Bias in RNA-seq Library Preparation: Current Challenges and Solutions

Sulfur Dioxide Alleviates Organ Damage and Inflammatory Response in Cecal Ligation and Puncture-Induced Sepsis Rat

The study aimed to elucidate the mechanisms by which sulfur dioxide (SO2) alleviates organ damage during sepsis using RNA-Seq technology. A cecal ligation and puncture (CLP) sepsis model was established in rats, and the effects of SO2 treatment on organ damage were assessed through histopathological examinations. RNA-Seq was performed to analyze differentially expressed genes (DEGs), and subsequent functional annotations and enrichment analyses were conducted. The CLP model successfully induced sepsis symptoms in rats. Histopathological evaluation revealed that SO2 treatment considerably reduced tissue damage across the heart, kidney, liver, and lungs. RNA-Seq identified 950 DEGs between treated and untreated groups, with significant enrichment in genes associated with ribosomal and translational activities, amino acid metabolism, and PI3K-Akt signaling. Furthermore, gene set enrichment analysis (GSEA) showcased enrichments in pathways related to transcriptional regulation, cellular migration, proliferation, and calcium-ion binding. In conclusion, SO2 effectively mitigates multi-organ damage induced by CLP sepsis, potentially through modulating gene expression patterns related to critical biological processes and signaling pathways. These findings highlight the therapeutic promise of SO2 in managing sepsis-induced organ damage.

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC-MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

The Impact of Blood Sample Processing on Ribonucleic Acid (RNA) Sequencing

In gene quantification and expression analysis, issues with sample selection and processing can be serious, as they can easily introduce irrelevant variables and lead to ambiguous results. This study aims to investigate the extent and mechanism of the impact of sample selection and processing on ribonucleic acid (RNA) sequencing. RNA from PBMCs and blood samples was investigated in this study. The integrity of this RNA was measured under different storage times. All the samples underwent high-throughput sequencing for comprehensive evaluation. The differentially expressed genes and their potential functions were analyzed after the samples were placed at room temperature for 0h, 4h and 8h, and different feature changes in these samples were also revealed. The sequencing results showed that the differences in gene expression were higher with an increased storage time, while the total number of genes detected did not change significantly. There were five genes showing gradient patterns over different storage times, all of which were protein-coding genes that had not been mentioned in previous studies. The effect of different storage times on seemingly the same samples was analyzed in this present study. This research, therefore, provides a theoretical basis for the long-term consideration of whether sample processing should be adequately addressed.

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

The Application of Long-Read Sequencing to Cancer

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response

BACKGROUND

The HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize "non-self" antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer.

METHODS

RNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles.

RESULTS

The genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls.

CONCLUSIONS

The integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient's tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.

RNA Analysis of Circulating Leukocytes in Patients with Alzheimer's Disease

Background

One of the key symptoms of Alzheimer's disease (AD) is the impairment of short-term memory. Hippocampal neurogenesis is essential for short-term memory and is known to decrease in patients with AD. Impaired short-term memory and impaired neurogenesis are observed in aged mice alongside changes in RNA expression of gap junction and metabolism-related genes in circulating leukocytes. Moreover, after penetrating the blood-brain barrier via the SDF1/CXCR4 axis, circulating leukocytes directly interact with hippocampal neuronal stem cells via gap junctions.

Objective

Evaluation of RNA expression profiles in circulating leukocytes in patients with AD.

Methods

Patients with AD (MMSE≧23, n = 10) and age-matched controls (MMSE≧28, n = 10) were enrolled into this study. RNA expression profiles of gap junction and metabolism-related genes in circulating leukocytes were compared between the groups (jRCT: 1050210166).

Results

The ratios of gap junction and metabolism-related genes were significantly different between patients with AD and age-matched controls. However, due to large inter-individual variations, there were no statistically significant differences in the level of single RNA expression between these groups.

Conclusions

Our findings suggest a potential connection between the presence of circulating leukocytes and the process of hippocampal neurogenesis in individuals with AD. Analyzing RNA in circulating leukocytes holds promise as a means to offer novel insights into the pathology of AD, distinct from conventional markers.

Microarray Analysis for Transcriptomic Profiling of Myocardium in Patients with Fatal Myocardial Infarction

Transcriptomic evidence from human myocardium in myocardial infarction (MI) is still not sufficient. Thus, there is a need for studies on human cardiac samples in relation to the clinical data of patients. The purpose of our pilot study was to investigate the transcriptomic profile of myocardium in the infarct zone, in comparison to the remote myocardium, in patients with fatal MI, via microarray analysis. This study included four patients with fatal MI type 1. We selected histologically verified samples from within the infarct area (n = 4) and remote myocardium (n = 4). The whole transcriptome was evaluated using microarray analysis. Differentially expressed genes (DEGs) clustered in the infarct area and in the remote myocardium allowed their differentiation. We identified a total of 1785 DEGs (8.32%) in the infarct area, including 1692 up-regulated (94.79%) and 93 down-regulated (5.21%) genes. The top 10 up-regulated genes were TRAIL, SUCLA2, NAE1, PDCL3, OSBPL5, FCGR2C, SELE, CEP63, ST3GAL3 and C4orf3. In the infarct area, we found up-regulation of seventeen apoptosis-related genes, eleven necroptosis-related, and six necrosis-related genes. Transcriptome profiling of the myocardium in patients with MI remains a relevant area of research for the formation of new scientific hypotheses and a potential way to increase the translational significance of studies into myocardial infarction.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Background

The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Methodology

In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response.

Results and discussion

The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers.

Conclusions

Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Unaltered hepatic wound healing response in male rats with ancestral liver injury

The possibility that ancestral environmental exposure could result in adaptive inherited effects in mammals has been long debated. Numerous rodent models of transgenerational responses to various environmental factors have been published but due to technical, operational and resource burden, most still await independent confirmation. A previous study reported multigenerational epigenetic adaptation of the hepatic wound healing response upon exposure to the hepatotoxicant carbon tetrachloride (CCl4) in male rats. Here, we comprehensively investigate the transgenerational effects by repeating the original CCl4 multigenerational study with increased power, pedigree tracing, F2 dose-response and suitable randomization schemes. Detailed pathology evaluations do not support adaptive phenotypic suppression of the hepatic wound healing response or a greater fitness of F2 animals with ancestral liver injury exposure. However, transcriptomic analyses identified genes whose expression correlates with ancestral liver injury, although the biological relevance of this apparent transgenerational transmission at the molecular level remains to be determined. This work overall highlights the need for independent evaluation of transgenerational epigenetic inheritance paradigms in mammals.

Microbiomes, Their Function, and Cancer: How Metatranscriptomics Can Close the Knowledge Gap

The interaction between the microbial communities in the human body and the onset and progression of cancer has not been investigated until recently. The vast majority of the metagenomics research in this area has concentrated on the composition of microbiomes, attempting to link the overabundance or depletion of certain microorganisms to cancer proliferation, metastatic behaviour, and its resistance to therapies. However, studies elucidating the functional implications of the microbiome activity in cancer patients are still scarce; in particular, there is an overwhelming lack of studies assessing such implications directly, through analysis of the transcriptome of the bacterial community. This review summarises the contributions of metagenomics and metatranscriptomics to the knowledge of the microbial environment associated with several cancers; most importantly, it highlights all the advantages that metatranscriptomics has over metagenomics and suggests how such an approach can be leveraged to advance the knowledge of the cancer bacterial environment.

Transcriptome-based nutrigenomics analysis reveals the roles of dietary taurine in the muscle growth of juvenile turbot (Scophthalmus maximus)

The present study explored transcriptomics and gene regulation variations in the muscle of turbot fed with dietary taurine. A 70-day feeding trial was conducted using turbot (initial body weight: 3.66 ± 0.02 g) fed with different levels of dietary taurine: 0 % (C), 0.4 % (T2), 1.2 % (T4) and 2.0 % (T6). Two methods were used to analyze and verify the taurine effects on muscle growth: (1) real-time quantitative PCR (qRT-PCR) for the key muscle growth-related genes and (2) transcriptomic analysis by next-generation sequencing (NGS). The results showed that 1.2 % of dietary taurine supplementation significantly increased the expression of muscle growth stimulatory genes, including TauT, myoD, Myf5, myogenin and follistatin. And also, the 1.2 % level significantly decreased the expression of the muscle growth-restricting gene (myostatin). Meanwhile, transcriptomics analysis found that 1.2 % dietary taurine supplementation significantly increased the number of up-regulated genes linked to metabolic pathways. In contrast, taurine significantly enriched the actin cytoskeleton and metabolic pathways in the T4 and T2 groups, respectively. These findings align with the gene ontology (GO) analysis, which indicated a higher number of cellular component (CC) gene expressions at a 1.2 % of dietary taurine compared to a 0.4 % of dietary taurine supplementation. In conclusion, dietary taurine had positive impacts on the growth-stimulatory genes. Moreover, 1.2 % of dietary taurine supplementation is important to the metabolic pathway enrichment.

Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics

Metatranscriptomics refers to the analysis of the collective microbial transcriptome of a sample. Its increased utilization for the characterization of human-associated microbial communities has enabled the discovery of many disease-state related microbial activities. Here, we review the principles of metatranscriptomics-based analysis of human-associated microbial samples. We describe strengths and weaknesses of popular sample preparation, sequencing, and bioinformatics approaches and summarize strategies for their use. We then discuss how human-associated microbial communities have recently been examined and how their characterization may change. We conclude that metatranscriptomics insights into human microbiotas under health and disease have not only expanded our knowledge on human health, but also opened avenues for rational antimicrobial drug use and disease management.

Promise and Perils of MicroRNA Discovery Research: Working Toward Quality Over Quantity

Since the first microRNA (miRNA) was described in 1993 in the humble worm Caenorhabditis elegans, the miRNA field has boomed, with more than 100 000 related patents filed and miRNAs now in ongoing clinical trials. Despite an advanced understanding of the biogenesis and action of miRNAs, applied miRNA research faces challenges and irreproducibility due to a lack of standardization. This review provides guidelines regarding miRNA investigation, while focusing on the pitfalls and considerations that are often overlooked in prevailing applied miRNA research. These include miRNA annotation and quantification, to modulation, target prediction, validation, and the study of circulating miRNAs.

Transcriptomic analysis of paired healthy human skeletal muscles to identify modulators of disease severity in DMD

Muscle damage and fibro-fatty replacement of skeletal muscles is a main pathologic feature of Duchenne muscular dystrophy (DMD) with more proximal muscles affected earlier and more distal affected later in the disease course, suggesting that different skeletal muscle groups possess distinctive characteristics that influence their susceptibility to disease. To explore transcriptomic factors driving differential gene expression and modulating DMD skeletal muscle severity, we characterized the transcriptome of vastus lateralis (VL), a more proximal and susceptible muscle, relative to tibialis anterior (TA), a more distal and protected muscle, in 15 healthy individuals using bulk RNA sequencing to identify gene expression differences that may mediate their relative susceptibility to damage with loss of dystrophin. Matching single nuclei RNA sequencing data was generated for 3 of the healthy individuals, to infer cell composition in the bulk RNA sequencing dataset and to improve mapping of differentially expressed genes to their cell source of expression. A total of 3,410 differentially expressed genes were identified and mapped to cell type using single nuclei RNA sequencing of muscle, including long non-coding RNAs and protein coding genes. There was an enrichment of genes involved in calcium release from the sarcoplasmic reticulum, particularly in the myofibers and these myofiber genes were higher in the VL. There was an enrichment of genes in "Collagen-Containing Extracellular Matrix" expressed by fibroblasts, endothelial, smooth muscle and pericytes, with most genes higher in the TA, as well as genes in "Regulation Of Apoptotic Process" expressed across all cell types. Previously reported genetic modifiers were also enriched within the differentially expressed genes. We also identify 6 genes with differential isoform usage between the VL and TA. Lastly, we integrate our findings with DMD RNA sequencing data from the TA, and identify "Collagen-Containing Extracellular Matrix" and "Negative Regulation Of Apoptotic Process" as differentially expressed between DMD compared to healthy. Collectively, these findings propose novel candidate mechanisms that may mediate differential muscle susceptibility in muscular dystrophies and provide new insight into potential therapeutic targets.

Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing

Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.

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.

Anti-Inflammatory Effects of Polyphenols from Plum (Prunus salicina Lindl) on RAW264.7 Macrophages Induced by Monosodium Urate and Potential Mechanisms

Acute gouty arthritis is an acute inflammatory reaction caused by the deposition of monosodium urate (MSU) crystals in joints and surrounding soft tissues. Controlling inflammation is the key to preventing acute gouty arthritis. Anti-inflammatory activities and the possible molecular mechanisms of plum (Prunus salicina Lindl cv. "furong") polyphenols (PSLP) on RAW264.7 macrophage cells induced by monosodium urate were investigated. PPSF significantly inhibited the activity of inflammatory factors such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-18 (IL-18). In addition, PPSF exhibited excellent activation of superoxide dismutase (SOD) activity and reduction of intracellular reactive oxygen species (ROS) and malondialdehyde (MDA) levels in RAW264.7 macrophages. The results of global screening of all transcripts by RNA-seq revealed 8585 differentially expressed genes between the PSLP-treated group and the MUS group. From GO analysis, PSLP could affect the occurrence and development of RAW264.7 macrophage inflammation through biological processes, such as organic substance metabolism, intracellular organelles, and binding function. The regulation mechanism of PSLP on MSU-induced RAW264.7 macrophage inflammation may be achieved through the HIF-1 signaling pathway, renal cell carcinoma, the ErbB signaling pathway, and the FoxO signaling pathway. Therefore, PSLP has great prospects in the prevention of gout and similar inflammatory diseases.

Identification of extremely GC-rich micro RNAs for RT-qPCR data normalization in human plasma

We aimed at extending the repertoire of high-quality miRNA normalizers for reverse transcription-quantitative PCR (RT-qPCR) of human plasma with special emphasis on the extremely guanine-cytosine-rich portion of the miRNome. For high-throughput selection of stable candidates, microarray technology was preferred over small-RNA sequencing (sRNA-seq) since the latter underrepresented miRNAs with a guanine-cytosine (GC) content of at least 75% (p = 0.0002, n = 2). miRNA abundances measured on the microarray were ranked for consistency and uniformity using nine normalization approaches. The eleven most stable sequences included miRNAs of moderate, but also extreme GC content (45%-65%: miR-320d, miR-425-5p, miR-185-5p, miR-486-5p; 80%-95%: miR-1915-3p, miR-3656-5p, miR-3665-5p, miR-3960-5p, miR-4488-5p, miR-4497 and miR-4787-5p). In contrast, the seven extremely GC-rich miRNAs were not found in the two plasma miRNomes screened by sRNA-seq. Stem-loop RT-qPCR was employed for stability verification in 32 plasma samples of healthy male Caucasians (age range: 18-55 years). In general, inter-individual variance of miRNA abundance was low or very low as indicated by coefficient of variation (CV) values of 0.6%-8.2%. miR-3665 and miR-1915-3p outperformed in this analysis (CVs: 0.6 and 2.4%, respectively). The eight most stable sequences included four extremely GC-rich miRNAs (miR-1915-3p, miR-3665, miR-4787-5p and miR-4497). The best-performing duo normalization factor (NF) for the condition of human plasma, miR-320d and miR-4787-5p, also included a GC-extreme miRNA. In summary, the identification of extremely guanine-cytosine-rich plasma normalizers will help to increase accuracy of PCR-based miRNA quantification, thus raise the potential that miRNAs become markers for psychological stress reactions or early and precise diagnosis of clinical phenotypes. The novel miRNAs might also be useful for orthologous contexts considering their conservation in related animal genomes.

RNA Preparation and RNA-Seq Bioinformatics for Comparative Transcriptomics

The principal transcriptome analysis is the determination of differentially expressed genes across experimental conditions. For this, the next-generation sequencing of RNA (RNA-seq) has several advantages over other techniques, such as the capability of detecting all the transcripts in one assay over RT-qPCR, such as its higher accuracy and broader dynamic range over microarrays or the ability to detect novel transcripts, including non-coding RNA molecules, at nucleotide-level resolution over both techniques. Despite these advantages, many microbiology laboratories have not yet applied RNA-seq analyses to their investigations. The high cost of the equipment for next-generation sequencing is no longer an issue since this intermediate part of the analysis can be provided by commercial or central services. Here, we detail a protocol for the first part of the analysis, the RNA extraction and an introductory protocol to the bioinformatics analysis of the sequencing data to generate the differential expression results.

Poly(a) selection introduces bias and undue noise in direct RNA-sequencing

Background

Genome-wide RNA-sequencing technologies are increasingly critical to a wide variety of diagnostic and research applications. RNA-seq users often first enrich for mRNA, with the most popular enrichment method being poly(A) selection. In many applications it is well-known that poly(A) selection biases the view of the transcriptome by selecting for longer tailed mRNA species.

Results

Here, we show that poly(A) selection biases Oxford Nanopore direct RNA sequencing. As expected, poly(A) selection skews sequenced mRNAs toward longer poly(A) tail lengths. Interestingly, we identify a population of mRNAs (> 10% of genes’ mRNAs) that are inconsistently captured by poly(A) selection due to highly variable poly(A) tails, and demonstrate this phenomenon in our hands and in published data. Importantly, we show poly(A) selection is dispensable for Oxford Nanopore’s direct RNA-seq technique, and demonstrate successful library construction without poly(A) selection, with decreased input, and without loss of quality.

Conclusions

Our work expands the utility of direct RNA-seq by validating the use of total RNA as input, and demonstrates important technical artifacts from poly(A) selection that inconsistently skew mRNA expression and poly(A) tail length measurements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08762-8.

PBSIM3: a simulator for all types of PacBio and ONT long reads

Long-read sequencers, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencers, have improved their read length and accuracy, thereby opening up unprecedented research. Many tools and algorithms have been developed to analyze long reads, and rapid progress in PacBio and ONT has further accelerated their development. Together with the development of high-throughput sequencing technologies and their analysis tools, many read simulators have been developed and effectively utilized. PBSIM is one of the popular long-read simulators. In this study, we developed PBSIM3 with three new functions: error models for long reads, multi-pass sequencing for high-fidelity read simulation and transcriptome sequencing simulation. Therefore, PBSIM3 is now able to meet a wide range of long-read simulation requirements.

Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

Purpose

To screen biomarkers in the serum of patients with sepsis by proteomics combined with RNA sequencing technology, and to find new diagnostic and therapeutic targets for sepsis.

Patients and Methods

Blood samples of 22 sepsis patients (sepsis group) and 10 healthy volunteers (normal group) were collected from January 2019 to December 2020. Data-independent acquisition (DIA) method was employed for protein profiling, RNA sequencing was employed for gene sequencing. Subsequently, quality control and differential analysis (FC≥2; FDR<0.05) of DIA data and RNA sequencing data were performed. Then we identified expression trend-consistent divergence factors by nine-quadrant analysis; subsequent protein-protein interaction (PPI) and gene ontology (GO) functional enrichment analysis of intersection factors was performed, and meta-analysis of targets at transcriptome level was implemented using public datasets. Finally, five Peripheral blood mononuclear cell (PBMC) samples (NC=2; SIRS=1; SEPSIS =2) were collected, and cell localization analysis of core genes was performed by 10× single-cell RNA sequencing (scRNA-seq).

Results

Compared with the normal group, there were 4681 differentially expressed genes and 202 differentially expressed proteins in the sepsis group. Among them, 25 factors were expressed in both proteome and transcriptome, and the analysis of PPI and GO found that they were mainly involved in biological processes such as white blood cell and neutrophil response, inflammatory and immune response. Four core genes GSTO1, C1QA, RETN, and GRN were screened by meta-analysis, all of which were highly expressed in the sepsis group compared with the normal group (P<0.05); scRNA-seq showed the core genes were mainly localized in macrophage cell lines.

Conclusion

The core genes GSTO1, C1QA, RETN and GRN are mainly expressed in macrophages, widely involved in inflammation and immune responses, and are highly expressed in plasma in the sepsis, suggesting that they may become potential research targets for sepsis.

Unique Molecular Identifier-Based High-Resolution HLA Typing and Transcript Quantitation Using Long-Read Sequencing

HLA typing provides essential results for stem cell and solid organ transplants, as well as providing diagnostic benefits for various rheumatology, gastroenterology, neurology, and infectious diseases. It is becoming increasingly clear that understanding the expression of patient HLA transcripts can provide additional benefits for many of these same patient groups. Our study cohort was evaluated using a long-read RNA sequencing methodology to provide rapid HLA genotyping results and normalized HLA transcript expression. Our assay used NGSEngine to determine the HLA genotyping result and normalized mRNA transcript expression using Athlon2. The assay demonstrated an excellent concordance rate of 99.7%. Similar to previous studies, for the class I loci, patients demonstrated significantly lower expression of HLA-C than HLA-A and -B (Mann-Whitney U, p value = 0.0065 and p value = 0.0154, respectively). In general, the expression of class II transcripts was lower than that of class I transcripts. This study demonstrates a rapid high-resolution HLA typing assay using RNA-Seq that can provide accurate HLA genotyping and HLA allele-specific transcript expression in 7-8 h, a timeline short enough to perform the assay for deceased donors.

Selective ablation of 3' RNA ends and processive RTs facilitate direct cDNA sequencing of full-length host cell and viral transcripts

Alternative splicing (AS) is necessary for viral proliferation in host cells and a critical regulatory component of viral gene expression. Conventional RNA-seq approaches provide incomplete coverage of AS due to their short read lengths and are susceptible to biases and artifacts introduced in prevailing library preparation methodologies. Moreover, viral splicing studies are often conducted separately from host cell transcriptome analysis, precluding an assessment of the viral manipulation of host splicing machinery. To address current limitations, we developed a quantitative full-length direct cDNA sequencing strategy to simultaneously profile viral and host cell transcripts. This nanopore-based approach couples processive reverse transcriptases with a novel one-step chemical ablation of 3' RNA ends (termed CASPR), which decreases ribosomal RNA reads and enriches polyadenylated coding sequences. We extensively validate our approach using synthetic reference transcripts and show that CASPR doubles the breadth of coverage per transcript and increases detection of long transcripts (>4 kb), while being functionally equivalent to PolyA+ selection for transcript quantification. We used our approach to interrogate host cell and HIV-1 transcript dynamics during viral reactivation and identified novel putative HIV-1 host factors containing exon skipping or novel intron retentions and delineated the HIV-1 transcriptional state associated with these differentially regulated host factors.

Machine Learning-Based Models for Detection of Biomarkers of Autoimmune Diseases by Fragmentation and Analysis of miRNA Sequences

Thanks to high-throughput data technology, microRNA analysis studies have evolved in early disease detection. This work introduces two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis. Based on work the authors published previously, both introduced models involve complete pipelines of text mining methods, integrated with traditional machine learning methods, and LSTM deep learning. This work also studies the fragmentation of miRNA sequences to reduce the needed processing time and computational power. Moreover, this work studies the impact of obtaining two different library preparation kits (NEBNEXT and NEXTFLEX) on the detection accuracy for rheumatoid arthritis. Additional experiments are applied to the proposed models based on three different transcriptomic datasets. The results denote that the transcriptomic fragmentation model reported a biomarker detection accuracy of 96.45% on a sequence fragment size of 0.2, indicating a significant reduction in execution power while retaining biomarker detection accuracy. On the other hand, the LSTM model obtained a promising detection accuracy of 72%, implying savings in feature engineering processing. Additionally, the fragmentation model and the LSTM model reported 22.4% and 87.5% less execution time than work in the literature, respectively, denoting a considerable execution power reduction.

Engineering synthetic RNA devices for cell control

The versatility of RNA in sensing and interacting with small molecules, proteins and other nucleic acids while encoding genetic instructions for protein translation makes it a powerful substrate for engineering biological systems. RNA devices integrate cellular information sensing, processing and actuation of specific signals into defined functions and have yielded programmable biological systems and novel therapeutics of increasing sophistication. However, challenges centred on expanding the range of analytes that can be sensed and adding new mechanisms of action have hindered the full realization of the field's promise. Here, we describe recent advances that address these limitations and point to a significant maturation of synthetic RNA-based devices.

Rapid Identification of DNA Fragments through Direct Sequencing with Electro-Optical Zero-Mode Waveguides

In contrast to sequence-specific techniques such as polymerase chain reaction, DNA sequencing does not require prior knowledge of the sample for surveying DNA. However, current sequencing technologies demand high inputs for a suitable library preparation, which typically necessitates DNA amplification, even for single-molecule sequencing methods. Here, electro-optical zero-mode waveguides (eZMWs) are presented, which can load DNA into the confinement of zero-mode waveguides with high efficiency and negligible DNA fragment length bias. Using eZMWs, highly efficient voltage-induced loading of DNA fragments of various sizes from ultralow inputs (nanogram-to-picogram levels) is observed. Rapid DNA fragment identification is demonstrated by burst sequencing of short and long DNA molecules (260 and 20 000 bp) loaded from an equimolar picomolar-level concentration mixture in just a few minutes. The device allows further studies in which low-input DNA capture is essential, for example, in epigenetics, where native DNA is required for obtaining modified base information.

DNA Methylation and Non-Coding RNAs during Tissue-Injury Associated Pain

While about half of the population experience persistent pain associated with tissue damages during their lifetime, current symptom-based approaches often fail to reduce such pain to a satisfactory level. To provide better patient care, mechanism-based analgesic approaches must be developed, which necessitates a comprehensive understanding of the nociceptive mechanism leading to tissue injury-associated persistent pain. Epigenetic events leading the altered transcription in the nervous system are pivotal in the maintenance of pain in tissue injury. However, the mechanisms through which those events contribute to the persistence of pain are not fully understood. This review provides a summary and critical evaluation of two epigenetic mechanisms, DNA methylation and non-coding RNA expression, on transcriptional modulation in nociceptive pathways during the development of tissue injury-associated pain. We assess the pre-clinical data and their translational implication and evaluate the potential of controlling DNA methylation and non-coding RNA expression as novel analgesic approaches and/or biomarkers of persistent pain.

The role of noncoding RNAs in Parkinson's disease: biomarkers and associations with pathogenic pathways

The discovery of various noncoding RNAs (ncRNAs) and their biological implications is a growing area in cell biology. Increasing evidence has revealed canonical and noncanonical functions of long and small ncRNAs, including microRNAs, long ncRNAs (lncRNAs), circular RNAs, PIWI-interacting RNAs, and tRNA-derived fragments. These ncRNAs have the ability to regulate gene expression and modify metabolic pathways. Thus, they may have important roles as diagnostic biomarkers or therapeutic targets in various diseases, including neurodegenerative disorders, especially Parkinson's disease. Recently, through diverse sequencing technologies and a wide variety of bioinformatic analytical tools, such as reverse transcriptase quantitative PCR, microarrays, next-generation sequencing and long-read sequencing, numerous ncRNAs have been shown to be associated with neurodegenerative disorders, including Parkinson's disease. In this review article, we will first introduce the biogenesis of different ncRNAs, including microRNAs, PIWI-interacting RNAs, circular RNAs, long noncoding RNAs, and tRNA-derived fragments. The pros and cons of the detection platforms of ncRNAs and the reproducibility of bioinformatic analytical tools will be discussed in the second part. Finally, the recent discovery of numerous PD-associated ncRNAs and their association with the diagnosis and pathophysiology of PD are reviewed, and microRNAs and long ncRNAs that are transported by exosomes in biofluids are particularly emphasized.