Summary The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biological processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clinical strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 molecular features in parallel with few or no false positive molecular discoveries. The low error rates allowed the first chemotaxonomic analyses of mycobacteria, which describe the extent of chemical change in each strain and identified particular strain-specific molecules for use as biomarkers.

The rRNA approach is the principal tool to study microbial diversity, but it has important biases. These include polymerase chain reaction (PCR) primers bias, and relative inefficiency of DNA extraction techniques. Such sources of potential undersampling of microbial diversity are well known, but the scale of the undersampling has not been quantified. Using a marine tidal flat bacterial community as a model, we show that even with unlimited sampling and sequencing effort, a single combination of PCR primers/DNA extraction technique enables theoretical recovery of only half of the richness recoverable with three such combinations. This shows that different combinations of PCR primers/DNA extraction techniques recover in principle different species, as well as higher taxa. The majority of earlier estimates of microbial richness seem to be underestimates. The combined use of multiple PCR primer sets, multiple DNA extraction techniques, and deep community sequencing will minimize the biases and recover substantially more species than prior studies, but we caution that even this-yet to be used-approach may still leave an unknown number of species and higher taxa undetected.

Microorganisms are spectacularly diverse phylogenetically, but available estimates of their species richness are vague and problematic. For example, for comparable environments, the estimated numbers of species range from a few dozen or hundreds to tens of thousands and even half a million. Such estimates provide no baseline information on either local or global microbial species richness. We argue that this uncertainty is due in large part to the way statistical tools are used, if not indeed misused, in biodiversity research. Here we develop a powerful synthetic statistical approach to quantify biodiversity. It provides statistically sound estimates of microbial richness at any level of taxonomic hierarchy. We apply this approach to a large original 16S rRNA dataset on marine bacterial diversity and show that the number of bacterial species in a sample from marine sediments is (2.4 ؎ 0.5 SE) ؋ 10 3 . We argue that our methodology provides estimates of microbial richness that are reliable and general, have biologically meaningful SEs, and meet other fundamental statistical standards. This approach can be an essential tool in biodiversity research, and the estimates of microbial richness presented here can serve as a baseline in microbial diversity studies.global biodiversity ͉ microorganisms ͉ number of species