Draft genome sequence of three hydrocarbon-degrading Pseudomonadota strains isolated from an abandoned century-old oil exploration well.

We present genome sequences of three Pseudomonadota strains isolated from an abandoned century-old oil exploration well. A Pseudomonas sp. genome showed a size of 5,378,420 bp, while Acinetobacter genomes sized 3,522,593 and 3,864,311 bp. Genomes included catabolic genes for benzoate, 4-hydroxybenzoate, salicylate, vanillate, indoleacetate, anthranilate, n-alkanes, 4-hydroxyphenylacetate, phenylacetate, among others.

Exploring environmental intra-species diversity through non-redundant pangenome assemblies.

At the genome level, microorganisms are highly adaptable both in terms of allele and gene composition. Such heritable traits emerge in response to different environmental niches and can have a profound influence on microbial community dynamics. As a consequence, any individual genome or population will contain merely a fraction of the total genetic diversity of any operationally defined "species", whose ecological potential can thus be only fully understood by studying all of their genomes and the genes therein. This concept, known as the pangenome, is valuable for studying microbial ecology and evolution, as it partitions genomes into core (present in all the genomes from a species, and responsible for housekeeping and species-level niche adaptation among others) and accessory regions (present only in some, and responsible for intra-species differentiation). Here we present SuperPang, an algorithm producing pangenome assemblies from a set of input genomes of varying quality, including metagenome-assembled genomes (MAGs). SuperPang runs in linear time and its results are complete, non-redundant, preserve gene ordering and contain both coding and non-coding regions. Our approach provides a modular view of the pangenome, identifying operons and genomic islands, and allowing to track their prevalence in different populations. We illustrate this by analysing intra-species diversity in Polynucleobacter, a bacterial genus ubiquitous in freshwater ecosystems, characterized by their streamlined genomes and their ecological versatility. We show how SuperPang facilitates the simultaneous analysis of allelic and gene content variation under different environmental pressures, allowing us to study the drivers of microbial diversification at unprecedented resolution.

Immunoanalytical Approach for Detecting and Identifying Ancestral Peptide Biomarkers in Early Earth Analogue Environments.

Several mass spectrometry and spectroscopic techniques have been used in the search for molecular biomarkers on Mars. A major constraint is their capability to detect and identify large and complex compounds such as peptides or other biopolymers. Multiplex immunoassays can detect these compounds, but antibodies must be produced for a large number of sequence-dependent molecular targets. Ancestral Sequence Reconstruction (ASR) followed by protein "resurrection" in the lab can help to narrow the selection of targets. Herein, we propose an immunoanalytical method to identify ancient and universally conserved protein/peptide sequences as targets for identifying ancestral biomarkers in nature. We have developed, tested, and validated this approach by producing antibodies to eight previously described ancestral resurrected proteins (three ß-lactamases, three thioredoxins, one Elongation Factor Tu, and one RuBisCO, all of them theoretically dated as Precambrian), and used them as a proxy to search for any potential feature of them that could be present in current natural environments. By fluorescent sandwich microarray immunoassays (FSMI), we have detected positive immunoreactions with antibodies to the oldest ß-lactamase and thioredoxin proteins (ca. 4 Ga) in samples from a hydrothermal environment. Fine epitope mapping and inhibitory immunoassays allowed the identification of well-conserved epitope peptide sequences that resulted from ASR and were present in the sample. We corroborated these results by metagenomic sequencing and found several genes encoding analogue proteins with significant matches to the peptide epitopes identified with the antibodies. The results demonstrated that peptides inferred from ASR studies have true counterpart analogues in Nature, which validates and strengthens the well-known ASR/protein resurrection technique and our immunoanalytical approach for investigating ancient environments and metabolisms on Earth and elsewhere.

Out of the blue: the independent activity of sulfur-oxidizers and diatoms mediate the sudden color shift of a tropical river.

BACKGROUND: Río Celeste ("Sky-Blue River") is a river located in the Tenorio National Park (Costa Rica) that has become an important hotspot for eco-tourism due to its striking sky-blue color. A previous study indicated that this color is not caused by dissolved chemical species, but by formation of light-scattering aluminosilicate particles at the mixing point of two colorless streams, the acidic Quebrada Agria and the neutral Río Buenavista. RESULTS: We now present microbiological information on Río Celeste and its two tributaries, as well as a more detailed characterization of the particles that occur at the mixing point. Our results overturn the previous belief that the light scattering particles are formed by the aggregation of smaller particles coming from Río Buenavista, and rather point to chemical formation of hydroxyaluminosilicate colloids when Quebrada Agria is partially neutralized by Río Buenavista, which also contributes silica to the reaction. The process is mediated by the activities of different microorganisms in both streams. In Quebrada Agria, sulfur-oxidizing bacteria generate an acidic environment, which in turn cause dissolution and mobilization of aluminum and other metals. In Río Buenavista, the growth of diatoms transforms dissolved silicon into colloidal biogenic forms which may facilitate particle precipitation. CONCLUSIONS: We show how the sky-blue color of Río Celeste arises from the tight interaction between chemical and biological processes, in what constitutes a textbook example of emergent behavior in environmental microbiology.

Coupled C, H, N, S and Fe biogeochemical cycles operating in the continental deep subsurface of the Iberian Pyrite Belt.

Microbial activity is a major contributor to the biogeochemical cycles that make up the life support system of planet Earth. A 613 m deep geomicrobiological perforation and a systematic multi-analytical characterization revealed an unexpected diversity associated with the rock matrix microbiome that operates in the subsurface of the Iberian Pyrite Belt (IPB). Members of 1 class and 16 genera were deemed the most representative microorganisms of the IPB deep subsurface and selected for a deeper analysis. The use of fluorescence in situ hybridization allowed not only the identification of microorganisms but also the detection of novel activities in the subsurface such as anaerobic ammonium oxidation (ANAMMOX) and anaerobic methane oxidation, the co-occurrence of microorganisms able to maintain complementary metabolic activities and the existence of biofilms. The use of enrichment cultures sensed the presence of five different complementary metabolic activities along the length of the borehole and isolated 29 bacterial species. Genomic analysis of nine isolates identified the genes involved in the complete operation of the light-independent coupled C, H, N, S and Fe biogeochemical cycles. This study revealed the importance of nitrate reduction microorganisms in the oxidation of iron in the anoxic conditions existing in the subsurface of the IPB.

A consensus protocol for the recovery of mercury methylation genes from metagenomes.

Mercury (Hg) methylation genes (hgcAB) mediate the formation of the toxic methylmercury and have been identified from diverse environments, including freshwater and marine ecosystems, Arctic permafrost, forest and paddy soils, coal-ash amended sediments, chlor-alkali plants discharges and geothermal springs. Here we present the first attempt at a standardized protocol for the detection, identification and quantification of hgc genes from metagenomes. Our Hg-cycling microorganisms in aquatic and terrestrial ecosystems (Hg-MATE) database, a catalogue of hgc genes, provides the most accurate information to date on the taxonomic identity and functional/metabolic attributes of microorganisms responsible for Hg methylation in the environment. Furthermore, we introduce "marky-coco", a ready-to-use bioinformatic pipeline based on de novo single-metagenome assembly, for easy and accurate characterization of hgc genes from environmental samples. We compared the recovery of hgc genes from environmental metagenomes using the marky-coco pipeline with an approach based on coassembly of multiple metagenomes. Our data show similar efficiency in both approaches for most environments except those with high diversity (i.e., paddy soils) for which a coassembly approach was preferred. Finally, we discuss the definition of true hgc genes and methods to normalize hgc gene counts from metagenomes.

Heterologous Expression of the Phytochelatin Synthase CaPCS2 from Chlamydomonas acidophila and Its Effect on Different Stress Factors in Escherichia coli.

Phytochelatins (PCs) are cysteine-rich small peptides, enzymatically synthesized from reduced glutathione (GSH) by cytosolic enzyme phytochelatin synthase (PCS). The open reading frame (ORF) of the phytochelatin synthase CaPCS2 gene from the microalgae Chlamydomonas acidophila was heterologously expressed in Escherichia coli strain DH5α, to analyze its role in protection against various abiotic agents that cause cellular stress. The transformed E. coli strain showed increased tolerance to exposure to different heavy metals (HMs) and arsenic (As), as well as to acidic pH and exposure to UVB, salt, or perchlorate. In addition to metal detoxification activity, new functions have also been reported for PCS and PCs. According to the results obtained in this work, the heterologous expression of CaPCS2 in E. coli provides protection against oxidative stress produced by metals and exposure to different ROS-inducing agents. However, the function of this PCS is not related to HM bioaccumulation.

The emergence of interstellar molecular complexity explained by interacting networks.

Recent years have witnessed the detection of an increasing number of complex organic molecules in interstellar space, some of them being of prebiotic interest. Disentangling the origin of interstellar prebiotic chemistry and its connection to biochemistry and ultimately, to biology is an enormously challenging scientific goal where the application of complexity theory and network science has not been fully exploited. Encouraged by this idea, we present a theoretical and computational framework to model the evolution of simple networked structures toward complexity. In our environment, complex networks represent simplified chemical compounds and interact optimizing the dynamical importance of their nodes. We describe the emergence of a transition from simple networks toward complexity when the parameter representing the environment reaches a critical value. Notably, although our system does not attempt to model the rules of real chemistry nor is dependent on external input data, the results describe the emergence of complexity in the evolution of chemical diversity in the interstellar medium. Furthermore, they reveal an as yet unknown relationship between the abundances of molecules in dark clouds and the potential number of chemical reactions that yield them as products, supporting the ability of the conceptual framework presented here to shed light on real scenarios. Our work reinforces the notion that some of the properties that condition the extremely complex journey from the chemistry in space to prebiotic chemistry and finally, to life could show relatively simple and universal patterns.

M&Ms: a versatile software for building microbial mock communities.

SUMMARY: Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze 16S rDNA sequencing data. As these tools need to be tested, it is important to simulate datasets that resemble samples from different environments. Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce different 16S rDNA datasets from reference sequences, based on pragmatic ecological parameters. It creates sequence libraries for 'in silico' microbial communities with user-controlled richness, evenness, microdiversity and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. AVAILABILITY AND IMPLEMENTATION: The source code of M&Ms is freely available at https://github.com/ggnatalia/MMs (GPL-3.0 License). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle.

BACKGROUND: Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions. RESULTS: The ß-diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R2 < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (Padj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals. CONCLUSIONS: Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH4 emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.

Metagenomic and metabolic analyses of poly-extreme microbiome from an active crater volcano lake.

El Chichón volcano is one of the most active volcanoes in Mexico. Previous studies have described its poly-extreme conditions and its bacterial composition, although the functional features of the complete microbiome have not been characterized yet. By using metabarcoding analysis, metagenomics, metabolomics and enzymology techniques, the microbiome of the crater lake was characterized in this study. New information is provided on the taxonomic and functional diversity of the representative Archaea phyla, Crenarchaeota and Euryarchaeota, as well as those that are representative of Bacteria, Thermotogales and Aquificae. With culture of microbial consortia and with the genetic information collected from the natural environment sampling, metabolic interactions were identified between prokaryotes, which can withstand multiple extreme conditions. The existence of a close relationship between the biogeochemical cycles of carbon and sulfur in an active volcano has been proposed, while the relationship in the energy metabolism of thermoacidophilic bacteria and archaea in this multi-extreme environment was biochemically revealed for the first time. These findings contribute towards understanding microbial metabolism under extreme conditions, and provide potential knowledge pertaining to "microbial dark matter", which can be applied to biotechnological processes and evolutionary studies.

Special Issue: Diversity of Extremophiles in Time and Space.

Extreme environments are fascinating ecosystems that have allowed us to increase our knowledge about the evolutionary processes of life [...].

A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding.

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.

Temperature and elemental sulfur shape microbial communities in two extremely acidic aquatic volcanic environments.

Aquatic environments of volcanic origin provide an exceptional opportunity to study the adaptations of microorganisms to early planet life conditions. Here, we characterized the prokaryotic communities and physicochemical properties of seepage sites at the bottom of the Poas Volcano crater and the Agrio River, two geologically related extremely acidic environments located in Costa Rica. Both locations hold a low pH (1.79-2.20) and have high sulfate and iron concentrations (Fe = 47-206 mg/L, SO42- = 1170-2460 mg/L), but significant differences in their temperature (90.0-95.0 ºC in the seepages at Poas Volcano, 19.1-26.6 ºC in Agrio River) and in the elemental sulfur content. Based on the analysis of 16S rRNA gene sequences, we determined that Sulfobacillus spp. represented more than half of the sequences in Poas Volcano seepage sites, while Agrio River was dominated by Leptospirillum and members of the archaeal order Thermoplasmatales. Both environments share some chemical characteristics and part of their microbiota, however, the temperature and the reduced sulfur are likely the main distinguishing features, ultimately shaping their microbial communities. Our data suggest that in the Poas Volcano-Agrio River system there is a common metabolism but with specialization of species that adapt to the physicochemical conditions of each environment.

SQMtools: automated processing and visual analysis of 'omics data with R and anvi'o.

BACKGROUND: The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches. RESULTS: Here we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi'o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment. CONCLUSIONS: Altogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands.

Microbial Community Structure Along a Horizontal Oxygen Gradient in a Costa Rican Volcanic Influenced Acid Rock Drainage System.

We describe the geochemistry and microbial diversity of a pristine environment that resembles an acid rock drainage (ARD) but it is actually the result of hydrothermal and volcanic influences. We designate this environment, and other comparable sites, as volcanic influenced acid rock drainage (VARD) systems. The metal content and sulfuric acid in this ecosystem stem from the volcanic milieu and not from the product of pyrite oxidation. Based on the analysis of 16S rRNA gene amplicons, we report the microbial community structure in the pristine San Cayetano Costa Rican VARD environment (pH = 2.94-3.06, sulfate ~ 0.87-1.19 g L-1, iron ~ 35-61 mg L-1 (waters), and ~ 8-293 g kg-1 (sediments)). San Cayetano was found to be dominated by microorganisms involved in the geochemical cycling of iron, sulfur, and nitrogen; however, the identity and abundance of the species changed with the oxygen content (0.40-6.06 mg L-1) along the river course. The hypoxic source of San Cayetano is dominated by a putative anaerobic sulfate-reducing Deltaproteobacterium. Sulfur-oxidizing bacteria such as Acidithiobacillus or Sulfobacillus are found in smaller proportions with respect to typical ARD. In the oxic downstream, we identified aerobic iron-oxidizers (Leptospirillum, Acidithrix, Ferrovum) and heterotrophic bacteria (Burkholderiaceae bacterium, Trichococcus, Acidocella). Thermoplasmatales archaea closely related to environmental phylotypes found in other ARD niches were also observed throughout the entire ecosystem. Overall, our study shows the differences and similarities in the diversity and distribution of the microbial communities between an ARD and a VARD system at the source and along the oxygen gradient that establishes on the course of the river.

Impacts of Saharan Dust Intrusions on Bacterial Communities of the Low Troposphere.

We have analyzed the bacterial community of a large Saharan dust event in the Iberian Peninsula and, for the first time, we offer new insights regarding the bacterial distribution at different altitudes of the lower troposphere and the replacement of the microbial airborne structure as the dust event receeds. Samples from different open-air altitudes (surface, 100 m and 3 km), were obtained onboard the National Institute for Aerospace Technology (INTA) C-212 aircrafts. Samples were collected during dust and dust-free air masses as well two weeks after the dust event. Samples related in height or time scale seems to show more similar community composition patterns compared with unrelated samples. The most abundant bacterial species during the dust event, grouped in three different phyla: (a) Proteobacteria: Rhizobiales, Sphingomonadales, Rhodobacterales, (b) Actinobacteria: Geodermatophilaceae; (c) Firmicutes: Bacillaceae. Most of these taxa are well known for being extremely stress-resistant. After the dust intrusion, Rhizobium was the most abundant genus, (40-90% total sequences). Samples taken during the flights carried out 15 days after the dust event were much more similar to the dust event samples compared with the remaining samples. In this case, Brevundimonas, and Methylobacterium as well as Cupriavidus and Mesorizobium were the most abundant genera.

Microbiota characterization of sheep milk and its association with somatic cell count using 16s rRNA gene sequencing.

This work aimed to use 16S ribosomal RNA sequencing with the Illumina MiSeq platform to describe the milk microbiota from 50 healthy Assaf ewes. The global observed microbial community for clinically healthy milk samples analysed was complex and showed a vast diversity. The core microbiota of the sheep milk includes five genera: Staphylococcus, Lactobacillus, Corynebacterium, Streptococcus and Escherichia/Shigella. Although there are some differences, some of these genera are common with the microbiota core pattern of milk from other species, especially with dairy cows. The microbial composition of the studied samples, based on the definition of amplicon sequence variants, was analysed through a correlation network. A preliminary analysis by grouping the milk samples based on their somatic cell count (SCC), which is considered an indicator of subclinical mastitis (SM), showed certain differences for the core of the samples identified as SM. The differences in the microbiota diversity pattern among samples might also suggest that subclinical mastitis would be associated with the significant increase in some genera that are inhabitants of the mammary gland and a remarkable concomitant reduction in the microbial diversity. Additionally, we have also presented here a preliminary analysis to assess the impact of the sheep milk microbiome on SCC, as an indicator of subclinical mastitis. The results here reported provide a first characterization of the sheep milk microbiota and settle the basis for future studies in this field.

Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes.

BACKGROUND: Metagenomes can be analysed using different approaches and tools. One of the most important distinctions is the way to perform taxonomic and functional assignment, choosing between the use of assembly algorithms or the direct analysis of raw sequence reads instead by homology searching, k-mer analysys, or detection of marker genes. Many instances of each approach can be found in the literature, but to the best of our knowledge no evaluation of their different performances has been carried on, and we question if their results are comparable. RESULTS: We have analysed several real and mock metagenomes using different methodologies and tools, and compared the resulting taxonomic and functional profiles. Our results show that database completeness (the representation of diverse organisms and taxa in it) is the main factor determining the performance of the methods relying on direct read assignment either by homology, k-mer composition or similarity to marker genes, while methods relying on assembly and assignment of predicted genes are most influenced by metagenomic size, that in turn determines the completeness of the assembly (the percentage of read that were assembled). CONCLUSIONS: Although differences exist, taxonomic profiles are rather similar between raw read assignment and assembly assignment methods, while they are more divergent for methods based on k-mers and marker genes. Regarding functional annotation, analysis of raw reads retrieves more functions, but it also makes a substantial number of over-predictions. Assembly methods are more advantageous as the size of the metagenome grows bigger.

Microdiversity ensures the maintenance of functional microbial communities under changing environmental conditions.

Microdiversity can lead to different ecotypes within the same species. These are assumed to provide stability in time and space to those species. However, the role of microdiversity in the stability of whole microbial communities remains underexplored. Understanding the drivers of microbial community stability is necessary to predict community response to future disturbances. Here, we analyzed 16S rRNA gene amplicons from eight different temperate bog lakes at the 97% OTU and amplicon sequence variant (ASV) levels and found ecotypes within the same OTU with different distribution patterns in space and time. We observed that these ecotypes are adapted to different values of environmental factors such as water temperature and oxygen concentration. Our results showed that the existence of several ASVs within a OTU favored its persistence across changing environmental conditions. We propose that microdiversity aids the stability of microbial communities in the face of fluctuations in environmental factors.