Eukaryotic Sequences in the 16SrRNA Metagenomic Dataset of Algal–bacterial Consortia of the White Sea Coastal Zone

The libraries of bacterial 16SrRNA gene fragment from algal–bacterial consortia of the White Sea coastal zone are analyzed. Up to 3% of the reads have revealed to correspond to eukaryotic rRNA. They related to following main eukaryotic clades: Discoba, Stramenopiles, Ciliata, Amoebozoa, and Nucletmycea. Amoebae, especially Vermamoeba, were present in all samples. In one sample, heterolobose amoeba Paravahlkampfia was detected. These microorganisms are parasites of microalgae, which can induce significant damage to industrial cultures. However, the data on their physiology and distribution are scarce. This study provides new evidence about the diversity of herbivorous eukaryotic microorganisms in natural algal‐containing consortia.     

Web Resources for Metagenomics Studies

The development of next-generation sequencing(NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.     

Biogeographic and Evolutionary Patterns of Trace Element Utilization in Marine Microbial World

Trace elements are required by all organisms, which are key components of many enzymes catalyzing important biological reactions. Many trace element-dependent proteins have been characterized; however, little is known about their occurrence in microbial communities in diverse environments, especially the global marine ecosystem. Moreover, the relationships between trace element utilization and different types of environmental stressors are unclear. In this study, we used metagenomic data from the Global Ocean Sampling expedition project to identify the biogeographic distribution of genes encoding trace element-dependent proteins (for copper, molybdenum, cobalt, nickel, and selenium) in a variety of marine and non-marine aquatic samples. More than 56,000 metalloprotein and selenoprotein genes corresponding to nearly 100 families were predicted, becoming the largest dataset of marine metalloprotein and selenoprotein genes reported to date. In addition, samples with enriched or depleted metalloprotein/selenoprotein genes were identified, suggesting an active or inactive usage of these micronutrients in various sites. Further analysis of interactions among the elements showed significant correlations between some of them, especially those between nickel and selenium/copper. Finally, investigation of the relationships between environmental conditions and metalloprotein/selenoprotein families revealed that many environmental factors might contribute to the evolution of different metalloprotein and/or selenoprotein genes in the marine microbial world. Our data provide new insights into the utilization and biological roles of these trace elements in extant marine microbes, and might also be helpful for the understanding of how these organisms have adapted to their local environments.     

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.     

The molecular basis for adaptive evolution in novel extradiol dioxygenases retrieved from the metagenome

Extradiol dioxygenase (EDO) catalyzes metal-dependent ring cleavage of catecholic substrates. We previously screened a metagenomic library of activated sludge used to treat industrial wastewater contaminated with phenols and cyanide to identify 43 EDO genes. Here, we have characterized the enzymes belonging to novel I.2.G, I.3.M and I.3.N subfamilies. The I.3.M and I.3.N EDOs were Fe(II) dependent and preferred bicyclic substrates, whereas the I.2.G EDOs were Mn(II) dependent, preferred monocyclic substrates and had the highest affinity for catechol reported thus far. The I.2.G EDOs were more tolerant against heat (60 °C for 1 h) and chemical inhibitors (H₂O₂ and NaCN) than I.3.M and I.3.N EDOs. Considering the dominance of the I.2.G EDOs over all retrieved EDOs (20 of 43 clones) and the presence of cyanide in the environment, this high affinity for substrate and structural robustness should provide survival advantages to host microorganisms. The 20 I.2.G EDOs were classified into six groups based on the amino acid sequence of the predicted ancestor, 1A1. Enzymes were chosen from each group and characterized. Two descendents, 1D2 and 5B2, each had a k _cat/ K _M approximately twofold higher than that of 1A1 and reduced thermal stability, suggesting that descendents of 1A1 have adapted evolutionarily by a trade-off of inherent stability for increased activity.     

Diversity of probiotic adhesion genes in the gastrointestinal tract of goats

Gastrointestinal (GI) microflora is an important system in the host, as it has both pathogenic and probiotic bacteria. Most of the studies were focused on the human gut microflora and the available information on the intestinal microflora of goats was limited. This urged the need to inspect the impacts of the goat's gut microflora. Metagenomic investigation of probiotic bacteria in the GI tract of goat is one of the challenging streams because of the less available data of the uncultivable bacteria. In our report, comparative analysis of metagenomic and enrichment samples of goat intestinal content was done and this approach will be helpful in analyzing the identification of uncultivable and cultivable probiotic bacteria. This study mainly focused on three key probiotic adhesion genes, such as EF-Tu, mapA, and mub. The GI of four different goats were investigated for these genes. The data from this study showed that there is a wide diversity of these genes among goat intestinal samples.     

环境微生物研究中机器学习算法及应用

微生物在环境中无处不在，它们不仅是生物地球化学循环和环境演化的关键参与者，也在环境监测、生态治理和保护中发挥着重要作用。随着高通量技术的发展，大量微生物数据产生，运用机器学习对环境微生物大数据进行建模和分析，在微生物标志物识别、污染物预测和环境质量预测等领域的科学研究和社会应用方面均具有重要意义。机器学习可分为监督学习和无监督学习2大类。在微生物组学研究当中，无监督学习通过聚类、降维等方法高效地学习输入数据的特征，进而对微生物数据进行整合和归类。监督学习运用有特征和标记的微生物数据集训练模型，在面对只有特征没有标记的数据时可以判断出标记，从而实现对新数据的分类、识别和预测。然而，复杂的机器学习算法通常以牺牲可解释性为代价来重点关注模型预测的准确性。机器学习模型通常可以看作预测特定结果的“黑匣子”，即对模型如何得出预测所知甚少。为了将机器学习更多地运用于微生物组学研究、提高我们提取有价值的微生物信息的能力，深入了解机器学习算法、提高模型的可解释性尤为重要。本文主要介绍在环境微生物领域常用的机器学习算法和基于微生物组数据的机器学习模型的构建步骤，包括特征选择、算法选择、模型构建和评估等，并对各种机器学习模型在环境微生物领域的应用进行综述，深入探究微生物组与周围环境之间的关联，探讨提高模型可解释性的方法，并为未来环境监测、环境健康预测提供科学参考。     

Genetic and functional properties of uncultivated MCG archaea assessed by metagenome and gene expression analyses

The Miscellaneous Crenarchaeota group (MCG) Archaea is one of the predominant archaeal groups in anoxic environments and may have significant roles in the global biogeochemical cycles. However, no isolate of MCG has been cultivated or characterized to date. In this study, we investigated the genetic organization, ecophysiological properties and evolutionary relationships of MCG archaea with other archaeal members using metagenome information and the result of gene expression experiments. A comparison of the gene organizations and similarities around the 16S rRNA genes from all available MCG fosmid and cosmid clones revealed no significant synteny among genomic fragments, demonstrating that there are large genetic variations within members of the MCG. Phylogenetic analyses of large-subunit+small-subunit rRNA, concatenated ribosomal protein genes and topoisomerases IB gene (TopoIB) all demonstrate that MCG constituted a sister lineage to the newly proposed archaeal phylum Aigarchaeota and Thaumarchaeota. Genes involved in protocatechuate degradation and chemotaxis were found in a MCG fosmid 75G8 genome fragment, suggesting that this MCG member may have a role in the degradation of aromatic compounds. Moreover, the expression of a putative 4-carboxymuconolactone decarboxylase was observed when the sediment was supplemented with protocatechuate, further supporting the hypothesis that this MCG member degrades aromatic compounds.     

沼气池宏基因组文库中未培养微生物β-葡萄糖苷酶基因的克隆与鉴定

以间接提取法提取了沼气池样品的微生物宏基因组DNA,用柯斯质粒载体pWEB:TNC构建了一个含三万个克隆的沼气池宏基因组文库,对文库中的克隆随机分析表明,该文库的外源片段平均长度为40 kb,文库的总容量为1 .2×106kb。对其中的一个在七叶苷平板上显色的阳性克隆pGXN100进行进一步亚克隆、测序和序列分析。结果表明,pGXN100上有一个全长为1 863bp的ORF,编码621个氨基酸组成的蛋白质。将该基因命名为Unglu100。与产气克雷伯菌属的一个β-葡萄糖苷酶基因AN292在核苷酸和氨基酸水平上分别有76%和85%的同源性,利用SMART软件进行预测表明,Unglu100可能是PTS中β-葡萄糖苷酶特异性的转运蛋白组件。     

Cloning and Heterologous Expression of the Vibrioferrin Biosynthetic Gene Cluster from a Marine Metagenomic Library

A biosynthetic gene cluster of siderophore consisting of five open reading frames (ORFs) was cloned by functional screening of a metagenomic library constructed from tidal-flat sediment. Expression of the cloned biosynthetic genes in Escherichia coli led to the production of vibrioferrin, a siderophore originally reported for the marine bacterium Vibrio parahaemolyticus. To the best of our knowledge, this is the first example of heterologous production of a siderophore by biosynthetic genes cloned from a metagenomic library. The cloned cluster was one of the largest of the clusters obtained by functional screening. In this study, we demonstrated and extended the possibility of function-based metagenomic research.