Nanopore sequencing produces long reads and offers unique advantages over next-generation sequencing, especially for the assembly of draft bacterial genomes with improved completeness. However, ...assembly errors can occur due to data characteristics and assembly algorithms. To address these issues, we developed MAECI, a pipeline for generating consensus sequences from multiple assemblies of the same nanopore sequencing data and error correction. Systematic evaluation showed that MAECI is an efficient and effective pipeline to improve the accuracy and completeness of bacterial genome assemblies. The available codes and implementation are at https://github.com/langjidong/MAECI.
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Nanopore sequencing technology (NST) has become a rapid and cost-effective method for the diagnosis and epidemiological surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ...during the coronavirus disease 2019 (COVID-19) pandemic. Compared with short-read sequencing platforms (e.g., Illumina’s), nanopore long-read sequencing platforms effectively shorten the time required to complete the detection process. However, due to the principles and data characteristics of NST, the accuracy of sequencing data has been reduced, thereby limiting monitoring and lineage analysis of SARS-CoV-2. In this study, we developed an analytical pipeline for SARS-CoV-2 rapid detection and lineage identification that integrates phylogenetic-tree and hotspot mutation analysis, which we have named NanoCoV19. This method not only can distinguish and trace the lineages contained in the alpha, beta, delta, gamma, lambda, and omicron variants of SARS-CoV-2 but is also rapid and efficient, completing overall analysis within 1 h. We hope that NanoCoV19 can be used as an auxiliary tool for rapid subtyping and lineage analysis of SARS-CoV-2 and, more importantly, that it can promote further applications of NST in public-health and -safety plans similar to those formulated to address the COVID-19 outbreak.
Particulate matter (PM) air pollution poses a formidable public health threat to the city of Beijing. Among the various hazards of PM pollutants, microorganisms in PM2.5 and PM10 are thought to be ...responsible for various allergies and for the spread of respiratory diseases. While the physical and chemical properties of PM pollutants have been extensively studied, much less is known about the inhalable microorganisms. Most existing data on airborne microbial communities using 16S or 18S rRNA gene sequencing to categorize bacteria or fungi into the family or genus levels do not provide information on their allergenic and pathogenic potentials. Here we employed metagenomic methods to analyze the microbial composition of Beijing’s PM pollutants during a severe January smog event. We show that with sufficient sequencing depth, airborne microbes including bacteria, archaea, fungi, and dsDNA viruses can be identified at the species level. Our results suggested that the majority of the inhalable microorganisms were soil-associated and nonpathogenic to human. Nevertheless, the sequences of several respiratory microbial allergens and pathogens were identified and their relative abundance appeared to have increased with increased concentrations of PM pollution. Our findings may serve as an important reference for environmental scientists, health workers, and city planners.
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Scleral hypoxia is a target for myopia control Wu, Hao; Chen, Wei; Zhao, Fei ...
Proceedings of the National Academy of Sciences - PNAS,
07/2018, Volume:
115, Issue:
30
Journal Article
Peer reviewed
Open access
Worldwide, myopia is the leading cause of visual impairment. It results from inappropriate extension of the ocular axis and concomitant declines in scleral strength and thickness caused by ...extracellular matrix (ECM) remodeling. However, the identities of the initiators and signaling pathways that induce scleral ECM remodeling in myopia are unknown. Here, we used single-cell RNA-sequencing to identify pathways activated in the sclera during myopia development. We found that the hypoxia-signaling, the eIF2-signaling, and mTOR-signaling pathways were activated in murine myopic sclera. Consistent with the role of hypoxic pathways in mouse model of myopia, nearly one third of human myopia risk genes from the genome-wide association study and linkage analyses interact with genes in the hypoxia-inducible factor-1α (HIF-1α)–signaling pathway. Furthermore, experimental myopia selectively induced HIF-1α up-regulation in the myopic sclera of both mice and guinea pigs. Additionally, hypoxia exposure (5% O₂) promoted myofibroblast transdifferentiation with down-regulation of type I collagen in human scleral fibroblasts. Importantly, the antihypoxia drugs salidroside and formononetin down-regulated HIF-1α expression as well as the phosphorylation levels of eIF2α and mTOR, slowing experimental myopia progression without affecting normal ocular growth in guinea pigs. Furthermore, eIF2α phosphorylation inhibition suppressed experimental myopia, whereas mTOR phosphorylation induced myopia in normal mice. Collectively, these findings defined an essential role of hypoxia in scleral ECM remodeling and myopia development, suggesting a therapeutic approach to control myopia by ameliorating hypoxia.
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Metagenomic sequencing has been widely used for the study of microbial communities from various environments such as soil, ocean, sediment and fresh water. Nonetheless, metagenomic sequencing of ...microbial communities in the air remains technically challenging, partly owing to the limited mass of collectable atmospheric particulate matter and the low biological content it contains. Here we present an optimized protocol for extracting up to tens of nanograms of airborne microbial genomic DNA from collected particulate matter. With an improved sequencing library preparation protocol, this quantity is sufficient for downstream applications, such as metagenomic sequencing for sampling various genes from the airborne microbial community. The described protocol takes ∼12 h of bench time over 2-3 d, and it can be performed with standard molecular biology equipment in the laboratory. A modified version of this protocol may also be used for genomic DNA extraction from other environmental samples of limited mass or low biological content.
In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell ...lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.
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This study proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was effectively applied to two benchmark datasets. It also explicitly outputs the genes involved in the prediction, which are enriched in functions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according ...to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (53%) alone. In conclusion, TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.
Background
Human papillomavirus (HPV) infection is the leading cause of cervical cancer. More and more studies discovered that cervical microbiota (CM) composition correlated with HPV infection and ...the development of cervical cancer. However, more studies need to be implemented to clarify the complex interaction between microbiota and the mechanism of disease development, especially in a specific area of China.
Materials and methods
In this study, 16S rDNA sequencing was applied on 276 Thin-prep Cytologic Test (TCT) samples of patients from the Sanmenxia area. Systematical analysis of the microbiota structure, diversity, group, and functional differences between different HPV infection groups and age groups, and co-occurrence relationships of the microbiota was carried out.
Results
The major microbiota compositions of all patients include
Lactobacillus iners
,
Escherichia coli
,
Enterococcus faecalis
, and
Atopobium vaginae
at species level, and
Staphylococcus
,
Lactobacillus
,
Gardnerella
,
Bosea
,
Streptococcus
, and
Sneathia
in genus level. Microbiota diversity was found significantly different between HPV-positive (Chao1 index: 98.8869,
p
< 0.01), unique-268 infected (infections with one of the HPV genotype 52, 56, or 58, 107.3885,
p
< 0.01), multi-268 infected (infections with two or more of HPV genotype 52, 56, and 58, 97.5337,
p
= 0.1012), other1 (94.9619,
p
< 0.05) groups and HPV-negative group (83.5299). Women older than 60 years old have higher microbiota diversity (108.8851,
p
< 0.01,
n
= 255) than younger women (87.0171,
n
= 21). The abundance of
Gardnerella
and
Atopobium vaginae
was significantly higher in the HPV-positive group than in the HPV-negative group, while
Burkholderiaceae
and
Mycoplasma
were more abundant in the unique-268 group compared to the negative group.
Gamma-proteobacteria
and
Pseudomonas
were found more abundant in older than 60 patients than younger groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG) analysis revealed the effects on metabolism by microbiota that the metabolism of cells, proteins, and genetic information-related pathways significantly differed between HPV-negative and positive groups. In contrast, lipid metabolism, signal transduction, and cell cycle metabolism pathway significantly differed between multi-268 and negative groups.
Conclusion
The HPV infection status and age of women were related to CM’s diversity and function pathways. The complex CM co-occurrent relationships and their mechanism in disease development need to be further investigated.
Drug-resistant bacteria are important carriers of antibiotic-resistant genes (ARGs). This fact is crucial for the development of precise clinical drug treatment strategies. Long-read sequencing ...platforms such as the Oxford Nanopore sequencer can improve genome assembly efficiency particularly when they are combined with short-read sequencing data.
Alcaligenes faecalis PGB1 was isolated and identified with resistance to penicillin and three other antibiotics. After being sequenced by Nanopore MinION and Illumina sequencer, its entire genome was hybrid-assembled. One chromosome and one plasmid was assembled and annotated with 4,433 genes (including 91 RNA genes). Function annotation and comparison between strains were performed. A phylogenetic analysis revealed that it was closest to A. faecalis ZD02. Resistome related sequences was explored, including ARGs, Insert sequence, phage. Two plasmid aminoglycoside genes were determined to be acquired ARGs. The main ARG category was antibiotic efflux resistance and β-lactamase (EC 3.5.2.6) of PGB1 was assigned to Class A, Subclass A1b, and Cluster LSBL3.
The present study identified the newly isolated bacterium A. faecalis PGB1 and systematically annotated its genome sequence and ARGs.
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