Tropical wetlands are the major natural source of methane released into the atmosphere, producing about 60% of all natural emissions. The great wetland areas of the Amazon basin are the largest source of methane in this region, contributing an estimated 5% of the total emissions from the world’s flooded areas. However, despite the important role that methanogenic archaea play in these environments, there have been few studies on the composition of their archaeal communities. In this survey, four 16S rRNA archaeal clone libraries from different depths were constructed to examine the archaeal community in an Amazon wetland soil. A total of 599 clones were used to perform diversity and phylogenetic analyses. A broad, diverse archaeal community was found at the site, with the diversity decreasing as the depth increased (Shannon index range: 2.40–1.94). Phylogenetic analysis revealed sequences belonging to two archaeal phyla, with 65% classified as Crenarchaeota and 35% classified as Euryarchaeota. Within the Euryarchaeota group, most sequences were clustered into the Methanococci and Methanomicrobia classes, two groups of methanogens. Based on the abundance of methanogenic organisms, culture–dependent isolation was used to isolate these organisms. To enhance the growth of methanogenic archaea, a modified atmosphere (H₂:CO₂ = 80:20) was established combined with an anoxic environment for 18 months. Among the isolates, the genera Methanosarcina and Methanobacterium were detected throughout the anaerobic in vitro cultivation, indicating a possible role for these organisms in methane production. In conclusion, these exploratory molecular and culture–dependent approaches enhance our understanding of the archaeal community and methanogenic archaea living in wetland soils of the eastern Amazon and their role in methane production. Full article

The high level of conservation of 16S ribosomal RNA gene (16S rRNA) in all Prokaryotes makes this gene an ideal tool for the rapid identification and classification of these microorganisms. Databases such as the Ribosomal Database Project II (RDP-II) and the Greengenes Project offer access to sets of ribosomal RNA sequence databases useful in identification of microbes in a culture-independent analysis of microbial communities. However, these databases do not contain all of the taxonomic levels attached to the published names of the bacterial and archaeal sequences. TaxCollector is a set of scripts developed in Python language that attaches taxonomic information to all 16S rRNA sequences in the RDP-II and Greengenes databases. These modified databases are referred to as TaxCollector databases, which when used in conjunction with BLAST allow for rapid classification of sequences from any environmental or clinical source at six different taxonomic levels, from domain to species. The TaxCollector database prepared from the RDP-II database is an important component of a new 16S rRNA pipeline called PANGEA. The usefulness of TaxCollector databases is demonstrated with two very different datasets obtained using samples from a clinical setting and an agricultural soil. The six TaxCollector scripts are freely available on http://taxcollector.sourceforge.net and on http://www.microgator.org. Full article

This study evaluated microbial communities of soil (0–10 cm) as affected by dryland cropping systems under different tillage practices after 5 years. The soil type was an Olton sandy loam with an average of 16.4% clay, 67.6% sand and 0.65 g kg⁻¹ of organic matter (OM). The cropping systems evaluated were grain sorghum (Sorghum bicolor L.)—cotton (Gossypium hirsutum) (Sr_g-Ct), cotton-winter rye (Secale cereale)-grain sorghum (Ct-Rye-Sr_g), and a rotation of forage (f) sorghum (Sorghum bicolor L. and Sorghum sudanense) with winter rye (Sr_f-Rye), which were under no-tillage (nt) and conventional tillage (ct) practices. Soil microbial communities under cotton based cropping systems (Sr_g-Ct and Ct-Rye-Sr_g) showed lower fungal:bacterial ratios compared to the soil under Sr_f-Rye. Soil under Sr_f-Rye showed higher population densities of Bacteroidetes and Proteobacteria while lower Actinobacteria compared to Sr_g-Ct and Ct-Rye-Sr_g. Chloroflexi, Gemmatimonadetes and Verrucomicrobiae were higher in tilled soil compared to the no-tilled plots. Regardless the limited irrigation available to sustain agricultural production within these dryland cropping systems, this study demonstrated that differences in microbial communities are more affected by crop rotation than tillage management history. Although soil fungal diversity was not analyzed in this study, pyrosequencing suggests that tillage practices can affect bacterial phyla distribution in this sandy soil. Full article

Humans harbor distinct commensal microbiota at various anatomic sites. There has been renewed interest in the contributions of microbiota activities to human health and disease. The microbiota of the gut is the most complex of all anatomic sites in terms of total numbers of bacteria that interact closely with the mucosal immune system and contribute various functions to host physiology. Especially in the proximal large intestine a diverse microbiota ferments complex substrates such as dietary fiber and host mucins, but also metabolizes bile acids and phytoestrogens that reach the large intestine. It is now well established that microbiota composition differs between but over time also within individuals. However, a thorough understanding of the sources of variations in microbiota composition, which is an important requirement for large population based microbiota studies is lacking. Microbiota composition varies depending on what kind of sample is collected, most commonly stool samples, stool swabs or superficial rectal or intestinal biopsies, and the time of collection. Microbiota dynamics are affected by life style factors including diet and exercise that determine what nutrients reach the proximal colon and how fast these nutrients pass through (transit time). Here we review sample collection issues in gut microbiota studies and recent findings about dynamics in microbiota composition. We recommend standardizing human microbiota analysis methods to facilitate comparison and pooling between studies. Finally, we outline a need for prospective microbiota studies in large human cohorts. Full article