Serial, non-invasive, real-time assessment of vaccine efficacy in mice challenged with the AlRv strain of <i>M. tuberculosis</i>.

<p>(A) Mean RLU count (± S.D.) assessed in live, anesthetized mice vaccinated with either sham (S) or rBCG30 (V) vaccines followed by high-dose (hi), low-dose (lo) or no aerosol infection with the AlRv strain. (B) Mean CFU and RLU counts (± SD) from lung homogenates obtained 1, 17 and 27 days after challenge with the AlRv strain.</p

Serial, non-invasive, real-time assessment of anti-tuberculosis activity in infected mice.

<p>(A) Mean RLU count (± SD) assessed daily in live, anesthetized mice and normalized to the baseline RLU value. Mean (B) lung and (C) spleen CFU counts (± SD) at baseline and after 3 days of treatment. Abbreviations: KAN, kanamycin; STR, streptomycin; PZA, pyrazinamide; MFX, moxifloxacin; RIF, rifampin; EMB, ethambutol; LZD, linezolid; INH, isoniazid. Doses (in mg/kg): KAN 150; STR 150; PZA 150; MFX 200; RIF 40; EMB 200; LZD 200; INH 10.</p

Growth comparison of WtRv and AlRv on the basis of RLU and CFU counts.

<p><i>In vitro</i> (A), in lung homogenates from BALB/c mice after high-dose aerosol infections (implanting 4.05±0.11 and 4.44±0.05 log₁₀ CFU, respectively) (B) or low-dose infections (implanting 2.15±0.08 and 2.78±0.07 log₁₀ CFU, respectively) (C), and in live, anesthetized mice after high dose infection (D).</p

Analysis of GFP expression in <i>P. aeruginosa</i> colony biofilms.

<p>Biofilms were developed for 48 h on membranes resting on tryptic soy agar plates. The two papers from which the experimental data in this figure were drawn used identical experimental systems <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Williamson2" target="_blank">[33]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Werner1" target="_blank">[34]</a>. A, oxygen concentration profile in mature colony biofilm. Reprinted with permission from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Werner1" target="_blank">[34]</a>; B, experimental GFP distribution (green) in frozen sections prior to induction (B1) and at 12 h post induction (B2). The transmission image in panel B1 was false colored blue to indicate the extent of biomass more clearly. The polymer membrane supporting the colony biofilm appears as a dark stripe in panel B2; the membrane detached from the biofilm specimen shown in panel B1 and so is absent. Reprinted with permission from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Williamson2" target="_blank">[33]</a>; C, Experimentally measured GFP fluorescent intensity in transects within the biofilm at various time points following induction (addition of arabinose). Zero on the x-axis corresponds to the biofilm-air interface; D, simulated GFP fluorescent intensity within the biofilm at various time points following induction. In panels C and D the air interface of the biofilm was on the left.</p

Simulated acid stress response in a <i>Staphylococcal</i> biofilm fermenting glucose to lactate.

<p>A, glucose and lactate concentrations. B, specific growth rate () and predicted spatial distribution of an acid stress response gene. Shown are results for the 48 h time point.</p

Spatial pattern of GFP fluorescence within a biofilm as a function of GFP stability.

<p>A, predicted GFP pattern dependence on the value of the turnover rate coefficient, . Grey shading indicates the growing region; outside this zone there is no growth because oxygen has been depleted. B, experimentally measured GFP distribution for an unstable GFP in a <i>P. aeruginosa</i> colony biofilm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Werner1" target="_blank">[34]</a>. Results from two locations in the biofilm section in Figure 4C of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Werner1" target="_blank">[34]</a> are shown.</p

Simulated distribution of <i>nirS</i> mRNA in <i>P. stutzeri</i> artificial biofilm and comparison to experimental data.

<p>A, simulated oxygen, nitrite, and predicted <i>nirS</i> mRNA distribution after 48 h. Shown in green is the <i>nirS</i> probe signal quantified from the image in panel B. The baseline and height are arbitrary. B, experimentally reported oxygen profile and <i>nirS</i> pattern demonstrated by FISH. Reprinted with permission from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083626#pone.0083626-Kofoed1" target="_blank">[36]</a>. Panel B shows a cross section of an artificial biofilm consisting of bacteria immobilized in agarose gel which was incubated in a medium containing oxygen and nitrite. The medium was on the left. Blue color (DAPI) indicates a relatively uniform distribution of biomass with depth while green color is from the <i>nirS</i>-specific probe.</p

Decay of GFP fluorescence in <i>P. aeruginosa</i> colony biofilms.

<p>The integrated fluorescence or area under the curve (AUC) was determined. Colony biofilms were grown under GFP-inducing conditions for 48 h then transferred to non-inducing conditions at time zero. Data from three replicate experiments are shown (symbols). Dashed lines are least-squares regressed lines to each of the experimental data sets. The negative slope of each line yields an estimate of the turnover rate coefficient, .</p

General Theory for Integrated Analysis of Growth, Gene, and Protein Expression in Biofilms

<div><p>A theory for analysis and prediction of spatial and temporal patterns of gene and protein expression within microbial biofilms is derived. The theory integrates phenomena of solute reaction and diffusion, microbial growth, mRNA or protein synthesis, biomass advection, and gene transcript or protein turnover. Case studies illustrate the capacity of the theory to simulate heterogeneous spatial patterns and predict microbial activities in biofilms that are qualitatively different from those of planktonic cells. Specific scenarios analyzed include an inducible GFP or fluorescent protein reporter, a denitrification gene repressed by oxygen, an acid stress response gene, and a quorum sensing circuit. It is shown that the patterns of activity revealed by inducible stable fluorescent proteins or reporter unstable proteins overestimate the region of activity. This is due to advective spreading and finite protein turnover rates. In the cases of a gene induced by either limitation for a metabolic substrate or accumulation of a metabolic product, maximal expression is predicted in an internal stratum of the biofilm. A quorum sensing system that includes an oxygen-responsive negative regulator exhibits behavior that is distinct from any stage of a batch planktonic culture. Though here the analyses have been limited to simultaneous interactions of up to two substrates and two genes, the framework applies to arbitrarily large networks of genes and metabolites. Extension of reaction-diffusion modeling in biofilms to the analysis of individual genes and gene networks is an important advance that dovetails with the growing toolkit of molecular and genetic experimental techniques.</p></div

Table1_Profile soil organic and inorganic carbon sequestration in maize cropland after long-term straw return.docx

Promoting cropland carbon (C) sequestration through straw return has always been the focus of numerous studies. However, there is still a lack of comprehensive understanding of the straw return effects on soil organic carbon (SOC) and soil inorganic carbon (SIC) sequestration. Therefore, the present study aims to investigate the effects of long-term straw return on SOC and SIC sequestration across the 0–100 cm soil profile in the maize planting cropland in Northeastern China. The results showed an increasing trend in SOC contents in the 0–100 cm soil profile following long-term straw return, while significant decreases in SIC contents were observed in the surface (0–20 cm) and subsoil (20–60 cm) layers, respectively. In addition, the SOC stock increased significantly in the subsoil layer following long-term straw return, by an average value of 44%, which is higher than those observed in other soil layers. On the other hand, the SIC stock in the subsoil layer increased by an average value of 24% and decreased in the surface and under-subsoil layers by average values of 53% and 33%, respectively. Moreover, the exchangeable calcium contents were positively correlated with SOC and SIC stock, demonstrating the soil calcium contributes to SOC and SIC sequestration. The present study highlighted the importance of the subsoil layer for effective straw return strategies in cropland to promote SOC and SIC sequestration in croplands.</p