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Objective Helicobacter pylori is associated with gastric inflammation, precancerous gastric atrophy (GA) and intestinal metaplasia (IM). We aimed to identify microbes that are associated with progressive inflammation, GA and IM 1 year after H. pylori eradication.DesignA total of 587 H. pylori–positive patients were randomised to receive H. pylori eradication therapy (295 patients) or placebo (292 patients). Bacterial taxonomy was analysed on 404 gastric biopsy samples comprising 102 pairs before and after 1 year H. pylori eradication and 100 pairs before and after 1 year placebo by 16S rRNA sequencing.ResultsAnalysis of microbial sequences confirmed the eradication of H. pylori in treated group after 1 year. Principal component analysis revealed distinct microbial clusters reflected by increase in bacterial diversity (p<0.00001) after H. pylori eradication. While microbial interactions remained largely unchanged after placebo treatment, microbial co-occurrence was less in treated group. Acinetobacter lwoffii, Streptococcus anginosus and Ralstonia were enriched while Roseburia and Sphingomonas were depleted in patients with persistent inflammation 1 year after H. pylori eradication. A distinct cluster of oral bacteria comprising Peptostreptococcus, Streptococcus, Parvimonas, Prevotella, Rothia and Granulicatella were associated with emergence and persistence of GA and IM. Probiotic Faecalibacterium praustznii was depleted in subjects who developed GA following H. pylori eradication. Functional pathways including amino acid metabolism and inositol phosphate metabolism were enriched while folate biosynthesis and NOD-like receptor signalling decreased in atrophy/IM-associated gastric microbiota.ConclusionThis study identified that gastric microbes contribute to the progression of gastric carcinogenesis after H. pylori eradication.

Non-coding RNAs play a significant role in viral infection cycles, with recent attention focused on circular RNAs (circRNAs) originating from various viral families. Notably, these circRNAs have been associated with oncogenesis and alterations in viral fitness. However, identifying their expression has proven more challenging than initially anticipated due to unique viral characteristics. This challenge has the potential to impede progress in our understanding of viral circRNAs. Key hurdles in working with viral genomes include: (1) the presence of repetitive regions that can lead to misalignment of sequencing reads, and (2) unconventional splicing mechanisms that deviate from conserved eukaryotic patterns. To address these challenges, we developed vCircTrappist, a bioinformatic pipeline tailored to identify backsplicing events and pinpoint loci expressing circRNAs in RNA sequencing data. Applying this pipeline, we obtained novel insights from both new and existing datasets encompassing a range of animal and human pathogens belonging to Herpesviridae, Retroviridae, Adenoviridae, Flaviviridae and Orthomyxoviridae families. Subsequent RT-PCR and Sanger sequencings validated the accuracy of the developed bioinformatic tool for a selection of new candidate virus-derived circRNAs. These findings demonstrate that vCircTrappist is an open and unbiased approach for comprehensive identification of virus-derived circRNAs.

Productivity of Indian mustard ( B. juncea ), a major oil yielding crop in rapeseed-mustard group is heavily inflicted by mustard aphid, L. erysimi . Mustard aphid, a specialist aphid species on rapeseed-mustard crops, rapidly multiplies and colonizes the plants leading to successful infestation. In contrary, legume specific cowpea aphid, A. craccivora when released on B. juncea plants fails to build up population and thus remains unsuccessful in infestation. In the present study, differential host response of B. juncea to the two aphid species, one being successful insect-pest and the other being unsuccessful on it has been studied based on transcriptome analysis. Differential feeding efficiency of the two aphid species on mustard plants was evident from the amount of secreted honeydews. Leaf-transcriptomes of healthy and infested plants, treated with the two aphid species, were generated by RNA sequencing on Illumina platform and de novo assembly of the quality reads. A comparative assessment of the differentially expressed genes due to treatments revealed a large extent of overlaps as well as distinctness with respect to the set of genes and their direction of regulation. With respect to host-genes related to transcription factors, oxidative homeostasis, defense hormones and secondary metabolites, L. erysimi led to either suppression or limited activation of the transcript levels compared to A . craccivora . Further, a comprehensive view of the DEGs suggested more potential of successful insect-pests towards transcriptional reprogramming of the host. qRT-PCR based validation of randomly selected up- and down-regulated transcripts authenticated the transcriptome data.

Key message We report the adaptation of the INTACT method for RNA-sequencing in the endosperm and demonstrate its feasibility for allele-specific expression analysis. Tissue-specific transcriptome analyses provide important insights into the developmental programs of defined cell types. The isolation of nuclei tagged in specific cell types (INTACT) is a versatile method that allows to isolate highly pure nuclei from defined tissue types that can be used for several downstream applications. Here, we describe the adaptation of INTACT from endosperm nuclei for high-throughput RNA-sequencing. By analyzing the ratio of parental reads and tissue-specific gene expression in the endosperm, we could assess the contamination level of our samples. Based on this analysis, we estimate that in most of the samples the contamination level is lower than in previously published datasets. We further show that the nuclear transcriptome and total transcriptome of the endosperm are well correlated. Together, our data show that INTACT of the endosperm is a reliable methodology for endosperm-specific transcriptome analysis that overcomes the limitation of time-consuming manual endosperm dissection that is connected with high levels of maternal tissue contamination. INTACT does not rely on expensive equipment and can be set up in every standard molecular biology laboratory, making it the method of choice for future molecular studies of the endosperm.

Staphylococcus epidermidis ( S. epidermidis ) is one of the primary pathogens in postoperative endophthalmitis, which is a devastating complication of cataract surgery and often results in irreversible visual loss and even blindness. Meanwhile, it is the most frequently isolated commensal bacterium in the healthy conjunctiva. In this study, we investigated the differentially expressed genes (DEGs) of S. epidermidis isolated from the patients with postoperative endophthalmitis and the healthy conjunctiva to predict their functions and pathways by Illumina high-throughput RNA sequencing. Using genome-wide transcriptional analysis, 281 genes (142 upregulated and 139 downregulated genes) were found to be differentially expressed (fold change ≥ 2, p  ≤ 0.05) in the strains from endophthalmitis. Ten randomly selected DEGs were further validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). GO enrichment analysis suggested that more DEGs were associated with the thioredoxin system and iron ion metabolism. KEGG pathway analysis revealed that more DEGs were associated with the pathways of the two-component system and pyruvate metabolism. Moreover, the gene SE1634 code for staphylococcal toxin was significantly upregulated in S. epidermidis strains of the endophthalmitis, which might be directly responsible for the pathogenesis of endophthalmitis. In conclusion, this research is helpful for further investigations on genes or pathways related with the pathogenesis and therapeutic targets of S. epidermidis endophthalmitis.

Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences. Measuring the expression of an organism's genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism's biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.

Extracellular RNAs (exRNAs) in human body fluids are emerging as effective biomarkers for detection of diseases. Saliva, as the most accessible and noninvasive body fluid, has been shown to harbor exRNA biomarkers for several human diseases. However, the entire spectrum of exRNA from saliva has not been fully characterized. Using high-throughput RNA sequencing (RNA-Seq), we conducted an in-depth bioinformatic analysis of noncoding RNAs (ncRNAs) in human cell-free saliva (CFS) from healthy individuals, with a focus on microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and circular RNAs (circRNAs). Our data demonstrated robust reproducibility of miRNA and piRNA profiles across individuals. Furthermore, individual variability of these salivary RNA species was highly similar to those in other body fluids or cellular samples, despite the direct exposure of saliva to environmental impacts. By comparative analysis of >90 RNA-Seq data sets of different origins, we observed that piRNAs were surprisingly abundant in CFS compared with other body fluid or intracellular samples, with expression levels in CFS comparable to those found in embryonic stem cells and skin cells. Conversely, miRNA expression profiles in CFS were highly similar to those in serum and cerebrospinal fluid. Using a customized bioinformatics method, we identified >400 circRNAs in CFS. These data represent the first global characterization and experimental validation of circRNAs in any type of extracellular body fluid. Our study provides a comprehensive landscape of ncRNA species in human saliva that will facilitate further biomarker discoveries and lay a foundation for future studies related to ncRNAs in human saliva.

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas. A multicenter study compares 13 commonly used single-cell RNA-seq protocols.

Large-scale sequencing of RNA from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states 1 . However, current short-read single-cell RNA-sequencing methods have limited ability to count RNAs at allele and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells 2 , 3 . Here we introduce Smart-seq3, which combines full-length transcriptome coverage with a 5′ unique molecular identifier RNA counting strategy that enables in silico reconstruction of thousands of RNA molecules per cell. Of the counted and reconstructed molecules, 60% could be directly assigned to allelic origin and 30–50% to specific isoforms, and we identified substantial differences in isoform usage in different mouse strains and human cell types. Smart-seq3 greatly increased sensitivity compared to Smart-seq2, typically detecting thousands more transcripts per cell. We expect that Smart-seq3 will enable large-scale characterization of cell types and states across tissues and organisms. Smart-seq3 enables isoform- and allele-specific reconstruction of RNA molecules.