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The advances in experimental methods and the development of high performance bioinformatic tools have substantially improved our understanding of microbial communities associated with human niches. Many studies have documented that changes in microbial abundance and composition of the human microbiome is associated with human health and diseased state. The majority of research on human microbiome is typically focused in the analysis of one level of biological information, i.e., metagenomics or metatranscriptomics. In this review, we describe some of the different experimental and bioinformatic strategies applied to analyze the 16S rRNA gene profiling and shotgun sequencing data of the human microbiome. We also discuss how some of the recent insights in the combination of metagenomics, metatranscriptomics and viromics can provide more detailed description on the interactions between microorganisms and viruses in oral and gut microbiomes. Recent studies on viromics have begun to gain importance due to the potential involvement of viruses in microbial dysbiosis.
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In addition, metatranscriptomic combined with metagenomic analysis have shown that a substantial fraction of microbial transcripts can be differentially regulated relative to their microbial genomic abundances. Thus, understanding the molecular interactions in the microbiome using the combination of metagenomics, metatranscriptomics and viromics is one of the main challenges towards a system level understanding of human microbiome. • Previous article in issue • Next article in issue.
Introduction Soil microbes play a primary role in ecosystem functions and sustainability, including agricultural ecosystems (; ). In agroecosystems, productivity, resilience to perturbations, nutrient cycling, and resistance to plagues is strongly influenced by soil microbial biodiversity (). Microbial communities change their composition and function as a consequence of environmental changes and farming practices (;; ); however, there is still little understanding about the nature and relative contribution of the specific factors that affect the composition and structure of soil microbial communities in time and space (;; ). In recent years, the composition and structure of microbial communities has been reported in many ecosystems; many studies on this topic have been published thanks to the development of high-throughput sequencing technologies (; ) and the use of analytic methods such as co-occurrence networks (). The use of these methods has helped identify some of the factors that contribute to soil microbial diversity and structure within agroecosystems (). In studies with maize and rice, for example, large effects on microbial diversity are associated with soil type and cultivation practices (; ). However, bacterial diversity surveys for agricultural soils have focused mainly on the characterization of microbial communities assessed in a single time-point and mostly on crop monocultures.
Crop polycultures, however, are very important because of their central role in the development of sustainable agriculture (; ). Moreover, they are often subjected to drastic environmental and management changes throughout the year, while being highly dependent on rainwater. For example, nearly three quarters of the agricultural production in rural Mexico is rainfed (). Given seasonal variation in rainfall, studies of polycultures should include longitudinal sampling that captures potential seasonal changes. The milpa is a traditional polyculture in Mexico and Mesoamerica that is based on maize and has been recognized as an invaluable repository of biological and cultural diversity (;; ).
The milpa typically includes intercropping of maize and common beans but often features additional crops such as tomato, squash, chili, jicama, and avocado. Over thousands of years, this polyculture has been adapted to a variety of climatic, edaphic, and cultural conditions, and it has been the foundation of food security in many Latin American rural communities (). The milpa system has been studied from different perspectives. Some of the bacterial diversity associated with milpa soils has been characterized but only for particular microbial species and families (, ).
Nevertheless, to our knowledge, no studies have been conducted on the structure and diversity of milpa-associated bacterial communities. Taking into account the recognized values of the milpa, it is of interest to investigate its associated microbiota, particularly for the conservation or restoration of the microorganism-mediated biogeochemical processes that can be the base of an input-free and sustainable agriculture (; ). In the present study, we report the composition and structure of soil prokaryotic communities associated with milpa plots in the central highlands of Mexico, in a region where small farmers practice rain-fed maize agriculture with several plants in association or in rotation (Figure ). Windows Xp Professional Oem Sp2 Iso Download more. Given the marked seasonality of milpa agriculture in this region we explore not only the composition and structure of soil prokaryotic communities but also their seasonal change along the cropping season. We hypothesize that nutrient profiles, bacterial diversity, bacterial composition, and co-occurrence networks exhibit seasonal changes.
For testing this hypothesis, we have collected soil samples from four plots at three key time points in the agricultural cycle. We determined the pH and the total content of nitrogen, carbon and phosphorus, and characterized the microbial community by means of high-throughput 16S rRNA amplicon sequencing. Finally, we interpreted the correlations among microbial taxa in terms of their ecological roles and putative interactions (; ). Bacterial Diversity and Community Structure Do Not Exhibit Seasonal Changes A total of 7,183 OTUs were identified from the 90 soil samples collected during three time points (after filtering to 119,062 reads the total number of reads for the collection of samples was 10,715,580). Considering all time points, the five dominant phyla were Proteobacteria (41.35%), Actinobacteria (17.33%), Acidobacteria (12.47%), Gemmatimonadetes (7.53%), and Verrucomicrobia (6.41%) (Figure ). According to Shannon index estimates, no significant differences in alpha diversity were found across time (Figure, ANOVA) or among plots with the exception of plot R, which differed from plots L and T only at t2 (Supplementary Figure S1; ANOVA F (3,35) = 3.747, P = 0.0196). Random subsamplings of the data set to balance sample size, showed that 99.5% of the time the effect of Time was not significant (ANOVA P >0.05).
Principal coordinate analyses (Supplementary Figure S2) showed no significant differences across time according to beta diversity estimates using Weighted Unifrac distances (Adonis test: Pseudo- F (2,87) = 1.4254, P = 0.102). However random subsampling of the Weighted Unifrac distance matrix indicated that, when sample size per time is equal (17 samples), the effect of time was significant 21.3% of the time (PERMANOVA P. Co-occurrence Patterns Show Significant Changes in Time We obtained co-occurrence networks for the four plots, the three time-points, and the combination of both categories.
The networks obtained for the three time points ( t1, t2, and t3) fitted a Power law ( R 2 = 0.75–0.81) and displayed strong modularity and hierarchical properties (see below), all of which have been associated with network complexity (; ). Moreover, a significant Power law fit was also observed in sub-networks that constitute taxonomic groups even when lower taxonomic hierarchies were used, or when taxa were taken inside modules. The comparisons between plots, which were made by pooling together the three time-points, showed no relevant differences in size and network indexes (Supplementary Table S3). In agreement with the network indexes, these networks looked similar and compact (Supplementary Figure S4). Networks inferred for each combination of plot and time-point retained some complexity properties as a power law distribution of degree and modularity. However, they were too small and did not displayed remarkable differences (see Supplementary File S1).
The comparison between time-points showed statistically relevant differences in their size, network indexes, and modularity (Table and Figure ). The differences were larger between t1 and t2 than between t2 and t3, in good agreement with the analyses of soil physicochemical parameters in which t1 and t2 showed larger differences than t2 and t3 (Table and Supplementary Table S3). For instance, the t1 and t2 networks shared 205 edges (which represent 12 and 31% of t1 and t2 networks, respectively) while t2 and t3 shared 252 edges (which represent 38 and 30% of t2 and t3 networks, respectively), and t3 and t1 shared 244 edges (representing 35 and 17%, respectively). The t1 network was more densely connected ( d = 0.038) than t2 and t3 ( d = 0.018 and 0.021, respectively; Table and Figure ). The overall differences between networks at different time-points were maintained after performing the robustness test suggested by (data not shown). Complex co-occurrence networks of microbial communities of milpa soil.
Genaric Mp4 Player Driver on this page. Networks correspond to three time points: t1 = before planting (dry season); t2 = during the early growth of plants (onset of the rainy season); and t3 = before harvest (end of the rainy season). Charts at left show the original networks with nodes colored by taxa while charts at right show the condensed networks where each circle represent a module with their size being equivalent to the size of the module (nr. Of nodes), and the taxa share displayed as a pie chart. Line thickness indicates amount of “flow” (edges) between modules. Network inference was done considering diversity at family level. Figures, and Supplementary Figure S3 show that taxa re-allocation in modules occurred extensively between time-points t1 and t2 and moderately between t2 and t3, especially for three phyla: Actinobacteria, Chloroflexi and Proteobacteria.
The proportional representation of taxa in modules was non-random ( t1: χ 2 = 263, d.f. Focused alluvial diagram of three times. Each column represents a time ( t1 = before planting (dry season); t2 = during the early growth of plants (onset of the rainy season); t3 = before harvest (end of the rainy season)) and the blocks at each time the network modules. The flow lines among times represent the module re-assignation of groups of OTUs (nodes). Colors correspond to taxa as indicated in the list, but only the two largest modules (at t2) were colored to avoid saturation of the figure.
The top graph is highlighting one dominant module and the bottom graph is showing another dominant module.