In patients with a change in LVEF of greater than 10%, greater ET1 expression was noted compared to those without a change in LVEF (p = 0.017). Increased ET1 expression in breast tumor tissue is associated with reduced LVEF. Future studies need to examine whether ET1 may be a tissue biomarker that helps predict the risk of developing CIC in women with breast cancer.As survival in breast cancer patients from newer therapies increases, concerns for chemotherapy-induced cardiotoxicity (CIC) have offset some of these benefits, manifesting as a decline in left ventricular ejection fraction (LVEF). Patients receiving anthracycline-based chemotherapy followed by trastuzumab are at risk for CIC. Previous research evaluating whether clinical biomarkers predict cardiotoxicity has been inconsistent. Recently, angiotensin II type 1 receptor (ATR1) and endothelin 1 (ET1) have been shown to play a role in breast tumor growth. We evaluated ATR1 and ET1 expression in breast cancer tissue and its association with CIC. A total of 33 paraffin-embedded breast tissue specimens from women with breast cancer treated with anthracycline-based chemotherapy and trastuzumab were analyzed by immunohistochemistry (IHC) and qRT-PCR. We found that ET1 expression was increased in patients with an LVEF ≤ 50% ( Breast cancer is the most common malignancy diagnosed in women with an incidence around 16% worldwide . AttemptThe accepted definition of CIC, as defined by the Cardiac Review and Evaluation Committee, is considered a reduction in LVEF ≥5% to <55% with symptoms of heart failure or reduction in LVEF ≥10% to <55% without symptoms ,5,6. TheThe mechanism behind the pathophysiology of CIC development is not well understood. Serum biomarkers such as troponin, natriuretic peptide and other novel biomarkers such as fatty acid binding protein, glycogen phosphorylase isoenzyme, interleukin-6, and myeloperoxidase have been studied as potential predictors of cardiotoxicity; however, studies examining these biomarkers have been inconclusive ,2,3,6,10Endothelin-1 (ET1) is a potent vasoconstrictor synthesized from the cleavage of big ET1 from pre-proET1 facilitated by endothelin-converting enzyme ,13. ET1 In addition to their known cardiovascular effects, ET1 and ATR1 have been shown to play a key role in tumor growth in breast cancer ,20,21,22Given their known cardiovascular effects and their role in tumor growth, angiogenesis, and poor outcomes in breast cancer, we evaluated the relationship of ET1 and ATR1 with the development of CIC. We hypothesized that ET1 and ATR1 expression would be elevated in women with breast cancer and CIC.This was a single center, retrospective study approved by our institutional IRB for analysis of biospecimens and chart review of subjects. A total of 175 patients from 2014 to 2017 who received anthracycline-based agents and subsequent trastuzumab were screened using eligibility criteria from an independent registry maintained within the Department of Oncology in Mayo Clinic Florida. We defined the development of CIC by echocardiography assessment of LVEF from ≥55% at baseline dropping to ≤50% following chemotherapy. Inclusion criteria consisted of women >18 years of age with surgically excised breast tumor, available tissue stored at Mayo Clinic Florida, availability of a sufficient amount of tissue available for analysis, and accessible echocardiographic assessment of LVEF prior to chemotherapy and following completion. Exclusion criteria included males, an incomplete chemotherapy regimen and/or no administration of trastuzumab, LVEF between 51 and 54%, or a drop in LVEF to ≤50% after initiation of chemotherapy with recovery to ≥55% following completion of chemotherapy, tissue samples of poor quality that could not be used for analysis, prior cardiac disease including atrial fibrillation, sustained ventricular tachycardia or structural abnormalities such as congenital heart disease, myocardial infarction, or valvular heart disease.After the application of eligibility criteria to the initial 175 patients, 52 were identified . Of the Demographics were recorded including age, race, co-morbidities and cardiovascular risk factors as well as hormone receptor status for estrogen and progesterone receptor . All patIHC was performed on breast cancer samples according to standard methods . Tissue qRT-PCR was performed on samples according to standard methods ,26,27. Hp < 0.05 considered statistically significant. The analysis was conducted using Prism (IHC/qRT-PCR) and R3.6.2 (hormone data).Continuous variables were summarized as mean  or standard deviation (hormone data) or range (minimum and maximum) where appropriate, and categorical variables were reported as frequency (percentage). A Wilcoxon rank sum test was used to compare continuous variables between patients with and without CIC, and Fisher’s exact test was used to compare categorical variables between the groups. All tests were two-sided with During the study period, 52 patients were found to be eligible based on application of inclusion criteria . Nineteen = 16/33, 48.5%,)  and least likely to be ER positive and PR negative  (p = 0.070).Estrogen receptor (ER) and progesterone receptor (PR)-positive breast tumors were most common within our study population overall  . Women w4, 7.1%) . CompareHyperlipidemia was more common in patients with preserved LV function (52.6% vs. 14.3% in those with CIC) in addition to baseline diastolic dysfunction (31.6% vs. 14.3% in those without CIC) . DiastolBaseline LVEF was similar between the two groups . Importantly, elevated ET1 expression correlated with a lower percentage of LVEF (p = 0.032). Patients with a reduction in LVEF of 10% or more after the administration of chemotherapy had more ET1 expression compared to those without a reduction (p = 0.026). The greater the reduction in LVEF, the greater the ET1 expression (p = 0.017). There was no significant relationship found between ET Receptor A with LVEF . Women who developed CIC were more likely to be White; however, this was primarily driven by our patient population where 78.6% (n = 25) of our study group was White. One African American patient, out of a total of four, developed CIC in our study. In the two patients of Middle Eastern descent, both developed CIC. Only one patient of Asian descent and one patient of Mexican descent were included in our study population, and neither developed CIC. Larger studies evaluating inherent risks of CIC in relation to ET1 expression based on ethnicity will need to be conducted. Studies such as these may play a role in more tailored therapy for women with breast cancer in this era of individualized medicine where health care disparities remain prevalent.We did not observe differences in traditional cardiovascular risk factors and co-morbidities between patients with CIC vs. those without in our study population. Interestingly, hyperlipidemia was more prevalent in the preserved LVEF group (n = 14) of women with breast cancer who underwent standard therapy (anthracycline followed by trastuzumab) developed CIC, defined as a drop from preserved baseline LVEF by echocardiography (LVEF ≥ 55%) to LVEF ≤ 50% following chemotherapy. We showed that a greater level of ET1 expression in tumor tissue was associated with CIC utilizing IHC and qRT-PCR. Women with an LVEF ≤ 50% following chemotherapy exhibited higher ET1 expression by qRT-PCR compared to those with a preserved LVEF of ≥55%. Additionally, greater ET1 expression was demonstrated in women with a larger reduction in baseline LVEF of ≥10% following chemotherapy.Evidence suggests that ATR1 and ET1 expression occurs specifically in breast tumor tissue of patients with breast cancer ,19,20,21These findings suggest the use of ET1 gene expression may help predict those at risk for the development of CIC in patients with breast cancer, but further research is needed. Assessment of this biomarker can be easily performed during the initial biopsy or at the time of surgical resection. Our findings suggest ET1 expression may correlate with CIC as measured by reduced LVEF. These findings also imply that greater ET1 expression may indicate a more severe reduction in LVEF, which is a known poor prognostic marker in heart failure. The mechanism of how ET1 contributes to heart failure remains unclear, but ET1 may be an early biomarker to identify CIC or predict those at risk for developing CIC. Yamashita and colleagues showed that plasma ET1 levels increased in five out of 30 patients during doxorubicin treatment . Two of Both the endothelin signaling pathway and the renin–angiotensin–aldosterone system are known to exert adverse effects on the cardiovascular system ,30,31. TIn the last three decades, studies have suggested that plasma ET1 levels may be a prognostic biomarker for heart failure ,35,36,37The use of endothelin receptor antagonists is well established in treatment of pre-capillary pulmonary arterial hypertension . HoweverElevated ATR1 expression in the breast tissue of CIC patients was not found in our study population. From prior work, patients with HER2 overexpression did not exhibit ATR1 ,45. ThisA review of studies shows mixed results in terms of hormone receptor status and association with ATR1 or ET1. One study found ATR1 expression was observed in patients with ER-positive and HER2-negative tumors, while another study found no association with hormone receptor status ,17,18. OOur study consisted of a small sample size after screening. Our population may not be well representative of the general population, particularly given the majority of patients were White. We also limited our study to women in order to avoid confounding factors associated with sex differences that have been implicated in the development of cardiomyopathy. Patients with an LVEF between 51 and 54% were excluded despite normal function, which was defined by clinical guidelines within this range due to the presence of other exclusion criteria .Confounding variables not accounted for in our study include socioeconomic status, diet, exercise, other co-morbidities including HIV status, the presence of obstructive sleep apnea, chronic obstructive lung disease or interstitial lung disease. In addition, we cannot rule out that myocarditis or idiopathic cardiomyopathy resulted in reduced LVEF. This is not a study focusing on the mechanism of developing CIC, but it may allow for future animal studies in order to better understand the relationship of ET1 to CIC.By utilizing IHC and qRT-PCR, we were able to demonstrate that greater ET1 expression in breast tumor tissue correlated with an LVEF ≤50% following chemotherapy for breast cancer. In addition, higher ET1 levels were associated with a greater reduction in LVEF compared to baseline. Our findings may aid in future research using novel biomarkers to potentially predict breast cancer patients at risk for developing CIC. In addition, targeted therapies to ET1 may potentially help prevent the development of CIC but will need further evaluation.Currently, the treatment of CIC manifesting as heart failure is managed similarly to other heart failure patients. Our findings may assist with modifying treatment options to tailor therapy to those who receive chemotherapy. In addition, as one of the first studies to find an association between ET1 and cardiotoxicity in breast cancer patients, our findings could lead to therapies that target these genes to prevent the development of heart failure following chemotherapy.Based on our findings, following ET1 expression in breast tumor tissue at the time of pathological diagnosis may aid in predicting those at risk for CIC. This may allow for individualized therapy to help prevent CIC or alter the course of treatment earlier. Further studies involving a larger and more diverse population will need to be conducted to evaluate the implications of our findings in detail.In patients with breast cancer treated with anthracycline-based chemotherapy and trastuzumab, the expression of endothelin-1 in breast tumor tissue may predict chemotherapy-induced cardiotoxicity as greater expression was seen in patients with LVEF ≤50% and with a greater reduction in LVEF ≥10% from baseline.Using tissue biomarkers to predict cardiotoxicity may help pave the way for targeted therapies that help treat or even prevent chemotherapy-induced cardiotoxicity.


Rp2 of 0.8581, RMSEP of 0.2951 and RPDp of 2.56 for ‘Beijing 553’ sweet potato, and the CARS–MLR model with Rp2 of 0.8153, RMSEP of 0.2744 and RPDp of 2.09 for ‘Red Banana’ sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA–SVR and CARS–MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in ‘Beijing 553’ and ‘Red Banana’ sweet potatoes. Hyperspectral images were acquired from 420 ROIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from ‘Beijing 553’ and ‘Red Banana’ sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA–SVR model with  Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in ‘Beijing 553’ and ‘Red Banana’ sweet potatoes. It contains plenty of starch, multiple vitamins, protein and inorganic salts such as calcium, phosphorus and iron. Sweet potato has been widely consumed for its functions of delaying aging, improving immunity and preventing cancer.1,2 Soluble solids mainly contain sugars, acids, vitamins and minerals,3,4 which are the important indicators to determine the taste of sweet potato. Consumers generally prefer sweet potato with high and uniform soluble solid content (SSC). However, SSC distribution in sweet potato varies greatly depending on growth environment such as temperature, moisture and light, which leads to the non-uniform spatial quality.5 Whether fresh sweet potatoes or dried chips or slices, SSC plays an important role in their quality attribution and commercial value. Therefore, a strong emphasis should be placed on visual detection of SSC distribution in sweet potato to determine its quality level and develop an online SSC-detection device.Sweet potato  spectroscopy has been widely employed to predict the gross SSC of diverse fruits such as pear,9,10 apples,11–13 watermelons,14 citrus,15 tomatoes,16 and sweet cherries,17 but was incapable of determining the change in SSC of different positions.Traditionally, destructive techniques are time-consuming and laborious for measurement of the SSC. Vibrational spectroscopic techniques offer the ability to measure internal quality attributes of agro-food with the ease of application, the non-destructive nature and rapidity.18 As an rapid and non-contact tool, hyperspectral imaging has been applied to measure the internal attributes of foodstuff including total volatile basic nitrogen (TVB-N) content in chicken,19 vitamin C content in head cabbage,20 as well as SSC and other quality parameters of various fruits.21–25 In particular, a partial least squares regression (PLSR) model was developed to map the SSC on apple slices by visible/near-infrared (Vis-NIR) hyperspectral imaging.26 A multispectral algorithm was proposed to detect and visual the early decay of citrus with fungal infection.27 Hyperspectral imaging was applied to visualize the spatial distribution of protein content in peanuts coupled with chemometrics.28 The dry matter in potatoes was detected by Vis-NIR hyperspectral system, and a PLSR model was develop to generate the visualization map for dry matter, with a determination coefficient of prediction set (Rp2) of 0.849 and root mean square error of prediction (RMSEP) of 0.878%.29 However, to the best of our knowledge, no attention has been paid respect to mapping the distribution of SSC in sweet potato.Hyperspectral imaging is a promising technique to obtain spatially resolved spectral information from a sample for present chemical mapping by advances in digital imaging and optics.The objective of this study was to explore the potential of hyperspectral imaging techniques for spatial prediction of soluble solids content in sweet potato. To this end, hyperspectral images were captured from two cultivars of sliced sweet potatoes, and prediction models were developed for spatial distribution of SSC using preprocessed spectral data and optimal wavelengths.22.1There were two cultivars of sweet potatoes used for the experiments. ‘Beijing 553’, a yellow-flesh cultivar, and ‘Red Banana’, with orange flesh, were purchased from the XinLv Vegetable Wholesale Market in Tai'an City, Shandong Province, China. A batch of 14 sweet potatoes without any bruises or defects and with uniform shape were selected for each cultivar, and transported to the postharvest engineering laboratory at Shandong Agricultural University, Tai'an City, Shandong Province, China. Then these samples were washed and dried under controlled conditions with 20 °C for 24 h, and weighed. The weight of ‘Beijing 553’ sweet potatoes ranged between 187.39 g and 234.18 g whereas the weight of ‘Red Banana’ sweet potatoes varied between 214.25 g and 267.38 g. Each sweet potato was cut into six slices with 15 mm thick from left to right along its long axis using a slicing tool , and residual pieces at both ends were excluded as shown in 2.2As shown in 2.3RW were captured by scanning the standard white plate with light turned on, and the dark reference image RD was obtained through covering the lens without illumination. The raw hyperspectral images R0 were calibrated as reflectance image R to eliminate the impacts of uneven illumination and dark current noise by the following expression.30Before collecting hyperspectral images, the light source was turned on and preheated for 15 minutes to ensure its stability. In order to capture clear and undistorted images, the exposure time was set to 10.38 ms, and the distance between the lens and the sample was 58.72 mm. The white reference image Sliced sweet potatoes were sequentially scanned in the order of slice A to slice F. The region of interest (ROI) (in this case marker) of the corrected images were extracted by ENVI 4.6 . The average spectrum of all the pixels in the ROI was calculated to provide the spectral data for the prediction model.2.4After hyperspectral images were acquired for each sliced sweet potatoes, a digital refractometer  was used to measure SSC. Flesh of sweet potato were first scooped out from each marker, and squeezed with manual juicer . Then the juice was sucked with a straw and dropped on prism plate of refractometer to show the SSC value on LCD. The measurement was repeated three times to calculate the average value for each marker.2.52.5.131The abnormal samples may seriously affect the performance of prediction models due to errors from instruments and operations. Here, Monte Carlo partial least squares (MCPLS) was used to remove these outliers from the samples using MATLAB 2011a  and The Unscrambler X 10.4  software. Firstly, some fractions of the samples were randomly selected to create the calibration set, and the remaining was assigned to prediction set. Then PLS models were developed multiple times until each sample was used more than once as the prediction set. As a result, each sample obtained a set of predictive residual errors (PRE). Mean value of predicted residual errors (MPRE) and standard deviation of predicted residual errors (STDPRE) were acquired for each sample in the prediction set. Those samples with larger MPRE and STDPRE were identified as abnormal samples.2.5.2x–y distance (SPXY) is an effective sample partition method, which takes into account both spectral characteristics and chemical properties while selecting samples. It has the advantage of improving the predictive ability of the model.32 In this study, 840 markers constituted the whole sample set, and were further divided into calibration set and prediction set using MATLAB 2011a software after elimination of abnormal samples.Sample set portioning based on joint 33 A relatively good pretreatment was determined by evaluating the performances of partial least squares regression (PLSR) model.To greatly improve the prediction ability of SSC in sweet potatoes, spectral data were pretreated to remove noise and other disturbances using baseline correction, de-trending, moving average smoothing (MA), multiplicative scatter correction (MSC), Savitzky–Golay (SG), and standard normal variate (SNV).2.5.334 The process of SPA was operated by a graphical user interface GUI_SPA in MATLAB 2011a.As a forward variable selection method, successive projection algorithm (SPA) can improve the speed and accuracy of modeling by reducing the collinearity and redundant information between variables. It starts with a certain wavelength and calculates the projection of the wavelength on the unselected wavelength in each iteration. The wavelength with the maximum projection value is selected as characteristic wavelength until the set number of wavelengths reaches. The optimal number of variables is determined by the lowest root mean square error of cross validation (RMSECV) in multiple linear regression (MLR) calibration.N-times adaptive reweighted sampling technique, the wavelength variables with large absolute value of regression coefficient are screened out from PLS model, and the wavelength variables with small weight are removed. After N-times sampling, N subsets of variables are obtained in an iterative manner. Based on 10-fold cross-validation, RMSECV values are calculated for each subset of variables in PLS model, and the subset with the smallest RMSECV is characterized as characteristic wavelengths.35 The process of CARS was operated in MATLAB 2011a.Competitive adaptive reweighting algorithm (CARS) is an effective wavelength variable selection method using the ‘survival of the fittest’ strategy in Darwin's evolution theory. Through 2.5.4Y and spectral data X. In the process of modeling, a smaller amount of new variables in the X space were extracted to best describe the Y space and reduce the dimensionality.36,37PLSR employs the information from full spectra to predict sample composition. It is used to model the maximum covariance or a linear relationship between reference values (in this case SSC) 38 During modeling, three parameters, insensitive loss coefficient ε, penalty factor C, width coefficient of kernel function  γ, were optimized using a grid search procedure.As an extension of support vector machine (SVM), support vector regression (SVR) attempts to cast the original data into a feature space of high dimensionality using nonlinear mapping functions. It conducts the linear relationship between the independent and the dependent variables by adopting the structural risk minimisation principle.39–41Y denotes SSC value, ai are the regression coefficient, Xi are the spectral data at different wavelength bands, and ε is the regression deviation.Multiple linear regression (MLR) is a widely used method for modeling the relationship between spectra data and chemical components by the linear equation defined as follows.Rc2), determination coefficient of prediction (Rp2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). Moreover, residual predictive deviation (RPD) was considered for calibration (RPDc) and prediction (RPDp).Each model was developed using The Unscrambler X 10.4 and Origin 2017  software, the performance of which was evaluated by a determination coefficient of calibration  and Rp2 (0.8581), lower RMSEC (0.2890) and RMSEP (0.2951). RPDc and RPDp were 2.68 and 2.56, respectively. For ‘Red Banana’ sweet potato, the more effective prediction results were emerged by MLR model using characteristic wavelengths selected by CARS (MLR–CARS), with Rc2 of 0.8385 and Rp2 of 0.8153, RMSEC of 0.2881 and RMSEP of 0.2744. RPDc and RPDp were 2.31 and 2.09, respectively. versus predicted SSC obtained by SPA–SVR and CARS–MLR models, and the predicted SSC values correlated well with measured ones.Prediction models for SSC were developed using PLSR, SVR and MLR combined with characteristic wavelengths, and their performances were presented in 3.7Spatial distribution maps of SSC in two cultivars of sweet potatoes could be obtained in a pixel-wise manner using SPA–SVR and CARS–MLR models. The specific steps were as follows: (1) obtaining the hyperspectral images of sweet potato slices at characteristic wavelengths; (2) extracting the reflectance of all pixels in the characteristic wavelength image; (3) calculating the SSC corresponding to each pixel point using the prediction models; (4) constructing the spatial distribution maps of SSC in sweet potato slices by pseudo-color processing over gray-scale image.43–46 In this study, the prediction results acquired from the mapping technique showed the significant differences in SSC occurring spatially within sweet potatoes, which demonstrated hyperspectral imaging as a powerful tool for spatial prediction of SSC in sweet potatoes, laying a foundation for develop an online SSC-detection device.Some studies have been investigated to predict the sugar content in potatoes (another tuber crop) using spectral profiles obtained by hyperspectral imaging, dielectric and nuclear magnetic resonance, and most types of spectral analyses presented a good predictive ability for the average SSC rather than spatial distribution of SSC within potato tubers.4Rc2 (0.8600) and Rp2 (0.8581), lower RMSEC (0.2890) and RMSEP (0.2951). RPDc and RPDp were 2.68 and 2.56, respectively. MLR–CARS model was the more effective for ‘Red Banana’ sweet potato, with Rc2 of 0.8385 and Rp2 of 0.8153, RMSEC of 0.2881 and RMSEP of 0.2744. RPDc and RPDp were 2.31 and 2.09, respectively. Distribution of SSC in sweet potatoes was visualized and mapped to allow quantifying the SSC level in a spatial way, showing the great potential for quality monitor of sweet potatoes.This study demonstrated that Vis-NIR hyperspectral imaging was capable of determining the spatial distribution of SSC in sweet potato. SPA–SVR model had the best performance for ‘Beijing 553’ sweet potato with higher The authors declare that there is no conflict of interests regarding the publication of this study.


Polar oceans belong to the most productive and rapidly changing environments, yet our understanding of this fragile ecosystem remains limited. Here we present an analysis of a unique set of DNA metabarcoding samples from the western Weddell Sea sampled throughout the whole water column and across five water masses with different characteristics and different origin. We focus on factors affecting the distribution of planktonic pico-nano eukaryotes and observe an ecological succession of eukaryotic communities as the water masses move away from the surface and as oxygen becomes depleted with time. At the beginning of this succession, in the photic zone, algae, bacteriovores, and predators of small eukaryotes dominate the community, while another community develops as the water sinks deeper, mostly composed of parasitoids (syndinians), mesoplankton predators (radiolarians), and diplonemids. The strongly correlated distribution of syndinians and diplonemids along the depth and oxygen gradients suggests their close ecological link and moves us closer to understanding the biological role of the latter group in the ocean ecosystem. DNA metabarcoding of pelagic protists in the Weddell Sea expands our understanding of their ecological role and interactions throughout the water column. Even in the photic layer, they are more diverse and abundant than primary eukaryotic producers4. Their distribution is mainly influenced by a combination of abiotic factors (with temperature and oxygen concentration being the most important) and biotic interactions5. Marine protist communities are now actively mapped by large metabarcoding projects12, but those are focused mostly on the tropical and temperate regions and on the sunlit ocean, where the bulk of the ocean’s productivity takes place. Although polar oceans belong to one of the most productive and rapidly changing ecosystems on Earth13, the Southern Ocean remains poorly represented in these large surveys of marine protists. With the exception of a single small-scale study that examined protists in several deep-ocean water masses14, most studies of protists in the Southern Ocean focused on the photic layer21. Given the unique conditions during the polar winter, it is reasonable to assume that the heterotrophic lifestyle is of particular importance in the polar environment. The dark ocean, namely the mesopelagic (200–1000 m) and bathypelagic (1000–4000 m) layers that form by far the most voluminous biome on Earth, is generally sparsely covered by metabarcoding surveys11. To our knowledge, studies that investigate detailed depth stratification of communities across all relevant protist groups are rare, and such studies reporting samples from the Southern Ocean are non-existent. Therefore, we aimed to fill this gap.Heterotrophic protists are a vital component of the ocean’s plankton throughout the water column22.The Weddell Sea hosts a well-known oceanographic feature, the Weddell Gyre. The formation/melting of sea‐ice and melting of shelf ice create specific conditions that make this region the most important site for deep and bottom water formation for the whole Southern Hemisphere, and one of few such locations around the globe. This is also a crucial region of the ocean where gas exchange between the ocean and the atmosphere occurs, affecting oxygen and carbon dioxide levels in the deep ocean (below 200 m) much further north25. Surface Water (SuW) from depths of less than 100 m is influenced by sea-ice melt in the open water regions in summer and therefore it has low salinity, a cold temperature below 0 °C, and a high oxygen concentration due to gas exchange between the ocean and the atmosphere. At depths between 400 m and 1600 m is the Warm Deep Water (WDW), characterized by a temperature above 0 °C and a salinity above 34.6. WDW circulates clockwise within the Weddell Gyre and originates in the Circumpolar Deep Water (CDW) of the Antarctic Circumpolar Current which enters the Weddell Gyre at its eastern edge. CDW has a higher temperature and salinity compared to WDW and, in turn, originates in the North Atlantic Deep Water (NADW). Therefore, WDW has a long history of being in the deep ocean with no contact with the atmosphere and as a result has a low oxygen concentration. Sandwiched between SuW and WDW is Modified Warm Deep Water (MWDW), which results from the upwelling of WDW and mixing with SuW, so that the temperature, salinity, and oxygen concentration of the MWDW are intermediate between those of WDW and SuW.Below we provide an overview of the Weddell Sea water masses relevant to our work, drawing on information published elsewhereOffshore the southern and western ice shelves of the Weddell Sea, extensive sea ice formation and brine precipitation in nearshore polynyas produce shelf water with high salinity (HSSW). HSSW  sinks along the continental slope and forms the Weddell Sea Bottom Water . When WSBW reaches the Weddell Gyre, mixing at the interface between the WSBW and WDW forms a new water mass, the Weddell Sea Deep Water (WSDW), characterized by a temperature between −0.7 °C and 0 °C and salinity above 34.63. The Weddell Gyre is an important site for the formation of the Antarctic Bottom Water (AABW), whose precursors are WSBW and WSDW. AABW is exported northward from the Weddell Gyre and constitutes a major ventilation component of the global abyssal ocean.22 as shown in Fig. Here we present a DNA metabarcoding study based on 110 marine planktonic samples collected on the western shelf of the Weddell Sea (eight locations) and in the Powell Basin (nine locations) Fig. . The samr = 0.83 and 0.74, respectively)  communities in our depth transects, the V9 18S rRNA barcode was amplified and sequenced from DNA isolated from the pico-nano size fraction 0.8 to 20 µm). For details on DNA sequencing, read processing, operational taxonomic unit (OTU) definition, and their taxonomic annotation, see “Methods”. After filtering out rare OTUs (with an abundance of 100 reads or less), we obtained 146.4 million reads  and 5048 eukaryotic OTUs . To investigate how eukaryotic pico-nano community composition depends on water masses, oxygen concentration and other environmental variables , we (1) performed non-metric multidimensional scaling (NMDS) of Bray-Curtis distances between communities, (2) inferred ecological boundaries , and (3) used Mantel tests to estimated correlations of the community distance matrix with Bray-Curtis distance matrices based on individual environmental variables. Comprehensive results of these analyses are shown in Suppl. Fig. 0 µm. Forly) Fig. . We beliNext, we subdivided the eukaryotic pico-nano communities into clades and considered only those represented by at least 200,000 reads resulting in 26 taxonomic groups (Suppl. Data 29 (see “Methods”). The relative abundance of indicator OTUs differs significantly among sample categories (or groups of categories), e.g., among water masses or groups of water masses. In the context of our dataset, all water masses have their own specific indicator syndinians and dinophytes . We argue that since these are the best indicator OTUs in the dataset, it is unlikely that a strong correlation of their relative abundance with the categorical abiotic variable is mediated by other OTUs in our dataset, with a weaker correlation to this abiotic variable . In other words, we believe that top indicator OTUs are probably more influenced by the abiotic variable than by the other OTUs in our dataset. MWDW, WDW, and WSDW form a group of water masses characterized by a substantial subset of these 100 best indicator OTUs: 23 syndinian, 8 diplonemid, 4 acantharean, and 4 dinophyte OTUs, among others  shapes prokaryotic communities in the Southern Ocean, controlling for effect of environmental variables and distance.For the mesopelagic and bathypelagic layers, the origin of a sample from a given water mass and its subsequent fate were shown to be the most important factors affecting the community composition of both prokaryotic9 conducted a global sampling in the bathypelagic zone mainly between 30°N and 30°S and reported metabarcoding (V4 region of the 18S rRNA gene) and metagenomic data for protists; NADW, WSDW, CDW, and their mixtures were sampled. Ascomycota and Basidiomycota were abundant in the bathypelagic waters of the Pacific and Indian Oceans (in CDW-enriched samples or in mixtures of the CDW and WSDW masses), whereas they were scarce elsewhere (in pure CDW and in NADW samples). In line with this study, fungi represent a minor component of deep-sea protist communities in the Weddell Sea. Polycystines (spummelarians and collodarians) were predominant in most CDW and NADW samples, chrysophytes were prominent in NADW and in northernmost CDW samples, and syndinians (mostly MALV-II) were widespread in all water masses9. Diplonemids were hardly detected in that study because their V4 rDNA region is generally longer36 than the 600 bp limit of the protocol used by Pernice et al.9.Pernice et al.14 studied protist communities (in the 2–200 µm size fraction) in thirteen samples from four Ross Sea water masses: from newly formed HSSW and Ice Shelf Water (ISW) with a high oxygen concentration and high prokaryotic abundance, from older AABW, and the oldest CDW with a low oxygen concentration and a high nitrate and silicate content. Zoccarato et al. generated a set of ~114,000 V9 rDNA reads for ~1700 OTUs. Overall, our results from the Weddell Sea, i.e., the relative abundance and OTU richness of predominant protist clades, are consistent with the results from the Ross Sea, where strong stratification of communities by water masses was also observed14. Of the four water masses studied by Zoccarato et al., CDW demonstrated the highest relative abundance and richness of radiolarians  and excavates (diplonemids). Dinoflagellates (dinophytes and syndinians) emerged as the most abundant and OTU-rich clade in all four water masses. In general, a unique protist community was found in CDW as compared to much “younger” water masses . This result is in line with our finding that the deep-sea protist community composed of syndinians, dinophytes, diplonemids, acanthareans, polycystines, and RAD-B radiolarians is most prominent in “old” water masses such as CDW or WDW that have spent a long time in the deep ocean.Zoccarato et al.9,14 by reporting data from an unsampled geographic region and unsampled water masses. It also substantially increases coverage of metabarcoding data, provides a more detailed and updated taxonomic annotation, and thus allows more accurate characterization of the protist community typical for “old” water masses which left the surface a long time ago. For example, we improve our understanding of the ecology of diplonemids, an important component of this deep-sea community. On the other hand, our results broadly confirm previous studies9,14 regarding the composition of this protist community and confirm the finding that the movement and mixing of different water masses is the most important factor shaping protist communities in the bathypelagic and mesopelagic zones. Below we discuss the key players in the deep-sea protist community one by one.Our study extends these two key studies summarized above 17.The abundance and especially the richness of Dinophyceae correlates poorly with the five other taxonomic groups characteristic to deep water masses  dinophytes are metabolically active in the Weddell Sea, as shown by Giner et al.7 for other regions of the ocean, using RNA:DNA ratios.In addition to living cells, DNA is also preserved in dead biomass and in an extracellular state in water7. Thus, relatively abundant polycystine radiolarians in the bathypelagic zone could be explained, at least in part, by dead cells. In addition, radiolarians were shown to be responsible for remarkable carbon export events and likely form a major component of the gelatinous material collected in sediment traps42. In marine plankton, polycystines are represented mainly by orders Spumellarida and Collodaria9. The latter form large colonies, harbor photosymbionts, and are reported from the surface during the summer blooms43. On the other hand, some spumellarids also inhabit the meso- and bathypelagic depths43. Therefore, the progressive increase in the relative abundance of polycystines may be caused by a cumulative effect of sinking dead collodarian biomass and a deeper spumellarid activity maximum. Acanthareans are a locally very abundant lineage of Radiolaria with largely unknown biology43. Some form large fast-sinking cysts 44, which are likely responsible for the activity peak observed for acanthareans in the mesopelagic zone7.RAD-B radiolarians and acanthareans are most metabolically active in the mesopelagic zone, whereas polycystines only at the surface45. Taking advantage of our homogeneous dataset and high-resolution depth transects, we refined the existing distribution curves for diplonemids46 across depth and oxygen gradients. We also showed that the relative abundances of syndinians and diplonemids and their OTU counts are tightly correlated 46 reach their highest metabolic activity in the mesopelagic zone, consistent with the relative abundance peaks observed in our study. Boeuf et al.40 reported much lower RNA-DNA ratios for both groups at a depth of 4000 m, but the ratio for excavates was an order of magnitude higher than that for syndinians. This again agrees with observed decrease in relative abundance of both groups throughout the bathypelagic zone reported here and with recently published data on the benthic diversity6.We also expand knowledge of one of the most overlooked heterotrophic protists in marine aphotic plankton: an abundant and extremely OTU-rich group called diplonemids49. While we know nothing about the biology of 99% of diplonemid diversity45, some representatives were described as putative parasitoids50 as well as bacteriovores52. The above-described patterns suggest that diplonemids are linked to syndinians either through direct trophic interactions or by utilizing the same food sources, possibly radiolarians also abundant in their mesopelagic habitat. Given our finding that the deep-sea protist community lives in “old” oxygen-depleted water masses, we suggest that attempts at cultivating these protists should be done at low oxygen concentrations. Protists such as deep-sea diplonemids and radiolarians have so far resisted all cultivation attempts.Syndinians  are globally abundant and diverse parasitoids of various planktonic lineages8. Moreover, it was demonstrated that the mesopelagic zone is a hotspot of protist metabolic activity7, and that corresponds to the fact that about 90% of carbon is respired in this layer53. However, here we present the most detailed picture of the vertical distribution of protist clades from the Southern Ocean and refine the description of protist distribution by moving from vertical depth stratification to ecological succession within moving water masses.The existence of distinct heterotrophic protist communities in the photic and non-photic layers of the water column was previously described on the global scale12 as well as in the benthos6. Thus, it is important to expand sampling in the Southern Ocean to obtain a truly global picture of protist diversity and biogeography.Protist communities in the polar oceans were shown to be distinct from each other and from the tropical/temperate cosmopolitan community. This was demonstrated in the photic zoneThis study is based on marine planktonic samples collected in the Weddell Sea from February to April 2019 during the PS118 expedition of the research vessel Polarstern . The water samples were taken by 12-liter Niskin bottles attached to a carousel  equipped with a CTD (SN 937). The system included two sensor pairs for conductivity  and temperature , one high-precision pressure sensor Digiquartz 410K-134 (SN 937), one oxygen sensor , one transmissometer , one fluorometer , and one altimeter . Information about samples including depth, geographical coordinates, fluorescence intensity, oxygen concentration, salinity, and water temperature are listed in Suppl. Data 54 . V9 amplicon barcoding libraries were sequenced using an Illumina HiSeq 4000 instrument at Genome Quebec, and paired-end 150 bp reads were generated. Primer sequences were removed from reads using cutadapt v. 1.15 under the following settings: --no-indels, --discard-untrimmed, --minimumlength 50, --overlap 4, -e 0.2, -a TTGTACACACCGCCC… GTAGGTGAACCTGCRGAAGG, -A CCTTCYGCAGGTTCACCTAC… GGGCGGTGTGTACAA. Reads were then merged using bbmerge under the default settings. Using bbduk, merged reads containing undetermined bases (Ns) were filtered out and reads having average Phred quality below 20 were discarded. Cleaned reads were dereplicated into barcodes using vsearch v. 2.7.1 under the default settings. Barcodes were grouped into OTUs using swarm v. 2.2.255 under the following settings: -d 1, -f, -z. OTUs were taxonomically annotated using the ggsearch36 tool from the FASTA package (ftp://ftp.ebi.ac.uk/pub/software/unix/fasta) under the following settings: -m 8 -d 0 -b 1 -E 10 -T 40 -w 199. As references for the annotation we used rRNA sequences from the PR2 database (https://github.com/pr2database/pr2database) supplemented by updated annotations for Discoba and Metamonada56 taken from the EukRef database (https://github.com/eukref/curation). The V9 region was extracted before the taxonomic assignment. We inspected the distribution of OTUs across abundance bins and the distribution of OTUs according to similarity to reference sequences and decided to exclude from downstream analyses OTUs having abundance below 100 reads and similarity to reference below 90%. At this step, 146.4 million reads  and 5,048 OTUs  were retained. Finally, we selected 26 eukaryotic clades  represented by at least 200,000 reads. These clades account for 94.6% of the original reads and 10.1% of the original OTUs.DNA was isolated from the filters using the PowerWater DNA Isolation Kit . The V9 region of the 18S rRNA gene was amplified using universal eukaryotic primersdecostand and metaMDS functions of the vegan package v.2.6-4. For NMDS analysis, we first explored as input either a Hellinger-transformed (using the decostand function) or non-transformed relative abundance matrix and tested dimensionalities from to 2 to 6 . Judging by the stress values across a range of dimensionalities, we chose Hellinger-transformed data and 2 dimensions (stress = 0.075). We also performed a split moving-window analysis of ecological differentiation58 that located significant changes in community composition across a gradient of an environmental variable . For this analysis, we used the smwda function of the EcolUtils v.0.1 package . The Bray-Curtis distance matrix based on Hellinger-transformed relative abundance values was used as an input.All statistical analyses and plotting were performed using R v. 4.2.1. Bray-Curtis distances between eukaryotic communities were calculated using the mantel function of the vegan package v.2.6-4 was used .The Mantel test was used to calculate correlation coefficients for the matrix of Bray-Curtis distances between the pico-nano eukaryotic communities  vs. a matrix of Bray-Curtis distances between the samples based on an abiotic variable (Euclidean distances were used in the case of geographic coordinates). The ggqqplot function of the ggpubr package v.0.4.0) and performing the Shapiro–Wilk test. If the Quantile-Quantile plots corresponded to a normal distribution and the p-value of the Shapiro-Wilk test exceeded 0.05, we considered the distributions to be normal and compared their variances using multiple ANOVA followed by the Tukey correction. Otherwise, we compared their variances using the non-parametric Kruskal–Wallis test followed by post-hoc Dunn’s test .The distributions of depth, oxygen concentration, salinity, temperature, and relative abundance values as well as OTU counts of six key players in the deep-sea eukaryotic community  over five water masses were tested for normality by creating Quantile-Quantile plots (with the mgcv package v.1.8-38. We fitted GAMs (y ~ s(x)) based on the beta distribution that is suitable for variables between 0 and 1 (relative abundance) or GAMs based on the gamma distribution that is suitable for positive continuous variables (OTU richness). In the latter case, the setting ‘link = identity’ was used. We calculated pairwise Spearman rank correlation coefficients for relative abundance and richness of the 26 selected eukaryotic clades and applied hierarchical clustering to this matrix .Distributions of biological variables (relative abundance and OTU richness) versus environmental variables  were approximated using generalized additive models (GAM) as implemented in the indicspecies package v.1.7.8 implementing Indicator Values introduced by Dufrene and Legendre30 and protocols introduced by De Cáceres and Legendre29. The matrix of Bray-Curtis distances between the pico-nano eukaryotic communities  was used as an input. We used the multipatt function with 10,000 permutations. Indicator OTUs were inferred for the water masses  or for ranges of oxygen concentration (and sets of ranges) defined by the ecological boundary approach.Indicator OTUs were identified using the Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Staphylococcus capitis, was cultured from fresh tissues. Meanwhile, scRNA-seq showed that intratumoral bacteria could be present in multiple cell types. Using 16S rRNA sequencing, we identified a total of 2,320,287 high-quality reads corresponding to 4,594 OTU  sequences. The most abundant bacterial orders include Burkholderiales, Pseudomonadales, Xanthomonadales, Bacillales and Clostridiales. Alpha and Beta diversity analysis revealed specific features in different tissues. In addition, the content of Paraburkholderia fungorum was significantly higher in the paracancerous tissues and negatively correlated with CA199 (Carbohydrate antigen199) levels. The results of in vitro and in vivo experiments suggest that P. fungorum possesses an antitumor activity against tumors. Metabolomics and transcriptomics showed that P. fungorum could inhibit tumor growth through alanine, aspartate and glutamate metabolism. We determined the characteristic profile of the intratumoral microbiota and the antitumor effect of P. fungorum in ICC.Intrahepatic cholangiocarcinoma (ICC) is a rare malignancy with a high prevalence in China. This study aimed to characterize the ICC tissues’ bacterial metagenomics signature and explore its antitumor potential for cancer. In this study, 16S rRNA sequencing was carried out on 99 tissues to characterize the features of intratumoral microbiota, followed by single-cell RNA sequencing (scRNA-seq) and multilevel validation. The presence of microbial DNA in tissues was determined using staining, fluorescence in situ hybridization (FISH), and transmission electron microscopy (TEM). A Gram-positive aerobic bacterium, identified as  As a common type of CHOL, intrahepatic cholangiocarcinoma (ICC) has a high incidence in China and Southeast Asia, with the disease having a degree of high malignancy and poor prognosis.Cholangiocarcinoma (CHOL) is a kind of malignant tumor occurring in the biliary system of the liver due to carcinoma of the biliary epithelium, which has less incidence than hepatocellular carcinoma (HCC)2 cardiovascular diseases,3 and tumors.4 Studies have shown the significance of that intestinal microbiota in maintaining the intestinal mucosal barrier and promoting the development and maturation of the immune system.6 An association exists between melanoma patients with different gut microbial compositions and different treatment outcomes.7 Microbial communities have been shown to parasitize the human liver and bile ducts, and they can affect liver metabolism, immune environment, and disease status.8 Clinical studies have found that intestinal microbiota imbalance is associated with the progression of nonalcoholic steatohepatitis (NASH) and cirrhosis.9–11 Another study reported that the intestinal microbiota and a functional Toll-like receptor 4 (TLR4), are required for hepatic fibrogenesis.12 Both clinical and animal studies have shown that microbial communities can stimulate hepatic inflammatory responses and liver fibrosis and induce IL-6 production, which in turn promotes the development of liver tumors.13 However, relatively few studies have been conducted on the relationship between microorganisms and bile duct cancer. Epidemiological studies have shown that Helicobacter pylori in bile is a risk factor for increased risk of bile duct cancer.14H. pylori located in the biliary tract can activate the NFKB signaling pathway, increase the transcriptional expression of vascular endothelial growth factor, and promote tumor angiogenesis.15 Therefore, a good understanding of how intrahepatic microbiota influences the development, progression and treatment of liver and bile duct disease deserves further investigation.Recent studies have found that human commensal microbes are closely associated with complex diseases such as mental disorders,16 A team of Israeli scientists found that in tumors such as brain, bone, and breast cancers, bacteria are present in most tumors and their adjacent normal tissues, and different types of tumors even have their unique community structure. However, whether and how bacteria within tumor cells contribute to tumorigenesis, progression, and response to treatment requires further in-depth study.17 Several researchers have studied the microbiome of tissues from patients with bile duct cancer associated with Schistosoma haematobium, but they have not further explored the biological functions of bacteria in the tissues.18With the development of metagenomic technology, researchers have discovered many novel microbial taxa in different environments. Metagenomic sequencing technology has a wide range of promising applications in clinical diagnosis, such as metagenomic sequencing of stool or blood samples to diagnose infectious diseases and malignant tumors.Paraburkholderia fungorum via in vitro and in vivo experiments, and omics data demonstrated that it may affect tumor growth by participating in amino acid metabolic pathways. All in all, our study provides a comprehensive overview of the microbiome with confirmation of the anti-tumor effects of bacteria and suggests potential approaches for ICC therapy.In this study, we sequenced 16S rRNA from the tissues of ICC patients and quantified the bacteria in the tissues of ICC patients using computational biology methods. Meanwhile, the presence of intracellular bacteria in bile duct cancer tissues was verified by bacterial culture and transmission electron microscopy (TEM) experiments. Moreover, we verified the biological function of We collected 94 clinical samples (42 paired tumor (T) and paracancerous (P) tissues, plus unpaired 3 tumor tissues and 7 paracancerous tissues) from 52 ICC patients. The clinical and biochemical features of ICC patients are presented in 19 to remove contaminating bacteria in the operating environment and to identify the specific microbial composition of ICC patient tissues. After removing contaminating bacteria from the negative control and filtering out individual low-quality samples, a final total of 2,320,287 sequencing reads with an average length of 413 bp was obtained. These reads correspond to 4,594 OTU  sequences with species taxonomic levels corresponding to 266 species, 602 genera, 282 families, 160 orders, 98 classes, 33 phyla, and 1 kingdom (Supplementary Table S2). Among them, bacteria with high abundance included Burkholderiales, Pseudomonadales, Xanthomonadales, Bacillales, Clostridiales, and Sphingomonadales . We used Decontamonadales . The resGiven the significant differences in the tumor microenvironment between normal liver tissues and paracancerous and cancerous tissues, we explored the compositional diversity of bacterial microbiota in these three groups of samples. A Venn diagram of the composition of the microbiota in the tissues showed that ICC cancer tissues had a greater variety of microorganisms . At the We next calculated the Beta diversity of community species using the NMDS analysis  method, which characterizes the similarities or differences in community composition among different subgroups. By calculation, we observed that there was a significant difference in beta diversity among the three groups of samples . However, there was also a great similarity between group P and group T relative to group N . As can Klebsiella pneumoniae, Pseudomonas azotoformans, S. capitis and P. fungorum were designed and hybridized to tissue sections of ICC. H&E staining and FISH fluorescence staining revealed the presence of DNA in the ICC tissues  from ICC patients, yielding a total of 11,357 cells for subsequent cluster analysis . The tSNterature .Figure 26 Therefore, we speculate that they can be directly involved in the body’s immune response.Next, the bacterial transcription products in each cell of the sample were calculated using scRNA-seq analysis. The results showed that bacteria in plasma were enriched in granulocytes, which belong to white blood cells. In paracancerous tissues, bacterial transcription products were most abundant in T cells, perhaps because T lymphocytes would be actively involved in the immune response in paracancerous tissues . The mosBacillus anthracis and P. azotoformans was found to be positively correlated with CA199  results showed a higher abundance of Proteobacteria in paracancerous tissues, while Armatimonadetes, Verrucomicrobia and Fusobacteria were more abundant in cancerous tissues . The LDAt amount . Howeverfungorum . The resBurkholderia include Ralstonia, Pelomonas, and Acidovorax. The genera Pseudomonas and Thermomonas were evolutionarily more related. The genera Afipia, Methylobacterium, Sphingomonas, and Paracoccus showed higher species similarity.We have computationally identified differentially characterized bacterial populations in tumor and paracancerous tissues, but the evolutionary relationships between the microbiota are unclear. To explore the relationships between bacteria in ICC tissues, we constructed a phylogenetic tree . The results showed that genera that differed relatively less from P. fungorum metabolites on cancer cell migration. Considering the inhibitory effect of S. aureus on hepatocellular carcinoma progression27 and that S. aureus and S. capitis are highly homologous,28 we performed in vitro and in vivo experiments using S. capitis obtained in culture as a positive control. The MTT assay results displayed that the supernatant of the P. fungorum solution (pre-filtered and devoid of viable bacteria) could inhibit cell proliferation, and the higher the concentration, the more effective the inhibition . The results of transwell assays demonstrated that the number of cells per unit area under electron microscopy was significantly reduced after the addition of P. fungorum supernatant, which represented a diminished proliferation and migration ability of cells , transwell and scratch assays were used to study the effect of of cells .Figure P. fungorum or S. capitis than in the control samples -based untargeted metabolomics was performed. The results of univariate statistical analysis showed that a total of 380 metabolites were significantly different  in tumors transplanted from experimental and control mice, of which 187 were expressed down-regulated and 193 were up-regulated . The reson trend , which mIn addition to probing metabolite changes with metabolomics of mouse transplanted tumors, transcriptomic assays were performed using supernatant-treated cell lines to detect gene expression changes. KEGG metabolic pathway enrichment analysis revealed that differentially expressed genes were mainly enriched in carbon metabolism, glycolysis/gluconeogenesis, amino acid biosynthesis, aminoacyl-tRNA biosynthesis, pyruvate metabolism, cancer central metabolism, and carbon metabolism pathways . By comparing the results with the metabolomics results, we infer that amino acid anabolism is closely related to the bacterial action on tumors.30 Previously, researchers in Singapore studied the intratumoral microbiota of Thai cholangiocarcinoma patients, some of whom were infected with Schistosoma hepatica, which is very different from the clinical background of our native Chinese cholangiocarcinoma patients.18 As far as the research methodology is concerned, in addition to the analysis of microbiome data, we went a step further and discussed the mechanism of microbial action on tumors at the molecular level. In addition, using fresh tissue homogenate cultures, we identified and isolated live bacteria from them, which demonstrated the intracellular presence of bacteria in tumor tissues in the form of live bacteria. By setting up a negative control, we used computational biology to remove contaminating bacteria and thus more accurately characterize the microbiota in ICC tissues. Previous researchers have also used large sample sizes to demonstrate that many of the bacteria in tumor tissues are intracellular and that they are present in both cancer cells and immune cells.17 This predicts that there is no distance barrier between bacteria and tumor cells, which implies a direct interaction between them. Additionally, some investigators have also found that the number of bacteria in adipose tissue correlates with indicators of immune cell infiltration, inflammation and metabolism.31 Therefore, based on the analysis of microbiome data from clinical samples, we have considered two research directions for subsequent experimental validation, one of which is the effect of bacteria on the tumor microenvironment, and the other is the effect of bacteria on tumor tissue metabolism.The presence of bacteria in human tumors has been reported for more than a hundred years, but a systematic and complete understanding of the characteristics and biological significance of bacteria is lacking. The low microbial content in tumor tissues makes it difficult to find an appropriate biological model to initiate systematic validation of them.P. fungorum was higher in paracancerous tissues, and the abundance of this bacterium showed a weak negative correlation with the level of CA199, a clinical cancer antigen indicator. Therefore, we speculate that metabolites of this bacterium may suppress tumor cells. Riquelme et al. found higher levels of microorganisms in the tumors of long-term surviving patients and showed immune activation relative to short-term surviving patients with pancreatic cancer.32 On this basis, it can be assumed that certain bacteria play an active role in tumor suppression and that they can be directly or indirectly involved in the “anti-tumor” process. However, it has also been found that treatment of xenograft colon cancer mice with metronidazole, an antibiotic that inhibits Bacillus nuclei in colorectal cancer tissues, can inhibit the proliferation of cancer cells.33 In summary, we conclude that for different tumor types, diverse intratumoral bacteria have distinct effects on tumor progression.The results of the microbiome data showed that most of the highly abundant bacteria were less expressed in ICC tissues relative to paracancerous tissues. Among the differences, Burkholderia blocked tumor cell migration and significantly inhibited the growth of transplanted tumors in mice with cholangiocarcinoma. Given this, we linked a nonpathogenic Escherichia coli strain capable of expressing CD47nb, which was designed by Sreyan et al. at Columbia University using a synthetic biology approach, where local injection of this bacterium in mouse lymphoma tumors stimulated an anti-tumor immune response that led to complete tumor regression in mice and even control of tumor lesions distal to the uninjected strain.34 Thus, changes in microbial abundance can also be considered as a process by which microorganisms interact with human host cells, with alterations that have multiple effects, one of which is on the tumor immune response. It has been shown that microorganisms in pancreatic cancer can induce immune infiltration and thus prolong patient survival.32 In addition, metabolites of the microbial community may influence tumor development. Several studies have reported that the metabolism of human intestinal bacteria can interact with the products of host genes.35 Metabolites between the host and its bacterial symbionts have also been reported to control host tumor immunity.36 Considering these two points, we should choose immunologically intact tumor-bearing mouse models. However, due to laboratory conditions and technical limitations, we were unable to successfully construct a spontaneous tumor mouse model of ICC. Therefore, we conducted an exploratory study to solve the problem by using a xenograft tumor model, focusing on the relationship between bacteria and tumor metabolism. In addition, the inhibition of transplant tumor growth by the live bacteria was more pronounced compared to the supernatant of the bacterial solution, which may be since the live bacteria metabolize more vigorously and produce more metabolites. It is noteworthy that the size of transplanted tumors in the experimental group with antibiotics added to the drinking water was significantly reduced compared to the control group, and the change in the size of transplanted tumors in this group was similar to that in the group containing only live bacteria. The reason for this may be that the addition of antibiotics to the drinking water did not ensure that the drug reached the tumor sites of the mice rapidly.Through in vitro and in vivo experiments, we verified that For the functional mechanism study, through microbiome data analysis, we identified microbial features of cancer and paracancerous tissue differences and predicted their association with amino acid metabolic pathways. We performed metabolomic assays on mouse transplanted tumors. By analyzing the differences in metabolites between experimental and control groups, we infer that the colony may influence tumor progression through the biological process of amino acid metabolism.32 Other similar studies have shown that gut microbes usually regulate tumor growth through tumor immunity and that intratumoral microbe may exert an essential role,37–39 implying a close relationship between intratumoral and gut microbiota. In particular, due to the presence of enterohepatic circulation (EHC), some substances are excreted into the intestine via the bile, reabsorbed in the intestine, and eventually returned to the liver via the portal vein.41 Considering that this mechanism of hepatic-intestinal circulation is likely to create conditions for cross-talks between intestinal microbes and intratumoral bacteria, we postulated that ICC intratumoral microbes are also derived from intestinal microbes to a large extent. However, further experiments are needed to verify the hypothesis.The discussion of the origin of intratumor microbes is a very interesting issue. Riquelme and his colleagues showed cross-talks between intra-tumor microbes in resected pancreatic adenocarcinoma, and intestinal bacterial microbiota, which can modulate the host’s antitumor immune response.In conclusion, our findings demonstrate that bacteria in ICC may play an important antitumor role and that bacterial biomass in the tumor correlates with clinical factors. Thus, DNA of bacterial microbiota can be used as a diagnostic marker or prognostic marker for tumors, or even as a target for clinical drug therapy. However, the molecular mechanisms of how exactly they interact with cancer cells remain to be deeply explored. We need to understand clearly the molecular mechanisms of how microbiota enter and reside in tumor tissues, and study how to utilize them as targets for tumor prevention.The samples were collected from patients diagnosed with ICC who had previously undergone curative resection surgery without any adjuvant treatment. Considering the possibility of endogenous contamination during the procedure, we tried to select clean tissue blocks for the procedure experiments. All patients were from Zhongshan Hospital, Fudan University. The study was approved by the Research Ethics Committee of Zhongshan Hospital, and written informed consent was obtained from each patient. Detailed information is available in the supplementary material.https://www.mpbio.com/116560000-fastdna-spin-kit-for-soil-samp-cf). DNA concentration and purity were measured using a NanoDrop 2000 UV-Vis spectrophotometer  according to the manufacturer’s directions. The OD260/OD280 ratios of all DNA samples were greater than 1.9, indicating that the quality of the DNA samples was up to standard. The average DNA concentration of all samples was 160.39 ± 84.87 ng/µL. The quality of the DNA samples was further assessed using 1% agarose gel electrophoresis.DNA from tissues was extracted using the same protocol as follows. Briefly speaking, tissues (30 ~ 50 mg) were ground in disposable grinding tubes (volume 2 mL) with one steel ball (diameter 0.6 cm) in each tube. The grinding tubes were pre-cooled with liquid nitrogen and the tissue was ground for 10 seconds at 50 Hz using a tissue grinder . DNA extraction was performed with FastDNA Spin Kit for soil  according to the instructions (The primer pairs we used were 338 F (5’-ACTCCTACGGGAGGCAGCA-3’) and 806 R (5’-GGACTACHVGGGTWTCTAAT-3’), and the V3-V4 region of the bacterial 16s rDNA gene was amplified in a GeneAmp PCR System 9700 Thermal Cycler  using TransStart Fastpfu DNA Polymerase  to amplify the V3-V4 region of the bacterial 16s rDNA gene. To ensure the accuracy and reliability of subsequent data analysis, we used low cycle number amplification whenever possible, using the same number of cycles for each sample. The reaction system contains 4 μL 5× FastPfu buffer, 2 μL 2.5 mM dNTPs, 0.8 μL forward primer (5 μM), 0.8 μL reverse primer (5 μM), 0.4 μL FastPfu polymerase, 0.2 μL BSA and 10 ng template DNA. The volume is then adjusted to 20 μL with water. The first round of PCR reaction consists of the following steps. 95°C for 3 min; 23 cycles ; 72°C for 10 min; and 10°C until stopped by the user. A second round of PCR was performed to increase the index, using the following procedure. 95°C, 3 min; 8 cycles ; 72°C for 10 min; and 10°C until stopped by the user.The amplification parameters were adjusted in preliminary experiments. PCR products were analyzed using 2% agarose gel electrophoresis. PCR products were classified as A (strong), B (moderate), and C (weak or invisible) according to their size and concentration. Samples A and B were regarded as qualified samples and sequenced. For samples with score C, DNA extraction and PCR amplification were replicated using residual tissue from the same specimen. For sequencing, three PCR experiments were conducted in parallel for each sample. PCR products from each sample were merged and subjected to 2% agarose gel electrophoresis. PCR fragments were recycled from the gels using the AxyPrep DNA Gel Extraction Kit . DNA was eluted using Tris-HCl buffer and its recovery and quality were evaluated by 2% agarose gel electrophoresis. DNA concentration was measured using a QuantiFluor-ST handheld fluorometer .Miseq libraries for sequencing are built by adding Illumina adapters to the ends of target DNA by PCR using the TruSeq DNA Sample Preparation Kit. PCR products were retrieved from agarose gels using the AxyPrep DNA Gel Extraction Kit. We eluted the DNA with Tris-HCl buffer and evaluated its quality by 2% agarose gel electrophoresis. Double-stranded DNA was denatured with NaOH to generate single-stranded DNA. Purified amplicons were pooled in equimolar patterns and analyzed on the Illumina MiSeq PE300 platform/NovaSeq PE250 platform  in paired-end mode.42 and cutadapter software.43 The reads sequences from double-end sequencing were merged with FLASH software.44 The specific QC steps are as follows: (1) filter the bases with a tail mass value below 20 in the reads, set a window of 50bp, and truncate the back-end bases from the window if the average mass value within the window is below 20; filter the reads below 50bp after QC, and remove the reads containing N bases; (2) merge pairs of reads into one sequence according to the overlapping relationship between PE reads, with a minimum overlap length of 10bp, and the maximum mismatch ratio allowed for the overlap region is 0.2; (3) distinguish samples according to the barcode and primers at the beginning and end of the sequence, and adjust the sequence orientation, the number of mismatches allowed for barcode is 0, and the maximum number of primer mismatches is 2. The clean data were processed into OTUs sequences with UPARSE software45 to complete OTU clustering. The 16S rRNA database Silva v138 (https://www.arb-silva.de/)46 was compared with the RDP Classifier software47 and annotated for each OTU sequence to generate OTU tables. In addition to the Silva database, we also annotated the OTU sequences with the 16S rRNA database RDP (http://rdp.cme.msu.edu/)48 and GreenGenes (http://greengenes.secondgenome.com/). Contaminating bacteria were removed with the Decontam program19 with the parameter “method = frequency”. Calculation of colony Alpha diversity using Mothur software.49 Qiime software50 was used to generate abundance tables for each taxonomic level of the microbiota and to calculate the Beta diversity of the microbiota. Phylogenetic trees were constructed and evolutionary analyses were performed using Mega software51 and IQ-TREE.52 Differential bacterial populations were identified on the Majorbio Cloud Platform using linear discriminant analysis .53 The PICRUST2 tool54combined with the KEGG database55 (https://www.genome.jp/kegg/) and the EggNOG database56 (https://eggnog.embl.de/) was used to predict biological functions.Quality control to remove low-quality reads data using fastqc softwareTM Tissue Preservation Solution (Singleron Biotechnol-ogies) within 2–8°C. Before digested in GEXSCOPETM Tissue Dissociation Solution (Singleron Biotechnologies) at 37°C, the specimens were firstly washed with HBSS for three times and grinded into 1–2 mm pieces. After digestion, cell debris was filtered out and the cells were centrifuged at 1000 rpm, 4°C, for 5 minutes and cell pellets were resuspended into 1 ml PBS. To remove red blood cells, 2 mL GEXSCOPETM Red Blood Cell Lysis Buffer (Singleron Biotechnologies) was added to the cell suspension and incubated at 25°C for 10 minutes. The mixture was centrifuged at 1000 rpm for 5 min and the cell pellet was then resuspended in PBS. Single cell suspension was loaded onto a microfluidic chip and single cell RNA-seq libraries were constructed according to the manufacturer’s instructions (Singleron Biotechno-logies). The resulting single cell RNA-seq libraries were sequenced on an Illumina HiSeq X10 instrument with 150bp paired-end reads.Fresh tissue samples were immediately stored upon collection in the GEXSCOPE58 and Bracken software59 to obtain the abundance matrix of the bacterial population. The raw gene expression matrix was imported and processed using the R package Seurat.60 Data were filtered by the following criteria: the sum of expression of all genes measured per cell (nCount_RNA) was greater than 201, the number of genes measured per cell (nFeature_RNA) was more than 6,000 or less than 201, and the percentage of mitochondrial genes (percent.mt) was no more than 20%. The gene expression matrix of the remaining high-quality cells was normalized by the total number of cellular UMIs. The normalized expression was scaled by the total number of cellular UMIs and the percentage of mitochondrial genes . Highly variable genes were calculated using the Seurat FindVariableGenes function with all parameters as default except select.method = “vst” and features = 2000. PCA analysis was then performed with highly variable genes, and significant principal components (top 10) were selected for dimensionality reduction, and clusters were identified using the FindClusters function . Dimensionality reduction and gene expression visualization were performed using tSNE and UMAP analysis. Also, the first 10 principal components were further dimensionality reduced using the RunUMAP function with default settings. Initial annotation of cell types was performed using SingleR,61 and the literature on marker genes was reviewed to correct the cell annotation results.The data analysis of the scRNA-seq follows a conventional approach with the following simple steps. QC, filtering, comparison and quantification of raw sequencing data using the SCOPE-tools toolkit to generate a single cell matrix. Single-cell sequencing reads data were processed using Kraken2 software16S rRNA sequencing, H&E staining, FISH, bacterial culture, single-cell RNA sequencing (scRNA-seq), quantitative PCR (q-PCR), in vitro and mouse experiments, and data analyses were performed, as described in the supplementary methods.Click here for additional data file.


Patients with obstructive sleep apnoea (OSA), male sex, obesity, older age or hypertension are prone to hypoxemia during flexible bronchoscopy. This study investigated whether using a high-flow nasal cannula (HFNC) could reduce the incidence of oxygen desaturation during bronchoscopy under deep sedation in patients at risk of hypoxemia.A total of 176 patients at risk of hypoxemia who underwent flexible bronchoscopy under deep sedation were randomly assigned to two groups: the HFNC group  and the facemask group .P < 0.001). The facemask group required more jaw thrust manoeuvres than the HFNC group . 8 patients (9.0%) in the facemask group and none in the HFNC group required bag-mask ventilation (P = 0.012).Oxygen desaturation occurred in 4 (4.6%) patients in the HFNC group and 26 (29.2%) patients in the facemask group (The use of an HFNC can reduce the incidence of oxygen desaturation and the requirement for airway intervention in patients at risk of hypoxemia during flexible bronchoscopy under deep sedation.www.chiCTR.org.cn Identifier: ChiCTR2100044105. Registered 11/03/2021. Flexible bronchoscopy (FB) is commonly performed under anaesthesia or sedation with a higher acceptability . HypoxemA high-flow nasal cannula (HFNC) can be used to provide an extremely high flow of heated and humidified gas with adjustable temperature and oxygen concentration . A numbewww.chiCTR.org.cn (ChiCTR2100044105) on 11/03/2021.This study was approved by the local ethics committee (IRB-2021-33). All participants signed a written informed consent form prior to the study. A total of 396 patients were screened, among whom 176 completed the study and had their results analysed at the Cancer Hospital of the University of Chinese Academy of Sciences  from March to April 2021. The study was registered at 2 < 90% without oxygen supply on admission); (5) upper respiratory tract infection or lung infection; and (6) refusal to participate in this study.Both outpatients and inpatients undergoing FB were recruited for this study. The most frequent indications for bronchoscopy were radiologic changes suggestive of tumours and endobronchial examination before surgery or before radiotherapy in patients with lung tumours. The inclusion criteria included (1) 18 to 80 years of age and (2) at risk of hypoxemia, defined as having a STOP-BANG   score ≥ The study flowchart is illustrated in Fig. The patients’ demographic information, such as sex, weight, height, smoking status, present illness and history of past illness, was collected. Additionally, the interincisor distance, thyromental distance, and modified Mallampati score (I-IV) were recorded by the investigators. After successful peripheral intravenous access, all patients, before sedation, received 20 min of nebulization with 10 ml of 2% lidocaine via a nebulizer facemask.The baseline oxygen saturation, heart rate, and blood pressure values of the patients were recorded by the investigators. Continuous electrocardiography and pulse oximetry were recorded, and noninvasive blood pressure of the patients was monitored every 5 min throughout the procedure. Patients in the HFNC group received humidified oxygen at a rate of 60 L/min and a concentration of 100% via using an HFNC , while those in the facemask group received oxygen at a flow rate of 6 L/min via a tight-fitting facemask  attached to a cycle system  and severe desaturation (SpO2 < 75% or 75% ≤ SpO2 < 90% lasting > 60 s), as recommended by the World Society of Intravenous Anaesthesia (SIVA) International Sedation Task Force  vs. 4[4.6%], No significant difference was detected in adverse reactions, including tachycardia, bradycardia, and hypotension, during bronchoscopy between the two groups Table . There wFurthermore, the groups did not differ in the patients’ haemodynamics during bronchoscopy Table . The lowThis study showed that the incidence of oxygen desaturation and the requirement of airway interventions such as jaw thrust manoeuvres and bag-mask ventilation in patients at risk of hypoxemia were significantly reduced with an HFNC during bronchoscopy under deep sedation.2 in the nasopharyngeal dead space with a high flow of oxygen [2O positive end-expiratory pressure, thereby increasing the end-expiratory lung volume, reopening the alveoli and preventing atelectasis [An HFNC can rapidly wash out COf oxygen , which clectasis . Additiolectasis , 21. ConOur results are consistent with previous studies showing that an HFNC may be a useful tool to avoid oxygen desaturation in patients undergoing bronchoscopy under deep sedation. Previous studies have shown that the use of an HFNC is effective for preoxygenation during intubation –25. In aThe HFNC method costs approximately ten times as much as the facemask technique; thus, it is impractical to apply it to all patients undergoing bronchoscopy, and it is essential to identify patients at high risk of developing hypoxemia. The STOP-BANG questionnaire is a favoured, straightforward, effective and highly sensitive screening tool to identify patients with OSA , 31. It Oxygen desaturation occurs frequently during the short length of sedation in patients at risk of hypoxemia undergoing FB. In the present study, the occurrence of desaturation was 29.2% in the facemask group, which was lower than that reported previously , which m2, and PaCO2 between the two groups could not be compared. Therefore, whether an HFNC has an effect on the retention of carbon dioxide within a short period is unknown. Second, as the gold standard for diagnosing OSA, polysomnography was not conducted; thus, few data could be provided about the exact number of patients with OSA. Third, the respiratory rate and tidal volume were not monitored, which made it difficult to explain the underlying cause of hypoventilation. The number of apnoea episodes during the procedure was not recorded. Fourth, patients older than 80 years, with upper respiratory tract infections or lung infections were excluded from the present study. Those patients were more prone to develop hypoxemia. Whether an HFNC can prevent the occurrence of desaturation in those patients needs further investigation. In addition, several studies have demonstrated that an increase in the flow rate with an HFNC can yield a higher FiO2 [The limitations of this study are as follows: first, to avoid increasing the trauma and economic burden of patients, arterial blood analysis was not performed; thus, the pH, PaOher FiO2 . MoreoveIn conclusion, the use of an HFNC significantly reduced the incidence of oxygen desaturation and the requirement for airway interventions in patients at risk of hypoxemia during bronchoscopy under deep sedation. These results might modify our clinical practice.


In this study, the asphaltene extracted from Luntai heavy oil was oxidized by a mixture of propionic anhydride and hydrogen peroxide without and with a catalyst. Elemental analysis and infrared spectroscopy results indicated the occurrence of oxygen addition, condensation, and side chain cleavage reactions in the oxidation process. Oxidation products were divided into methanol solubles and methanol insolubles. The H/C and O/C atomic ratios of the MeOHS in the oxidation products without a catalyst were higher than those of the Luntai asphaltene. MeOHS had fewer aromatic rings than Luntai asphaltene. Compared with the oxidative reaction without a catalyst, the total mass of oxidation products and the proportion of MeOHS in oxidation products both increased after catalytic oxidation. This low-temperature oxidation technology can be used to upgrade asphaltenes, and thus can promote the exploitation and processing of heavy oil. The rapid rise in the world economy is driving a continuous increase in oil demand. Petroleum will remain one of the main energy sources for a long time; however, its production systems are facing very serious challenges. Conventional oil resources cannot meet the demand to support global economic development. Heavy oil has been gradually developed as an unconventional oil resource for its enormous reserves. However, it exhibits high viscosity, large specific gravity, and considerable flow resistance. The viscosity of heavy oil increases exponentially with the increasing asphaltenes’ concentration . The den3O4, Fe3O4, MaO, NiO, and PdO), and then their catalytic oxidation effect was investigated by thermogravimetric analysis at temperatures above 200 °C. The catalytic activities were closely related to the adsorption affinity, acidity and basicity, and morphology of the catalyst surface. The catalytic oxidation process upgraded the asphaltenes, thereby promoting the exploitation and processing of heavy oil. Can asphaltenes be oxidized at low-temperatures below 100 °C?Asphaltenes can be oxidized by hydrogen peroxide, air, sodium hypochlorite, or other oxidants. The ruthenium ion catalytic oxidation (RICO) method has a high selectivity for aromatic carbon, and it can decompose asphaltenes into various compounds, including benzoic acid, fatty acid, and carbon dioxide . HoweverIn this study, hydrogen peroxide and propionic anhydride  were useThe heptane-insoluble fraction  was obtained from Luntai heavy oil. The mass concentration of hydrogen peroxide was 30 wt.%, and other reagents such as xylene, n-heptane, propionic anhydride, and methanol were all of analytical grades.2 atmosphere at 400 °C at a heating rate of 2.5 °C/min, and then kept at a constant temperature for 2 h. Then the temperature was increased to 1000 °C at a heating rate of 2.5 °C/min, and maintained for 3 h. The black powder obtained after cooling was a carbon-doped nitrogen-supported iron catalyst [Iron nitrate nonahydrate and 2-methylimidazole were separately weighed at a 1:8 molar ratio, dissolved in methanol, and then mixed to obtain a homogenous solution. The mixture was placed in a water bath at 30 °C for 12 h, and then left to stand at 50 °C for another 12 h. The solid product was centrifuged, washed with methanol, dried at room temperature, and ground to a fine powder. The powder was calcined under an Ncatalyst .The catalyst was characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM).The Luntai asphaltene was ground to a fine powder. About 0.5 g of asphaltene powder, 10 wt.% catalyst relative to asphaltene, and about 6 g propionic anhydride were added to a three-necked flask. The obtained mixture was magnetically stirred in a water bath for 15 min. Then, about 30 mL 30 wt.% hydrogen peroxide was added, and the mixture was heated to 50 °C under the hydraulic reflux and reacted for 24 h. The obtained product was dried in an oven at 60 °C. The dried residue was mixed with methanol and then dispersed by ultrasonic vibration, followed by centrifugation to separate methanol solubles (MeOHS) and methanol insolubles (MeOHI). Finally, MeOHS and MeOHI were dried at 60 °C to remove methanol. The effect of oxidation was evaluated by the proportion of MeOHS in the oxidation product.The elemental compositions of Luntai asphaltene, MeOHS, and MeOHI after oxidation of asphaltene were analyzed by a fully automatic VARIO EL Ⅲ instrument.Infrared spectra of Luntai asphaltene, MeOHS, and MeOHI were obtained by Fourier transform infrared spectrophotometer (Nicolet 6700).The Luntai asphaltene powder was oxidated by propionic anhydride and hydrogen peroxide without a catalyst. The oxidation products were separated into MeOHS and MeOHI. It can be seen from The elemental compositions and atomic mass of Luntai asphaltene, MeOHS, and MeOHI after reaction are given in It can be seen from Infrared spectra of Luntai asphaltene, MeOHS, and MeOHI after oxidation reaction are shown in −1 was significantly enhanced, assigned to the carboxyl group (C=O) characteristic absorption peak [−1 was also significantly enhanced, corresponding to phenolic hydroxyl or tertiary alcohol. The peak for the C-O bending vibration of primary alcohol appeared near 1037 cm−1. These enhanced IR peaks indicated the occurrence of the oxidation reaction, and the resulting oxygen-containing groups included carboxyl, phenolic hydroxyl, tertiary alcohol, and primary alcohol groups. Luntai asphaltene and MeOHI show a strong peak near 750 cm−1, corresponding to aromatic rings, while in MeOHS, no strong peak appeared in this region, suggesting a lower number of aromatic rings in MeOHS, compared to Luntai asphaltene and MeOHI.Comparing the infrared spectra before and after the oxidation reaction, it can be seen that the peak near 1711 cmion peak . The peaThe catalyst was prepared according to the method described in The catalytic activity will be judged according to the following two points: the increase in total mass of the asphaltene and the proportion of MeOHS in the oxidation product.The Luntai asphaltene powder was oxidated by propionic anhydride and hydrogen peroxide with the catalyst prepared in It can be seen from Infrared spectra of MeOHS and MeOHI after catalytic oxidation reaction are shown in −1, corresponding to aromatic rings, while that peak is absent in MeOHS, indicating that MeOHS did not have as many aromatic rings as MeOHI. Therefore, the ferric ions in the catalyst reduce the side chain of the asphaltene molecular structure and the hydrogen on the aromatic ring during the catalytic oxidation process, thereby oxidizing into oxygen-containing compounds such as carbonyl and alcohol hydroxyl groups [Infrared analysis results show the occurrence of oxidation reactions, and the resulting oxygen-containing groups include carboxyl, phenolic hydroxyl, tertiary alcohol, and primary alcohol groups. MeOHI shows a strong peak near 750 cml groups .Many oxygen-containing groups were added to the product after catalytic oxidation. Compared with the case without a catalyst, the total mass and the proportion of MeOHS both increased after catalytic oxidation. It indicates that the nitrogen-doped, carbon-supported iron catalyst promotes the oxidation reaction of Luntai asphaltene.The Luntai asphaltene was oxidized by hydrogen peroxide and propionic anhydride at a low temperature of 50 °C. The total mass of asphaltene after oxidation increased by 56 wt.%. The oxidation products were divided into 44 wt.% MeOHS and 56 wt.% MeOHI. The H/C and O/C atomic ratios of the MeOHS were higher than those of the Luntai asphaltene. MeOHS had fewer aromatic rings than Luntai asphaltene and MeOHI, and the H/C atomic ratio in MeOHS was as large as 1.5.The Luntai asphaltene was catalytically oxidized by hydrogen peroxide and propionic anhydride at 50 °C with the nitrogen-doped, carbon-supported iron catalyst. The total mass of asphaltene increased by 68 wt.% after catalytic oxidation. The oxidation products were divided into 46 wt.% MeOHS and 54 wt.% MeOHI. Compared with the reaction without a catalyst, the total mass of oxidation products and the proportion of MeOHS both increased after catalytic oxidation.This low-temperature catalytic oxidation technology can be used to upgrade asphaltenes and promote the exploitation and processing of heavy oil.