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trial showing the beneficial effect of the strain Ligilactobacillus salivarius CECT 30632 (previously known as L. salivarius MP101) on the functional and nutritional status, and on the nasal and fecal inflammatory profiles of elderly residing in a nursing home highly affected by the pandemic. The objective of this post-hoc analysis was to elucidate if there were changes in the nasal and fecal bacteriomes of a subset of these patients as a result of the administration of the strain for 4 months and, also, its impact on their fecal fatty acids profiles. Culture-based methods showed that, while L. salivarius (species level) could not be detected in any of the fecal samples at day 0, L. salivarius CECT 30632 (strain level) was present in all the recruited people at day 120. Paradoxically, the increase in the L. salivarius counts was not reflected in changes in the metataxonomic analysis of the nasal and fecal samples or in changes in the fatty acid profiles in the fecal samples of the recruited people. Overall, our results indicate that L. salivarius CECT 30632 colonized, at least temporarily, the intestinal tract of the recruited elderly and may have contributed to improvements in their functional, nutritional, and immunological status, without changing the general structure of their nasal and fecal bacteriomes when assessed at the genus level. They also suggest the ability of low abundance bacteria to train immunity., Sección Dptal. de Nutrición y Ciencia de los Alimentos (Veterinaria), Fac. de Veterinaria, TRUE, pub

Aim of the study: The gut microbiota is thought to be a crucial factor in the development of irritable bowel syndrome (IBS), and its metabolic byproducts, including short-chain fatty acids (SCFAs), are known to impact gut function and host health. However, the precise role of SCFAs in IBS remains a subject of debate. Methods: In this study, we analyzed the bacterial community structure by 16S rRNA gene profiling and measured SCFA levels by UPLC-MS/MS in fecal samples from healthy controls (HC; n=100) and non-constipated IBS patients (IBS-D and IBS-M; NC-IBS; n=240) recruited from several hospitals in Italy. Results and Conclusions: Our results indicate a significant difference between the fecal microbiomes of NC-IBS patients and HC subjects, with HC showing greater intra-sample biodiversity. Specifically, we observed a general expansion of Actinobacteria (particularly the species Collinsella aerofaciens) and a reduction of Bacteroidetes, butyrate, valerate, and succinate in the fecal microbiome of NC-IBS subjects. Furthermore, the implementation of a probability distribution distance metric enabled the classification of non-constipated IBS patients into two distinct subgroups based on their fecal SCFA levels (fecal catabotypes FC-L and FC-H) (see Figure 1), each characterized by unique bacterial signatures. Notably, we found that the FC-H and FC-L fecal catabotypes are associated with different IBS symptom severity, with a stronger direct relationship between fecal SCFAs and IBS symptoms observed in the FC-H catabotype. Our results suggest that the FC-H catabotype, with higher SCFA levels, may represent a distinct clinical phenotype of IBS that could have implications for diagnosis and treatment. This study provides a new perspective on the intricate relationship between the gut microbiome and bowel symptoms in IBS, highlighting the importance of personalized strategies for its management.

The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in- silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3–V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3–V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3–V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3–V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3–V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Cons

Edible mushrooms contain biologically active compounds with antioxidant, antimicrobial, immunomodulatory and anticancer properties. The link between their anticancer and immunomodulatory properties with their possible prebiotic activity on gut micro-organisms has been the subject of intense research over the last decade. Lyophilized Pleurotus eryngii (PE) mushrooms, selected due to their strong lactogenic effect and anti-genotoxic, immunomodulatory properties, underwent in vitro static batch fermentation for 24 h by fecal microbiota from eight elderly apparently healthy volunteers (>65 years old). The fermentation-induced changes in fecal microbiota communities were examined using Next Generation Sequencing of the hypervariable regions of the 16S rRNA gene. Primary processing and analysis were conducted using the Ion Reporter Suite. Changes in the global metabolic profile were assessed by 1H NMR spectroscopy, and metabolites were assigned by 2D NMR spectroscopy and the MetaboMiner platform. PLS-DA analysis of both metataxonomic and metabolomic data showed a significant cluster separation of PE fermented samples relative to controls. DEseq2 analysis showed that the abundance of families such as Lactobacillaceae and Bifidobacteriaceae were increased in PE samples. Accordingly, in metabolomics, more than twenty metabolites including SCFAs, essential amino acids, and neurotransmitters discriminate PE samples from the respective controls, further validating the metataxonomic findings.

Background: A growing body of evidence demonstrates a different bacterial composition in the oral cavity of patients with oral lichen planus (OLP). Patients and methods: Buccal swab samples were collected from affected and non-affected sites of six patients with reticular OLP and the healthy oral mucosa of six control subjects. 16S rRNA gene MiSeq sequencing and mass spectrometry-based proteomics were utilised to identify the metataxonomic and metaproteomic profiles of the oral microbiome in both groups. Results: From the metataxonomic analysis, the most abundant species in the three subgroups were Streptococcus oralis and Pseudomonas aeruginosa, accounting for up to 70% of the total population. Principal Coordinates Analysis showed differential clustering of samples from the healthy and OLP groups. ANCOM-BC compositional analysis revealed multiple species (including P. aeruginosa and several species of Veillonella, Prevotella, Streptococcus and Neisseria) significantly over-represented in the control group and several (including Granulicatella elegans, Gemella haemolysans and G. parahaemolysans) in patients with OLP. The metaproteomic data were generally congruent and revealed that several Gemella haemolysans-belonging peptidases and other proteins with inflammatory and virulence potential were present in OLP lesions. Conclusion: Our data suggest that several bacterial species are associated with OLP. Future studies with larger cohorts should be conducted to determine their role in the aetiology of OLP and evaluate their potential as disease biomarkers.

The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3–V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3–V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3–V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3–V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3–V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the ba, The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3–V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3–V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3–V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3–V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3–V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the

The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3–V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3–V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3–V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3–V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3–V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the ba, The Lactobacillaceae are lactic acid bacteria harnessed to deliver important outcomes across numerous industries, and their unambiguous, species-level identification from mixed community environments is an important endeavor. Amplicon-based metataxonomics using short-read sequencing of partial 16S rRNA gene regions is widely used to support this, however, the high genetic similarity among Lactobacillaceae species restricts our ability to confidently describe these communities even at genus level. Long-read sequencing (LRS) of the whole 16S rRNA gene or the near complete rRNA operon (16S-ITS-23S) has the potential to improve this. We explored species ambiguity amongst Lactobacillaceae using in-silico tool RibDif2, which identified allele overlap when various partial and complete 16S rRNA gene and 16S-ITS-23S rRNA regions were amplified. We subsequently implemented LRS by MinION™ to compare the capacity of V3–V4, 16S and 16S-ITS-23S rRNA amplicons to accurately describe the diversity of a 20-species Lactobacillaceae mock community in practice. In-silico analysis identified more instances of allele/species overlap with V3–V4 amplicons (n = 43) compared to the 16S rRNA gene (n = 11) and partial (n = up to 15) or complete (n = 0) 16S-ITS-23S rRNA amplicons. With subsequent LRS of a DNA mock community, 80% of target species were identified using V3–V4 amplicons whilst the 16S rRNA gene and 16S-ITS-23S rRNA region amplicons resulted in 95 and 100% of target species being identified. A considerable reduction in false-positive identifications was also seen with 16S rRNA gene (n = 3) and 16S-ITS-23S rRNA region (n = 9) amplicons compared with V3–V4 amplicons (n = 43). Whilst the target species affected by allele overlap in V3–V4 and 16S rRNA gene sequenced mock communities were predicted by RibDif2, unpredicted species ambiguity was observed in 16S-ITS-23S rRNA sequenced communities. Considering the average nucleotide identity (ANI) between ambiguous species (~97%) and the

CRUE-CSIC (Acuerdos Transformativos 2022), The ecological dysbiosis of a biofilm includes not only bacterial changes but also changes in their metabolism. Related to oral biofilms, changes in metabolic activity are crucial endpoint, linked directly to the pathogenicity of oral diseases. Despite the advances in caries research, detailed microbial and metabolomic etiology is yet to be fully clarified. To advance this knowledge, a meta-taxonomic approach based on 16S rRNA gene sequencing and an untargeted metabolomic approach based on an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry analysis (UHPLC/Q-TOF-MS) were conducted. To this end, an in vitro biofilm model derived from the saliva of healthy participants were developed, under commensal and cariogenic conditions by adding sucrose as the disease trigger. The cariogenic biofilms showed a significant increase of Firmicutes phyla (p = 0.019), due to the significant increase in the genus Streptococcus (p = 0.010), and Fusobacter (p < 0.001), by increase Fusobacterium (p < 0.001) and Sphingomonas (p = 0.024), while suffered a decrease in Actinobacteria (p < 0.001). As a consequence of the shift in microbiota composition, significant extracellular metabolomics changes were detected, showed 59 metabolites of the 120 identified significantly different in terms of relative abundance between the cariogenic/commensal biofilms (Rate of change > 2 and FDR < 0.05). Forty-two metabolites were significantly higher in abundance in the cariogenic biofilms, whereas 17 metabolites were associated significantly with the commensal biofilms, principally related protein metabolism, with peptides and amino acids as protagonists, latter represented by histidine, arginine, l-methionine, glutamic acid, and phenylalanine derivatives., Depto. de Medicina, Fac. de Odontología, Fac. de Medicina, TRUE, pub

Elderly was the most affected population during the first COVID-19 and those living in nursing homes represented the most vulnerable group, with high mortality rates, until vaccines became available. In a previous article, we presented an open-label trial showing the beneficial effect of the strain Ligilactobacillus salivarius CECT 30632 (previously known as L. salivarius MP101) on the functional and nutritional status, and on the nasal and fecal inflammatory profiles of elderly residing in a nursing home highly affected by the pandemic. The objective of this post-hoc analysis was to elucidate if there were changes in the nasal and fecal bacteriomes of a subset of these patients as a result of the administration of the strain for 4 months and, also, its impact on their fecal fatty acids profiles. Culture-based methods showed that, while L. salivarius (species level) could not be detected in any of the fecal samples at day 0, L. salivarius CECT 30632 (strain level) was present in all the recruited people at day 120. Paradoxically, the increase in the L. salivarius counts was not reflected in changes in the metataxonomic analysis of the nasal and fecal samples or in changes in the fatty acid profiles in the fecal samples of the recruited people. Overall, our results indicate that L. salivarius CECT 30632 colonized, at least temporarily, the intestinal tract of the recruited elderly and may have contributed to improvements in their functional, nutritional, and immunological status, without changing the general structure of their nasal and fecal bacteriomes when assessed at the genus level. They also suggest the ability of low abundance bacteria to train immunity.

In this work, a total of 72 commercial table olive packages obtained from different international markets were analysed to determine their fungal biodiversity. Viable fungal counts ranged from the detection threshold (<1.6 log CFU/g in 25% of cases) to a maximum of 5.86 log CFU/g. Assignation of fungal taxonomy was carried out through a metataxonomic analysis of the ITS region, which revealed that almost half of the total sequences obtained from all packages corresponded to the Pichia genus (44.08%), followed by Citeromyces (14.45%), Candida (8.07%), and Wickerhamomyces (6.95%). In lower proportions were also detected other genera such as Starmerella (3.60%), Saccharomyces (2.24%), Debaryomyces (2.08%), and Dekkera (2.05%). The statistical analysis allowed to link certain taxa to specific types of elaboration (lye treated, green, and black natural olives), presentation (pitted, whole, or sliced samples), and packaging material/system (glass, PET, plastic bags, and vacuum). Likewise, Zygotorulaspora genus was especially sensitive to the presence of potassium sorbate, while other genera such as Sporobolomyces, Moniliella, and Gibellulopsis were more abundant in packages treated with this preservative. Lastly, potential pathogenic fungal genera such as Alternaria, Kodamaea, Lodderomyces, Malasessia, or Aspergillus were detected in low proportions (<0.3%), although with higher representation in some individual samples. Our results contribute to improving our knowledge of the fungal population associated with this ready-to-eat fermented vegetable, providing us a strong tool to assess the safety, stability, and quality of the final product.

The effluents from industries processing vegetable oils are extremely rich in sulfates, often exceeding the maximum concentration allowed to release them to the environment. Biological sulfate reduction is a promising alternative for the removal of sulfates in this type of wastewater, which has other particularities such as an acidic pH. The ability to reduce sulfates has been widely described for a particular bacterial group (SRB: sulfate-reducing bacteria), although the reports describing its application for the treatment of sulfate-rich industrial wastewaters are scarce. In this work, we describe the use of a natural SRB-based consortium able to remove above 30% of sulfates in the wastewater from one of the largest edible oil industries in Peru. Metataxonomic analysis was used to analyse the interdependencies established between SRB and the native microbiota present in the wastewater samples, and the performance of the consortium was quantified for different sulfate concentrations in laboratory-scale reactors. Our results pave the way towards the use of this consortium as a low-cost, sustainable alternative for the treatment of larger volumes of wastewater coming from this type of industries.

Citrus is the most cultivated fruit crop worldwide. The modern citrus industry needs new bioproducts to overcome phytopathological threats, tolerate stresses and increase yield and quality. Mutualistic microbes from roots significantly impact host physiology and health and are a potentially beneficial resource. The bacterial microbiome can be surveyed to select potentially host-beneficial microbes. To achieve this goal, a prevalent “core-citrus” bacterial microbiome was obtained by picking those operational taxonomic units (OTUs) shared among samples within and across two Citrus rootstock genotypes grown in the same soil for more than 20 years. A sub-selection of main OTUs from the defined “core-citrus” microbiome was made based on abundance, host-enriched versus bulk soil, and rhizosphere-indicator species. In parallel, an extensive census of the cultivable microbiota was performed to collect a large number of bacterial citrus isolates. Metataxonomic data were linked to cultured microbes, matching 16S rRNA gene sequences from bacterial isolates with those counterpart OTU reference sequences from the selected bacterial “core-citrus” microbiome. This approach allowed selection of potentially host-beneficial bacteria to mine for agricultural probiotics in future biotechnological applications required for the citrus industry.

The study of the food microbiome has gained considerable interest in recent years, mainly due to the wide range of applications that can be derived from the analysis of metagenomes. Among these applications, it is worth mentioning the possibility of using metagenomic analyses to determine food authenticity, to assess the microbiological safety of foods thanks to the detection and tracking of pathogens, antibiotic resistance genes and other undesirable traits, as well to identify the microorganisms responsible for food processing defects. Metataxonomics and metagenomics are currently the gold standard methodologies to explore the full potential of metagenomes in the food industry. However, there are still a number of challenges that must be solved in order to implement these methods routinely in food chain monitoring, and for the regulatory agencies to take them into account in their opinions. These challenges include the difficulties of analysing foods and food-related environments with a low microbial load, the lack of validated bioinformatics pipelines adapted to food microbiomes and the difficulty of assessing the viability of the detected microorganisms. This review summarizes the methods of microbiome analysis that have been used, so far, in foods and food-related environments, with a specific focus on those involving Next-Generation Sequencing technologies.

The current understanding of human gut microbial community is mainly limited to taxonomic features at the genus level. Here, we examined the human gut microbial community at the species level by metataxonomics. To achieve this purpose, a high-throughput approach involving operational phylogenetic unit analysis of the near full-length 16S ribosomal RNA (rRNA) gene sequence was used. A total of 1,235 species-level phylotypes (SLPs) were classified in the feces of 120 Chinese healthy individuals, including 461 previously classified species, 358 potentially new species, and 416 potentially new taxa, which were categorized into low, medium, and high prevalent bacteria groups based on their prevalence. Each individual harbored 186 ± 51 SLPs on average. There was no universal bacterial species shared by all the individuals. However, 90 ± 19 of 116 SLPs were shared in the high prevalent bacteria group. Thirty-two out of thirty-eight species in the high prevalent bacteria group detected in this study were also found in at least one previous study on human gut microbiota based on either culture-dependent or culture-independent approaches. Through compositional analysis, a hierarchical clustering of the prevalence and relative abundance of the 1,235 SLPs revealed two types of gut microbial communities, which were dominated by Prevotella copri and Bacteroides vulgatus, respectively. The type dominated by P. copri was more prevalent in northern China, while the B. vulgatus-dominant type was more prevalent in southern China. Therefore, P- and B-type gut microbial communities in China were proposed. It was found that 166 out of 461 known bacterial species have been previously reported as potential pathogens, and the individuals sampled for this study harbored 20 of these potential pathogenic species on average. The top two most abundant and prevalent potential pathogenic species were Klebsiella pneumoniae and Bacteroides fragilis.

Copyright © 2019 Kioroglou, Mas and Portillo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Metataxonomic analysis represents a fast and cost-effective approach for acquiring informative insight into the composition of the microbiome of samples with variable diversity, such as wine samples. Nevertheless, it comprises a vast amount of laboratory procedures and bioinformatic frameworks each one associated with an inherent variability of protocols and algorithms, respectively. As a solution to the bioinformatic maze, QIIME bioinformatic framework has incorporated benchmarked, and balanced parameters as default parameters. In the current study, metataxonomic analysis of two types of mock community standards with the same microbial composition has been performed for evaluating the effectivess of QIIME balanced default parameters on a variety of aspects related to different laboratory and bioinformatic workflows. These aspects concern NGS platforms, PCR protocols, bioinformatic pipelines, and taxonomic classification algorithms. Several qualitative performance expectations have been the outcome of the analysis, rendering the mock community a useful evaluation tool.

Background: New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies. Findings: We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io). Conclusions: Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.

Metataxonomic 16S rDNA based studies are a commonplace and useful tool in the research of the microbiome, but they do not provide the full investigative power of metagenomics and metatranscriptomics for revealing the functional potential of microbial communities. However, the use of metagenomic and metatranscriptomic technologies is hindered by high costs and skills barrier necessary to generate and interpret the data. To address this, a tool for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was developed for inferring the functional potential of an observed microbiome profile, based on 16S data. This allows functional inferences to be made from metataxonomic 16S rDNA studies with little extra work or cost, but its accuracy relies on the availability of completely sequenced genomes of representative organisms from the community being investigated. The rumen microbiome is an example of a community traditionally underrepresented in genome and sequence databases, but recent efforts by projects such as the Global Rumen Census and Hungate 1000 have resulted in a wide sampling of 16S rDNA profiles and almost 500 fully sequenced microbial genomes from this environment. Using this information, we have developed “CowPI,” a focused version of the PICRUSt tool provided for use by the wider scientific community in the study of the rumen microbiome. We evaluated the accuracy of CowPI and PICRUSt using two 16S datasets from the rumen microbiome: one generated from rDNA and the other from rRNA where corresponding metagenomic and metatranscriptomic data was also available. We show that the functional profiles predicted by CowPI better match estimates for both the meta-genomic and transcriptomic datasets than PICRUSt, and capture the higher degree of genetic variation and larger pangenomes of rumen organisms. Nonetheless, whilst being closer in terms of predictive power for the rumen microbiome, there were differences when compared to bot

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.