High proliferation rate and robustness are vital characteristics of bacterial pathogens that successfully colonize their hosts. The observation of drastically slow growth in some pathogens is thus paradoxical and remains unexplained. In this study, we sought to understand the slow (fastidious) growth of the plant pathogen Xylella fastidiosa Using genome-scale metabolic network reconstruction, modeling, and experimental validation, we explored its metabolic capabilities. Despite genome reduction and slow growth, the pathogen’s metabolic network is complete but strikingly minimalist and lacking in robustness. Most alternative reactions were missing, especially those favoring fast growth, and were replaced by less efficient paths. We also found that the production of some virulence factors imposes a heavy burden on growth. Interestingly, some specific determinants of fastidious growth were also found in other slow-growing pathogens, enriching the view that these metabolic peculiarities are a pathogenicity strate support the idea that the fragility of the metabolic network may have been shaped during evolution to lead to the self-limiting behavior of X. fastidiosa. Copyright © 2020 Gerlin et al.Prochlorococcus cyanobacteria grow in diurnal rhythms driven by diel cycles. Their ecology depends on light, nutrients, and top-down mortality processes, including lysis by viruses. Cyanophage, viruses that infect cyanobacteria, are also impacted by light. For example, the extracellular viability and intracellular infection kinetics of some cyanophage vary between light and dark conditions. Nonetheless, it remains unclear whether light-dependent viral life history traits scale up to influence population-level dynamics. SB431542 datasheet Here, we examined the impact of diel forcing on both cellular- and population-scale dynamics in multiple Prochlorococcus-phage systems. To do so, we developed a light-driven population model, including both cellular growth and viral infection dynamics. We then tested the model against measurements of experimental infection dynamics with diel forcing to examine the extent to which population level changes in both viral and host abundances could be explained by light-dependent life history traitslular- and population-level dynamics for some host-virus systems. However, we also found that additional mechanisms, including lysis saturation, are required to explain observed host-virus dynamics at the population scale. This study provides the basis for future work to understand the intertwined fates of Prochlorococcus and associated viruses in the surface ocean. Copyright © 2020 Demory et al.Type 2 diabetes (T2D) is a complex metabolic syndrome characterized by insulin dysfunction and abnormalities in glucose and lipid metabolism. The gut microbiome has been recently identified as an important factor for development of T2D. In this study, a total of 102 subjects were recruited, and we have looked at the gut microbiota of prediabetics (PreDMs) (n = 17), newly diagnosed diabetics (NewDMs) (n = 11), and diabetics on antidiabetic treatment (KnownDMs) (n = 39) and compared them with healthy nondiabetics (ND) (n = 35). Twenty-five different serum biomarkers were measured to assess the status of diabetes and their association with gut microbiota. Our analysis revealed nine different genera as differentially abundant in four study groups. Among them, Akkermansia, Blautia, and Ruminococcus were found to be significantly (P less then 0.05) decreased, while Lactobacillus was increased in NewDMs compared to ND and recovered in KnownDMs. Akkermansia was inversely correlated with HbA1c and positively correla attempted to investigate gut microbiota of ND, PreDMs, NewDMs, and KnownDMs. We found that the genera Akkermansia and Blautia decreased significantly (P less then 0.05) in treatment-naive diabetics and were restored in KnownDMs on antidiabetic treatment. To the best of our knowledge, comparative studies on shifts in the microbial community in individuals of different diabetic states are lacking. Understanding the transition of microbiota and its association with serum biomarkers in diabetics with different disease states may pave the way for new therapeutic approaches for T2D. Copyright © 2020 Gaike et al.BACKGROUND The effect of isolated small airway dysfunction (SAD) on exercise remains incompletely characterized. We sought to quantify the relationship between isolated SAD, identified with lung testing, and the respiratory response to exercise. METHODS We conducted a prospective evaluation of service members with new-onset dyspnea. All subjects underwent plethysmography, diffusing capacity of the lung for carbon monoxide (DLCO), impulse oscillometry, high-resolution computed tomography (HRCT), and cardiopulmonary exercise testing (CPET). In subjects with normal basic spirometry, DLCO, and HRCT, SAD measures were analyzed for associations with ventilatory parameters at submaximal exercise and at maximal exercise during CPET. RESULTS We enrolled 121 subjects with normal basic spirometry (ie, FEV1, FVC, and FEV1/FVC), DLCO, and HRCT. Mean age and body mass index were 37.4 ± 8.8 y and 28.4 ± 3.8 kg/m2, respectively, and 110 (90.9%) subjects were male. The prevalence of SAD varied from 2.5% to 28.8% depending on the ventilatory response to exercise. Copyright © 2020 by Daedalus Enterprises.BACKGROUND Detection of diaphragmatic muscle activity during invasive ventilation may provide valuable information about patient-ventilator interactions. Transesophageal electromyography of the diaphragm (tEAdi) is used in neurally adjusted ventilatory assist. This technique is invasive and can only be applied with one specific ventilator. Surface electromyography of the diaphragm (sEAdi) is noninvasive and can potentially be applied with all types of ventilators. The primary objective of our study was to compare the ability of diaphragm activity detection between sEAdi and tEAdi. METHODS In this single-center pilot study, sEAdi and tEAdi recordings were obtained simultaneously for 15 min in adult subjects in the ICU who were invasively ventilated. The number of breathing efforts detected by sEAdi and tEAdi were determined. The percentage of detected breathing efforts by sEAdi compared with tEAdi was calculated. Temporal and signal strength relations on optimum recordings of 10 breaths per subject were also compared.