Intratumoral NKT cell accumulation promotes antitumor immunity in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) is a potentially lethal disease lacking effective treatments. Its immunosuppressive tumor microenvironment (TME) allows it to evade host immunosurveillance and limits response to immunotherapy. Here, using the mouse KRT19-deficient (sgKRT19-edited) PDA model, we find that intratumoral accumulation of natural killer T (NKT) cells is required to establish an immunologically active TME. Mechanistically, intratumoral NKT cells facilitate type I interferon (IFN) production to initiate an antitumor adaptive immune response, and orchestrate the intratumoral infiltration of T cells, dendritic cells, natural killer cells, and myeloid-derived suppressor cells. At the molecular level, NKT cells promote the production of type I IFN through the interaction of their CD40L with CD40 on myeloid cells. To evaluate the therapeutic potential of these observations, we find that administration of folinic acid to mice bearing PDA increases NKT cells in the TME and improves their response to anti-PD-1 antibody treatment. In conclusion, NKT cells have an essential role in the immune response to mouse PDA and are potential targets for immunotherapy.

Comprehensive evaluation and screening of phytochemical compounds and their hypolipidemic activities of lotus leaf based on HPLC-Q-TOF-MS and spectral-effect analysis.

This study aimed to identify and quantify the primary components in lotus leaf and to explore the hypolipidemic components through spectral-effect relationships and chemometric methods. Utilizing a data-dependent acquisition-diagnostic fragment ion/characteristic neutral loss screening strategy (DFI-NLS), a reliable HPLC-Q-TOF-MS analysis was conducted, identifying 77 compounds, including 36 flavonoids, 21 alkaloids, 3 terpenoids, 11 organic acids, 4 phenols, 1 lignin and 1 unsaturated hydrocarbon. A straightforward HPLC-DAD method was developed for the simultaneous determination of seven major components in lotus leaf, and quercetin-3-O-glucuronide (Q3GA) was identified as the most abundant component. The HPLC fingerprints of 36 lotus leaf sample batches were assessed using chemometric approaches such as principal component analysis and hierarchical cluster analysis. The hypolipidemic effect of these samples was analyzed by measuring total cholesterol (TC) and total triglycerides (TG) levels in palmitic acid (PA) and oleic acid (OA)-induced lipid modeling in HepG-2 cells, employing partial least squares regression and grey relation analysis to investigate the spectral-effect relationship of the lotus leaf. The in vivo hypolipidemic effect of these compounds was assessed using an egg yolk powder-induced high-fat zebrafish model. The findings indicated that peak No.11 (Q3GA) in the chemical fingerprint was significantly associated with hypolipidemic activity, suggesting it as a potential hypolipidemic compound in lotus leaf. In summary, this study facilitates the exploration of the phytochemical compounds and their bioactive properties in the lotus leaf.

Light Chain Q166C Mutation Permits One-step Site Specific Conjugation on Monoclonal Antibodies.

Engineered cysteines are frequently used for site-specific conjugation in antibody-drug conjugate (ADC) development. When cysteine-engineered mAbs are produced in the cell culture process, the sulfhydryl groups on the engineered cysteines are mostly in an oxidized form. The oxidized cysteines require multiple steps (such as reduction, reoxidation, and buffer exchanges) to reactivate for bioconjugation, which complicates the ADC production process and reduces yields. In this study, we identified a Q166C mutation in the light chain that allows the presence of free sulfhydryl groups during cell culture and purification process. This mutation is in the constant region and away from sites involved in antigen binding or Fc-mediated functions. The free sulfhydryl reacts readily with maleimide in a mild solution at a high conjugation rate. This is only the second such site reported (the first one is Q124C in the light chain). Using the Q166C mutation, we conjugated an anti-angiopoietin-2 (Ang-2) peptide on bevacizumab, an anti-vascular endothelial growth factor (VEGF) antibody, to construct a peptide antibody conjugate, Ava-Plus, which could block two pro-angiogenic factors simultaneously. Ava-Plus showed high affinity for both VEGF and Ang-2 and demonstrated higher activity than bevacizumab in in vitro cell migration and in vivo mouse xenograft models.

Mitochondrial elongation factor 4 modulates energy metabolism and promotes breast cancer metastasis by orchestration of mitochondrial translation.

To cope with the requirements of energy and building blocks for rapid proliferation, cancer cells reprogram their metabolic pathways profoundly, especially in oxygen- and nutrients-deficient tumor microenvironments. However, functional mitochondria and mitochondria-dependent oxidative phosphorylation are still necessary for the tumorigenesis and metastasis of cancer cells. We show here that mitochondrial elongation factor 4 (mtEF4) is commonly upregulated in breast tumors compared to adjacent non-cancerous tissues, and is relevant to tumor progression and poor prognosis. Down regulation of mtEF4 in breast cancer cells impairs the assembly of mitochondrial respiration complexes, decreases mitochondrial respiration, reduces ATP production, attenuates the formation of lamellipodia, and suppresses cell motility in vitro and cancer metastasis in vivo. On the contrary, upregulation of mtEF4 elevates the mitochondrial oxidative phosphorylation, which contributes to the migratory capacities of breast cancer cells. mtEF4 also increases the potential of glycolysis, probably via an AMPK-related mechanism. In summary, we provide direct evidences that the aberrantly upregulated mtEF4 contributes to the metastasis of breast cancer by coordinating metabolic pathways.

Effects of acute normovolemic hemodilution on allogeneic blood transfusion & coagulation in orthognathic surgery: A randomized study.

BACKGROUND: Acute normovolemic hemodilution (ANH) is one of the important techniques predominantly used in cardiac, hepatic, and vascular surgery for decreasing allogeneic blood transfusion. However, the effect of ANH in orthognathic surgery has been rarely studied. Therefore, this study aims to assess the ANH-mediated reduction in the allogeneic red blood cell transfusion for orthognathic surgery patients. STUDY DESIGN AND METHODS: In this single-center study, 18-80 years old patients were recruited. Patients with hemoglobin ≥11 g/dL and normal coagulation function were randomly divided into ANH or standard treatment group. RESULTS: Ninety six patients underwent ANH, and 101 patients received standard treatment. No differences in demographic or major pre-operative characteristics were observed between the two groups. One patient in the ANH and three patients in the standard treatment group received allogeneic blood [3(2.97%) vs. 1(1.16%), control vs. ANH, p = .395]. Multivariate logistic regression analysis revealed that ANH treatment was not associated with transfusion of allogeneic blood (p = .763). After retransfusing autologous blood, PT and APTT in the ANH group significantly increased compared to standard treatment group (PT: -1.73 ± 1.09 vs. -2.15 ± 1.06, p = .035; APTT: -6.39 ± 5.76 vs. -8.16 ± 5.70, p = .031; control vs. ANH). No significant differences between the two groups were observed for changes in coagulation parameters at first postoperative day. However, platelet counts in the ANH group decreased compared to the standard group. No significant difference in major adverse outcomes was observed between the two groups. CONCLUSION: ANH did not reduce the incidence of allogeneic transfusion in patients undergoing orthognathic surgery.

Processed product (Pinelliae Rhizoma Praeparatum) of Pinellia ternata (Thunb.) Breit. Alleviates the allergic airway inflammation of cold phlegm via regulation of PKC/EGFR/MAPK/PI3K-AKT signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Pinelliae Rhizoma Praeparatum (PRP) is a traditional processed product of Pinellia ternata (Thunb.) Berit., which mainly used for treating cold asthma (CA). However, the mechanism of action of PRP for treating CA have not been fully elucidated. AIM OF THE STUDY: To investigate the core active constituents and the pharmacological mechanism of PRP against CA. MATERIALS AND METHODS: Ovalbumin (OVA) and cold water-induced cold asthma model were established in male mice. The effects of water extract from PRP were evaluated by general morphological observation, expectorant activity, airway hyperresponsiveness, mucus hypersecretion, inflammatory cytokines, etc. Additionally, the mRNA and protein expression of mucin 5AC (MUC5AC) and aquaporin 5 (AQP5) in vivo and in vitro were detected by immunohistochemistry (IHC), qRT-PCR, and western blotting. The mechanisms of action were investigated through network pharmacology and transcriptomic, and validated through western blotting and molecular docking. RESULTS: PRP exhibited a favorable expectorant activity, and significantly reduced the airway inflammation, mucus secretion, and hyperresponsiveness in cold asthma model. It also reduced the levels of IL-4, IL-5, IL-8, and IL-13 in bronchoalveolar lavage fluid (BALF) and IL-4 and total IgE in serum, while obviously increased the levels of IL-10 and IFN-γ in serum for asthmatic mice. Meanwhile, PRP also attenuated the pathological changes and mucus production in cold asthmatic mice. Moreover, the downregulation of MUC5AC and upregulation of AQP 5 were detected by western blotting and qRT-PCR after administration with PRP both in vivo and in vitro. PRP expectedly inhibited the protein expression of PKC-α, SRC, p-EGFR, p-ERK1/2, p-JNK, p-p38, p-PI3K, and p-Akt levels in vivo. CONCLUSIONS: These combined data showed that PRP suppressed the allergic airway inflammation of CA by regulating the balance of Th1 and Th2 cytokines and the possible involvement of the PKC/EGFR/MAPK/PI3K-Akt signaling pathway. Pentadecanoic acid, licochalcone A, ß-sitosterol, etc. were considered as main active ingredients of PRP against CA. This study provides a novel perspective of the classical herbal processed product PRP in the treatment of CA.

Carcinomas assemble a filamentous CXCL12-keratin-19 coating that suppresses T cell-mediated immune attack.

Cancer immunotherapy frequently fails because most carcinomas have few T cells, suggesting that cancers can suppress T cell infiltration. Here, we show that cancer cells of human pancreatic ductal adenocarcinoma (PDA), colorectal cancer, and breast cancer are coated with transglutaminase-2 (TGM2)-dependent covalent CXCL12-keratin-19 (KRT19) heterodimers that are organized as filamentous networks. Since a dimeric form of CXCL12 suppresses the motility of human T cells, we determined whether this polymeric CXCL12-KRT19 coating mediated T cell exclusion. Mouse tumors containing control PDA cells exhibited the CXCL12-KRT19 coating, excluded T cells, and did not respond to treatment with anti-PD-1 antibody. Tumors containing PDA cells not expressing either KRT19 or TGM2 lacked the CXCL12-KRT19 coating, were infiltrated with activated CD8+ T cells, and growth was suppressed with anti-PD-1 antibody treatment. Thus, carcinomas assemble a CXCL12-KRT19 coating to evade cancer immune attack.

PathAL: An Active Learning Framework for Histopathology Image Analysis.

Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for analyzing histopathology images. However, training these models relies heavily on a large number of samples manually annotated by experts, which is cumbersome and expensive. In addition, it is difficult to obtain a perfect set of labels due to the variability between expert annotations. This paper presents a novel active learning (AL) framework for histopathology image analysis, named PathAL. To reduce the required number of expert annotations, PathAL selects two groups of unlabeled data in each training iteration: one "informative" sample that requires additional expert annotation, and one "confident predictive" sample that is automatically added to the training set using the model's pseudo-labels. To reduce the impact of the noisy-labeled samples in the training set, PathAL systematically identifies noisy samples and excludes them to improve the generalization of the model. Our model advances the existing AL method for medical image analysis in two ways. First, we present a selection strategy to improve classification performance with fewer manual annotations. Unlike traditional methods focusing only on finding the most uncertain samples with low prediction confidence, we discover a large number of high confidence samples from the unlabeled set and automatically add them for training with assigned pseudo-labels. Second, we design a method to distinguish between noisy samples and hard samples using a heuristic approach. We exclude the noisy samples while preserving the hard samples to improve model performance. Extensive experiments demonstrate that our proposed PathAL framework achieves promising results on a prostate cancer Gleason grading task, obtaining similar performance with 40% fewer annotations compared to the fully supervised learning scenario. An ablation study is provided to analyze the effectiveness of each component in PathAL, and a pathologist reader study is conducted to validate our proposed algorithm.

A multi-resolution model for histopathology image classification and localization with multiple instance learning.

Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra-observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group ≥ 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images.

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset.

A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.

Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition. In this paper, we propose a multi-scale U-Net model to classify images at the pixel-level using 224 histological image tiles from radical prostatectomies of 20 patients. Our model was evaluated by a patient-based 10-fold cross validation, and achieved a mean Jaccard index of 65.8% across 4 classes (stroma, Gleason 3, Gleason 4 and benign glands), and 75.5% for 3 classes (stroma, benign glands, prostate cancer), outperforming other methods.

The rLrp of Mycobacterium tuberculosis inhibits proinflammatory cytokine production and downregulates APC function in mouse macrophages via a TLR2-mediated PI3K/Akt pathway activation-dependent mechanism.

We demonstrate that Mycobacterium tuberculosis recombinant leucine-responsive regulatory protein (rLrp) inhibits lipopolysaccharide (LPS)-induced tumor necrosis factor alpha (TNF-α), interleukin-6, and interleukin-12 production and blocks the nuclear translocation of subunits of the nuclear-receptor transcription factor NF-κB (Nuclear factor-kappa B). Moreover, rLrp attenuated LPS-induced DNA binding and NF-κB transcriptional activity, which was accompanied by the degradation of inhibitory IκBα and a consequent decrease in the nuclear translocation of the NF-κB p65 subunit. RLrp interfered with the LPS-induced clustering of TNF receptor-associated factor 6 and with interleukin-1 receptor-associated kinase 1 binding to TAK1. Furthermore, rLrp did not attenuate proinflammatory cytokines or the expression of CD86 and major histocompatibility complex class-II induced by interferon-gamma in the macrophages of Toll-like receptor 2 deletion (TLR2-/-) mice and in protein kinase b (Akt)-depleted mouse cells, indicating that the inhibitory effects of rLrp were dependent on TLR2-mediated activation of the phosphatidylinositol 3-OH kinase (PI3K)/Akt pathway. RLrp could also activate the PI3K/Akt pathway by stimulating the rapid phosphorylation of PI3K, Akt, and glycogen synthase kinase 3 beta in macrophages. In addition, 19 amino acid residues in the N-terminus of rLrp were determined to be important and required for the inhibitory effects mediated by TLR2. The inhibitory function of these 19 amino acids of rLrp raises the possibility that mimetic inhibitory peptides could be used to restrict innate immune responses in situations in which prolonged TLR signaling has deleterious effects. Our study offers new insight into the inhibitory mechanisms by which the TLR2-mediated PI3K/Akt pathway ensures the transient expression of potent inflammatory mediators.

TLR2 and TLR4 signaling pathways are required for recombinant Brucella abortus BCSP31-induced cytokine production, functional upregulation of mouse macrophages, and the Th1 immune response in vivo and in vitro.

Brucella abortus is a zoonotic Gram-negative pathogen that causes brucelosis in ruminants and humans. Toll-like receptors (TLRs) recognize Brucella abortus and initiate antigen-presenting cell activities that affect both innate and adaptive immunity. In this study, we focused on recombinant Brucella cell-surface protein 31 (rBCSP31) to determine its effects on mouse macrophages. Our results demonstrated that rBCSP31 induced TNF-α, IL-6 and IL-12p40 production, which depended on the activation of mitogen-activated protein kinases (MAPKs) by stimulating the rapid phosphorylation of p38 and JNK and the activation of transcription factor NF-κB in macrophages. In addition, continuous exposure (>24 h) of RAW264.7 cells to rBCSP31 significantly enhanced IFN-γ-induced expression of MHC-II and the ability to present rBCSP31 peptide to CD4(+) T cells. Furthermore, we found that rBCSP31 could interact with both TLR2 and TLR4. The rBCSP31-induced cytokine production by macrophages from TLR2(-/-) and TLR4(-/-) mice was lower than that from C57BL/6 macrophages, and the activation of NF-κB and MAPKs was attenuated in macrophages from TLR2(-/-) and TLR4(-/-) mice. In addition, CD4(+) T cells from C57BL/6 mice immunized with rBCSP31 produced higher levels of IFN-γ and IL-2 compared with CD4(+) T cells from TLR2(-/-) and TLR4(-/-) mice. Macrophages from immunized C57BL/6 mice produced higher levels of IL-12p40 than those from TLR2(-/-) and TLR4(-/-) mice. Furthermore, immunization with rBCSP31 provided better protection in C57BL/6 mice than in TLR2(-/-) and TLR4(-/-) mice after B. abortus 2308 challenge. These results indicate that rBCSP31 is a TLR2 and TLR4 agonist that induces cytokine production, upregulates macrophage function and induces the Th1 immune response.

Recombinant MPT83 derived from Mycobacterium tuberculosis induces cytokine production and upregulates the function of mouse macrophages through TLR2.

TLR2 recognizes components of Mycobacterium tuberculosis and initiates APC activities that influence both innate and adaptive immunity. M. tuberculosis lipoproteins are an important class of TLR2 ligands. In this study, we focused on recombinant MPT83 (rMPT83) to determine its effects on mouse macrophages. We demonstrated that rMPT83 induced the production of TNF-α, IL-6, and IL-12 p40 and that cytokine induction depended on activated MAPKs, because we observed the rapid phosphorylation of ERK1/2, p38, and JNK in macrophages. Additionally, neutralizing Abs against TLR2 significantly inhibited cytokine secretion and reduced or attenuated the rMPT83-induced activation of p38 and JNK in RAW264.7 cells, a mouse macrophage cell line. Furthermore, rMPT83-induced cytokine production was significantly lower in macrophages from TLR2(-/-) mice than in macrophages from wild-type mice. We further found that prolonged exposure (>24 h) of RAW264.7 cells or macrophages from wild-type and TLR2(-/-) mice to rMPT83 resulted in a significant enhancement of IFN-γ-induced MHC class II expression and an enhanced ability of macrophages to present the rMPT83 peptide to CD4(+) T cells. These results indicated that rMPT83 is a TLR2 agonist that induces the production of cytokines by macrophages and upregulates macrophage function.