The effect of pH on Marinobacter hydrocarbonoclasticus denitrification pathway and nitrous oxide reductase

Increasing atmospheric concentration of N₂O has been a concern, as it is a potent greenhouse gas and promotes ozone layer destruction. In the N-cycle, release of N₂O is boosted upon a drop of pH in the environment. Here, Marinobacter hydrocarbonoclasticus was grown in batch mode in the presence of nitrate, to study the effect of pH in the denitrification pathway by gene expression profiling, quantification of nitrate and nitrite, and evaluating the ability of whole cells to reduce NO and N₂O. At pH 6.5, accumulation of nitrite in the medium occurs and the cells were unable to reduce N₂O. In addition, the biochemical properties of N₂O reductase isolated from cells grown at pH 6.5, 7.5 and 8.5 were compared for the first time. The amount of this enzyme at acidic pH was lower than that at pH 7.5 and 8.5, pinpointing to a post-transcriptional regulation, though pH did not affect gene expression of N₂O reductase accessory genes. N₂O reductase isolated from cells grown at pH 6.5 has its catalytic center mainly as CuZ(4Cu1S), while that from cells grown at pH 7.5 or 8.5 has it as CuZ(4Cu2S). This study evidences that an in vivo secondary level of regulation is required to maintain N₂O reductase in an active state.

Genomic organization, gene expression and activity profile of Marinobacter hydrocarbonoclasticus denitrification enzymes

Background

Denitrification is one of the main pathways of the N-cycle, during which nitrate is converted to dinitrogen gas, in four consecutive reactions that are each catalyzed by a different metalloenzyme. One of the intermediate metabolites is nitrous oxide, which has a global warming impact greater then carbon dioxide and which atmospheric concentration has been increasing in the last years. The four denitrification enzymes have been isolated and biochemically characterized from Marinobacter hydrocarbonoclasticus in our lab.

Methods

Bioinformatic analysis of the M. hydrocarbonoclasticus genome to identify the genes involved in the denitrification pathway. The relative gene expression of the gene encoding the catalytic subunits of those enzymes was analyzed during the growth under microoxic conditions. The consumption of nitrate and nitrite, and the reduction of nitric oxide and nitrous oxide by whole-cells was monitored during anoxic and microoxic growth in the presence of 10 mM sodium nitrate at pH 7.5.

Results

The bioinformatic analysis shows that genes encoding the enzymes and accessory factors required for each step of the denitrification pathway are clustered together. An unusual feature is the co-existence of genes encoding a q- and a c-type nitric oxide reductase, with only the latter being transcribed at similar levels as the ones encoding the catalytic subunits of the other denitrifying enzymes, when cells are grown in the presence of nitrate under microoxic conditions. Using either a batch- or a closed system, nitrate is completely consumed in the beginning of the growth, with transient formation of nitrite, and whole-cells can reduce nitric oxide and nitrous oxide from mid-exponential phase until being collected (time-point 50 h).

Discussion

M. hydrocarbonoclasticus cells can reduce nitric and nitrous oxide in vivo, indicating that the four denitrification steps are active. Gene expression profile together with promoter regions analysis indicates the involvement of a cascade regulatory mechanism triggered by FNR-type in response to low oxygen tension, with nitric oxide and nitrate as secondary effectors, through DNR and NarXL, respectively. This global characterization of the denitrification pathway of a strict marine bacterium, contributes to the understanding of the N-cycle and nitrous oxide release in marine environments.

Sleep biosignature of Type 2 diabetes: a case-control study

AIM

To determine whether or not the sleep disturbances associated with Type 2 diabetes affect the structure of sleep.

METHODS

We designed a case-control study in 76 patients with Type 2 diabetes and 76 control subjects without Type 2 diabetes, matched by age, gender, BMI and waist and neck circumferences. A subgroup of 32 patients with Type 2 diabetes was also matched with 64 control subjects without Type 2 diabetes according to apnoea-hypopnoea index score. Examination included an overnight full polysomnography.

RESULTS

No differences in the percentage of time spent in either rapid eye movement or non-rapid eye movement sleep were observed between groups; however, patients with Type 2 diabetes had more microarousal events during sleep than control subjects [41.4 (total range 4.0-104.4) vs 20.7 (total range 1.3-94.5) events/h; P < 0.001]. These differences were mainly observed during the non-rapid eye movement sleep [7.4 (total range 0-107.2) vs 0.2 (total range 0-65.2) events/h; P < 0.001]. In addition, sleep variables related to oxygen saturation measures, such as the percentage of time spent with oxygen saturation ≤90%, were significantly greater during the rapid eye movement sleep in patients with Type 2 diabetes [20.3 (total range 0-99.2) vs. 10.5 (total range 0-94.0)%; P = 0.047]. This pattern was maintained in the subgroup of patients matched by apnoea-hypopnaea index. Finally, stepwise regression analyses showed that apnoea-hypopnoea index, the presence of Type 2 diabetes and fasting plasma glucose value were independently associated with the number of microarousals (R² =0.667).

CONCLUSIONS

Type 2 diabetes is associated with an altered sleep structure, with different effects according to rapid eye movement (increase in nocturnal hypoxia) or non-rapid eye movement (increase in sleep fragmentation) sleep.

Global Assessment of the Impact of Type 2 Diabetes on Sleep through Specific Questionnaires. A Case-Control Study

Abstract

Type 2 diabetes (T2D) is an independent risk factor for sleep breathing disorders. However, it is unknown whether T2D affects daily somnolence and quality of sleep independently of the impairment of polysomnographic parameters.

Material and Methods

A case-control study including 413 patients with T2D and 413 non-diabetic subjects, matched by age, gender, BMI, and waist and neck circumferences. A polysomnography was performed and daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). In addition, 135 subjects with T2D and 45 controls matched by the same previous parameters were also evaluated through the Pittsburgh Sleep Quality Index (PSQI) to calculate sleep quality.

Results

Daytime sleepiness was higher in T2D than in control subjects (p = 0.003), with 23.9% of subjects presenting an excessive daytime sleepiness (ESS>10). Patients with fasting plasma glucose (FPG ≥13.1 mmol/l) were identified as the group with a higher risk associated with an ESS>10 (OR 3.9, 95% CI 1.8–7.9, p = 0.0003). A stepwise regression analyses showed that the presence of T2D, baseline glucose levels and gender but not polysomnographic parameters (i.e apnea-hyoapnea index or sleeping time spent with oxigen saturation lower than 90%) independently predicted the ESS score. In addition, subjects with T2D showed higher sleep disturbances [PSQI: 7.0 (1.0–18.0) vs. 4 (0.0–12.0), p<0.001].

Conclusion

The presence of T2D and high levels of FPG are independent risk factors for daytime sleepiness and adversely affect sleep quality. Prospective studies addressed to demonstrate whether glycemia optimization could improve the sleep quality in T2D patients seem warranted.

The historical pathway towards more accurate homogenisation

Abstract. In recent years increasing effort has been devoted to objectively evaluate the efficiency of homogenisation methods for climate data; an important effort was the blind benchmarking performed in the COST Action HOME (ES0601). The statistical characteristics of the examined series have significant impact on the measured efficiencies, thus it is difficult to obtain an unambiguous picture of the efficiencies, relying only on numerical tests. In this study the historical methodological development with focus on the homogenisation of surface temperature observations is presented in order to view the progress from the side of the development of statistical tools. The main stages of this methodological progress, such as for instance the fitting optimal step-functions when the number of change-points is known (1972), cutting algorithm (1995), Caussinus – Lyazrhi criterion (1997), are recalled and their effects on the quality-improvement of homogenisation is briefly discussed. This analysis of the theoretical properties together with the recently published numerical results jointly indicate that, MASH, PRODIGE, ACMANT and USHCN are the best statistical tools for homogenising climatic time series, since they provide the reconstruction and preservation of true climatic variability in observational time series with the highest reliability. On the other hand, skilled homogenizers may achieve outstanding reliability also with the combination of simple statistical methods such as the Craddock-test and visual expert decisions. A few efficiency results of the COST HOME experiments are presented to demonstrate the performance of the best homogenisation methods.