E6 Protein Expressed by High-Risk HPV Activates Super-Enhancers of the EGFR and c-MET Oncogenes by Destabilizing the Histone

The high-risk (HR) human papillomaviruses (HPV) are causative agents of anogenital tract dysplasia and cancers and a fraction of head and neck cancers. The HR HPV E6 oncoprotein possesses canonical oncogenic functions, such as p53 degradation and telomerase activation. It is also capable of stimulating expression of several oncogenes, but the molecular mechanism underlying these events is poorly understood. Here, we provide evidence that HPV16 E6 physically interacts with histone H3K4 demethylase KDM5C, resulting in its degradation in an E3 ligase E6AP- and proteasome-dependent manner. Moreover, we found that HPV16-positive cancer cell lines exhibited lower KDM5C protein levels than HPV-negative cancer cell lines. Restoration of KDM5C significantly suppressed the tumorigenicity of CaSki cells, an HPV16-positive cervical cancer cell line. Whole genome ChIP-seq and RNA-seq results revealed that CaSki cells contained super-enhancers in the proto-oncogenes EGFR and c-MET. Ectopic KDM5C dampened these super-enhancers and reduced the expression of proto-oncogenes. This effect was likely mediated by modulating H3K4me3/H3K4me1 dynamics and decreasing bidirectional enhancer RNA transcription. Depletion of KDM5C or HPV16 E6 expression activated these two super-enhancers. These results illuminate a pivotal relationship between the oncogenic E6 proteins expressed by HR HPV isotypes and epigenetic activation of super-enhancers in the genome that drive expression of key oncogenes like EGFR and c-MET. Significance: This study suggests a novel explanation for why infections with certain HPV isotypes are associated with elevated cancer risk by identifying an epigenetic mechanism through which E6 proteins expressed by those isotypes can drive expression of key oncogenes.</p

Heterogeneity in the diagnosis and prognosis of ischemic stroke subtypes: 9-year follow-up of 22,000 cases in Chinese adults

Background: Reliable classification of ischemic stroke (IS) etiological subtypes is required in research and clinical practice, but the predictive properties of these subtypes in population studies with incomplete investigations are poorly understood. Aims: To compare the prognosis of etiologically classified IS subtypes and use machine learning (ML) to classify incompletely investigated IS cases. Methods: In a 9-year follow-up of a prospective study of 512,726 Chinese adults, 22,216 incident IS cases, confirmed by clinical adjudication of medical records, were assigned subtypes using a modified Causative Classification System for Ischemic Stroke (CCS) (large artery atherosclerosis (LAA), small artery occlusion (SAO), cardioaortic embolism (CE), or undetermined etiology) and classified by CCS as “evident,” “probable,” or “possible” IS cases. For incompletely investigated IS cases where CCS yielded an undetermined etiology, an ML model was developed to predict IS subtypes from baseline risk factors and screening for cardioaortic sources of embolism. The 5-year risks of subsequent stroke and all-cause mortality (measured using cumulative incidence functions and 1 minus Kaplan–Meier estimates, respectively) for the ML-predicted IS subtypes were compared with etiologically classified IS subtypes. Results: Among 7443 IS subtypes with evident or probable etiology, 66% had SAO, 32% had LAA, and 2% had CE, but proportions of SAO-to-LAA cases varied by regions in China. CE had the highest rates of subsequent stroke and mortality (43.5% and 40.7%), followed by LAA (43.2% and 17.4%) and SAO (38.1% and 11.1%), respectively. ML provided classifications for cases with undetermined etiology and incomplete clinical data (24% of all IS cases; n = 5276), with area under the curves (AUC) of 0.99 (0.99–1.00) for CE, 0.67 (0.64–0.70) for LAA, and 0.70 (0.67–0.73) for SAO for unseen cases. ML-predicted IS subtypes yielded comparable subsequent stroke and all-cause mortality rates to the etiologically classified IS subtypes. Conclusion: This study highlighted substantial heterogeneity in prognosis of IS subtypes and utility of ML approaches for classification of IS cases with incomplete clinical investigations

The fragmentomic property of plasma cell-free DNA enables the non-invasive detection of diabetic nephropathy in patients with diabetes mellitus

BackgroundDiabetic nephropathy (DN) is one of the most prevalent complications of diabetes mellitus (DM). However, there is still a lack of effective methods for non-invasive diagnosis of DN in clinical practice. We aimed to explore biomarkers from plasma cell-free DNA as a surrogate of renal biopsy for the differentiation of DN patients from patients with DM.Materials and methodsThe plasma cell-free DNA (cfDNA) was sequenced from 53 healthy individuals, 53 patients with DM but without DN, and 71 patients with both DM and DN. Multidimensional features of plasma DNA were analyzed to dissect the cfDNA profile in the DM and DN patients and identify DN-specific cfDNA features. Finally, a classification model was constructed by integrating all informative cfDNA features to demonstrate the clinical utility in DN detection.ResultsIn comparison with the DM patients, the DN individuals exhibited significantly increased cfDNA concentration in plasma. The cfDNA from the DN patients showed a distinct fragmentation pattern with an altered size profile and preferred motifs that start with “CC” in the cfDNA ending sites, which were associated with deoxyribonuclease 1 like 3 (DNASE1L3) expression in the kidney. Moreover, patients with DM or DN were found to carry more alterations in whole-genome cfDNA coverage when compared with healthy individuals. We integrated DN-specific cfDNA features (cfDNA concentration, size, and motif) into a classification model, which achieved an area under the receiver operating characteristic curve (AUC) of 0.928 for the differentiation of DN patients from DM patients.ConclusionOur findings showed plasma cfDNA as a reliable non-invasive biomarker for differentiating DN patients from DM patients. The utility of cfDNA in clinical practice in large prospective cohorts is warranted

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe

Internal and External Factors Related to Burnout among Iron and Steel Workers: A Cross-Sectional Study in Anshan, China.

Burnout is a syndrome of emotional exhaustion, cynicism and reduced professional efficacy, which can result from long-term work stress. Although the burnout level is high among iron and steel workers, little is known concerning burnout among iron and steel worker. This study aimed to evaluate the burnout and to explore its associated internal and external factors in iron and steel workers.A cross-sectional survey was conducted in iron and steel workers at the Anshan iron-steel complex in Anshan, northeast China. Self-administered questionnaires were distributed to 1,600 workers, and finally 1,300 questionnaires were returned. Burnout was measured using the Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS). Effort-reward imbalance (ERI), perceived organizational support (POS), and psychological capital (PsyCap) were measured anonymously. A hierarchical regression model was applied to explore the internal and external factors associated with burnout.Mean MBI-GS scores were 13.11±8.06 for emotional exhaustion, 6.64±6.44 for cynicism, and 28.96±10.39 for professional efficacy. Hierarchical linear regression analysis showed that ERI and POS were the most powerful predictors for emotional exhaustion and cynicism, and PsyCap was the most robust predictor for high professional efficacy.Chinese iron and steel workers have a high level of burnout. Burnout might be associated with internal and external factors, including ERI, POS, and PsyCap. Further studies are recommended to develop an integrated model including both internal and external factors, to reduce the level of ERI, and improve POS and workers' PsyCap, thereby alleviating the level of burnout among iron and steel workers

Characterization of Effects of Different Tea Harvesting Seasons on Quality Components, Color and Sensory Quality of &ldquo;Yinghong 9&rdquo; and &ldquo;Huangyu&rdquo; Large-Leaf-Variety Black Tea

Harvesting seasons are crucial for the physicochemical qualities of large-leaf-variety black tea. To investigate the effect of harvesting seasons on physicochemical qualities, the color and sensory characteristics of black tea produced from &ldquo;Yinghong 9&rdquo; (Yh) and its mutant &ldquo;Huangyu&rdquo; (Hy) leaves were analyzed. The results demonstrated that Hy had better chemical qualities and sensory characteristics, on average, such as a higher content of tea polyphenols, free amino acids, caffeine, galloylated catechins (GaCs) and non-galloylated catechins (NGaCs), while the hue of the tea brew (&Delta;E*ab and &Delta;b*) increased, which meant that the tea brew was yellower and redder. Moreover, the data showed that the physicochemical qualities of SpHy (Hy processed in spring) were superior to those of SuHy (Hy processed in summer) and AuHy (Hy processed in autumn), and 92.6% of the total variance in PCA score plots effectively explained the separation of the physicochemical qualities of Yh and Hy processed in different harvesting seasons. In summary, Hy processed in spring was superior in its physicochemical qualities. The current results will provide scientific guidance for the production of high-quality large-leaf-variety black tea in South China

Means, standard deviations (SD), range, and correlations of continuous variables.

<p>* p<0.05,</p><p>** p<0.01</p><p>Note: ERR = effort/reward ratio; POS = perceived organizational support; PsyCap = psychological capital</p><p>Means, standard deviations (SD), range, and correlations of continuous variables.</p

Mean MBI-GS scores of iron and steel worker according to sociodemographic feature.

<p>^a,b,c Calculated by least-significant-difference (LSD), mean scores for the three dimensions of burnout with different superscripts differ significantly at the p<0.05 level.</p><p>Mean MBI-GS scores of iron and steel worker according to sociodemographic feature.</p

Gross Ecosystem Productivity Dominates the Control of Ecosystem Methane Flux in Rice Paddies

Although rice paddy fields are one of the world’s largest anthropogenic sources of methane CH4, the budget of ecosystem CH4 and its’ controls in rice paddies remain unclear. Here, we analyze seasonal dynamics of direct ecosystem-scale measurements of CH4 flux in a rice-wheat rotation agroecosystem over 3 consecutive years. Results showed that the averaged CO2 uptakes and CH4 emissions in rice seasons were 2.2 and 20.9 folds of the wheat seasons, respectively. In sum, the wheat-rice rotation agroecosystem acted as a large net C sink (averaged 460.79 g C m−2) and a GHG (averaged 174.38 g CO2eq m−2) source except for a GHG sink in one year (2016) with a very high rice seeding density. While the linear correlation between daily CH4 fluxes and gross ecosystem productivity (GEP) was not significant for the whole rice season, daily CH4 fluxes were significantly correlated to daily GEP both before (R2: 0.52–0.83) and after the mid-season drainage (R2: 0.71–0.79). Furthermore, the F partial test showed that GEP was much greater than that of any other variable including soil temperature for the rice season in each year. Meanwhile, the parameters of the best-fit functions between daily CH4 fluxes and GEP shifted between rice growth stages. This study highlights that GEP is a good predictor of daily CH4 fluxes in rice paddies