The Protective Effects of the Violacein Pigment Against UV-C Irradiation in Chromobacterium violaceum

Author Institution: Tippecanoe High School, Tipp City, OHChromobacterium violaceum is a gram-negative bacteria found in tropical regions. C. violaceum has the distinct phenotypic characteristic of a deep violet pigment called violacein. Violacein has a high molar extinction in methanol, suggesting that it is protective against visible light. The purpose of this study was to establish the protective effects of violacein against UV-induced cellular damage. It was hypothesized that violacein protects DNA and proteins (e.g. catalase) from UV-C induced damage. Wild-type (WT) C. violaceum was mutagenized with N-methyl-N’-nitro-N-nitrosoguanidine to produce mutants with varying amounts of violacein. Mutants CV9, CV13, and CV14 (non-pigmented) produced less pigmentation than WT and retained colony morphology, while mutants H19, H20, and H21 (hyper-producers) over-expressed violacein but had an altered petite morphology. UV-induced DNA damage was assayed through sub-culture post-irradiation at 6,000μW*s-1*cm-2 at λ=253.7nm. Sub-cultures of WT and hyper-producers showed reduced viability after 48 hours; nonpigmented mutants showed no growth, suggesting violacein is protective against UV-induced DNA damage. UV-induced catalase damage was assayed pre- and post-irradiation. Catalase activity in WT and hyper-producers significantly decreased post-irradiation; catalase activities of non-pigmented mutants significantly increased post-irradiation. Increased catalase activity in non-pigmented mutants can potentially be explained by the increased induction of catalase genes in response to elevated reactive oxidative species, presumably from lack of pigmentation. Taken together, these results support the hypothesis that violacein is protective against UV-induced cellular damage

Computational evidence for an early, amplified systemic inflammation program in polytrauma patients with severe extremity injuries

Extremity and soft tissue injuries contribute significantly to inflammation and adverse in-hospital outcomes for trauma survivors; accordingly, we examined the complex association between clinical outcomes inflammatory responses in this setting using in silico tools. Two stringently propensity-matched, moderately/severely injured (Injury Severity Score > 16) patient sub-cohorts of ~30 patients each were derived retrospectively from a cohort of 472 blunt trauma survivors and segregated based on their degree of extremity injury severity (above or below 3 on the Abbreviated Injury Scale). Serial blood samples were analyzed for 31 plasma inflammatory mediators. In addition to standard statistical analyses, Dynamic Network Analysis (DyNA) and Principal Component Analysis (PCA) were used to model systemic inflammation following trauma. Patients in the severe extremity injury sub-cohort experienced longer intensive care unit length of stay (LOS), total LOS, and days on a mechanical ventilator, with higher Marshall Multiple Organ Dysfunction (MOD) Scores over the first 7 days post-injury as compared to the mild/moderate extremity injury sub-cohort. The higher severity cohort had statistically significant elevated lactate, base deficit, and creatine phosphokinase on first blood draw, along with significant changes in multiple circulating inflammatory mediators. DyNA pointed to a sustained role for type 17 immunity in both sub-cohorts, along with IFN-γ in the severe extremity injury group. DyNA network complexity increased over 7 days post-injury in the severe injury group, while generally decreasing over this same time period in the mild/moderate injury group. PCA suggested a more robust activation of multiple pathways in the severe extremity injury group as compared to the mild/moderate injury group. These studies thus point to the possibility of self-sustaining inflammation following severe extremity injury vs. resolving inflammation following less severe extremity injury

Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation

Significance: Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. Recent Advances: Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies. Critical Issues: Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments. Future Directions: These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets. Antioxid. Redox Signal. 23, 1370?1387.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140310/1/ars.2015.6398.pd

Evidence for involvement of non‐classical pathways in the protection from UV‐induced DNA damage by vitamin D‐related compounds

The vitamin D hormone, 1,25dihydroxyvitamin D3 (1,25(OH)2D3), and related compounds derived from vitamin D3 or lumisterol as a result of metabolism via the enzyme CYP11A1, have been shown, when applied 24 hours before or immediately after UV irradiation, to protect human skin cells and skin from DNA damage due to UV exposure, by reducing both cyclobutane pyrimidine dimers (CPD) and oxidative damage in the form of 8‐oxo‐7,8‐dihydro‐2’‐ deoxyguanosine (8‐OHdG). We now report that knockdown of either the vitamin D receptor or the endoplasmic reticulum protein ERp57 by siRNA abolished the reductions in UV‐induced DNA damage with 20‐hydroxyvitamin D3 or 24‐hydroxylumisterol3, as previously shown for 1,25(OH)2D3. Treatment with 1,25(OH)2D3 reduced oxygen consumption rates in UV‐exposed and sham‐exposed human keratinocytes and reduced phosphorylation of CREB (cyclic AMP response binding element protein). Both these actions have been shown to inhibit skin carcinogenesis after chronic UV exposure, consistent with the anticarcinogenic activity of 1,25(OH)2D3. The requirement for a vitamin D receptor for the photoprotective actions of 1,25(OH)2D3 and of naturally occurring CYP11A1‐derived vitamin D related compounds may explain why mice lacking the vitamin D receptor in skin are more susceptible to UV‐induced skin cancers, whereas mice lacking the 1α‐hydroxylase and thus unable to make 1,25(OH)2D3 are not more susceptible. This article is protected by copyright. All rights reserved

The Impact of the COVID-19 Pandemic on Racial Disparities in Patients Undergoing Total Shoulder Arthroplasty in the United States

INTRODUCTION: The purpose of this study was to assess racial disparities in total shoulder arthroplasty (TSA) in the US and to determine whether these disparities were affected by the COVID-19 pandemic. METHODS: Centers for Medicare and Medicaid Services (CMS) 100% sample was used to examine primary TSA volume from April-December from 2019-2020. Utilization was assessed for White/Black/Hispanic/Asian populations to determine if COVID-19 affected these groups differently. A regression model adjusted for age/sex/CMS-Hierarchical Condition Categories (HCC) score, dual enrollment (proxy for socioeconomic status), time fixed effects, and Core-based Statistical Area (CBSA) fixed effects was used to study difference across groups. RESULTS: In 2019, TSA volume/1000 beneficiaries was 1.51 for White and 0.57 for non-White, a 2.6-fold difference. In 2020, the rate of TSA in White patients (1.30/1000) was 2.9 times higher than non-White (0.45/1000) during the COVID-19 pandemic (P\u3c0.01). There was an overall 14% decrease in TSA volume/1000 Medicare beneficiaries in 2020; non-White patients had a larger percentage decrease in TSA volume than White (21% vs. 14%, estimated difference;8.7%,p = 0.02). Black patients experienced the most pronounced disparity with estimated difference of 10.1%,p = 0.05, compared with White patients. Similar disparities were observed when categorizing procedures into anatomic and reverse TSA, but not proximal humerus fracture. CONCLUSIONS: During the COVID-19 pandemic, overall TSA utilization decreased by 14% with White patients experiencing a decrease of 14%, and non-White patients experiencing a decrease of 21%. This trend was observed for elective TSA while disparities were less apparent for proximal humerus fracture

Common variation in PHACTR1 is associated with susceptibility to cervical artery dissection

Cervical artery dissection (CeAD), a mural hematoma in a carotid or vertebral artery, is a major cause of ischemic stroke in young adults although relatively uncommon in the general population (incidence of 2.6/100,000 per year). Minor cervical traumas, infection, migraine and hypertension are putative risk factors, and inverse associations with obesity and hypercholesterolemia are described. No confirmed genetic susceptibility factors have been identified using candidate gene approaches. We performed genome-wide association studies (GWAS) in 1,393 CeAD cases and 14,416 controls. The rs9349379[G] allele (PHACTR1) was associated with lower CeAD risk (odds ratio (OR) = 0.75, 95% confidence interval (CI) = 0.69-0.82; P = 4.46 × 10(-10)), with confirmation in independent follow-up samples (659 CeAD cases and 2,648 controls; P = 3.91 × 10(-3); combined P = 1.00 × 10(-11)). The rs9349379[G] allele was previously shown to be associated with lower risk of migraine and increased risk of myocardial infarction. Deciphering the mechanisms underlying this pleiotropy might provide important information on the biological underpinnings of these disabling conditions

Comprehensive review:Computational modelling of Schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence.Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes