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In the past ten years, the research and application of microbiome has continued to increase. The microbiome has gradually become the research focus in the fields of life science, environmental science, and medicine. Meanwhile, many countries and organizations around the world are launching their own microbiome projects and conducting a multi-faceted layout, striving to gain a strategic position in this promising field. In addition, whether it is scientific research or industrial applications, there has been a climax of research and a wave of investment and financing, accordingly, products and services related to the microbiome are constantly emerging. However, due to the rapid development of microbiome sequencing and analysis related technologies and methods, the research and application from various countries have not yet unified on the standards of technology, programs, and data. Domestic industry participants also have insufficient understanding of the microbiome. New methods, technologies, and theories have not yet been fully accepted and used. In addition, some of the existing standards and guidelines are too general with poor practicality. This not only causes obstacles in the integration of scientific research data and waste of resources, but also gives related companies unfair competition opportunity. More importantly, China still lacks national standards related to the microbiome, and the national microbiome project is still in the process of preparation. In this context, the experts and practitioners of the microbiome worked together and developed the consensus of experts. It can not only guide domestic scientific research and industrial institutions to regulate the production, learning and research of the microbiome, the application can also provide reference technical basis for the relevant national functional departments, protect the scale and standardized corporate company's interests, strengthen industry self-discipline, avoid unregulated enterprises from disrupting the market, and ultimately promote the benign development of microbiome-related industries.
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Humanos , China , Consenso , Industrias , MicrobiotaRESUMEN
Exploring the mechanisms of maintaining microbial community structure is important to understand biofilm development or microbiota dysbiosis. In this paper, we propose a functional gene-based composition prediction (FCP) model to predict the population structure composition within a microbial community. The model predicts the community composition well in both a low-complexity community as acid mine drainage (AMD) microbiota, and a complex community as human gut microbiota. Furthermore, we define community structure shaping (CSS) genes as functional genes crucial for shaping the microbial community. We have identified CSS genes in AMD and human gut microbiota samples with FCP model and find that CSS genes change with the conditions. Compared to essential genes for microbes, CSS genes are significantly enriched in the genes involved in mobile genetic elements, cell motility, and defense mechanisms, indicating that the functions of CSS genes are focused on communication and strategies in response to the environment factors. We further find that it is the minority, rather than the majority, which contributes to maintaining community structure. Compared to health control samples, we find that some functional genes associated with metabolism of amino acids, nucleotides, and lipopolysaccharide are more likely to be CSS genes in the disease group. CSS genes may help us to understand critical cellular processes and be useful in seeking addable gene circuitries to maintain artificial self-sustainable communities. Our study suggests that functional genes are important to the assembly of microbial communities.
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Humanos , Microbioma Gastrointestinal , Genética , Genes Microbianos , Microbiota , Genética , Minería , Modelos Genéticos , Contaminación del AguaRESUMEN
Objective To observe the changes of inflammatory factors in acute lung injury (ALI) in mice induced by lipopolysaccharide (LPS), and to explore the influence of different doses of LPS on ALI onset and progress at different time points. Methods Intratracheally, LPS at the dosages of 2.5, 5.0, 7.5 and 10.0 mg/kg were administered to a total of 210 C57BL/6 mice, and according to the difference in dosage, they were divided into four groups. The ALI model was replicated by intratracheally dropping of LPS. And a normal control group and a normal saline control group were established (each, n=10). The changes of index of pathological lung tissue and lung tissue wet/dry (W/D) ratio were observed at 1, 2, 4, and 8 hours after injury, and simultaneously, the levels of norepinephrine (NE), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and protein in serum and bronchoalveolar lavage fluid (BALF) were detected. Results ①The degree of lung injury induced by LPS was dose-and time-dependent.②With the increase of LPS dosage and prolongation of time, in LPS group, the lung W/D ratio and the index of pathological lung tissue were increased;additionally, the levels of NE, TNF-α, IL-6 and protein in serum or BALF were also significantly increased. The critical occurrence point of acute respiratory distress syndrome (ARDS) with specific characteristics was at 5.0 mg/kg of LPS acting for 4 hours [lung W/D ratio: 4.97±0.41, index of pathological changes of lung tissue (score): 5.60±1.52; serum NE (ng/L): 379.99±27.65, TNF-α (ng/L): 159.15±20.62, IL-6 (ng/L): 177.15±29.13;BALF NE (mg/kg):105.85±13.66, TNF-α(mg/kg):227.22±48.01, IL-6 (mg/kg):251.55±54.08, total protein (g/L):1.59±0.37]. The injury induced by LPS acting for 8 hours in the dosage group 10.0 mg/kg was the most significant in comparisons with other groups of dosages at the same time points [lung W/D ratio:5.10±0.18 vs. 5.01±0.43, 5.01±0.19, 4.91±0.30; index of pathological changes of lung tissue (score): 9.20±1.48 vs. 8.00±1.00, 6.00±1.22, 4.40±0.89;serum NE (ng/L): 447.43±34.63 vs. 419.23±30.62, 391.16±54.91, 372.59±51.52; TNF-α(ng/L): 205.99±31.31 vs. 181.01±25.11, 161.01±13.98, 138.83±28.95; IL-6 (ng/L): 233.76±34.84 vs. 206.21±26.68, 186.58±26.54, 156.99±28.83;BALF NE (mg/kg):190.82±41.75 vs. 153.30±35.42, 122.64±25.15, 80.23±13.69;TNF-α(mg/kg):305.24±72.99 vs. 292.77±38.07, 249.60±35.20, 193.63±10.83; IL-6 (mg/kg): 354.81±67.79 vs. 303.02±54.24, 272.43±32.34, 197.64±12.35;total protein (g/L):2.31±0.30 vs. 2.02±0.26, 1.62±0.19, 1.10±0.24, P<0.05 or P<0.01]. Conclusions The severity of ALI induced by LPS in mice was positively correlated to LPS dosage and duration of its action. After administration of LPS 5 mg/kg for 4 hours, remarkable characteristic manifestations of ARDS occur in mice, reaching the critical point.
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Accurate prediction of the translation initiation site (TIS) is an important issue for prokaryotic genome annotation. However, it is still a challenge for the existing methods to predict the TIS in the genomes over a wide variety of GC content. Besides, the existing methods have not yet undergone a comprehensive evaluation, leaving prediction reliability as a largely open problem. A new algorithm MED-StartPlus, a tool that predicts TIS in prokaryotic genomes with a wide variety of GC content was presented. It makes several efforts to model the nucleotide composition bias, the regulatory motifs upstream of the TIS, the sequence patterns around the TIS, and the operon structure. Tests on hundreds of reliable data sets, with TISs confirmed by experiments or having annotated functions, show that the new method achieves a totally high accuracy of TIS prediction. Compared with existing TIS predictors, the method reports a totally higher performance, especially for genomes that are GC-rich or have complex initiation mechanisms. The potential application of the method to improve the TIS annotation deposited in the public database was also proposed.
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Light, auxin and brassinosteroid play important roles in plant growth and development. Genetic analysis has demonstrated complex interactions between their signaling pathways, but the gene regulatory mechanisms connecting these pathways are poorly understood. CYP72B1 and AUR3 are two important genes responsive to light, auxin and brassinosteroid at transcription level. To understand the regulation mechanism of the two genes, a new tool called OCMMat was developed for identifying cis-elements, OCMMat combines both the over-representation property of regulatory elements in co-expressed genes and the conservation property in orthologous genes, for the latter, it was estimated by an enrichment score of regulatory element in orthologous promoter sequences. Using this tool, 3 regulatory motifs shared by genes CYP72B1 and AUR3 were reported, motif GAGACA which is the same as a known cis-element AuxRE, motif AAGAAAAA containing the sequence of GT element and the third ATCATG which is a new one named EDIB element. The space and the order of AAGAAAAA and EDIB show the same pattern in promoters of both the co-expressed genes and the orthologs of CYP72B1. Based on the sequence analysis and the literature knowledge to date, a model was proposed for describing the transcriptional regulatory mechanism of CYP72B1 and AUR3 in response to light, auxin and brassinosteroid. The model presents how the signaling pathways of light, auxin and brassinosteroid are interplaying at gene transcription level. In response to light, the transcription factors GT factor and an unknown protein repress the expression of CYP72B1 and AUR3, the hormone pathways are not interfered and thus work in their own way. While in the absence of light stimulation, CYP72B1 and AUR3 are expressed and the products, in turn, inhibit both the auxin pathway and the brassinosteroid pathways. On the other side, at high hormone level, gene expression is up-regulated through ARF binding, the gene products inhibit the hormone pathways in a feedback manner, and meanwhile, rescues the light signal through the photoreceptor phyB.
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The recent outbreak of pneumonia in Wuhan, China caused by the 2019 Novel Coronavirus (2019-nCoV) emphasizes the importance of detecting novel viruses and predicting their risks of infecting people. In this report, we introduced the VHP (Virus Host Prediction) to predict the potential hosts of viruses using deep learning algorithm. Our prediction suggests that 2019-nCoV has close infectivity with other human coronaviruses, especially the severe acute respiratory syndrome coronavirus (SARS-CoV), Bat SARS-like Coronaviruses and the Middle East respiratory syndrome coronavirus (MERS-CoV). Based on our prediction, compared to the Coronaviruses infecting other vertebrates, bat coronaviruses are assigned with more similar infectivity patterns with 2019-nCoVs. Furthermore, by comparing the infectivity patterns of all viruses hosted on vertebrates, we found mink viruses show a closer infectivity pattern to 2019-nCov. These consequences of infectivity pattern analysis illustrate that bat and mink may be two candidate reservoirs of 2019-nCov.These results warn us to beware of 2019-nCoV and guide us to further explore the properties and reservoir of it. One Sentence SummaryIt is of great value to identify whether a newly discovered virus has the risk of infecting human. Guo et al. proposed a virus host prediction method based on deep learning to detect what kind of host a virus can infect with DNA sequence as input. Applied to the Wuhan 2019 Novel Coronavirus, our prediction demonstrated that several vertebrate-infectious coronaviruses have strong potential to infect human. This method will be helpful in future viral analysis and early prevention and control of viral pathogens.
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The SARS-CoV-2 pandemic has raised the concern for identifying hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features automatically, to predict host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool applicable to any novel virus and overcame the limitation of the sequence similarity-based methods, reaching a satisfactory AUC of 0.987 on the five-classification. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existed tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of COVID-19, we inferred minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, the large-scale genome analysis, based on DeepHoFs computation for the later world-wide pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.