Investigation of Indazole Unbinding Pathways in CYP2E1 by Molecular Dynamics Simulations

Human microsomal cytochrome P450 2E1 (CYP2E1) can oxidize not only low molecular weight xenobiotic compounds such as ethanol, but also many endogenous fatty acids. The crystal structure of CYP2E1 in complex with indazole reveals that the active site is deeply buried into the protein center. Thus, the unbinding pathways and associated unbinding mechanisms remain elusive. In this study, random acceleration molecular dynamics simulations combined with steered molecular dynamics and potential of mean force calculations were performed to identify the possible unbinding pathways in CYP2E1. The results show that channel 2c and 2a are most likely the unbinding channels of CYP2E1. The former channel is located between helices G and I and the B-C loop, and the latter resides between the region formed by the F-G loop, the B-C loop and the β1 sheet. Phe298 and Phe478 act as the gate keeper during indazole unbinding along channel 2c and 2a, respectively. Previous site-directed mutagenesis experiments also supported these findings

Metagenomic next-generation sequencing of the 2014 Ebola Virus disease outbreak in the Democratic Republic of the Congo

We applied metagenomic next-generation sequencing (mNGS) to detect Zaire Ebola virus (EBOV) and other potential pathogens from whole-blood samples from 70 patients with suspected Ebola hemorrhagic fever during a 2014 outbreak in Boende, Democratic Republic of the Congo (DRC) and correlated these findings with clinical symptoms. Twenty of 31 patients (64.5%) tested in Kinshasa, DRC, were EBOV positive by quantitative reverse transcriptase PCR (qRT-PCR). Despite partial degradation of sample RNA during shipping and handling, mNGS followed by EBOV-specific capture probe enrichment in a U.S. genomics laboratory identified EBOV reads in 22 of 70 samples (31.4%) versus in 21 of 70 (30.0%) EBOV-positive samples by repeat qRT-PCR (overall concordance = 87.1%). Reads from Plasmodium falciparum (malaria) were detected in 21 patients, of which at least 9 (42.9%) were coinfected with EBOV. Other positive viral detections included hepatitis B virus (n = 2), human pegivirus 1 (n = 2), Epstein-Barr virus (n = 9), and Orungo virus (n = 1), a virus in the Reoviridae family. The patient with Orungo virus infection presented with an acute febrile illness and died rapidly from massive hemorrhage and dehydration. Although the patient's blood sample was negative by EBOV qRT-PCR testing, identification of viral reads by mNGS confirmed the presence of EBOV coinfection. In total, 9 new EBOV genomes (3 complete genomes, and an additional 6 ≥50% complete) were assembled. Relaxed molecular clock phylogenetic analysis demonstrated a molecular evolutionary rate for the Boende strain 4 to 10× slower than that of other Ebola lineages. These results demonstrate the utility of mNGS in broad-based pathogen detection and outbreak surveillance

Canagliflozin and Cardiovascular and Renal Outcomes in Type 2 Diabetes Mellitus and Chronic Kidney Disease in Primary and Secondary Cardiovascular Prevention Groups

Background: Canagliflozin reduces the risk of kidney failure in patients with type 2 diabetes mellitus and chronic kidney disease, but effects on specific cardiovascular outcomes are uncertain, as are effects in people without previous cardiovascular disease (primary prevention). Methods: In CREDENCE (Canagliflozin and Renal Events in Diabetes With Established Nephropathy Clinical Evaluation), 4401 participants with type 2 diabetes mellitus and chronic kidney disease were randomly assigned to canagliflozin or placebo on a background of optimized standard of care. Results: Primary prevention participants (n=2181, 49.6%) were younger (61 versus 65 years), were more often female (37% versus 31%), and had shorter duration of diabetes mellitus (15 years versus 16 years) compared with secondary prevention participants (n=2220, 50.4%). Canagliflozin reduced the risk of major cardiovascular events overall (hazard ratio [HR], 0.80 [95% CI, 0.67-0.95]; P=0.01), with consistent reductions in both the primary (HR, 0.68 [95% CI, 0.49-0.94]) and secondary (HR, 0.85 [95% CI, 0.69-1.06]) prevention groups (P for interaction=0.25). Effects were also similar for the components of the composite including cardiovascular death (HR, 0.78 [95% CI, 0.61-1.00]), nonfatal myocardial infarction (HR, 0.81 [95% CI, 0.59-1.10]), and nonfatal stroke (HR, 0.80 [95% CI, 0.56-1.15]). The risk of the primary composite renal outcome and the composite of cardiovascular death or hospitalization for heart failure were also consistently reduced in both the primary and secondary prevention groups (P for interaction >0.5 for each outcome). Conclusions: Canagliflozin significantly reduced major cardiovascular events and kidney failure in patients with type 2 diabetes mellitus and chronic kidney disease, including in participants who did not have previous cardiovascular disease

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

<i>TRI14</i> Is Critical for <i>Fusarium graminearum</i> Infection and Spread in Wheat

Trichothecenes are sesquiterpenoid toxins produced by diverse ascomycetes, including Fusarium. The trichothecene analog deoxynivalenol (DON) produced by the Fusarium head blight (FHB) pathogen Fusarium graminearum is a virulence factor on wheat and a major food and feed safety concern. In Fusarium, the trichothecene biosynthetic gene (TRI) cluster consists of 7–14 genes. Most TRI cluster genes are conserved and their specific roles in trichothecene biosynthesis have been determined. An exception is TRI14, which is not required for DON synthesis in vitro but is required for spread of F. graminearum in wheat heads. In the current study, gene expression analyses revealed that TRI14 was highly induced in infected wheat heads. We demonstrated that TRI14 was not only required for F. graminearum spread but also important for initial infection in wheat. Although a prior study did not detect DON in infected seeds, our analyses showed significantly less DON and fungal biomass in TRI14-mutant (designated ∆tri14)-inoculated heads than wild-type-inoculated heads. Gene expression comparison showed that the level of expression of TRI genes was similar in the wheat tissues infected with ∆tri14 or the wild type, indicating the reduced toxin levels caused by ∆tri14 may be due to less fungal growth. ∆tri14 also caused less lesion and grew less in wheat coleoptiles than the wild type. The growth of ∆tri14 in carboxymethylcellulose medium was more sensitive to hydrogen peroxide than the wild type. The data suggest that TRI14 plays a critical role in F. graminearum growth, and potentially protects the fungus from plant defense compounds

Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods

Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally; computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multiclassification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction