Genetic reprogramming with stem cells regenerates glomerular epithelial podocytes in Alport syndrome.

Glomerular filtration relies on the type IV collagen (ColIV) network of the glomerular basement membrane, namely, in the triple helical molecules containing the α3, α4, and α5 chains of ColIV. Loss of function mutations in the genes encoding these chains (Col4a3, Col4a4, and Col4a5) is associated with the loss of renal function observed in Alport syndrome (AS). Precise understanding of the cellular basis for the patho-mechanism remains unknown and a specific therapy for this disease does not currently exist. Here, we generated a novel allele for the conditional deletion of Col4a3 in different glomerular cell types in mice. We found that podocytes specifically generate α3 chains in the developing glomerular basement membrane, and that its absence is sufficient to impair glomerular filtration as seen in AS. Next, we show that horizontal gene transfer, enhanced by TGFß1 and using allogenic bone marrow-derived mesenchymal stem cells and induced pluripotent stem cells, rescues Col4a3 expression and revive kidney function in Col4a3-deficient AS mice. Our proof-of-concept study supports that horizontal gene transfer such as cell fusion enables cell-based therapy in Alport syndrome.

Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM), which has an inherent ability to image biological samples without harsh labeling methods while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Mouse Models to Evaluate the Functional Role of the Tumor Microenvironment in Cancer Progression and Therapy Responses.

The tumor microenvironment (TME) is a complex ecosystem of both cellular and noncellular components that functions to impact the evolution of cancer. Various aspects of the TME have been targeted for the control of cancer; however, TME composition is dynamic, with the overall abundance of immune cells, endothelial cells (ECs), fibroblasts, and extracellular matrix (ECM) as well as subsets of TME components changing at different stages of progression and in response to therapy. To effectively treat cancer, an understanding of the functional role of the TME is needed. Genetically engineered mouse models have enabled comprehensive insight into the complex interactions within the TME ecosystem that regulate disease progression. Here, we review recent advances in mouse models that have been employed to understand how the TME regulates cancer initiation, progression, metastasis, and response to therapy.

The collagen landscape in cancer: profiling collagens in tumors and in circulation reveals novel markers of cancer-associated fibroblast subtypes.

Cancer-associated fibroblasts (CAFs) deposit and remodel collagens in the tumor stroma, impacting cancer progression and efficacy of interventions. CAFs are the focus of new therapeutics with the aim of normalizing the tumor microenvironment. To do this, a better understanding of CAF heterogeneity and collagen composition in cancer is needed. In this study, we sought to profile the expression of collagens at multiple levels with the goal of identifying cancer biomarkers. We investigated the collagen expression pattern in various cell types and CAF subtypes in a publicly available single-cell RNA sequencing (RNA-seq) dataset of pancreatic ductal adenocarcinoma. Next, we investigated the collagen expression profile in tumor samples across cancer types from The Cancer Genome Atlas (TCGA) database and evaluated if specific patterns of collagen expression were associated with prognosis. Finally, we profiled circulating collagen peptides using a panel of immunoassays to measure collagen fragments in the serum of cancer patients. We found that pancreatic stellate cells and fibroblasts were the primary producers of collagens in the pancreas. COL1A1, COL3A1, COL5A1, COL6A1 were expressed in all CAF subtypes, whereas COL8A1, COL10A1, COL11A1, COL12A1 were specific to myofibroblast CAFs (myCAF) and COL14A1 specific to inflammatory CAFs (iCAF). In TCGA database, myCAF collagens COL10A1 and COL11A1 were elevated across solid tumor types, and multiple associations between high expression and worse survival were found. Finally, circulating collagen biomarkers were elevated in the serum of patients with cancer relative to healthy controls with COL11A1 (myCAF) having the best diagnostic accuracy of the markers measured. In conclusion, CAFs express a noncanonical collagen profile with specific collagen subtypes associated with iCAFs and myCAFs in PDAC. These collagens are deregulated at the cellular, tumor, and systemic levels across different solid tumors and associate with survival. These findings could lead to new discoveries such as novel biomarkers and therapeutic targets. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Engineered immunomodulatory extracellular vesicles derived from epithelial cells acquire capacity for positive and negative T cell co-stimulation in cancer and autoimmunity.

Extracellular vesicles (EVs) are generated by all cells and systemic administration of allogenic EVs derived from epithelial and mesenchymal cells have been shown to be safe, despite carrying an array of functional molecules, including thousands of proteins. To address whether epithelial cells derived EVs can be modified to acquire the capacity to induce immune response, we engineered 293T EVs to harbor the immunomodulatory CD80, OX40L and PD-L1 molecules. We demonstrated abundant levels of these proteins on the engineered cells and EVs. Functionally, the engineered EVs efficiently elicit positive and negative co-stimulation in human and murine T cells. In the setting of cancer and auto-immune hepatitis, the engineered EVs modulate T cell functions and alter disease progression. Moreover, OX40L EVs provide additional benefit to anti-CTLA-4 treatment in melanoma-bearing mice. Our work provides evidence that epithelial cell derived EVs can be engineered to induce immune responses with translational potential to modulate T cell functions in distinct pathological settings.

Rapid and high yield isolation of extracellular vesicles with purity by application of size exclusion fast performance liquid chromatography.

Extracellular Vesicles (EVs) have emerged as potential biomarkers for diagnosing a range of diseases without invasive procedures. Extracellular vesicles also offer an advantage compared to synthetic vesicles, for delivery of various drugs. However, limitations in segregating EVs from soluble proteins have led to inconsistent EV retrieval rates with low levels of purity. Here, we report a new high-yield (>95%) and rapid (<20 min) EV isolation method called S ize E xclusion - F ast P erformance L iquid C hromatography (SE-FPLC). We show SE-FPLC can effectively isolate EVs from multiple sources including EVs derived from human and mouse cells and serum. The results indicate that SE-FPLC can successfully remove highly abundant protein contaminants such as albumin and lipoprotein complexes, which can represent a major hurdle in large scale isolation of EVs for clinical translation. Additionally, the high-yield nature of SE- FPLC allows for easy industrial upscaling of extracellular vesicles production for various clinical utilities. Moreover, SE-FPLC enables analysis of very small volumes of blood for use in point-of-care diagnostics in the clinic. Collectively, SE-FPLC offers many advantages over current EV isolation methods and offers rapid clinical utility potential.

Exosomes modified with anti-MEK1 siRNA lead to an effective silencing of triple negative breast cancer cells.

Triple negative breast cancer (TNBC) is a highly heterogenous disease not sensitive to endocrine or HER2 therapy and standardized treatment regimens are still missing. Therefore, development of novel TNBC treatment approaches is of utmost relevance. Herein, the potential of MAPK/ERK downregulation by RNAi-based therapeutics in a panel of mesenchymal stem-like TNBC cell lines was uncovered. Our data revealed that suppression of one of the central nodes of this signaling pathway, MEK1, affects proliferation, migration, and invasion of TNBC cells, that may be explained by the reversion of the epithelial-mesenchymal transition phenotype, which is facilitated by the MMP-2/MMP-9 downregulation. Moreover, an exosome-based system was successfully generated for the siRNA loading (iExoMEK1). Our data suggested absence of modification of the physical properties and general integrity of the iExoMEK1 comparatively to the unmodified counterparts. Such exosome-mediated downregulation of MEK1 led to a tumor regression accompanied by a decrease of angiogenesis using the chick chorioallantoic-membrane model. Our results highlight the potential of the targeting of MAPK/ERK cascade as a promising therapeutic approach against TNBC.

KRASG12D inhibition reprograms the microenvironment of early and advanced pancreatic cancer to promote FAS-mediated killing by CD8+ T cells.

The KRASG12D mutation is present in nearly half of pancreatic adenocarcinomas (PDAC). We investigated the effects of inhibiting the KRASG12D mutant protein with MRTX1133, a non-covalent small molecule inhibitor of KRASG12D, on early and advanced PDAC and its influence on the tumor microenvironment. Employing 16 different models of KRASG12D-driven PDAC, we demonstrate that MRTX1133 reverses early PDAC growth, increases intratumoral CD8+ effector T cells, decreases myeloid infiltration, and reprograms cancer-associated fibroblasts. MRTX1133 leads to regression of both established PanINs and advanced PDAC. Regression of advanced PDAC requires CD8+ T cells and immune checkpoint blockade (ICB) synergizes with MRTX1133 to eradicate PDAC and prolong overall survival. Mechanistically, inhibition of KRASG12D in advanced PDAC and human patient derived organoids induces FAS expression in cancer cells and facilitates CD8+ T cell-mediated death. Collectively, this study provides a rationale for a synergistic combination of MRTX1133 with ICB in clinical trials.

Elimination of oncogenic KRAS in genetic mouse models eradicates pancreatic cancer by inducing FAS-dependent apoptosis by CD8+ T cells.

Oncogenic KRASG12D (KRAS∗) is critical for the initiation and maintenance of pancreatic ductal adenocarcinoma (PDAC) and is a known repressor of tumor immunity. Conditional elimination of KRAS∗ in genetic mouse models of PDAC leads to the reactivation of FAS, CD8+ T cell-mediated apoptosis, and complete eradication of tumors. KRAS∗ elimination recruits activated CD4+ and CD8+ T cells and promotes the activation of antigen-presenting cells. Mechanistically, KRAS∗-mediated immune evasion involves the epigenetic regulation of Fas death receptor in cancer cells, via methylation of its promoter region. Furthermore, analysis of human RNA sequencing identifies that high KRAS expression in PDAC tumors shows a lower proportion of CD8+ T cells and demonstrates shorter survival compared with tumors with low KRAS expression. This study highlights the role of CD8+ T cells in the eradication of PDAC following KRAS∗ elimination and provides a rationale for the combination of KRAS∗ targeting with immunotherapy to control PDAC.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1) and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Identification of unique α4 chain structure and conserved antiangiogenic activity of α3NC1 type IV collagen in zebrafish.

BACKGROUND: Type IV collagen is an abundant component of basement membranes in all multicellular species and is essential for the extracellular scaffold supporting tissue architecture and function. Lower organisms typically have two type IV collagen genes, encoding α1 and α2 chains, in contrast with the six genes in humans, encoding α1-α6 chains. The α chains assemble into trimeric protomers, the building blocks of the type IV collagen network. The detailed evolutionary conservation of type IV collagen network remains to be studied. RESULTS: We report on the molecular evolution of type IV collagen genes. The zebrafish α4 non-collagenous (NC1) domain, in contrast with its human ortholog, contains an additional cysteine residue and lacks the M93 and K211 residues involved in sulfilimine bond formation between adjacent protomers. This may alter α4 chain interactions with other α chains, as supported by temporal and anatomic expression patterns of collagen IV chains during the zebrafish development. Despite the divergence between zebrafish and human α3 NC1 domain (endogenous angiogenesis inhibitor, Tumstatin), the zebrafish α3 NC1 domain exhibits conserved antiangiogenic activity in human endothelial cells. CONCLUSIONS: Our work supports type IV collagen is largely conserved between zebrafish and humans, with a possible difference involving the α4 chain.

The role of extracellular vesicles in cancer.

Intercellular communication is a key feature of cancer progression and metastasis. Extracellular vesicles (EVs) are generated by all cells, including cancer cells, and recent studies have identified EVs as key mediators of cell-cell communication via packaging and transfer of bioactive constituents to impact the biology and function of cancer cells and cells of the tumor microenvironment. Here, we review recent advances in understanding the functional contribution of EVs to cancer progression and metastasis, as cancer biomarkers, and the development of cancer therapeutics.

Antigen-presenting type-I conventional dendritic cells facilitate curative checkpoint blockade immunotherapy in pancreatic cancer.

Inflammation and tissue damage associated with pancreatitis can precede or occur concurrently with pancreatic ductal adenocarcinoma (PDAC). We demonstrate that in PDAC coupled with pancreatitis (ptPDAC), antigen-presenting type-I conventional dendritic cells (cDC1s) are specifically activated. Immune checkpoint blockade therapy (iCBT) leads to cytotoxic CD8 + T cell activation and eradication of ptPDAC with restoration of lifespan even upon PDAC re-challenge. Such eradication of ptPDAC was reversed following specific depletion of dendritic cells. Employing PDAC antigen-loaded cDC1s as a vaccine, immunotherapy-resistant PDAC was rendered sensitive to iCBT with a curative outcome. Analysis of the T-cell receptor (TCR) sequences in the tumor infiltrating CD8 + T cells following cDC1 vaccination coupled with iCBT identified unique CDR3 sequences with potential therapeutic significance. Our findings identify a fundamental difference in the immune microenvironment and adaptive immune response in PDAC concurrent with, or without pancreatitis, and provides a rationale for combining cDC1 vaccination with iCBT as a potential treatment option.

Oncogenic Kras G12D specific non-covalent inhibitor reprograms tumor microenvironment to prevent and reverse early pre-neoplastic pancreatic lesions and in combination with immunotherapy regresses advanced PDAC in a CD8 + T cells dependent manner.

Pancreatic ductal adenocarcinoma (PDAC) is associated with mutations in Kras, a known oncogenic driver of PDAC; and the KRAS G12D mutation is present in nearly half of PDAC patients. Recently, a non-covalent small molecule inhibitor (MRTX1133) was identified with specificity to the Kras G12D mutant protein. Here we explore the impact of Kras G12D inhibition by MRTX1133 on advanced PDAC and its influence on the tumor microenvironment. Employing different orthotopic xenograft and syngeneic tumor models, eight different PDXs, and two different autochthonous genetic models, we demonstrate that MRTX1133 reverses early PDAC growth, increases intratumoral CD8 + effector T cells, decreases myeloid infiltration, and reprograms cancer associated fibroblasts. Autochthonous genetic mouse models treated with MRTX1133 leads to regression of both established PanINs and advanced PDAC. Regression of advanced PDAC requires CD8 + T cells and immune checkpoint blockade therapy (iCBT) synergizes with MRTX1133 to eradicate PDAC and prolong overall survival. Mechanistically, inhibition of mutant Kras in advanced PDAC and human patient derived organoids (PDOs) induces Fas expression in cancer cells and facilitates CD8 + T cell mediated death. These results demonstrate the efficacy of MRTX1133 in different mouse models of PDAC associated with reprogramming of stromal fibroblasts and a dependency on CD8 + T cell mediated tumor clearance. Collectively, this study provides a rationale for a synergistic combination of MRTX1133 with iCBT in clinical trials.

Single extracellular vesicle imaging and computational analysis identifies inherent architectural heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM) which has an inherent ability to image biological samples without harsh labeling methods and while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources such as cancer cells, normal cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical (S. Spherical), rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution EV images and offer insights into their potential biological impact.

Oncogenic collagen I homotrimers from cancer cells bind to α3ß1 integrin and impact tumor microbiome and immunity to promote pancreatic cancer.

In contrast to normal type I collagen (Col1) heterotrimer (α1/α2/α1) produced by fibroblasts, pancreatic cancer cells specifically produce unique Col1 homotrimer (α1/α1/α1). Col1 homotrimer results from epigenetic suppression of the Col1a2 gene and promotes oncogenic signaling, cancer cell proliferation, tumor organoid formation, and growth via α3ß1 integrin on cancer cells, associated with tumor microbiome enriched in anaerobic Bacteroidales in hypoxic and immunosuppressive tumors. Deletion of Col1 homotrimers increases overall survival of mice with pancreatic ductal adenocarcinoma (PDAC), associated with reprograming of the tumor microbiome with increased microaerophilic Campylobacterales, which can be reversed with broad-spectrum antibiotics. Deletion of Col1 homotrimers enhances T cell infiltration and enables efficacy of anti-PD-1 immunotherapy. This study identifies the functional impact of Col1 homotrimers on tumor microbiome and tumor immunity, implicating Col1 homotrimer-α3ß1 integrin signaling axis as a cancer-specific therapeutic target.

Quantification of Phosphonate Drugs by 1H-31P HSQC Shows That Rats Are Better Models of Primate Drug Exposure than Mice.

The phosphonate group is a key pharmacophore in many antiviral, antimicrobial, and antineoplastic drugs. Due to its high polarity and short retention time, detecting and quantifying such phosphonate-containing drugs with LC/MS-based methods are challenging and require derivatization with hazardous reagents. Given the emerging importance of phosphonate-containing drugs, developing a practical, accessible, and safe method for their quantitation in pharmacokinetics (PK) studies is desirable. NMR-based methods are often employed in drug discovery but are seldom used for compound quantitation in PK studies. Here, we show that proton-phosphorous (1H-31P) heteronuclear single quantum correlation (HSQC) NMR allows for the quantitation of the phosphonate-containing enolase inhibitor HEX in plasma and tissues at micromolar concentrations. Although mice were shown to rapidly clear HEX from circulation (over 95% in <1 h), the plasma half-life of HEX was more than 1 h in rats and nonhuman primates. This slower clearance rate affords a significantly higher exposure of HEX in rat models compared to that in mouse models while maintaining a favorable safety profile. Similar results were observed for the phosphonate-containing antibiotic, fosfomycin. Our study demonstrates the applicability of the 1H-31P HSQC method to quantify phosphonate-containing drugs in complex biological samples and illustrates an important limitation of mice as preclinical model species for phosphonate-containing drugs.