Integration of transcriptome and metabolome provides unique insights to pathways associated with obese breast cancer patients

Information regarding transcriptome and metabolome has significantly contributed to identifying potential therapeutic targets for the management of a variety of cancers. Obesity has profound effects on both cancer cell transcriptome and metabolome that can affect the outcome of cancer therapy. The information regarding the potential effects of obesity on breast cancer (BC) transcriptome, metabolome, and its integration to identify novel pathways related to disease progression are still elusive. We assessed the whole blood transcriptome and serum metabolome, as circulating metabolites, of obese BC patients compared them with non-obese BC patients. In these patients' samples, 186 significant differentially expressed genes (DEGs) were identified, comprising 156 upregulated and 30 downregulated. The expressions of these gene were confirmed by qRT-PCR. Furthermore, 96 deregulated metabolites were identified as untargeted metabolomics in the same group of patients. These detected DEGs and deregulated metabolites enriched in many cellular pathways. Further investigation, by integration analysis between transcriptomics and metabolomics data at the pathway levels, revealed seven unique enriched pathways in obese BC patients when compared with non-obese BC patients, which may provide resistance for BC cells to dodge the circulating immune cells in the blood. In conclusion, this study provides information on the unique pathways altered at transcriptome and metabolome levels in obese BC patients that could provide an important tool for researchers and contribute further to knowledge on the molecular interaction between obesity and BC. Further studies are needed to confirm this and to elucidate the exact underlying mechanism for the effects of obesity on the BC initiation or/and progression

Glow curve analysis of glassy system dosimeter subjected to photon and electron irradiations

The current paper illustrates glow curve analysis of newly developed Borate glass dosimeters. A series of dosimetric properties including dose response for photons and electrons, energy response, optical fading, and precision were determined. Glow curve deconvolution based on the general order kinetics equation was applied to extract the trapping parameters. Excellent fitting was obtained with the superposition of three-second order glow peaks. The quality of fitting was monitored through the r2 value which is always in excess of 0.9998. Thermoluminescence (TL) measurements showed that the material exhibits good linear dose–response over the delivered range of absorbed dose from 0.5 to 4 Gy for photons and electrons irradiation with low energy dependence. The material exhibits large signal loss when exposed to direct sunlight and moderate signal loss when exposed to fluorescent light. Therefore, it is recommended to use the current dosimeters indoor and to avoid prolonged direct exposure to fluorescent light. This combination of properties makes the material suitable for radiation dosimetry

Estimating the quality of 3D protein models using the ModFOLD7 server

Assessing the accuracy of 3D models has become a keystone in the protein structure prediction field. ModFOLD7 is our leading resource for Estimates of Model Accuracy (EMA), which has been upgraded by integrating a number of the pioneering pure-single- and quasi-single-model approaches. Such an integration has given our latest version the strengths to accurately score and rank predicted models, with higher consistency compared to older EMA methods. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (1) ModFOLD7_rank, which is optimized for ranking/selection, (2) ModFOLD7_cor, which is optimized for correlations of predicted and observed scores, and (3) ModFOLD7 global for balanced performance. ModFOLD7 has been ranked among the top few EMA methods according to independent blind testing by the CASP13 assessors. Another evaluation resource for ModFOLD7 is the CAMEO project, where the method is continuously automatically evaluated, showing a significant improvement compared to our previous versions. The ModFOLD7 server is freely available at http://www.reading.ac.uk/bioinf/ModFOLD/

Critically ill patients with diabetes and Middle East respiratory syndrome:a multi-center observational study

Background: Diabetes is a risk factor for infection with coronaviruses. This study describes the demographic, clinical data, and outcomes of critically ill patients with diabetes and Middle East Respiratory Syndrome (MERS).Methods: This retrospective cohort study was conducted at 14 hospitals in Saudi Arabia (September 2012–January 2018). We compared the demographic characteristics, underlying medical conditions, presenting symptoms andsigns, management and clinical course, and outcomes of critically ill patients with MERS who had diabetes compared to those with no diabetes. Multivariable logistic regression analysis was performed to determine ifdiabetes was an independent predictor of 90-day mortality.Results: Of the 350 critically ill patients with MERS, 171 (48.9%) had diabetes. Patients with diabetes were more likely to be older, and have comorbid conditions, compared to patients with no diabetes. They were more likely topresent with respiratory failure requiring intubation, vasopressors, and corticosteroids. The median time to clearance of MERS-CoV RNA was similar (23 days (Q1, Q3: 17, 36) in patients with diabetes and 21.0 days (Q1, Q3: 10, 33) in patients with no diabetes). Mortality at 90 days was higher in patients with diabetes (78.9% versus 54.7%, p <0.0001). Multivariable regression analysis showed that diabetes was an independent risk factor for 90-day mortality(odds ratio, 2.09; 95% confidence interval, 1.18–3.72).Conclusions: Half of the critically ill patients with MERS have diabetes; which is associated with more severe disease. Diabetes is an independent predictor of mortality among critically patients with MERS

Critically ill patients with diabetes and Middle East respiratory syndrome:a multi-center observational study

Background: Diabetes is a risk factor for infection with coronaviruses. This study describes the demographic, clinical data, and outcomes of critically ill patients with diabetes and Middle East Respiratory Syndrome (MERS).Methods: This retrospective cohort study was conducted at 14 hospitals in Saudi Arabia (September 2012–January 2018). We compared the demographic characteristics, underlying medical conditions, presenting symptoms andsigns, management and clinical course, and outcomes of critically ill patients with MERS who had diabetes compared to those with no diabetes. Multivariable logistic regression analysis was performed to determine ifdiabetes was an independent predictor of 90-day mortality.Results: Of the 350 critically ill patients with MERS, 171 (48.9%) had diabetes. Patients with diabetes were more likely to be older, and have comorbid conditions, compared to patients with no diabetes. They were more likely topresent with respiratory failure requiring intubation, vasopressors, and corticosteroids. The median time to clearance of MERS-CoV RNA was similar (23 days (Q1, Q3: 17, 36) in patients with diabetes and 21.0 days (Q1, Q3: 10, 33) in patients with no diabetes). Mortality at 90 days was higher in patients with diabetes (78.9% versus 54.7%, p <0.0001). Multivariable regression analysis showed that diabetes was an independent risk factor for 90-day mortality(odds ratio, 2.09; 95% confidence interval, 1.18–3.72).Conclusions: Half of the critically ill patients with MERS have diabetes; which is associated with more severe disease. Diabetes is an independent predictor of mortality among critically patients with MERS

Structure-Based Development of Small Molecule PFKFB3 Inhibitors: A Framework for Potential Cancer Therapeutic Agents Targeting the Warburg Effect

Cancer cells adopt glycolysis as the major source of metabolic energy production for fast cell growth. The HIF-1-induced PFKFB3 plays a key role in this adaptation by elevating the concentration of Fru-2,6-BP, the most potent glycolysis stimulator. As this metabolic conversion has been suggested to be a hallmark of cancer, PFKFB3 has emerged as a novel target for cancer chemotherapy. Here, we report that a small molecular inhibitor, N4A, was identified as an initial lead compound for PFKFB3 inhibitor with therapeutic potential. In an attempt to improve its potency, we determined the crystal structure of the PFKFB3•N4A complex to 2.4 Å resolution and, exploiting the resulting molecular information, attained the more potent YN1. When tested on cultured cancer cells, both N4A and YN1 inhibited PFKFB3, suppressing the Fru-2,6-BP level, which in turn suppressed glycolysis and, ultimately, led to cell death. This study validates PFKFB3 as a target for new cancer therapies and provides a framework for future development efforts

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

KSHV PAN RNA Associates with Demethylases UTX and JMJD3 to Activate Lytic Replication through a Physical Interaction with the Virus Genome

Kaposi's sarcoma-associated herpesvirus (KSHV) is the cause of Kaposi's sarcoma and body cavity lymphomas. KSHV lytic infection produces PAN RNA, a highly abundant noncoding polyadenylated transcript that is retained in the nucleus. We recently demonstrated that PAN RNA interacts with several viral and cellular factors and can disregulate the expression of genes that modulate immune response. In an effort to define the role of PAN RNA in the context of the virus genome we generated a recombinant BACmid that deleted the PAN RNA locus. Because of the apparent duplication of the PAN RNA locus in BAC36, we generated BAC36CR, a recombinant BACmid that removes the duplicated region. BAC36CR was used as a template to delete most of the PAN RNA locus to generate BAC36CRΔPAN. BAC36CRΔPAN failed to produce supernatant virus and displayed a general decrease in mRNA accumulation of representative immediate early, early and late genes. Most strikingly, K-Rta expression was decreased in lytically induced BAC36CRΔPAN-containing cell lines at early and late time points post induction. Expression of PAN RNA in trans in BAC36CRΔPAN containing cells resulted in an increase in K-Rta expression, however K-Rta over expression failed to rescue BAC36CRΔPAN, suggesting that PAN RNA plays a wider role in virus replication. To investigate the role of PAN RNA in the activation of K-Rta expression, we demonstrate that PAN RNA physically interacts with the ORF50 promoter. RNA chromatin immunoprecipitation assays show that PAN RNA interacts with demethylases JMJD3 and UTX, and the histone methyltransferase MLL2. Consistent with the interaction with demethylases, expression of PAN RNA results in a decrease of the repressive H3K27me3 mark at the ORF50 promoter. These data support a model where PAN RNA is a multifunctional regulatory transcript that controls KSHV gene expression by mediating the modification of chromatin by targeting the KSHV repressed genome

Phosphofructo-2-kinase/Fructose-2,6-bisphosphatase Modulates Oscillations of Pancreatic Islet Metabolism

Pulses of insulin from pancreatic beta-cells help maintain blood glucose in a narrow range, although the source of these pulses is unclear. It has been proposed that a positive feedback circuit exists within the glycolytic pathway, the autocatalytic activation of phosphofructokinase-1 (PFK1), which endows pancreatic beta-cells with the ability to generate oscillations in metabolism. Flux through PFK1 is controlled by the bifunctional enzyme PFK2/FBPase2 (6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase) in two ways: via (1) production/degradation of fructose-2,6-bisphosphate (Fru2,6-BP), a potent allosteric activator of PFK1, as well as (2) direct activation of glucokinase due to a protein-protein interaction. In this study, we used a combination of live-cell imaging and mathematical modeling to examine the effects of inducibly-expressed PFK2/FBPase2 mutants on glucose-induced Ca2+ pulsatility in mouse islets. Irrespective of the ability to bind glucokinase, mutants of PFK2/FBPase2 that increased the kinase:phosphatase ratio reduced the period and amplitude of Ca2+ oscillations. Mutants which reduced the kinase:phosphatase ratio had the opposite effect. These results indicate that the main effect of the bifunctional enzyme on islet pulsatility is due to Fru2,6-BP alteration of the threshold for autocatalytic activation of PFK1 by Fru1,6-BP. Using computational models based on PFK1-generated islet oscillations, we then illustrated how moderate elevation of Fru-2,6-BP can increase the frequency of glycolytic oscillations while reducing their amplitude, with sufficiently high activation resulting in termination of slow oscillations. The concordance we observed between PFK2/FBPase2-induced modulation of islet oscillations and the models of PFK1-driven oscillations furthermore suggests that metabolic oscillations, like those found in yeast and skeletal muscle, are shaped early in glycolysis