Chapter 36 : Metagenomics of Meat and Poultry

The pangenome and core genome, based on the Edwards-Venn cogwheel and designed by O. Zhaxybayeva, Dartmouth College. The pangenome of a group refers to the sum of all the genes that are present in members of the group. Pangenomes comprise the core genome, which comprises the genes found in all members of a group of interest, and the accessory genome, genes that are present in only one or a few members of the group. The concept of a pangenome has led to the idea that steps in metabolic pathways may be distributed over several individuals within a community. The Black Queen hypothesis suggests that the combination of leaky functions—genes that produce a product that is shared with others in the community—with a selection for small genomes will lead to a situation in which leaky functions are encoded in the genomes of only a fraction of community members that produce this function as a common good. The pangenomes of many taxa seem to be open (that is, of an unlimited size), although the combination of limited population size and limited time of divergence from a common ancestor certainly limits the numbers of genes actually present in a given taxon. Estimated pangenome sizes taking population size and divergence time into consideration can be large; for example, the Prochlorococcus pangenome has been estimated to contain approximately 58,000 genes, whereas the individual genomes of the members of this genus contain only about 2,000 genes each. Reprinted from reference 1 .

The cost of sequencing per megabase pair from 2001 to 2015. Reprinted from reference 87 .

Flow diagram of a typical metagenome project. Dashed arrows indicate steps that can be omitted. Reprinted from reference 4 .

Coverage of the archaeal 16S rRNA gene by sequences included in RIM-DB. Sequence variability is expressed as a Shannon index for each alignment position using a 50-bp moving average. Sequence coverage per base is indicated by the heat map and was calculated using a 50-bp moving average. Approximate positions of frequently targeted regions (V1-V2, V3-V5, and V6-V8) for amplicon sequencing are shown for orientation and nucleotide numbering and correspond to positions in the E. coli 16S rRNA gene. Reprinted from reference 88 .

Rarefaction plot of individual liver sample from Chao1 measurements. The leveling off in all samples indicates that an appropriate sampling depth was reached to estimate the diversity of the community. Reprinted with permission from reference 72 .

Demonstration of cumulative sum scaling (B) and rarefying (C) of raw sequences (A).

Examples of common ways metagenomic data are presented. (A) Taxonomy plot (reprinted from reference 47 ); (B) nonmetric multidimensional scaling ordination plots (reprinted from reference 47 ); (C) heat maps (reprinted from reference 75 ); (D) phylogenic relationship trees (reprinted from reference 89 ); (E) principal coordinate analysis (reprinted from reference 89 ); (F) network analysis (reprinted from reference 90 ).

Contribution of different factors to the overall cost of a sequencing project across time. (Left) The four-step process: (i) experimental design and sample collection, (ii) sequencing, (iii) data reduction and management, and (iv) downstream analysis. (Right) Changes over time of the relative impact of these four components of a sequencing experiment. BAM, binary sequence alignment/map; CRAM, compression algorithm; MRF, mapped read format; RPKM, reads per kilobase million; TAR, transcriptionally active region; VCF, variant call format. Reprinted from reference 91 .

Because metagenomics is primarily associated with short-read sequences, species-specific differentiation is difficult due to different species containing the same homologous sections of nucleotides. Therefore, the section of the genome sequenced may be in a species-specific region of the genome (A or C) or may be shared by both bacteria (B) and would therefore not allow differentiation.

Hierarchy of commensals (not to scale). Pathogenic commensals make up only a tiny fraction of the total microbial environment. Greatly underrated and understudied are the multitudes of “core” and transient colonizers, i.e., commensals that constitute the major reservoirs of resistance genes. To some degree, commensals can be distinguished by their place in the environment and the relationships with their hosts. Some colonizers of the skin, oropharynx, and intestinal tract rarely if ever cause disease (e.g., the lactic acid bacteria). Yet another group is considered generally nonpathogenic, but when imbalances or shifts occur in the selective pressures on their microbial niches, these species can be propelled to the status of pathogens, made more problematic if they have acquired resistance or virulence genes from neighboring commensals. These constitute the group of opportunistic or pathogenic commensals. Staphylococcus aureus, which regularly or transiently colonizes about 80% of humans, now frequently crosses this border. Staphylococcus epidermidis and other traditionally commensal coagulase-negative staphylococci only occasionally cause nosocomial infections and only under extreme selective pressures, such as those exerted by indwelling catheters and depressed immunologic states. Organisms that commonly harbor native or “constitutive” resistance in their chromosomes (e.g., Pseudomonas, Stenotrophomonas, and Acinetobacter) also may emerge under these conditions. Most commensals, however, exist as environmental residents of soil and water habitats, many of which may become transient colonizers of humans and animals through the food chain and other routes of exposure. The accumulated evidence suggests widespread gene exchange among these groups. Reprinted from reference 60 .

Significant co-occurrence and coexclusion relationships between bacterial operational taxonomic units. Spearman's rank correlation matrix of operational taxonomic units with an abundance of ≥0.1% in at least five samples. Only phylotypes assigned to the phyla Proteobacteria and Firmicutes were considered. Strong correlations are indicated by large circles, whereas weak correlations are indicated by small circles. The colors of the scale bar denote the nature of the correlation, with 1 indicating a perfectly positive correlation (dark blue) and −1 indicating a perfectly negative correlation (dark red) between two phylotypes. Only significant correlations (false discovery rate < 0.05) are shown. Reprinted from reference 66 .

Relative abundances of sequence reads assigned as indicated. f, family-level classification. Reprinted from reference 67 .

Incidence of operational taxonomic unit tracebacks based on bTEFAP (bacterial tag-encoded FLX amplicon pyrosequencing) analysis of all the DNA samples directly extracted from meat at different times and under different storage conditions. Only taxa with an incidence above 9% in at least one sample are shown, and only percentages above 1% are displayed in the histograms. Shown are results for storage in air (A), MAP (B), vacuum packaging (C), and active vacuum packaging (D). Reprinted from reference 75 .

The rare microbiome in Salame Piacentino DOP samples: distribution of families accounting for less than 5% of the total bacterial diversity in the 63 samples analyzed. The total number of sequences retrieved and the number of genera represented are reported for each rare family. A heat-mapping color scale indicates the relative distribution of positive samples among producers, ripening times, and presence or absence of starter addition. Reprinted from reference 92 .