QS9: Host Biofilm Interaction In Breast Implant Illness

Purpose: Breast Implant Illness (BII) is patient-described constellation of symptoms that are believed to be related to their breast implant. The symptoms described include fibromyalgia, chronic fatigue and a host of other symptoms that are often associated with autoimmune illnesses. In this work, we report that bacterial biofilm associated with breast implant, metabolize fatty acid oleic acid present in the breast tissue milieu to oxylipins, one such oxylipin identified from this study is (10S)-hydroxy-(8E)-octadecenoic acid (10-HOME). We hypothesize that immunomodulatory effects of oxylipin 10-HOME produced by biofilm present on the implant could be correlated with BII pathogenesis. Methods: Capsulectomy and breast implants from clinically indicated procedures for patients requesting prosthetic removal were collected using clinical parameters outlined in previous studies, and questionnaire screened for the commonly reported symptoms associated with BII. Predictive variables included age, diabetes status, co-morbidities, nature and duration of implant. Scanning electron microscopy (SEM), Wheat Germ Agglutinin (WGA) and 16SrRNA sequencing were used for bacterial biofilm bacterial identification. 10-HOME was quantitated through targeted and untargeted lipidomic analyses using LC-MS-MS. Results: Sixty eight Implant, associated capsules and breast tissue specimen were collected for BII (n=46) and two control groups, group I, (non-BII, n=14) patients with breast implants, no BII symptoms. Group II (normal tissue, n = 8), patients without an implant, whose breast tissue was removed due to surgical procedures. Bacterial biofilm was detected through SEM in both BII and non BII cohorts. However, WGA analysis (quantitative analysis) indicated increased abundance of biofilm in the BII cohort (n=7, p=0.0036). 16SrRNA (genomic) sequencing identified increased abundance of Staphylococcus epidermidis (Fisher’s exact test, p<0.001) in the BII group (63.04%) compared to non-BII group (14.3%) and the normal group. The BII group was 9.8 times significantly more likely to have Staphylococcus epidermidis colonization compared to the non-BII group (p=0.003, logistic regression), compared to normal, it is 17.4 times significantly more likely to have Staphylococcus epidermidis (p=0.0021). Elevated levels of 10-HOME BII compared to non-BII samples, (p < 0.0001) were observed through mass spectrometry. Positive correlation was observed between bacterial abundance and concentration of 10-HOME in BII subjects (R2=0.88). Similar correlation was observed in BII subjects with Staphylococcus epidermidis (R2=0.77). Conclusion: This study investigated the biofilm hypothesis of breast implant illness through a host-pathogen interaction. The breast microenvironment led to formation of biofilm derived 10-HOME from host oleic acid. The study provides the first evidence of a possible correlation between bacterial biofilm and biofilm derived 10-HOME in the context of 10-HOME. In consideration of reports of biofilm association with other metal implants, the findings of this study can possibly explain autoimmune response associated with those implants

Biofilm Derived Oxylipin Mediated Autoimmune Response in Breast Implant Subjects

Over 10 million women worldwide have breast implants for breast cancer/prophylactic reconstruction or cosmetic augmentation. In recent years, a number of patients have described a constellation of symptoms that are believed to be related to their breast implants. This constellation of symptoms has been named Breast Implant Illness (BII). The symptoms described include chronic fatigue, joint pain, muscle pain and a host of other manifestations often associated with autoimmune illnesses. In this work, we report that bacterial biofilm is associated with BII. We postulate that the pathogenesis of BII is mediated via a host-pathogen interaction whereby the biofilm bacteria Staphylococcus epidermidis interacts with breast lipids to form the oxylipin 10-HOME. The oxylipin 10-HOME was found to activate CD4+ T cells to Th1 subtype. An increased abundance of CD4+Th1 was observed in the breast tissue of BII subjects. The identification of a mechanism of immune activation associated with BII via a biofilm enabled pathway provides insight into the pathogenesis for implant-associated autoimmune symptoms

Staphylococcus aureus Biofilm Infection Compromises Wound Healing by Causing Deficiencies in Granulation Tissue Collagen

Objective: The objective of this work was to causatively link biofilm properties of bacterial infection to specific pathogenic mechanisms in wound healing. Background: Staphylococcus aureus is one of the four most prevalent bacterial species identified in chronic wounds. Causatively linking wound pathology to biofilm properties of bacterial infection is challenging. Thus, isogenic mutant stains of S. aureus with varying degree of biofilm formation ability was studied in an established preclinical porcine model of wound biofilm infection. Methods: Isogenic mutant strains of S. aureus with varying degree (ΔrexB > USA300 > ΔsarA) of biofilm-forming ability were used to infect full-thickness porcine cutaneous wounds. Results: Compared with that of ΔsarA infection, wound biofilm burden was significantly higher in response to ΔrexB or USA300 infection. Biofilm infection caused degradation of cutaneous collagen, specifically collagen 1 (Col1), with ΔrexB being most pathogenic in that regard. Biofilm infection of the wound repressed wound-edge miR-143 causing upregulation of its downstream target gene matrix metalloproteinase-2. Pathogenic rise of collagenolytic matrix metalloproteinase-2 in biofilm-infected wound-edge tissue sharply decreased collagen 1/collagen 3 ratio compromising the biomechanical properties of the repaired skin. Tensile strength of the biofilm infected skin was compromised supporting the notion that healed wounds with a history of biofilm infection are likely to recur. Conclusion: This study provides maiden evidence that chronic S. aureus biofilm infection in wounds results in impaired granulation tissue collagen leading to compromised wound tissue biomechanics. Clinically, such compromise in tissue repair is likely to increase wound recidivism

Regulation of miR-146a by RelA/NFkB and p53 in STHdhQ111/HdhQ111 Cells, a Cell Model of Huntington's Disease

Huntington's disease (HD) is caused by the expansion of N-terminal polymorphic poly Q stretch of the protein huntingtin (HTT). Deregulated microRNAs and loss of function of transcription factors recruited to mutant HTT aggregates could cause characteristic transcriptional deregulation associated with HD. We observed earlier that expressions of miR-125b, miR-146a and miR-150 are decreased in STHdhQ111/HdhQ111 cells, a model for HD in comparison to those of wild type STHdhQ7/HdhQ7 cells. In the present manuscript, we show by luciferase reporter assays and real time PCR that decreased miR-146a expression in STHdhQ111/HdhQ111 cells is due to decreased expression and activity of p65 subunit of NFkB (RelA/NFkB). By reporter luciferase assay, RT-PCR and western blot analysis, we also show that both miR-150 and miR-125b target p53. This partially explains the up regulation of p53 observed in HD. Elevated p53 interacts with RelA/NFkB, reduces its expression and activity and decreases the expression of miR-146a, while knocking down p53 increases RelA/NFkB and miR-146a expressions. We also demonstrate that expression of p53 is increased and levels of RelA/NFkB, miR-146a, miR-150 and miR-125b are decreased in striatum of R6/2 mice, a mouse model of HD and in cell models of HD. In a cell model, this effect could be reversed by exogenous expression of chaperone like proteins HYPK and Hsp70. We conclude that (i) miR-125b and miR-150 target p53, which in turn regulates RelA/NFkB and miR-146a expressions; (ii) reduced miR-125b and miR-150 expressions, increased p53 level and decreased RelA/NFkB and miR-146a expressions originate from mutant HTT (iii) p53 directly or indirectly regulates the expression of miR-146a. Our observation of interplay between transcription factors and miRNAs using HD cell model provides an important platform upon which further work is to be done to establish if such regulation plays any role in HD pathogenesis

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Laser Capture Microdissection in the Spatial Analysis of Epigenetic Modifications in Skin: A Comprehensive Review

Each cell in the body contains an intricate regulation for the expression of its relevant DNA. While every cell in a multicellular organism contains identical DNA, each tissue-specific cell expresses a different set of active genes. This organizational property exists in a paradigm that is largely controlled by forces external to the DNA sequence via epigenetic regulation. DNA methylation and chromatin modifications represent some of the classical epigenetic modifications that control gene expression. Complex tissues like skin consist of heterogeneous cell types that are spatially distributed and mixed. Furthermore, each individual skin cell has a unique response to physiological and pathological cues. As such, it is difficult to classify skin tissue as homogenous across all cell types and across different environmental exposures. Therefore, it would be prudent to isolate targeted tissue elements prior to any molecular analysis to avoid a possibility of confounding the sample with unwanted cell types. Laser capture microdissection (LCM) is a powerful technique used to isolate a targeted cell group with extreme microscopic precision. LCM presents itself as a solution to tackling the problem of tissue heterogeneity in molecular analysis. This review will cover an overview of LCM technology, the principals surrounding its application, and benefits of its application to the newly defined field of epigenomics, in particular of cutaneous pathology. This presents a comprehensive review about LCM and its use in the spatial analysis of skin epigenetics. Within the realm of skin pathology, this ability to isolate tissues under specific environmental stresses, such as oxidative stress, allows a far more focused investigation