spyder, a new method for in silico design and assessment of 16S rRNA gene primers for molecular microbial ecology

Monitoring antibiotic resistance genes in wastewater environments: The challenges of filling a gap in the One-Health cycle

Antibiotic resistance (AR) is a global problem requiring international cooperation and coordinated action. Global monitoring must rely on methods available and comparable across nations to quantify AR occurrence and identify sources and reservoirs, as well as paths of AR dissemination. Numerous analytical tools that are gaining relevance in microbiology, have the potential to be applied to AR research. This review summarizes the state of the art of AR monitoring methods, considering distinct needs, objectives and available resources. Based on the overview of distinct approaches that are used or can be adapted to monitor AR, it is discussed the potential to establish reliable and useful monitoring schemes that can be implemented in distinct contexts. This discussion places the environmental monitoring within the One-Health approach, where two types of risk, dissemination across distinct environmental compartments, and transmission to humans, must be considered. The plethora of methodological approaches to monitor AR and the variable features of the monitored sites challenge the capacity of the scientific community and policy makers to reach a common understanding. However, the dialogue between different methods and the production of action-oriented data is a priority. The review aims to warm up this discussion.

Modeling Microbial Community Networks: Methods and Tools

In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities' organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.

Blowing in the wind: Bacteria and fungi are spreading from public restroom hand dryers

This study aimed to identify and quantify fungi and bacteria in the airflow of restroom hand dryers in public areas. Airflow from restroom hand dryers in 8 retail locations was tested using three types of culture media, followed by PCR and sequence analysis to identify microbial species. Both bacterial and fungal colonies were detected in all locations. The number of colonies did not vary significantly across different locations, suggesting a similar level of microbial spread by hand dryers between different types of commercial stores. Molecular analysis revealed 24 bacterial species and 40 fungal species. Of these species, 48% (31/64) have been reported to be implicated in various infections in humans, primarily those with underlying medical conditions. This study is the first to demonstrate the spread of fungi by the airflow of restroom hand dryers, and the first to show the prevalence of different fungal and bacterial species spread by restroom hand dryers in common public areas.

ARGA, a pipeline for primer evaluation on antibiotic resistance genes

Molecular biology techniques have assisted in the investigation of antibiotic resistance genes (ARGs) from various environments. However, their accuracy relies on primer quality and data interpretation, both of which require a full-coverage sequence database for ARGs. Here, based upon the abandoned Antibiotic Resistance Genes Database (ARDB), we created an updated sequence database of antibiotic resistance genes (SDARG). A total of 1,260,069 protein sequences and 1,164,479 nucleotide sequences, 56 times more sequences than ARDB, from 448 types of ARGs (enabling resistance to 18 categories of antibiotics) were collected and integrated with different hierarchical credibility and full-scale taxonomic information. Based on this comprehensive sequence database, an online pipeline - ARG analyzer (ARGA, http://mem.rcees.ac.cn:8083/) was developed to assess current ARGs primers, as well as annotate ARGs from environmental metagenomes. Thereafter, a list of 658 published primer pairs, targeting 173 ARGs, was evaluated using ARGA and integrated in ARGA as ARGs primer database. The results showed that 65.05% primers are of high specificity (≥90%), while only 29.79% primers cover >50% of targeted sequences, indicating a divergence in the quality of current ARG primers. Hence, primer assessment or redesign is highly recommended to improve the accuracy of ARGs studies. ARGs primer database was attached in ARGA to provide researchers alternatives to better survey ARGs in the environment.

Optimizing PCR primers targeting the bacterial 16S ribosomal RNA gene

BACKGROUND

Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies.

RESULTS

We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S .

CONCLUSIONS

Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.

Functional metagenomics: a tool to gain knowledge for agronomic and veterinary sciences

The increased global demand for food production has motivated agroindustries to increase their own levels of production. Scientific efforts have contributed to improving these production systems, aiding to solve problems and establishing novel conceptual views and sustainable alternatives to cope with the increasing demand. Although microorganisms are key players in biological systems and may drive certain desired responses toward food production, little is known about the microbial communities that constitute the microbiomes associated with agricultural and veterinary activities. Understanding the diversity, structure and in situ interactions of microbes, together with how these interactions occur within microbial communities and with respect to their environments (including hosts), constitutes a major challenge with an enormous relevance for agriculture and biotechnology. The emergence of high-throughput sequencing technologies, together with novel and more accessible bioinformatics tools, has allowed researchers to learn more about the functional potential and functional activity of these microbial communities. These tools constitute a relevant approach for understanding the metabolic processes that can occur or are currently occurring in a given system and for implementing novel strategies focused on solving production problems or improving sustainability. Several 'omics' sciences and their applications in agriculture are discussed in this review, and the usage of functional metagenomics is proposed to achieve substantial advances for food agroindustries and veterinary sciences.

Exploring abundance, diversity and variation of a widespread antibiotic resistance gene in wastewater treatment plants

An updated sul1 gene sequence database was constructed and new degenerate primers were designed to better investigate the abundance, diversity, and variation of a ubiquitous antibiotic resistance gene, sul1, with PCR-based methods in activated sludge from wastewater treatment plants (WWTPs). The newly designed degenerate primers showed high specificity and higher coverage in both in-silico evaluations and activated sludge samples compared to previous sul1 primers. Using the new primers, the abundance and diversity of sul1 gene, together with 16S rRNA gene, in activated sludge from five WWTPs in summer and winter were determined by quantitative PCR and MiSeq sequencing. The sul1 gene was found to be prevalent and displayed a comparable abundance (0.081 copies per bacterial cell in average) to the total bacteria across all samples. However, compared to the significant seasonal and geographical divergences in the quantity and diversity of bacterial communities in WWTPs, there were no significant seasonal or geographical variations of representative clusters of sul1 gene in most cases. Additionally, the representative sul1 clusters showed fairly close phylogeny and there was no obvious correlation between sul1 gene and the dominant bacterial genera, as well as the int1 gene, suggesting that bacterial hosts of sul1 gene is not stable, the sul1 gene may be carried by mobile genetic elements, sometimes integrated with class 1 integrons and sometimes not. Thus mobile genetic elements likely play a greater role than specific microbial taxa in determining the composition of sul1 gene in WWTPs.

Diet complexity and estrogen receptor β status affect the composition of the murine intestinal microbiota

Intestinal microbial dysbiosis contributes to the dysmetabolism of luminal factors, including steroid hormones (sterones) that affect the development of chronic gastrointestinal inflammation and the incidence of sterone-responsive cancers of the breast, prostate, and colon. Little is known, however, about the role of specific host sterone nucleoreceptors, including estrogen receptor β (ERβ), in microbiota maintenance. Herein, we test the hypothesis that ERβ status affects microbiota composition and determine if such compositionally distinct microbiota respond differently to changes in diet complexity that favor Proteobacteria enrichment. To this end, conventionally raised female ERβ(+/+) and ERβ(-/-) C57BL/6J mice (mean age of 27 weeks) were initially reared on 8604, a complex diet containing estrogenic isoflavones, and then fed AIN-76, an isoflavone-free semisynthetic diet, for 2 weeks. 16S rRNA gene surveys revealed that the fecal microbiota of 8604-fed mice and AIN-76-fed mice differed, as expected. The relative diversity of Proteobacteria, especially the Alphaproteobacteria and Gammaproteobacteria, increased significantly following the transition to AIN-76. Distinct patterns for beneficial Lactobacillales were exclusive to and highly abundant among 8604-fed mice, whereas several Proteobacteria were exclusive to AIN-76-fed mice. Interestingly, representative orders of the phyla Proteobacteria, Bacteroidetes, and Firmicutes, including the Lactobacillales, also differed as a function of murine ERβ status. Overall, these interactions suggest that sterone nucleoreceptor status and diet complexity may play important roles in microbiota maintenance. Furthermore, we envision that this model for gastrointestinal dysbiosis may be used to identify novel probiotics, prebiotics, nutritional strategies, and pharmaceuticals for the prevention and resolution of Proteobacteria-rich dysbiosis.

Effect of antimicrobial growth promoter administration on the intestinal microbiota of beef cattle

Background

Antimicrobial growth promoters (AGPs) are antimicrobial agents administered to livestock in feed for prolonged periods to enhance feed efficiency. Beef cattle are primarily finished in confined feeding operations in Canada and the USA, and the administration of AGPs such as chlortetracycline and sulfamethazine (Aureo S-700 G) is the standard. The impacts of AGPs on the intestinal microbiota of beef cattle are currently uncertain; it is documented that AGPs administered to beef cattle pass through the rumen and enter the intestine. To ascertain the impacts of Aureo S-700 G on the small and large intestinal microbiota of beef cattle (mucosa-associated and within digesta), terminal restriction fragment length polymorphism (T-RFLP) analysis and quantitative PCR (qPCR) for total bacteria were applied. Beef cattle were maintained in an experimental feedlot (five replicate pens per treatment), and AGP treatment cattle were administered Aureo S-700 G in feed, whereas control cattle were administered no antimicrobials. As the intestinal microbiota of beef cattle has not been extensively examined, clone library analysis was applied to ascertain the primary bacterial constituents of the intestinal microbiota.

Results

Comparative T-RFLP and qPCR analysis (n = 122 samples) revealed that bacterial community fingerprints and bacterial load within digesta differed from those associated with mucosa. However, the administration of Aureo S-700 G did not affect bacterial community fingerprints or bacterial load within the small and large intestine relative to control cattle. Analysis of >1500 near full length 16S rDNA clones revealed considerably greater bacterial diversity in the large relative to the small intestine of beef cattle. Mucosa-associated bacterial communities in the jejunum were dominated by Proteobacteria, and differed conspicuously from those in the ileum and large intestine. Although the ileum contained bacterial clones that were common to the jejunum as well as the cecum, Firmicutes clones associated with mucosa dominated in the ileum, cecum, and descending colon. In the descending colon, clone library analysis did not reveal a difference in the richness or diversity of bacterial communities within digesta relative to those associated with mucosa. However, T-RFLP analysis indicated a significant difference in T-RF relative abundance (i.e. difference in relative taxon abundance) between mucosa-associated and digesta communities attributed in part to the differential abundance of Bacteriodes, Alistipes, Oscillibacter, and unclassified Clostridiales.

Conclusions

These data demonstrate that there was no significant difference in the composition of the predominant intestinal bacteria constituents within animals administered Aureo S-700 G and those not administered AGPs after a 28 day withdrawal period.

Evaluation of marine bacteroidetes-specific primers for microbial diversity and dynamics studies

Assumptions on the matching specificity of group-specific bacterial primers may bias the interpretation of environmental microbial studies. As available sequence data continue growing, the performance of primers and probes needs to be reevaluated. Here, we present an evaluation of several commonly used and one newly designed Bacteroidetes-specific primer (CF418). First, we revised the in silico primer coverage and specificity with the current SILVA and RDP databases. We found minor differences with previous studies, which could be explained by the chosen databases, taxonomies, and matching criteria. We selected eight commonly used Bacteroidetes primers and tested them with a collection of assorted marine bacterial isolates. We also used the denaturing gradient gel electrophoresis (DGGE) approach in environmental samples to evaluate their ability to yield clear and diverse band patterns corresponding to Bacteroidetes phylotypes. Among the primers tested, CF968R did not provide satisfactory results in DGGE, although it exhibited the highest in silico coverage for Flavobacteria. Primers CFB560 and CFB555 presented undesirable features, such as requiring nested protocols or presence of degeneracies. Finally, the new primer CF418 and primer CF319a were used to explore the Bacteroidetes dynamics throughout a 1-year cycle in Mediterranean coastal waters (Blanes Bay Microbial Observatory). Both primers provided clear and diverse banding patterns, but the low specificity of CF319a was evidenced by 83.3 % of the bands sequenced corresponding to nontarget taxa. The satisfactory DGGE banding patterns and the wide diversity of sequences retrieved from DGGE bands with primer CF418 prove it to be a valuable alternative for the study of Bacteroidetes communities, recovering a wide range of phylotypes within the group.