transcriptomes of a salt-tolerant Tunisian landrace (Boulifa) exposed to 2, 8, and 24 h salt stress were compared with pre-exposure plants to identify candidate genes and pathways underlying barley's response. Expression of 3585 genes was upregulated and 5586 downregulated in leaves, while expression of 13,200 genes was upregulated and 10,575 downregulated in roots. Regulation of gene expression was severely impacted in roots, highlighting the complexity of salt stress response mechanisms in this tissue. Functional analyses in both tissues indicated that response to salt stress is mainly achieved through sensing and signaling pathways, strong transcriptional reprograming, hormone osmolyte and ion homeostasis stabilization, increased reactive oxygen scavenging, and activation of transport and photosynthesis systems. A number of candidate genes involved in hormone and kinase signaling pathways, as well as several transcription factor families and transporters, were identified. This study provides valuable information on early salt-stress-responsive genes in roots and leaves of barley and identifies several important players in salt tolerance.

Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia

Autosomal dominant polycystic kidney disease (ADPKD) affects more than 12 million people worldwide. Mutations in PKD1 and PKD2 cause cyst formation through unknown mechanisms. To unravel the pathogenic mechanisms in ADPKD, multiple studies have investigated transcriptional mis-regulation in cystic kidneys from patients and mouse models, and numerous dysregulated genes and pathways have been described. Yet, the concordance between studies has been rather limited. Furthermore, the cellular and genetic diversity in cystic kidneys has hampered the identification of mis-expressed genes in kidney epithelial cells with homozygous PKD mutations, which are critical to identify polycystin-dependent pathways. Here we performed transcriptomic analyses of Pkd1- and Pkd2-deficient mIMCD3 kidney epithelial cells followed by a meta-analysis to integrate all published ADPKD transcriptomic data sets. Based on the hypothesis that Pkd1 and Pkd2 operate in a common pathway, we first determined transcripts that are differentially regulated by both genes. RNA sequencing of genome-edited ADPKD kidney epithelial cells identified 178 genes that are concordantly regulated by Pkd1 and Pkd2. Subsequent integration of existing transcriptomic studies confirmed 31 previously described genes and identified 61 novel genes regulated by Pkd1 and Pkd2. Cluster analyses then linked Pkd1 and Pkd2 to mRNA splicing, specific factors of epithelial mesenchymal transition, post-translational protein modification and epithelial cell differentiation, including CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6. Taken together, this model-based integrative analysis of transcriptomic alterations in ADPKD annotated a conserved core transcriptomic profile and identified novel candidate genes for further experimental studies.

RNA Sequencing of Human Peripheral Nerve in Response to Injury: Distinctive Analysis of the Nerve Repair Pathways

The development of regenerative therapies for central nervous system diseases can likely benefit from an understanding of the peripheral nervous system repair process, particularly in identifying potential gene pathways involved in human nerve repair. This study employed RNA sequencing (RNA-seq) technology to analyze the whole transcriptome profile of the human peripheral nerve in response to an injury. The distal sural nerve was exposed, completely transected, and a 1 to 2 cm section of nerve fascicles was collected for RNA-seq from six participants with Parkinson’s disease, ranging in age between 53 and 70 yr. Two weeks after the initial injury, another section of the nerve fascicles of the distal and pre-degenerated stump of the nerve was dissected and processed for RNA-seq studies. An initial analysis between the pre-lesion status and the postinjury gene expression revealed 3,641 genes that were significantly differentially expressed. In addition, the results support a clear transdifferentiation process that occurred by the end of the 2-wk postinjury. Gene ontology (GO) and hierarchical clustering were used to identify the major signaling pathways affected by the injury. In contrast to previous nonclinical studies, important changes were observed in molecular pathways related to antiapoptotic signaling, neurotrophic factor processes, cell motility, and immune cell chemotactic signaling. The results of our current study provide new insights regarding the essential interactions of different molecular pathways that drive neuronal repair and axonal regeneration in humans.

Role of tRNAs in Breast Cancer Regulation

Increased proliferation and protein synthesis are characteristics of transformed and tumor cells. Although the components of the translation machinery are often dysregulated in cancer, the role of tRNAs in cancer cells has not been well studied. Nevertheless, the number of related studies has recently started increasing. With the development of high throughput technologies such as next-generation sequencing, genome-wide differential tRNA expression patterns in breast cancer-derived cell lines and breast tumors have been investigated. The genome-wide transcriptomics analyses have been linked with many studies for functional and phenotypic characterization, whereby tRNAs or tRNA-related fragments have been shown to play important roles in breast cancer regulation and as promising prognostic biomarkers. Here, we review their expression patterns, functions, prognostic value, and potential therapeutic use as well as related technologies.

New insights on the effects of ionic liquid structural changes at the gene expression level: Molecular mechanisms of toxicity in Daphnia magna

Knowledge on the molecular basis of ionic liquids' (ILs) ecotoxicity is critical for the development of these designer solvents as their structure can be engineered to simultaneously meet functionality performance and environmental safety. The molecular effects of ILs were investigated by using RNA-sequencing following Daphnia magna exposure to imidazolium- and cholinium-based ILs: 1-ethyl-3-methylimidazolium chloride ([C₂mim]Cl), 1-dodecyl-3-methylimidazolium chloride ([C₁₂mim]Cl) and cholinium chloride ([Chol]Cl)-; the selection allowing to compare different families and cation alkyl chains. ILs shared mechanisms of toxicity focusing e.g. cellular membrane and cytoskeleton, oxidative stress, energy production, protein biosynthesis, DNA damage, disease initiation. [C₂mim]Cl and [C₁₂mim]Cl were the least and the most toxic ILs at the transcriptional level, denoting the role of the alkyl chain as a driver of ILs toxicity. Also, it was reinforced that [Chol]Cl is not devoid of environmental hazardous potential regardless of its argued biological compatibility. Unique gene expression signatures could also be identified for each IL, enlightening specific mechanisms of toxicity.

Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales

Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identifying unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challenging. As an environmental stress, the acoustic radiation has a significant effect on the alteration of lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics applications. This review concluded that a combination of many of the omics areas may elucidate the metabolism of lipids and possible hazards on lipids in toothed whales by radiation.

Systems Wide Analysis of CCM Signaling Complex Alterations in CCM-Deficient Models Using Omics Approaches

Omics research has garnered popularity recently to integrate in-depth analysis of alterations at the molecular level to elucidate observable phenotypes resulting from knockdown/knockout models. Genomics, performed through RNA-seq, allows the user to evaluate alterations at the transcription level, oftentimes more sensitive than other types of analysis, especially when attempting to understand lack of observation of an expected phenotype. Proteomics facilitates an understanding of mechanisms being altered at the translational level allowing for an understanding of multiple layers of regulation occurring, elucidating discrepancies between what is seen at the RNA level compared to what is translated to a functional protein. Here we describe the methods currently being used to evaluate CCM-deficient strains in human brain microvascular endothelial cells (HBMVEC), zebrafish embryos as well as in vivo mouse model to evaluate impacts on various signaling cascades resulting from deficiencies in KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). The integration of data from genomics and proteomics analysis allows for the composition of interactomes, elucidating systems wide impacts resulting from disruption of the CCM signaling complex (CSC).

Genome-wide transcriptome reveals mechanisms underlying Rlm1-mediated blackleg resistance on canola

Genetic resistance to blackleg (Leptosphaeria maculans, Lm) of canola (Brassica napus, Bn) has been extensively studied, but the mechanisms underlying the host-pathogen interaction are still not well understood. Here, a comparative transcriptome analysis was performed on a resistant doubled haploid Bn line carrying the resistance gene Rlm1 following inoculation with a virulent (avrLm1) or avirulent (AvrLm1) Lm isolate on cotyledons. A total of 6999 and 3015 differentially expressed genes (DEGs) were identified, respectively, in inoculated local tissues with compatible (susceptible) and incompatible (resistant) interactions. Functional enrichment analysis found several biological processes, including protein targeting to membrane, ribosome and negative regulation of programmed cell death, were over-represented exclusively among up-regulated DEGs in the resistant reaction, whereas significant enrichment of salicylic acid (SA) and jasmonic acid (JA) pathways observed for down-regulated DEGs occurred only in the susceptible reaction. A heat-map analysis showed that both biosynthesis and signaling of SA and JA were induced more significantly in the resistant reaction, implying that a threshold level of SA and JA signaling is required for the activation of Rlm1-mediated resistance. Co-expression network analysis revealed close correlation of a gene module with the resistance, involving DEGs regulating pathogen-associated molecular pattern recognition, JA signaling and transcriptional reprogramming. Substantially fewer DEGs were identified in mock-inoculated (control) cotyledons, relative to those in inoculated local tissues, including those involved in SA pathways potentially contributing to systemic acquired resistance (SAR). Pre-inoculation of cotyledon with either an avirulent or virulent Lm isolate, however, failed to induce SAR on remote tissues of same plant despite elevated SA and PR1 protein. This study provides insights into the molecular mechanism of Rlm1-mediated resistance to blackleg.

Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies

Detection of differentially expressed genes (DEGs) between different biological conditions is a key data analysis step of most RNA-sequencing studies. Conventionally, computational tools have used gene-level read counts as input to test for differential gene expression between sample condition groups. Recently, it has been suggested that statistical testing could be performed with increased power at a lower feature level prior to aggregating the results to the gene level. In this study, we systematically compared the performance of calling the DEGs when using read count data at different levels (gene, transcript, and exon) as input, in the context of two publicly available data sets. Additionally, we tested two different methods for aggregating the lower feature-level p-values to gene-level: Lancaster and empirical Brown’s method. Our results show that detection of DEGs is improved compared to the conventional gene-level approach regardless of the lower feature-level used for statistical testing. The overall best balance between accuracy and false discovery rate was obtained using the exon-level approach with empirical Brown’s aggregation method, which we provide as a freely available Bioconductor package EBSEA (https://bioconductor.org/packages/release/bioc/html/EBSEA.html).

Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors

In recent years, cancer immunotherapy experienced remarkable developments and it is nowadays considered a promising therapeutic frontier against many types of cancer, especially hematological malignancies. However, in most types of solid tumors, immunotherapy efficacy is modest, partly because of the limited accessibility of lymphocytes to the tumor core. This immune exclusion is mediated by a variety of physical, functional and dynamic barriers, which play a role in shaping the immune infiltrate in the tumor microenvironment. At present there is no unified and integrated understanding about the role played by different postulated models of immune exclusion in human solid tumors. Systematically mapping immune landscapes or "topographies" in cancers of different histology is of pivotal importance to characterize spatial and temporal distribution of lymphocytes in the tumor microenvironment, providing insights into mechanisms of immune exclusion. Spatially mapping immune cells also provides quantitative information, which could be informative in clinical settings, for example for the discovery of new biomarkers that could guide the design of patient-specific immunotherapies. In this review, we aim to summarize current standard and next generation approaches to define Cancer Immune Topographies based on published studies and propose future perspectives.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

RNA-Sequencing Analysis Pipeline for Prognostic Marker Identification in Cancer

Sequencing analysis finds many applications in various fields of biology from comparative genomics to clinical research. Recent studies, using high-throughput sequencing method, has generated terabytes of data. It is challenging to interpret and draw a meaningful conclusion without the proper understanding of various steps involved in the analysis of such data. This chapter deals with the pipeline to be followed to process the raw RNA sequencing (RNA-Seq) reads, align, assemble, and quantify them in order to draw significant clinical conclusions from them.

Experimental and Computational Workflow for RNA Sequencing in Mycobacterium tuberculosis : From Total RNA to Differentially Expressed Genes

RNA sequencing (RNAseq) in bacteria has become a transformative tool for many applications, including the identification of mechanisms that contribute to pathogenesis, environmental adaptation, and drug response. The kinds of analysis outputs achievable from RNA-seq depend heavily on several key technical parameters during the sample preparation, sequencing, and data processing steps. In this chapter, we will describe the process of preparing Mycobacterium tuberculosis samples into sequencing libraries, selecting the appropriate sequencing platform, and performing data processing compatible with gene expression quantification. We will also discuss how each parameter could affect outcomes. The protocols described below produce consistently high yields. This chapter should inform on the technical considerations that impact sequencing output and enable the reader to decide on the best parameters to implement based on their own experimental goals.

Automated Isoform Diversity Detector (AIDD): a pipeline for investigating transcriptome diversity of RNA-seq data

Background

As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism—RNA editing due to post-transcriptional changes of individual nucleotides—remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise.

Results

Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities.

Conclusions

AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

miRNA Targets: From Prediction Tools to Experimental Validation

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

Integration of whole-exome and anchored PCR-based next generation sequencing significantly increases detection of actionable alterations in precision oncology

BACKGROUND

Frequency of clinically relevant mutations in solid tumors by targeted and whole-exome sequencing is ∼30%. Transcriptome analysis complements detection of actionable gene fusions in advanced cancer patients. Goal of this study was to determine the added value of anchored multiplex PCR (AMP)-based next-generation sequencing (NGS) assay to identify further potential drug targets, when coupled with whole-exome sequencing (WES).

METHODS

Selected series of fifty-six samples from 55 patients enrolled in our precision medicine study were interrogated by WES and AMP-based NGS. RNA-seq was performed in 19 cases. Clinically relevant and actionable alterations detected by three methods were integrated and analyzed.

RESULTS

AMP-based NGS detected 48 fusions in 31 samples (55.4%); 31.25% (15/48) were classified as targetable based on published literature. WES revealed 29 samples (51.8%) harbored targetable alterations. TMB-high and MSI-high status were observed in 12.7% and 1.8% of cases. RNA-seq from 19 samples identified 8 targetable fusions (42.1%), also captured by AMP-based NGS. When number of actionable fusions detected by AMP-based NGS were added to WES targetable alterations, 66.1% of samples had potential drug targets. When both WES and RNA-seq were analyzed, 57.8% of samples had targetable alterations.

CONCLUSIONS

This study highlights importance of an integrative genomic approach for precision oncology, including use of different NGS platforms with complementary features. Integrating RNA data (whole transcriptome or AMP-based NGS) significantly enhances detection of potential targets in cancer patients. In absence of fresh frozen tissue, AMP-based NGS is a robust method to detect actionable fusions using low-input RNA from archival tissue.

Multi-Faceted Systems Biology Approaches Present a Cellular Landscape of Phenolic Compound Inhibition in Saccharomyces cerevisiae

Synthetic biology has played a major role in engineering microbial cell factories to convert plant biomass (lignocellulose) to fuels and bioproducts by fermentation. However, the final product yield is limited by inhibition of microbial growth and fermentation by toxic phenolic compounds generated during lignocellulosic pre-treatment and hydrolysis. Advances in the development of systems biology technologies (genomics, transcriptomics, proteomics, metabolomics) have rapidly resulted in large datasets which are necessary to obtain a holistic understanding of complex biological processes underlying phenolic compound toxicity. Here, we review and compare different systems biology tools that have been utilized to identify molecular mechanisms that modulate phenolic compound toxicity in Saccharomyces cerevisiae. By focusing on and comparing functional genomics and transcriptomics approaches we identify common mechanisms potentially underlying phenolic toxicity. Additionally, we discuss possible ways by which integration of data obtained across multiple unbiased approaches can result in new avenues to develop yeast strains with a significant improvement in tolerance to phenolic fermentation inhibitors.

OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data

Over the past decade, a large amount of RNA sequencing (RNA-seq) data were deposited in public repositories, and more are being produced at an unprecedented rate. However, there are few open source tools with point-and-click interfaces that are versatile and offer streamlined comprehensive analysis of RNA-seq datasets. To maximize the capitalization of these vast public resources and facilitate the analysis of RNA-seq data by biologists, we developed a web application called OneStopRNAseq for the one-stop analysis of RNA-seq data. OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis. Users only need to select the desired analyses and genome build, and provide a Gene Expression Omnibus (GEO) accession number or Dropbox links to sequence files, alignment files, gene-expression-count tables, or rank files with the corresponding metadata. Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.