Soil represents a very heterogeneous environment for its microbiota. Among the soil inhabitants, bacteria and fungi are important organisms as they are involved in key biogeochemical cycling processes. A main energy source driving the system is formed by plants through the provision of plant-fixed (reduced) carbon to the soil, whereas soil nitrogen and phosphorus may move from the soil back to the plant. The carbonaceous compounds released form the key energy and nutrient sources for the soil microbiota. In the grossly carbon-limited soil, the emergence of plant roots and the formation of their associated mycorrhizae thus create nutritional hot spots for soil-dwelling bacteria. As there is natural (fitness) selection on bacteria in the soil, those bacteria that are best able to benefit from the hot spots have probably been selected. The purpose of this review is to examine the interactions of bacteria with soil fungi in these hot spots and to highlight the key mechanisms involved in the selection of fungal-responsive bacteria. Salient bacterial mechanisms that are involved in these interactions have emerged from this examination. Thus, the efficient acquisition for specific released nutrients, the presence of type-III secretion systems and the capacity of flagellar movement and to form a biofilm are pinpointed as key aspects of bacterial life in the mycosphere. The possible involvement of functions present on plasmid-borne genes is also interrogated.

Plasmid pTer331 from the bacterium Collimonas fungivorans Ter331 is a new member of the pIPO2/pSB102 family of environmental plasmids. The 40 457-bp sequence of pTer331 codes for 44 putative ORFs, most of which represent genes involved in replication, partitioning and transfer of the plasmid. We confirmed that pTer331 is stably maintained in its native host. Deletion analysis identified a mini-replicon capable of replicating autonomously in Escherichia coli and Pseudomonas putida. Furthermore, plasmid pTer331 was able to mobilize and retromobilize IncQ plasmid pSM1890 at typical rates of 10(-4) and 10(-8), respectively. Analysis of the 91% DNA sequence identity between pTer331 and pIPO2 revealed functional conservation of coding sequences, the deletion of DNA fragments flanked by short direct repeats (DR), and sequence preservation of long DRs. In addition, we experimentally established that pTer331 has no obvious contribution in several of the phenotypes that are characteristic of its host C. fungivorans Ter331, including the ability to efficiently colonize plant roots. Based on our findings, we hypothesize that cryptic plasmids such as pTer331 and pIPO2 might not confer an individual advantage to bacteria, but, due to their broad-host-range and ability to retromobilize, benefit bacterial populations by accelerating the intracommunal dissemination of the mobile gene pool.

Background Skin autofluorescence (SAF) is a non-invasive marker of tissue accumulation of advanced glycation endproducts (AGE). Recently, we demonstrated in the general population that elevated SAF levels predict the development of type 2 diabetes (T2D), cardiovascular disease (CVD) and mortality. We evaluated whether elevated SAF may predict the development of CVD and mortality in individuals with T2D. Methods We included 2349 people with T2D, available baseline SAF measurements (measured with the AGE reader) and follow-up data from the Lifelines Cohort Study. Of them, 2071 had no clinical CVD at baseline. 60% were already diagnosed with diabetes (median duration 5, IQR 2–9 years), while 40% were detected during the baseline examination by elevated fasting blood glucose ≥7.0 mmol/l) and/or HbA1c ≥6.5% (48 mmol/mol). Results Mean (±SD) age was 57 ± 12 yrs., BMI 30.2 ± 5.4 kg/m2. 11% of participants with known T2D were treated with diet, the others used oral glucose-lowering medication, with or without insulin; 6% was using insulin alone. Participants with known T2D had higher SAF than those with newly-detected T2D (SAF Z-score 0.56 ± 0.99 vs 0.34 ± 0.89 AU, p < 0.001), which reflects a longer duration of hyperglycaemia in the former group. Participants with existing CVD and T2D had the highest SAF Z-score: 0.78 ± 1.25 AU. During a median follow-up of 3.7 yrs., 195 (7.6%) developed an atherosclerotic CVD event, while 137 (5.4%) died. SAF was strongly associated with the combined outcome of a new CVD event or mortality (OR 2.59, 95% CI 2.10–3.20, p < 0.001), as well as incidence of CVD (OR 2.05, 95% CI 1.61–2.61, p < 0.001) and death (OR 2.98, 2.25–3.94, p < 0.001) as a single outcome. In multivariable analysis for the combined endpoint, SAF retained its significance when sex, systolic blood pressure, HbA1c, total cholesterol, eGFR, as well as antihypertensive and statin medication were included. In a similar multivariable model, SAF was independently associated with mortality as a single outcome, but not with incident CVD. Conclusions Measuring SAF can assist in prediction of incident cardiovascular disease and mortality in individuals with T2D. SAF showed a stronger association with future CVD events and mortality than cholesterol or blood pressure levels.

Increased skin autofluorescence (SAF) predicts the development of diabetes-related complications and cardiovascular disease. We assessed the performance of a simple model which includes SAF to identify individuals at high risk for undiagnosed and incident type 2 diabetes, in 58,377 participants in the Lifelines Cohort Study without known diabetes. Newly-diagnosed diabetes was defined as fasting blood glucose ≥ 7.0 mmol/l and/or HbA1c ≥ 6.5% (≥ 48 mmol/mol) or self-reported diabetes at follow-up. We constructed predictive models based on age, body mass index (BMI), SAF, and parental history of diabetes, and compared to results with the concise FINDRISC model. At 2nd visit to Lifelines, 1113 (1.9%) participants were identified with undiagnosed diabetes and 1033 (1.8%) participants developed diabetes during follow-up. A model comprising age, BMI and SAF yielded an AUC of 0.783 and was non-inferior to the concise FINDRISC model, which had an AUC of 0.797 to predict new diabetes. At a score of 5.8, sensitivity was 78% and specificity of 66%. Model 2 which also incorporated parental diabetes history, had an AUC of 0.792, and a sensitivity of 74% and specificity of 70% at a score of 6.5. Net reclassification index (NRI) did not improve significantly (NRI 1.43% (− 0.50–3.37 p = 0.15). The combination of an easy to perform SAF measurement with age and BMI is a good alternative screening tool suitable for medical and non-medical settings. Parental history of diabetes did not significantly improve model performance in this homogeneous cohort.