Existing clinical methods for prognosis evaluating for Epithelial Ovarian Cancer (EOC) patients had defects of invasive, unsystematic and subjective and little data are available for individualizing treatment, therefore, to identify potential prognostic markers and new therapeutic targets for EOC is urgently required.PEX3, DNAJB9, RB1, HSP90AB1 and CXCR4) were confirmed and the autophagy-related risk model was established by the 5 prognostic DEARGs. The accuracy and robustness of the prognostic risk model for survival prediction were evaluated and verified by analyzing the correlation between EOC patients’ survival status, clinicopathological features and risk scores.Expression of 232 autophagy-related genes (ARGs) in 354 EOC and 56 human ovarian surface epithelial specimens from 7 independent laboratories were analyzed, 31 mRNAs were identified as DEARGs. We did functional and pathway enrichment analysis and constructed protein–protein interaction network for all DEARGs. To screen out candidate DEARGs related to EOC patients’ survival and construct an autophagy-related prognostic risk signature, univariate and multivariate Cox proportional hazards models were established separately. Finally, 5 optimal independent prognostic DEARGs (The autophagy-related prognostic risk model can be independently used to predict overall survival in EOC patients, it can also potentially assist in individualizing treatment and biomarker development. Ovarian cancer (OC) has the highest morbidity and mortality in the female genital tract . It is tEstablishing prognosis models for EOC patients is an important part of risk evaluation and treatment, can also greatly assists in biomarker development . From clAutophagy is a homeostatic mechanism that can maintain cell survival by recycling organelles and macromolecules . Varioushttps://www.ncbi.nlm.nih.gov/geo/). The brief information of 7 GEO datasets [containing 410 human ovarian surface epithelial (HOSE) and epithelial ovarian cancer (EOC) specimens] from 7 independent laboratories was extracted and listed in Table https://portal.gdc.cancer.gov/). We downloaded 232 genes identified so far to be involved in autophagy from the Human Autophagy Database (HADb).The gene expression profiling data sets  were obtained from Gene Expression Omnibus database  > 1.Data preprocessing was performed before difference analysis for 7 independent GEO datasets. We did batch normalization for all expression profiling data through ComBat algorithm in R to eliminate the systematic variations among different studies. The differentially expressed autophagy-related genes (DEARGs) between 354 EOC tissues and 56 HOSE tissues were screened out by the Wilcoxon signed-rank test. The cutoff criteria were adjusted https://string-db.org/). The PPI network was constructed for all DEARGs and visualized with the cut-off criterion of interaction score > 0.4. To visualized the PPI network and highlighted the hub genes, we make use of the Cytoscape software to perform deeply biological network analysis.PPI analysis is a protein correlation analysis that can effectively reveal the molecular mechanisms of crucial cellular activities in carcinogenesis. It is constructed based on the STRING database  analysis can annotate characteristics of a set of genes, such as involved cellular components (CC), molecular functions (MF) and biological processes (BP). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to reveal the involved pathways of all DEARGs. These analyses were done by clusterProfiler package of R with P < 0.05 were regarded as candidate genes related to EOC patients’ survival.To identify DEARGs whose expression profiles had a significant correlation with the overall survival (OS) of patients with EOC, we performed the univariate Cox regression model. The DEARGs with the threshold of The survival-related DEARGs screened out through the univariate Cox proportional hazards model were incorporated into a multivariate Cox regression analysis to weeded out the DEARGs which might not be an independent index in prognosis monitoring. Then, we can obtain a set of optimal prognostic DEARGs, and establish a risk score model using these genes.We can get the risk score for each EOC patient through the followed formula,The risk score was calculated using the gene expression value multiplied by the regression coefficients of each individual gene. The genes in the formula mean the independent prognostic DEARGs we screened before. The regression coefficients were obtained from the multivariate Cox regression model and stand for the relative weight of selected genes. It is a measure of prognostic risk for EOC patients. With the median risk score as the cutoff value, patients were divided into low-risk group and high-risk group. A high-risk score means a poor prognosis.PEX3, DNAJB9, RB1, HSP90AB1 and CXCR4) in an Ovarian Cystadenocarcinoma case set using an open-access tool cBioPortal .We draw the survival curves by Kaplan–Meier plotter and assessed the difference in the survival status between the high-risk group and low-risk groups to verify the validity and robustness of the OS risk prognostic model. Then, the relationship between risk score and survival status of EOC patients was visualized. We studied genetic alterations of 5 independent prognostic DEARGs  were obtained. We list the details such as log2FoldChange and statistical significance of all DEARGs in Table 1b, the fold change patterns of 31 DEARGs were showed in a heat map. The expression of 31 DEARGs between EOC tissues and HOSE tissues was visualized by scatter plots .The expression values of 13,045 genes in 410 samples (354 EOC and 56 HOSE specimens) were normalized with R package and showed in Fig. 1a). A PPI network of DEARGs was constructed with STRING database and visualized the interaction of the PPI network using Cytoscape software, there are 12 hub genes with interaction degree > 5 showed as a diamond and arranged in a circle . GO analysis shows that DEARGs are enriched in several hypoxia-related biological processes (BP), including autophagy, macroautophagy, response to hypoxia and response to oxygen levels. Regarding the molecular function (MF), the DEARGs played vital parts in some functions, such as ubiquitin-like protein ligase binding, chaperone binding and heat shock protein binding. In terms of the cellular components (CC), the proteins encoded by the DEARGs are components of melanosome, pigment granule and secretory granule lumen . KEGG analysis showed the main pathways that DEARGs involved containing autophagy, and protein processing in the endoplasmic reticulum .We displayed the distribution of all DEARGs with a volcano plot Fig. a. A PPI p < 0.05) . To improve the validity and robustness, 9 prognostic-related DEARGs obtained from the above univariate analysis were further subjected to the multivariate Cox regression analysis. Finally, 5 DEARGs  were screened out as optimal independent prognosis biomarkers and applied to construct an autophagy-related risk model . The risk score of OS for EOC patients was calculated according to the following formula: risk score =  +  +  +  + . The risk scores of 374 EOC patients were calculated through the above formula, and patients were divided into high-risk (n = 187) and low-risk group (n = 187) with the median of the risk score as the cutoff value.Altogether RNA-seq and clinical data of 379 EOC tissue specimens were obtained from the TCGA database and 374 EOC tissue specimens contained complete clinical follow-up information were subjected to univariate Cox regression analyses to evaluate the association between 31 screened DEARGs’ expression profiles and overall survival in 374 EOC patients. The results of univariate Cox regression analyses revealed that 9 DEARGs were significantly associated with the prognosis of EOC patients . Kaplan-Meier plot was drawn to compare the OS difference between high-risk and low-risk group. EOC patients in the low-risk group had obviously better survival outcomes than the high-risk group (p = 9.606E-07) illustrated that the risk score based on the autophagy-related risk model correlated with EOC patients’ prognosis . Figure.4c visualized the risk scores from low to high. The mortality of EOC patients increased as the risk score increased . The heatmap in Fig. 4e displayed the expression patterns of 5 prognostic DEARGs in different risk groups.The OncoPrint in cBioPortal is a compact and efficient graphical summary of genomic alterations in tumor specimens. According to previous results of DEARGs screening and autophagy-related signature establishment, the risk score calculated based on the expression of p = 0.211)  or pathological stage IIIC-IV and pathological stage I-IIIB (p = 0.195) , although elder patients seemed had a higher risk score than younger patients and high pathological stage patients seemed had a higher risk score than low pathological stage patients. Risk score was higher in histological grade G3–4 than in G1–2 (p = 0.011) . Obviously, the majority of EOC patients are diagnosed at a late stage, so most patients are diagnosed with pathological stage III or histological grade 3–4. The association between the expression level of 5 prognostic DEARGs which used to construct the risk model and clinical pathological parameters of EOC are shown in Fig. 5a, b, c.The univariate and multivariate Cox proportional hazard regression analyses were performed to determine the correlation between the risk score and clinicopathological features, and to further evaluated whether the autophagy-related risk prognostic model constructed with DEARGs could independent of other clinicopathological features as a predictor of OS for EOC patients. There is no difference of risk scores between age > 65 and age ≤ 65 , risk scores based on the autophagy-related genes signature could also be applied in clinical to provide a handy and better prognosis monitoring. The DEARGs can also help facilitate personalized target treatment and early screening.Our study developed a risk prognostic model to predict individuals’ clinical outcomes, molecular signature combined with clinical features make the model steady and credible. Our results proved that the risk model constructed by 5 DEARGs p-value< 0.05 and |log2FC| > 1.0. From 31 DEARGs, 5 prognostic DEARGs  were identified to construct a risk score prediction model, the risk score was calculated with the expression values of these genes and regression coefficients. Combined validation analysis of molecular signature and clinical characteristics, the risk score prediction model can robustly estimate the OS of EOC patients. The prognosis DEARGs also provide new possibilities for EOC therapeutic intervention. Meanwhile, our study can reveal the molecular mechanisms behind EOC from a brand-new insight.Our study analyzed transcriptome expression profiles of 354 EOC tissue samples and 56 HOSE samples from 7 independent laboratories and evaluated the expression pattern of 232 ARGs in the two groups. There were 17 up-regulated DEARGs and 14 down-regulated DEARGs in EOC with the filter criteria of adjusting


For the last two decades there has been wide ranging debate about the status of macroautophagy during mitosis. Because metazoan cells undergo an “open” mitosis in which the nuclear envelope breaks down, it has been proposed that macroautophagy must be inhibited to maintain genome integrity. While many studies have agreed that the number of autophagosomes is greatly reduced in cells undergoing mitosis, there has been no consensus on whether this reflects decreased autophagosome synthesis or increased autophagosome degradation. Reviewing the literature we were concerned that many studies relied too heavily on autophagy assays that were simply not appropriate for a relatively brief event such as mitosis. Using highly dynamic omegasome markers we have recently shown unequivocally that autophagosome synthesis is repressed at the onset of mitosis and is restored once cell division is complete. This is accomplished by CDK1, the master regulator of mitosis, taking over the function of MTORC1, to ensure autophagy is repressed during mitosis. For better or worse, LC3B lipidation combined with lysosomal inhibitors is widely used as a “gold-standard” assay for autophagy. However, several groups have identified instances when it is inappropriate to use such assays; for example, lysosomal inhibitors such as ammonia and chloroquine actually promote LC3 lipidation by activating non-canonical autophagy. Furthermore, LC3 puncta (another common marker) can persist for approximately 30 min, making it difficult to deduce whether LC3 puncta observed during the early phases of mitosis originate from autophagosome synthesis during the preceding interphase or during mitosis itself. As an alternative we elected to study autophagy by visualizing ATG13 and WIPI2 puncta. These are early, highly dynamic structures; for example, ATG13 localizes to the developing omegasome but leaves it prior to autophagosomal budding, thus persisting for only ~3 min and enabling a far more accurate dissection of rates of autophagosome biogenesis between interphase and mitotic cells. In this way, using both fixed and live-cell imaging techniques, we were able to demonstrate that autophagy initiation is clearly repressed in mitosis, including in cells undergoing a normal, unperturbed mitosis [Curiously, we observed that autophagy is repressed during mitosis even when cells are deprived of amino acids or treated with the ATP-competitive MTOR inhibitor AZD8055. Indeed, phosphorylation of RPS6KB/S6K at the known MTORC1 target site, threonine 389, is reduced in mitotic cells, in line with observations made by independent groups. To understand why MTORC1 is seemingly unimportant in the regulation of autophagy during mitosis we again employed imaging. MTORC1 is recruited to lysosomes where it is activated, enabling it to phosphorylate its substrates such as ULK1, RPS6KB and EIF4EBP1/4E-BP1. By imaging endogenous GFP-RPTOR we demonstrated that MTORC1 fails to localize to lysosomes during mitosis, providing a likely mechanism for MTORC1 inactivation during mitosis. CDK1 is the master regulator of mitosis and we showed that RPTOR undergoes CDK1-dependent mitotic phosphorylation, in line with previous findings; more importantly, phosphomimetic mutation of known CDK1-dependent phosphorylation sites on RPTOR impairs the RPTOR-RRAGA interaction, suggesting that mitotic phosphorylation of RPTOR mediates MTORC1 inactivation. The continued localization of TFEB to lysosomes during mitosis suggests that RRAG function remains intact.in vitro at these sites.Because MTORC1 is inactive during mitosis, it might be expected that its known substrates involved in autophagy regulation  would not be phosphorylated. However, this would not be consistent with the repression of autophagy. Indeed, we observed that all of these proteins are hyperphosphorylated during mitosis, including at known repressive sites within these proteins that are usually phosphorylated by MTORC1. These sites are phosphorylated in mitosis even in the presence of MTOR inhibitors  arguing that MTORC1 cannot be the relevant regulatory kinase during mitosis. Instead, we suggest that CCNB1-CDK1 represses autophagy during mitosis because: (i) these sites are proline-directed serine/threonine sites, satisfying the minimum consensus motif of CDK1; (ii) their phosphorylation in cells is reversed by CDK1 inhibitors and (iii) CCNB1-CDK1 can phosphorylate fragments of these autophagy regulators Finally, we wanted to define the functional consequence of these phosphorylation events. However, it was not possible to investigate this by mutation of the relevant phosphorylation sites because this would undermine normal regulation of autophagy by MTORC1 in interphase. Furthermore, MTORC1- and CDK1-dependent phosphorylation events extend across multiple autophagy proteins suggesting a system-wide shutdown of autophagy, which is unlikely to be rescued by mutation of a single protein. Instead we assessed known functional outcomes of the repressive phosphorylation of these proteins. TFEB is exported from the nucleus just prior to nuclear envelope breakdown, coinciding with CDK1 activation, and ULK1 activity is dramatically reduced in mitotically-arrested cells, as measured by phospho-ATG14 (S29). Recent advances in development of antibodies for another ULK1 target substrate, phospho-ATG16L1 (S278), may allow future independent confirmation of our conclusion that ULK1 activity is inhibited in mitotic cells. In addition, we speculate that other autophagy regulators may come under the control of CCNB1-CDK1 during mitosis.Conceptually it makes sense for CDK1 to coordinate the inhibition of MTORC1 (through RPTOR phosphorylation) and to also take over its functions by phosphorylating MTORC1 sites in autophagy regulators. This mechanism insulates autophagy from nutrient status, thereby ensuring that the genome is protected from nonselective macroautophagy throughout mitosis. Critically, we must emphasize again our rejection of LC3 puncta or LC3 processing as markers of autophagy during this dynamic cell cycle event; our unambiguous demonstration of autophagy repression during mitosis was dependent on the use of dynamic markers that were fit for the experimental purpose. Indeed, we think mitosis acts as an excellent case study for why authors should consider using omegasome markers as a direct and unambiguous readout of autophagosome biogenesis.


Vertebrate genomes are marked by notably high levels of 5-cytosine DNA methylation (5meC). The clearest function of DNA methylation among members of the subphylum is repression of potentially deleterious transposable elements (TEs). However, enrichment in the bodies of protein coding genes and pericentromeric heterochromatin indicate an important role for 5meC in those genomic compartments as well. Moreover, DNA methylation plays an important role in silencing of germline-specific genes. Impaired function of major components of DNA methylation machinery results in lethality in fish, amphibians and mammals. Despite such apparent importance, mammals exhibit a dramatic loss and regain of DNA methylation in early embryogenesis prior to implantation, and then again in the cells specified for the germline. In this minireview we will highlight recent studies that shine light on two major aspects of embryonic DNA methylation reprogramming: (1) The mechanism of DNA methylation loss after fertilization and (2) the protection of discrete loci from ectopic DNA methylation deposition during reestablishment. Finally, we will conclude with some extrapolations for the evolutionary underpinnings of such extraordinary events that seemingly put the genome under unnecessary risk during a particularly vulnerable window of development. Cryptococcus neoformans that DNA methylation patterns have endured for millions of years exclusively through a maintenance mechanism, as there is an absence of de novo DNA methylation enzymes in the genome  is a modification conserved across all kingdoms of eukaryotes. Most generally it is found in the CpG dinucleotide context, and there are factors that ensure faithful daughter strand methylation after each round of DNA synthesis  family of dioxygenases, although the mechanism is far from intuitive: iterative oxidation of 5meC to hydroxymethylcytosine (5hmC), formylcytosine (5fC), and finally carboxylcytosine (5caC) will trigger the thymine DNA glycosylase-dependent base excision repair (TDG BER) pathway to replace modified cytosines with unmodified versions . While there are many intricate layers to UHRF1/DNMT1 regulation, at its most fundamental level the mechanism is elegantly simple: UHRF1 recognizes hemimethylated CpG sites after DNA replication, and recruits DNMT1 to methylated the cytosine on the daughter strand , mESCs exhibit globally low levels of DNA methylation—more or less on par with the ICM cells from which ESCs are derived -marked regions, respectively, via binding by their PWWP domains  promoters, which are distinguished by their markedly high CpG content. Roughly two-thirds of promoters are CGIs, and comprise most housekeeping and developmental genes -deposited H3K27me3 (Azuara et al., Dppa2/4 mutant mice exhibit developmental defects far after the embryonic stages where developmental genes are enriched for bivalent marks (Madan et al., Dppa2/4 mutant mESCs, a subset of DPPA2/4 targets lose both H3K4me3 and H3K27me3 enrichment, indicating a role for these transcription factors in recruiting both silencing and activating complexes (Eckersley-Maslin et al., de novo DNA methyltransferases (In addition to TET and polycomb action, there must be a sequence-based recruitment of H3K4 methyltransferase complexes, i.e., through transcription factors. CGIs serve as important platforms of transcription factor binding, which is associated with alterations in DNA methylation level (Lienert et al., sferases .in vitro model of global DNA demethylation (Gretarsson and Hackett, Dppa2/4 were two of the top hits from the screen, but mutations did not lead to global DNA methylation perturbations. Consistent with the study from Eckersley-Maslin, DNA methylation abnormalities were restricted to a proportion of bivalently marked gene promoters. Curiously, DPPA2/4 also target LINE1 retrotransposons, although their role in transposon regulation is less clear. It should be noted that DNA methylation defects do not occur at all DPPA2/4 targets in the double mutant background; moreover, there are bivalent promoters that are not DPPA2/4 targets. That is all to say, DPPA2/4 are only two of the transcription factors important for DNA methylation protection during epigenetic reprogramming and there is a high probability others exist. Nevertheless, these are two important studies that undergird a model wherein sequence-specific transcription factors not only prime genes for proper expression during development, but also play a protective role against ectopic silencing.Gretarsson and Hackett ultimately arrived at similar conclusions, although they discovered a role for DPPA2/4 through different means. Using a clever mESC-based CRISPR screen, they searched for mutants that failed to activate a methylated reporter during an de novo DNA methylation of promoters can lead to stable silencing of key developmental regulators. Perhaps more worrisome, the absence of DNA methylation can result not only in the ectopic expression of protein coding genes, but massive transposon derepression, which can have far ranging and deleterious effects (Walsh et al., de novo or maintenance DNA methylation machinery die shortly after gastrulation (Li et al., DNA methylation, if misregulated, can have dire consequences. As discussed above, the improper Dppa3 gene, Mulholland et al. revealed it is not found outside mammals. Amazingly, when the mouse DPPA3 protein was incubated with the egg extracts of the amphibian Xenopus laevis, the mouse protein inhibited the frog UHRF1. Furthermore, when the fertilized eggs from the model fish medaka were injected with Dppa3 mRNA, the embryos exhibited dramatic hypomethylation (Mulholland et al., Dppa3 arose in the mammalian lineage in concert with the role of transposon expression regulating the early transcription program. This certainly is possible, however it does not explain why global demethylation occurs exclusively in mammals, and also why it happens twice in development.In investigating the evolutionary conservation of the Dppa3 is only present in placental mammals; marsupials and egg-laying monotremes lack the gene. Classic experiments in the 1980s demonstrated that mouse paternal and maternal genomes are not equivalent—androgenetic diploids exhibit robust placenta, whereas conversely gynogenetic diploids harbor severely undersized placenta (Surani and Barton, Dppa3 evolved in order to prevent either the paternal or maternal genome exerting too much control with regards to inherent conflict. Imprint control regions, in turn, evolved mechanisms to escape DNA methylation erasure during embryogenesis. In the case of marsupials, there is evidence of germline reprogramming and genomic imprinting (Renfree et al., It is notable that MVCG wrote the manuscript and prepared the figures.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The role of the ATP binding cassette transporter A1 (ABCA1) in maintaining cellular lipid homeostasis in cardiovascular disease is well established. More recently, the important beneficial role played by ABCA1 in modulating pathogenic disease mechanisms, such as inflammation, in a broad range of chronic conditions has been realised. These studies position ABCA1 as a potential therapeutic target in a diverse range of diseases where inflammation is an underlying cause. Chronic respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD) are driven by inflammation, and as such, there is now a growing recognition that we need a greater understanding of the signaling pathways responsible for regulation of ABCA1 expression in this clinical context. While the signaling pathways responsible for cholesterol-mediated ABCA1 expression have been clearly delineated through decades of studies in the atherosclerosis field, and thus far appear to be translatable to the respiratory field, less is known about the cholesterol-independent signaling pathways that can modulate ABCA1 expression in inflammatory lung disease. This review will identify the various signaling pathways and ligands that are associated with the regulation of ABCA1 expression and may be exploited in future as therapeutic targets in the setting of chronic inflammatory lung diseases. ATP binding cassette transporter A1 (ABCA1) is a transmembrane transporter protein ubiquitously expressed in human tissue, and found in abundance in the liver and lungs (reviewed in ). Thus, ABCA1 transporter plays an important role in maintaining cellular cholesterol homeostasis by participating in the reverse cholesterol transport pathway (RCT) . ABCA1 eMutations in ABCA1 can cause Tangier disease, a rare genetic disorder characterized by a substantial reduction in HDL levels . StudiesEvidence from both in vitro and in vivo studies have shown ABCA1 expression is altered in lung diseases such as COPD , 13. In Cellular cholesterol is linked with numerous inflammatory functions of the lungs . Hence, Considering the relationship between cholesterol homeostasis and airway inflammation, the therapeutic focus has been on reducing intracellular cholesterol synthesis using cholesterol-lowering drugs, like statins . There hMost studies exploring the role and function of ABCA1 have been conducted in cholesterol associated disease states such as atherosclerosis . AtherosNevertheless, growing evidence has clearly implicated ABCA1 as a target of interest in diseases typified by inflammation. The potential anti-inflammatory effects of ABCA1 have thus led to increasing interest towards investigating the benefit of ABCA1 in inflammatory lung diseases , 36. WhiCholesterol homeostasis is maintained through feed-forward and feed-back mechanisms involving ABCA1 and other partners. Notably, ABCA1 expression is regulated through key signaling pathways mediated by excess cholesterol ,25-epoxycholesterol  is such an example, where it is produced from acetyl coenzyme during a gap in the mevalonate pathway [Cholesterol in cells can be converted into oxysterols, which in turn can potently upregulate ABCA1 expression . In cell pathway . Similar pathway .Table 1Given the important role played by LXR in this pathway, it follows that a potential therapeutic target to modulate cholesterol homeostasis is by increasing ABCA1 expression through the LXR dependent pathway . To thisAnother pathway involved in the regulation of cellular cholesterol homeostasis is mediated by the sterol regulatory element binding protein 2 (SREBP-2) transcription factor . SREBP-2Recent investigations have revealed that a number of inflammatory mediators and noxious exposures (such as cigarette smoke) that drive lung disease also have an impact on ABCA1 expression. Clinically-used and experimental drugs also target ABCA1, with some key in vitro studies performed in cells with relevance to respiratory disease listed in Table Tumour Necrosis Factor (TNF) is a proinflammatory cytokine that is highly expressed in patients with COPD . TNF is Toll like receptor 4 (TLR4) is a primary source of innate immune system defence . This prThe cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) pathway is responsible for regulating multiple cellular functions, including anti-inflammatory responses . Activat2-adrenergic receptor agonists and phosphodiesterase inhibitors) are widely used treatments in respiratory medicine today.However, while transcriptional regulation differs, cells from both human and mice are responsive to cAMP, albeit via dissimilar mechanisms. Post-translationally, the upregulation of ABCA1 phosphorylation and cholesterol efflux in this signaling cascade is initiated when apoA-1 interacts with ABCA1 during the process of reverse cholesterol transport . The intSignal transducer and activator of transcription 3 (STAT3) plays a complex role in regulating inflammation that occurs with airway injury . CigaretDuring acute and chronic airway inflammation there is a significant increase in immune and structural cells that undergo apoptosis . The cleRecent studies have reported a pathway regulated by the phagocytic receptor brain specific angiogenesis inhibitor 1 (BAI1), operates in a similar manner to the way in which MerTK recognises the PtdSer on apoptotic cells . In brieApoA-1 is an acceptor for cellular cholesterol from peripheral cells that directly interacts with ABCA1 to forms nascent lipid-poor HDL . Murphy Intriguingly, apoA-1 represses LPS-induced proinflammatory cytokine production via the TLR4/myeloid88 pathway in macrophages . This wa2-adrenergic receptor agonist) and phosphodiesterase inhibitors. Although some studies have examined the impact of current respiratory medications on ABCA1 expression, there is much scope for further investigation. The effect of these medicines on the signaling pathways regulating ABCA1 expression will be outlined below.Current pharmacological treatments for people with respiratory disease include the use of corticosteroids, bronchodilators  inhibitors are a treatment option in COPD. Similar to the effect of β pathway . The PDE pathway ., however, Niesor et al. showed that simvastatin and atorvastatin reduced ABCA1 expression and increased miR33, supporting the possibility that a repressive effect on ABCA1-mediated anti-inflammatory functions could have played a role in the equivocal outcomes shown in STATCOPE [Statins were postulated as possible treatment for inflammatory lung diseases including COPD. However, clinical trials like STATCOPE were shown to have an overall insignificant impact on patient outcomes , 113. PrSTATCOPE . It is pGiven that the LXR transcription factor is a potent inducer of ABCA1 expression coupled with evidence of anti-inflammatory impact , it follIn recent years, our understanding of the role and function of ABCA1 has evolved from the traditional function in maintaining cellular cholesterol homeostasis to include its potent anti-inflammatory function. This has led to the possibility of exploiting known and novel signaling pathways to upregulate ABCA1 expression in a number of chronic disease settings. Notably, signaling pathways implicated in inflammatory responses are responsible for regulating ABCA1  and its ability to repress inflammation exists. Currently unknown is the extent of the therapeutic benefit that may be achieved by targeting ABCA1 in inflammatory lung diseases like COPD. Despite the primary focus on the potential of ABCA1 as a therapeutic target in atherosclerosis, its dual role in repressing inflammation while maintaining cholesterol homeostasis represents a promising therapeutic target for inflammatory lung diseases in the future.


COVID19 has caused a global and ongoing pandemic. The need for population seroconversion data is apparent to monitor and respond to the pandemic. Using a lateral flow assay (LFA) testing platform, the seropositivity in 63 New York Blood Center (NYBC) Convelescent Plasma (CP) donor samples were evaluated for the presence of COVID19 specific IgG and IgM.CP donors showed diverse antibody result. Convalescent donor plasma contains SARS-CoV-2 specific antibodies. Weak antibody bands may identify low titer CP donors. LFA tests can identify antibody positive individuals that have recovered from COVID19. Confirming suspected cases using antibody detection could help inform the patient and the community as to the relative risk to future exposure and a better understanding of disease exposure. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over 4012,000 infections and  > 32,000 deaths in New York State alone [However, a crucial step in understanding the test characteristics is to ensure the assay detects antibodies in individuals with a previous documented disease. One study suggests that 75% of patients with a confirmed PCR test had a positive antibody IgG and 20% were weakly positive . Another® SARS-CoV-2 IgG/IgM Rapid Test Cassettes in determining the presence of binding antibodies in convalescent plasma (CP) donor samples with previously documented COVID19.In response to this need for antibody testing, a lateral flow assay (LFA) was developed to provide rapid point of care diagnostic testing of COVID19 antibodies. The LFA test is able to detect specific SARS-CoV-2 antibodies and differentiate between IgG and IgM immunoglobin classes in a rapid, point of care test using either whole blood, plasma or serum . The tes® SARS-CoV-2 (COVID-19) IgG/IgM Rapid Test Cassettes were used to determine the presence of SARS-CoV-2-specific IgG and IgM. The manufacturer of the Cassette  validated this immunoassay for the qualitative detection of IgG and IgM antibodies to SARS-CoV-2 and these data were submitted to FDA as part of their Emergency Use Authorization [Convalescent donor plasma was collected by the New York Blood Center (NYBC) with written consent from patients in accordance with NYBC Institutional Review Board protocols. All donors had self-reported documented COVID19 disease by positive SARS-CoV-2 RT-PCR test (manufacturer and documentation not provided from referring institution of CP donors), had complete resolution of symptoms at least 14 days prior to donation, and otherwise met all criteria for donating blood consistent with FDA’s policy on the Collection of COVID-19 Convalescent Plasma [rization .To perform assays, 20 mL of human plasma was applied to the sample pad followed by two drops of proprietary running buffer. Tests were analyzed after 15 min. Following incubation, high resolution images were taken of detection zone and saved as JPEG for reference and analysis. Positive and negative IgG/IgM band determinations were made by visual inspection with accordance to manufacturer instructions  were IgGNeg and IgMPos and 38.1% (24/63) were IgGPos and IgMNeg  were IgMNeg. These data suggest that LFA tests possess a high degree of sensitivity  and specificity  for detecting SARS-CoV-2 specific antibodies. Given all CP donors were collected more than 14 days since date of last symptom, when the IgM tests would have performed, it is not surprising that the IgM results were low since IgM immunoglobins likely develop early in response to infection [With regard to negative samples, 11.1% (7/63) of CP donors were IgGnd 38.1% 4/63 wereWeak antibody bands may identify low titer CP donors. Recent studies suggest that a significant percentage of convalescent individuals may have low SARS-CoV-2 IgG or IgM titers , 9. We aTo confirm reproducibility, we re-tested random samples n = 16) to explore whether CP donor samples could provide reproducible results  IgG/IgM LFA test possesses high sensitivity and specificity for COVID19 antibodies. The LFA test was easy to use with properly trained staff. Results were interpretable within 15 min and the internal procedural control confirmed that sufficient specimen volume, adequate membrane wicking and correct procedural technique were used. Since documented positive PCR tests or comparison to other antibody testing platforms were unavailable, we cannot state that the 7 negative donors in fact were infected or if they have antibody. Even if CP donor infection data were available, it may also be possible, and is probable, that some CP donors produced low amounts of antibodies that is specific to the immunological response unique to each individual, thus, below the detection limit of the LFA. The IgG results are consistent with the manufacturer’s 97.4% clinical performance data which showed positive IgG agreement with known positive RT-PCR test. The IgM results are consistent with recently published data which shows that IgM can persist more than 23 days after symptom onset and can be earlier, synchronous or later than IgG.Our study analyzed blood samples from COVID19 convalescent plasma donors to determine whether antibodies are detected using a LFA in this population. We found that the CLUNGENEMost (90%) COVID19 convalescent donors seroconverted, demonstrating the potential of LFA tests to identify antibody positive individuals that have recovered from COVID19. Confirming suspected SARS-CoV-2 cases using antibody detection at the point of care could help inform the patient and the community as to the relative risk to future SARS-CoV2- exposure and a better understanding of disease exposure. However, a coherent description of the immunological response and antiviral antibody activity  is warranted to definitively use antibody presence to prognose future disease potential . This stSamples were not tested for virus neutralization; therefore neutralizing activities of the detected IgG antibodies are not known.The small sample size of patients and the absence of documented PCR test results makes it difficult to determine the relationship between antibody response and clinical course.More detailed investigation of the reproducibility of the test is warrented.Our study has several limitations, including but not limited to:


Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds , organized point clouds , or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy. Flat surfaces are pervasive in engineered structures and also occur in natural terrain. For example, structures such as walls, floors, rooftops, and roadways are often flat or “flat-like”. Similarly, home and office furnishings are typically composed of multiple flat surfaces. Sensors such as LiDAR and RGBD cameras generate dense 3D point clouds of these predominately flat surface environments. This observation has been exploited for tasks in localization and mapping , digitalα-shapes but operate strictly on 2D data, requiring the projection of each 3D planar point cloud and expensive triangulation . We use a standard 2D peak detector algorithm to find local peaks in the image. A peak is in the center of a 3 × 3 pixel group if it is the maximum in the group and its value is higher than a user-configurable l unique dominant plane normals are returned as a list Polylidar3D uses the Fast Gaussian Accumulator (FastGA) to estimate dominant plane normals. Triangle normals from the half-edge triangular mesh are input to FastGA. Not all triangle normals are needed to achieve acceptable results, so a user-configurable percent sampling parameter  be used .The following sections build upon our previous work in polygon extraction from 2D triangular meshes. l dominant plane normals The main input for planar segmentation is the half-edge triangular mesh, composed of k triangles in Algorithm 4: Group Assignment.We first create triangle group array y OpenMP . Figure task-based parallelism, where l dominant plane normals create l separate tasks of regions growing. These tasks are executed in parallel by a threadpool and can themselves spawn additional dynamic tasks [g. The routine begins by creating empty sets to store planar triangular segments and their corresponding polygonal representations, denoted t verified to belong to group g (Line 5). Subroutine ExtractPlanarSegment uses the seed triangle to create edge-connected triangular subsets from Algorithm 5: Region Growing Task.Region growing is decomposed using ic tasks . Each inic tasks  and was ic tasks . AlgoritData Structure InitializationExtract Exterior Hull/ShellExtract Interior HolesPolygon extraction is performed on each planar mesh segment ous work  is used ous work . For exaThe data structure initialization identifies all boundary half-edges inside The only modification to our previous work occurs in projecting the boundary edges. This is needed in finding x-y plane where triangulation has already taken place; this guarantees edges do not cross. However planar segments from user-provided meshes and organized point clouds may be projected to arbitrary geometric planes. Additionally the tolerance in “flatness” of the planar triangular segment is user-configurable. This issue, if it occurs, is managed in polygon post-processing as described in Given noisy and dense planar mesh segments, border edges may cross during projection to the geometric plane. When this occurs an invalid polygon will be generated, most often a small self-intersection. This issue does not occur with unorganized point clouds because they are projected to the ous work .OpenMP for data parallelism which is carried out in “hot” loops that are iteration independent, e.g., triangle group assignment in Algorithm 4 as well as computing triangle normals. We use the MARL library to handle task scheduling and synchronization primitives [Planar segmentation and polygon extraction use both data and task-based parallelism. We use imitives . Note thPolygon is simplified with parameter Polygon is buffered outward by parameter Polygon is buffered inward by parameter Polygon is removed if its area is less than Interior holes are removed if area is less than The polygons returned by Polylidar3D can be further processed to improve visualization and filter superfluous polygons and/or holes. All operations are implemented on the 2D projection of the polygon on its geometric plane. The following sequential operations are executed:The simplification algorithm is used to remove redundant vertices and “smooth” the polygon . The α pstance β . A positstance β . An examWe present several examples of our methods applied to real-world and synthetic 3D data. All experiments/benchmarks use the same consumer desktop computer. The CPU is an AMD Ryzen 3900X 12 Core CPU with a frequency at 4.2 GHz equipped with 32 GB of RAM. Note that all results obtained with CPU parallelization are annotated with number of threads used; the default is four. An NVIDIA GeForce RTX 2070 Super is used for GPU acceleration.k unit normals into the accumulator with k varied over the tests. Per K-D trees [K-D tree Gaussian Accumulator implementation that uses nanoflann, a high performance C++ K-D tree library [This section evaluates the Fast Gaussian Accumulator (FastGA) proposed for dominant plane normal estimation. We specifically analyze CPU execution time needed to integrate a set of -D trees . To allo library . A leaf  library . All ben library .K-D tree index is not part of execution timing. Results of integrating all k normals for each test set are shown in K-D tree, though the K-D tree implementation is also fast and could be used as an alternative method if desired. We can conclude that exploiting the known fixed structure of triangular cells on S2 (using space filling curves and sorted integer search) outperforms a general purpose K-D tree method.A GA with refinement level four (5120 triangle cells) was used for all tests. The first test generated 100,000 randomly distributed surface normals on the unit sphere and integrated them into the GA. The second test integrated all 60,620 triangle normals from the basement mesh previously shown in scikit-image image processing library [scipy library [Peak detection is currently implemented in Python using the  library . Agglome library . The unwz component represents elevation. Therefore points of a flat rooftop surface are already aligned with the This section presents qualitative results of Polylidar3D extracting flat rooftops in cities from airborne LiDAR point clouds of buildings. These unorganized point clouds are captured from an overhead viewpoint but are typically angled based on sensor location; wall surfaces can therefore be visible. Point cloud g Open3D . Flat suThe KITTI Vision Benchmark Suite provides raw datasets of Velodyne LiDAR point clouds, color video, and calibration data captured from a car while driving in Karlsruhe, Germany . CalibraRoads are not truly flat; they often have elevation changes such as a raised center line for drainage. For this reason we do not use the point-to-plane distance parameter in Polylidar3D to allow flexibility in capturing semi-flat ground surfaces in street environments. Polylidar3D processed 6608 frames from 24 recorded “drives” from the raw KITTI dataset. Mean execution timings for each processing step are presented in We used an Intel RealSense D435i to capture depth and RGB frames in two home environment scenes. The D435i uses stereo infrared cameras to generate a depth map. Depth noise grows quadratically with distance, and empirical evidence indicates as much as four centimeter RMS error at a two meter distance . The IntThe first scene is composed of 2246 frames (74 s) with the camera traversing from one side of a basement to the other. While walking the camera is pointed in many directions including the floor, ceiling, and walls. Multiple dominant planar surfaces are captured at the same time. Small surfaces are explicitly removed by filtering planes and polygons that do not meet minimum number of triangles and area constraints. The second scene is composed of 2735 frames (91 s) with the camera moving on the main floor through dining area, kitchen, and living room. The camera is pointed in many directions and with many dominant planar surfaces extracted as polygons. We also evaluated Polylidar3D on SynPEB, a challenging benchmark dataset used to evaluate plane segmentation algorithms, created by the authors of PPE . This syWe used the training set to tune our methods parameters including mesh smoothing , dominant plane normal estimation (FastGA), and plane/polygon extraction. We found that the most important parameter was the number of iterations of Laplacian smoothing needed. We trained a linear regression model to predict the most suitable number of iterations given an estimate of point cloud noise. All parameters used for test set reproduction are shown in f representing the percent of ground truth planes identified, k indicating percent of the point cloud correctly identified, and RMSE quantifying accuracy of each plane fit. Variables f metric of 47.3% indicates that Polylidar3D did not capture most of the planes in the benchmark, however the k metric of 78.3% indicates our algorithm did well in capturing the large dominant planes comprising most of the point cloud. Additionally there are fewer spurious, over segmented, and under segmented planes generated by Polylidar3D than with other methods. The RMSE value is also the lowest, indicating the predicated planes have a good fit. Plane segmentation is accomplished in significantly less time, especially in comparison to the front runner PPE. PPE’s f and k metrics indicate it does an excellent job of capturing the numerous small planes in the scene, but it fails more often in capturing the large dominant planes. Polylidar3D also uniquely generates concave polygons which provide a condensed representation of identified planes.ion. See ,67 for dThis section provides execution timing analysis of our accelerated mesh smoothing algorithms on organized point clouds (OPC) per n filter . Note thWe apply Polylidar3D on two meshes of an indoor home environment. Both meshes were generated by gathering color and depth frames from an Intel RealSense D435i camera and integrating them into a triangular mesh using methods from Zhou et al.  implemenThis section explores how Polylidar3D scales with additional CPU threads. We specifically focus on plane/polygon extraction in Polylidar3D’s back-end. Both meshes are used in these steps and we limit the dominant plane normals to the top four in the scene, i.e., only floors and walls are extracted. K-D tree methods and shown to be two times faster and effective at identifying dominant plane normals. Data and task-based parallelism is also exploited to efficiently allocate work to available CPU cores.Results show Polylidar3D successfully extracts flat surfaces as polygons with interior holes from unorganized/organized point clouds and user-provided meshes. One of Polylidar3D’s primary strengths is its polygon extraction speed. The key to this speed is the fast construction of half-edge triangular meshes used directly in polygon extraction. No secondary re-triangulation is necessary after planar segmentation. Our Fast Gaussian Accumulator was benchmarked against competing Results also illustrate limitations. First, rooftop and ground extraction in Polylidar3D planar segmentation expects a mesh to be reasonably smoothed. The amount of smoothing depends on user-specified parameters for surface extraction and the noise of the input data. If only large distinct flat surfaces are required then minimal smoothing is necessary. We define distinct surfaces as plane normals that are well-separated on the Gaussian Accumulator  surface normals, it is possible they will appear near each other on the GA and be merged. Noise in the mesh affects how close these two peaks can be on the GA and still be detected as distinct peaks. As the mesh is further smoothed (with edge-preservation filters) the noise is reduced and the peaks become more defined. This is exactly what had to be done to detect the numerous noisy planes in the SynPEB benchmark. Also, group assignment in Algorithm 4 will assign common triangles to these detected peaks if they meet a user defined angular threshold k at 78% shows Polylidar3D doing an excellent job of capturing large dominant planes. Scenes in this benchmark are the antithesis for what Polylidar3D was designed for , yet we show Polylidar3D still performs well in important metrics such as minimizing the number of over/under-segmented planes, spurious predictions, and execution time. We believe these metrics taken as a whole demonstrate Polylidar3D’s efficiency and reliability for polygon extraction of dominant planar surfaces.Only dominant planes, flat surfaces that account for most of the 3D data, can be reliably captured from organized point clouds and user-provided meshes. We see numerous qualitative examples of this from RGBD sensor data, the SynPEB benchmark, and user-provided meshes. Polylidar3D is only able to extract 47.3% of the average 42.6 planes in the SynPEB test scenes. However the percent of point cloud metric Each of the experimental result sections use different sensors resulting in dissimilar point cloud characteristics such as density, spatial distribution, noise, and accuracy to the ground truth surface. These characteristics have been studied both for Airborne Laser Scanning (ALS) technology ,71, VeloThe Polylidar3D parameters in Our Fast Gaussian Accumulator (FastGA) algorithm has several parameters which are influenced both by point cloud characteristics as well as real-time computational needs. First the Plane and polygon extraction algorithm parameters are influenced by density, point spacing, and noise in the point cloud. The parameter Polygon post-processing parameter values are chosen more from scene context than from underlying point cloud properties. For example parameters n is the number of points. This is limited by the 2D and 2.5D Delaunay triangulation in the front-end which can only be completed in n being integrated into the sphere. The search process for the histogram cell is likewise independent of n and only influenced by the refinement level of the GA which is known at compile time. Region growing for planar segmentation is also completed in The time complexity of Polylidar3D varies depending upon the data input. Unorganized 2D and 3D point clouds have a total time complexity of gn) time . However(n) time . Any plaThere are three significant techniques that will improve Polylidar3D’s robustness in future work: polygon merging, time integration, and integrating intensity/color data. Many planar segmentation algorithms perform “plane“ merging of extracted segments (point sets) which are deemed similar by Euclidean distance and plane-fit error tolerance ,11,21,23Polylidar3D processes each point cloud distinctly. Time integration incorporates data from multiple data frames in a sequence by filtering and refining extracted polygons based on previous results. In a static scene with fixed sensor viewpoint time integration can reduce the variance of polygons produced over time. All linear rings of the polygon (both hull and holes) can be explicitly tracked using meta-data previously discussed and removed if certain thresholds are not met. With a dynamic scene or moving sensor time integration would require significant extension to Polylidar3D to incorporate additional data such as sensor (vehicle) motion estimates and even semantic scene information. Additional work investigating the use of Bayesian filtering will be done.Data such as intensity and/or color of the point cloud can be used to further determine similarity between neighbors in the point cloud during region growing. Such data has been shown to improve results for point cloud registration  and meshThis paper introduced Polylidar3D, a non-convex polygon extraction method capturing flat surfaces from a variety of 3D data sources. Front-end methods transform unorganized point clouds, organized point clouds, and 3D triangular meshes to a common half-edge triangular mesh format. Back-end core algorithms perform mesh smoothing, dominant plane normal estimation, planar segmentation, and polygon extraction. A novel Gaussian accumulator, FastGA, was demonstrated robust and quick at detecting dominant plane normals in a 3D scene. These dominant plane normals are used to parallelize planar segmentation and polygon extraction. Polylidar3D is evaluated in five separate experiments with airborne LiDAR point clouds, automotive LiDAR point clouds, RGBD videos, synthetic LiDAR benchmark data, and meshes of indoor environments. Qualitative and quantitative results demonstrate Polylidar3D’s speed and versatility. All of Polylidar3D is open source and available to be freely used and improved upon by the community ,16,44.


Hechtian adhesion between cell wall and plasma membrane transduces wall stress that opens Ca2+ channels and reorients auxin efflux “PIN” proteins; they control the auxin-activated proton pump that dissociates Ca2+ bound by periplasmic arabinogalactan proteins (AGP-Ca2+) hence the source of cytosolic Ca2+ waves that activate exocytosis of wall precursors, AGPs and PIN proteins essential for morphogenesis. This novel approach identifies the critical determinants of an algorithm that generates phyllotaxis spiral and Fibonaccian symmetry: these determinants in order of their relative contribution are: (1) size of the apical meristem and the AGP-Ca2+ capacitor; (2) proton pump activity; (3) auxin efflux proteins; (4) Ca2+ channel activity; (5) Hechtian adhesion that mediates the cell wall stress vector. Arguably, AGPs and the AGP-Ca2+ capacitor plays a decisive role in phyllotaxis periodicity and its evolutionary origins.Phyllotaxis describes the periodic arrangement of plant organs most conspicuously floral. Oscillators generally underlie periodic phenomena. A hypothetical algorithm generates phyllotaxis regulated by the Hechtian growth oscillator of the stem apical meristem (SAM) protoderm. The oscillator integrates biochemical and mechanical force that regulate morphogenetic gradients of three ionic species, auxin, protons and Ca These hydroxyproline-rich glycoproteins, especially the arabinogalactan proteins (AGPs), are involved in a hypothetical Hechtian growth oscillator. It involves transduction of the wall stress–strain to the plasma membrane where an auxin-activated proton pump dissociates AGP-Ca2+. Elevated cytosolic Ca2+-activates exocytosis thus regulating plant growth. Discussion of the Hechtian Oscillator vis-a-vis the role of the primary cell wall in plant morphogenesis [Agnes Arber  in “The 2+ release from arabinogalactan proteins (AGPs) of the cell surface; this hypothesis accounts for the origin of oscillations in molecular detail absent from previous models of tip growth [2+ waves) that regulate growth and differentiation  support that hypothesis. Recent characterization of their AGPs shows an elevated glucuronic acid content suggestive of enhanced Ca2+ binding in high salt [The Hechtian Oscillator exemplifies the pollen tube paradigm of rapid tip growth in particular ,7. The rt stress  may enhaigh salt . “cycles of auxin build-up and depletion accompany, and may direct, different stages of primordium development,” further confirmed by a more recent study of Arabidopsis embryogenesis [Heisler et al.  concludeogenesis . Auxin w+ dissociation of periplasmic AGP-Ca2+ [2+ that activates exocytosis in the AGP-rich protoderm. Indeed, ubiquitous distribution of AGPs throughout the Plant Kingdom [Thus, HAGP-Ca2+  is the i Kingdom ,35 impli Kingdom  confirm  Kingdom . Therefo+-ATPase proton pump by increasing its phosphorylation which increases the rate of proton extrusion [+-ATPase “turbo-molecular” proton pump. The molecular pathway involves successive glutamate protonation and deprotonation from the cytoplasmic side to the periplasm and cell wall  explanation invokes low pH-dependent wall loosening “enzymes” and expansins all of unknown specificity [2+ that provides an alternative reinterpretation of “acid growth” based on a visual analogy of the plasma membrane depicted as a metaphorical “molecular pin-ball machine” in . When activated by auxin, the proton pump shoots fast protons into the periplasm where they dislodge Ca2+ ions from the periplasmic AGP glycomodules; i.e., proton efflux generates Ca2+ influx. Thus, free Ca2+ ions then enter the cytosol via stretch-activated Ca2+channels. The rapid increase of cytosolic Ca2+ [2+ homeostasis is thus the major function of the proton pump rather than the regulation of wall pH.Proton extrusion and a concomitantly low wall pH associated with cell extension so-called “acid growth”  exemplifcificity  but ignohine” in  that reglic Ca2+  activatelic Ca2+  . For example in a floral phyllotaxis, N is a function of the stem apical meristem (SAM) size and the magnitude of major variables that define the symmetry and periodicity of new primordia. To sustain their growth, rapidly expanding cells demand auxin by generating the cell wall stress vector (CWsv) that redirects PIN proteins thus channeling auxin towards these incipient primordia (a) with auxin maxima and minima corresponding to future primordia and boundary tipping points, respectively. Generally, increasing the magnitude of a variable increases auxin transport towards a primordium hence rapidly depleting distal cells. Reversal of PIN protein orientation then generates a new primordium. Thus, an increased auxin depletion rate increases the number of new primordia. However, they also depend on Ca2+ availability determined by the expression of AGPs. We predict that AGPs strongly expressed in the protoderm will increase the periodicity of primordia while weaker expression will decrease it. Thus, to that extent, the algorithm is semi-quantitative and dependent on strong expression of AGPs in the protoderm of Arabidopsis meristems [Protoderm cell expansion generates new primordia ;Auxin efflux activity: A2+ channels: Cch;Ca2+ influx;Exocytosis is a complex variable regulated by Caad;Hechtian adhesion: Hsv;Cell wall stress vector: CWStem apical meristem protoderm SAMPsv vector determines auxin, proton and Ca2+ ion fluxes involving four phases of the Hechtian Oscillator proton pump that regulate cell expansion.The cell wall stress CW2+ influx and minimal activity of the oscillator.Phase I. Quiescent: Minimal cell wall stress corresponds to minimal Ca2+ channels; entry of Ca2+ initiates auxin binding to the proton pump and initial oscillator activation leading to Phase III.Phase II. Activation: Cell expansion increases wall stress, demand for auxin and opens Ca2+and supplies the Ca2+ channels thus generating cytosolic Ca2+waves that activate exocytosis, notably of wall precursors and plasticizers but also enabling dynamic redirection of PIN proteins. Addition of precursors reinforce the wall and slow its expansion, leading to Phase IV:Phase III. Maximum Activation: Occurs at the high auxin levels supplied by redirected auxin efflux PIN proteins; accelerated proton extrusion dissociates glycomodule AGP-Ca2+ channels.Phase IV. Return to quiescent phase: Stress relaxation of reinforced wall closes Ca2+ recycles via AGP precursors and Ca2+transporters replenish periplasmic AGP-Ca2+Cytosolic CaWhen attenuation of the stress vector reaches a tipping point of minimal oscillator activity and least rapid cell expansion, distant cells expand more rapidly and now exert a new stress vector in an opposing direction thus generating a new primordium that contributes to phyllotactic symmetry.a stem apical meristem protoderm generates new primordia as a function of the activity of five major variables or determinants shown in the speculative graph taking petal phyllotaxis as an example ranging from 3 to 34 petals. It assumes that the cell wall stress vector CWsv is essentially constant for a given SAMPa. SAMP2+ capacitor determines the periodicity of primordia initiation (cf. Protoderm AGP content (activity) is the primary determinant based on the simple hypothesis that size of the AGP-Cation cf.  where a The beguiling simplicity of the above plot inferred largely from biochemical observations is in strong contrast to previous complex formulations based largely on mathematical/geometrical logic.The evolutionary history of the stem apical meristem from a relatively simple arrangement of apical cells in the Bryophytes and ferns culminates in the morphogenetic protoderm of the angiosperms. Here, we conjecture that hybridization may solve the riddle of Fibonacci phyllotaxis and its evolutionary origins. 2+ signal that depends on the AGP-Ca2+ capacitor size, proton flux and Ca2+ channel status. Thus, cells of the protoderm with a large AGP-Ca2+ capacitor will increase cytosolic Ca2+ rapidly and therefore generate numerous primordia within a shorter range than in a protoderm with weaker AGP expression and therefore with more cells between primordia. For example, lower activity of the proton pump and a smaller AGP-Ca2+ capacitor increase spacing between primordia. Unlike the animal kingdom, plants have an enormous propensity for polyploidy and hybridization that we suggest provides a simple biochemical explanation for an evolutionary origin of the well-known Fibonacci series of floral organs exemplified by the 3, 5, 8, 13, 21 and 34 petals (Ranunculus ficaria (13), Erigeron canadensis (21). Can hybridization between contiguous members of the series generate a Fibonacci sequence? If so, how? Consider a hybrid expressing the sum of AGPs from both parents! If AGPs play a dominant role in defining phyllotaxis, then a hybrid of two-fold and threefold symmetry, with a corresponding increased size of the AGP-Ca2+ capacitor, would generate the most common five-fold symmetry, and so on for subsequent members of the series (“a consequence of optimal space filling in systems adding new units at a pole.” Thus, we infer that hybridization generates floral Fibonacci phyllotaxis and accounts for the evolutionary origin of a discrete series rather than a smooth arithmetic progression. This suggestion has the merit of simplicity based on Occam’s Razor in contrast to all preceding mathematical conjectures [Strong AGP expression predicts numerous closely spaced primordia  that gen4 petals , e.g., Re series . Thompsojectures  and is s“It was obviously my hope that the chemiosmotic rationale of vectorial metabolism and biological energy transfer might one day come to be generally accepted, and I have done my best to argue in favour of that state of affairs for more than twenty years…was it not the great Max Planck who remarked that a new scientific idea does not triumph by convincing its opponents, but rather because its opponents eventually die?” Although Mitchell’s unconventional ideas were initially rejected, they were finally recognized. Their universal applicability has become apparent more recently. In simple photoautotrophs, light-driven proton gradients involve bacteriorhodopsin [2+ rather than just two. Thompson’s classical “Growth and Form”  exemplifhodopsin , while i2+ channels that generate cytosolic Ca2+ gradients. Thus, the Hechtian growth oscillator is an extrapolation of Mitchell’s chemiosmosis that unifies physics and chemistry in a minimalist approach to regulating plant growth. Indeed, precursors to life surely involve proton gradients as a basis of prebiotic energy transduction and the universal proton pump of exoplanet life in the habitable zone.However, these gradients do not arise by simple diffusion but are regulated by auxin efflux “PIN” proteins whose discovery began with Rubery and Sheldrake’s  classic


We evaluate whether transrectal ultrasonography (TRUS)-guided prostate biopsy might lead to spillage of tumor cells into peripheral blood as a result of disruption of the epithelial barrier and ultimately result in metastasis. Eighty-eight patients underwent TRUS-guided prostate needle biopsy due to prostate-specific antigen (PSA) increase or abnormal digital rectal examination at the Samsun Research and Training Hospital  between April 2016 and September 2018. Approximately 10 mL of whole blood was collected from patients before, 1 week after, and 1 month after biopsy. Samples were analyzed for CD117 positivity and prostate-specific membrane antigen (PSMA) levels using flow cytometry. Patients with pathologically determined prostate cancer and without CD117 positivity before biopsy were included in the study. The study group thus consisted of 55 patients.Subjects’ PSA levels ranged from 2.3 to 40.0 ng/mL (median: 7.9 ng/mL), and their Gleason score was a median of 7 (range: 5–9). PSMA levels ranged between 9.3 ng/mL and 118.5 ng/mL and CD117 antigen levels between 0 and 5. We detected no CD117-positive cells in blood samples collected 7 days or 1 month after biopsy.We detected no circulating tumor cells in the peripheral circulation following biopsy. Prostate needle biopsy seems to be a safe method in terms of spillage of tumor cells into blood circulation as a possible cause of further metastasis. Prostate cancer is the most common noncutaneous cancer in men in the United States [1]. Transrectal ultrasonography (TRUS)-guided biopsy of the prostate has been routinely used for the detection of prostatic malignant diseases since being introduced by Hodge et al. in 1989 [2]. In addition to advanced biopsy techniques, prostate-specific antigen (PSA) screening has led to an increase in the early detection of prostate cancer. However, biopsy is still necessary for definite pathological diagnosis. Circulating tumor cells (CTCs) circulate in the peripheral blood and are released from the primary tumor or metastatic site. They are thought to play a critical role in the hematogenous spread of malignancy and leading metastases [3]. CD117 (c-kit) is a 145-kDa molecular-weight cell surface antigen and protooncogene. It is a receptor tyrosine kinase located in the structure of the single chain type 1 glycoprotein consisting of 976 amino acids and in the plasma membrane. At the beginning of the 1990s, stem cell factor (SCF) was identified as a c-kit ligand [4]. c-kit encodes a transmembrane tyrosine kinase receptor [5], a member of the platelet-derived growth factor (PDGF) and colony-stimulating factor-1 (CSF-I) receptors. The kinase activity of CD117 has been shown to play a role in the pathophysiology of tumors such as mast cell leukemia, germ cell tumors, gastrointestinal tumors, acute myeloblastic leukemia, neuroblastoma, melanoma, ovarian and breast cancers, and small-cell lung cancer [6]. There is evidence that c-kit and SCF together play a role in various hematological and nonhematological malignancies [7]. Prostate-specific membrane antigen (PSMA) is a type II integral membrane glycoprotein with enzymatic functions that acts as glutamate-preferring carboxypeptidase (glutamate) in the human prostate tissue and that plays a role in folic acid utilization and metabolism [8]. The PSMA protein has a unique three-part structure consisting of an interior of 19 amino acids, a transmembrane part of 24 amino acids, and a 707-amino acid exterior [9]. Although PSMA is present in the prostate secretory-acinar epithelium, it is overexpressed in prostate cancer, which is reflected in elevated blood serum levels [10]. Increased expression has been reported in patients with hematogenous micrometastasis in prostate carcinomas [11]. PSMA expression has been shown in various cancers, including kidney, colon, and breast carcinomas as well as the prostate, in addition to various benign changes [11]. Increased expression has been reported in patients with prostate cancer with hematogenous micrometastases. PSMA expression has also been documented in extraprostatic tissues, including the small bowel and brain [11].The enumeration of CTCs is regarded as a valuable predictor of possible metastasis and prognosis [12]. CTCs have been found to be useful and may also be prognostic predictors in various stages of prostate cancer, extending from localized disease through metastasis [13]. CTC numbers have been shown to be predictive in the differentiation of unfavorable and favorable groups using discrete cut-off values (≥5 CTCs/7.5 mL of blood vs. ≤4 CTCs) in patients with progressive metastatic breast, colon, or prostate cancer [14].The purpose of this study was to evaluate whether TRUS-guided prostate biopsy may lead to spillage of tumor cells into peripheral blood as a result of the disruption of the epithelial barrier in terms of its value in predicting future metastasis with the aid of surface biomarkers CD117 and PSMA using flow cytometry. To the best of our knowledge, this is the first prospective study to investigate the correlation between prostate biopsy and CTC numbers via flow cytometric analysis. Eighty-eight patients admitted to our department due to PSA increase or abnormal digital rectal examination and scheduled for TRUS-guided prostate biopsy between April 2016 and September 2018 were included in the study. Approval for the study was granted by the Ondokuz Mayıs University Ethics Committee, Samsun, Turkey. Detailed written informed consent was obtained from all patients. Approximately 10 mL of whole blood was collected from all patients before biopsy and sent immediately for flow cytometric analysis. All patients then underwent transrectal needle biopsy with 12 cores under TRUS guidance. All specimens were examined by the same pathologist. Cases reported as nonmalignant and patients with metastatic disease were excluded from the study. Cases pathologically determined as prostate cancer and without CD117 positivity prior to biopsy were included in the study. These patients underwent CD117 analysis by flow cytometer 1 week and 1 month after biopsy. Plasma PSMA levels were measured in the same samples at the same time. Before each analysis via flow cytometry, control samples of other origins already present in our laboratory and known to be CD117-positive were studied, and the study samples were analyzed subsequently. Flow cytometric analysis of CD117 lymphocytes isolated from whole blood was performed with density gradient centrifugation using Biocoll separating solution  according to the manufacturer’s instructions. Pelleted lymphocytes were resuspended in bovine serum albumin stain buffer . Isolated lymphocytes were incubated with fluorescently conjugated antibodies .Samples were analyzed on a FACSCanto II flow cytometer with 2 lasers and 8 colors . APC was detected using 633-nm and 640-nm filters. A total of 100,000 events were acquired for each specimen, and data were analyzed with FACSDIVA software . Data were expressed as percentages of positive cells with CD117 via flow cytometry.Concentrations of human PSMA in plasma were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits . Enzymatic reactions were quantified on an automatic microplate photometer. PSMA levels were expressed as ng/mL. The mean interassay coefficient of variation (CV) % and intraassay CV % for PSMA were <12% and <10%, respectively. All assays were conducted according to the manufacturer’s instructions. Samples exhibiting higher concentrations were diluted and measured in duplicate. Statistical analysis was performed with IBM SPSS 23 software . The normality of data was assessed using the Kolmogorov–Smirnov test. The Spearman correlation and Friedman tests were used to compare data without normal distribution. Correlation tests were used to determine whether correlation existed among age and PSA values and Gleason scores. Data are expressed as median (min–max). P < 0.05 was considered statistically significant.Upon pathological examination of TRUS-guided biopsies, 28 (31.8%) of the 88 cases were identified as benign while metastatic disease was detected in five cases using other diagnostic methods (whole-body bone scan). These 33 (37.5%) patients were therefore excluded from the study, which finally involved 55 (62.5%) cases pathologically identified as prostate adenocarcinoma. The patients’ median age was 66.5 years (range: 56–78). The median PSA level was 7.9 ng/mL (range: 2.3–40.0) and median Gleason score was 7 (range: 5–9). We detected a statistically significant positive but weak correlation between patients’ ages and PSA values . No correlation was observed between age and Gleason scores . PSMA levels ranged between 9.3 ng/mL and 118.5 ng/mL and CD117 antigen levels between 0 and 5. No CD117-positive cells were detected in blood samples from patients 7 days or 1 month after biopsy. PSMA data are shown in the Table. The Figure shows the CD117 results from a control specimen and samples from patients prior to biopsy and 7 days and 1 month after biopsy via flow cytometry. These results revealed that no CTCs exceeding the critical level of five cells per 7.5 mL of peripheral blood were observed upon flow cytometric analysis. Despite recent advances in PSA-based screening in the form of free and complexed PSA, PSA density, PSA velocity, and age-specific ranges, and imaging methods such as multiparametric magnetic resonance imaging, TRUS-guided prostate biopsy remains the standard technique for routine biopsy as well as for precise identification of malignant tissue. Due to some limitations of PSA in the context of pathological stages of the disease, there has recently been considerable interest in CTCs, particularly focusing on different stages of prostate cancer. CTCs in whole-blood samples have been used as an alternative marker in disease determination and prognosis, and in the search for more reliable tests for diagnosing malignant diseases with localized and metastatic phases [15]. Detecting CTCs with high sensitivity and specificity is an important objective of CTC studies in prostate cancer and other solid tumors. Improvements in CTC capture by novel capture antibodies , negative selection methods, novel CTC chip designs that enhance CTC yield, and improved CTC molecular profiling technologies will assist further exploration of CTCs and their implications for metastatic prostate cancer. The CellSearch System is the first standardized system approved by the Federal Drug Administration for detecting and quantifying CTCs in peripheral blood [16]. Detection by CellSearch depends on EpCAM expression in CTCs, which are subsequently identified as nucleated cells positive for cytokeratin 8, 18, or 19 expression and negative for leukocyte antigen CD45 expression by immunofluorescence staining. This CTC detection technology has been widely used in prostate cancer research. CellSearch results are highly reproducible between laboratories, and the results are stable for samples transported for as long as 72 h [17].In general, a threshold of 5 cells per 7.5 mL of peripheral blood has been used to estimate prognosis, and cell counts above this level are widely interpreted as a sign of metastatic disease [18]. Allard et al. detected CTCs in 107 of 188 patients with metastatic prostate cancer compared to 1 of 344 healthy male controls [13]. In a study evaluating whether CTCs in peripheral blood samples correlate with tumor volume, pathological stage, and Gleason score in men with localized prostate cancer, Davis et al. observed no correlation between number of CTCs and known prognostic factors in these subjects [19]. Interestingly, their CTC positivity rates were almost identical for patients with prostate cancer (21%) and the healthy controls (20%). In addition, 84% of patients with preliminary positive CTC values were negative by 6 weeks after surgery. CTCs provide real-time and easy access to tumor cells, but there are also limitations to CTC studies. De Bono et al. reported a nondetection rate of CTCs of more than 50% in patients with metastatic castration-resistant prostate cancer [12].Miyamoto et al. reported that some CTCs may entirely lack epithelial biomarkers [20]. There are several possible explanations for this lack of CTC detection, including loss of rare cells through multiple capture and purification steps, strict characterization definitions, inefficient magnetic separation of labeled cells throughout a large population of unlabeled cells, and other technical issues. PSMA is a type II integral membrane glycoprotein that exists in the prostate secretory-acinar epithelium and is highly expressed in prostate cancer. Recent evidence suggests that PSMA is also expressed in the tumor-associated neovasculature [21]. Increased expression has been reported in patients with prostate cancer with hematogenous micro metastases. PSMA expression has also been documented in extraprostatic tissues, including the small bowel and brain [11].CD117 is a cell surface antigen and protooncogene with a molecular weight of 145 kDa identified as a receptor tyrosine kinase on the plasma membrane in the early 1990s. Activation of tyrosine kinase by somatic mutation has been documented in several malignancies, including gastrointestinal stromal tumor, seminoma, acute myelogenous leukemia, and mastocytosis. A pathophysiological role of this kinase by means of paracrine or autocrine activation has also been postulated in some other malignancies, including small-cell lung and ovarian cancer [6]. Mainetti et al. observed that bone-induced CD117 antigen-positive cells increased the invasion and migration of prostate cancer cells in mice with prostate cancer with bone metastasis [22].Why did we employ flow cytometry for the analysis and detection of cells? Flow cytometry is considered a reliable method for the detection and analysis of rare CTCs. Bhagwat and Carpenter described flow cytometry as a powerful cell analysis technique that is being increasingly used in this field and that permits easy recovery of viable cells for molecular analysis. They also proposed it as an attractive technology for cancer research and as a diagnostic tool [23].CTCs can be detected by flow cytometry, which provides sensitive detection of CD117 antigen positivity. To the best of our knowledge, this is the first study evaluating flow cytometric detection of CD117 antigen-positive cells. CD117 antigens were analyzed in circulating cells before and after prostate biopsy in order to establish whether these cells may lead to possible further metastasis. We determined no CD117 antigen-positive cells in blood specimens obtained after biopsy from patients without CD117 antigen positivity before biopsy. These results suggest that no CTC spillage into the peripheral circulation occurred following biopsy. Our hypothesis that TRUS-guided prostate biopsy may lead to spillage of tumor cells into peripheral blood is therefore invalid.In conclusion, our results reveal that TRUS-guided prostate needle biopsy does not result in clinically significant levels of CTCs in peripheral blood. We therefore conclude that prostate needle biopsy is a safe method in terms of the spillage of tumor cells into the peripheral blood as a result of prostatic epithelial barrier disruption.Dr. Naci Murat, Assistant Professor of Biostatistics at Ondokuz Mayıs University, Department of Biostatistics, analyzed the data.


How often are cell-free DNA (cfDNA) studies in prostate cancer confounded by clonal hematopoiesis (CHIP) variants in genes used for poly(ADP) ribose polymerase inhibitor (PARPi) eligibility?ATM.In this case series study of 69 men with advanced prostate cancer, 7 (10%) had CHIP variants in genes used for US Food and Drug Administration-approved indications of PARPi treatment, most frequently in Men with prostate cancer are at high risk of being misdiagnosed as being eligible for PARPi therapy using current cfDNA tests; assays should use a whole-blood control sample to distinguish CHIP variants from prostate cancer. This case series study examines the prevalence of clonal hematopoiesis and its potential interference in prostate cancer cell-free DNA testing. Cell-free DNA (cfDNA) testing is increasingly used in the treatment of patients with advanced prostate cancer. Clonal hematopoiesis of indeterminate potential (CHIP) can interfere with cfDNA testing and cause incorrect interpretation of results. There is an urgent need to better understand this problem following recent US Food and Drug Administration approval of poly(ADP) ribose polymerase inhibitors (PARPi) for metastatic prostate cancer based on variants in DNA repair genes that can be affected by CHIP.To determine the prevalence of clinically relevant CHIP interference in prostate cancer cfDNA testing.We report a case series of 69 patients with advanced prostate cancer  who had cfDNA variant testing with a large panel cancer next generation sequencing assay (UW-OncoPlexCT). To determine the source of variants in plasma, we tested paired cfDNA and whole blood control samples. The study was carried out in an academic medical center system reference laboratory.Prevalence and gene spectrum of CHIP interference in patients with prostate cancer undergoing cfDNA testing.ATM (n = 5), BRCA2 (n = 1), and CHEK2 (n = 1). Overall, CHIP variants accounted for almost half of the somatic DNA repair gene variants detected. Participant CHIP variants were exponentially correlated with older age (R2 = 0.82). CHIP interference variants could be distinguished from prostate cancer variants using a paired whole-blood control.We detected CHIP variants at 2% or more variant fraction in cfDNA from 13 of 69 men with prostate cancer . Seven men  had CHIP variants in DNA repair genes used to determine PARPi candidacy, including ATM. Clinical cfDNA testing should include a paired whole-blood control to exclude CHIP variants and avoid misdiagnosis.In this case series, approximately 10% of men with advanced prostate cancer had CHIP interference in plasma cfDNA in DNA repair genes that are used for eligibility of PARPi therapy, most frequently in  Yet most commercial labs perform cfDNA testing using a plasma-only approach that cannot reliably distinguish variants derived from prostate cancer vs those arising from CHIP. To improve cfDNA assay performance, we developed an approach (UW-OncoPlexCT) that simultaneously analyzes plasma and paired whole-blood control samples.4 Using this paired testing approach we sought to determine to what degree CHIP interferes with the results of prostate cancer cfDNA testing.Clonal hematopoiesis of indeterminate potential (CHIP) is a known confounder of cfDNA testing.7 We defined CHIP interference as a pathogenic variant with variant allele fractions (VAFs) of at least 2% in both the whole blood and plasma. Germline variants were distinguished from CHIP clones by tumor sequencing. Sequencing data analysis and variant interpretation were performed by an expert molecular pathologist (C.C.P.). All data were manually reviewed in the integrated genomics viewer (IGV) to exclude sequencing artifacts. Data were generated and preprocessed by the University of Washington NGS Laboratory and Analytics group. This study was performed in accordance with the Declaration of Helsinki guidelines and approved by the University of Washington/Fred Hutchinson Cancer Consortium institutional review board and all patients provided written informed consent.We retrospectively reviewed cfDNA study results from 69 patients with advanced prostate cancer  sequenced by our Clinical Laboratory Improvement Amendments (CLIA)-certified and College of American Pathologists (CAP)-accredited clinical UW-OncoPlexCT protocol. Plasma cfDNA and a paired whole-blood control sample were tested in every patient.ATM n = 5, BRCA2, n = 1 and CHEK2, n = 1) . Seven patients  had CHIP variants in DNA repair genes that are used for PARPi selection   Table)..ATM n = and TP53  . We detected CHIP in 0% (0/6) of men aged 40 to 50 years, 12.5% (2/16) of men aged 51 to 60 years, 6.3% (1/16) of men aged 61 to 70 years, 20.8% (5/24) of men aged 71 to 80 years, and 71% (5/7) of men aged 81 to 90 years : CHIP interference somatic , non-CHIP somatic (n = 9), germline (n = 6) , B. We cBRCA2 CHIP interference had cfDNA testing done in parallel by an outside commercial laboratory using a plasma-only assay, which was unknown to our laboratory at the time of testing. The BRCA2 CHIP clone was clinically reported by the commercial lab with the recommendation to use PARPi therapy.The patient with 11 We are concerned that CHIP interference is causing false-positive cfDNA biomarker assessments that may result in patient harm from inappropriate treatment, and delays in delivering alternative effective treatment options. Without performing a whole-blood control, 7 of 69 patients (10%) would have been misdiagnosed and incorrectly deemed eligible for PARP-inhibitor therapy based on CHIP interference in plasma. In fact, 1 patient in this series had a BRCA2 CHIP clone that had been previously reported by a commercial lab with the recommendation to use a PARPi. To mitigate these risks, cfDNA results should be compared to results from whole-blood control or tumor tissue.12We found that a strikingly high proportion of DNA repair gene variants in the plasma of patients with advanced prostate cancer are attributable to CHIP. The CHIP variants were strongly correlated with increased age, and even higher than expected by age group. The high rate of CHIP may also be influenced by prior exposure to chemotherapy.13 highlighted inaccuracies of commercial laboratory cfDNA testing in patients with prostate cancer. In that report, cfDNA samples from 40 patients were sent to 2 separate CLIA-certified laboratories and only 9 of 40 (23%) demonstrated congruence  of positive findings.13 The consistent findings included ATM and TP53 variants in patients with low PSA at the time of blood draw, raising suspicion that these may be CHIP clones. The CHIP interference in cfDNA testing has also been reported in other cancer types. In renal-cell carcinoma (RCC), for example, CHIP was found to affect cfDNA results in 43% of patients.14Challenges of accurate cfDNA testing are beginning to be described. A recent reportATM accounted for the majority of clinically relevant CHIP interference in our series. The ATM gene has been described as a frequent CHIP clone in clinical cancer predisposition testing, along with CHEK2 and TP53.10 We speculate that CHIP interference in cfDNA testing could be affecting results of PARPi clinical studies of patients with metastatic prostate cancer. Trials allowing plasma-only cfDNA testing for enrollment may have included patients with false-positive results associated with CHIP in DNA repair genes, particularly in ATM.15 We speculate that this could be contributing to low PARPi response rates reported in patients with ATM variants, such as recently reported from the TRITON2 study.15Overall, This study has several limitations including relatively small sample size, the retrospective nature of the study, and heterogeneity in patient populations and prior therapies.Findings of this study suggest that CHIP substantially interferes with plasma cfDNA testing in patients with advanced prostate cancer. There is a risk for widespread misdiagnosis and overtreatment of men with PARPi using currently available commercial cfDNA assays. We recommend that all cfDNA testing in patients with prostate cancer include a whole-blood control to distinguish CHIP from prostate cancer variants.