Flow cytometric analysis identifies changes in S and M phases as novel cell cycle alterations induced by the splicing inhibitor isoginkgetin

The spliceosome is a large ribonucleoprotein complex that catalyzes the removal of introns from RNA polymerase II-transcribed RNAs. Spliceosome assembly occurs in a stepwise manner through specific intermediates referred to as pre-spliceosome complexes E, A, B, B* and C. It has been reported that small molecule inhibitors of the spliceosome that target the SF3B1 protein component of complex A lead to the accumulation of cells in the G1 and G2/M phases of the cell cycle. Here we performed a comprehensive flow cytometry analysis of the effects of isoginkgetin (IGG), a natural compound that interferes with spliceosome assembly at a later step, complex B formation. We found that IGG slowed cell cycle progression in multiple phases of the cell cycle (G1, S and G2) but not M phase. This pattern was somewhat similar to but distinguishable from changes associated with an SF3B1 inhibitor, pladienolide B (PB). Both drugs led to a significant decrease in nascent DNA synthesis in S phase, indicative of an S phase arrest. However, IGG led to a much more prominent S phase arrest than PB while PB exhibited a more pronounced G1 arrest that decreased the proportion of cells in S phase as well. We also found that both drugs led to a comparable decrease in the proportion of cells in M phase. This work indicates that spliceosome inhibitors affect multiple phases of the cell cycle and that some of these effects vary in an agent-specific manner despite the fact t

Effects of PB on S and M phases of the cell cycle.

<p>HCT116 and p53KO cells were exposed to either 0 or 25 nM PB for 24 hours. HCT116 (A) and p53KO cells (B) were BrdU labelled for 1 hour prior to collection. BrdU incorporation was then assessed by two-parameter flow cytometric analysis. (C) The proportion of cells incorporating BrdU was determined and this is expressed relative to untreated control cells. (D) Phospho-H3 was detected as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191178#pone.0191178.g002" target="_blank">Fig 2</a>. Each value in (C) and (D) represents the mean (+/- SEM) determined from 3 independent experiments. In (C), the mean values for both cell lines at 10 and 25 nM PB were significantly different from 1 by single sample T test (P < 0.05). In (D), ** indicates that the values were significantly different (P<0.01) by Student T test.</p

IGG induces S phase arrest in HCT116 and p53KO cells.

<p>Two-parameter flow cytometry analysis of BrdU incorporation and DNA content was performed following a 24-hour exposure of HCT116 (A) and p53 KO (B) cells to 30 μM IGG. (C) The proportion of BrdU positive cells was determined and expressed relative to untreated controls. (D) Relative replication is an estimate of the efficiency of DNA replication in the BrdU positive population of cells. (E) Cell cycle distribution of no drug- (ND), vehicle control- (DMSO) and 30 IGG-treated cells was estimated from dot plots like those presented in A and B. (F) HCT116 cells were exposed to either DMSO for 48 hours (DMSO) or IGG for 24 hours (dashed line) followed by either an additional 24 hours in IGG or fresh medium with DMSO (REV). Data obtained from multiple experiments were expressed as the mean percentage of BrdU positive cells. Each value in C-F represents the mean (+/- SEM) determined from 4, 4, 6 and 3 independent experiments, respectively. In C and D, ** indicates that the values were significantly different (P < 0.01) by two way ANOVA followed by Bonferroni post hoc tests. In E and F, * and ** denote that the value was significantly different (P < 0.05 or P< 0.01) by one-way ANOVA followed by Tukey multiple comparisons test.</p

Similar effects on cell growth but differences in cell cycle distribution in IGG treated HCT116 and p53KO cells.

<p>HCT116 and p53KO cells were incubated in 15 μM IGG, 30 μM IGG or vehicle control for either 8 or 24 hours. One-parameter flow cytometric analysis of PI stained cells was used to determine cell cycle distributions (G₁, S and G₂/M) based on DNA content, using Modfit 4.1 cell cycle analysis software. (A) Representative histograms are presented for samples collected 24 hours following exposure to 30 μM IGG and its vehicle control. (B and C) The compiled cell cycle results from similar analysis of HCT116 cells collected at 8 (B) and 24 (C) hours following treatment with the indicated concentration of IGG (open symbols) and the corresponding vehicle controls (closed symbols). (D) HCT116 and p53KO (left and right panels, respectively) were incubated in growth medium alone (circles), DMSO (triangles) or IGG (inverted triangles) along with BrdU for the indicated period. The proportion of cells incorporating BrdU at each time point was estimated by flow cytometry. (E and F) p53KO cells were incubated in IGG for either 8 (D) or 24 hours (E) at the indicated concentration (open symbols) or with an equivalent volume of DMSO (closed symbols). Each value in B through F represents the mean (+/- SEM) determined from a minimum of 3 independent experiments. *** indicates that the value is significant different (P<0.001) from controls (DMSO and no drug) by one way ANOVA followed by Tukey multiple comparisons test.</p

Markers of M phase are decreased in response to IGG.

<p>(A) HCT116 cells were incubated in IGG for 24 hours. Protein lysates were collected and analyzed by immunoblot using antibodies raised against the indicated proteins. (B) p53 KO cells were treated as described in (A) except that colcemid, a microtubule-depolymerizing agent, was included in lane 6. HCT116 (C) and p53KO (D) cells were exposed to DMSO, IGG or colcemid for 24 hours and the M phase-specific phosphorylation of histone H3 was detected using a phosphospecific antibody coupled with flow cytometry. ‘M’ denotes the mitotic population. The proportion of phospho-H3 positive cells was determined from 3 independent experiments (E and F). *, ** and *** denote that the values were significantly different (P 0.05, 0.01, 0.001, respectively) by one way ANOVA followed by a Tukey multiple comparisons test. Statistical analysis of colcemid, the positive control, was not included for clarity.</p

AN ELECTRONIC MEDICAL RECORD BASED ALGORITHM TO TAILOR CARDIOVASCULAR DISEASE PREVENTION USING LIPOPROTEIN(A), APOLIPOPROTEIN B, CHOLESTEROL AND MYOCARDIAL INFARCTION DIAGNOSIS: ABCDS PREVENTION PROGRAM

Therapeutic Area: CVD Prevention – Primary and Secondary; ASCVD/CVD Risk Assessment; Preventive Cardiology Best Practices Background: According to the 2022 American Heart Association Heart Disease and Stroke Statistics, coronary heart disease remains the leading cause of death attributable to cardiovascular disease (CVD). Opportunity exists to utilize electronic medical records (EMRs) and biomarkers to facilitate early identification of patients at high risk for CVD. Additionally, automatic or opt-out orders are EMR-based tools that have the potential to improve referral rates to prevention programs. The role of cardiovascular biomarkers and electronic medical records (EMRs) in optimizing identification and referral of patients at risk for CVD are explored in the ABCDs PREVENTION program. Methods: A multidisciplinary team of cardiologists, internists, engineers, and clinical informaticists defined the logic for the guideline based ABCDs PREVENTION tool. The EMR algorithm used the cardiovascular risk biomarker thresholds of lipoprotein(a) > 70 nmol/L, apolipoprotein B > 90 mg/dL, low-density lipoprotein cholesterol  > 150 mg/dL, and triglycerides > 200 mg/dL, and/or a diagnosis of ST-elevation myocardial infarction (STEMI) or non-ST-elevation MI (NSTEMI) based on ICD-10 codes to generate automatic referrals to (1) cardiac rehabilitation (CR), (2) the advanced lipid disorders clinic, and/or (3) Corrie Cardiovascular Health Program (Figure 1). Results: In a test environment, the algorithm was applied to 27 patients identified by the clinical team with STEMI or NSTEMI. The algorithm was 90% successful in triggering automatic referrals to CR and Corrie. Fail rates can be attributed to our current algorithm not detecting some ICD codes related to NSTEMI. The automatic referral to lipid disorders clinic based on abnormal lipid biomarkers is now live and undergoing automation optimization to validate accuracy. Conclusion: Building an EMR-based algorithm to individualize CVD prevention using cardiovascular risk biomarkers and diagnoses may enable early identification and intervention on high-risk patients. Future directions include applying the algorithm to clinical decision support tools as well as an automated order set to increase referral rates to evidenced-based programs focused on primary and secondary CVD prevention. Ultimately, use analysis will determine if the algorithm improves referral rates to CR, lipid clinic, and the Corrie Cardiovascular Health Program to improve access to these evidence-based services

Are Caregivers’ Feeding Competence and Autonomy Associated with Healthier Restaurant Food Purchases for Their Child at Fast Food or Counter Service Restaurants? A Cross-Sectional Study in a Diverse Sample of U.S. Caregivers

This study examined the cross-sectional relationship between caregivers’ perceived competence and autonomy (as defined by the Self-Determination Theory) and their fast food or counter service restaurant food purchases (side dishes, beverage, and dessert) for their child. A U.S. national convenience sample of caregivers with at least one 3–12-year-old child completed an online survey with questions adapted from the Intrinsic Motivation Inventory that measured perceived competence and autonomy for feeding fruits and vegetables and limiting sugar-sweetened beverages (SSBs) and desserts. The survey included four questions asking about their fast food or counter service restaurant food purchases (side dish, beverage, and dessert). We applied logistic and multinomial logistic regression models to examine the associations between competence or autonomy and restaurant orders. Competence and autonomy were associated with ordering fruits and vegetables as side dishes (OR [95% CI], 1.14 [1.06, 1.24] and 1.09 [1.03, 1.14], respectively). However, higher competence was also associated with ordering desserts at restaurants and higher autonomy was associated with lower odds of ordering water. These findings will inform interventions and programs that aim to support caregivers’ psychological needs, like competence and autonomy, to promote supportive environments and healthier restaurant purchases for their children

The Virtual Inclusive Digital Health Intervention Design to Promote Health Equity (iDesign) Framework for Atrial Fibrillation: Co-design and Development Study

BackgroundSmartphone ownership and mobile app use are steadily increasing in individuals of diverse racial and ethnic backgrounds living in the United States. Growing adoption of technology creates a perfect opportunity for digital health interventions to increase access to health care. To successfully implement digital health interventions and engage users, intervention development should be guided by user input, which is best achieved by the process of co-design. Digital health interventions co-designed with the active engagement of users have the potential to increase the uptake of guideline recommendations, which can reduce morbidity and mortality and advance health equity. ObjectiveWe aimed to co-design a digital health intervention for patients with atrial fibrillation, the most common cardiac arrhythmia, with patient, caregiver, and clinician feedback and to describe our approach to human-centered design for building digital health interventions. MethodsWe conducted virtual meetings with patients with atrial fibrillation (n=8), their caregivers, and clinicians (n=8). We used the following 7 steps in our co-design process: step 1, a virtual meeting focused on defining challenges and empathizing with problems that are faced in daily life by individuals with atrial fibrillation and clinicians; step 2, a virtual meeting focused on ideation and brainstorming the top challenges identified during the first meeting; step 3, individualized onboarding of patients with an existing minimally viable version of the atrial fibrillation app; step 4, virtual prototyping of the top 3 ideas generated during ideation; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study. ResultsThe top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support. ConclusionsWe co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators

Advancing the genetic counseling profession through research: Identification of priorities by the National Society of Genetic Counselors research task force

To help advance research critical to the achievement of the National Society of Genetic Counselors’ (NSGC) strategic objectives, coordination and prioritization of society resources are needed. NSGC convened a task force to advance research necessary for the achievement of our strategic objectives by reviewing existing society-supported research efforts identifying gaps in current research, and coordinating society resources, the task force was formed in order to coordinate and prioritize society resources to advance research critical to the achievement of our strategic objectives. The task force developed a research agenda outlining high-priority research questions for the next 5 years. The questions are organized into four domains: (a) Genetic Counseling Clients; (b) Genetic Counseling Process and Outcomes; (c) Value of Genetic Counseling Services; and (d) Access to Genetic Counseling Services. This framework can be used to advocate for research and funding priorities within NSGC and with other key research entities to stimulate the growth and advancement of the genetic counseling profession

Functional cytochrome P450 1A enzymes are induced in mouse and human islets following pollutant exposure

Aims/hypothesis: Exposure to environmental pollution has been consistently linked to diabetes incidence in humans, but the potential causative mechanisms remain unclear. Given the critical role of regulated insulin secretion in maintaining glucose homeostasis, environmental chemicals that reach the endocrine pancreas and cause beta cell injury are of particular concern. We propose that cytochrome P450 (CYP) enzymes, which are inv