Effect of abattoir and cut on variations in microbial communities of vacuum-packaged beef.

This report builds on the earlier studies of the shelf-life of chilled Australian vacuum packaged (VP) beef primals (striploin and cube roll), products distinguished in the global marketplace for unusually long shelf-life. Notable findings in those studies were a shelf-life of at least 26weeks at -0.5°C, low microbial counts, and relatively high sensory scores. However, growth rates for total viable counts (TVC) and lactic acid bacteria (LAB) varied among the different abattoirs. The present study adds to these findings, by providing greater definition about temporal changes in bacterial communities using terminal restriction fragment length polymorphism (TRFLP) and clone library analyses of 16S ribosomal RNA (16S rRNA) gene, and measuring statistical associations among abattoir, beef cut, storage time and sensory attributes. Bacterial communities changed over time, with Carnobacterium spp. typically predominating (29-97%) at the end of storage. Variation in TRFLP profiles showed that different Carnobacterium strains predominated in different abattoirs, and that additional variation was due to the presence of other taxa typical of VP meat microbiomes. TRFLP-based community structure correlated significantly (P≤0.01) with sensorial characteristics, such as vacuum integrity, confinement odour, and intact pack appearance of beef. This study shows that Carnobacterium spp. predominate on extended shelf-life VP beef primals, while other taxa may produce subtle effects on shelf-life duration.

Utility of gel-free, label-free shotgun proteomics approaches to investigate microorganisms.

This review will examine the current situation with label-free, quantitative, shotgun-oriented proteomics technology and discuss the advantages and limitations associated with its capability in capturing and quantifying large portions of proteomes of microorganisms. Such an approach allows (1) comparisons between physiological or genetic states of organisms at the protein level, (2) 'painting' of proteomic data onto genome data-based metabolic maps, (3) enhancement of the utility of genomic data and finally (4) surveying of non-genome sequenced microorganisms by taking advantage of available inferred protein data in order to gain new insights into strain-dependent metabolic or physiological capacities. The technology essentially is a powerful addition to systems biology with a capacity to be used to ask hypothesis-driven 'top-down' questions or for more empirical 'bottom-up' exploration.