Metatranscriptome of human lung microbial communities in a cohort of mechanically ventilated COVID-19 Omicron patients.

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) infected a substantial proportion of Chinese population, and understanding the factors underlying the severity of the disease and fatality is valuable for future prevention and clinical treatment. We recruited 64 patients with invasive ventilation for COVID-19 and performed metatranscriptomic sequencing to profile host transcriptomic profiles, plus viral, bacterial, and fungal content, as well as virulence factors and examined their relationships to 28-day mortality were examined. In addition, the bronchoalveolar lavage fluid (BALF) samples from invasive ventilated hospital/community-acquired pneumonia patients (HAP/CAP) sampled in 2019 were included for comparison. Genomic analysis revealed that all Omicron strains belong to BA.5 and BF.7 sub-lineages, with no difference in 28-day mortality between them. Compared to HAP/CAP cohort, invasive ventilated COVID-19 patients have distinct host transcriptomic and microbial signatures in the lower respiratory tract; and in the COVID-19 non-survivors, we found significantly lower gene expressions in pathways related viral processes and positive regulation of protein localization to plasma membrane, higher abundance of opportunistic pathogens including bacterial Alloprevotella, Caulobacter, Escherichia-Shigella, Ralstonia and fungal Aspergillus sydowii and Penicillium rubens. Correlational analysis further revealed significant associations between host immune responses and microbial compositions, besides synergy within viral, bacterial, and fungal pathogens. Our study presents the relationships of lower respiratory tract microbiome and transcriptome in invasive ventilated COVID-19 patients, providing the basis for future clinical treatment and reduction of fatality.

Lightweight Differentiated Transmission Based on Fuzzy and Random Modeling in Underwater Acoustic Sensor Networks.

Energy-efficient and reliable underwater acoustic communication attracts a lot of research due to special marine communication conditions with limited resources in underwater acoustic sensor networks (UASNs). In their final analysis, the existing studies focus on controlling redundant communication and route void that greatly influence UASNs' comprehensive performances. Most of them consider directional or omnidirectional transmission for partial optimization aspects, which still have many extra data loads and performance losses. This paper analyzes the main issue sources causing redundant communication in UASNs, and proposes a lightweight differentiated transmission to suppress extra communication to the greatest extent as well as balance energy consumption. First, the layered model employs layer ID to limit the scale of the data packet header, which does not need depth or location information. Second, the layered model, fuzzy-based model, random modeling and directional-omnidirectional differentiated transmission mode comb out the forwarders step by step to decrease needless duplicated forwarding. Third, forwarders are decided by local computation in nodes, which avoids exchanging controlling information among nodes. Simulation results show that our method can efficiently reduce the network load and improve the performance in terms of energy consumption balance, network lifetime, data conflict and network congestion, and data packet delivery ratio.

Disturbed gut virome with potent interferonogenic property in systemic lupus erythematosus.

Accumulating evidence suggests an essential role of disturbed gut microbiota in the etiopathogenesis of systemic lupus erythematosus (SLE), but it remains unclear as to gut virome. In this study, fecal virus-like particles (VLPs) isolated from 76 non-treated SLE patients and 75 healthy controls were subjected to gut virome profiling. The proportion of bacteriophages was significantly elevated in the SLE gut, and the altered viral taxa were correlated with clinical parameters. Gut virome and bacteriome were closely associated with each other in SLE patients. The combination of gut viral and bacterial markers displayed better performance in distinguishing SLE patients from healthy controls. Further, VLPs from non-treated SLE patients promoted interferon-α production in an epithelial cell line and human immune cells. Intriguingly, the interferon-stimulatory capacity diminished in VLPs from post-treated SLE patients. Our findings may shed novel insights into SLE pathogenesis. Further in-depth understanding of gut virome might help develop future biomarkers and therapeutics for SLE patients.

MetaSVs: A pipeline combining long and short reads for analysis and visualization of structural variants in metagenomes.

Structural variants (SVs, including large-scale insertions, deletions, inversions, and translocations) significantly impact the functions of genes in the microbial genome, and SVs in the microbiome are associated with diverse biological processes and human diseases. With the advancements in sequencing and bioinformatics technologies, increasingly, sequencing data and analysis tools are already being extensively utilized for microbiome SV analyses, leading to a higher demand for more dedicated SV analysis workflows. Moreover, due to the unique detection biases of various sequencing technologies, including short-read sequencing (such as Illumina platforms) and long-read sequencing (e.g., Oxford Nanopore and PacBio), SV discovery based on multiple platforms is necessary to comprehensively identify the wide variety of SVs. Here, we establish an integrated pipeline MetaSVs combining Nanopore long reads and Illumina short reads to analyze SVs in the microbial genomes from gut microbiome and further identify differential SVs that can be reflective of metabolic differences. Our pipeline provides researchers easy access to SVs and relevant metabolites in the microbial genomes without the requirement of specific technical expertise, which is particularly useful to researchers interested in metagenomic SVs but lacking sophisticated bioinformatic knowledge.

Short- and long-read metagenomics expand individualized structural variations in gut microbiomes.

In-depth profiling of genetic variations in the gut microbiome is highly desired for understanding its functionality and impacts on host health and disease. Here, by harnessing the long read advantage provided by Oxford Nanopore Technology (ONT), we characterize fine-scale genetic variations of structural variations (SVs) in hundreds of gut microbiomes from healthy humans. ONT long reads dramatically improve the quality of metagenomic assemblies, enable reliable detection of a large, expanded set of structural variation types (notably including large insertions and inversions). We find SVs are highly distinct between individuals and stable within an individual, representing gut microbiome fingerprints that shape strain-level differentiations in function within species, complicating the associations to metabolites and host phenotypes such as blood glucose. In summary, our study strongly emphasizes that incorporating ONT reads into metagenomic analyses expands the detection scope of genetic variations, enables profiling strain-level variations in gut microbiome, and their intricate correlations with metabolome.

Correlation Analysis of the Microbiome and Immune Function in the Lung-Gut Axis of Critically Ill Patients in the ICU.

Objective: Critical illnesses in the intensive care unit (ICU) have been a global burden. We aimed to determine the correlation between the lung and gut in critically ill patients to find novel evidence of the lung-gut axis, which may be a new treatment for patients with critical illness in the ICU. Methods: We collected bronchoalveolar lavage specimens and fecal samples of 31 patients with critical illness within 24 h after admission. Metagenomics was used to detect lung and intestinal samples. Immune cells were detected by flow cytometry. Results: There are 86 common species in both lung and gut. The abundance of Enterococcus faecium is high in both the lung and gut of patients with critical illness in the respiratory intensive care unit (RICU). Corynebacterium striatum in the lung and gut is correlated with different immune cells. In addition, C. striatum in the lung and gut might share the same source, supporting the concept of a gut-lung axis in humans. Conclusions: The microbiome in the lung and gut showed a correlation to some extent, and C. striatum in the lung and gut might share the same source. In addition, the microbiome showed a correlation with immunity, indicating a potential therapeutic target in patients with critical illness. The lung-gut axis might play an important role in patients with critical illness in the RICU.

Targeting RNA with Next- and Third-Generation Sequencing Improves Pathogen Identification in Clinical Samples.

Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture-based diagnosis in clinics is increasingly supplemented by metagenomic next-generation-sequencing (mNGS). Here, RNA/cDNA-targeted sequencing (meta-transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta-transcriptomics using third-generation sequencing, mtTGS) to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple-strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct-RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA-targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT.

A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19.

Exosomes represent a subtype of extracellular vesicle that is released through retrograde transport and fusion of multivesicular bodies with the plasma membrane1. Although no perfect methodologies currently exist for the high-throughput, unbiased isolation of pure plasma exosomes2,3, investigation of exosome-enriched plasma fractions of extracellular vesicles can confer a glimpse into the endocytic pathway on a systems level. Here we conduct high-coverage lipidomics with an emphasis on sterols and oxysterols, and proteomic analyses of exosome-enriched extracellular vesicles (EVs hereafter) from patients at different temporal stages of COVID-19, including the presymptomatic, hyperinflammatory, resolution and convalescent phases. Our study highlights dysregulated raft lipid metabolism that underlies changes in EV lipid membrane anisotropy that alter the exosomal localization of presenilin-1 (PS-1) in the hyperinflammatory phase. We also show in vitro that EVs from different temporal phases trigger distinct metabolic and transcriptional responses in recipient cells, including in alveolar epithelial cells, which denote the primary site of infection, and liver hepatocytes, which represent a distal secondary site. In comparison to the hyperinflammatory phase, EVs from the resolution phase induce opposing effects on eukaryotic translation and Notch signalling. Our results provide insights into cellular lipid metabolism and inter-tissue crosstalk at different stages of COVID-19 and are a resource to increase our understanding of metabolic dysregulation in COVID-19.

Integrated gut virome and bacteriome dynamics in COVID-19 patients.

SARS-CoV-2 is the cause of the current global pandemic of COVID-19; this virus infects multiple organs, such as the lungs and gastrointestinal tract. The microbiome in these organs, including the bacteriome and virome, responds to infection and might also influence disease progression and treatment outcome. In a cohort of 13 COVID-19 patients in Beijing, China, we observed that the gut virome and bacteriome in the COVID-19 patients were notably different from those of five healthy controls. We identified a bacterial dysbiosis signature by observing reduced diversity and viral shifts in patients, and among the patients, the bacterial/viral compositions were different between patients of different severities, although these differences are not entirely distinguishable from the effect of antibiotics. Severe cases of COVID-19 exhibited a greater abundance of opportunistic pathogens but were depleted for butyrate-producing groups of bacteria compared with mild to moderate cases. We replicated our findings in a mouse COVID-19 model, confirmed virome differences and bacteriome dysbiosis due to SARS-CoV-2 infection, and observed that immune/infection-related genes were differentially expressed in gut epithelial cells during infection, possibly explaining the virome and bacteriome dynamics. Our results suggest that the components of the microbiome, including the bacteriome and virome, are affected by SARS-CoV-2 infections, while their compositional signatures could reflect or even contribute to disease severity and recovery processes.

[Virome: the next hotspot in microbiome research].

Virome is the collective term for the viral collection or viral metagenomes that are distributed in various environments. Viruses can be found in bodies of water, glaciers, plants, animals, and even some viruses, which are classified as eukaryotes, prokaryotes and subviruses. Viruses play very important role in maintaining environmental homeostasis and ecosystem balance, and are especially closely related to human health. In recent years, with the advancement of sequencing technology and data analysis, we are able to gain more insights into the virome and explore its potential role in the ecological niche by metagenomic sequencing. A large amount of viral data have been obtained from glaciers, oceans, and various plants and animals, and numerous unknown viruses have been discovered. Virome has been studied mainly through metagenomic data mining, as well as virus-like particles separation and enrichment. To date, several different methods for viral isolation and enrichment exist, and numerous bioinformatic analyses of the virome have been performed. However, there is a lack of specific and complete reviews on the enrichment and data analysis methods for the virome. Thus, our review will summarize viral isolation and enrichment methods and data analysis, and present some of the landmark research conducted by the enrichment method, to provide a reference for researchers of interest and further advance the field of virome research.

Community Curation and Expert Curation of Human Long Noncoding RNAs with LncRNAWiki and LncBook.

In recent years, the number of human long noncoding RNAs (lncRNAs) that have been identified has increased exponentially. However, these lncRNAs are poorly annotated compared to protein-coding genes, posing great challenges for a better understanding of their functional significance and elucidating their complex functioning molecular mechanisms. Here we employ both community and expert curation to yield a comprehensive collection of human lncRNAs and their annotations. Specifically, LncRNAWiki (http://lncrna.big.ac.cn/index.php/Main_Page) uses a wiki-based community curation model, thus showing great promise in dealing with the flood of biological knowledge, while LncBook (http://bigd.big.ac.cn/lncbook) is an expert curation-based database that provides a complement to LncRNAWiki. LncBook features a comprehensive collection of human lncRNAs and a systematic curation of lncRNAs by multi-omics data integration, functional annotation, and disease association. These protocols provide step-by-step instructions on how to browse and search a specific lncRNA and how to obtain a range of related information including expression, methylation, variation, function, and disease association. © 2019 by John Wiley & Sons, Inc.

Characterization and identification of long non-coding RNAs based on feature relationship.

MOTIVATION: The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. RESULTS: Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. AVAILABILITY AND IMPLEMENTATION: LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LncBook: a curated knowledgebase of human long non-coding RNAs.

Long non-coding RNAs (lncRNAs) have significant functions in a wide range of important biological processes. Although the number of known human lncRNAs has dramatically increased, they are poorly annotated, posing great challenges for better understanding their functional significance and elucidating their complex functioning molecular mechanisms. Here, we present LncBook (http://bigd.big.ac.cn/lncbook), a curated knowledgebase of human lncRNAs that features a comprehensive collection of human lncRNAs and systematic curation of lncRNAs by multi-omics data integration, functional annotation and disease association. In the present version, LncBook houses a large number of 270 044 lncRNAs and includes 1867 featured lncRNAs with 3762 lncRNA-function associations. It also integrates an abundance of multi-omics data from expression, methylation, genome variation and lncRNA-miRNA interaction. Also, LncBook incorporates 3772 experimentally validated lncRNA-disease associations and further identifies a total of 97 998 lncRNAs that are putatively disease-associated. Collectively, LncBook is dedicated to the integration and curation of human lncRNAs as well as their associated data and thus bears great promise to serve as a valuable knowledgebase for worldwide research communities.

Editome Disease Knowledgebase (EDK): a curated knowledgebase of editome-disease associations in human.

RNA editing, as an essential co-/post-transcriptional RNA modification type, plays critical roles in many biological processes and involves with a variety of human diseases. Although several databases have been developed to collect RNA editing data in both model and non-model animals, there still lacks a resource integrating associations between editome and human disease. In this study, we present Editome-Disease Knowledgebase (EDK; http://bigd.big.ac.cn/edk), an integrated knowledgebase of RNA editome-disease associations manually curated from published literatures. In the current version, EDK incorporates 61 diseases associated with 248 experimentally validated abnormal editing events located in 32 mRNAs, 16 miRNAs, 1 lncRNA and 11 viruses, and 44 aberrant activities involved with 6 editing enzymes, which together are curated from more than 200 publications. In addition, to facilitate standardization of editome-disease knowledge integration, we propose a data curation model in EDK, factoring an abundance of relevant information to fully capture the context of editome-disease associations. Taken together, EDK is a comprehensive collection of editome-disease associations and bears the great utility in aid of better understanding the RNA editing machinery and complex molecular mechanisms associated with human diseases.

ICG: a wiki-driven knowledgebase of internal control genes for RT-qPCR normalization.

Real-time quantitative PCR (RT-qPCR) has become a widely used method for accurate expression profiling of targeted mRNA and ncRNA. Selection of appropriate internal control genes for RT-qPCR normalization is an elementary prerequisite for reliable expression measurement. Here, we present ICG (http://icg.big.ac.cn), a wiki-driven knowledgebase for community curation of experimentally validated internal control genes as well as their associated experimental conditions. Unlike extant related databases that focus on qPCR primers in model organisms (mainly human and mouse), ICG features harnessing collective intelligence in community integration of internal control genes for a variety of species. Specifically, it integrates a comprehensive collection of more than 750 internal control genes for 73 animals, 115 plants, 12 fungi and 9 bacteria, and incorporates detailed information on recommended application scenarios corresponding to specific experimental conditions, which, collectively, are of great help for researchers to adopt appropriate internal control genes for their own experiments. Taken together, ICG serves as a publicly editable and open-content encyclopaedia of internal control genes and accordingly bears broad utility for reliable RT-qPCR normalization and gene expression characterization in both model and non-model organisms.