- Open Access
Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy
Biotechnology for Biofuels volume 9, Article number: 26 (2016)
Biogas production is an economically attractive technology that has gained momentum worldwide over the past years. Biogas is produced by a biologically mediated process, widely known as “anaerobic digestion.” This process is performed by a specialized and complex microbial community, in which different members have distinct roles in the establishment of a collective organization. Deciphering the complex microbial community engaged in this process is interesting both for unraveling the network of bacterial interactions and for applicability potential to the derived knowledge.
In this study, we dissect the bioma involved in anaerobic digestion by means of high throughput Illumina sequencing (~51 gigabases of sequence data), disclosing nearly one million genes and extracting 106 microbial genomes by a novel strategy combining two binning processes. Microbial phylogeny and putative taxonomy performed using >400 proteins revealed that the biogas community is a trove of new species. A new approach based on functional properties as per network representation was developed to assign roles to the microbial species. The organization of the anaerobic digestion microbiome is resembled by a funnel concept, in which the microbial consortium presents a progressive functional specialization while reaching the final step of the process (i.e., methanogenesis). Key microbial genomes encoding enzymes involved in specific metabolic pathways, such as carbohydrates utilization, fatty acids degradation, amino acids fermentation, and syntrophic acetate oxidation, were identified. Additionally, the analysis identified a new uncultured archaeon that was putatively related to Methanomassiliicoccales but surprisingly having a methylotrophic methanogenic pathway.
This study is a pioneer research on the phylogenetic and functional characterization of the microbial community populating biogas reactors. By applying for the first time high-throughput sequencing and a novel binning strategy, the identified genes were anchored to single genomes providing a clear understanding of their metabolic pathways and highlighting their involvement in anaerobic digestion. The overall research established a reference catalog of biogas microbial genomes that will greatly simplify future genomic studies.
Biogas production from agricultural and industrial wastes allows the simultaneous treatment of organic residues with generation of a versatile energy carrier (i.e., methane), which in turn can be transformed into electricity and heat . The biogas is produced through a biologically mediated process, the so called “anaerobic digestion” (AD), which is divided into four steps, namely hydrolysis, acidogenesis, acetogenesis, and methanogenesis . All steps are executed by an extremely specialized and complex microbial community, in which different members have distinct roles in a collective organization . These intricate sets of relationships between the microorganisms hamper the investigation of the microbial community through traditional microbiological methods. To address this problem is mandatory to go beyond a simple identification of the microbial species, unveiling their functional roles in the biogas production system.
In most of the shotgun sequencing studies performed on the AD microbial community, the functional characterization of the microbes was performed directly on the short reads  without prior assembly, or at best, gene finding was performed on a small number of short scaffolds [5, 6]. However, it is well known that a high-quality assembly strongly improves the reliability of gene finding and annotation . Only recently, the metagenome of a single biogas plant was assembled but it was not binned to extract genomes .
Till now, the identification of species in the biogas community was performed using sequence similarity search against reference genomes present in public databases [5, 9, 10]. However, these genomes were isolated from different environments and, even if they belong to phylogenetically related groups, they might have different functional properties. For this reason, a “predictive metagenomic approach,” based on 16S gene sequencing, was tentatively applied to the AD microbial community , but it was found that this strategy is more reliable when adequate reference genomes are available in the public databases [3, 11]. Another weak point of many metagenomic studies is that they are conducted without performing assembly and binning processes. It is known that short reads are error prone and contain only minimal signal for homology searches, hampering direct annotation against reference databases . As a consequence, the AD microbiome still remains a “black box” due to the small number of complete microbial genomes obtained from the biogas community [13, 14]. Therefore, the development of an appropriate database representing the biogas microbial community will allow the correlation of genome characteristics, phylogenetic, and metabolic properties of these uncultivated microorganisms.
To achieve this outcome is mandatory to perform a de novo assembly of the shotgun sequence data. This process offers several advantages for analyzing metagenomics datasets, as for example: (a) improved accuracy of sequences obtained by removing random sequencing errors, (b) more reliable gene finding and annotation process, (c) significant reduction of the data for subsequent processing and obviously, (d) possibility to discover novel genomic elements. Despite these advantages, de novo assembly in microbial community is extremely complex because it results in a large set of scaffolds that are difficult to be classified in single biological entities [i.e., Genomes Bin (GB)]. This classification was previously performed on other microbial communities with different methods such as tetranucleotide composition assignment , tetranucleotide composition combined with abundance of the scaffold in a small number of conditions , and binning of co-abundant genes across a series of metagenomic samples . These methods are based on the rationale that the relative concentrations of a microbial species can change in different contexts; thus, scaffolds can be assembled in the same individual genome if their coverage changes concertedly. This process is called binning. Nevertheless, procedures based on nucleotide genomic composition can be inconsistent due to uneven tetranucleotide distribution into the same genome. On the other hand, abundance-based methods cannot give comprehensive segregation of all entities in complex samples [16, 18] and a reliable binning of co-abundant genes requires numerous samples. Recently, fully automated binning procedures have been developed such as CONCOCT , GroopM , or MetaBAT .
In this study, we applied a novel two-stage approach strategy, combining the two procedures previously proposed [16, 17]. This allowed the extraction of 106 GBs from the biogas microbial community which adds a new chapter in the study of the anaerobic digestion (AD). Annotation of the identified genes and functional analysis of the species gave for the first time a clear understanding of the AD microbiome and allowed to establish a reference collection of biogas microbial genomes that will greatly simplify future genomic studies.
Results and discussion
Approximately, 340 millions high-quality paired-end reads (~51 gigabases of metagenomic sequence) were obtained from high throughput sequencing of 15 samples collected from 8 anaerobic digesters, representing conventional biogas reactors. The assembly of the reads resulted in 409,831 scaffolds (~686 Mbp) ranging in size from 500 to 313,754 bp (N50 2338). The percentage of reads aligned to the assembly varied from 57 to 73 % (with a mean average of 67 %) as shown in Additional file 1: Table S1. No differences were found between the samples included in the assembly and those used only for the binning process, suggesting that the assembly was fairly representative for all the reactors. It should be mentioned that ~242 Mbp are in scaffolds larger than 5 kbp (“Methods” and Additional file 1). After the assembly process, the gene finding and annotation are more reliable and led to the identification of nearly one million protein encoding genes, 23.6 % of which could be assigned to GBs (Additional file 2). The protein encoding genes were annotated using COG , KEGG , and Pfam  (Additional file 2). The results showed that 569,645 genes (60.8 %) had a match in the COG database, 418,103 (44.6 %) in KEGG and 579,337 (61.8 %) had a protein domain annotated in Pfam. Finally, 277,604 genes (29.6 %) were completely unknown. The number of predicted proteins is approximately 70 times and 3.7 times more than those obtained in the two best previous assemblies of a biogas microbial community reported in the literature [6, 8].
The number of genes belonging to each KEGG category in the assembly was compared with the scaffold coverage, which is directly related to species abundance (Additional file 1 and Additional file 1: Figure S2). This means that the categories with the higher ratio between “coverage” and “number of genes in the category” are those associated with most abundant GBs. This analysis allowed an evaluation of the relevance of the KEGG classes considering both the number of genes in the pathway and the abundance of the species in the microbial community. From these data, it was evident that some metabolic pathways included genes with a high average coverage because they were encoded (also) in the genomes of the more abundant species of the microbial community as shown in Additional file 1: Figure S2. The metabolic pathway of methanogenesis is the most straightforward example indicating that some methanogenic archaea (i.e., Eu01) dominate the microbial community in terms of abundance. We can assume that, for the same reason, the riboflavin KEGG pathway, which led to the biosynthesis of the proteins’ cofactor F430 involved in methanogenesis , is one of the highly ranked in the list.
On the contrary, the KEGG pathway modules related to the degradation of xenobiotic compounds like “styrene,” “naphthalene,” “fluorobenzoate,” and “aromatic compounds” were mainly encoded in low abundant species and frequently belong to scaffolds that could not be assigned through the binning process. For example, only 22 % of the genes involved in “xylene degradation” were binned vs. 36 % of the “RNA-transport” and 33 % of the “riboflavin metabolism” (Additional file 2). This suggests that the degradation of xenobiotic compounds is specific to the rare biosphere in the biogas reactors. The only notable exception is “nitrotoluene degradation” but this is expected as the degradation of this compound and incorporation into the bacterial biomass in anaerobic conditions has been previously demonstrated [26–28].
Carbohydrate phosphotransferase (PTS) system, despite being represented by 1261 genes, is the second least abundant category considering the ratio “coverage/number of genes.” This suggests that mainly low abundant community members utilize this system to transport sugars. This result is totally unexpected, as PTS is widely spread among bacteria . However, our data evidence that, in the AD microbiome, ABC transporters are more frequent in the high abundant species.
Moreover, it was found that “nitrogen metabolism” includes genes mostly represented in low abundance species. This could be due to the average low nitrogen concentration contained in cattle manure (in comparison for example to pig or poultry manure) . It can be expected that, due to the high dynamicity of the biogas community [3, 14, 31], modification of the manure composition (for example a higher quantity of ammonia) can lead to an increase in the abundance of some species that in our experiment are associated to the rare biosphere.
Binning process and taxonomic classification
Mapping reads from each sampling point to the assembled scaffolds indicated that the microbial species were differentially represented due to heterogeneous manure feedstock composition. The differences in the microbial abundance allowed the clustering of the scaffolds and resulted in the extraction of 106 GBs from the total assembly. A detailed explanation of the binning assembly procedure is reported in Additional file 1 together with a schematic representation of the binning strategy in Additional file 1: Figure S3. In the first part of our procedure, high-quality GBs were manually extracted using the procedure of Albertsen et al. . These GBs served as internal controls and were used to drive the second part of the binning. By an automatic extraction process based on clustering of scaffolds having similar coverage profiles, 61 additional GBs were identified. The estimated completeness of the GBs, based on the presence of 107 conserved marker genes , ranged from 15 % to more than 99 % (with a mean of 83 %) (Additional file 3). In order to validate this finding, an additional analysis was performed using CheckM , which gave as output very similar values (85 % completeness on average). The level of genome contamination was estimated both considering the number of duplicated essential genes and also with CheckM; the contamination was found to be extremely low and ranging on average between 3 and 5 % depending on the method used (Additional file 3). With our procedure, we have successfully identified 60 genomes with estimated completeness higher than 90 % considering the 107 essential genes, or 51 genomes according to CheckM. The result obtained is of a very high quality if compared with previous studies obtained from single-cell genome sequencing, where genome completeness averages around 40 % .
An additional analysis was performed using MetaBAT software  in order to evaluate the performance of our binning strategy. Considering as thresholds a) genome completeness higher than 90 % and b) contamination level lower than 20 %, MetaBAT managed to extract 42 GBs, while our binning strategy led to the identification of 51 GBs. Even by lowering the completeness threshold (e.g., to 70 %), our binning strategy was able to extract more GBs. The outcome of this comparison validated the high accuracy and efficiency of the binning strategy presented in the current manuscript.
Taxonomic assignment showed that none of the GBs could be assigned to species level, only 10 GBs were assigned to the genus level, while the vast majority were assigned to phylum level (Table 1; Additional file 3). This confirms that most of the species in the biogas microbial community were not previously characterized at a genomic level . The more affordable taxonomic assignments were obtained for Euryarchaeota Eu01, Eu02, Eu04, Eu05 suggesting that archaea are better characterized than bacteria in the biogas community (with the exception of Eu03). On the contrary, bacteria are completely unknown at genomic level. The results revealed that the biogas microbial community is dominated by the phylum Firmicutes (69 GBs) followed by the phyla Proteobacteria (10 GBs) and Bacteroidetes (6 GBs), which is in accordance with other studies [36–40] (Fig. 1). Sixty-nine of the GBs belong to Firmicutes (Fig. 5; Additional file 3). The species included in this division are extremely relevant from a functional point of view since they are involved in many metabolic processes including the degradation of carbohydrates, fatty acids utilization, Wood–Ljungdahl pathway (WLP) (homoacetogenesis) or syntrophic acetate oxidation (SAO). The comprehensive high-resolution microbial tree (Fig. 1) evidenced that these GBs can be subdivided into six main sub-groups (Additional file 3). Three GBs belong to Eubacteriaceae, 17 to the family Clostridiaceae, seven to the family Syntrophomonadaceae and 22 can be assigned only to the class Clostridia and are distantly related to the other Firmicutes. It is worth mentioning that the GBs assigned to the class Clostridia are the most cryptic inside the community, as they are distantly related to other Firmicutes, showing deeply branched GBs (Fig. 1). Moreover, four of the GBs initially assigned to the Firmicutes using the 107 essential genes were then re-assigned to the family Acholeplasmataceae, of the phylum Tenericutes (Te01, Te02, Te03, Te04) using Phylophlan.
Proteobacteria is the second most abundant group (10 GBs), including Alcaligenaceae (Pr05, Pr06, Pr10), a group of three GBs that can only be assigned to the Gammaproteobacteria group (Pr01, Pr02, Pr04), two GBs belonging to the Deltaproteobacteria (Pr09, Pr11) and one belonging to the Campylobacteraceae (Pr07). Alcaligenaceae are not frequently reported in analysis of the biogas reactors [41, 42] and the analysis of their genomic composition can provide a first glimpse into their possible role. GB Pr09 has been tentatively assigned to the Desulfomicrobium group and it is one of the most interesting members of Proteobacteria because it is competing with methanogens in anaerobic enrichment cultures degrading oleate and palmitate [43, 44]. Finally, Pr11 is relevant because members of the Desulfobacterales are involved in acetate oxidation by parallel reduction of sulfur, a key process in the biogas microbial community .
Bacteroidetes is the third most abundant group (6 GBs) which is composed of two subgroups: Porphyromonadaceae (Ba03, Ba05, Ba06) and Rikenellaceae (Ba01, Ba02, Ba07). Both subgroups are dominant microorganisms in biogas plants [46, 47].
Synergistetes, which was a recently introduced phylum having only Synergistaceae family, was represented by 6 GBs in our study.
The remaining GBs are included in the phyla Actinobacteria (Ac01), Thermotogae (Th01, Th02), and Spirochaete (Sp01, Sp02). The abundance of all these GBs was low in the samples examined. Species of the phylum Thermotogae were identified also in thermophilic biogas-production plants utilizing renewable primary products for biomethanation, even at low abundances [48–50]. Their role in utilization of complex carbohydrates has been recently suggested on the basis of the gene content of Defluviitoga tunisiensis . The low frequency of Spirochaetes is in agreement with relevant works in anaerobic digesters and their abundance seems to be highly variable depending on the operational conditions of the reactor . Also, Actinobacteria have been previously reported at low abundance in biogas reactors [53, 54] but in the cited research their functional role was difficult to be predicted due to the lack of genomic sequences and their highly variable physiological and metabolic properties.
In the tree of life obtained using PhyloPhlAn, Tm01 was one of the most difficult taxonomic assignments as this GB was deeply branched from the candidate phylum TM7 composed only by the Candidatus Saccharimonas aalborgensis . Despite its genome that is not completely closed (72 % completeness), it is one of the most complete TM7 reported in database and its small genome size (~1.2 Mbp) confirms data reported in the literature indicating that it is one of the smallest in the AD microbial community.
Functional characterization of the biogas microbial community
In the cited literature, the role of the majority of microbial groups involved in biogas production has been hypothesized considering the functional characteristics of distantly related species. Nevertheless, the lack of genome sequences prevents a clear understanding of their physiology and behavior. Therefore, our analyses targeted to give answers to two questions; (a) how much specialized are the microorganisms, and (b) which are their roles in the metabolic pathways of AD process.
In order to elucidate the microorganisms’ specialization, we converted the results from SEED analysis (“Methods” and Additional file 1) into a Network Representation of the Biogas Functional Organization (NRBFO). For the construction of the NRBFO, we selected only the GBs that were ranked among the top 1/8 of each SEED functional category based on the number of corresponding genes. If one GB belonged in two categories, these were connected with an edge (Fig. 2; Additional file 4). This revealed that the GBs in the AD microbiome could be classified into two distinct groups according to their functional properties.
The first group consists of GBs specialized on a single metabolic process, as the ones enriched in genes belonging to the general functional category of “carbohydrate utilization and metabolism” (blue nodes in Fig. 2). More specifically, some GBs were only involved in “central carbohydrate metabolism,” others in “aminosugars utilization,” or “di- and oligosaccharides utilization,” and so on, generating a very complex and faceted organization inside the microbial community. On the contrary, the second group includes GBs possessing multifunctional roles (i.e., they have high number of genes in more SEED categories). These GBs are inside the nodes connected by thick edges in the network (Fig. 2). It was found that 10 GBs have high number of SEED feature counts both in “sugar fermentation” and “fatty acids oxidation,” Three of these GBs belong to Syntrophomonadaceae family (Fi07, Fi08, Fi09), two belong to Alcaligenaceae family (Pr05, Pr10), two to Gammaproteobacteria (Pr01, Pr02) and three to Clostridia class (Fi12, Fi62, Fi68) (Additional file 5). It is known that common functionalities can be shared by species of the same taxonomic group . However, our analysis proved that in some cases, species of completely different taxonomic groups can share the same functional role and therefore compete for the same niche.
As an additional step, the species were functionally classified considering the proposed organization of the AD process, which is divided in four layers (i.e., hydrolysis, acidogenesis, acetogenesis, and methanogenesis) (Fig. 3; Additional file 1: Figures S4–S7; Additional files 5, 6, 7, 8). In order to do this, a putative functional role for the GBs was assigned taking into account their annotation obtained by COG, KEGG, SEED, and Pfam (Additional file 1). The assignment showed that the AD microbiome bears resemblance to a funnel concept; during the initial step of organic substrate degradation (i.e., carbohydrates, proteins, and lipids), a wide variety of GBs (even belonging to different phyla) are involved. In contrast, while proceeding to the next steps of the AD process (i.e., acetogenesis, acidogenesis, and methanogenesis), the involved GBs become gradually more specialized.
Particular attention was drawn to key functional steps of the AD process in order to elucidate the role of GBs. For example, proteins involved in polysaccharide degradation are important as it is well known that the raw manure contains a high fraction of fibers due to animal nutrition. These proteins were identified using the SEED annotation (polysaccharides category) and also by selecting those with significant matches to at least one of the carbohydrate-binding modules proposed by Hess et al. . Analysis of the Pfam domains was performed in order to minimize the dependence on the overall sequence similarity of candidate genes to known carbohydrate-active enzymes. Out of 7161 carbohydrate-binding proteins found in the global assembly, 1896 of them (~26 %) were assigned to specific GBs. Most of the GBs with high number of carbohydrate-binding modules belong to Clostridiales and considering the similarity of the 107 essential genes obtained using BLAST, can be related to Ruminoclostridium or Clostridium. These genera are well known for their involvement in polysaccharides degradation, and some species have been previously isolated in biogas plants . The carbohydrates utilization process involves numerous species which are specialized in degradation of different carbohydrates groups (Fig. 3). These microorganisms cooperate with species involved in lipids or proteins degradation to generate the byproducts for the subsequent steps of methanogenesis.
In the fermentation of sugars to organic acids, an important role is played by the Wood-Ljungdahl pathway (WLP), which is characteristic for some acetogenic bacteria and archaea . In this process, carbon dioxide is reduced to carbon monoxide and then converted to acetyl-CoA, with hydrogen serving as electron donor. From KEGG analysis performed on selected genes of the WLP, it was found that a specific subset of 8 bacterial species (Fig. 3; Additional file 1: Figure S6) features a complete or nearly-complete pathway. All these bacteria were assigned to Firmicutes and more specifically to Clostridia sp. (Fi12, Fi13, Fi38, Fi46, and Fi62), Clostridiales sp. (Fi61), Peptococcaceae sp. (Fi18), and Tepidianaerobacter sp. (Fi34). It is known that specific microbes are capable to perform also the reverse WLP (i.e., the so called Syntrophic Acetate Oxidation, SAO), which includes the same genes of the WLP. By this pathway, they oxidize acetate to hydrogen and carbon dioxide when growing syntrophically with hydrogenotrophic methanogens that utilize the hydrogen and carbon dioxide produced to generate methane . The overall process can be viewed as an additional mechanism of methane formation from acetate, and was originally proposed by Barker  and later confirmed by Zinder and Koch . The mechanism was initially described in thermophilic anaerobic processes [58, 60, 61] and was later on identified to occur also in reactors operating at mesophilic temperatures [62–64].
Another finding is related to the synergistic behavior between Synergistetes with other microorganisms. SEED subsystem revealed that the most similar sequenced species to Sy02, Sy03, Sy05, and Sy06 is Thermanaerovibrio acidaminovorans DSM 6589 and for Sy01 is Anaerobaculum hydrogeniformans ATCC BAA-1850. Therefore, the presence of numerous ABC transporters for branched-chain amino acids (AA) (Additional file 5) together with the large number of genes involved in AA metabolism (Additional file 1) indicates that Synergistetes, similarly to T. acidaminivorans, could operate synergistically with other species, to ferment AAs to acetate and propionate .
Archaeal community characterization
As previously discussed, the archaeal species are the best characterized in the biogas community. Archaea are dominated by the hydrogenotrophic methanogen Eu01 belonging to the Methanoculleus genus. Eu01, together with Eu02 (another Methanoculleus sp.), features all the central enzymes and complexes of methanogenesis: Mcr, Mtr, Fpo, and Hdr (Fig. 4). In addition, they feature all the complementary genes necessary for the reduction of CO2 to methane: fmd/fwd, ftr, mch, mtd, and mer. On the contrary, they both lack the gene phosphate acetyltransferase (pta), involved in the conversion of acetate to methane (aceticlastic pathway) and also all the genes coding for the methylamine and methanol corrinoid proteins, essential for the conversion of methyl groups from methanol and methylamines to methane (methylotrophic pathway).
Eu04, belonging to the Methanosarcinales genus, has a very low abundance (Fig. 5). It features all the genes belonging to the methylotrophic and aceticlastic pathway. Interestingly, it lacks the gene coding for Mtd, which catalyzes the fourth reaction in the reduction of CO2 to methane. As stated before, Eu01 and Eu02 are instead able to perform all the reactions of this pathway, and the fact that these two GBs are approximately 4000 and 270 folds more abundant than Eu04 (Fig. 5) is an indication that the hydrogenothrophic pathway is the most favorable at the tested conditions. This finding is in accordance with several studies performed in similar conditions [5, 6, 10].
Interestingly, the second most abundant archaeon is represented by a completely new Euryarchaeota (Eu03). It is remarkable that Eu03 was the second most abundant archaeal species in the microbial community. It has very small genome size (~1.76 Mbp) similarly to Candidatus Methanoplasma termitum and Ca. Methanomassiliicoccus intestinalis (1.48 and 1.93 Mbp, respectively). Comparative analysis of the methane pathway (performed on all the archaeal GBs) using KEGG (Fig. 4) revealed that, differently from the other archaeal GBs, Eu03 completely lacks the coenzyme F420 biosynthesis pathway. This feature is also evident in the recently sequenced Ca. M. termitum . Interestingly, it was possible to identify pivotal methanogenic genes in Eu03, including some belonging to the methylotrophic pathway, which are instead absent in the Methanomassiliicoccales species previously sequenced. Due to the small number of archaeal genomes in public databases, all the taxonomic analyses performed on the essential genes (Additional file 3) failed to assign Eu03 to any previously defined lineage. Only by using BLASTP similarity search and analysis of the 16S rRNA, it was possible to identify a distant correlation with the recently discovered seventh order of methanogens, the Methanomassiliicoccales (previously referred to as “Methanoplasmatales”).
It is important to highlight that this new uncultured archaeon (Eu03) was identified, quantified, and assigned to a putative functional role only thanks to the binning strategy, which is the fundament of the present work.
This study demonstrated that the metagenomic assembly and binning of the shotgun sequences obtained from biogas reactors allowed the identification of 106 GBs that can be assigned into the context of the biogas degradation food chain by means of bioinformatic analysis. This is a major step forward in the characterization of the biogas microbial community especially when compared to previous studies, where the functional roles have been inferred from those assigned to the more similar species identified considering 16S rRNA similarity. In the case of the biogas microbial community, the identified GBs are distantly related to species for which the genomes are available in the databases and, as previously discussed, a predictive metagenomics approach is not accurate. This is clearly demonstrated by the high fraction of new GBs identified and assigned only at high taxonomic level, as for example the newly identified methanogenic archaea (Eu03). Another concluding remark drawn by the binning process revealed that approximately 70 % of the assembly cannot be assigned to a specific GB. This suggests the presence of more than 450 GBs in the biogas microbial community. As this is the first attempt to deeply characterize the AD microbiome, it is expected that further studies performed under different operational conditions (e.g., different temperatures and substrate) will allow in the next future to enrich the genome database. Finally, this study opens new avenues in deciphering the functional interactions between microbial species involved in the AD process and provides a solid reference that will greatly simplify further metatranscriptomics and metaproteomics analyses.
Biogas reactors’ configuration
Eight laboratory scale Continuous Stirred Tank Reactors (CSTR) operating at thermophilic conditions (54 ± 1 °C) were selected for sampling as shown in Additional file 1: Figure S1. The influent substrate of the reactors was cattle manure with varying chemical composition. The reactor’s operating temperature and influent feedstock composition were chosen to resemble typical conditions occurring in centralized full-scale biogas plants. The Organic Loading Rate (OLR) of all reactors varied between 1.9–2.9 gVS/L reactor-day and the hydraulic retention time was kept constant at 15 days. The initial inoculum used derived from Snertinge biogas plant, Denmark.
Eighteen samples for microbial analyses (~15 ml each) were collected at various times during the operation of the reactors (Additional file 9). The samples were denoted with the name of the reactor followed by a letter to designate the sampling time period (e.g., CSTR01a and CSTR01b).
Barley residues present in the manure were removed by filtering with a 100-μm nylon cell strainer filter. The filtered sample was centrifuged at 5000 rpm for 10 min and the supernatant was discarded leaving ~2 g of material. Genomic DNA was extracted from these 2 g of material using the RNA PowerSoil® DNA Elution Accessory Kit (MO BIO laboratories, Carlsbad, CA, USA). The quality and the quantity of the extracted DNA were determined both using NanoDrop (ThermoFisher Scientific, Waltham, MA, USA) and Qubit fluorometer (Life Technologies, Carlsbad, CA, USA).
Genomic DNA extracted from the samples was prepared for sequencing using two different procedures. A pool obtained using identical quantities of the samples CSTR01a, CSTR02a, CSTR03a, CSTR01b, CSTR02b, and CSTR03b was used to prepare libraries using TruSeq DNA PCR-free Kit v2 (Illumina, San Diego, CA, USA). Nextera DNA Library Preparation Kit (Illumina, San Diego, CA, USA) was used to prepare libraries for all the individual samples from the reactors (Additional file 1: Figure S1). All the samples (both pooled and individual) were paired-end sequenced (2 × 150 bp) using Illumina HiSeq 2500 (Illumina, San Diego, CA, USA). One lane of the sequencer was allocated to the pooled sample prepared with the TruSeq kit (~250 millions filtered reads) and one to the samples prepared using the Nextera kit (from 26–58 millions filtered reads for each sample). The TruSeq DNA PCR-free kit was used due to its superior coverage of DNA regions, which are traditionally difficult to sequence, such as high GC-rich regions. The sequences obtained from samples CSTR01a, CSTR02a, CSTR03a, CSTR01b, CSTR02b, and CSTR03b were assembled since they represented more than 50 % of the total obtained reads.
Sequence data reported in this study have been submitted to the National Center for Biotechnology Information (BioProject PRJNA283298). Raw sequence data have been deposited at Sequence Read Archive under accession SRP058179 and Whole Genome Shotgun projects have been deposited at DDBJ/EMBL/GenBank under the accession LFRM00000000-LFTS00000000. The versions described in this paper are the first version LFRM01000000-LFTS01000000.
Reads trimming and de novo metagenome assembly
Reads in FASTQ format were quality-filtered and the adaptors were removed using Trimmomatic software . Overlapped paired-ends were merged using Flash  with standard parameters, except from the maximum overlap parameter, which was set to 150. Assembly was performed using both paired-end reads (with insert size equal to 470 bp for TrueSeq and 280 bp for Nextera) and single-end reads (both those merged using Flash and those which only one end passed the filtering step). Reads were imported to CLC Genomics workbench v. 5.1 (CLC Bio, Aarhus, DK, USA) and assembled using CLC’s de novo assembly algorithm, using a kmer of 63, a bubble size of 60 and a minimum scaffold length of 500 bp.
Gene finding and annotation
Gene finding on the scaffolds obtained from the assembly was performed using Prodigal, run in metagenomic mode . Conserved protein families and domains were identified using reverse position-specific BLAST algorithm (RPSBLAST of NCBI BLAST+) performed on all predicted proteins, and using COG only  and Pfam  RPSBLAST databases. Only results with e-value lower than 1e-5 were considered, and additionally for COG only the best match was considered. KEGG annotation was performed using usearch7.0.1090_i86linux32 (-ublast) on the KEGG Orthology (KO) database  with e value cutoff 1e-5 (-maxhits 1) (http://www.drive5.com/usearch/). From the output file, KEGG pathway modules were identified using KOBAS . After the binning process, scaffolds assigned to each GB were re-annotated via Rapid Annotation using Subsystem Technology (RAST) server . The entire protein set of the five archaeal GBs were analyzed using KEGG Automatic Annotation Server (KAAS) .
Taxonomic and functional analysis of the metagenome assembly
All the scaffolds obtained from the shotgun assembly were uploaded to the MG-RAST metagenomics analysis server  and analyzed using standard parameters, except from the minimum alignment length that was set to 100 bp. It should be noted that scaffold coverage was not assigned at the uploading. Results were visualized using KRONA software .
Taxonomic assignment of the GBs was performed with four different methods, and the detailed procedure is reported in the Additional file 1; results were then compared to extract the best possible one (Additional file 3). Briefly, the essential genes associated to each GB were checked by sequence similarity to the NR database using BLASTN, with e-value threshold 1e-5. The taxonomic assignment of the best match was recovered and sequence similarity of 95, 85, and 75 % or better was used for species, genus, and phylum level taxonomical assignment , respectively. A similar analysis was performed using BLASTP. The phylogenetic result and the microbial tree of life (Fig. 1; Additional file 10) were determined using Phylophlan  and the scaffolds of each GB were analyzed using Phylopythia .
Taxonomy was determined from NR database alignment, while functional classification was determined using COG  and SEED  (Additional file 1: Figure S8). Results are available at the MG-RAST database (meta-assembly) under accession number 4636806.3.
Hypergeometric analysis was performed to calculate the probability of observing the number of genes belonging to a specific functional category in each GB . The probability P of finding at least k genes of a specific functional category within a group of n genes (the total number of genes of a GB) is given by
where f is the total number of genes of a specific functional category determined considering all the GBs together, g is the total number of genes determined in all the GBs. Finally, we recursively repeated the calculation on SEED functional categories, KEGG pathways, and finally on COG functional categories. All the statistical calculations were performed using the R package.
Calculation of the scaffold coverage
Reads obtained individually (using the Nextera kit) for 18 samples collected from all the reactors were aligned on the scaffolds larger than 500 bp with Bowtie2 software  and scaffold coverage was determined with the genomecov software of the BEDTools package . Coverage was normalized considering the number of aligned reads and using the sample with the lower number as a reference. The coverage obtained was considered both for comparison between the number of genes of each KEGG pathway and its average coverage (Additional file 1 and Additional file 1: Figure S2), and also for the “refinement” of the binning process (“Methods” “Binning refinement through identification of co-abundant scaffolds,” a more detailed description is reported in Additional file 1).
Identification of conserved marker genes
A set of 107 Hidden Markov Models of essential single-copy genes  were searched against the predicted open reading frames using HMMER3 (http://hmmer.janelia.org/) , following the strategy of Albertsen et al. . The results were used to predict completeness and level of duplication of the GBs identified using the script “determine_bins_completenes.pl.”
Identification of 16S rRNA genes
Scaffolds encoding the 16S rRNA genes were identified using the method described by Albertsen et al. . Taxonomical assignment of the 16S rRNA genes was determined using RDP classifier  with a confidence threshold of 0.8.
Binning of genomes using tetranucleotide composition and coverage
Initial binning was performed using the procedure of Albersten et al.  which is based on “sequence composition-independent binning and tetranucleotide binning.” During the first step, distinct groups of scaffolds were identified based on their coverage similarity in a pair of samples. During the second step, principal component analysis of tetranucleotide frequencies was used to separate species present in the same coverage-defined GBs.
Binning refinement through identification of co-abundant scaffolds
The GBs extracted with the procedure described above are of high quality and were used as “internal controls” to verify the binning procedure based on the coverage strategy. MeV software  was used to examine the coverage profile of the scaffolds, which contain the essential single copy genes and are assigned to the GBs in all the 18 samples. Using Euclidean distance calculation (single linkage) on the coverage profiles of the scaffolds, the GBs were separated and manually checked. Visual analysis of the clusters previously assigned to the GBs allowed the selection of those that were clearly separated from the others. Subsequently, each group of scaffolds was used to generate a “canopy profile.” Each profile was used to extract (from the entire list of scaffolds) those having Euclidean distance smaller than 1 SD from the distribution of the “canopy scaffolds.” This step was performed using the script “extract_scaffold_euclidean.pl.” Finally, the paired-end connections between scaffolds were used to assign scaffolds to the GBs using the procedure reported by Albersten et al. . Due to the high number of GBs, the procedure was not performed “manually” but implemented in the script “recover_interacting_scaffold.pl.” To minimize the misassignments, only scaffolds having average coverage within threefolds from the interacting scaffold and having a number of paired-end connections of at least 1/3rd of the scaffold average coverage were considered. Genome contamination, which can inflate genome completeness estimates, was determined both by checking the number of essential genes present in more than one copy on a single GB and also using CheckM software . More details regarding the binning procedure are reported in Additional file 1 (Binning strategy). The extracted GBs and the scripts used for binning refinement can be downloaded from http://www.biogasmicrobiome.com together with the manual and the test files. MetaBAT  was used in order to evaluate the performance of the binning strategy proposed in the current manuscript. The software was executed using default parameters but also “–sensitive” and “–specific.” The results reported refer to the “–sensitive” test.
Recovery of the multifasta files and of the protein sequences encoded by the GBs
Using the IDs of the scaffolds, it was possible to recover the multifasta file from the entire metagenome assembly multifasta using the script “extract_sequences_from_fasta.pl.” Moreover, with the same script, it was possible to extract the protein sequences from the fasta file containing all the proteins predicted using Prodigal software .
Clusters of Orthologous Groups
continuous stirred tank rreactor
KEGG Automatic Annotation Server
Kyoto Encyclopedia of Genes and Genomes
Network Representation of the Biogas Functional Organization
Organic Loading Rate
rapid annotation using subsystems technology
syntrophic acetate oxidation
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SC designed the strategy for metagenomic data analysis, wrote perl scripts, and drafted the manuscript; LT monitored bioreactors performance, collected the samples and prepared DNA for sequencing, analyzed metagenomic data, and revised the manuscript; PGK designed experiments, set up bioreactors, monitored bioreactors performance, analyzed biochemical parameters, and drafted the manuscript; DDF designed experiments, monitored bioreactors performance, analyzed archaeal species, and revised the manuscript; GV interpret metagenomic data and revised the manuscript; IA designed and supervised experiments and drafted the manuscript. All authors read and approved the final manuscript.
This work was supported by the Danish Council for Strategic Research under the project “SYMBIO—Integration of biomass and wind power for biogas enhancement and upgrading via hydrogen assisted anaerobic digestion,” contract 12-132654.
Illumina sequencing was performed at the Ramaciotti Centre for Genomics (Sydney, Australia). Information regarding the custom software developed for use in this research and the genome database are available at http://www.biogasmicrobiome.com together with the manual and the test files.
The authors declare that they have no competing interests.
Stefano Campanaro, Laura Treu, Panagiotis G. Kougias and Davide De Francisci contributed equally to this work
Additional file 1. Additional text file that contains supplementary information regarding: assembly, gene finding and annotation, comparison between number of genes belonging to each KEGG pathway and coverage, binning strategy, taxonomic assignment of the GBs, functional roles of the microbial species, number of genes for each KEGG pathways modules identified in the genome bins, number of genes identified in the genome bins for some selected KEGG pathways, methanogenic archaea.
Additional file 2. Gene annotation. Annotation of the genes identified in the assembly using COG, KEGG and Pfam databases.
Additional file 3. Taxonomy assignment and characteristics of the GBs. The taxonomy of the GBs identified was determined using different methods and a taxonomic assignment was suggested considering the results obtained. In columns (A–V) are reported: (A) the acronym of the GBs as reported in the main text (Sp = Spirochetes; Sy = Synergistetes; Th = Thermotogae; Pr = Proteobacteria; Fi = Firmicutes; Te = Tenericutes; Ac = Actinobacteria; Ba = Bacteroidetes; Tm = TM7 phylum; Eu = Euryarchaeota) the Phylum was determined using Phylophlan and, secondly, the results obtained from BLASTP search versus nr databases filtered using MEGAN; (B) the tentative taxonomic assignment; (C) domain of the genome bin; (D) phylum; (E) taxonomic level considered for the name assignment (the result obtained using Phylopythia was used when more than 50 % of the genome bin sequence was assigned to the same taxonomic group); (F) confidence for taxonomic assignment obtained using Phylophlan; (G–I) domain, phylum, class determined using Phylophlan; (J) taxonomic assignment determined using Phylopythia; (K) percentage of the genome assigned as reported in “J”; (L) taxonomic level reported in “J”; (M) number of genes having BLASTP e-value lower than 1*E-5; (N) average similarity for BLASTP results; (O) number of genes having BLASTN e-value lower than 1*E-5; (P) average similarity for BLASTN results; (Q) the species having the highest number of best match in BLASTP column “M”; (R) taxonomy assignment obtained using RDP classifier on the 16S rRNA gene, similarity, contig where the 16S gene was identified; (S) total length of the scaffolds assigned to the genome bin; (T) number of scaffolds, (U) scaffolds N50, (V) scaffolds N90, (W) average scaffolds length, (X) number of contigs determined after splitting scaffolds on stretched of 10 or more unknown bases “N”, (Y) contigs N50, (Z) contigs N90, (AA) average contigs length, (AB) number of protein encoding genes identified using SEED subsystem; (AC) number of protein encoding genes identified using Prodigal; (AD) total number of essential genes identified, (AE) univocal number of essential genes (removed those in multiple copies); (AF) estimated completeness of the GB; (AG) average number of essential genes in phylum 1. Bold text in columns (A, U, Y, Z, AA, AF) refers to GBs that satisfy the Human Microbiome Project quality criteria; (AH) estimated contamination level determined considering the univocal number of essential genes and the total number of essential genes in multiple copies; (AI) estimated completeness of the GB determined using CheckM software; (AJ) estimated level of contamination determined using CheckM software.
Additional file 4. Input files used for the network Representation of the Biogas Functional Organization (NRBFO). Functional analysis of the microbial community begins with the identification of the GBs enriched in SEED subsystem counts (ranked among the top one eighth of each SEED category). Starting from the entire list of SEED sub-categories (Data Set 6) 56 were selected (the more relevant for the characterization of the biogas community) and the GBs with high number of subsystem counts were identified (“node abundance” worksheet). The subsystem categories were compared in order to identify if they shared the same GBs and the couples with three or more common genome bins were identified (“edge_size_connections” worksheet). The number of GBs identified for each category was used to set the node size and the number of GBs shared was used to set the edge size (Fig. 2).
Additional file 5. Functional characterization of the GBs according to SEED. (“SEED” worksheet) GBs were annotated using SEED subsystem and for each category (row 1) the subcategories are reported (row 2). Numbers refer to the subsystem feature counts. The SEED categories are reported in columns highlighted in grey (C, R, X, AF, AH, AL, AP, AX, BN, BR, BV, BZ, CF, CJ, CO, CV, DG, DN, DT, DW, DZ, EH, ER, EW, FH, FL, FN). In columns at right of each category are reported the results for the subcategories (for example, columns D-Q refer to the “super-category” “C”). Rows 110–114 report the median, the third and the first quartile results for each column. In red and in blue are highlighted the GBs having high and low numbers of subsystem feature counts (those in the third and first quartile of each category). (“hypergeometric” worksheet) For each GB and each SEED functional category the P value obtained from hypergeometric distribution is reported.
Additional file 6. Functional characterization of the GBs according to COG. (“COG_gene_numb” worksheet) GBs were annotated using COG. Numbers refer to the genes identified on each GB for each COG category. The COG categories are reported in columns (C–AA), the GBs are reported in rows (2–107). (“COG_perc” worksheet) Percentages of genes belonging to COG categories are calculated with respect to the total number of COG results for each GB (note that some genes belong to more than one COG category). In red and green are highlighted, for each COG category, the GBs having the 10 highest and the 10 lowest percentages. (“hypergeometric” worksheet). For each GB and each COG functional category, the P value obtained from hypergeometric distribution is reported.
Additional file 7. Functional characterization of the GBs according to KEGG. (“KEGG” worksheet) GBs were annotated using KEGG. Numbers refer to the genes identified on each GB for each KEGG pathway. The KEGG pathways are reported in columns (C–EZ), the GBs are reported in rows (2–107). (“hypergeometric” worksheet) For each GB and each KEGG pathway, the P value obtained from hypergeometric distribution is reported.
Additional file 8. Database resources used for functional characterization of the GBs. Functional processes of the GBs analyzed in the AD community are reported in the first column. The SEED subsystems, KEGG pathway maps, COG categories and Pfam domains used are reported in columns 3 and 4.
Additional file 9. Metadata regarding the operational conditions of the reactors. Metadata regarding the operational parameters of the reactors including pH, methane yield and volatile fatty acid concentration.
Additional file 10. Suggested viewer: FigTree http://tree.bio.ed.ac.uk/software/figtree/. Newick format of the file representing the microbial tree of life. The tree reports the 106 GBs of the AD microbial community together with other 3,737 microbial genomes. The file was obtained with PhyloPhlAn using 400 broadly conserved proteins used to extract phylogenetic signal.
About this article
Cite this article
Campanaro, S., Treu, L., Kougias, P.G. et al. Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy. Biotechnol Biofuels 9, 26 (2016). https://doi.org/10.1186/s13068-016-0441-1
- Anaerobic digestion
- Next-generation sequencing
- Microbial community structure


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There’s a crazy amount of comics released each week. Here’s a look at some of the strangest, coolest, and funniest things you might have missed in last week’s comics. These examples should give you more of a reason to pick up these titles at your local comic shop. You can also find more current and past examples on the Best Stuff Instagram page.
There may be some minor spoilers below.
The Amazing Spider-Man #8
Best New Snack Item
Peter Parker is out with Mary Jane and she introduces him to a new snack item–Hong Kong egg waffle with rainbow vanilla ice, caramel drizzle and…Fruity Pebbles. Sounds crazy. It could be really bad, but I’d definitely give it a try.
Best Example Why Peter And MJ Are Great Together
Peter and Mary Jane deserve to be together. They had it all before, and then it was all taken away. Now that they’ve found their way back together, we can only hope the comic gods look happily upon this couple. Look how cute they are together.
Detective Comics #991
Best Example You Don’t Need A Shotgun To Call Shotgun
Batman and Commissioner Gordon are to teaming up with Two-Face. It’s a long story. Check them out as they zip along in the Batmobile. Two-Face obviously called “shotgun” despite the face he’s holding two different kinds of pistols. It’s surprising Batman allowed them in the car. Perhaps that’s why he’s sticking his head (and guns) outside.
The Punisher #3
Best Reason To Avoid Commuting To Work
The strange team-ups continue. Daredevil wants Punisher to turn himself in, but there’s a gaggle of Ninja attacking both of them. Seeing them in action would make the commute home interesting, but making sure you avoid getting a sword jabbed through your head might not be easy.
Best Reason Not To Lose Your Head When Dealing With The Punisher
If you’re going to go after the Punisher, you need to know what you’re getting into. When this dude got overpowered, you can see he starts to lose it. And then he loses his head…literally.
Old Lady Harley #1
Best Fast Food Ad
In the dark apocalyptic future shown in Old Lady Harley, Condiment King has his own restaurant chain. He even has a pretty good catchphrase, “It’s better than eating nothing!”
Grossest Food Item This Week
Poutine may not be for everyone. Some people love it, for sure. In the Canadian stores, they an Eyeball Poutine option. Granted, there appears to be a zombie situation there. Can’t imagine anyone else wanting to order this.
Spider-Geddon: Spider-Girls #1
Best Example That A Joke Shouldn’t Be Overused
Spider-Man is the king of bad jokes. It can’t be easy trying to always come up with witty banter. It seems he has repeated some of his jokes on occasion including referencing a line from Michael Jackson’s “Smooth Criminal” along with his daughter Annie. And you thought your dad’s dad jokes were bad.
The Silencer #10
Best Homage This Week
Apparently in the DC Universe, there is a knockoff Superman theme park. When it’s attacked, some of the park employees suffer injuries. In an homage to the death of Supergirl in Crisis on Infinite Earths, we see a recreation of that tragic moment. You should see what happens to these two right after…
Black Panther Vs. Deadpool #1
Best Rain Man Reference
In typical Deadpool fashion, in the middle of a fight, Deadpool yells out a line from the Tom Cruise/Dustin Hoffman movie, Rain Man.
Best Way To Disarm Deadpool
It’s easy to imagine how annoying being around Deadpool could be. After sneaking his way into Wakanda while dressed as a mooing elephant (really), he gets into Black Panther’s face after T’Challa refused to give him a bullet-sized amount of Vibranium. Black Panther blasts Deadpool and even manages to make a joke about it. Who knew Black Panther could be funny?
Wonder Woman #57
Best Well-Deserved Slap On The Head
John Constantine can be a smug and tiresome fellow. Zatanna has a bit of history with him and seems to know how to handle him. Sometimes a good smack to the back of the head is needed to rein someone back to reality.
Return Of Wolverine #2
Most Surprising Development For Wolverine
Wolverine is back. That’s no surprise. What is a surprise is he seems to have a new ability. Logan isn’t quite sure what’s going on but discovered his claws can heat up. They can heat up so intensely that they’ll ignite gasoline. Guess it’ll be like a built in car cigarette lighter for his cigars–if he’s still smoking them after his return.
Justice League Odyssey #2
Best Example Having Good Trash Talk Is Important
Jessica Cruz found herself facing off against Darkseid. While he was just recently reduced to the body of a baby, saying he needs his diaper changed as trash talk is a little weak. But in her defense, she did suffer from agoraphobia and locked herself in her apartment for some time. She just didn’t get out and didn’t have the need to perfect the art of trash talk.
X-Men Red #9
Best Example Why Being Able To Talk To The Opposite Sex Is Important
The X-Men are working on undoing Cassandra Nova’s evil plans. While trying to change the negative sentiment against mutants created by Cassandra, Nezhno awkwardly tries talking to Trinary. Gabby points out she can’t feel pain, but this exchange is too painful for even her.
Action Comics #1004
Best Kiss This Week
Lois Lane was supposed to be out in space with her son and father-in-law. She secretly returned to Earth and has been working incognito. She didn’t even tell Clark she came back. When he discovers she returned, the two reunite and share a passionate and loving kiss.
Okay, that’s enough. Get a room you two.
That’s it for this week. Be sure to pick up these comics at your local comic shop, if you haven’t already if any of these moments strikes your fancy.

domain: entertainment


Join us for this talk on Breaking Strings: Explorations of Mistakes in Music Therapy on November 11th.
In this innovative volume of Breaking Strings, Avi Gilboa from and Laurien Hakvoort use broken strings on an instrument as a metaphor to refer to those frustrating and sometimes upsetting moments when things go wrong in music therapy.
Although there are myriad ways in which music therapists can make mistakes and deal with the consequences, there is limited coverage of this subject in the professional literature. This book explores theoretical ideas and research on mistakes in music therapy. It also presents a series of authentic case studies in which 17 music therapists – ranging from novice to experienced practitioners, from 13 different countries – describe and reflect on mistakes in their own practice.
This book provides a starting point for both students and the wider music therapy community to discuss, analyze, and ultimately understand and learn from mistakes as part of their professional development.
Avi Gilboa (Israel), PhD, received his BA in Psychology and Music at Bar-Ilan University, Israel and subsequently his diploma in Music Therapy and his PhD in Cognitive Psychology. He has been a music therapist since 2001 in various settings and with different clinical populations. He is associate professor and head of the Music Therapy program at Bar-Ilan university. He supervises many music therapy PhD students and is supervisor of music therapy students and graduates.
Laurien Hakvoort (Netherlands), PhD, RMTh, NMT-Fellow, holds a BA and MA in Music Therapy from Amersfoort and University of the Pacific, California, USA, respectively. She obtained her PhD in Behavioral Sciences from Tilburg University. She has been a music therapist since 1994 and has worked as a music therapist in forensic psychiatry for 17 years and currently runs a private practice, Muzis.net. She is a music therapy lecturer on the bachelor’s and (pre-)master’s programs at ArtEZ University of the Arts, Enschede, the Netherlands and a freelance researcher.
Melissa Mercadal-Brotons, PhD, MT-BC, MTAE. Music therapist specialised in the area of gerontology and dementias. Member of the Research Group PSICOPERSONA (FPCCE-Blanquerna, URL). Director of the Master Programme in Music Therapy (idEC-UPF, Barcelona). Coordinator of Research and Master Programmes (Escola Superior de Música de Catalunya-ESMUC). EMTC Spanish Delegate. Chair of the Publications Commission (WFMT).
This event is free and open to the public. Please register for the event below.


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At the Ranch this summer, we have been suffering through the blistering heat and humidity. To cool off, we scheduled a Colorado Club Beat the Heat Happy Hour while also celebrating Colorado Day. We had a great crowd come to the La Mesa room at La Hacienda Club, to wipe off the sweat and indulge in cool drinks and great eats from the Bistro Veinte.
It was called a social and it was not just to sit around and complain about the weather, it was filled with a “Funtastic Colorado” trivia contest.
Our MC for the night was flaming-hot Pete Watson, entertaining the club with his humor and posing challenging trivia questions. Pondering over the trivia questions about our Centennial State, Colorado, working in teams to quickly answer the challenge. In the end, first place went to the team of Bob Thompson, Ron and Carol Beeley, and Richard Juneau. Second place went to the team of Sol and Carol Ginsberg and Dale and Val Zupancic.
You can always be assured our ever-popular 50/50 raffle will be available at the club’s events. On this night, Linda Harvey won first place while Jan Martin won second.
The next Colorado Club event will be a Football Smackdown with the Washington State Club for the Monday Night Football game between the Seattle Seahawks and the Denver Broncos on Sept. 12. Since Denver recruited Russell Wilson from the Seahawks, you know there will be a good rivalry between the two ball clubs. Get your Bronco gear out. You may win a prize for the most outrageous fan attire.
Look for more Colorado Club events this fall and winter.

domain: sports


Argonne National Laboratory Postdoctoral Appointee in Lemont, Illinois
Requisition Number: 403356 Location: Lemont, IL
Functional Area: Research and Development Division: ES-Energy Systems
Employment Category: Temporary 6 Months or Greater Education Required: Doctorate Degree
Level (Grade): 700 Shift: 8:30 - 5:00
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We are looking for a motivated, recently graduated Polymer Scientist with expertise in specialty polymers synthesis and characterization of physicochemical and mechanical behavior of polymeric materials. The individual we're seeking will preferably have expertise in polymer physics and characterization with a strong emphasis on nanoscale phenomena such as polymer adhesion, cohesion and other surface science phenomena.
Knowledge, Skills and Experience
- Considerable knowledge and experience in one or more of the following areas: organic chemical synthesis, polymer synthesis, polymer characterization.
- Considerable hands-on, practical laboratory experience with organic chemical processing and analytical methods specifically related to characterization of polymeric materials.
- Comfortable with working in a rapid pace environment.
- Communicate effectively and on a timely basis with the laboratory supervisory personnel on all developments or matters resulting from laboratory work activities.
- This level of knowledge and experience is typically achieved through Ph.D. degree in Organic Chemistry, specifically in organic synthesis of polymers, supramolecular architecture, polymers mechanical and chemical characterization.
As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.


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Where is Hinduism persecuted?
According to the United States Commission on International Religious Freedom (USCIRF), Hindus are among those persecuted in Bangladesh, with hundreds of cases of “killings, attempted killings, death threats, assaults, rapes, kidnappings, and attacks on homes, businesses, and places of worship” on religious minorities …
Why is there conflict between Muslims Hinduism?
The roots of violence against Muslims lie in India’s history, stemming from lingering resentment toward the Islamic domination of India during the Middle Ages, policies established by the colonial government during the period of British rule, the violent partition of India into a Muslim Pakistan, and a secular Hindu- …
What are the major issues in Hinduism?
Few of the prominent issues include caste system, Sati practice, dowry system etc. Hinduism takes a very strong stand on unity and considering all the Hindus as family regardless of age, gender, beliefs or Hindus living in different countries.
What’s the most persecuted religion in the world?
The following statistics from Pew Research Center show that Jews and Hindus are the “most likely to live in countries where their groups experience harassment” (99%), followed closely by Muslims (97%).
Why are Hindus and Muslims fighting in India?
The 2020 Delhi riots, which left more than 53 dead and hundreds injured including both Hindus and Muslims, were triggered by protests against a citizenship law seen by many critics as anti-Muslim and part of Prime Minister Narendra Modi’s Hindu nationalist agenda.
What is the core human problem in Hinduism?
The main human problem according to Hinduism involves the cycle of death and rebirth that humans must experience. This cycle, known as reincarnation,…
What is the most popular religion?
Adherents in 2020
What does Hinduism say about other religions?
Hindus believe that each person is intrinsically divine and the purpose of life is to seek and realise the divinity within all of us. The Hindu belief is totally non-exclusive and accepts all other faiths and religious paths.
How can I become a Hindu?
There is no official conversion process or ceremony for converting to the Hindu faith. To become a follower, one needs only to have the will and the commitment to study the scriptures and abide the proper practices.
What are the deadliest religious conflicts in history?
Here are some of history’s deadliest religious conflicts. The term “Reconquista” refers to a series of wars beginning with the victory of Don Pelayo of Asturias at Covadonga around A.D. 720, as per Britannica. It pitted medieval Iberia’s Muslim and Christian states.
What are the ethnic and religious conflicts in India?
Ethnic and Religious Conflicts in India 1 (1) Hindus (a) Caste Hindus 61.22 (b) Scheduled Castes 14.60 (c) Scheduled Tribes 06.90 2 (2) Muslims 11.21 3 (3) Christians 2.60 4 (4) Sikhs 1.89 5 (5) Buddhists 0.70 6 (6) Jains 0.47 7 (7) Other religions (including Tribals, Parsis and Jews) 0.40 8 (8) Religion not stated 0.01
What is the Sikh-Hindu conflict in Punjab?
SIKH-HINDU CONFLICT IN PUNJAB. Starting in August 1980, mounting communal tension between Hindus and Sikhs in the state of Punjab led to violent clashes, in the last year in particular. Unlike Assam, Punjab is a state with the highest per capita income.
What do Hindus do with the dead?
Most Hindus cremate their dead, but Sadhus (Hindu saints) and infants are traditionally buried or immersed in the river. Autopsy, embalming and organ donation are acceptable for Hindus, and there are no specific rules for performing autopsies in Hinduism.

domain: sociology


General Motors claims to have cracked the code for building cheaper electric vehicles than they currently offer. During the Barclay’s Investment conference, GM CEO Mary Barra said that their innovations in battery technology and other breakthroughs mean that they can build a more affordable electric car than their current Chevrolet Bolt EV, Forbes reports.
According to Forbes, GM plans to launch a new EV platform in 2021 which will lay the foundation for the release of 20 new electric vehicles by 2023. They expect these vehicles to be versatile enough to carry nine different body styles in various sizes, and brands for the U.S. market, China, and beyond.
But they aren’t just waiting on 2021 to release new electric vehicles. Barra told the conference that GM plans to continue to develop the Chevrolet Bolt platform and release three new EVs by 2020. Two of these vehicles will be crossovers and they will share features already offered in the Bolt EV.
As Forbes notes, all of this sounds like a warning shot for Tesla Motors and a plan to overshadow their release of their mass-market EV, the Tesla Model 3.
The Model 3 manufacturer has been beset by problems of late and competitors like GM and Daimler (Mercedes) seem to be using that to their advantage.
As the Inquisitr previously reported, Tesla CEO Elon Musk has admitted that the Model 3 is in production hell. Apparently the “production bottleneck” stems from an issue with the automated equipment at their Gigafactory in Nevada. Tesla recently acquired Perbix Machine Company, a firm that specializes in advanced automated manufacturing equipment. There’s speculation that the purchase was made to help solve some of the Model 3’s production issues.
Tesla suppliers have also revealed that the premium electric automaker has slashed parts orders for the Model 3. One supplier said that their orders were cut by 40 percent and that another order was pushed back by a couple months.
Tesla’s planned crossover version of the Model 3, the Model Y, could be a competitor for the EVs that GM plans to introduce by 2020. But according to Seeking Alpha, Tesla first has to build a factory for it and test a new manufacturing system. They estimate that the Model Y will be ready by 2022 and not 2021, as was originally projected.
So, there’s a chance that GM will be first to market with their crossovers, two years before Tesla which may not be good news for Elon Musk and Co.
[Featured Image by GM Corporate Newsroom]


The Universal Holster is a hook and loop mount made for most handguns. Compatible with these popular firearms: Glock 17, Beretta 92, 1911 models, and more.
- Resizable touch-fastener strap guards trigger
- Secures to touch-fastener-faced surfaces and concealed carry compartments
- Adjustable backstrap cord with pull tab
- Fits most pistols
- 600D polyester construction with PVC backing
- Size: 5.75"W x 3"H x 1"D


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Herbarium specimen: Carex pulicaris
|Taxon:||Cyperaceae: Carex pulicaris L. ("Flea Sedge")|
named by Arthur Oliver Chater 1963
|Filed in taxon folder:||Cyperaceae: Carex pulicaris L. ("Flea Sedge")|
|Collected by:||Denis John Carr|
|Locality:||Great Britain, VC69 Westmorland, Nor Moss Tarn, Windermere, SD39, Boggy area.|
|ex herb:||University of Manchester|
|Institution:||Aberystwyth University Herbarium (ABS)|
|notes:||There is also a specimen of C. echinata on the sheet.|
Inferred details are marked.
This sheet was documented by volunteers from the herbarium@home project.
Log-in to edit this specimen or crop examples of handwriting.
Documented by johnhawksford on 14th January 2008.

domain: biology


Fully-functional robot hand is 3D-printed and won’t break the bank
Via Springwise: Amputees going through the traditional routes for a replacement appendage either have to make do with hook systems developed in the 1940s or pay upwards of USD 10,000 if they want functionality or realistic-looking prosthetics. We’ve already seen Robohand develop an affordable, open-source prosthetics system, and now the UK’s Open Hand Project is doing something similar, with its 3D-printed, robotic hand that retails for under USD 1,000.
Having successfully surpassed its funding target on Indiegogo, the group is now working to distribute its first product, the Dextrus, a hand made of electric motors and cables that mimic the abilities of a real hand. Its affordability comes down to the fact that most of its parts are 3D-printed — allowing anyone to make alterations on the fly according to their needs — and the open-source nature of the project, which is releasing the device without a patent. The Dextrus uses electrodes to read signals from the remaining muscles in the user’s arm and features standard connectors to enable it to be attached to existing prostheses.


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The next great eclipse is upon us, with viewers across North America witnessing the moon passing in front of the Sun. It’s an amazing experience, but also an opportunity to do science. Let’s talk about what we can learn from this momentous event.
Whenever astronomers discover something surprising, the answer often turns out to be dust. Dust obscuring our view, dust changing the polarity, dust warming things up, dust cooling things down. It’s always dust. Until it isn’t.
If you’re in dark skies and look up, you’re certain to see a satellite. Lots of them. But how can you know which one you’re seeing, and how can you improve your chances of a sighting? Today we’ll talk about how to see satellites, or avoid seeing them.
Astronomers came together in January to present their newest research, and not surprisingly, the Winter AAS meeting was heavy on news from JWST. What were some of the new results that were announced?
It’s always sad to say goodbye, but when we send our robotic emissaries out into the cosmos, it’s just a matter of time before they shut down. Today we’re going to say goodbye to a few missions which have reached the end of their lives. But they were very good robots.
Well, this is it, we’re finally going to talk about the James Webb Space Telescope. After decades of development, delays and budget creep, the powerful infrared observatory is at its final home at the L2 Lagrange Point. Yesterday we saw the first scientific images from the telescope, and according to Pamela’s rules, we’re finally allowed to talk about it.
There are general-purpose telescopes and missions that astronomers can use to study specific objects. And there are survey missions that look at the entire sky, which astronomers can use to answer questions about the Universe. We’ve talked about surveys in the past, but the landscape is changing fast so it’s time for an update.
A recent image from the South African Meerkat telescope blew our minds. It was a high resolution image of the center of the Milky Way, showing delicate filaments and other structures. What was so mind blowing is that this was an image from a radio telescope. Today we’re going to talk about why this was such an accomplishment and what the future holds for radio astronomy.
In a rare moment of weakness, Pamela has decided she’s open to the possibility that a future exists. That missions, telescopes and spacecraft are going to be built and they’re going to do some science. Today we’ll talk about what we’re looking forward to before she changes her mind and ruins Fraser’s naive optimism for the future.
With all the success of James Webb so far, it’s looking like science’s huge gamble is going to pay off, but there were years of delays and budget overruns. What impacts did these delays have on science, careers, and the future of space exploration?
We’ve reached the end of 2021, and this is the last episode of the year. Let’s look back at the big space events of the last year and talk about what we’re looking forward to in 2022.
You’re familiar with the Hubble Space Telescope, of course, but it’s just one of NASA’s Great Observatories. After Hubble came three more incredible observatories, each greater than the last. Together, they would fill in almost the entire electromagnetic spectrum.
Last week we talked about the various ways that astronomers could detect the presence of intelligent civilizations by observing technosignatures. This week we’ll give you an update on the state of searching for extraterrestrials. This field has gone from a collection of pariahs to a completely legitimate field of research. What’s changed?
When you think about the world’s observatories, I’m sure you’re imagining huge telescopes perched atop mountain peaks, or space telescopes like Hubble. But you might be surprised to learn that some telescopes are carried high into the atmosphere on board balloons. What can they accomplish?
Day by day, we’re losing our connection with the night sky. Already 1/3rd of humanity lives in so much light pollution that they can’t see the Milky Way without a drive. And now satellite constellations are adding additional light pollution, even in the darkest skies on Earth.
Today, we gaze into the future of space and astronomy. What upcoming missions and events are we excited about?
Discovering comets is one of the fields that amateurs can still make a regular contribution to astronomy. But more and more telescopes are getting found by spacecraft, automated systems and machine learning. This week, we’ll talk about how comets are discovered and how you can get your name on one.
As everyone knows, the Universe owes us a bright comet. There have been a lot of promising candidates, but in the end, they always fail to live up to our expectations. Comets keep on breaking up with us.
For the longest time astronomers could only study the skies with telescopes. But then new techniques and technologies were developed to help us see in different wavelengths. Now astronomers can study objects in both visible light, neutrinos, gravitational waves and more. The era of multi-messenger astronomy is here.
This week we’re live at the American Astronomical Society’s 235th meeting in Honolulu, Hawai’i. We learned about new planets, black holes and star formation, but the big issue hanging over the whole conference is the protests and politics over the new Thirty Meter Telescope due for construction on Mauna Kea.
Ancient peoples had no light pollution, and they knew the night skies very well. In fact, they depends on them to know when to plant and when to harvest. Today Pamela talks about the archeoastronomical sites of the American Southwest. In this episode we mentioned...
We always focus on the missions, but there’s an important glue that holds the whole system together. The Deep Space Network. Today we’re going to talk about how this system works and how it communicates with all the spacecraft out there in the Solar System.
This is our second episode in a two part series where we look at Transients in astronomy. In last week’s episode, we talked about things that change here in our own Solar System. Now we’ll talk about everything else in the Milky Way and beyond.
Astronomers have found that sometimes the Universe changes. Things move, things explode, things get brighter or dimmer. In fact, knowing this has helped astronomers discover some very important aspects of the Universe. Today we begin a two part series on Transients and their role in astronomy.
Another update show, this time on the various generations of stars, let’s get into it.

domain: astronomy