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

Detecting gene expression in Caenorhabditis elegans

Reliable methods for detecting and analyzing gene expression are necessary tools for understanding development and investigating biological responses to genetic and environmental perturbation. With its fully sequenced genome, invariant cell lineage, transparent body, wiring diagram, detailed anatomy, and wide array of genetic tools, Caenorhabditis elegans is an exceptionally useful model organism for linking gene expression to cellular phenotypes. The development of new techniques in recent years has greatly expanded our ability to detect gene expression at high resolution. Here, we provide an overview of gene expression methods for C. elegans, including techniques for detecting transcripts and proteins in situ, bulk RNA sequencing of whole worms and specific tissues and cells, single-cell RNA sequencing, and high-throughput proteomics. We discuss important considerations for choosing among these techniques and provide an overview of publicly available online resources for gene expression data.

The Evolution of Next-Generation Sequencing Technologies

The genetic information that dictates the structure and function of all life forms is encoded in the DNA. In 1953, Watson and Crick first presented the double helical structure of a DNA molecule. Their findings unearthed the desire to elucidate the exact composition and sequence of DNA molecules. Discoveries and the subsequent development and optimization of techniques that allowed for deciphering the DNA sequence has opened new doors in research, biotech, and healthcare. The application of high-throughput sequencing technologies in these industries has positively impacted and will continue to contribute to the betterment of humanity and the global economy. Improvements, such as the use of radioactive molecules for DNA sequencing to the use of florescent dyes and the implementation of polymerase chain reaction (PCR) for amplification, led to sequencing a few hundred base pairs in days, to automation, where sequencing of thousands of base pairs in hours became possible. Significant advances have been made, but there is still room for improvement. Here, we look at the history and the technology of the currently available next-generation sequencing platforms and the possible applications of such technologies to biomedical research and beyond.

SLAM-seq reveals independent contributions of RNA processing and stability to gene expression in African trypanosomes

Gene expression is a multi-step process that converts DNA-encoded information into proteins, involving RNA transcription, maturation, degradation, and translation. While transcriptional control is a major regulator of protein levels, the role of post-transcriptional processes such as RNA processing and degradation is less well understood due to the challenge of measuring their contributions individually. To address this challenge, we investigated the control of gene expression in Trypanosoma brucei, a unicellular parasite assumed to lack transcriptional control. Instead, mRNA levels in T. brucei are controlled by post-transcriptional processes, which enabled us to disentangle the contribution of both processes to total mRNA levels. In this study, we developed an efficient metabolic RNA labeling approach and combined ultra-short metabolic labeling with transient transcriptome sequencing (TT-seq) to confirm the long-standing assumption that RNA polymerase II transcription is unregulated in T. brucei. In addition, we established thiol (SH)-linked alkylation for metabolic sequencing of RNA (SLAM-seq) to globally quantify RNA processing rates and half-lives. Our data, combined with scRNA-seq data, indicate that RNA processing and stability independently affect total mRNA levels and contribute to the variability seen between individual cells in African trypanosomes.

Proteomics Can Rise to the Challenge of Pseudogenes' Coding Nature

Throughout the past decade, technological advances in genomics and transcriptomics have revealed pervasive translation throughout mammalian genomes. These putative proteins are usually excluded from proteomics analyses, as they are absent from common protein repositories. A sizable portion of these noncanonical proteins is translated from pseudogenes. Pseudogenes are commonly termed defective copies of coding genes unable to produce proteins. Here, we suggest that proteomics can help in their annotation. First, we define important terms and review specific examples underlining the caveats in pseudogene annotation and their coding potential. Then, we will discuss the challenges inherent to pseudogenes that have thus far rendered complex their confidence in omics data. Finally, we identify recent developments in experimental procedures, instrumentation, and computational methods in proteomics that put the field in a unique position to solve the pseudogene annotation conundrum.

Challenges and opportunities in processing NanoString nCounter data

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.

Exploring transcription modalities from bimodal, single-cell RNA sequencing data

There is a growing interest in generating bimodal, single-cell RNA sequencing (RNA-seq) data for studying biological pathways. These data are predominantly utilized in understanding phenotypic trajectories using RNA velocities; however, the shape information encoded in the two-dimensional resolution of such data is not yet exploited. In this paper, we present an elliptical parametrization of two-dimensional RNA-seq data, from which we derived statistics that reveal four different modalities. These modalities can be interpreted as manifestations of the changes in the rates of splicing, transcription or degradation. We performed our analysis on a cell cycle and a colorectal cancer dataset. In both datasets, we found genes that are not picked up by differential gene expression analysis (DGEA), and are consequently unnoticed, yet visibly delineate phenotypes. This indicates that, in addition to DGEA, searching for genes that exhibit the discovered modalities could aid recovering genes that set phenotypes apart. For communities studying biomarkers and cellular phenotyping, the modalities present in bimodal RNA-seq data broaden the search space of genes, and furthermore, allow for incorporating cellular RNA processing into regulatory analyses.

Transcriptomic Analysis of Blood Collagen-Induced Arthritis Mice Exposed to 0.1 THz Reveals Inhibition of Genes and Pathways Involved in Rheumatoid Arthritis

Inflammation plays an essential role in the phases of rheumatoid arthritis (RA) as the joints secrete a range of molecules that modulate the inflammatory process. While therapies based on physical properties have shown effectiveness in a range of animal experimental models, the understanding of their biological mechanisms remains unclear. The aim of this study was to investigate the immunomodulatory effects of a 0.1 terahertz (THz) wave in rheumatoid arthritis in an attempt to dissect the molecular pathways implicated. The collagen-induced rheumatoid arthritis (CIA) model joint mice were irradiated daily for 30 min over a period of 2 weeks with continuous 0.1 terahertz waves. High-throughput bulk RNA sequencing of the murine blood was performed to analyze and characterize the differences in gene expression changes between the control (Ctrl), CIA (RA), and CIA exposed to THz. Differentially expressed genes, canonical pathway analysis, gene set enrichment, and protein-protein interaction were further run on the selected DEGs. We found that terahertz exposure downregulated gene ontologies representing the "TGF-β signaling pathway", "apoptosis", "activation of T cell receptor signaling pathway", and "non-canonical NF-κB signal transduction". These observations were further confirmed by a decreased level in the expression of transcription factors Nfib and Nfatc3, and an increased level of Lsp1. In addition, the expression of Mmp8 was significantly restored. These results indicate that THz ultimately attenuates the inflammatory response of hemocytes through the T cell and NF-κB pathway, and these changes are reverberated in the blood transcriptome. In this first report of transcriptome sequencing in a model of rheumatoid arthritis exposed to terahertz waves, the downregulated DEGs were associated with anti-inflammatory effects.

Developing a peripheral blood RNA-seq based NETseq ensemble classifier: A potential novel tool for non-invasive detection and treatment response assessment in neuroendocrine tumor patients receiving 177Lu-DOTATATE PRRT

Neuroendocrine tumors (NETs) are presented with metastases due to delayed diagnosis. We aimed to identify NET-related biomarkers from peripheral blood. The development and validation of a multi-gene NETseq ensemble classifier using peripheral blood RNA-Seq is reported. RNA-Seq was performed on peripheral blood samples from 178 NET patients and 73 healthy donors. Distinguishing gene features were identified from a learning cohort (59 PRRT-naïve GEP-NET patients and 38 healthy donors). Ensemble classifier combining the output of five machine learning algorithms viz. Random Forest (RF), Extreme Gradient Boosting (XGBOOST), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Logistic Regression (LR) were trained and independently validated in the evaluation cohort (n = 106). The response to PRRT was evaluated in the PRRT cohort (n = 46) and the PRRT response monitoring cohort (n = 16). The response to 177Lu-DOTATATE PRRT was assessed using RECIST 1.1 criteria. The Ensemble classifier trained on 61 gene features, distinguished NET from healthy samples with 100% accuracy in the learning cohort. In an evaluation cohort, the classifier achieved 93% sensitivity (95% CI: 87.8%-98.03%) and 91.4% specificity (95% CI: 82.1%-100%) for PRRT-naïve GEP-NETs (AUROC = 95.4%). The classifier returned >87.5% sensitivity across different tumor characteristics and outperformed serum Chromogranin A sensitivity (χ2 = 21.89, p = 4.161e-6). In the PRRT cohort, RECIST 1.1 responders showed significantly lower NETseq prediction scores after 177Lu-DOTATATE PRRT, in comparison to the non-responders. In an independent response monitoring cohort, paired samples (before PRRT and after 2nd or 3rd cycle of PRRT) were analyzed. The NETseq prediction score significantly decreased in partial responders (p = .002) and marginally reduced in stable disease (p = .068). The NETseq ensemble classifier identified PRRT-naïve GEP-NETs with high accuracy (≥92%) and demonstrated a potential role in early treatment response monitoring in the PRRT setting. This blood-based, non-invasive, multi-analyte molecular method could be developed as a valuable adjunct to conventional methods in the detection and treatment response assessment in NET patients.

Y and mitochondrial chromosomes in the heterogeneous stock rat population

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial (MT) Chromosomes. We genotyped the Y and MT Chromosomes in heterogeneous stock (HS) rats (Rattus norvegicus), an outbred population created from 8 inbred strains. We identified 8 distinct Y and 4 distinct MT Chromosomes among the 8 founders. However, only 2 types of each nonrecombinant chromosome were observed in our modern HS rat population (generations 81-97). Despite the relatively large sample size, there were virtually no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and MT Chromosomes were strongly associated with the expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern HS rats there are no Y and MT Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and MT Chromosomes that do not appear in modern HS rats, nor do they address effects that may exist in other rat populations, or in other species.

A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging

Single-cell RNA sequencing has enabled the study of aging at a molecular scale. While substantial progress has been made in measuring age-related gene expression, the underlying patterns and mechanisms of aging transcriptomes remain poorly understood. To address this gap, we propose a physics-inspired, data-analysis approach to extract additional insights from single-cell RNA sequencing data. By considering the genome as a many-body interacting system, we leverage central idea of the Renormalization Group to construct an approach to hierarchically describe aging across a spectrum of scales for the gene expresion. This framework provides a quantitative language to study the multiscale patterns of aging transcriptomes. Overall, our study demonstrates the value of leveraging theoretical physics concepts like the Renormalization Group to gain new biological insights from complex high-dimensional single-cell data.

Genome-Wide Analysis of Caffeoyl-CoA-O-methyltransferase (CCoAOMT) Family Genes and the Roles of GhCCoAOMT7 in Lignin Synthesis in Cotton

Caffeoyl coenzyme A-O-methyltransferase (CCoAOMT) has a critical function in the lignin biosynthesis pathway. However, its functions in cotton are not clear. In this research, we observed 50 CCoAOMT genes from four cotton species, including two diploids (Gossypium arboretum, 9, and Gossypium raimondii, 8) and two tetraploids (Gossypium hirsutum, 16, and Gossypium barbadense, 17), performed bioinformatic analysis, and focused on the involvement and functions of GhCCoAOMT7 in lignin synthesis of Gossypium hirsutum. CCoAOMT proteins were divided into four subgroups based on the phylogenetic tree analysis. Motif analysis revealed that all CCoAOMT proteins possess conserved Methyltransf_3 domains, and conserved structural features were identified based on the genes' exon-intron organization. A synteny analysis suggested that segmental duplications were the primary cause in the expansion of the CCoAOMT genes family. Transcriptomic data analysis of GhCCoAOMTs revealed that GhCCoAOMT2, GhCCoAOMT7, and GhCCoAOMT14 were highly expressed in stems. Subcellular localization experiments of GhCCoAOMT2, GhCCoAOMT7, and GhCCoAOMT14 showed that GhCCoAOMT2, GhCCoAOMT7, and GhCCoAOMT14 were localized in the nucleus and plasma membrane. However, there are no cis-regulatory elements related to lignin synthesis in the GhCCoAOMT7 gene promoter. GhCCoAOMT7 expression was inhibited by virus-induced gene silencing technology to obtain gene silencing lines, the suppression of GhCCoAOMT7 expression resulted in a 56% reduction in the lignin content in cotton stems, and the phloroglucinol staining area corresponding to the xylem was significantly decreased, indicating that GhCCoAOMT7 positively regulates lignin synthesis. Our results provided fundamental information regarding CCoAOMTs and highlighted their potential functions in cotton lignin biosynthesis and lignification.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Dual-RNA-sequencing to elucidate the interactions between sorghum and Colletotrichum sublineola

In warm and humid regions, the productivity of sorghum is significantly limited by the fungal hemibiotrophic pathogen Colletotrichum sublineola, the causal agent of anthracnose, a problematic disease of sorghum (Sorghum bicolor (L.) Moench) that can result in grain and biomass yield losses of up to 50%. Despite available genomic resources of both the host and fungal pathogen, the molecular basis of sorghum-C. sublineola interactions are poorly understood. By employing a dual-RNA sequencing approach, the molecular crosstalk between sorghum and C. sublineola can be elucidated. In this study, we examined the transcriptomes of four resistant sorghum accessions from the sorghum association panel (SAP) at varying time points post-infection with C. sublineola. Approximately 0.3% and 93% of the reads mapped to the genomes of C. sublineola and Sorghum bicolor, respectively. Expression profiling of in vitro versus in planta C. sublineola at 1-, 3-, and 5-days post-infection (dpi) indicated that genes encoding secreted candidate effectors, carbohydrate-active enzymes (CAZymes), and membrane transporters increased in expression during the transition from the biotrophic to the necrotrophic phase (3 dpi). The hallmark of the pathogen-associated molecular pattern (PAMP)-triggered immunity in sorghum includes the production of reactive oxygen species (ROS) and phytoalexins. The majority of effector candidates secreted by C. sublineola were predicted to be localized in the host apoplast, where they could interfere with the PAMP-triggered immunity response, specifically in the host ROS signaling pathway. The genes encoding critical molecular factors influencing pathogenicity identified in this study are a useful resource for subsequent genetic experiments aimed at validating their contributions to pathogen virulence. This comprehensive study not only provides a better understanding of the biology of C. sublineola but also supports the long-term goal of developing resistant sorghum cultivars.

The Mclust Analysis of Tumor Budding Unveils the Role of the Collagen Family in Cervical Cancer Progression

In RNA-seq data analysis, condensing the gene count matrix size is pivotal for downstream investigations, particularly pathway analysis. For this purpose, harnessing machine learning attracts increasing interest, while conventional methodologies depend on p-value comparisons. In this study, 20 tissue samples from real-world cervical cancers were subjected to sequencing, followed by the application of the Mclust algorithm to delineate an optimal cluster. By stratifying tumor budding into high and low groups and quantifying the epithelial-to-mesenchymal transition (EMT) score to scrutinize tumor budding, we discerned 24 EMT-related genes, with 5 showing strong associations with cervical cancer prognosis. Our observations elucidate a biological flow wherein EMT, Matrix Metallopep-tidase 2 (MMP2), and extracellular matrix (ECM) degradation are interconnected, ultimately leading to collagen type VI and exacerbating the prognosis of cervical cancer. The present study underscores an alternative method for selecting useful EMT-related genes by employing an appropriate clustering algorithm, thereby avoiding classical methods while unveiling novel insights into cervical cancer etiology and prognosis. Moreover, when comparing high and low tumor budding, collagen type VI emerges as a potential gene marker for the prognosis of cervical cancer.

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

Non-Thermal Effects of Terahertz Radiation on Gene Expression: Systematic Review and Meta-Analysis

With the advancement of terahertz technology, unveiling the mysteries of terahertz has had a profound impact on the field of biomedicine. However, the lack of systematic comparisons for gene expression signatures may diminish the effectiveness and efficiency of identifying common mechanisms underlying terahertz effects across diverse research findings. We performed a comprehensive review and meta-analysis to compile patterns of gene expression profiles associated with THz radiation. Thorough bibliographic reviews were conducted, utilizing the PubMed, Embase, Web of Science, and ProQuest databases to extract references from published articles. Raw CEL files were obtained from Gene Expression Omnibus and preprocessed using Bioconductor packages. This systematic review (Registration No. CDR42024502937) resulted in a detailed analysis of 13 studies (14 papers). There are several possible mechanisms and pathways through which THz radiation could cause biological changes. While the established gene expression results are largely associated with immune response and inflammatory markers, other genes demonstrated transcriptional outcomes that may unravel unknown functions. The enrichment of genes primarily found networks associated with broader stress responses. Altogether, the findings showed that THz can induce a distinct transcriptomic profile that is not associated with a microthermal cellular response. However, it is impossible to pinpoint a single gene or family of genes that would accurately and reliably justify the patterns of gene expression response under THz exposure.

Long-term exposure to diesel exhaust particles induces concordant changes in DNA methylation and transcriptome in human adenocarcinoma alveolar basal epithelial cells

BACKGROUND

Diesel exhaust particles (DEP), which contain hazardous compounds, are emitted during the combustion of diesel. As approximately one-third of the vehicles worldwide use diesel, there are growing concerns about the risks posed by DEP to human health. Long-term exposure to DEP is associated with airway hyperresponsiveness, pulmonary fibrosis, and inflammation; however, the molecular mechanisms behind the effects of DEP on the respiratory tract are poorly understood. Such mechanisms can be addressed by examining transcriptional and DNA methylation changes. Although several studies have focused on the effects of short-term DEP exposure on gene expression, research on the transcriptional effects and genome-wide DNA methylation changes caused by long-term DEP exposure is lacking. Hence, in this study, we investigated transcriptional and DNA methylation changes in human adenocarcinoma alveolar basal epithelial A549 cells caused by prolonged exposure to DEP and determined whether these changes are concordant.

RESULTS

DNA methylation analysis using the Illumina Infinium MethylationEPIC BeadChips showed that the methylation levels of DEP-affected CpG sites in A549 cells changed in a dose-dependent manner; the extent of change increased with increasing dose reaching the statistical significance only in samples exposed to 30 µg/ml DEP. Four-week exposure to 30 µg/ml of DEP significantly induced DNA hypomethylation at 24,464 CpG sites, which were significantly enriched for DNase hypersensitive sites, genomic regions marked by H3K4me1 and H3K27ac, and several transcription factor binding sites. In contrast, 9,436 CpG sites with increased DNA methylation levels were significantly overrepresented in genomic regions marked by H3K27me3 as well as H3K4me1 and H3K27ac. In parallel, gene expression profiling by RNA sequencing demonstrated that long-term exposure to DEP altered the expression levels of 2,410 genes, enriching 16 gene sets including Xenobiotic metabolism, Inflammatory response, and Senescence. In silico analysis revealed that the expression levels of 854 genes correlated with the methylation levels of the DEP-affected cis-CpG sites.

CONCLUSIONS

To our knowledge, this is the first report of genome-wide transcriptional and DNA methylation changes and their associations in A549 cells following long-term exposure to DEP.

Comprehensive Transcriptome Analysis Expands lncRNA Functional Profiles in Breast Cancer

Breast cancer is a heterogeneous disease that arises as a multi-stage process involving multiple cell types. Patients diagnosed with the same clinical stage and pathological classification may have different prognoses and therapeutic responses due to alterations in molecular genetics. As an essential marker for the molecular subtyping of breast cancer, long non-coding RNAs (lncRNAs) play a crucial role in gene expression regulation, cell differentiation, and the maintenance of genomic stability. Here, we developed a modular framework for lncRNA identification and applied it to a breast cancer cohort to identify novel lncRNAs not previously annotated. To investigate the potential biological function, regulatory mechanisms, and clinical relevance of the novel lncRNAs, we elucidated the genomic and chromatin features of these lncRNAs, along with the associated protein-coding genes and putative enhancers involved in the breast cancer regulatory networks. Furthermore, we uncovered that the expression patterns of novel and annotated lncRNAs identified in breast cancer were related to the hormone response in the PAM50 subtyping criterion, as well as the immune response and progression states of breast cancer across different immune cells and immune checkpoint genes. Collectively, the comprehensive identification and functional analysis of lncRNAs revealed that these lncRNAs play an essential role in breast cancer by altering gene expression and participating in the regulatory networks, contributing to a better insight into breast cancer heterogeneity and potential avenues for therapeutic intervention.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

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.

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Meta-analysis towards FSHD reveals misregulation of neuromuscular junction, nuclear envelope, and spliceosome

Facioscapulohumeral muscular dystrophy (FSHD) is one of the most common autosomal dominant muscle disorders, yet no cure or amelioration exists. The clinical presentation is diverse, making it difficult to identify the actual driving pathomechanism among many downstream events. To unravel this complexity, we performed a meta-analysis of 13 original omics datasets (in total 171 FSHD and 129 control samples). Our approach confirmed previous findings about the disease pathology and specified them further. We confirmed increased expression of former proposed DUX4 biomarkers, and furthermore impairment of the respiratory chain. Notably, the meta-analysis provides insights about so far not reported pathways, including misregulation of neuromuscular junction protein encoding genes, downregulation of the spliceosome, and extensive alterations of nuclear envelope protein expression. Finally, we developed a publicly available shiny app to provide a platform for researchers who want to search our analysis for genes of interest in the future.

DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data

Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of gene expression profiles for individual cells. However, scRNA-seq is still too expensive to be used for large-scale population studies, and bulk RNA-seq is still widely used in such situations. An essential challenge is to deconvolve cellular composition for bulk RNA-seq data based on scRNA-seq data. Here, we present DeepDecon, a deep neural network model that leverages single-cell gene expression information to accurately predict the fraction of cancer cells in bulk tissues. It provides a refining strategy in which the cancer cell fraction is iteratively estimated by a set of trained models. When applied to simulated and real cancer data, DeepDecon exhibits superior performance compared to existing decomposition methods in terms of accuracy.

OMD Curation Toolkit: a workflow for in-house curation of public omics datasets

BACKGROUND

Major advances in sequencing technologies and the sharing of data and metadata in science have resulted in a wealth of publicly available datasets. However, working with and especially curating public omics datasets remains challenging despite these efforts. While a growing number of initiatives aim to re-use previous results, these present limitations that often lead to the need for further in-house curation and processing.

RESULTS

Here, we present the Omics Dataset Curation Toolkit (OMD Curation Toolkit), a python3 package designed to accompany and guide the researcher during the curation process of metadata and fastq files of public omics datasets. This workflow provides a standardized framework with multiple capabilities (collection, control check, treatment and integration) to facilitate the arduous task of curating public sequencing data projects. While centered on the European Nucleotide Archive (ENA), the majority of the provided tools are generic and can be used to curate datasets from different sources.

CONCLUSIONS

Thus, it offers valuable tools for the in-house curation previously needed to re-use public omics data. Due to its workflow structure and capabilities, it can be easily used and benefit investigators in developing novel omics meta-analyses based on sequencing data.

Establishment and comparison of human term placenta-derived trophoblast cells†

Research on the biology of fetal-maternal barriers has been limited by access to physiologically relevant cells, including trophoblast cells. In this study, we describe the development of a human term placenta-derived cytotrophoblast immortalized cell line (hPTCCTB) derived from the basal plate. Human-term placenta-derived cytotrophoblast immortalized cell line cells are comparable to their primary cells of origin in terms of morphology, marker expression, and functional responses. We demonstrate that these can transform into syncytiotrophoblast and extravillous trophoblasts. We also compared the hPTCCTB cells to immortalized chorionic trophoblasts (hFM-CTC), trophoblasts of the chorionic plate, and BeWo cells, choriocarcinoma cell lines of conventional use. Human-term placenta-derived cytotrophoblast immortalized cell line and hFM-CTCs displayed more similarity to each other than to BeWos, but these differ in syncytialization ability. Overall, this study (1) demonstrates that the immortalized hPTCCTB generated are cells of higher physiological relevance and (2) provides a look into the distinction between the spatially distinct placental and fetal barrier trophoblasts cells, hPTCCTB and hFM-CTC, respectively.

Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model

Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association between gut microbiota and KRAS mutation in CRC remains enigmatic. We conducted 16S rRNA sequencing on stool samples from 94 CRC patients and employed the linear discriminant analysis effect size algorithm to identify distinct gut microbiota between KRAS mutant and KRAS wild-type CRC patients. Transcriptome sequencing data from nine CRC patients were transformed into a matrix of immune infiltrating cells, which was then utilized to explore KRAS mutation-associated biological functions, including Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways. Subsequently, we analyzed the correlations among these KRAS mutation-associated gut microbiota, host immunity, and KRAS mutation-associated biological functions. At last, we developed a predictive random forest (RF) machine learning model to predict the KRAS mutation status in CRC patients, based on the gut microbiota associated with KRAS mutation. We identified a total of 26 differential gut microbiota between both groups. Intriguingly, a significant positive correlation was observed between Bifidobacterium spp. and mast cells, as well as between Bifidobacterium longum and chemokine receptor CX3CR1. Additionally, we also observed a notable negative correlation between Bifidobacterium and GOMF:proteasome binding. The RF model constructed using the KRAS mutation-associated gut microbiota demonstrated qualified efficacy in predicting the KRAS phenotype in CRC. Our study ascertained the presence of 26 KRAS mutation-associated gut microbiota in CRC and speculated that Bifidobacterium may exert an essential role in preventing CRC progression, which appeared to correlate with the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:proteasome binding. Furthermore, the RF model constructed on the basis of KRAS mutation-associated gut microbiota exhibited substantial potential in predicting KRAS mutation status in CRC patients.IMPORTANCEGut microbiota has emerged as an essential player in the onset and development of colorectal cancer (CRC). However, the relationship between gut microbiota and KRAS mutation in CRC remains elusive. Our study not only identified a total of 26 gut microbiota associated with KRAS mutation in CRC but also unveiled their significant correlations with tumor-infiltrating immune cells, immune-related genes, and biological pathways (Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways). We speculated that Bifidobacterium may play a crucial role in impeding CRC progression, potentially linked to the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:Proteasome binding. Furthermore, based on the KRAS mutation-associated gut microbiota, the RF model exhibited promising potential in the prediction of KRAS mutation status for CRC patients. Overall, the findings of our study offered fresh insights into microbiological research and clinical prediction of KRAS mutation status for CRC patients.

Integration of Computational Pipeline to Streamline Efficacious Drug Nomination and Biomarker Discovery in Glioblastoma

Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation.

Molecular mechanisms of macrophage immunomodulation mediated by Areca inflorescence polysaccharides based on RNA-seq analysis

The elucidation of the immunomodulatory molecular mechanisms of polysaccharides has contributed to their further development and application. In this study, the effect of Areca inflorescence polysaccharide (AFP2a) on macrophage activation was confirmed and the detailed mechanisms were investigated based on a comprehensive transcriptional study and specific inhibitors. The results showed that AFP2a induced macrophage activation (M1 polarization), promoting macrophage proliferation, reactive oxygen species production, nitric oxide and cytokine release, and costimulatory molecule expression. RNA-seq analysis identified 5919 differentially expressed genes (DEGs). For DEGs, GO, KEGG, and Reactome enrichment analyses and PPI networks were conducted, elucidating that AFP2a activated macrophages mainly by triggering the Toll-like receptor cascade and corresponding adapter proteins (TIRAP and TRIF), thereby resulting in downstream NF-κB, TNF, and JAK-STAT signaling pathway expression. The inhibition assay revealed that TLR4 and TLR2 were essential for the recognition of AFP2a. This work provides an in-depth understanding of the immunoregulatory mechanism of AFP2a while offering a molecular basis for AFP2a to serve as a potential natural immunomodulator.

New Empirical Bayes Models to Jointly Analyze Multiple RNA-Sequencing Data in a Hypophosphatasia Disease Study

Hypophosphatasia is a rare inherited metabolic disorder caused by the deficiency of tissue-nonspecific alkaline phosphatase. More severe and early onset cases present symptoms of muscle weakness, diminished motor coordination, and epileptic seizures. These neurological manifestations are poorly characterized. Thus, it is urgent to discover novel differentially expressed genes for investigating the genetic mechanisms underlying the neurological manifestations of hypophosphatasia. RNA-sequencing data offer a high-resolution and highly accurate transcript profile. In this study, we apply an empirical Bayes model to RNA-sequencing data acquired from the spinal cord and neocortex tissues of a mouse model, individually, to more accurately estimate the genetic effects without bias. More importantly, we further develop two integration methods, weighted gene approach and weighted Z method, to incorporate two RNA-sequencing data into a model for enhancing the effects of genetic markers in the diagnostics of hypophosphatasia disease. The simulation and real data analysis have demonstrated the effectiveness of our proposed integration methods, which can maximize genetic signals identified from the spinal cord and neocortex tissues, minimize the prediction error, and largely improve the prediction accuracy in risk prediction.

Validation of RNA Extraction Methods and Suitable Reference Genes for Gene Expression Studies in Developing Fetal Human Inner Ear Tissue

A comprehensive gene expression investigation requires high-quality RNA extraction, in sufficient amounts for real-time quantitative polymerase chain reaction and next-generation sequencing. In this work, we compared different RNA extraction methods and evaluated different reference genes for gene expression studies in the fetal human inner ear. We compared the RNA extracted from formalin-fixed paraffin-embedded tissue with fresh tissue stored at -80 °C in RNAlater solution and validated the expression stability of 12 reference genes (from gestational week 11 to 19). The RNA from fresh tissue in RNAlater resulted in higher amounts and a better quality of RNA than that from the paraffin-embedded tissue. The reference gene evaluation exhibited four stably expressed reference genes (B2M, HPRT1, GAPDH and GUSB). The selected reference genes were then used to examine the effect on the expression outcome of target genes (OTOF and TECTA), which are known to be regulated during inner ear development. The selected reference genes displayed no differences in the expression profile of OTOF and TECTA, which was confirmed by immunostaining. The results underline the importance of the choice of the RNA extraction method and reference genes used in gene expression studies.

Comamonas-dominant microbial community in carbon poor aquitard sediments revealed by metagenomic-based growth rate investigation

The microbiological ecology of a low-nutrient shallow aquifer with high arsenic content in the Yinchuan Plain was investigated in this study. Amplicon sequencing data from five samples (depths: 1.5 m, 3.5 m, 11.2 m, 19.3 m, and 25.5 m) revealed diverse and adaptable microbial community. Among the microbial community, Comamonas was the most prominent, accounting for 10.52 % of the total. This genus displayed high growth rates, with a maximum growth rate of 12.06 d-1 and a corresponding doubling time of 1.38 days, as determined through an analysis of codon usage bias. Functional annotation of Metagenome-Assembled Genomes (MAGs) for samples at 1.5 m and 11.2 m depths revealed Comamonas' metabolic versatility, including various carbon pathways, assimilative sulfate reduction (ASR), and dissimilatory reduction to ammonium (DNRA). The TPM (Transcripts Per Kilobase of exon model per Million mapped reads) of MAGs at 11.2 m sample was 15.7 and 12.3. The presence of arsenic resistance genes in Comamonas aligns with sediment arsenic levels (65.8 mg/kg for 1.5 m depth, 32.8 mg/kg for 11.2 m depth). This study highlights the role of Comamonas as a 'generalist' bacteria in challenging oligotrophic sediments, emphasizing the significance of such organisms in community stability and ecological functions. ENVIRONMENTAL IMPLICATION: Low-biomass limits the microbial activity and biogeochemical study in oligotrophic environments, which is the typical condition for underground aquatic ecosystems. Facilitated by growth rate estimation, our research focuses on active functional microorganisms and their biogeochemical metabolic in oligotrophic aquifer sediments, revealing their impact on the environment and response to arsenic threats. Findings illuminate the metabolic advantage of a 'generalist life-style' in carbon-scarce environments and contribute to a broader understanding of bacterial ecosystems and environmental impacts in oligotrophic aquifer sediments worldwide.

Genome-wide identification and structural analysis of the BMP gene family in Triplophysa dalaica

BACKGROUND

Bone morphogenetic proteins (BMPs) are part of the transforming growth factor beta (TGF-β) superfamily and play crucial roles in bone development, as well as in the formation and maintenance of various organs. Triplophysa dalaica, a small loach fish that primarily inhabits relatively high elevations and cooler water bodies, was the focus of this study. Understanding the function of BMP genes during the morphogenesis of T. dalaica helps to clarify the mechanisms of its evolution and serves as a reference for the study of BMP genes in other bony fishes. The data for the T. dalaica transcriptome and genome used in this investigation were derived from the outcomes of our laboratory sequencing.

RESULTS

This study identified a total of 26 BMP genes, all of which, except for BMP1, possess similar TGF-β structural domains. We conducted an analysis of these 26 BMP genes, examining their physicochemical properties, subcellular localization, phylogenetic relationships, covariance within and among species, chromosomal localization, gene structure, conserved motifs, conserved structural domains, and expression patterns. Our findings indicated that three BMP genes were associated with unstable proteins, while 11 BMP genes were located within the extracellular matrix. Furthermore, some BMP genes were duplicated, with the majority being enriched in the GO:0008083 pathway, which is related to growth factor activity. It was hypothesized that genes within the BMP1/3/11/15 subgroup (Group I) play a significant role in the growth and development of T. dalaica. By analyzing the expression patterns of proteins in nine tissues (gonad, kidney, gill, spleen, brain, liver, fin, heart, and muscle), we found that BMP genes play diverse regulatory roles during different stages of growth and development and exhibit characteristics of division of labor.

CONCLUSIONS

This study contributes to a deeper understanding of BMP gene family member expression patterns in high-altitude, high-salinity environments and provides valuable insights for future research on the BMP gene family in bony fishes.

MCPIP1 Inhibits Hepatic Stellate Cell Activation in Autocrine and Paracrine Manners, Preventing Liver Fibrosis

BACKGROUND & AIMS

Hepatic fibrosis is characterized by enhanced deposition of extracellular matrix (ECM), which results from the wound healing response to chronic, repeated injury of any etiology. Upon injury, hepatic stellate cells (HSCs) activate and secrete ECM proteins, forming scar tissue, which leads to liver dysfunction. Monocyte-chemoattractant protein-induced protein 1 (MCPIP1) possesses anti-inflammatory activity, and its overexpression reduces liver injury in septic mice. In addition, mice with liver-specific deletion of Zc3h12a develop features of primary biliary cholangitis. In this study, we investigated the role of MCPIP1 in liver fibrosis and HSC activation.

METHODS

We analyzed MCPIP1 levels in patients' fibrotic livers and hepatic cells isolated from fibrotic murine livers. In vitro experiments were conducted on primary HSCs, cholangiocytes, hepatocytes, and LX-2 cells with MCPIP1 overexpression or silencing.

RESULTS

MCPIP1 levels are induced in patients' fibrotic livers compared with their nonfibrotic counterparts. Murine models of fibrosis revealed that its level is increased in HSCs and hepatocytes. Moreover, hepatocytes with Mcpip1 deletion trigger HSC activation via the release of connective tissue growth factor. Overexpression of MCPIP1 in LX-2 cells inhibits their activation through the regulation of TGFB1 expression, and this phenotype is reversed upon MCPIP1 silencing.

CONCLUSIONS

We demonstrated that MCPIP1 is induced in human fibrotic livers and regulates the activation of HSCs in both autocrine and paracrine manners. Our results indicate that MCPIP1 could have a potential role in the development of liver fibrosis.

CrustyBase v.2.0: new features and enhanced utilities to support open science

BACKGROUND

Transcriptomes present a rich, multi-dimensional subset of genomics data. They provide broad insights into genetic sequence, and more significantly gene expression, across biological samples. This technology is frequently employed for describing the genetic response to experimental conditions and has created vast libraries of datasets which shed light on gene function across different tissues, diseases, diets and developmental stages in many species. However, public accessibility of these data is impeded by a lack of suitable software interfaces and databases with which to locate and analyse them. BODY: Here we present an update on the status of CrustyBase.org, an online resource for analysing and sharing crustacean transcriptome datasets. Since its release in October 2020, the resource has provided many thousands of transcriptome sequences and expression profiles to its users and received 19 new dataset imports from researchers across the globe. In this article we discuss user analytics which point towards the utilization of this resource. The architecture of the application has proven robust with over 99.5% uptime and effective reporting of bugs through both user engagement and the error logging mechanism. We also introduce several new features that have been developed as part of a new release of CrustyBase.org. Two significant features are described in detail, which allow users to navigate through transcripts directly by submission of transcript identifiers, and then more broadly by searching for encoded protein domains by keyword. The latter is a novel and experimental feature, and grants users the ability to curate gene families from any dataset hosted on CrustyBase in a matter of minutes. We present case studies to demonstrate the utility of these features.

CONCLUSION

Community engagement with this resource has been very positive, and we hope that improvements to the service will further enable the research of users of the platform. Web-based platforms such as CrustyBase have many potential applications across life science domains, including the health sector, which are yet to be realised. This leads to a wider discussion around the role of web-based resources in facilitating an open and collaborative research community.

An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

Effects of a second iron-dextran injection administered to piglets during lactation on differential gene expression in liver and duodenum at weaning

Six female littermate piglets were used in an experiment to evaluate the mRNA expression in tissues from piglets given one or two 1 mL injections of iron dextran (200 mg Fe/mL). All piglets in the litter were administered the first 1 mL injection < 24 h after birth. On day 7, piglets were paired by weight (mean body weight = 1.72 ± 0.13 kg) and one piglet from each pair was randomly selected as control (CON) and the other received a second injection (+Fe). At weaning on day 22, each piglet was anesthetized, and samples of liver and duodenum were taken from the anesthetized piglets and preserved until mRNA extraction. differential gene expression data were analyzed with a fold change cutoff (FC) of |1.2| P < 0.05. Pathway analysis was conducted with Z-score cutoff of P < 0.05. In the duodenum 435 genes were significantly changed with a FC ≥ |1.2| P < 0.05. In the duodenum, Claudin 1 and Claudin 2 were inversely affected by + Fe. Claudin 1 (CLDN1) plays a key role in cell-to-cell adhesion in the epithelial cell sheets and was upregulated (FC = 4.48, P = 0.0423). Claudin 2 (CLDN2) is expressed in cation leaky epithelia, especially during disease or inflammation and was downregulated (FC = -1.41, P = 0.0097). In the liver, 362 genes were expressed with a FC ≥ |1.2| P < 0.05. The gene most affected by a second dose of 200 mg Fe was hepcidin antimicrobial peptide (HAMP) with a FC of 40.8. HAMP is a liver-produced hormone that is the main circulating regulator of Fe absorption and distribution across tissues. It also controls the major flows of Fe into plasma by promoting endocytosis and degradation of ferroportin (SLC4A1). This leads to the retention of Fe in Fe-exporting cells and decreased flow of Fe into plasma. Gene expression related to metabolic pathway changes in the duodenum and liver provides evidence for the improved feed conversion and growth rates in piglets given two iron injections preweaning with contemporary pigs in a companion study. In the duodenum, there is a downregulation of gene clusters associated with gluconeogenesis (P < 0.05). Concurrently, there was a decrease in the mRNA expression of genes for enzymes required for urea production in the liver (P < 0.05). These observations suggest that there may be less need for gluconeogenesis, and possibly less urea production from deaminated amino acids. The genomic and pathway analyses provided empirical evidence linking gene expression with phenotypic observations of piglet health and growth improvements.

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

Modular Splicing Is Linked to Evolution in the Synapse-Specificity Molecule Kirrel3

Kirrel3 is a cell-adhesion molecule that instructs the formation of specific synapses during brain development in mouse and Kirrel3 variants may be risk factors for autism and intellectual disabilities in humans. Kirrel3 is predicted to undergo alternative splicing but brain isoforms have not been studied. Here, we present the first in-depth characterization of Kirrel3 isoform diversity in brain using targeted, long-read mRNA sequencing of mouse hippocampus. We identified 19 isoforms with predicted transmembrane and secreted forms and show that even rare isoforms generate detectable protein in the brain. We also analyzed publicly-available long-read mRNA databases from human brain tissue and found 11 Kirrel3 isoforms that, similar to mouse, encode transmembrane and secreted forms. In mice and humans, Kirrel3 diversity arises from alternative, independent use of protein-domain coding exons and alternative early translation-stop signals. Intriguingly, the alternatively spliced exons appear at branch points in the chordate phylogenetic tree, including one exon only found in humans and their closest living relatives, the great apes. Together, these results validate a simple pipeline for analyzing isoform diversity in genes with low expression and suggest that Kirrel3 function is fine-tuned by alternative splicing and may play a role in brain evolution.

Y and Mitochondrial Chromosomes in the Heterogeneous Stock Rat Population

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial Chromosomes. We genotyped the Y and mitochondrial chromosomes in heterogeneous stock rats (Rattus norvegicus), which were created in 1984 by intercrossing eight inbred strains and have subsequently been maintained as an outbred population for 100 generations. As the Y and mitochondrial Chromosomes do not recombine, we determined which founder had contributed these chromosomes for each rat, and then performed association analysis for all complex traits (n=12,055; intersection of 12,116 phenotyped and 15,042 haplotyped rats). We found the eight founders had 8 distinct Y and 4 distinct mitochondrial Chromosomes, however only two of each were observed in our modern heterogeneous stock rat population (Generations 81-97). Despite the unusually large sample size, the p-value distribution did not deviate from expectations; there were no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and mitochondrial Chromosomes were strongly associated with expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern heterogeneous stock rats there are no Y and mitochondrial Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and mitochondrial Chromosomes that do not appear in modern heterogeneous stock rats, nor do they address effects that may exist in other rat populations, or in other species.

Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases

Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review.

Transcriptome software results show significant variation among different commercial pipelines

BACKGROUND

We have been documenting the biological responses to low levels of radiation (natural background) and very low level radiation (below background), and thus these studies are testing mild external stimuli to which we would expect relatively mild biological responses. We recently published a transcriptome software comparison study based on RNA-Seqs from a below background radiation treatment of two model organisms, E. coli and C. elegans (Thawng and Smith, BMC Genomics 23:452, 2022). We reported DNAstar-D (Deseq2 in the DNAstar software pipeline) to be the more conservative, realistic tool for differential gene expression compared to other transcriptome software packages (CLC, Partek and DNAstar-E (using edgeR). Here we report two follow-up studies (one with a new model organism, Aedes aegypti and another software package (Azenta) on transcriptome responses from varying dose rates using three different sources of natural radiation.

RESULTS

When E. coli was exposed to varying levels of K40, we again found that the DNAstar-D pipeline yielded a more conservative number of DEGs and a lower fold-difference than the CLC pipeline and DNAstar-E run in parallel. After a 30 read minimum cutoff criterion was applied to the data, the number of significant DEGs ranged from 0 to 81 with DNAstar-D, while the number of significant DEGs ranged from 4 to 117 and 14 to 139 using DNAstar-E and the CLC pipelines, respectively. In terms of the extent of expression, the highest foldchange DEG was observed in DNAstar-E with 19.7-fold followed by 12.5-fold in CLC and 4.3-fold in DNAstar-D. In a recently completed study with Ae. Aegypti and using another software package (Azenta), we analyzed the RNA-Seq response to similar sources of low-level radiation and again found the DNAstar-D pipeline to give the more conservative number and fold-expression of DEGs compared to other softwares. The number of significant DEGs ranged 31-221 in Azenta and 31 to 237 in CLC, 19-252 in DNAstar-E and 0-67 in DNAStar-D. The highest fold-change of DEGs were found in CLC (1,350.9-fold), with DNAstar-E (5.9 -fold) and Azenta (5.5-fold) intermediate, and the lowest levels of expression (4-fold) found in DNAstar-D.

CONCLUSIONS

This study once again highlights the importance of choosing appropriate software for transcriptome analysis. Using three different biological models (bacteria, nematode and mosquito) in four different studies testing very low levels of radiation (Van Voorhies et al., Front Public Health 8:581796, 2020; Thawng and Smith, BMC Genomics 23:452, 2022; current study), the CLC software package resulted in what appears to be an exaggerated gene expression response in terms of numbers of DEGs and extent of expression. Setting a 30-read cutoff diminishes this exaggerated response in most of the software tested. We have further affirmed that DNAstar-Deseq2 gives a more conservative transcriptome expression pattern which appears more suitable for studies expecting subtle gene expression patterns.

Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

TrACES of time: Transcriptomic analyses for the contextualization of evidential stains - Identification of RNA markers for estimating time-of-day of bloodstain deposition

Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future.

Merging short and stranded long reads improves transcript assembly

Long-read RNA sequencing has arisen as a counterpart to short-read sequencing, with the potential to capture full-length isoforms, albeit at the cost of lower depth. Yet this potential is not fully realized due to inherent limitations of current long-read assembly methods and underdeveloped approaches to integrate short-read data. Here, we critically compare the existing methods and develop a new integrative approach to characterize a particularly challenging pool of low-abundance long noncoding RNA (lncRNA) transcripts from short- and long-read sequencing in two distinct cell lines. Our analysis reveals severe limitations in each of the sequencing platforms. For short-read assemblies, coverage declines at transcript termini resulting in ambiguous ends, and uneven low coverage results in segmentation of a single transcript into multiple transcripts. Conversely, long-read sequencing libraries lack depth and strand-of-origin information in cDNA-based methods, culminating in erroneous assembly and quantitation of transcripts. We also discover a cDNA synthesis artifact in long-read datasets that markedly impacts the identity and quantitation of assembled transcripts. Towards remediating these problems, we develop a computational pipeline to "strand" long-read cDNA libraries that rectifies inaccurate mapping and assembly of long-read transcripts. Leveraging the strengths of each platform and our computational stranding, we also present and benchmark a hybrid assembly approach that drastically increases the sensitivity and accuracy of full-length transcript assembly on the correct strand and improves detection of biological features of the transcriptome. When applied to a challenging set of under-annotated and cell-type variable lncRNA, our method resolves the segmentation problem of short-read sequencing and the depth problem of long-read sequencing, resulting in the assembly of coherent transcripts with precise 5' and 3' ends. Our workflow can be applied to existing datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.

A fast and globally optimal solution for RNA-seq quantification

Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.

Single-Cell Transcriptomics for Unlocking Personalized Cancer Immunotherapy: Toward Targeting the Origin of Tumor Development Immunogenicity

Cancer immunotherapy is a promising approach for treating malignancies through the activation of anti-tumor immunity. However, the effectiveness and safety of immunotherapy can be limited by tumor complexity and heterogeneity, caused by the diverse molecular and cellular features of tumors and their microenvironments. Undifferentiated tumor cell niches, which we refer to as the "Origin of Tumor Development" (OTD) cellular population, are believed to be the source of these variations and cellular heterogeneity. From our perspective, the existence of distinct features within the OTD is expected to play a significant role in shaping the unique tumor characteristics observed in each patient. Single-cell transcriptomics is a high-resolution and high-throughput technique that provides insights into the genetic signatures of individual tumor cells, revealing mechanisms of tumor development, progression, and immune evasion. In this review, we explain how single-cell transcriptomics can be used to develop personalized cancer immunotherapy by identifying potential biomarkers and targets specific to each patient, such as immune checkpoint and tumor-infiltrating lymphocyte function, for targeting the OTD. Furthermore, in addition to offering a possible workflow, we discuss the future directions of, and perspectives on, single-cell transcriptomics, such as the development of powerful analytical tools and databases, that will aid in unlocking personalized cancer immunotherapy through the targeting of the patient's cellular OTD.

A multi-platform normalization method for meta-analysis of gene expression data

Transcriptomic profiling is a mainstay of translational cancer research and is often used to identify cancer subtypes, stratify responders vs. non-responders patients, predict survival, and identify potential targets for therapeutic intervention. Analysis of gene expression data gathered by RNA sequencing (RNA-seq) and microarray is generally the first step in identifying and characterizing cancer-associated molecular determinants. The methodological advancements and reduced costs associated with transcriptomic profiling have increased the number of publicly available gene expression profiles for cancer subtypes. Data integration from multiple datasets is routinely done to increase the number of samples, improve statistical power, and provide better insight into the heterogeneity of the biological determinant. However, utilizing raw data from multiple platforms, species, and sources introduces systematic variations due to noise, batch effects, and biases. As such, the integrated data is mathematically adjusted through normalization, which allows direct comparison of expression measures among studies while minimizing technical and systemic variations. This study applied meta-analysis to multiple independent Affymetrix microarray and Illumina RNA-seq datasets available through the Gene Expression Omnibus (GEO) and The Cancer Gene Atlas (TCGA). We have previously identified a tripartite motif containing 37 (TRIM37), a breast cancer oncogene, that drives tumorigenesis and metastasis in triple-negative breast cancer. In this article, we adapted and assessed the validity of Stouffer's z-score normalization method to interrogate TRIM37 expression across different cancer types using multiple large-scale datasets.

Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes

Premise

Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein-coding gene predictions.

Methods

The impact of repeat masking, long-read and short-read inputs, and de novo and genome-guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity.

Results

Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono-exonic/multi-exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA-read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence-based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome-guided transcriptome assemblies, or full-length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post-processing with functional and structural filters is highly recommended.

Discussion

While the annotation of non-model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions.