RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Insight into the complexity of male infertility: a multi-omics review

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

A preliminary study of gene expression changes in Koalas Infected with Koala Retrovirus (KoRV) and identification of potential biomarkers for KoRV pathogenesis

BACKGROUND

Koala retrovirus (KoRV), a major pathogen of koalas, exists in both endogenous (KoRV-A) and exogenous forms (KoRV-A to I and K to M) and causes multiple disease phenotypes, including carcinomas and immunosuppression. However, the direct association between the different KoRV subtypes and carcinogenesis remains unknown. Differentially expressed gene (DEG) analysis of peripheral blood mononuclear cells (PBMCs) of koalas carrying both endogenous (KoRV-A) and exogenous (KoRV-A, B, and C) subtypes was performed using a high-throughput RNA-seq approach. PBMCs were obtained from three healthy koalas: one infected with endogenous (KoRV-A; Group I) and two infected with exogenous (KoRV-B and/or KoRV-C; Group II) subtypes. Additionally, spleen samples (n = 6) from six KoRV-infected deceased koalas (K1- K6) and blood samples (n = 1) from a live koala (K7) were collected and examined to validate the findings.

RESULTS

All koalas were positive for the endogenous KoRV-A subtype, and eight koalas were positive for KoRV-B and/or KoRV-C. Transcription of KoRV gag, pol, and env genes was detected in all koalas. Upregulation of cytokine and immunosuppressive genes was observed in koalas infected with KoRV-B or KoRV-B and -C subtypes, compared to koalas infected with only KoRV-A. We found 550 DEG signatures with significant (absolute p < 0.05, and absolute log2 Fold Change (FC) > 1.5) dysregulation, out of which 77.6% and 22.4% DEGs were upregulated (log2FC > 1.5) and downregulated (log2FC <  - 1.5), and downregulated (log2 FC <  - 1), respectively. We identified 17 unique hub genes (82.3% upregulated and 17.7% down-regulated), with KIF23, CCNB2, POLR3F, and RSL24D1 detected as the potential hub genes modified with KoRV infection. Real-time RT-qPCR was performed on seven koalas to ascertain the expression levels of four potential hub genes, which were subsequently normalized to actin copies. Notably, all seven koalas exhibited distinct expression signatures for the hub genes, especially, KIF23 and CCNB2 show the highest expression in healthy koala PBMC, and POLR3F shows the highest expression in koala with lymphoma (K1).

CONCLUSION

Thus, it can be concluded that multiple KoRV subtypes affect disease progression in koalas and that the predicted hub genes could be promising prognostic biomarkers for pathogenesis.

Transcriptome analysis uncovers the expression of genes associated with growth in the gills and muscles of white shrimp (Litopenaeus vannamei) with different growth rates

Litopenaeus vannamei is a crucial species in aquaculture. The gene expression patterns associated with distinct growth rates are not well understood. To investigate this, we used RNA-seq to study the underlying growth mechanism of L. vannamei with varying growth rates. Individuals of higher growth performance (HG), middle growth performance (MG), and lower growth performance (LG) were examined. A total of 8422 and 4560 differentially expressed genes (DEGs) were identified in gill and muscle samples, respectively. Genes related to growth were significantly up-regulated in HG gills, such as cuticle protein, chitin synthase, pupal cuticle protein, titin myosin G heavy chain, and myosin heavy chain 10. The GO enrichment analysis revealed that the DEGs of HG gills were significantly enriched in "structural constituent of cuticle", "primary metabolic process" and "chitin binding". The growth-related genes were highly expressed in HG muscle, such as myosin heavy chain, myosin heavy chain type A and myosin 3. The GO enrichment analysis revealed that the DEGs of HG muscle were significantly enriched in "myosin filament", "myosin complex" and "myofibril". These findings provide insights into mechanisms underlying the growth performance of superior L. vannamei, and identify candidate genes for genetic improvement programs aimed at enhancing this trait.

Machine Learning Analysis of RNA-Seq Data Identifies Key Gene Signatures and Pathways in Mpox Virus-Induced Gastrointestinal Complications Using Colon Organoid Models

Mpox, caused by the Mpox virus (MPXV), emerged globally in 2022 with the Clade IIb strain, presenting a critical public health challenge. While MPXV is primarily characterized by fever and rash, gastrointestinal (GI) complications, such as diarrhea and proctitis, have also been observed. This study is a reanalysis of GSE219036 without own data and focuses on the impact of MPXV infection on the colon, using human-induced pluripotent stem cell-derived colon organoids as a model. We applied a tailored statistical framework for RNA-seq data, Generalized Linear Models with Quasi-Likelihood F-tests and Relaxed Magnitude-Altitude Scoring (GLMQL-RMAS), to identify differentially expressed genes (DEGs) across MPXV clades: MPXV I (Zr-599 Congo Basin), MPXV IIa (Liberia), and MPXV IIb (2022 MPXV). Through a novel methodology called Cross-RMAS, we ranked genes by integrating statistical significance and biological relevance across all clades. Machine learning analysis using the genes identified by Cross-RMAS, demonstrated 100% accuracy in differentiating between the different MPXV strains and mock samples. Furthermore, our findings reveal that MPXV Clade I induces the most extensive alterations in gene expression, with significant upregulation of stress response genes, such as HSPA6 and FOS, and downregulation of genes involved in cytoskeletal organization and vesicular trafficking, such as PSAP and CFL1. In contrast, Clade IIb shows the least impact on gene expression. Through Gene Ontology (GO) analysis, we identified pathways involved in protein folding, immune response, and epithelial integrity that are disrupted in infected cells, suggesting mechanisms by which MPXV may contribute to GI symptoms.

Effect of Gossypol on Gene Expression in Swine Granulosa Cells

Gossypol (GP), a polyphenolic compound in cottonseed, has notable effects on female reproduction and the respiratory system in pigs. This study aimed to discern the alterations in gene expression within swine granulosa cells (GCs) when treated with two concentrations of GP (6.25 and 12.5 µM) for 72 h, in vitro. The analysis revealed significant changes in the expression of numerous genes in the GP-treated groups. A Gene Ontology analysis highlighted that the differentially expressed genes (DEGs) primarily pertained to processes such as the mitotic cell cycle, chromosome organization, centromeric region, and protein binding. Pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated distinct impacts on various pathways in response to different GP concentrations. Specifically, in the GP6.25 group, pathways related to the cycle oocyte meiosis, progesterone-mediated oocyte maturation, and p53 signaling were prominently affected. Meanwhile, in the GP12.5 group, pathways associated with PI3K-Akt signaling, focal adhesion, HIF-1 signaling, cell cycle, and ECM-receptor interaction showed significant alterations. Notably, genes linked to female reproductive function (CDK1, CCNB1, CPEB1, MMP3), cellular component organization (BIRC5, CYP1A1, TGFB3, COL1A2), and oxidation-reduction processes (PRDX6, MGST1, SOD3) exhibited differential expression in GP-treated groups. These findings offer valuable insights into the changes in GC gene expression in pigs exposed to GP.

Single-cell profiling of cellular changes in the somatic peripheral nerves following nerve injury

Injury to the peripheral nervous system disconnects targets to the central nervous system, disrupts signal transmission, and results in functional disability. Although surgical and therapeutic treatments improve nerve regeneration, it is generally hard to achieve fully functional recovery after severe peripheral nerve injury. A better understanding of pathological changes after peripheral nerve injury helps the development of promising treatments for nerve regeneration. Single-cell analyses of the peripheral nervous system under physiological and injury conditions define the diversity of cells in peripheral nerves and reveal cell-specific injury responses. Herein, we review recent findings on the single-cell transcriptome status in the dorsal root ganglia and peripheral nerves following peripheral nerve injury, identify the cell heterogeneity of peripheral nerves, and delineate changes in injured peripheral nerves, especially molecular changes in neurons, glial cells, and immune cells. Cell-cell interactions in peripheral nerves are also characterized based on ligand-receptor pairs from coordinated gene expressions. The understanding of cellular changes following peripheral nerve injury at a single-cell resolution offers a comprehensive and insightful view for the peripheral nerve repair process, provides an important basis for the exploration of the key regulators of neuronal growth and microenvironment reconstruction, and benefits the development of novel therapeutic drugs for the treatment of peripheral nerve injury.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BACKGROUND

Derivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.

RESULTS

The results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism's specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.

CONCLUSIONS

While we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

BayesAge 2.0: A Maximum Likelihood Algorithm to Predict Transcriptomic Age

Aging is a complex biological process influenced by various factors, including genetic and environmental influences. In this study, we present BayesAge 2.0, an improved version of our maximum likelihood algorithm designed for predicting transcriptomic age (tAge) from RNA-seq data. Building on the original BayesAge framework, which was developed for epigenetic age prediction, BayesAge 2.0 integrates a Poisson distribution to model count-based gene expression data and employs LOWESS smoothing to capture non-linear gene-age relationships. BayesAge 2.0 provides significant improvements over traditional linear models, such as Elastic Net regression. Specifically, it addresses issues of age bias in predictions, with minimal age-associated bias observed in residuals. Its computational efficiency further distinguishes it from traditional models, as reference construction and cross-validation are completed more quickly compared to Elastic Net regression, which requires extensive hyperparameter tuning. Overall, BayesAge 2.0 represents a notable advance in transcriptomic age prediction, offering a robust, accurate, and efficient tool for aging research and biomarker development.

Universal CRISPR-Cas12a and Toehold RNA Cascade Reaction on Paper Substrate for Visual Salmonella Genome Detection

Salmonella, the most prevalent food-borne pathogen, poses significant medical and economic threats. Swift and accurate on-site identification and serotyping of Salmonella is crucial to curb its spread and contamination. Here, a synthetic biology cascade reaction is presented on a paper substrate using CRISPR-Cas12a and recombinase polymerase amplification (RPA), enabling the programming of a standard toehold RNA switch for a genome of choice. This approach employs just one toehold RNA switch design to differentiate between two different Salmonella serotypes, i.e., S. Typhimurium and S. Enteritidis, without the need for reengineering the toehold RNA switch. The sensor exhibits high sensitivity, capable of visually detecting as few as 100 copies of the whole genome from a model Salmonella pathogen on a paper substrate. Furthermore, this robust assay is successfully applied to detect whole genomes in contaminated milk and lettuce samples, demonstrating its potential in real sample analysis. Due to its versatility and practical features, genomes from different organisms can be detected by merely changing a single RNA element in this universal cell-free cascade reaction.

Transcriptomic analysis provides insights into the immune responsive genes in the Procambarus clarkii hepatopancreas challenged with Vibrio parahaemolyticus

Procambarus clarkii is an economically important species in China; however, its high mortality rate due to pathogenic bacteria, particularly Vibrio parahaemolyticus, results in significant economic loss. This study aimed to understand the immune response of crayfish to bacterial infection by comparing and analyzing transcriptome data of hepatopancreatic tissue from P. clarkii challenged with V. parahaemolyticus or treated with PBS. Physiological indices (TP, Alb, ACP, and AKP) were analyzed, and tissue sections were prepared. After assembling and annotating the data, 18,756 unigenes were identified. A comparison of the expression levels of these unigenes between the control and V. parahaemolyticus groups revealed 4037 DEGs, with 2278 unigenes upregulated and 1759 downregulated in the V. parahaemolyticus group. GO (Gene Ontology) enrichment analysis shows that the DGEs are mainly enriched in cellular anatomical activity, bindinga and cellular process, enrichment analysis of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways showed that DGEs were mainly enriched in Base excision repair, Phagosome and Longevity regulating pathway. At the same time, lysosome was also enriched. The phagosome and lysosome pathways play a crucial role in the immune defense of crayfish against V. parahaemolyticus injection that will be highlighted. In addition, the expression levels of six selected immune-related DEGs were measured using qRT-PCR, which validated the results of RNA-seq analysis. This study provides a new perspective on the immune system and defense mechanisms of P. clarkii and a valuable foundation for further investigation of the molecular immune mechanisms of this species.

Genomic reproducibility in the bioinformatics era

In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms

BACKGROUND

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals.

METHODS

We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

RESULTS

Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)].

CONCLUSIONS

Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

IMPACT

HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Developing a simple and rapid method for cell-specific transcriptome analysis through laser microdissection: insights from citrus rind with broader implications

BACKGROUND

With the rapid development of single-cell sequencing technology, histological studies are no longer limited to conventional homogenized tissues. Laser microdissection enables the accurate isolation of specific tissues or cells, and when combined with next-generation sequencing, it can reveal important biological processes at the cellular level. However, traditional laser microdissection techniques have often been complicated and time-consuming, and the quality of the RNA extracted from the collected samples has been inconsistent, limiting follow-up studies. Therefore, an improved, simple, and efficient laser microdissection method is urgently needed.

RESULTS

We omitted the sample fixation and cryoprotectant addition steps. Instead, fresh samples were embedded in Optimal Cutting Temperature medium within 1.5 ml centrifuge tube caps, rapidly frozen with liquid nitrogen, and immediately subjected to cryosectioning. A series of section thicknesses of citrus rind were tested for RNA extraction, which showed that 18 μm thickness yielded the highest quality RNA. By shortening the dehydration time to one minute per ethanol gradient and omitting the tissue clearing step, the resulting efficient dehydration and preserved morphology ensured high-quality RNA extraction. We also propose a set of laser microdissection parameters by adjusting the laser power to optimal values, reducing the aperture size, and lowering the pulse frequency. Both the epidermal and subepidermal cells from the citrus rind were collected, and RNA extraction was completed within nine hours. Using this efficient method, the transcriptome sequencing of the isolated tissues generated high-quality data with average Q30 values and mapping rates exceeding 91%. Moreover, the transcriptome analysis revealed significant differences between the cell layers, further confirming the effectiveness of our isolation approach.

CONCLUSIONS

We developed a simple and rapid laser microdissection method and demonstrated its effectiveness through a study based on citrus rind, from which we generated high-quality transcriptomic data. This fast and efficient method of cell isolation, combined with transcriptome sequencing not only contributes to precise histological studies at the cellular level in citrus but also provides a promising approach for cell-specific transcriptome analysis in a broader range of other plant tissues.

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

Comparative genomic and transcriptomic analyses provide new insight into symbiotic host specificity

Host specificity plays important roles in expanding the host range of rhizobia, while the genetic information responsible for host specificity remains largely unexplored. In this report, the roots of four symbiotic systems with notable different symbiotic phenotypes and the control were studied at four different post-inoculation time points by RNA sequencning (RNA-seq). The differentially expressed genes (DEGs) were divided into "found only in soybean or Lotus," "only expressed in soybean or Lotus," and "expressed in both hosts" according to the comparative genomic analysis. The distributions of enriched function ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways vary significantly in different symbiotic systems. Host specific genes account for the majority of the DEGs involved in response to stimulus, associated with plant-pathogen interaction pathways, and encoding resistance (R) proteins, the symbiotic nitrogen fixation (SNF) proteins and the target proteins in the SNF-related modules. Our findings provided molecular candidates for better understanding the mechanisms of symbiotic host-specificity.

Analysis of RNA and Its Modifications

Ribonucleic acids (RNAs) are key biomolecules responsible for the transmission of genetic information, the synthesis of proteins, and modulation of many biochemical processes. They are also often the key components of viruses. Synthetic RNAs or oligoribonucleotides are becoming more widely used as therapeutics. In many cases, RNAs will be chemically modified, either naturally via enzymatic systems within a cell or intentionally during their synthesis. Analytical methods to detect, sequence, identify, and quantify RNA and its modifications have demands that far exceed requirements found in the DNA realm. Two complementary platforms have demonstrated their value and utility for the characterization of RNA and its modifications: mass spectrometry and next-generation sequencing. This review highlights recent advances in both platforms, examines their relative strengths and weaknesses, and explores some alternative approaches that lie at the horizon.

Precision diagnostics in transplanted organs using microarray-assessed gene expression: concepts and technical methods of the Molecular Microscope® Diagnostic System (MMDx)

There is a major unmet need for improved accuracy and precision in the assessment of transplant rejection and tissue injury. Diagnoses relying on histologic and visual assessments demonstrate significant variation between expert observers (as represented by low kappa values) and have limited ability to assess many biological processes that produce little histologic changes, for example, acute injury. Consensus rules and guidelines for histologic diagnosis are useful but may have errors. Risks of over- or under-treatment can be serious: many therapies for transplant rejection or primary diseases are expensive and carry risk for significant adverse effects. Improved diagnostic methods could alleviate healthcare costs by reducing treatment errors, increase treatment efficacy, and serve as useful endpoints for clinical trials of new agents that can improve outcomes. Molecular diagnostic assessments using microarrays combined with machine learning algorithms for interpretation have shown promise for increasing diagnostic precision via probabilistic assessments, recalibrating standard of care diagnostic methods, clarifying ambiguous cases, and identifying potentially missed cases of rejection. This review describes the development and application of the Molecular Microscope® Diagnostic System (MMDx), and discusses the history and reasoning behind many common methods, statistical practices, and computational decisions employed to ensure that MMDx scores are as accurate and precise as possible. MMDx provides insights on disease processes and highly reproducible results from a comparatively small amount of tissue and constitutes a general approach that is useful in many areas of medicine, including kidney, heart, lung, and liver transplants, with the possibility of extrapolating lessons for understanding native organ disease states.

Identification of pigmentation genes in skin, muscle and tail of a Thai-flag variety of Siamese fighting fish Betta splendens

Pigmentation genes expressed in skin, body muscle and tail of Thai-flag compared with Blue, White and Red varieties of Siamese fighting fish Betta splendens were identified. In total, 22,919 new unigenes were found. Pearson correlation and PCA analysis revealed that expression profiles of genes in muscle, skin and tail across solid color variety were similar. In contrast, those in skin and red tail part of Thai-flag were closely related but they showed different expression profiles with the white tail part. Moreover, 21,347-64,965 SNPs were identified in exonic regions of identified genes. In total, 28,899 genes were differentially expressed between paired comparisons of libraries where 13,907 genes (48.12 %) were upregulated and 14,992 genes (51.88 %) were downregulated. DEGs between paired libraries were 106-5775 genes relative to the compared libraries (56-2982 and 50-2782 for upregulated and downregulated DEGs). Interestingly, 432 pigmentation genes of B. splendens were found. Of these, 297 DEGs showed differential expression between varieties. Many DEGs in melanogenesis (Bsmcr1r, Bsmcr5r, and Bsslc2a15b), tyrosine metabolism (Bstyr, Bstyrp1b and Bsdct), stripe repressor (BsAsip1 and BsAsip2b), pteridine (Bsgch2) and carotenoid (BsBco2) biosynthesis were downregulated in the Thai-flag compared with solid color varieties. Expression of Bsbco1l, Bsfrem2b, Bskcnj13, Bszic2a and Bspah in skin, muscle and tail of Thai-flag, Blue, Red and White varieties was analyzed by qRT-PCR and revealed differential expression between fish varieties and showed anatomical tissue-preferred expression patterns in the same fish variety. The information could be applied to assist genetic-based development of new B. splendens varieties in the future.

Transcriptomics reveal useful resources for examining fruit development and variation in fruit size in Coccinia grandis

Introduction

The Cucurbitaceae family comprises many agronomically important members, that bear nutritious fruits and vegetables of great economic importance. Coccinia grandis, commonly known as Ivy gourd, belongs to this family and is widely consumed as a vegetable. Members of this family are known to display an impressive range of variation in fruit morphology. Although there have been studies on flower development in Ivy gourd, fruit development remains unexplored in this crop.

Methods

In this study, comparative transcriptomics of two Ivy gourd cultivars namely "Arka Neelachal Kunkhi" (larger fruit size) and "Arka Neelachal Sabuja" (smaller fruit size) differing in their average fruit size was performed. A de novo transcriptome assembly for Ivy gourd was developed by collecting fruits at different stages of development (5, 10, 15, and 20 days after anthesis i.e. DAA) from these two varieties. The transcriptome was analyzed to identify differentially expressed genes, transcription factors, and molecular markers.

Results

The transcriptome of Ivy gourd consisted of 155205 unigenes having an average contig size of 1472bp. Unigenes were annotated on publicly available databases to categorize them into different biological functions. Out of these, 7635 unigenes were classified into 38 transcription factor (TF) families, of which Trihelix TFs were most abundant. A total of 11,165 unigenes were found to be differentially expressed in both the varieties and the in silico expression results were validated through real-time PCR. Also, 98768 simple sequence repeats (SSRs) were identified in the transcriptome of Ivy gourd.

Discussion

This study has identified a number of genes, including transcription factors, that could play a crucial role in the determination of fruit shape and size in Ivy gourd. The presence of polymorphic SSRs indicated a possibility for marker-assisted selection for crop breeding in Ivy gourd. The information obtained can help select candidate genes that may be implicated in regulating fruit development and size in other fruit crops.

RNA-seq combined network pharmacology reveals that Fu-Gan-Wan (FGW) inhibits liver fibrosis via NF-κB/CCL2/CCR2 and lipid peroxidation via Nrf2/HMOX1 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Liver fibrosis is a serious complication of liver disease characterized by excessive collagen deposition, without effective therapeutic agents in the clinic. Fu-Gan-Wan (FGW) is an empirical formula used for the clinical treatment of hepatitis and cirrhosis. It has been shown to reverse experimental liver fibrosis. However, its corresponding mechanisms remain unclear.

AIM OF THE REVIEW

This study aimed to elucidate the key pathways and target genes of FGW in attenuating liver fibrosis.

MATERIALS AND METHODS

The therapeutic effects of different doses of FGW on liver fibrosis were investigated using a 2 mL/kg 15% CCl4-induced mouse model. Then, RNA-seq combined with network pharmacology was used to analyze the key biological processes and signaling pathways underlying the anti-liver fibrosis exertion of FGW. These findings were validated in a TGF-β1-induced model of activation and proliferation of mouse hepatic stellate cell line JS-1. Finally, the key signaling pathways and molecular targets were validated using animal tissues, and the effect of FGW on tissue lipid peroxidation was additionally observed.

RESULTS

We found that 19.5 g/kg FGW significantly down-regulated CCl4-induced elevation of hepatic ALT and AST, decreased collagen deposition, and inhibited the expression of pro-fibrotic factors α-SMA, COL1α1, CTGF, TIMP-1, as well as pro-inflammatory factor TGF-β1. Additionally, FGW at doses of 62.5, 125, and 250 μg/mL dose-dependently blocked JS-1 proliferation, migration, and activation. Furthermore, RNA-seq identified the NF-κB signaling pathway as a key target molecular pathway for FGW against liver fibrosis, and network pharmacology combined with RNA-seq focused on 11 key genes. Significant changes were identified in CCL2 and HMOX1 by tissue RT-PCR, Western blot, and immunohistochemistry. We further demonstrated that FGW significantly attenuated CCl4-induced increases in p-p65, CCL2, CCR2, and HMOX1, while significantly elevating Nrf2. Finally, FGW significantly suppressed the accumulation of lipid peroxidation products MDA and 4-HNE and reconfigured the oxidation-reduction balance, including promoting the increase of antioxidants GPx, GSH, and SOD, and the decrease of peroxidation products ROS and GSSG.

CONCLUSIONS

This study demonstrated that FGW exhibits potential in mitigating CCl4-induced hepatic fibrosis, lipid peroxidation, and iron metabolism disorders in mice. This effect may be mediated through the NF-κB/CCL2/CCR2 and Nrf2/HMOX1 pathways.

Transcriptome Complexity Disentangled: A Regulatory Molecules Approach

Transcription factors (TFs) and microRNAs (miRNAs) are fundamental regulators of gene expression, cell state, and biological processes. This study investigated whether a small subset of TFs and miRNAs could accurately predict genome-wide gene expression. We analyzed 8895 samples across 31 cancer types from The Cancer Genome Atlas and identified 28 miRNA and 28 TF clusters using unsupervised learning. Medoids of these clusters could differentiate tissues of origin with 92.8% accuracy, demonstrating their biological relevance. We developed Tissue-Agnostic and Tissue-Aware models to predict 20,000 gene expressions using the 56 selected medoid miRNAs and TFs. The Tissue-Aware model attained an R 2 of 0.70 by incorporating tissue-specific information. Despite measuring only 1/400th of the transcriptome, the prediction accuracy was comparable to that achieved by the 1000 landmark genes. This suggests the transcriptome has an intrinsically low-dimensional structure that can be captured by a few regulatory molecules. Our approach could enable cheaper transcriptome assays and analysis of low-quality samples. It also provides insights into genes that are heavily regulated by miRNAs/TFs versus alternative mechanisms. However, model transportability was impacted by dataset discrepancies, especially in miRNA distribution. Overall, this study demonstrates the potential of a biology-guided approach for robust transcriptome representation.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

Characterization of a pleiotropic regulator MtrA in Streptomyces avermitilis controlling avermectin production and morphological differentiation

BACKGROUND

The macrolide antibiotic avermectin, a natural product derived from Streptomyces avermitilis, finds extensive applications in agriculture, animal husbandry and medicine. The mtrA (sav_5063) gene functions as a transcriptional regulator belonging to the OmpR family. As a pleiotropic regulator, mtrA not only influences the growth, development, and morphological differentiation of strains but also modulates genes associated with primary metabolism. However, the regulatory role of MtrA in avermectin biosynthesis remains to be elucidated.

RESULTS

In this study, we demonstrated that MtrA, a novel OmpR-family transcriptional regulator in S. avermitilis, exerts global regulator effects by negatively regulating avermectin biosynthesis and cell growth while positively controlling morphological differentiation. The deletion of the mtrA gene resulted in an increase in avermectin production, accompanied by a reduction in biomass and a delay in the formation of aerial hyphae and spores. The Electrophoretic Mobility Shift Assay (EMSA) revealed that MtrA exhibited binding affinity towards the upstream region of aveR, the intergenic region between aveA1 and aveA2 genes, as well as the upstream region of aveBVIII in vitro. These findings suggest that MtrA exerts a negative regulatory effect on avermectin biosynthesis by modulating the expression of avermectin biosynthesis cluster genes. Transcriptome sequencing and fluorescence quantitative PCR analysis showed that mtrA deletion increased the transcript levels of the cluster genes aveR, aveA1, aveA2, aveC, aveE, aveA4 and orf-1, which explains the observed increase in avermectin production in the knockout strain. Furthermore, our findings demonstrate that MtrA positively regulates the cell division and differentiation genes bldM and ssgC, while exerting a negative regulatory effect on bldD, thereby modulating the primary metabolic processes associated with cell division, differentiation and growth in S. avermitilis, consequently impacting avermectin biosynthesis.

CONCLUSIONS

In this study, we investigated the negative regulatory effect of the global regulator MtrA on avermectin biosynthesis and its effects on morphological differentiation and cell growth, and elucidated its transcriptional regulatory mechanism. Our findings indicate that MtrA plays crucial roles not only in the biosynthesis of avermectin but also in coordinating intricate physiological processes in S. avermitilis. These findings provide insights into the synthesis of avermectin and shed light on the primary and secondary metabolism of S. avermitilis mediated by OmpR-family regulators.

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.

Discovery of an independent poor-prognosis subtype associated with tertiary lymphoid structures in breast cancer

Introduction

Tertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited.

Methods

In our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts.

Results

A notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype.

Discussion

Our study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.

Rickettsia transmission from whitefly to plants benefits herbivore insects but is detrimental to fungal and viral pathogens

Bacterial endosymbionts play important roles in the life histories of herbivorous insects by impacting their development, survival, reproduction, and stress tolerance. How endosymbionts may affect the interactions between plants and insect herbivores is still largely unclear. Here, we show that endosymbiotic Rickettsia belli can provide mutual benefits also outside of their hosts when the sap-sucking whitefly Bemisia tabaci transmits them to plants. This transmission facilitates the spread of Rickettsia but is shown to also enhance the performance of the whitefly and co-infesting caterpillars. In contrast, Rickettsia infection enhanced plant resistance to several pathogens. Inside the plants, Rickettsia triggers the expression of salicylic acid-related genes and the two pathogen-resistance genes TGA 2.1 and VRP, whereas they repressed genes of the jasmonic acid pathway. Performance experiments using wild type and mutant tomato plants confirmed that Rickettsia enhances the plants' suitability for insect herbivores but makes them more resistant to fungal and viral pathogens. Our results imply that endosymbiotic Rickettsia of phloem-feeding insects affects plant defenses in a manner that facilitates their spread and transmission. This novel insight into how insects can exploit endosymbionts to manipulate plant defenses also opens possibilities to interfere with their ability to do so as a crop protection strategy.

IMPORTANCE

Most insects are associated with symbiotic bacteria in nature. These symbionts play important roles in the life histories of herbivorous insects by impacting their development, survival, reproduction as well as stress tolerance. Rickettsia is one important symbiont to the agricultural pest whitefly Bemisia tabaci. Here, for the first time, we revealed that the persistence of Rickettsia symbionts in tomato leaves significantly changed the defense pattern of tomato plants. These changes benefit both sap-feeding and leaf-chewing herbivore insects, such as increasing the fecundity of whitefly adults, enhancing the growth and development of the noctuid Spodoptera litura, but reducing the pathogenicity of Verticillium fungi and TYLCV virus to tomato plants distinctively. Our study unraveled a new horizon for the multiple interaction theories among plant-insect-bacterial symbionts.

Unlocking melanoma Suppression: Insights from Plasma-Induced potent miRNAs through PI3K-AKT-ZEB1 axis

INTRODUCTION

Melanoma is a rare but highly malignant form of skin cancer. Although recent targeted and immune-based therapies have improved survival rates by 10-15%, effective melanoma treatment remains challenging. Therefore, novel, combinatorial therapy options such as non-thermal atmospheric pressure plasma (NTP) are being investigated to inhibit and prevent chemoresistance. Although several studies have reported the apoptotic and inhibitory effects of reactive oxygen species produced by NTP in the context of melanoma, the intricate molecular network that determines the role of microRNAs (miRNAs) in regulating NTP-mediated cell death remains unexplored.

OBJECTIVES

This study aimed to explore the molecular mechanisms and miRNA networks regulated by NTP-induced oxidative stress in melanoma cells.

METHODS

Melanoma cells were exposed to NTP and then subjected to high-throughput miRNA sequencing to identify NTP-regulated miRNAs. Various biological processes and underlying molecular mechanisms were assessed using Alamar Blue, propidium iodide (PI) uptake, cell migration, and clonogenic assays followed by qRT-PCR and flow cytometry.

RESULTS

NTP exposure for 3 min was sufficient to modulate the expression of several miRNAs, inhibiting cell growth. Persistent NTP exposure for 5 min increased differential miRNA regulation, PI uptake, and the expression of genes involved in cell cycle arrest and death. qPCR confirmed that miR-200b-3p and miR-215-5p upregulation contributed to decreased cell viability and migration. Mechanistically, inhibiting miR-200b-3p and miR-215-5p in SK-2 cells enhancedZEB1, PI3K, and AKT expression, increasing cell proliferation and viability.

CONCLUSION

This study demonstrated that NTP exposure for 5 min results in the differential regulation of miRNAs related to the PI3K-AKT-ZEB1 axis and cell cycle dysregulation to facilitate melanoma suppression.

Characterization of non-invasive oropharyngeal samples and nucleic acid isolation for molecular diagnostics

Molecular diagnostics is an increasingly important clinical tool, especially in routine sampling. We evaluated two non-invasive methods (oral swabs and mouthwashes) for sampling nucleic acids from the oral/pharyngeal area. We created a workflow from sample collection (n = 59) to RT-qPCR based analysis. The samples were further characterized in terms of their cellular composition as well as the purity, degradation and microbial content of the derived DNA/RNA. We determined the optimal housekeeping genes applicable for these types of samples. The cellular composition indicated that mouthwashes contained more immune cells and bacteria. Even though the protocol was not specifically optimized to extract bacterial RNA it was possible to derive microbial RNA, from both sampling methods. Optimizing the protocol allowed us to generate stable quantities of DNA/RNA. DNA/RNA purity parameters were not significantly different between the two sampling methods. Even though integrity analysis demonstrated a high level of degradation of RNA, corresponding parameters confirmed their sequencing potential. RT-qPCR analysis determined TATA-Box Binding Protein as the most favorable housekeeping gene. In summary, we have developed a robust method suitable for multiple downstream diagnostic techniques. This protocol can be used as a foundation for further research endeavors focusing on developing molecular diagnostics for the oropharyngeal cavity.

Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR

Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

CCN3, POSTN, and PTHLH as potential key regulators of genomic integrity and cellular survival in iPSCs

Reprogramming human somatic cells into a pluripotent state, achieved through the activation of well-defined transcriptional factors known as OSKM factors, offers significant potential for regenerative medicine. While OSKM factors are a robust reprogramming method, efficiency remains a challenge, with only a fraction of cells undergoing successful reprogramming. To address this, we explored genes related to genomic integrity and cellular survival, focusing on iPSCs (A53T-PD1) that displayed enhanced colony stability. Our investigation had revealed three candidate genes CCN3, POSTN, and PTHLH that exhibited differential expression levels and potential roles in iPSC stability. Subsequent analyses identified various protein interactions for these candidate genes. POSTN, significantly upregulated in A53T-PD1 iPSC line, showed interactions with extracellular matrix components and potential involvement in Wnt signaling. CCN3, also highly upregulated, demonstrated interactions with TP53, CDKN1A, and factors related to apoptosis and proliferation. PTHLH, while upregulated, exhibited interactions with CDK2 and genes involved in cell cycle regulation. RT-qPCR validation confirmed elevated CCN3 and PTHLH expression in A53T-PD1 iPSCs, aligning with RNA-seq findings. These genes' roles in preserving pluripotency and cellular stability require further exploration. In conclusion, we identified CCN3, POSTN, and PTHLH as potential contributors to genomic integrity and pluripotency maintenance in iPSCs. Their roles in DNA repair, apoptosis evasion, and signaling pathways could offer valuable insights for enhancing reprogramming efficiency and sustaining pluripotency. Further investigations are essential to unravel the mechanisms underlying their actions.

JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics

Combinatorial patterns of epigenetic features reflect transcriptional states and functions of genomic regions. While many epigenetic features have correlated relationships, most existing data normalization approaches analyze each feature independently. Such strategies may distort relationships between functionally correlated epigenetic features and hinder biological interpretation. We present a novel approach named JMnorm that simultaneously normalizes multiple epigenetic features across cell types, species, and experimental conditions by leveraging information from partially correlated epigenetic features. We demonstrate that JMnorm-normalized data can better preserve cross-epigenetic-feature correlations across different cell types and enhance consistency between biological replicates than data normalized by other methods. Additionally, we show that JMnorm-normalized data can consistently improve the performance of various downstream analyses, which include candidate cis-regulatory element clustering, cross-cell-type gene expression prediction, detection of transcription factor binding and changes upon perturbations. These findings suggest that JMnorm effectively minimizes technical noise while preserving true biologically significant relationships between epigenetic datasets. We anticipate that JMnorm will enhance integrative and comparative epigenomics.

Ambiguous genes due to aligners and their impact on RNA-seq data analysis

The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.

Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms

Introduction

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals.

Methods

We included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

Results

Cluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]).

Conclusions

A single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

Approaches and challenges in genome-wide circular RNA identification and quantification

Numerous circular RNAs (circRNAs) produced from back-splicing of exon(s) have been recently revealed on a genome-wide scale across species. Although generally expressed at a low level, some relatively abundant circRNAs can play regulatory roles in various biological processes, prompting continuous profiling of circRNA in broader conditions. Over the past decade, distinct strategies have been applied in both transcriptome enrichment and bioinformatic tools for detecting and quantifying circRNAs. Understanding the scope and limitations of these strategies is crucial for the subsequent annotation and characterization of circRNAs, especially those with functional potential. Here, we provide an overview of different transcriptome enrichment, deep sequencing and computational approaches for genome-wide circRNA identification, and discuss strategies for accurate quantification and characterization of circRNA.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

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 .

O. Improved meta-analysis pipeline ameliorates distinctive gene regulators of diabetic vasculopathy in human endothelial cell (hECs) RNA-Seq data

Enormous gene expression data generated through next-generation sequencing (NGS) technologies are accessible to the scientific community via public repositories. The data harboured in these repositories are foundational for data integrative studies enabling large-scale data analysis whose potential is yet to be fully realized. Prudent integration of individual gene expression data i.e. RNA-Seq datasets is remarkably challenging as it encompasses an assortment and series of data analysis steps that requires to be accomplished before arriving at meaningful insights on biological interrogations. These insights are at all times latent within the data and are not usually revealed from the modest individual data analysis owing to the limited number of biological samples in individual studies. Nevertheless, a sensibly designed meta-analysis of select individual studies would not only maximize the sample size of the analysis but also significantly improves the statistical power of analysis thereby revealing the latent insights. In the present study, a custom-built meta-analysis pipeline is presented for the integration of multiple datasets from different origins. As a case study, we have tested with the integration of two relevant datasets pertaining to diabetic vasculopathy retrieved from the open source domain. We report the meta-analysis ameliorated distinctive and latent gene regulators of diabetic vasculopathy and uncovered a total of 975 i.e. 930 up-regulated and 45 down-regulated gene signatures. Further investigation revealed a subset of 14 DEGs including CTLA4, CALR, G0S2, CALCR, OMA1, and DNAJC3 as latent i.e. novel as these signatures have not been reported earlier. Moreover, downstream investigations including enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs revealed durable disease association signifying their potential as novel transcriptomic biomarkers of diabetic vasculopathy. While the meta-analysis of individual whole transcriptomic datasets for diabetic vasculopathy is exclusive to our comprehension, however, the novel meta-analysis pipeline could very well be extended to study the mechanistic links of DEGs in other disease conditions.

Protocol for transcriptome assembly by the TransBorrow algorithm

High-throughput RNA-seq enables comprehensive analysis of the transcriptome for various purposes. However, this technology generally generates massive amounts of sequencing reads with a shorter read length. Consequently, fast, accurate, and flexible tools are needed for assembling raw RNA-seq data into full-length transcripts and quantifying their expression levels. In this protocol, we report TransBorrow, a novel transcriptome assembly software specifically designed for short RNA-seq reads. TransBorrow is employed in conjunction with a splice-aware alignment tool (e.g. Hisat2 and Star) and some other transcriptome assembly tools (e.g. StringTie, Cufflinks, and Scallop). The protocol encompasses all necessary steps, starting from downloading and processing raw sequencing data to assembling the full-length transcripts and quantifying their expressed abundances. The execution time of the protocol may vary depending on the sizes of processed datasets and computational platforms.

Effect of Mowing on Wheat Growth at Seeding Stage

Winter wheat is used as forage at the tillering stage in many countries; however, the regrowth pattern of wheat after mowing remains unclear. In this study, the growth patterns of wheat were revealed through cytological and physiological assessments as well as transcriptome sequencing. The results of agronomic traits and paraffin sections showed that the shoot growth rate increased, but root growth was inhibited after mowing. The submicroscopic structure revealed a decrease in heterochromatin in the tillering node cell and a change in mitochondrial shape in the tillering node and secondary root. Analysis of the transcriptome showed the number of differentially expressed genes (DEGs) involved in biological processes, cellular components, and molecular functions; 2492 upregulated DEGs and 1534 downregulated DEGs were identified. The results of the experimental study showed that mowing induced expression of DEGs in the phenylpropanoid biosynthesis pathway and increased the activity of PAL and 4CL. The upregulated DEGs in the starch and sucrose metabolism pathways and related enzyme activity alterations indicated that the sugar degradation rate increased. The DEGs in the nitrogen metabolism pathway biosynthesis of the amino acids, phenylpropanoid biosynthesis metabolism, and in the TCA pathway also changed after mowing. Hormone content and related gene expression was also altered in the tillering and secondary roots after mowing. When jasmonic acid and ethylene were used to treat the wheat after mowing, the regeneration rate increased, whereas abscisic acid inhibited regrowth. This study revealed the wheat growth patterns after mowing, which could lead to a better understanding of the development of dual-purpose wheat.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Characterization of 3D organotypic epithelial tissues reveals tonsil-specific differences in tonic interferon signaling

Three-dimensional (3D) culturing techniques can recapitulate the stratified nature of multicellular epithelial tissues. Organotypic 3D epithelial tissue culture methods have several applications, including the study of tissue development and function, drug discovery and toxicity testing, host-pathogen interactions, and the development of tissue-engineered constructs for use in regenerative medicine. We grew 3D organotypic epithelial tissues from foreskin, cervix, and tonsil-derived primary cells and characterized the transcriptome of these in vitro tissue equivalents. Using the same 3D culturing method, all three tissues yielded stratified squamous epithelium, validated histologically using basal and superficial epithelial cell markers. The goal of this study was to use RNA-seq to compare gene expression patterns in these three types of epithelial tissues to gain a better understanding of the molecular mechanisms underlying their function and identify potential therapeutic targets for various diseases. Functional profiling by over-representation and gene set enrichment analysis revealed tissue-specific differences: i.e., cutaneous homeostasis and lipid metabolism in foreskin, extracellular matrix remodeling in cervix, and baseline innate immune differences in tonsil. Specifically, tonsillar epithelia may play an active role in shaping the immune microenvironment of the tonsil balancing inflammation and immune responses in the face of constant exposure to microbial insults. Overall, these data serve as a resource, with gene sets made available for the research community to explore, and as a foundation for understanding the epithelial heterogeneity and how it may impact their in vitro use. An online resource is available to investigate these data (https://viz.datascience.arizona.edu/3DEpiEx/).

Transcriptome analysis of immune-related genes of Asian corn borer (Ostrinia furnacalis [Guenée]) after oral bacterial infection

The Asian corn borer (Ostrinia furnacalis) is an important agricultural pest causing serious damage to economic crops, such as corn and sorghum. The gut is the first line of defense against pathogens that enter through the mouth. Staphylococcus aureus was used to infect the O. furnacalis midgut to understand the midgut immune mechanism against exogenous pathogens to provide new ideas and methods for the prevention and control of O. furnacalis. A sequencing platform was used for genome assembly and gene expression. The unigene sequences were annotated and functionally classified by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Significant differences were found in the induced expression profiles before and after infection. Some differentially expressed genes have important relations with lipid metabolism and immune mechanism, suggesting that they play an important role in the innate immune response of O. furnacalis. Furthermore, quantitative real-time polymerase chain reaction assay was used to identify the key genes involved in the signaling pathway, and the expression patterns of these key genes were confirmed. The results could help study the innate immune system of lepidopteran insects and provide theoretical support for the control of related pests and the protection of beneficial insects.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Simultaneous profiling of chromatin architecture and transcription in single cells

The three-dimensional structure of chromatin plays a crucial role in development and disease, both of which are associated with transcriptional changes. However, given the heterogeneity in single-cell chromatin architecture and transcription, the regulatory relationship between the three-dimensional chromatin structure and gene expression is difficult to explain based on bulk cell populations. Here we develop a single-cell, multimodal, omics method allowing the simultaneous detection of chromatin architecture and messenger RNA expression by sequencing (single-cell transcriptome sequencing (scCARE-seq)). Applying scCARE-seq to examine chromatin architecture and transcription from 2i to serum single mouse embryonic stem cells, we observe improved separation of cell clusters compared with single-cell chromatin conformation capture. In addition, after defining the cell-cycle phase of each cell through chromatin architecture extracted by scCARE-seq, we find that periodic changes in chromatin architecture occur in parallel with transcription during the cell cycle. These findings highlight the potential of scCARE-seq to facilitate comprehensive analyses that may boost our understanding of chromatin architecture and transcription in the same single cell.

Exploring Genetic Markers: Mitochondrial DNA and Genomic Screening for Biodiversity and Production Traits in Donkeys

Donkeys (Equus asinus) play a pivotal role as essential livestock in arid and semi-arid regions, serving various purposes such as transportation, agriculture, and milk production. Despite their significance, donkey breeding has often been overlooked in comparison to other livestock species, resulting in limited genetic improvement programs. Preserving donkey genetic resources within each country necessitates the establishment of breed conservation programs, focusing on managing genetic diversity among populations. In recent years, significant strides have been made in sequencing and analyzing complete mitochondrial DNA (mtDNA) molecules in donkeys. Notably, numerous studies have honed in on the mitochondrial D-loop region, renowned for its remarkable variability and higher substitution rate within the mtDNA genome, rendering it an effective genetic marker for assessing genetic diversity in donkeys. Furthermore, genetic markers at the RNA/DNA level have emerged as indispensable tools for enhancing production and reproduction traits in donkeys. Traditional animal breeding approaches based solely on phenotypic traits, such as milk yields, weight, and height, are influenced by both genetic and environmental factors. To overcome these challenges, genetic markers, such as polymorphisms, InDel, or entire gene sequences associated with desirable traits in animals, have achieved widespread usage in animal breeding practices. These markers have proven increasingly valuable for facilitating the selection of productive and reproductive traits in donkeys. This comprehensive review examines the cutting-edge research on mitochondrial DNA as a tool for assessing donkey biodiversity. Additionally, it highlights the role of genetic markers at the DNA/RNA level, enabling the informed selection of optimal production and reproductive traits in donkeys, thereby driving advancements in donkey genetic conservation and breeding programs.

Toxicological and pharmacokinetic properties of sucralose-6-acetate and its parent sucralose: in vitro screening assays

The purpose of this study was to determine the toxicological and pharmacokinetic properties of sucralose-6-acetate, a structural analog of the artificial sweetener sucralose. Sucralose-6-acetate is an intermediate and impurity in the manufacture of sucralose, and recent commercial sucralose samples were found to contain up to 0.67% sucralose-6-acetate. Studies in a rodent model found that sucralose-6-acetate is also present in fecal samples with levels up to 10% relative to sucralose which suggest that sucralose is also acetylated in the intestines. A MultiFlow® assay, a high-throughput genotoxicity screening tool, and a micronucleus (MN) test that detects cytogenetic damage both indicated that sucralose-6-acetate is genotoxic. The mechanism of action was classified as clastogenic (produces DNA strand breaks) using the MultiFlow® assay. The amount of sucralose-6-acetate in a single daily sucralose-sweetened drink might far exceed the threshold of toxicological concern for genotoxicity (TTCgenotox) of 0.15 µg/person/day. The RepliGut® System was employed to expose human intestinal epithelium to sucralose-6-acetate and sucralose, and an RNA-seq analysis was performed to determine gene expression induced by these exposures. Sucralose-6-acetate significantly increased the expression of genes associated with inflammation, oxidative stress, and cancer with greatest expression for the metallothionein 1 G gene (MT1G). Measurements of transepithelial electrical resistance (TEER) and permeability in human transverse colon epithelium indicated that sucralose-6-acetate and sucralose both impaired intestinal barrier integrity. Sucralose-6-acetate also inhibited two members of the cytochrome P450 family (CYP1A2 and CYP2C19). Overall, the toxicological and pharmacokinetic findings for sucralose-6-acetate raise significant health concerns regarding the safety and regulatory status of sucralose itself.

Transcriptome analysis provides insights into the stress response in cultivated peanut (Arachis hypogaea L.) subjected to drought-stress

BACKGROUND

Peanut (Arachis hypogaea L.) is one of the valuable oilseed crops grown in drought-prone areas worldwide. Drought severely limits peanut production and productivity significantly.

METHOD AND RESULTS

In order to decipher the drought tolerance mechanism in peanut under drought stress, RNA sequencing was performed in TAG - 24 (drought tolerant genotype) and JL-24 (drought susceptible genotype). Approximately 51 million raw reads were generated from four different libraries of two genotypes subjected to drought stress exerted by 20% PEG 6000 stress and control conditions, of which ~ 41 million (80.87%) filtered reads were mapped to the Arachis hypogaea L. reference genome. The transcriptome analysis detected 1,629 differentially expressed genes (DEGs), 186 genes encoding transcription factors (TFs) and 30,199 SSR among the identified DEGs. Among the differentially expressed TF encoding genes, the highest number of genes were WRKY followed by bZIP, C2H2, and MYB during drought stress. The comparative analysis between the two genotypes revealed that TAG-24 exhibits activation of certain key genes and transcriptional factors that are involved in essential biological processes. Specifically, TAG-24 showed activation of genes involved in the plant hormone signaling pathway such as PYL9, Auxin response receptor gene, and ABA. Additionally, genes related to water deprivation such as LEA protein and those involved in combating oxidative damage such as Glutathione reductase were also found to be activated in TAG-24.

CONCLUSION

This genome-wide transcription map, therefore, provides a valuable tool for future transcript profiling under drought stress and enriches the genetic resources available for this important oilseed crop.