Mass production and use of antibiotics and antimicrobials in medicine and agriculture have existed for over 60 years, and has substantially benefited public health and agricultural productivity throughout the world. However, there is growing evidence that resistance to antibiotics (AR) is increasing both in benign and pathogenic bacteria, posing an emerging threat to public and environmental health in the future. Although evidence has existed for years from clinical data of increasing AR, almost no quantitative environmental data exist that span increased industrial antibiotic production in the 1950s to the present; i.e., data that might delineate trends in AR potentially valuable for epidemiological studies. To address this critical knowledge gap, we speculated that AR levels might be apparent in historic soil archives as evidenced by antibiotic resistance gene (ARG) abundances over time. Accordingly, DNA was extracted from five long-term soil-series from different locations in The Netherlands that spanned 1940 to 2008, and 16S rRNA gene and 18 ARG abundances from different major antibiotic classes were quantified. Results show that ARG from all classes of antibiotics tested have significantly increased since 1940, but especially within the tetracyclines, with some individual ARG being >15 times more abundant now than in the 1970s. This is noteworthy because waste management procedures have broadly improved and stricter rules on nontherapeutic antibiotic use in agriculture are being promulgated. Although these data are local to The Netherlands, they suggest basal environmental levels of ARG still might be increasing, which has implications to similar locations around the world.

The abundance of six tetracycline resistance genes tet(O), tet(Q), tet(W), tet(M), tet(B) and tet(L), were quantified over time in wastewater lagoons at concentrated animal feeding operations (CAFO) to assess how feedlot operation affects resistance genes in downstream surface waters. Eight lagoons at five cattle feedlots in the Midwestern United States were monitored for 6 months. Resistance and 16S-rRNA gene abundances were quantified using real-time PCR, and physicochemical lagoon conditions, tetracycline levels, and other factors (e.g. feedlot size and weather conditions) were monitored over time. Lagoons were sorted according to antibiotic use practice at each site, and designated as 'no-use', 'mixed-use' or 'high-use' for comparison. High-use lagoons had significantly higher detected resistance gene levels (tet(R); 2.8 x 10(6) copies ml(-1)) relative to no-use lagoons (5.1 x 10(3) copies ml(-1); P < 0.01) and mixed-use lagoons (7.3 x 10(5) copies ml(-1); P = 0.076). Bivariate correlation analysis on pooled data (n = 54) confirmed that tet(R) level strongly correlated with feedlot area (r = 0.67, P < 0.01) and 'total' bacterial 16S-rRNA gene level in each lagoon (r = 0.51, P < 0.01), which are both characteristic of large CAFOs. tet(M) was the most commonly detected gene, both in absolute number and normalized to 16S-rRNA gene level, although tet(O), tet(Q) and tet(W) levels were also high in the mixed and high-use lagoons. Finally, resistance gene levels were highly seasonal with abundances being 10-100 times greater in the autumn versus the summer. Results show that antibiotic use strategy strongly affects both the abundance and seasonal distribution of resistance genes in associated lagoons, which has implications on water quality and feedlot management practices.

Methanobactins (mbs) are a class of copper-binding peptides produced by aerobic methane oxidizing bacteria (methanotrophs) that have been linked to the substantial copper needs of these environmentally important microorganisms. The only characterized mbs are those from Methylosinus trichosporium OB3b and Methylocystis strain SB2. M. trichosporium OB3b produces a second mb (mb-Met), which is missing the C-terminal Met residue from the full-length form (FL-mb). The as-isolated copper-loaded mbs bind Cu(I). The absence of the Met has little influence on the structure of the Cu(I) site, and both molecules mediate switchover from the soluble iron methane mono-oxygenase to the particulate copper-containing enzyme in M. trichosporium OB3b cells. Cu(II) is reduced in the presence of the mbs under our experimental conditions, and the disulfide plays no role in this process. The Cu(I) affinities of these molecules are extremely high with values of (6-7) × 10(20) M(-1) determined at pH ≥ 8.0. The affinity for Cu(I) is 1 order of magnitude lower at pH 6.0. The reduction potentials of copper-loaded FL-mb and mb-Met are 640 and 590 mV respectively, highlighting the strong preference for Cu(I) and indicating different Cu(II) affinities for the two forms. Cleavage of the disulfide bridge results in a decrease in the Cu(I) affinity to ∼9 × 10(18) M(-1) at pH 7.5. The two thiolates can also bind Cu(I), albeit with much lower affinity (∼ 3 × 10(15) M(-1) at pH 7.5). The high affinity of mbs for Cu(I) is consistent with a physiological role in copper uptake and protection.

Biological nitrification (that is, NH 3 -NO 2 À -NO 3 À ) is a key reaction in the global nitrogen cycle (Ncycle); however, it is also known anecdotally to be unpredictable and sometimes fails inexplicably. Understanding the basis of unpredictability in nitrification is critical because the loss or impairment of this function might influence the balance of nitrogen in the environment and also has biotechnological implications. One explanation for unpredictability is the presence of chaotic behavior; however, proving such behavior from experimental data is not trivial, especially in a complex microbial community. Here, we show that chaotic behavior is central to stability in nitrification because of a fragile mutualistic relationship between ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), the two major guilds in nitrification. Three parallel chemostats containing mixed microbial communities were fed complex media for 207 days, and nitrification performance, and abundances of AOB, NOB, total bacteria and protozoa were quantified over time. Lyapunov exponent calculations, supported by surrogate data and other tests, showed that all guilds were sensitive to initial conditions, suggesting broad chaotic behavior. However, NOB were most unstable among guilds and displayed a different general pattern of instability. Further, NOB variability was maximized when AOB were most unstable, which resulted in erratic nitrification including significant NO 2 À accumulation. We conclude that nitrification is prone to chaotic behavior because of a fragile AOB-NOB mutualism, which must be considered in all systems that depend on this critical reaction.