Synaptotagmin-mediated vesicle fusion regulates cell migration.

Chemokines and other chemoattractants direct leukocyte migration and are essential for the development and delivery of immune and inflammatory responses. To probe the molecular mechanisms that underlie chemoattractant-guided migration, we did an RNA-mediated interference screen that identified several members of the synaptotagmin family of calcium-sensing vesicle-fusion proteins as mediators of cell migration: SYT7 and SYTL5 were positive regulators of chemotaxis, whereas SYT2 was a negative regulator of chemotaxis. SYT7-deficient leukocytes showed less migration in vitro and in a gout model in vivo. Chemoattractant-induced calcium-dependent lysosomal fusion was impaired in SYT7-deficient neutrophils. In a chemokine gradient, SYT7-deficient lymphocytes accumulated lysosomes in their uropods and had impaired uropod release. Our data identify a molecular pathway required for chemotaxis that links chemoattractant-induced calcium flux to exocytosis and uropod release.

Structural and Thermodynamic Properties of Nanoparticle-Protein Complexes: A Combined SAXS and SANS Study.

We propose a novel method for determining the structural and thermodynamic properties of nanoparticle-protein complexes under physiological conditions. The method consists of collecting a full set of small-angle X-ray and neutron-scattering measurements in solutions with different concentrations of nanoparticles and protein. The nanoparticle-protein dissociation process is described in the framework of the Hill cooperative model, based on which the whole set of X-ray and neutron-scattering data is fitted simultaneously. This method is applied to water solutions of gold nanoparticles in the presence of human serum albumin without any previous manipulation and can be, in principle, extended to all systems. We demonstrate that the protein dissociation constant, the Hill coefficient, and the stoichiometry of the nanoparticle-protein complex are obtained with a high degree of confidence.

Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. METHODS: In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. FINDINGS: In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937). INTERPRETATION: Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. FUNDING: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.

Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.

Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied for histopathological confirmation, thus lowering the specificity of noninvasive BCC diagnosis. Recently, reflectance confocal microscopy, a noninvasive diagnostic technique that can image skin lesions at cellular level resolution, has shown to improve specificity in BCC diagnosis and reduced the number needed to biopsy by 2-3 times. In this study, we developed and evaluated a deep learning-based artificial intelligence model to automatically detect BCC in reflectance confocal microscopy images. The proposed model achieved an area under the curve for the receiver operator characteristic curve of 89.7% (stack level) and 88.3% (lesion level), a performance on par with that of reflectance confocal microscopy experts. Furthermore, the model achieved an area under the curve of 86.1% on a held-out test set from international collaborators, demonstrating the reproducibility and generalizability of the proposed automated diagnostic approach. These results provide a clear indication that the clinical deployment of decision support systems for the detection of BCC in reflectance confocal microscopy images has the potential for optimizing the evaluation and diagnosis of patients with skin cancer.

Interpretable deep learning of myelin histopathology in age-related cognitive impairment.

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aß) plays a major role in Alzheimer's type age-related cognitive impairment, in addition to other etiopathologies such as Aß-independent hyperphosphorylated tau, cerebrovascular disease, and myelin damage, which also warrant further investigation. Classical methods, even in the setting of the gold standard of postmortem brain assessment, involve semi-quantitative ordinal staging systems that often correlate poorly with clinical outcomes, due to imperfect cognitive measurements and preconceived notions regarding the neuropathologic features that should be chosen for study. Improved approaches are needed to identify histopathological changes correlated with cognition in an unbiased way. We used a weakly supervised multiple instance learning algorithm on whole slide images of human brain autopsy tissue sections from a group of elderly donors to predict the presence or absence of cognitive impairment (n = 367 with cognitive impairment, n = 349 without). Attention analysis allowed us to pinpoint the underlying subregional architecture and cellular features that the models used for the prediction in both brain regions studied, the medial temporal lobe and frontal cortex. Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with a modest accuracy that was significantly greater than chance. Attention-based interpretation studies of the features most associated with cognitive impairment in the top performing models suggest that they identified myelin pallor in the white matter. Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive impairment that can be translated to the study of other neurobiological disorders.

BLT1-mediated T cell trafficking is critical for rejection and obliterative bronchiolitis after lung transplantation.

Leukotriene B4 is a lipid mediator that recently has been shown to have potent chemotactic activity for effector T lymphocytes mediated through its receptor, BLT1. Here, we developed a novel murine model of acute lung rejection to demonstrate that BLT1 controls effector CD8+ T cell trafficking into the lung and that disruption of BLT1 signaling in CD8+ T cells reduces lung inflammation and mortality in the model. In addition, we used BLT1-deficient mice and a BLT1 antagonist in two tracheal transplant models of lung transplantation to demonstrate the importance of BLT1 for the recruitment of T cells into tracheal allografts. We also show that BLT1-mediated CD8+ T cell recruitment plays an important role in the development of airway fibroproliferation and obliteration. Finally, in human studies of lung transplant recipients, we found that BLT1 is up-regulated on T lymphocytes isolated from the airways of patients with obliterative bronchiolitis. These data demonstrate that BLT1 contributes to the development of lung rejection and obliterative bronchiolitis by mediating effector T lymphocyte trafficking into the lung. This is the first report that describes a pathologic role for BLT1-mediated T lymphocyte recruitment in disease and identifies BLT1 as a potential therapeutic target after lung transplantation.

Chemokine receptor CXCR3 and its ligands CXCL9 and CXCL10 are required for the development of murine cerebral malaria.

Cerebral malaria is a significant cause of global mortality, causing an estimated two million deaths per year, mainly in children. The pathogenesis of this disease remains incompletely understood. Chemokines have been implicated in the development of cerebral malaria, and the IFN-inducible CXCR3 chemokine ligand IP-10 (CXCL10) was recently found to be the only serum biomarker that predicted cerebral malaria mortality in Ghanaian children. We show that the CXCR3 chemokine ligands IP-10 and Mig (CXCL9) were highly induced in the brains of mice with murine cerebral malaria caused by Plasmodium berghei ANKA. Mice deficient in CXCR3 were markedly protected against cerebral malaria and had far fewer T cells in the brain compared with wild-type mice. In competitive transfer experiments, CXCR3-deficient CD8(+) T cells were 7-fold less efficient at migrating into the infected brains than wild-type CD8(+) T cells. Adoptive transfer of wild-type CD8(+) effector T cells restored susceptibility of CXCR3-deficient mice to cerebral malaria and also restored brain proinflammatory cytokine and chemokine production and recruitment of T cells, independent of CXCR3. Mice deficient in IP-10 or Mig were both partially protected against cerebral malaria mortality when infected with P. berghei ANKA. Brain immunohistochemistry revealed Mig staining of endothelial cells, whereas IP-10 staining was mainly found in neurons. These data demonstrate that CXCR3 on CD8(+) T cells is required for T cell recruitment into the brain and the development of murine cerebral malaria and suggest that the CXCR3 ligands Mig and IP-10 play distinct, nonredundant roles in the pathogenesis of this disease.

An ectromelia virus protein that interacts with chemokines through their glycosaminoglycan binding domain.

Poxviruses encode a number of secreted virulence factors that modulate the host immune response. The vaccinia virus A41 protein is an immunomodulatory protein with amino acid sequence similarity to the 35-kDa chemokine binding protein, but the host immune molecules targeted by A41 have not been identified. We report here that the vaccinia virus A41 ortholog encoded by ectromelia virus, a poxvirus pathogen of mice, named E163 in the ectromelia virus Naval strain, is a secreted 31-kDa glycoprotein that selectively binds a limited number of CC and CXC chemokines with high affinity. A detailed characterization of the interaction of ectromelia virus E163 with mutant forms of the chemokines CXCL10 and CXCL12alpha indicated that E163 binds to the glycosaminoglycan binding site of the chemokines. This suggests that E163 inhibits the interaction of chemokines with glycosaminoglycans and provides a mechanism by which E163 prevents chemokine-induced leukocyte migration to the sites of infection. In addition to interacting with chemokines, E163 can interact with high affinity with glycosaminoglycan molecules, enabling E163 to attach to cell surfaces and to remain in the vicinity of the sites of viral infection. These findings identify E163 as a new chemokine binding protein in poxviruses and provide a molecular mechanism for the immunomodulatory activity previously reported for the vaccinia virus A41 ortholog. The results reported here also suggest that the cell surface and extracellular matrix are important targeting sites for secreted poxvirus immune modulators.

CXCR3 requires tyrosine sulfation for ligand binding and a second extracellular loop arginine residue for ligand-induced chemotaxis.

CXCR3 is a G-protein-coupled seven-transmembrane domain chemokine receptor that plays an important role in effector T-cell and NK cell trafficking. Three gamma interferon-inducible chemokines activate CXCR3: CXCL9 (Mig), CXCL10 (IP-10), and CXCL11 (I-TAC). Here, we identify extracellular domains of CXCR3 that are required for ligand binding and activation. We found that CXCR3 is sulfated on its N terminus and that sulfation is required for binding and activation by all three ligands. We also found that the proximal 16 amino acid residues of the N terminus are required for CXCL10 and CXCL11 binding and activation but not CXCL9 activation. In addition, we found that residue R216 in the second extracellular loop is required for CXCR3-mediated chemotaxis and calcium mobilization but is not required for ligand binding or ligand-induced CXCR3 internalization. Finally, charged residues in the extracellular loops contribute to the receptor-ligand interaction. These findings demonstrate that chemokine activation of CXCR3 involves both high-affinity ligand-binding interactions with negatively charged residues in the extracellular domains of CXCR3 and a lower-affinity receptor-activating interaction in the second extracellular loop. This lower-affinity interaction is necessary to induce chemotaxis but not ligand-induced CXCR3 internalization, further suggesting that different domains of CXCR3 mediate distinct functions.

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder-decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection (FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8 dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder-decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.

Development of a novel chemokine-mediated in vivo T cell recruitment assay.

Trafficking of leukocytes to sites of inflammation is an important step in the establishment of an immune response. Chemokines are critical regulators of leukocyte trafficking and are widely studied molecules for their important role in disease and for their potential as new therapeutic targets. The ability of chemokines to induce leukocyte recruitment has been mainly measured by in vitro chemotaxis assays, which lack many components of the complex biological process of leukocyte migration and therefore provide incomplete information about chemokine function in vivo. In vivo assays to study the activity of chemokines to induce leukocyte recruitment have been difficult to establish. We describe here the development of a robust in vivo recruitment assay for CD8(+) and CD4(+) T lymphocytes induced by the CXCR3 ligands IP-10 (CXCL10) and I-TAC (CXCL11). For this assay, in vitro activated T lymphocytes were adoptively transferred into the peritoneum of naïve mice. Homing of these transferred T lymphocytes into the airways was measured following intratracheal instillation of chemokines. High recruitment indices were achieved that were dependent on chemokine concentration and CXCR3 expression on the transferred lymphocytes. Recruitment was also inhibited by antibodies to the chemokine. The assay models the natural condition of chemokine-mediated lymphocyte migration into the airways as chemokines are expressed in the airways during inflammation. The nature of this model allows flexibility to study wildtype and mutant chemokines and chemokine receptors and the ability to evaluate chemokine antagonists and antibodies in vivo. This assay will therefore help elucidate a deeper understanding of the chemokine system in vivo.

Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.

Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens of thousands of digital slides per month. The resulting vast digital archives form the basis of clinical use in digital pathology and allow large scale machine learning in computational pathology. One of the most crucial bottlenecks of high-throughput scanning is quality control (QC). Currently, digital slides are screened manually to detected out-of-focus regions, to compensate for the limitations of scanner software. We present a solution to this problem by introducing a benchmark dataset for blur detection, an in-depth comparison of state-of-the art sharpness descriptors and their prediction performance within a random forest framework. Furthermore, we show that convolution neural networks, like residual networks, can be used to train blur detectors from scratch. We thoroughly evaluate the accuracy of feature based and deep learning based approaches for sharpness classification (99.74% accuracy) and regression (MSE 0.004) and additionally compare them to domain experts in a comprehensive human perception study. Our pipeline outputs spacial heatmaps enabling to quantify and localize blurred areas on a slide. Finally, we tested the proposed framework in the clinical setting and demonstrate superior performance over the state-of-the-art QC pipeline comprising commercial software and human expert inspection by reducing the error rate from 17% to 4.7%.

Crystal structures of oligomeric forms of the IP-10/CXCL10 chemokine.

We have determined the structure of wild-type IP-10 from three crystal forms. The crystals provide eight separate models of the IP-10 chain, all differing substantially from a monomeric IP-10 variant examined previously by NMR spectroscopy. In each crystal form, IP-10 chains form conventional beta sheet dimers, which, in turn, form a distinct tetrameric assembly. The M form tetramer is reminiscent of platelet factor 4, whereas the T and H forms feature a novel twelve-stranded beta sheet. Analytical ultracentrifugation indicates that, in free solution, IP-10 exists in a monomer-dimer equilibrium with a dissociation constant of 9 microM. We propose that the tetrameric structures may represent species promoted by the binding of glycosaminoglycans. The binding sites for several IP-10-neutralizing mAbs have also been mapped.

CXCL10 can inhibit endothelial cell proliferation independently of CXCR3.

CXCL10 (or Interferon-inducible protein of 10 kDa, IP-10) is an interferon-inducible chemokine with potent chemotactic activity on activated effector T cells and other leukocytes expressing its high affinity G protein-coupled receptor CXCR3. CXCL10 is also active on other cell types, including endothelial cells and fibroblasts. The mechanisms through which CXCL10 mediates its effects on non-leukocytes is not fully understood. In this study, we focus on the anti-proliferative effect of CXCL10 on endothelial cells, and demonstrate that CXCL10 can inhibit endothelial cell proliferation in vitro independently of CXCR3. Four main findings support this conclusion. First, primary mouse endothelial cells isolated from CXCR3-deficient mice were inhibited by CXCL10 as efficiently as wildtype endothelial cells. We also note that the proposed alternative splice form CXCR3-B, which is thought to mediate CXCL10's angiostatic activity, does not exist in mice based on published mouse CXCR3 genomic sequences as an in-frame stop codon would terminate the proposed CXCR3-B splice variant in mice. Second, we demonstrate that human umbilical vein endothelial cells and human lung microvascular endothelial cells that were inhibited by CXL10 did not express CXCR3 by FACS analysis. Third, two different neutralizing CXCR3 antibodies did not inhibit the anti-proliferative effect of CXCL10. Finally, fourth, utilizing a panel of CXCL10 mutants, we show that the ability to inhibit endothelial cell proliferation correlates with CXCL10's glycosaminoglycan binding affinity and not with its CXCR3 binding and signaling. Thus, using a very defined system, we show that CXCL10 can inhibit endothelial cell proliferation through a CXCR3-independent mechanism.

Inhibition of pulmonary fibrosis in mice by CXCL10 requires glycosaminoglycan binding and syndecan-4.

Pulmonary fibrosis is a progressive, dysregulated response to injury culminating in compromised lung function due to excess extracellular matrix production. The heparan sulfate proteoglycan syndecan-4 is important in mediating fibroblast-matrix interactions, but its role in pulmonary fibrosis has not been explored. To investigate this issue, we used intratracheal instillation of bleomycin as a model of acute lung injury and fibrosis. We found that bleomycin treatment increased syndecan-4 expression. Moreover, we observed a marked decrease in neutrophil recruitment and an increase in both myofibroblast recruitment and interstitial fibrosis in bleomycin-treated syndecan-4-null (Sdc4-/-) mice. Subsequently, we identified a direct interaction between CXCL10, an antifibrotic chemokine, and syndecan-4 that inhibited primary lung fibroblast migration during fibrosis; mutation of the heparin-binding domain, but not the CXCR3 domain, of CXCL10 diminished this effect. Similarly, migration of fibroblasts from patients with pulmonary fibrosis was inhibited in the presence of CXCL10 protein defective in CXCR3 binding. Furthermore, administration of recombinant CXCL10 protein inhibited fibrosis in WT mice, but not in Sdc4-/- mice. Collectively, these data suggest that the direct interaction of syndecan-4 and CXCL10 in the lung interstitial compartment serves to inhibit fibroblast recruitment and subsequent fibrosis. Thus, administration of CXCL10 protein defective in CXCR3 binding may represent a novel therapy for pulmonary fibrosis.

Chapter 18. A chemokine-mediated in vivo T-cell recruitment assay.

The ability of chemokines to induce the migration of cells expressing their cognate G-protein-coupled receptor is a characteristic property of chemokine function. To study this important function, in vitro chemotaxis assays are most often used, which, although useful, lack many components of the complex in vivo trafficking process. Reliable in vivo recruitment assays have been very difficult to establish. We describe a robust in vivo T-cell recruitment assay for adoptively transferred T lymphocytes in mice. Instillation of the CXCR3 chemokine ligands IP-10/CXCL10 or I-TAC/CXCL11 into the airways results in robust recruitment of transferred T lymphocytes. The assay thereby models the natural environment of chemokine function, as chemokines are expressed in the airways during inflammation, inducing selective leukocyte homing. This assay is particularly useful for the analysis of chemokine and chemokine receptor mutants in structure function studies and for testing the in vivo efficacy of inhibitory chemokine and chemokine receptor antibodies and small molecule antagonists.

An antibody to IP-10 is a potent antagonist of cell migration in vitro and in vivo and does not affect disease in several animal models of inflammation.

IP-10 secretion is induced by pro-inflammatory cytokines and mediates the migration of CXCR3+ cells. Its elevation in clinical samples has been associated with multiple inflammatory diseases and its antagonism has been reported to be effective in several animal models of inflammatory disease. We generated a mouse anti-mouse IP-10 monoclonal antibody (mAb; Clone 20A9) that specifically bound murine IP-10 with high affinity and inhibited in vitro IP-10 induced BaF3/mCXCR3 cell migration with an IC(50) of approximately 4 nM. The 20A9 mAb was completely absorbed in vivo and had dose proportional pharmacokinetic exposure with a serum half life of 2.4-6 days. The 20A9 mAb inhibited IP-10 mediated T-cell recruitment to the airways, indicating that it is effective in vivo. However, administration of the 20A9 mAb had no significant effect on disease in mouse models of delayed type hypersensitivity, collagen induced arthritis, cardiac allograft transplantation tolerance, EAE or CD4+ CD45RBHi T-cell transfer-induced IBD. These data suggest that the 20A9 mAb can antagonize IP-10 mediated chemotaxis in vitro and in vivo and that this is insufficient to cause a therapeutic benefit in multiple mouse models of inflammatory disease.

The lysophosphatidic acid receptor LPA1 links pulmonary fibrosis to lung injury by mediating fibroblast recruitment and vascular leak.

Aberrant wound-healing responses to injury have been implicated in the development of pulmonary fibrosis, but the mediators directing these pathologic responses have yet to be fully identified. We show that lysophosphatidic acid levels increase in bronchoalveolar lavage fluid following lung injury in the bleomycin model of pulmonary fibrosis, and that mice lacking one of its receptors, LPA1, are markedly protected from fibrosis and mortality in this model. The absence of LPA1 led to reduced fibroblast recruitment and vascular leak, two responses that may be excessive when injury leads to fibrosis rather than to repair, whereas leukocyte recruitment was preserved during the first week after injury. In persons with idiopathic pulmonary fibrosis, lysophosphatidic acid levels in bronchoalveolar lavage fluid were also increased, and inhibition of LPA1 markedly reduced fibroblast responses to the chemotactic activity of this fluid. LPA1 therefore represents a new therapeutic target for diseases in which aberrant responses to injury contribute to fibrosis, such as idiopathic pulmonary fibrosis.