Citations

Abstract Eosinophil-derived neurotoxin (EDN) and eosinophil cationic protein (ECP) are generally regarded as eosinophil-specific proteins. We tested whether EDN and ECP are present in mature neutrophils. By indirect immunofluorescence, both eosinophils and neutrophils stained with antibodies to EDN and ECP. Lysates of purified (<0.1% eosinophil contamination) neutrophils contained EDN, 112 ± 4 ng/106 cells, and ECP, 163 ± 2 ng/106 cells, whereas eosinophil major basic protein (MBP) was not detectable. Electron microscopic examination of immunogold-labeled buffy coat cells stained with EDN antibody showed that EDN is localized to neutrophil granules. Finally, EDN mRNA was detected in lysates of highly purified neutrophils (0.001% eosinophil contamination) by the reverse transcription-polymerase chain reaction. We conclude that proteins that are either identical to or immunologically cross-reactive with EDN and ECP are present in neutrophils and that EDN is synthesized and localized to neutrophil granules. Thus, caution must be exercised in interpreting the presence of EDN and ECP as specific markers of eosinophil-associated inflammation in human disease.

Abstract Summary Gap-filling is a necessary step to produce quality genome-scale metabolic reconstructions capable of flux-balance simulation. Most available gap-filling tools use an organism-agnostic approach, where reactions are selected from a database to fill gaps without consideration of the target organism. Conversely, our likelihood based gap-filling with probabilistic annotations selects candidate reactions based on a likelihood score derived specifically from the target organism’s genome. Here, we present two new implementations of probabilistic annotation and likelihood based gap-filling: a web service called ProbAnnoWeb, and a standalone python package called ProbAnnoPy. Availability and implementation Our tools are available as a web service with no installation needed (ProbAnnoWeb) at probannoweb.systemsbiology.net, and as a local python package implementation (ProbAnnoPy) at github.com/PriceLab/probannopy. Supplementary information Supplementary data are available at Bioinformatics online.

OBJECTIVE Intensive treatment (INT) of type 1 diabetes reduces the incidence of cardiovascular disease (CVD) events compared with conventional treatment (CONV), but it also results in more weight gain. Our objective was to examine whether excessive weight gain from INT of type 1 diabetes is independently associated with subsequent CVD events. RESEARCH DESIGN AND METHODS Quartiles (Q) of weight gain in 1,213 participants aged 18 years and older at enrollment in the Diabetes Control and Complications Trial (DCCT) were determined within randomized treatment groups (INT vs. CONV) using change in BMI from baseline to the closeout DCCT visits. Effects of this weight gain on CVD risk factors and outcomes during an additional 20 years of observational follow-up were then determined. RESULTS The Q4 INT group experienced greater proportional weight gain (median change in BMI, 6.08 kg/m2), increases in CVD risk factors, and need for medications for hypertension and lipids compared with the Q1–3 INT and comparable CONV groups. Over a mean of 26 years of follow-up, the numbers of major and total CVD events were not statistically different in Q4 compared with Q1–3 of either the INT or CONV group. By year 14, however, the incident CVD event curve became significantly higher in the Q4 INT group than in the Q1–3 INT groups (P = 0.024) and was similar to that for the CONV group. CONCLUSIONS For the first 13 years after DCCT, INT for type 1 diabetes reduced macrovascular events compared with CONV, even when excessive weight gain occurred. After this, total CVD events significantly increased in the Q4 INT group, becoming equivalent to those in the CONV group. Longer follow-up is needed to determine whether this trend continues and results in more major CVD events.