Hormonal contraceptives are among the most effective forms of reversible contraception, but many other compounds, including some antiretrovirals, have clinically meaningful drug–drug interactions (DDIs) with hormonal contraceptives. Islatravir is a novel human immunodeficiency virus nucleoside reverse transcriptase translocation inhibitor currently in clinical development for treatment and prevention of HIV infection. A phase 1 clinical trial was conducted to evaluate the DDI of islatravir and the combination of oral contraceptive levonorgestrel (LNG)/ethinyl estradiol (EE).This was an open‐label, two‐period, fixed‐sequence, DDI clinical trial in healthy, postmenopausal or bilaterally oophorectomized females aged 18 through 65 years in the United States between October 2016 and January 2017. A single dose of LNG 0.15 mg/EE 0.03 mg was given followed by a 7‐day washout. Islatravir, 20 mg, was then dosed once weekly for 3 weeks; a single dose of LNG 0.15 mg/EE 0.03 mg was given concomitantly with the third dose of islatravir. Pharmacokinetic samples for plasma LNG and EE concentrations were collected pre‐dose and up to 120 hours post‐dose in each period. Safety and tolerability were assessed throughout the trial by clinical assessments, laboratory evaluations and examination of adverse events.0–inf and Cmax were 1.13  and 0.965 , respectively. For EE, the GMRs (90% CI) for AUC0–inf and Cmax were 1.05  and 1.02 , respectively. Co‐administration of all three drugs was generally well tolerated.Fourteen participants were enrolled. The pharmacokinetics of LNG and EE were not meaningfully altered by co‐administration with islatravir. For the comparison of (islatravir + LNG/EE)/, the geometric mean ratios (GMRs)  for LNG AUCThe results of this trial support the use of LNG/EE contraceptives in combination with islatravir without dose adjustment. Approximately 5500 young women aged 15–24 years become infected with HIV weekly worldwide. In sub‐Saharan Africa, among 15‐ to 24‐year olds, women are twice as likely to be living with HIV as men, and in adolescents aged 15–19, five of six new HIV infections occur in girls , 7.Tmax) of 0.5 hours and a plasma half‐life (t1/2) of ∼50–60 hours [Tmax between 6 and 24 hours and decline with a half‐life of ∼120–210 hours [Islatravir MK‐8591) is a nucleoside reverse transcriptase translocation inhibitor with potent antiviral activity against wild‐type and drug‐resistant HIV‐1 [591 is a 60 hours . Intrace10 hours .Tmax of ∼1.5 hours. The terminal t1/2 of LNG and EE after single oral doses is approximately 34 and 18 hours, respectively [Hormonal contraceptives are the most widely used form of highly effective reversible contraception and typically contain a progestin with or without an estrogenic component. Levonorgestrel (LNG)/ethinyl estradiol (EE) is a popular fixed‐dose combination oral contraceptive. LNG does not undergo first‐pass metabolism, instead undergoing phase I metabolism likely mediated in part via CYP enzymes , 13. Firectively .The potential for clinically significant interactions between contraceptives and a number of ARVs can complicate the choice of both hormonal contraceptive and ARV regimen , 16. Co‐22.1Protocol MK‐8591‐006 was an open‐label, two‐period, fixed‐sequence, DDI trial to assess the effects of multiple, once‐weekly oral doses of islatravir on the single‐dose pharmacokinetics of the combined oral contraceptive LNG/EE conducted in the United States between October 2016 and January 2017. The trial was conducted in accordance with principles of Good Clinical Practice and was approved by Novum Independent Institutional Review Board .2.22. Levels of sex hormone binding globulin (SHBG) can fluctuate during the menstrual cycle, which affects the pharmacokinetics of hormonal contraceptives and can confound the interpretation of pharmacokinetic data. Therefore, this study enrolled healthy, postmenopausal or oophorectomized women to minimize any within participant variability in the pharmacokinetics of combination oral contraceptive pills, following once‐weekly administration of islatravir. Key exclusion criteria included pregnancy, lactation and clinically significant health conditions.Eligible participants were healthy females 18 and 65 years, who were postmenopausal or bilaterally oophorectomized, had estradiol concentrations ≤35 pg/ml, had FSH levels ≥40 mIU/ml and had a body mass index (BMI) of 19–30 kg/m2.3Participants received a single dose of 0.15 mg LNG/0.03 mg EE  on day 12.4Plasma for the measurement of LNG/EE concentration in period 1 was collected pre‐dose and 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, 24, 48, 72, 96, and 120 hours post‐dose. For period 2, plasma for the measurement of LNG/EE concentration was collected pre‐dose on day 15 and at the same timepoints as period 1 post‐dose. Safety and tolerability were assessed throughout the trial by clinical assessments, laboratory evaluations and examination of AEs.2.5®, Version 9.4). Plasma LNG and EE concentrations and actual sampling times were used to estimate LNG and EE pharmacokinetic parameters. Cmax and Tmax values were determined from the observed plasma concentration time data. AUC0–last was calculated using the linear trapezoidal method for ascending concentrations and the log trapezoidal method for descending concentrations. AUC0–∞ for the analytes was calculated as AUC0–last+Cest,t/Kel, where Cest,t is the concentration at the last blood sampling timepoint as predicted from the terminal‐phase linear regression. For each participant, Kel (λz) was estimated from the negative slope of the dataset with the best‐fit least‐squares linear regression analysis of the terminal ln‐linear concentration time data, and the apparent terminal t1/2 was calculated as ln(2)/λz. At least three data points in the terminal phase were used for Kel calculations.The plasma concentrations of LNG and EE were determined by InVentiv Health Clinique, Inc.   via a validated liquid chromatography‐tandem mass spectrometry assay. For LNG and EE, the calibration ranges were 10–10,000 pg/mL and 1–200 pg/mL, respectively. The pharmacokinetic parameter values were calculated using the software Statistical Analysis System  for the true mean difference  for each parameter on the log scale was computed from the above linear mixed effect model. The 90% CI on the log scale was exponentiated to obtain the 90% CI for the true geometric mean ratio (GMR)  for each parameter. Cmax was analysed in a similar fashion as AUC0–∞. Assuming 10 participants with available pharmacokinetic data, non‐negative correlation among the four test statistics and true GMRs of 0.95 for both parameters, there was at least 80% probability that both 90% CIs would satisfy their corresponding target intervals simultaneously.Individual AUC32  and a median BMI of 27.7 kg/m0–inf was 13% higher with LNG/EE co‐administered with islatravir compared to LNG/EE alone. LNG Cmax, apparent terminal t1/2 and Tmax were similar for both treatments  and pruritus ; none of which were considered by the investigator to be drug related.Administration of LNG/EE alone and with islatravir was generally well tolerated. There were no deaths or serious AEs reported. Seventeen treatment‐emergent AEs were reported by 7 of 14 participants; three occurred after administration of LNG/EE alone, eight occurred after administration of islatravir alone and six occurred after co‐administration of LNG/EE and islatravir. All reported AEs were mild in intensity and resolved by the end of trial. The most common AEs (occurring in two or more participants) were abnormal urinalysis , is committed to providing qualified scientific researchers access to anonymized patient‐level data and clinical study reports from the company's clinical trials for the purpose of conducting legitimate scientific research. The company is also obligated to protect the rights and privacy of trial participants and, as such, has a procedure in place for evaluating and fulfilling requests for sharing company clinical trial data with qualified external scientific researchers. The process includes submission of data requests to the MSD data sharing website available at:


This study analyzes the pattern of use of single agent anticancer therapy (SAACT) in the treatment and survival of advanced hepatocellular carcinoma (aHCC) before and after sorafenib was FDA approved in 2007.Adult patients diagnosed with HCC and treated with only ACT from 2004 – 2014 were identified in NCDB database. Patients were analyzed during three time frames: 2004–2006 (pre‐sorafenib (PS)), 2007–2010 (early sorafenib (ES)) and 2011–2014 (late sorafenib (LS)). Cox proportional hazards models and Kaplan‐Meier method were used for analyses.p < 0.0001). During this later period, the highest proportion of SAACT is among academic and integrated network facilities (23.3%) as compared to community facilities . The median overall survival of patients with aHCC treated only with SAACT improved significantly over time from 8.0 months (m) (95% CI: 7.4–8.8) to 10.7 m (10.4–11.2) to 15.6 m . Multivariate analysis indicates worse outcomes for patients treated at community cancer programs , patients without insurance  and estimated household income of <$63,000 .The NCDB contained 31,107 patients with HCC diagnosed from 2004–2014 and treated with ACT alone. Patients were generally men (78.0%), >50 years of age (92.5%). A significant increase in the rate of adaption of SAACT was observed over time: 6.2% PS, 15.2% ES, and 22.2% LS (aHCC patients treated only with ACT have experienced an overall improvement in survival, but significant differences exist between facility type, insurance status, and income. Prior to 2007, patients with advanced HCC (aHCC) were treated with transarterial embolization and radioembolization with limited long‐term control. Sorafenib was the first targeted agent for aHCC to get FDA approval in November 2007. Sorafenib is an oral multi kinase inhibitor that inhibits both intracellular and surface kinases which are involved in tumor signaling. Inhibition of these kinases affect tumor proliferation, angiogenesis and cell death.Since the approval of sorafenib, limited data exist regarding its pattern of adoption and socioeconomic factors that may affect outcomes among the general aHCC patient population in the United Sates.2The NCDB is jointly sponsored by the American College of Surgeons and the American Cancer Society and is a clinical oncology database sourced from hospital registry data collected from more than 1,500 Commission on Cancer accredited facilities. We received approval from the NCDB to conduct this study and obtained a dataset for HCC from 2004–2014. Using NCDB Participant User File, patients ≥18 years with diagnosis of hepatocellular carcinoma were identified by ICD code. We submitted our research protocol to the IRB for review and it was determined that approval was not required.Data variables including age, sex, co‐morbidity index (Charlson/Deyo score), treatment type , time of treatment, AFP , facility type , insurance type  and estimated household income  were obtained. Of note, systemic anticancer therapies (ACT) are categorized as chemotherapy in NCDB, and it does not differentiate between conventional chemotherapy (ex. fluorouracil) and targeted therapies such as sorafenib. For facility type, the academic and integrative network categories were combined as many academic centers are categorized as integrative network if they include multiple sites. When assessing for ACT and SAACT utilization, we excluded all patients that received any type of surgery, locoregional therapy, or radiation treatment. In general, we assessed outcomes according to three time frames: 2004–2006 (pre‐sorafenib), 2007–2010(early sorafenib) and 2011–2014 (late sorafenib). Cox proportional hazards models were used for univariate and multivariable analyses. Kaplan‐Meier method was used for survival analysis. Patients diagnosed in 2015 were excluded from the database due to insufficient data. SAS version 9.4 was used to perform all analyses, and R version 3.6.3 was used to generate all figures.3A total of 31,107 patients were identified in the NCDB database who were diagnosed with HCC and received treatment from 2004 – 2014. The majority were men (78.0%), White (73.0%), and between the ages of 50–69 years (66.0%). Other racial groups included Black (16.1%), Asian and Pacific Islanders (7.4%), and American Indian (0.8%). Nearly two‐thirds of patients (63.9%) were noted to have an elevated AFP, and 76.0% had a Charlson Deyo score of 0–1. Approximately three‐fourths of patients (72.9%) were estimated to have a household median income <$63,000. Almost all patients had insurance including Medicare (43.4%), private insurance (32.9%), and other insurance (17.0%). About five percent were noted to have no insurance. Patients were treated at three types of facilities: academic/integrated cancer centers (69.3%), comprehensive community centers (25.5%), and community centers (4.0%).  received SAACT. Soon after sorafenib approval, overall ACT utilization for aHCC increased significantly to approximately 23.2%. For those receiving ACT in the early sorafenib period of 2007–2010, 66.0% of patients received SAACT, and by the late sorafenib period of 2011–2014, 26.3% of all aHCC patients received ACT with 84.5% receiving SAACT  in the late sorafenib period  improved from 10.0 m (95% CI: 9.5–10.6) in the pre‐sorafenib period and to 12.5 m (95% CI: 12.0–12.9) in the early sorafenib period to eventually 16.0 m .Worse outcomes for cancer patients have been associated with low socioeconomic factors, and similarly we found that having no insurance status and a household annual income <$63 K are associated with worse survival.5Our study has limitations including use of retrospective data from the NCDB. These data are thought to only represent 60–70% of cancer care in the US. Retrospective data have limitations but these can help us understand trends in a large population over a certain period of time. In this study, we presumed that SAACT was sorafenib because it was the only FDA‐approved therapy during the study period evaluated. Regorafenib was approved in 2017 and thus likely not significantly represented in the time frame from 2004 to 2014. Thus, we feel confident that these data primarily represent sorafenib utilization. The NCDB does not provide some key information such as performance status nor the risk factors associated with HCC development, which are known prognostic factors. The Charlson/Deyo co‐morbidity index provides an indicator of health status, but does not fully replace the performance status. Additionally, the Charlson/Deyo scores for this cohort seemed low given 48% were reported to have a score of 0 despite the majority of patients having cirrhosis, which should have been scored at least as 1 for mild cirrhosis or 3 for moderate‐severe cirrhosis. This likely indicates that a significant proportion of participating CoC sites may not have appropriately scored the Charlson/Deyo index properly, and thus the dataset may underestimate the co‐morbidities of this cohort. We are unable to re‐assess this because the database does not provide individual medical diagnoses. The comprehensive community cancer centers and community cancer centers represent a smaller proportion of the total data set, 25.5% and 4.0%, respectively.In summary, with improvements in the treatment of aHCC including systemic treatment options with newer targeted therapies and immunotherapy such as atezoluzimab and bevacizumab, patients with aHCC will continue to have improvements in survival.The authors have declared no conflicts of interest.Aman Opneja ‐ conceptualization, formal analysis, investigation, methodology, writing ‐ original draft, and writing ‐ review and editing. Gino Cioffi ‐ conceptualization, formal analysis, investigation, methodology, writing ‐ original draft, and writing ‐ review and editing. Asrar Alahmadi ‐ formal analysis, writing ‐ original draft, and writing ‐ review and editing. Nelroy Jones ‐ formal analysis, writing ‐ original draft, and writing ‐ review and editing. Tin‐Yun Tang ‐ writing ‐ review and editing. Nirav Patil ‐ formal analysis, methodology, writing ‐ original draft, and writing ‐ review and editing. David L. Bajor ‐ writing ‐ review and editing. Joel N. Saltzman ‐ writing ‐ review and editing. Amr Mohamed ‐ writing ‐ review and editing. Ankit Mangla ‐ writing ‐ review and editing. Jill Barnholtz‐Sloan ‐ conceptualization, formal analysis, investigation, methodology, writing ‐ original draft, and writing ‐ review and editing. Richard T. Lee ‐ conceptualization, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, writing ‐ original draft, and writing ‐ review and editing.


Data are steadily accruing that demonstrate that intestinal tumors are frequently derived from multiple founding cells, resulting in tumors comprised of distinct ancestral clones that might cooperate or alternatively compete, thereby potentially impacting different phases of the disease process.We sought to determine whether tumors with a multi‐ancestral architecture involving at least two distinct clones show increased tumor number, growth, progression, or resistance to drug intervention.Min allele of Apc were generated that were mosaic with only a subset of cells in the intestinal epithelium expressing an activated form of PI3K, a key regulatory kinase affecting several important cellular processes. These cells were identifiable as they fluoresced green, whereas all other cells fluoresced red.Mice carrying the Cell lineage tracing revealed that many intestinal tumors from our mouse model were derived from at least two founding cells, those expressing the activated PI3K (green) and those which did not (red). Heterotypic tumors with a multi‐ancestral architecture as evidenced by a mixture of green and red cells exhibited increased tumor growth and invasiveness. Clonal architecture also had an impact on tumor response to low‐dose aspirin. Aspirin treatment resulted in a greater reduction of heterotypic tumors derived from multiple founding cells as compared to tumors derived from a single founding cell.These data indicate that genetically distinct tumor‐founding cells can contribute to early intratumoral heterogeneity. The coevolution of the founding cells and their progeny enhances colon tumor progression and impacts the response to aspirin. These findings are important to a more complete understanding of tumorigenesis with consequences for several distinct models of tumor evolution. They also have practical implications to the clinic. Mouse models with heterogenous tumors are likely better for predicting drug efficacy as compared to models in which the tumors are highly homogeneous. Moreover, understanding how interactions among different populations in a single heterotypic tumor with a multi‐ancestral architecture impact response to a single agent and combination therapies are necessary to fully develop personalized medicine. This intratumoral diversity is a serious clinical concern, as it contributes to tumor progression, chemotherapy resistance, tumor recurrence, and poor clinical prognosis.A number of models for tumor evolution have been postulated over the years. A longstanding model is the clonal sweep model. It advanced the notion that a tumor emerged following the neoplastic transformation of a single tumor‐founding cell and that the tumor progressed from a benign to malignant state as progeny acquired new molecular alterations. Defects in DNA repair, telomere maintenance, and chromosome segregation can result in the slow accumulation of point mutations, larger‐scale genomic rearrangements, and copy number alterations. About 60% of colorectal cancers develop through a chromosomal instability pathway and 15% exhibit a high frequency of microsatellite instability.Other models do account for intratumoral heterogeneity, including the cancer punctuated equilibrium model, the Big Bang model, and the multi‐ancestral model. The cancer punctuated equilibrium model postulates that each cancer is initiated from a single founder but the progeny rapidly acquire molecular changes and the newly formed clones gradually equilibrate over long periods of time until the next burst of molecular changes Figure .16, 1KRAS, PIK3CA encoding PI3K, or TP53, which are commonly mutated in colorectal cancers.While multi‐ancestral adenomas and adenocarcinomas have been identified in both sporadic and familial cases of colorectal cancer in humans, previous studies did not address the fate of ancestral clones that carry mutations in key tumor driver genes such as Recently, activated PI3K signaling has been shown to drive rapid tumor initiation, growth, and progression within the mammalian colon of transgenic mice, indicating that PI3K is a key tumor driver within the colon.We use a similar transgenic mouse model in this study. The distal small intestine and colon are a mosaics composed of cells expressing the activated form of PI3K and those that do not. The two populations are distinguishable because the former fluoresce green, whereas the latter fluoresce red. This model allowed us to determine whether activation of a strong tumor driver like PI3K within a subset of tumor‐founding cells was sufficient to drive an early clonal sweep or whether tumors would maintain early diversity through tumor growth and progression. The results underscore the importance of early genetic diversity arising from multiple tumor‐founding cells as it impacts many facets of tumor biology including initiation, growth, progression, and response to intervention.22.1+,+,ApcMin/+,+ mice were backcrossed to B6 mice for 10 generations to generate B6.Fabp1‐Cre+ congenic mice. Mice were interbred to create experimental animals and controls. Additional information on the breeding scheme is available in the Supplementary Methods. Fabp1‐Cre+ mT/mG+ Pik3ca*+ApcMin/+ mice were considered experimental mice to assess clonal architecture and its impact on tumor response to aspirin or GDC‐0941, whereas Fabp1‐Cre+ mT/mG+ApcMin/+, mT/mG+ Pik3ca*+ApcMin/+, and mT/mG+ApcMin/+ mice were ApcMin/+. Comparisons were made to ensure the observed effects were not due to Fabp1‐Cre+ or the mT/mG+ transgene  as a control or else the Teklad Custom Aspirin diet  with their progeny continuing to receive the diet fed to the females until the study was terminated. Between 30 and 40 days of age, the aspirin‐treated and non‐treated control mice underwent colonoscopy as described previously.2.3Between 40 and 45 days of age, the experimental mice were treated with GDC‐0941 . After treatment, the experimental mice  that were treated with GDC‐0941 and control mice  that were treated with vehicle alone were euthanatized, dissected, and imaged. The number of tumors was scored and each tumor was analyzed to determine its clonal origin/architecture.2.4Intestinal tracts were removed at necropsy, opened longitudinally, rinsed with PBS, fixed in 4% paraformaldehyde for 2 days at 4°C, and then stored in 70% ethanol at 4°C. To document the pattern of mosaicism, the small intestine and colon were mounted in 0.4% agarose and imaged with a Nikon Eclipse TI‐S microscope or Zeiss AxioPlan II.2.5The intestinal epithelium in experimental and control mice is a mosaic of GFP‐positive and of RFP‐positive crypts. The ability to detect multi‐ancestral tumors depends on the pattern of mosaicism. To quantify the mosaicism, whole‐tissue fluorescent images that were taken on a Nikon Eclipse TI‐S microscope were exported from ND2 files to single channel TIFF files prior to analysis. All images were processed using a macro for the Fiji image‐processing package of ImageJ to ensure consistency. Additional details on the image‐processing steps are available in the Supplemental Methods. For quality control, binary images were compared to color overlays; when necessary, the images were manually manipulated to more accurately reflect the patchwork of the tissue.+) and half the cells express Cre recombinase (GFP+).A variegation score was assigned to each binary image  were excised, embedded in paraffin, and cut into 5 μm sections. Every 20th section was stained with hematoxylin and eosin (H&E).4 solution in PBS for 10 min; blocking in 5% dehydrated milk in PBS for at least 1 h; incubation in anti‐tdTomato  and anti‐EGFP  primary antibodies overnight; incubation in anti‐rabbit  and anti‐mouse  secondary antibodies overnight; and staining of nuclei with 4′,6‐diamidino‐2‐phenylindole (DAPI) by applying ProLong Gold with DAPI mounting media .To determine whether cells from distinct lineages were neoplastic, slides of interest were stained by immunofluorescence for β‐catenin as described previouslyImages of H&E and immunohistochemical stained slides were acquired on a Nikon Eclipse TI‐S microscope using a halogen light source, no color filters, and either a 4× or 10× objective lens. Images of immunofluorescent stained slides were acquired on either a Zeiss AxioPlan II or Nikon Eclipse TI‐S microscope as previously described with either a 10× or 20× objective lens.The phenotype of each tumor was determined by a board‐certified pathologist (KAM). H&E‐stained slides were examined to identify neoplastic cells and areas of invasion and then compared to the immunofluorescent stained slides to determine which neoplastic cells were red and which neoplastic cells were green. Homotypic tumors were composed entirely of either RFP+ neoplastic cells or GFP+ neoplastic cells, whereas heterotypic tumors with a multi‐ancestral architecture were composed of a mixture of RFP‐positive and GFP‐positive neoplastic cells.2.72HPO4, 8 mM KH2PO4, 96.2 mM NaCl, 1.6 mM KCl, 43.4 mM sucrose, 54.9 mM D‐sorbital, and 0.5 mM DL‐Dithiothreitol) in a 15 m tube for 1 h on ice. After the incubation, samples were rinsed with chelation buffer and PBS. The samples were then suspended in DMEM containing 2.5% FBS, 50 U/ml Penicillin, 50 μg/ml Streptomycin, 1 mg/ml Collagenase Type XI (Sigma C9697), and 1.25 mg/ml Dispase II (Sigma D4963), placed for 45 min at 37°C, and shaken vigorously every 15 min. The supernatant from the digest was transferred to a clean 15 ml tube and cells were pelleted. The cells were washed three times and resuspended in cold PBS at a concentration of 600 cells/μl.Tumor samples were isolated for organoid culture based on an adaptation of a published protocol.Cell suspensions were mixed 1:1 with Growth‐Factor Reduced Matrigel (Corning #354230) and 50 μl drops were placed in the center of wells in a 24 well plate. All samples were plated in duplicate. Matrigel was allowed to solidify for 15 min at 37°C. Wells were cultured with 500 μl of advanced DMEM containing 50 U/ml Penicillin, 50 μg/ml Streptomycin, 1× Glutamax (Gibco), 1× HEPES, 1× B‐27 Supplement (Gibco), 1× N‐2 Supplement (Gibco), and 500 ng/ml mouse recombinant EGF. Media was changed every other day.To collect longitudinal data on growth patterns of tumor cells, six tumors were isolated as described above and plated in duplicate: two in which all neoplastic epithelial cells expressed tdTomato (“homotypic red”), two in which all neoplastic epithelial cells expressed EGFP (“homotypic green”), and two composed of a mixture of epithelial cells expressing tdTomato and EGFP (“heterotypic”). In addition, cells from each homotypic red tumor were mixed 50:50 with cells from each homotypic green tumor and plated in duplicate.A total of 299 organoids was imaged daily for 7 days. To collect longitudinal data, three random areas were selected from each well and imaged on day 0 before organoid formation; these areas were then imaged daily on days 1–7. White light, red fluorescent, and green fluorescent images were collected with a Nikon Eclipse TI‐S microscope with a 4× objective lens as previously described.To determine growth patterns, images were used to measure the maximum diameter of every organoid that was observed throughout the longitudinal experiment at each time point. The diameter growth rate for each organoid was calculated as the change in maximum diameter from the first time point to the final time point the organoid was observed, divided by the number of days the organoid was observed.2.85 to 3 × 106 cells in a volume of less than 400 μl were injected subcutaneously in the flank. The recipients were monitored weekly to assess tumor development and growth. The tumors were measured using veneer calipers. When engrafted tumors reached a volume of 2 cm3, the recipients were euthanized.Tumor cells were isolated as described above. After final washes, tumor samples were engrafted onto the flank of naïve B6 recipients. Minced tumors, cells from the digest, cells from sorting, or organoids were mixed at a 1:1 ratio with Matrigel (Corning #356234). For sorting, cells were suspended in PBS + 2% FBS + 1 mM EDTA, sorted into ADF + 2% FBS, spun down, and then resuspended in culture medium prior to injection. The samples consisting of 3 × 102.9Differences in tumor multiplicities and sizes between groups of mice were tested for statistical significance using the two‐sided Wilcoxon rank sum test. Proportions of tumors and clones within tumors that were invasive were tested with a two‐sided Fisher's exact test.33.1+ mT/mG+ double transgenic mice, Cre recombinase is expressed in approximately 50% of the epithelial cells in the distal small intestine and colon during embryogenesis; cells are then labeled by a reporter that expresses either a derivative of a red fluorescent protein (tdTomato) in the absence of Cre‐mediated recombination or an enhanced green fluorescent protein (EGFP) following Cre‐mediated recombination. In this way, clones are labeled prior to tumor initiation. The validity of this model for tracing ancestral clones in tumors was previously demonstrated in ApcMin/+ mice, which develop tumors throughout the entire length of the intestinal tract.+ mT/mG+ApcMin/+ mice are either wholly red or wholly green, the pattern of mosaicism that is established during embryogenesis is stable over time, and some  tumors were multi‐ancestral as they were composed of a mixture of wholly red or wholly green dysplastic crypts.To trace individual clones through tumor initiation, growth, and progression, we utilized transgenic animals that stably express reporter genes to differentially label clones in the intestinal tract. In Fabp1‐CrePik3ca* transgene was used. This transgene results in the expression of constitutively activated PI3K within cells that express Cre recombinase.+ mT/mG+ Pik3ca*+ApcMin/+ animals were a mixture of tdTomato‐labeled (red) cells harboring a mutant Apc allele and EGFP‐labeled (green) cells harboring a mutant Apc allele and expressing activated PI3K. As tumors with a multi‐ancestral origin/architecture can only be detected when they arise from crypts expressing different colored labels, and because they arise as a consequence of short‐range interactions among neighboring crypts,+ mT/mG+ Pik3ca*+ApcMin/+ mice. A high score indicates more intermingling . A difference in tumor maximum diameter between experimental and controls was also observed . These effects were expected based on the results of a previous study.+ mT/mG+ Pik3ca*+ApcMin/+ mice with PI3K activated developed 3‐fold more tumors on average in the distal quarter of the small intestine (SI‐4) than ApcMin/+ controls without PI3K activated . Moreover, Fabp1‐Cre+ mT/mG+ Pik3ca*+ApcMin/+ mice developed on average 13‐fold more tumors in the colon (CO) ApcMin/+ controls . Expression of the mT/mG reporter transgene did not significantly change tumor number   of the small intestine; Supplemental Table To determine whether the s Figure . All (10) Figure  and S4. 3.3+ mT/mG+ Pik3ca*+ApcMin/+ mice were sectioned and stained  tumors from Fabp1‐Cred Figure . H&E sli3.4p < 0.001). We next sought to determine whether the clones were cooperating to their mutual advantage . tdTomato‐labeled cells showed a similar trend but the change was not statically significant. They were invasive in 25% (2/8) of homotypic red tumors, which lacked a neoplastic EGFP‐labeled component, and in 60% (18/30) of heterotypic tumors in which the tdTomato‐labeled cells had neoplastic EGFP‐labeled partners . In addition, both clones invaded farther in invasive multi‐ancestral tumors than in their homotypic counterparts  of non‐invasive tumors were heterotypic compared to 57% (29/51) of adenocarcinomas . The numbers of homotypic green and heterotypic tumors were less in the aspirin‐treated cohort than the non‐treated control cohort , whereas the number of homotypic red was unchanged when comparing the two cohorts  . Thus, aspirin appears to inhibit the development of Pik3ca‐mutant tumors in the laboratory mice, recapitulating its effect on PIK3CA‐mutant cancers in humans.Epidemiological studies have found that low‐dose aspirin reduces the risk of colorectal cancer recurrence in patients treated for PIK3CA‐mutant cancers.) Figure . The eff+ mT/mG+ Pik3ca*+ApcMin/+ mice with GDC‐0941, an inhibitor of PI3K . The change in the distribution of tumor types was significant when comparing the GDC‐0941 treated cohort to the non‐treated control cohort .Preliminary data indicate that susceptible cells might be even protected by resistant cells. We treated Fabp1‐Cre3.6+ mT/mG+ Pik3ca*+ApcMin/+ mice  and tumor cells with a relatively low risk of malignant transformation  resulted in a greater invasion of even the green cells. Other studies of clonal architecture made similar observations. Marusyk et al.Our study has implications for the clinic. The research team of Dr. Liskay reported that a critical mass of Apc‐deficient cells must form to efficiently establish a tumor.Our study has limitations. The Min mouse is a model of familial adenomatous polyposis which is a hereditary form of colorectal cancer. Data are steadily accruing indicating that tumors with a multi‐ancestral architecture can form in patients with sporadic disease. Our findings are likely not only relevant to hereditary forms of colorectal cancers. Another limitation of our study is that Cre recombination is not equally efficient for all transgenes in all cells.In the age of genomics and sequencing, a major focus of cancer research is upon identifying the genetic aberrations within clones. This focus emphasizes genetic heterogeneity as a potential mechanism for tumor adaptability and survivability; in the presence of insults such as immune system activation, hypoxic conditions, or chemotherapy, a genetically heterogeneous tumor has numerous opportunities to contain a pre‐existing resistant clone. In this view of tumor heterogeneity, each clone exists independent of all other clones. Another component of tumor heterogeneity, however, is cooperation between clones.The authors have no conflict of interest to declare.Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Writing—Original Draft, A.A.L.; Investigation, Methodology, B.J.G.; Investigation, Methodology, A.M.W.; Investigation, Methodology, E.R.W; Investigation, Methodology, S.W.; Investigation, Methodology, C.D.Z.; Formal Analysis, Investigation, Writing—Review & Editing, K.A.M.; Conceptualization, Formal Analysis, Investigation, Writing—Review & Editing, D.A.D.; Funding Acquisition, Investigation, N.K.; Investigation, Methodology, Q.R.; Conceptualization, Investigation, Methodology, Writing—Review & Editing, C.K.S.; Investigation, Methodology, A.R.S.; Formal Analysis, Investigation, Methodology, Writing—Review & Editing, D.M.A.; Data Curation, Visualization, Writing—Review & Editing, L.C.; Funding Acquisition, Investigation, Methodology, H.M.; Formal Analysis, Supervision, Writing—Review & Editing, M.A.N.; Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Writing—Review & Editing, R.B.H.All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data. Animal studies were conducted under protocols approved by the School of Medicine and Public Health Institutional Animal Care and Use Committee at the University of Wisconsin in compliance with policies established by the Office of Laboratory Animal Welfare at the National Institutes of Health.Appendix S1: Supporting InformationClick here for additional data file.


Lipoprotein(a)[Lp(a)] has been considered as an independent risk factor for coronary artery disease (CAD). The present study aimed to evaluate the association between baseline serum Lp(a) and CAD progression determined by angiographic score.A total of 814 patients who had undergone two or more coronary computed tomography angiography at least 6 months apart were consecutively enrolled and the coronary severity was determined by the Gensini score system. Patients were stratified into two groups according to Lp(a)>300 mg/L and Lp(a) ≤ 300 mg/L or classified as “progressors” and “non‐progressors” based on the Gensini score rate of change per year. The association of continuous Lp(a) and Lp(a)>300 mg/L with CAD progression were respectively assessed by logistic regression analysis. Moreover, further evaluation of those association was performed in subgroups of the study population.p<.001) and Lp(a)>300 mg/L  were positively associated with CAD progression after adjusting for confounding factors. In addition, those relation seemed to be more prominent in subjects with lower body mass index .Patients in the “progressors” group had significant higher Lp(a) levels. Furthermore, the multivariate logistic regression analysis showed that elevated Lp(a)  is positively and independently associated with angiographic progression of CAD, particularly in participants with relatively low body mass index. Therefore, Lp(a) could be a potent risk factor for CAD progression, assisting in early risk stratification in cardiovascular patients. Despite great advances in its diagnosis, treatment, and prevention, adverse cardiovascular events do not seem to decline on the background of optimal medical therapy.Lipoprotein(a)[Lp(a)] is composed of a low‐density lipoprotein(LDL)‐like particle with its apolipoprotein (apo)B100, which covalently bound to a characteristic glycoprotein apolipoprotein a[apo(a)] by a disulfide bond.Hence, in the present study, we aimed to analyze the association between baseline Lp(a) levels and angiographic progression of CAD by Gensini score system according to the results of coronary CTA in suspected CAD patients at outpatient department.22.12, acute inflammatory disease, 0.5<Gensini score rate of change per year≤1, or missing data. Eventually, 814 patients were analyzed. , an abnormal echocardiogram, positive treadmill exercise test or elevated cardiovascular risk. Based on the severity of angina‐like symptoms or coronary CTA images, some of them were admitted for further treatment. Among them, 421 patients receiving percutaneous coronary intervention or coronary bypass surgery were excluded. In addition, other exclusion criteria included patients with malignancy, estimated glomerular filtration rate(eGFR)<60 ml/min/1.73m2.22. Hypertension (HTN) was defined as systolic blood pressure (SBP) ≥ 140 mm Hg and/or diastolic blood pressure ≥90 mmHg or currently using antihypertensive agents. Diabetes (DM) was defined as fasting serum glucose≥7.0 mmol/L or random serum glucose≥11.1 mmol/L or the 2‐h serum glucose of the oral glucose tolerance test≥11.1 mmol/L or using anti‐diabetic medication. Smoking status was defined as a person who smoked at the time of initial coronary CTA or who had quit smoking within the year before initial coronary CTA. Obstructive CAD was defined as coronary stenosis of 50% or more in the epicardial coronary artery determined by initial coronary CTA.Clinical parameters including age, sex, height, weight, systolic and diastolic blood pressure (DBP), heart rate, comorbidities, and medications were collected at outpatient or admission. Body mass index (BMI) was calculated from weight in kg divided by height in mBlood samples were obtained from each patient in the fasting state at outpatient or after admission and then tested in laboratory medicine as soon as possible. The serum lipid profiles including total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL‐C), high density lipoprotein cholesterol (HDL‐C), apoA1, apoB100, and Lp(a) were determined by automatic biochemical analyzer . In addition, in detail, TC and TG were measured with enzyme colorimetry method, while LDL‐C and HDL‐C were determined by the direct method. ApoA1 and apoB100 were measured with immunoturbidimetry method. The serum concentration of Lp(a) was analyzed using latex immunoturbidimetry method. Due to racial differences, the optimal risk cutoffs for cardiovascular disease were still not clearly determined. The European Atherosclerosis Society proposed Lp(a) <50 mg/dl as the appropriate cutoffs, while China and the United States recommended Lp(a) <30 mg/dl as a desirable level.2.3The 320‐slice coronary CTA was performed by a predefined standard operating procedure as previously described.All coronary CTA images were reconstructed and analyzed using a dedicated workstation  by experienced radiologists, which in accordance with the Society of Cardiovascular Computed Tomography guidelines.2.4SD, or median with interquartile range, as appropriate. Student t test or Mann–Whitney U‐test was used to determine the differences in continuous variables between groups. Categorical variables were expressed as frequencies with percentage and analyzed by Chi square tests. Logistic regression analyses were conducted to explore the association between baseline Lp(a) and angiographic progression of CAD. Firstly, univariate logistic regression analyses were performed. Baseline Lp(a) was analyzed as a continuous variable or a categorical variable with a cutoff value as 300 mg/L according to Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adult,p‐value of <.05 was defined as statistically significant.All statistical analyses were performed using SPSS 25.0 software  for Windows. Continuous variables were shown as mean ± 33.1A total of 814 eligible patients were entered into this study. The baseline serum Lp(a) values ranged from 3.00 to 1575.00 mg/L . Table 3.2p = .018).Subjects were divided into two groups on the basis of the presence of angiographic CAD progression. As is shown in Table 3.3p = .018).Table 3.4p = .002). In the multivariate analysis adjusting for age and sex in Model 1 showed the odds ratio for progression was 1.284(95% CI 1.095–1.506 p = .002). In Model 2, results seemed to enhanced these correlation after adjustment of clinical comorbidities, smoking status and BMI . In Model 3 including other serum lipids, HbA1c, FPG, creatinine, UA and WBC as covariates together, increasing Lp(a) level was still positively correlated to CAD progression .As a log‐transformed continuous variable, univariate logistic regression analysis demonstrated that increasing Lp(a) level was positively associated with CAD progression .This positive correlation remained statistically significant after adjusting for age, sex, BMI, history of HTN, DM, initially obstructive CAD, smoking status, HbA1c, FPG, non‐HDL, HDL‐C, LDL‐C, creatinine, UA, and WBC . Results of logistic regression analyses are summarized in Table The association between baseline Lp(a) and CAD progression was also explored by categorizing the Lp(a) with a cutoff value of 300 mg/L. In unadjusted multivariable logistic regression analysis revealed that the odds ratio for CAD progression increased in patients with higher Lp(a) group .Further assessment of the association between baseline serum Lp(a) and CAD progression was performed in various subgroups of the study populationFigure . After a4In the present study, our results demonstrated that elevated baseline serum Lp(a) was positively associated with angiographic progression of coronary artery disease, particularly in patients with relatively lower BMI, independent of traditional cardiovascular risk factors, including age, sex, BMI, history of HTN, DM, initially obstructive CAD, smoking status, HbA1c, FPG, non‐HDL, HDL‐C, LDL‐C, creatinine, UA and WBC. Our findings contribute to a growing body of literature supporting the hypothesis that Lp(a) is correlated with progression of CAD.Many previous studies have described the correlation between Lp(a) and coronary atherosclerosis progression using invasive imaging methods. In one study performed in 76 patients with acute coronary syndrome (ACS) who underwent intravascular ultrasound(IVUS) at baseline and at 10‐month follow‐up after percutaneous coronary intervention, the results showed that patients with serum levels of Lp(a) >20 mg/dl had slight plaque progression despite of statin therapy, suggesting Lp(a) levels could be an alternative predictor of further plaque progression.p = 0.871), implying the potential predictive value of Lp(a) on the basis of comparable LDL‐C levels. Interestingly, a meta‐analysis with 11 studies including 18 979 patients was performed to assess the prognostic value of Lp(a), the authors confirmed that the correlation between Lp(a) and cardiovascular events may be attenuated in patients with lower levels of LDL‐C.Conversely, Puri et al. found no evidence of a correlation of Lp(a) with coronary atheroma progression rate during 2 years high‐intensity statin therapy in sub‐analysis of the Study of Coronary Atheroma by Intravascular Ultrasound: Effect of Rosuvastatin Versus Atorvastatin (SATURN) trial.Another interesting finding of the present study was that the association of baseline Lp(a) and CAD progression seemed to be more prominent in subjects with relatively lower BMI, even after controlling for conventional covariates. In prior studies, obesity has been considered as an independent risk factor for CAD, as well as increased rates of adverse cardiovascular events.In the present study, there were several limitations. First, due to the variable numbers of the KIV type2 domains, method we used in the present study may be influenced by the apo(a) sizes. The results of Lp(a) levels may be overestimated or underestimated and may cause inaccurate evaluation of a patient's risk for CAD progression. Second, since this was a retrospective, longitudinal study conducted at a single‐center, residual confounders are hard to avoid. For example, diet, excise, alcohol consumption patterns, and genetic background were not monitored during the study, which was variable. Third, this study only enrolled patients who took repeated coronary CTA for suspected CAD, which could lead to a selection bias. Fourth, only quantitative CTA analysis and angiographic score were performed due to noninvasive and convenience for clinical application, qualitative analysis of coronary plaques was unavailable. Fifth, as Lp(a) was genetically determined by LPA gene, with little influence from lifestyle interventions, therefore, Lp(a) levels did not fluctuate significantly during the whole life.5In conclusion, the current study found that baseline serum Lp(a) concentration was significantly associated with angiographic progression of CAD, particularly in those with relatively low BMI. Therefore, Lp(a) could be a potent risk factor for progression of CAD, assisting in early risk stratification in cardiovascular patients. Further studies will clearly be required to confirm our findings.No conflict of interest was declared.Conceived and designed the study: Lin Chen and Zhen Wu; clinical data acquisition: Binghan Zheng and Yongxia Wu; data analysis: Xujing Xie, Zefeng Chen and Zhen Wu; statistical analysis: Xing Shui and Zheqi Wen; Manuscript drafting: Xing Shui, Zheqi Wen and Zefeng Chen; Each author contributed important intellectual content during manuscript writing or revision.Supplementary Table S1 Baseline clinical and biochemical data according to the CAD progressionClick here for additional data file.


Listeria monocytogenes PlcB, Clostridium perfringens PLC, and Pseudomonas aeruginosa PlcH are the most deeply understood. In silico predictions of substrates docking with these three bacterial enzymes provide evidence that they interact with different substrates at the same active site. This review discusses structural aspects, substrate specificity, and the mechanism of action of those bacterial enzymes on target cells and animal infection models to shed light on their roles in pathogenesis.Bacterial phospholipases and sphingomyelinases are lipolytic esterases that are structurally and evolutionarily heterogeneous. These enzymes play crucial roles as virulence factors in several human and animal infectious diseases. Some bacterial phospholipases C (PLCs) have both phosphatidylcholinesterase and sphingomyelinase C activities. Among them,  in silico substrate docking analysis are discussed, as well as their mechanisms of toxicity in cultured cells and animal models.The aim of this review is to discuss similarities and differences among the most explored bacterial enzymes that have both phospholipase C and sphingomyelinase C activities. The structural aspects of three of those enzymes, their substrate specificity, and  CHOChinese hamster ovaryCpPLCClostridium perfringens PLCEDTAethylenediaminetetraacetic acidEGTAegtazic acidHUVEChuman umbilical vein endothelialLLOlisteriolysin OLmPlcBListeria monocytogenes PLCMplListerial metalloproteasePaPlcHPseudomonas aeruginosa PlcHPCphosphatidylcholinePEphosphatidylethanolaminePGphosphatidylglycerolPIphosphatidylinositolPLCsphospholipases CPSphosphatidylserineROSreactive oxygen speciesSMsphingomyelinTrkATyr kinase A receptorBacterial phospholipases and sphingomyelinases are a structurally and evolutionarily heterogeneous group of lipolytic esterases , 2, 3. TListeria monocytogenes PLC (LmPlcB), Clostridium perfringens PLC (CpPLC), and Pseudomonas aeruginosa PlcH (PaPlcH) are the most deeply explored. CpPLC and LmPlc are zinc metalloenzymes, whereas PaPlcH belongs to the PLC/phosphatase superfamily. This review aims to discuss the similarities and differences among those bacterial enzymes. Although the regulation of their expression is beyond the scope of this review, the structural aspects of LmPlcB, CpPLC, and PaPlcH, their substrate specificity and mechanisms of toxicity both in cultured cells and animal models are discussed below.Although most bacterial lipolytic esterases have either phospholipase or sphingomyelinase activity, some of them cleave glycerophospholipids and sphingomyelin (SM) . Among tL. monocytogenes is a Gram‐positive facultative intracellular bacterium that causes severe foodborne infections [L. monocytogenes multiplies and spreads from cell to cell using an actin‐based mechanism of motility [L. monocytogenes secretes several toxins that favor phagosomal escape, including LmPlcB and a phosphatidylinositol‐PLC (PI‐PLC) [fections . The bacfections , 4. Oncemotility , 4. Thismotility , 4. L. m(PI‐PLC) , 5. Once(PI‐PLC) , 4.C. perfringens is a Gram‐positive anaerobic bacterium that produces more than 17 toxins [C. perfringens strains and is the main virulence factor in gas gangrene in humans [C. perfringens escape the phagosome and persist in the cytoplasm of macrophages during the earliest infection stages [7 toxins . This ba7 toxins , 9. CpPLn humans . This din humans . Gas gann humans . CpPLC hn stages . Then, Cn stages .P. aeruginosa is a Gram‐negative opportunistic pathogen associated with acute and chronic infections in predisposed human subjects [P. aeruginosa lung infections are common and severe in chronic obstructive pulmonary disease and cystic fibrosis [P. aeruginosa PlcH releases phosphocholine from SM and phosphatidylcholine (PC), the major components of the pulmonary surfactant, thus aiding in bacterial dispersion [subjects . P. aerufibrosis . P. aeruspersion .B. cereus PLC [+2 ions at the active site, although LmPlcB has a considerably weaker affinity for Zn+2 than the B. cereus enzyme [LmPlcB is a 33 kDa single‐domain zinc metalloenzyme secreted as a precursor. Its activation requires the cleavage of an amino‐terminal pro‐peptide by either a listerial metalloprotease (Mpl) or host proteases, resulting in the active 29 kDa enzyme , 14, 15.https://www.uniprot.org/) to build a structural model with SWISS‐MODEL [http://swissmodel.expasy.org/) using B. cereus PLC (PDB accession No. 1ah7) as a template. The predicted structure shows a α‐helical single‐domain protein with three zinc atoms, as visualized using Discovery Studio Visualizer  was retrieved from the UniProt database   is shown in Fig. CpPLC is a 45 kDa zinc metalloenzyme composed of two domains joined by a short flexible linker region , 20. The. cereus  and L. menes PLC , and theof CpPLC , and theof CpPLC . The C‐tbstrates , 24. A ‘ domains . The strB. cereus PLC [+2 binding sites in the C‐terminal domain and orienting its active catalytic site to interact directly with membrane phospholipids [B. cereus helix hairpin is replaced with a tryptophan residue, which, together with several hydrophobic residues in the C‐terminal domain , is well placed to interact with the hydrophobic membrane interior. In addition, the C‐terminal domain, analogous to C2 domains, binds Ca+2 ions but does not complete their coordination spheres, and has many positively charged residues (such as Lys300) placed to interact with the phosphate at the membrane surface [B. cereus homologue [Structurally, CpPLC lacks two helices in the N‐terminal domain present in reus PLC , resultiholipids . The abs surface . These aomologue , 19.F. tularensis [M. tuberculosis, Bordetella spp., F. tularensis, and Burkholderia spp, and homologs in fungi (Aspergillus fumigatus) and plants (Arabidopsis) [PaPlcH does not share any structural similarity with LmPlcB or CpPLC, but has 23% sequence identity, as the acid phosphatase AcpA from larensis , 27. Thuidopsis) , 30, 31.https://www.uniprot.org/) [F. tularensis Acid Phosphatase A (PDB accession No 2d1g) as a template [http://swissmodel.expasy.org/). The predicted structure  was retrieved from the UniProt database (ot.org/) . The PaPtemplate  , phosphatidylserine (PS), PI, cardiolipin, phosphatidylglycerol (PG), plasmalogens, and plasmenylethanolamine , 32. The product . On largl spread . Its enzl spread , 37, 38 l spread , 40. Thepreading . Interessurvival .Docking studies with PC and SM were used for visualizing their interaction with the LmPlcB catalytically active site Fig. .LmPlcB is catalytically active at a wide pH range (5.0–8.0) , 32, altC. perfringens to infect different tissues and help the bacterium escape the early phagosome toward the macrophage cytoplasm [CpPLC hydrolyzes PC and SM , 47, 48,ytoplasm , 51. Usiytoplasm .Docking studies with PC and SM were used for visualizing their interaction with the CpPLC catalytically active site Fig. .+2 and several divalent cations [PaPlcH hydrolyzes mainly PC and SM, but it also acts on cardiolipin, PE, and PG when assayed on large unilamellar vesicles , 54. How cations . Further cations .Figure PaPlcH partially purified from a clinical isolate showed enzymatic activity from pH 5.5–8, optimal at 7–8 . HoweverThe substrates for the bacterial enzymes discussed in this review and their corresponding optimal pH are summarized in Table Iron is an essential resource for bacteria, and in vertebrate hosts, high amounts of iron are incorporated into hemoglobin . Thus, aLmPlcB is hemolytic, and this activity requires the presence of PC in the plasma membrane of the target erythrocytes . ErythroC. perfringens supernatants evidenced their hemolytic activity when injected into pigeons, sheep, and rabbits [+2 or the Zn+2 binding domain, which affect catalytic activity against aggregated substrates, lack hemolytic activity [Initial studies with  rabbits . This ac rabbits , 57. Fur rabbits , 59, 60. rabbits . Later s rabbits , 65, 66.activity , 63, 64.A two‐stage interaction of CpPLC and human erythrocytes was shown using thermal shape fluctuation spectroscopy . This fiPaPlcH caused hot‐cold hemolysis of human and sheep erythrocytes . The hotin vitro and in vivo [CpPLC causes platelet aggregation  in vivo , 71, 72. in vivo . The eff in vivo . Similar in vivo .The cellular effects of PLCs with phosphatidylcholinesterase and SMase activities have been evaluated on different cell lines, as discussed below.L. monocytogenes observed under electron microscopy showed significantly less lysis of the two‐membrane vacuoles that surround the bacteria after cell‐to‐cell spread than the wild‐type [A double mutant of both the LmPlcB and the PI‐PLC genes shows severely diminished cell‐to‐cell spread effect in a plaque assay with L2 fibroblasts . Accordiild‐type . Infectiild‐type .L. monocytogenes mutants lacking LLO and LmPlcB or Mpl showed that the last two are required for bacterial growth and lysis of the primary vacuole in epithelial cells, suggesting that the relative importance of LLO, the phospholipases, and Mpl varies in different cell types [L.fnd="ER"> monocytogenes‐containing primary vacuoles in some epithelial cell lines, such as Hep‐2 and HeLa [In murine bone marrow‐derived macrophages, both PI‐PLC and LmPlcB facilitate membrane disruption by acting on the inner membrane of the spreading vacuole, whereas LLO is required to degrade the outer membrane . Studiesll types . Accordiand HeLa , 36 or dand HeLa . LmPlcB and HeLa . An LLO and HeLa . In a pland HeLa . Furtherand HeLa , 77.H‐uridine and detecting the leakage of radioactive compounds, CpPLC treatment for 30 min was found to result in 25% of radioactivity released at concentrations of 16 µg·mL−1, reaching 100% maximum release after 1 h associated with the hydrolysis of membrane phospholipids [The first report of the action of CpPLC on nucleated cells was to determine the membrane damage effect on human diploid fibroblasts and Hela cells . By labeholipids . Sarcomaholipids . CpPLC‐tholipids . Later, holipids  due to aholipids , 82. Sinholipids . Muscle holipids , 85. Thiholipids . This hyholipids , 86, 87.Several other cell types, such as cultured intestinal epithelial cells, Chinese hamster ovary (CHO), and Hela cells, have been used to study the effects of DAG generation induced by CpPLC, such as arachidonic acid release, PKC, PLA2, and calcium activation , 91, 92.P. aeruginosa extracellular protease LepA [PaPlcH induces the release of prostaglandins and leukotrienes in human granulocytes and mouse peritoneal cells . PaPlcH ase LepA .PaPlcH suppresses respiratory burst in neutrophils by interfering with a PKC‐dependent pathway . Thus, aPaPlcHR is slightly cytotoxic to lung epithelial cells from a patient with cystic fibrosis and to HeLa cells, and highly toxic (at picomolar concentrations) to CHO and HUVEC . PaPlcHRStudying the effects of bacterial PLCs with phosphatidylcholinesterase and SMase activities or bacterial mutant strains lacking their corresponding genes in animal models has revealed important insights of these enzymes in virulence.L. monocytogenes mutant strain lacking LmPlcB is 20‐fold less lethal to mice [mpl mutant that fails to produce mature LmPlcB showed impaired virulence in mice compared with a wild‐type strain and, at later stages of infection, exhibits less growth in mouse liver and spleen [While LmPlcB is not toxic even when up to 25 µg is injected intravenously into mice , a L. mo to mice . An mpl d spleen . It has d spleen . Later, d spleen .C. perfringens [C. perfringens plc mutant cannot induce gas gangrene in mice [+2 binding, or lacking surface‐exposed hydrophobic residues, failed to produce myonecrosis in a gas gangrene murine model [in vivo [C. perfringens, which could contribute to myonecrosis progression [CpPLC is the main virulence factor in gas gangrene , 10. Intfringens . A C. pe in mice . CpPLC i in mice . CpPLC vne model , 64, 113[in vivo , 114. St[in vivo . On the gression .P. aeruginosa sepsis [P. aeruginosa strain PaPlcH mutant exhibits decreased virulence in a murine burn infection model [P. aeruginosa, a PaPlcH mutant showed reduced virulence compared to the wild‐type strain [PaPlcH causes dermonecrosis, vascular permeability, and death when injected intraperitoneally into mice . This ena sepsis . PaPlcH a sepsis . A P. aeon model . Similare strain . The nume strain .P. aeruginosa, and activates biofilm formation and anaerobic metabolism [P. aeruginosa lung infections by inhibiting the function of the cystic fibrosis transmembrane conductance regulator Cl channel in epithelial cells, leading to thick mucus production that clogs the airways, which fosters bacterial growth [Phosphorylcholine release from the pulmonary surfactant lipids by PlcH during lung infections provides nutrients to tabolism , 120. Ful growth .in silico docking assays while acting over different substrates, they generate 1,2‐diacylglycerol and ceramide, which change the biophysical properties of cellular target membranes. These products can undergo spontaneous transbilayer movement and recruit signaling proteins, affecting cellular processes such as cell cycle arrest, stress responses, autophagy, apoptosis, and cytokine production. The particular role of the bacterial enzymes in pathogenesis depends on the context of the infection. LmPlcB is activated when L. monocytogenes reaches the acidic environment of late endosomes‐lysosomes, aiding bacterial escape toward the cytosol and cell‐to‐cell propagation. On the other hand, when C. perfringens reaches a certain density in soft tissues and CpPLC is produced, this toxin induces endothelial damage and platelet aggregation, which favors ischemia, thus providing an optimal environment for bacterial growth. Finally, PaPlcH secreted by P. aeruginosa, hydrolyzes target phospholipids where the infection is being established, helps bacteria to obtain substrates from the host, and triggers signaling pathways that lead to inflammation. Several other bacteria such as Burkholderia pseudomallei and Mycobacterium tuberculosis produce enzymes homologous to PaPlcH, but their role in pathogenesis remains to be clarified. The data already available for LmPlcB, CpPLC, and PaPlcH can provide insights to further our understanding of their mechanism of action.Bacterial PLCs with phosphatidylcholinesterase and sphingomyelinase C activities, such as LmPlcB, CpPLC, and PaPlcH, can exert diverse toxic effects. As observed in The authors declare no conflict of interest.LM‐G wrote the first draft of the manuscript; FV‐R did the docking studies; MF‐D and AA‐G review and substantively edited the manuscript. All authors read and approved the final version and take responsibility for the overall content and integrity of the work.


Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture‐based diagnosis in clinics is increasingly supplemented by metagenomic next‐generation‐sequencing (mNGS). Here, RNA/cDNA‐targeted sequencing (meta‐transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms  to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple‐strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct‐RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA‐targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT. Targeting ribonucleic acid (RNA), the authors develop meta‐transcriptomic‐based pathogen detection using next‐generation sequencing (mtNGS) with Illumina and third generation sequencing (mtTGS) using Oxford Nanopore Technology (ONT), and show they outperform the current DNA‐based approaches in efficiency and accuracy, covering wider range of pathogens including bacteria, viruses and fungi. Further, direct RNA sequencing with ONT shows promising advantages and warrants further investigation.  However, mNGS still faces numerous obstacles in terms of its clinical application, especially when the materials used tend to be low in both volume and microbial quantity, such as throat swab samples, bronchoalveolar lavage fluid samples (BALF), blood samples, and cerebrospinal fluid samples (CSF). Simultaneously, host cells and thus nucleotides tend to constitute the majority in such samples , dramatically reducing the efficiency of sequencing for microbial identification.Infection continues to be an immense threat to human health worldwide, casting a heavy burden on clinical diagnoses and treatments.1, 2 Thus, RNA viruses will be neglected from solely DNA‐based mNGS, sometimes hindering important diagnoses. Even for bacteria and fungi, despite relatively few studies on RNA‐based profiling or metatranscriptomics, the capability of revealing functionally active members/genes has provided novel insights into the human microbiome in host and health; in addition, while the bacterial genome has just a few copies of rDNA, rRNA molecules exist on a magnitude of thousands in active cells, and RNA targeting could, in theory, avoid dominant but dormant microbes making up a large percentage of sequencing reads.Current platforms for next‐generation sequencing (NGS) are widely utilized for many applications, including pathogen identification, but they can only sequence DNA molecules and produce relatively short reads. While DNA composes the genetic material for pathogenic bacteria and fungi, RNA viruses compose a large fraction of infectious pathogens as well, including the influenza that causes seasonal flu as well as global pandemic and, at the time of this work, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) responsible for the coronavirus disease 2019 (COVID‐19) pandemic and global unrest.11 Thu ONT, in contrast, could produce longer reads  and generate sequences in real time, and it has already been recently applied in clinical applications for the rapid identification of pathogens targeting microbial DNA. In addition, ONT offers novel prospects for clinical application due to its capacity for direct RNA sequencing. In view of these rationales, we carried out a prospective study of targeting RNA (thus metatranscriptome) for microbial pathogen profiling in representative clinical samples, demonstrating its improvement from DNA‐targeted sequencing approaches, and we further explored the potential of using the ONT platform for pathogen identification with either cDNA sequencing or direct RNA sequencing.Interesting features from third‐generation sequencing (TGS), such as Oxford Nanopore Technology (ONT), might further improve sequencing‐based pathogen identification. NGS usually produces fragmented, short reads that need to be assembled before taxonomic assignments and/or antibiotic resistance determination, and the time required for sequencing  on current NGS platforms also takes a few days. However, shortening the diagnosis time can greatly benefit patients; for instance, intensive‐care unit (ICU) patients face life‐threatening infections, such as sepsis, encephalitis, meningitis, toxin‐induced cardiocirculatory shock, failures of central nervous system function, respiratory insufficiency or multiorganic failure, which can become lethal within hours; and consequently, their survival rate is estimated to increase 7% by each shortened hour for pathogen diagnosis.18 ONT22.1Table In this study, we collected 39 bronchoalveolar lavage fluid (BALF) samples, 14 blood samples, and nine cerebrospinal fluid (CSF) samples, each from different patients diagnosed with infections. Samples were obtained from two medical centers in Beijing and one in Guangzhou , it is first demonstrated the higher efficiency of mtNGS for microbial profiling in general, including a higher proportion of microbial reads than with the mNGS approach and, in particular, elevated copy numbers of taxonomy‐resolving genes (16S and 23S rDNA/rRNA genes for bacteria). Although host DNA was predominant in DNA‐based mNGS , microbial cDNA was significantly increased and enriched in the mtNGS results   and individually in blood  and CSF samples , while relatively more complex communities, such as BALF, showed low, nonsignificant correlations   as well as in each individual type of sample   Figure . Using 2e Figure . Using M) Figure . Our res2.3r2 = 0.31 in all samples, 0.33 in BALF, 0.42 in blood and 0.21 in CSF samples, all p < 0.05). More importantly, the relative abundances of major virus groups were between one to two magnitudes higher in mtNGS than in mNGS, further demonstrating the higher efficiency in viral detection and profiling using mtNGS , in contrast to 694 viruses using mNGS . Importantly, 271 RNA viruses, including known infectious viruses such as hepatitis C virus , human coronavirus 229E, and human parainfluenza virus 3, were identified using mtNGS assay. In mtNGS and mNGS approach, 619 shared viruses were identified in all samples and their abundances showed significant correlations among different categories of samples  on average , the relative ratio of 18S reads increased more than fivefold to 0.000071% on average  in mtNGS , and in mNGS, 1004 ARGs were identified in all samples , while the mtNGS data also revealed 914 in all samples , with high correlations between DNA and cDNA  for shared ARGs. Although mtNGS did not differ greatly from mNGS in the total number of ARGs discovered, further inspection revealed the ARGs with higher occurrences , with mtNGS alone discovering 45.9% (1034/2250 occurrences) and mtNGS and mNGS providing a shared identification of 37.4% of such ARGs (838/2250 occurrences), while only 16.8% (378/2250 occurrences) were found by mNGS , 42 had positive bacterial culture results, and a total of 61 strains of bacteria were identified. The majority were from BALF samples with 29 samples and 47 isolates, and the rest were from four blood samples with four isolates and nine CSF samples with ten isolates. The most common species were Acinetobacter baumannii (eleven strains), Staphylococcus aureus (eight strains), and Klebsiella pneumoniae (nine strains), and the rest of the bacterial species had fewer than five total isolates in our samples. Surprisingly, mNGS was only capable of reporting one strain of Klebsiella pneumoniae using 16S rDNA mapping and four strains of bacteria using 23S rDNA. Even by expanding the number of genetic markers using MetaPhlAn, the number of positive discoveries  achieved only 26 . In contrast, mtNGS significantly improved the positive discovery using single phylogenetic markers, identifying 25 strains using 16S rRNA (25‐fold increase compared with mNGS) and 40 using 23S rRNA (tenfold increase). In terms of the MetaPhlAn‐based approach using multiple markers, mtNGS achieved nearly the same amount of positive discovery (25), missing Staphylococcus aureus, Streptococcus pneumoniae, and Escherichia coli independently in three samples but simultaneously discovering Escherichia coli and Enterococcus independently in another two samples that were not detected by mNGS , culture‐based results were crosschecked for the clinical samples used in this study with our mNGS and mtNGS data. Of all the samples subjected to mNGS and mtNGS  and Saccharomyces (two samples), using culture‐based methods. Here, only positive identification was achieved with mtNGS for Saccharomyces using 18S rRNA as a genetic marker, and mNGS could not positively identify any fungal pathogens.We then expanded the comparison of mtNGS versus mNGS to examine their power to discover pathogenic viruses and fungi, and it is found that despite the overall low number of viral/fungal pathogens diagnosed in clinics, the performance of mtNGS still surpassed that of mNGS. Viral pathogens were only identified in blood samples using a corresponding polymerase chain reaction (PCR) detection kit for clinically relevant viruses, with nine samples positive for either influenza A virus (six samples), hepatitis C virus (two samples), or human rhinovirus (one sample). Among those, mtNGS identified hepatitis C virus in two samples and one influenza A virus sample. Similarly, in terms of fungal pathogens, eight BALF samples were identified to have nine isolates of fungi, mainly As for the abundance of sequencing in each sample, MetaPhlAn (multimarker) approach showed that the bacterial pathogens detected by conventional methods were among the most abundant taxa discovered in the mNGS (median rank 2nd) and mtNGS (median rank 3rd), while depending on single markers such as 23S still identified the corresponding bacteria among the top ten species , though using 16S marker only revealed one positive identification in mNGS, and low ranking for pathogens in mtNGS. . ONT allows relatively quick library preparations of DNA libraries within hours, and combined with real‐time sequencing and base‐calling on an individual‐read basis, these features have already been used to accelerate the clinical diagnosis of pathogens using DNA.20 In Figure Microbial compositional analyses revealed further improvements in mtTGS in terms of sequencing efficiency. Compared with mtNGS, mtTGS provided similar percentage of microbial reads, and both were higher than mNGS. However, the percentage of reads that could be mapped to fungi (18S) increased significantly in all three sample types, as well as the total reads that could be mapped to the database used in MetaPhlAn for bacteria identification, all with a high fold change  Figure 4. PotenWhen comparing the identity of the identified bacteria and fungi, it is found that the highest number of shared microbes discovered by more than two methods  were between mtTGS and mtNGS using 16S rRNA, 23S rRNA, or 18S rRNA markers, demonstrating a high agreement between metatranscriptomic‐based methods on different platforms. Additionally, for bacteria identified by MetaPhlAn and viruses, the highest number of shared taxa were discovered by all three methods, revealing an overall high accordance between sequencing methods when using multiple markers or genome sequences. However, this agreement could not be established for ARG genes, as mtTGS identified only 0.0056–13% of ARG genes identified in mtNGS. We speculate that this was due to the lower number of mtTGS reads (only 0.2–15% of those in mtNGS) as a result of the relatively low output of the ONT platform, preventing the discovery of low‐abundance genes.Finally, crosschecking the previously mentioned clinical diagnosis and identification by mtTGS confirmed that the higher mapping rate against the MetaPhlAn database further increased the positive discovery rate compared with that of mtNGS. In a total of 28 samples with bacterial culture and mtTGS results , 43 isolates of bacteria belonging to 19 species were identified, in which mtTGS managed to positively discover 34 using 16S rRNA, 36 using 23S rRNA, and 37 using MetaPhlAn, showing a considerable increase from 22, 34, and 19 in mtNGS, respectively. It is attributed that this improvement to the higher read length and consequently higher mapping rates to reference databases, facilitating the positive identification of reads belonging to those pathogens. As to the sequences abundance in each sample, mtTGS results showed increased detection even using single markers such as 16S and 23S, and improvement in ranking of the main pathogens , as we attributed to increased sequence lengths; but seemingly using multimarker (MetaPhlAn) is slightly problematic as the ranks of key pathogens are low (median 20th), which could result from the high error rate of ONT reads and consequently misidentifications on individual marker genes. In discovering of pathogenic fungi, mtTGS showed increased detection by using 18S markers . While in pathogenic virus detection, mtNGS was also more sensitive than those two approaches, mNGS and mtTGS  and sequencing results from 11 samples, including eight BALF, two blood samples, and one CSF sample, resulting in 12–11 500 kb total nucleotides in each sample , fungi (fold change: 0–305984.484), and viruses (fold change: 17.8–3663) (–6–1.8 × 10–5%), and we managed to establish a considerable positive correlation of the read number  to that of the viral load as determined by RT‐PCR , direct RNA sequencing with ONT generated 14–1474 Mb sequences  using the ONT platform, targeted sequencing was applied by further increasing the percentage of microbial reads. ONT is able to utilize a “read‐until” strategy in which when reads with partial sequences match a defined database, the electric current in the corresponding sequencing nanopore can be reversed and sequencing terminates for particular reads. Using the host genome and transcriptome as a reference to remove part of the host cDNA reads from ONT cDNA sequencing might further improve the percentage of microbial cDNA in the final sequencing results. Thus, experimenting with two recent developments in targeted sequencing based on ONT, Readfish,25 andCompared with the ONT mtTGS results (with an average of 93.97% host reads), it is observed that detectable but small decreases in host reads during targeted cDNA reads by Readfish (average of 93.15% host reads). UNCALLED, on the other hand, resulted in an average of 96.76% host reads in the sequencing results  further improved the mapping rate of reads against a multimarker database for microbial identification, a feature that we attributed to the longer read length on ONT platforms. The shorter library preparation time and per‐read sequencing output also results in a greatly reduced total time for clinical sample analysis, which has already been demonstrated for the ONT platform; in the lab, RNA extraction can be performed similarly to DNA extraction. Our approach requires only one extra step of reverse transcription, with the remaining library preparation and sequencing remaining unchanged. In addition, ONT currently enables microbial composition analysis “on‐the‐fly”, and real‐time base calling generates sequences for finished reads continuously, using both ONT‐provided EPI2ME and custom software. We also primarily tested the ability of direct RNA sequencing and targeted sequencing , which are currently only possible with ONT. In both applications, we observed a potential for further improving efficiency; however, those methods require further improvement to be effectively utilized in microbial profiling and clinical diagnosis. Being relatively cheap and straightforward, mNGS (mainly using Illumina platforms) is the major sequencing‐based pathogen detection, and mtNGS developed in our study (added reverse transcription) only incurs very limited extra cost. In comparison, TGS platforms such as ONT and Pacbio are still in their early developing stage and relatively costly, especially considering the reduced sequencing outcome. Yet, the current stage of TGS mirrors the beginning era of NGS a decade ago, with its potential being explored in many applicational fronts first and the costs driven down gradually. While sequencing‐based approaches should be considered important but not sole methods for pathogen identification in clinics, many other types of diagnostic data such as immune cell and cytokine profiling, medical imaging, etc. merit being combined for consideration in treating infections, especially by accumulating large amounts and diverse types of data and applying machine‐learning methods that are increasingly applied in medical science.The congruence between sequencing results and clinical diagnosis was improved in our study with the adaptation of mtNGS and mtTGS, yet the pitfall remains that culture‐based methods might themselves be prone to bias, and the main pathogen might not be among the top taxa in sequencing results  in terms of sequencing efficiency. Further improvement using direct RNA sequencing showed preliminary potential, and with foreseeable maturation of the platforms, we expect it to be utilized in a wider range of applications. Developments in reference databases as well as computational methods are also warranted to fully explore the potential in the future RNA‐oriented clinical diagnosis of pathogenic microbes.4This study was approved by Ethical approval no S2019‐266‐02 of the Chinese PLA General Hospital, S2020008 of Peking University Third Hospital, 2019‐KY‐081‐01 of Zhujiang Hospital affiliated with Southern Medical University, and only involved the use of residual samples that were sent to these three hospitals. All subjects in our study provided written informed consent, and these samples were anonymized by the removal of any patient identifiable information.A total of 62 samples , 39 bronchoalveolar lavage fluid samples (BALF), and 14 blood samples) were selected for this study, and all of the 62 samples were sequenced by Illumina platform (both mNGS and mtNGS), 43 samples were sequenced by ONT mtTGS, eleven were sequenced by ONT direct RNA sequencing  was used for metagenome library construction. Without rRNA depletion, a KAPA mRNA Library Preparation kit  was used for metagenome library construction. Sequencing was performed using NovaSeq (PE150). For double‐strand cDNA synthesis, 100 pmol of primer Rrm and Klenow fragment  were added. Random amplification was conducted with 8 µL of the double‐stranded cDNA template in a final reaction volume of 200 µL, which contained 4 × 10−6m primer Rm (5’‐GCCGGAGCTCTGCAGAATTC‐3’), 90 × 10−6m dNTPs each, 80 × 10−6m Mg2+, 10x buffer, and 1 U of KOD‐Plus DNA polymerase . The amplification product was purified using agarose gel electrophoresis and a QIAquick Gel Extraction Kit . MinION cDNA library preparation was performed according to the manufacturer's instructions for native barcoding kit  and genomic DNA ligation kit . When multiplexing, every nine or ten cDNA samples were pooled together. ONT MinKNOW software (v20.06.4) was used to collect raw sequencing data, and Guppy (v4.0.15) was used for real‐time base calling of the raw data. Before ONT direct RNA sequencing, poly(A) tails were added to the purified RNA in a reaction containing 30 µL purified RNA (10–200 ng RNA), 4 µL 10 x E. coli Poly(A) Polymerase Reaction Buffer, 4 µL 10 × 10−3m adenosine triphosphate (ATP), 1.5 µL E. coli Poly(A) Polymerase , and 1 µL RNase Inhibitor . The reaction was incubated at 37 °C for 30 min. Then, direct RNA library preparation was performed according to the manufacturer's instructions for direct RNA sequencing .For ONT cDNA sequencing, first‐strand cDNA was synthesized in a 20‐µL reaction mixture with 13 µL of purified RNA from each sample and 100 pmol of primer Rrm (5’‐GACCATCTAGCGACCTCCAC‐NNNNNN‐3’), as previously described.34 For Then, the “readfish targets” command was started after the first mux scan during the sequencing by MinKNOW (v3.6.3) and Guppy basecaller server (v3.1.5). The experimental type as host depletion  was also set in the TOML file. Sequencing runs utilized a MinION with four NVIDIA Quadro GV100 GPUs by remote basecall. For UNCALLED, human cDNA and noncoding RNA  were used to build a burrows‐wheeler alignment (BWA) index as a reference to reduce the alignment time. Then, real‐time ReadUntil was started using the command “uncalled real‐time” with the parameter “–deplete” to eject reads mapping to the reference before the sequencing run. The sequencing runs utilized a MinION with a 16‐core central processing unit (CPU) for UNCALLED.Standard DNA ligation sequencing libraries  were prepared from cDNA as described above. For Readfish, the host genome reference  index using minimap2 was first constructed.35 TheIllumina raw reads were processed using Kneaddata (v0.7.4) to trim and filter low‐quality sequences, as well as contaminate human reads with the human genome reference (Hg 38). Profiling the composition of microbial communities was performed using MetaPhlAn2 (v2.7.5) by mapping reads to clade‐specific markers, SILVA‐provided comprehensive, quality checked, and regularly updated datasets of aligned small (16S) and large subunit (23S) ribosomal RNA (rRNA) sequences for bacteria, and 18S rRNA sequences for fungi. Bacterial annotation names were extracted from the bed files, and when the genus name existed, the bacterial name was a combination of the family name and the genus name, separated by an underscore, and if the genus name did not exist, the last annotation classification level was taken. The virus composition was determined by aligning the reads to the reference genome in the National Center for Biotechnology Information (NCBI) virus genome database. For ONT sequencing data analysis, adaptor and barcode sequences of ONT sequencing raw data were trimmed using Porechop (v0.2.3) with default parameters. Trimmed reads were analyzed using minimap2 to identify the 16S, 23S, MetaPhlAn, fungi and virus composition, which aligned reads to the reference genome in the Silva, MetaPhlAn2, and NCBI virus genome databases, respectively. Supplementary and secondary mapping reads from the bam file were removed, and the count matrix of the mapping reads was calculated using the “samtools view” and the “samtools idxstats” parameters, respectively. For ARG detection, the resistance gene identifier is used to predict resistomes from protein or nucleotide data based on homology and single nucleotide polymorphism (SNP) models. The application uses reference data from the CARD, a rigorously curated collection of characterized, peer‐reviewed resistance determinants and associated antibiotics organized by the Antibiotic Resistance Ontology, and AMR gene detection models.r2 and p values between the relative abundance measured by different sequencing platforms were determined by the stat_cor function in the ggpubr package using a linear model. The Spearman correlation coefficient rho between the percentage of SARS‐CoV‐2 mapped reads and the viral load of SARS‐CoV‐2 was determined by the stat_cor function in ggpubr package. Two‐tailed p‐values <0.05 were considered significant unless otherwise stated. Venn diagrams were drawn using the VennDiagram package to compare data from different sequencing platforms. Heatmaps was plotted using the ComplexHeatmap package. The R scripts and associated data used in this study, as well as the bacterial annotation bed files, have been uploaded to github (https://github.com/gaimingle10/PathIden).All statistics were performed in R (v3.6.1). Relative abundance was calculated as the number of reads mapped to pathogens or other microorganisms divided by the overall number of reads for each sample, followed by a log10 transformation. The relevant sample sizes are described in the corresponding figure legends and tables. Comparisons between two groups were performed using the Mann–Whitney–Wilcoxon rank sum test, while comparisons among three or more groups were performed using the Kruskal–Wallis test. The correlation coefficient The authors declare no conflict of interest.N.Z., J.C., J.X., B.L., B.L., and D.C. contributed equally to this work. J.W., H.Z., and L.X. designed the study. J.W., N.Z. and J.C. wrote the paper. B.L., B.L., and D.C. collected the clinical samples. N.Z., W.Z., Y.Z., and X.Z. facilitated multiplatform sequencing. J.C., J.X., B.X., and L.C. analyzed the sequencing data. Z.D., K.W., F.X., K.X., and W.Y. helped the collection of clinical samples.Supporting InformationClick here for additional data file.Supplemental Table 1Click here for additional data file.Supplemental Table 2Click here for additional data file.Supplemental Table 3Click here for additional data file.Supplemental Table 4Click here for additional data file.Supplemental Table 5Click here for additional data file.Supplemental Table 6Click here for additional data file.Supplemental Table 7Click here for additional data file.Supplemental Table 8Click here for additional data file.Supplemental Table 9Click here for additional data file.Supplemental Table 10Click here for additional data file.


The geriatric nutritional risk index (GNRI), originally developed as a nutritional assessment tool to evaluate mortality and morbidity in older hospitalized patients , is regarded as a prognostic factor in several cancers. Body composition is also an important consideration when predicting the prognosis of patients with cancer. This study aimed to investigate the relationship between the GNRI and psoas muscle volume (PMV) for survival outcomes in patients with pancreatic cancer.3/m3 for men and 44.1 mm3/m3 for women) and low PMV (PMV ≤ 61.5 mm3/m3 for men and 44.1 mm3/m3 for women) groups.This retrospective study evaluated the prognostic significance of the GNRI and PMV in 105 consecutive patients aged ≥65 years who underwent pancreatectomy for histologically confirmed pancreatic cancer. The patients were divided into high (GNRI > 98) and low GNRI groups (GNRI ≤98), and into high (PMV > 61.5 mmP <  0.001). The C-index of the combination of the GNRI and PMV for predicting 5-year OS was greater than the C-indices of either the GNRI or PMV alone. Multivariate analysis revealed that the combination of the GNRI and PMV was an independent prognostic factor in patients aged ≥65 years with pancreatic cancer (P = 0.003).Both the 5-year overall survival (OS) and recurrence-free survival (RFS) rates were significantly greater among patients in the high GNRI group than among patients in the low GNRI group. Similarly, both the 5-year OS and RFS rates were significantly greater among patients in the high PMV group than among patients in the low PMV group. Patients were stratified into three groups: those with both high GNRI and high PMV; those with either high GNRI or high PMV (but not both); and those with both low GNRI and low PMV. Patients with both low GNRI and low PMV had a worse 5-year OS rate, compared with patients in other groups (The combination of the GNRI and PMV might be useful to predict prognosis in patients aged ≥65 years with pancreatic cancer. Various tumor-specific factors and individual factors are reportedly associated with the prognosis of patients with cancer. In recent years, the evaluation of nutritional status and body composition in patients with cancer have received attention in the context of survival outcomes; their potential prognostic values have been reported in various malignant diseases.The geriatric nutritional risk index (GNRI), first reported by Bouillanne et al., is a novel and well-established objective nutritional assessment tool for the prediction of malnutrition-related risks of mortality and morbidity, including infection and bedsores, in hospitalized older patients  . The GNRBody composition is also considered important for predicting survival outcomes. Skeletal muscle wasting  contributes to poor prognosis in patients with cancer. This is at least partly because of hypercatabolism related to the exhaustion of skeletal muscle mass in patients with advanced stages of cancer –7. MalnuPancreatic cancer is considered one of the most lethal cancers with a 5-year overall survival (OS) rate of < 6% . The proTherefore, this study was performed to evaluate the prognostic significance of the GNRI and to examine the relationship between the GNRI and skeletal muscle volume  in terms of prognosis prediction in patients aged ≥65 years with pancreatic cancer.In this study, in accordance with the World Health Organization classification, older individuals were defined as those aged ≥65 years. We retrospectively analyzed the medical records of 105 consecutive older patients with histologically confirmed pancreatic ductal adenocarcinoma who had undergone pancreatectomy with regional lymphadenectomy at our institution between January 2006 and December 2019. The patients enrolled in this study constituted 103 patients with resectable pancreatic cancer and 2 patients with borderline resectable pancreatic cancer. Neoadjuvant chemotherapy was administered to 12 patients; 10 with resectable pancreatic cancer and 2 with borderline resectable pancreatic cancer. We collected all clinicopathological data from their records. All patients in this study were of Japanese ethnicity. The patients’ histopathological findings, such as tumor size, lymph node metastasis, distant metastasis, and histological differentiation, were classified in accordance with the 8th edition of the International Union Against Cancer TNM classification system [The GNRI was calculated using the following formula: GNRI = [14.89 × serum albumin level (g/dL)] + [41.7 × actual body weight/ideal body weight] [3) for each patient was measured by the analysis of preoperative computed tomography images using SYNAPSE VINCENT . It was then divided by the cube of height (m3) to produce normalized PMV values (mm3/m3).Total PMV  rates were estimated by the Kaplan–Meier method; prognostic differences were compared among groups by using the log-rank test.P <  0.1 in univariate analysis were entered into multivariate analysis. P values < 0.05 were considered statistically significant.Receiver operating characteristic analysis was used to calculate the cutoff value of PMV. The concordance index (C-index) was used to evaluate the combination of the GNRI and PMV, and GNRI and PMV, separately, to predict 5-year OS. Univariate and multivariate analyses were performed using Cox proportional hazards models to identify factors with prognostic significance for OS. Variables with All statistical analyses were performed using IBM SPSS Statistics for Windows .3/m3 for men and 44.1 mm3/m3 for women. Based on the GNRI and PMV cutoff values, the patients were grouped as high GNRI  or low GNRI , and as high PMV  or low PMV .The median follow-up interval in this study was 26.6 months . The mean GNRI of all patients was 97.3 ± 10.2. The mean PMVs were 64.2 ± 13.8 for male patients and 48.4 ± 8.5 for female patients. In accordance with a previous report [The relationships between clinicopathological characteristics and the GNRI or PMV are summarized in Table P <  0.001; RFS, P = 0.001; Fig. P <  0.001; RFS: P <  0.001; Fig. P = 0.006; RFS: P = 0.010; Fig. n = 42); B, patients with high GNRI or high PMV (but not both) (n = 31); and C, patients with both low GNRI and low PMV (n = 32). Figure P <  0.001, Fig. P <  0.001, Fig. The 5-year OS and RFS rates, stratified according to both the GNRI and PMV, are shown in Fig. oth) n = 1; and C,oth) n = 1; and C,oth) n = 1; and C,P = 0.002), along with BMI , tumor size , and lymph node metastasis  in patients aged ≥65 years who underwent resection of pancreatic cancer  with pancreatic cancer. This study also indicated that the combination of the GNRI and PMV was an independent prognostic factor in older patients with pancreatic cancer; notably, patients with both low GNRI and low PMV had significantly worse prognosis than patients with other combinations of GNRI and PMV.Malnutrition is closely associated with poor survival in patients with cancer of the digestive system –12. CancThe GNRI is clearly an easily measurable and readily available nutritional marker in older patients. However, body composition should also be considered to accurately evaluate nutritional status in older patients because there are considerable differences in skeletal muscle volume among these patients. Although substantial loss of muscle mass is generally observed in older patients, skeletal muscle volume in these patients is particularly influenced by their lifestyles and cultural backgrounds. The exhaustion of skeletal muscle is caused by age, as well as malnutrition, inflammatory disease, endocrine changes, and malignancy . SarcopeThis study had several limitations. First, it was a retrospective cohort study with a small population of only Asian individuals, which might have led to bias and limited the generalizability of the findings. Second, although the cutoff value of the GNRI in this study was set at 98, this value was originally defined as a nutrition-related risk index to predict mortality and morbidity in hospitalized patients aged ≥65 years. Therefore, the optimal cutoff value of the GNRI for OS in older patients with pancreatic cancer remains unclear. Third, the definition of “older” has not been standardized among studies of older patients. Finally, we measured PMV to establish sarcopenia in older patients. However, no consensus has been established concerning methods to measure skeletal muscle volume because body composition—with respect to skeletal muscle mass and strength—in older patients is known to vary according to ethnicity, body size, lifestyle, and cultural background. A large prospective study involving individuals with various ethnicities is necessary to confirm our findings.The combination of the GNRI and PMV might be useful to predict prognosis in older patients with pancreatic cancer. Notably, patients with low GNRI and low PMV had the worst pancreatic cancer prognosis. Nutritional management  and daily regular exercise might contribute to improved prognosis in older patients with pancreatic cancer.