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PubMed2006

ProteinChip technology is a useful method in the pathogenesis and diagnosis of endometriosis: a preliminary study.

OBJECTIVE: To test the feasibility of ProteinChip (Ciphergen Biosystems, Inc., Fremont, CA) technology as a proteomic tool in discovering and identifying proteins that are differentially expressed in endometrium, endometriotic tissue, and normal peritoneum from women with and without endometriosis. DESIGN: Differential analysis of protein expression in women with and without endometriosis. SETTING: University hospital. PATIENT(S): A total of nine patients during their secretory phase (days 20-22) were selected for this study on the basis of cycle phase and presence/or absence of endometriosis. INTERVENTION(S): Twelve tissues used in the study included six endometrial biopsies from women with mild endometriosis (n = 3) and a normal pelvis (n = 3) as well as paired samples of peritoneal endometriotic lesions (n = 3) and macroscopically normal peritoneum biopsies (n = 3) from three women with endometriosis. MAIN OUTCOME MEASURE(S): Numerous expression differences were observed in the above comparisons, representing both up-regulation and down-regulation in protein and peptide expression levels. RESULT(S): Endometrial expression for a number of proteins and peptides in the range of 2.8-12.3 kDa was 3-24 times lower in women with endometriosis than in those without endometriosis. When compared with normal peritoneum, endometriotic lesions showed an increased expression for a set of proteins and peptides in the range of 3-96 kDa, and especially an up-regulated cluster of proteins between 22 and 23 kDa, identified to be transgelin, a smooth muscle actin-binding protein. CONCLUSION(S): This preliminary study demonstrated that differential protein profiling by using ProteinChip array technology is feasible, reproducible, and may be developed into a powerful tool for endometriosis research.

Biomarkers↗

PubMed2005

Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy.

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology with an unpredictable response to treatment. We examined two groups of patients to determine whether there are serum protein profiles reflective of active disease and predictive of response to therapy. The first group (n = 8) responded to conventional therapy. The second group (n = 15) responded to an experimental antibody to the IL-6 receptor (MRA). Paired sera from each patient were analyzed before and after treatment, using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Despite the small number of patients, highly significant and consistent differences were observed before and after response to therapy in all patients. Of 282 spectral peaks identified, 23 had mean signal intensities significantly different (P < 0.001) before treatment and after response to treatment. The majority of these differences were observed regardless of whether patients responded to conventional therapy or to MRA. These peaks represent potential biomarkers of active disease. One such peak was identified as serum amyloid A, a known acute-phase reactant in SJIA, validating the SELDI-TOF MS platform as a useful technology in this context. Finally, profiles from serum samples obtained at the time of active disease were compared between the two patient groups. Nine peaks had mean signal intensities significantly different (P < 0.001) between active disease in patients who responded to conventional therapy and in patients who failed to respond, suggesting a possible profile predictive of response. Collectively, these data demonstrate the presence of serum proteomic profiles in SJIA that are reflective of active disease and suggest the feasibility of using the SELDI-TOF MS platform used as a tool for proteomic profiling and discovery of novel biomarkers in autoimmune diseases.

Anti-Inflammatory Agents↗

PubMed2006

Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia.

BACKGROUND: The identification of schizophrenia biomarkers is a crucial step towards improving current diagnosis, developing new presymptomatic treatments, identifying high-risk individuals and disease subgroups, and assessing the efficacy of preventative interventions at a rate that is not currently possible. METHODS AND FINDINGS: (1)H nuclear magnetic resonance spectroscopy in conjunction with computerized pattern recognition analysis were employed to investigate metabolic profiles of a total of 152 cerebrospinal fluid (CSF) samples from drug-naïve or minimally treated patients with first-onset paranoid schizophrenia (referred to as "schizophrenia" in the following text) and healthy controls. Partial least square discriminant analysis showed a highly significant separation of patients with first-onset schizophrenia away from healthy controls. Short-term treatment with antipsychotic medication resulted in a normalization of the disease signature in over half the patients, well before overt clinical improvement. No normalization was observed in patients in which treatment had not been initiated at first presentation, providing the first molecular evidence for the importance of early intervention for psychotic disorders. Furthermore, the alterations identified in drug-naïve patients could be validated in a test sample set achieving a sensitivity and specificity of 82% and 85%, respectively. CONCLUSIONS: Our findings suggest brain-specific alterations in glucoregulatory processes in the CSF of drug-naïve patients with first-onset schizophrenia, implying that these abnormalities are intrinsic to the disease, rather than a side effect of antipsychotic medication. Short-term treatment with atypical antipsychotic medication resulted in a normalization of the CSF disease signature in half the patients well before a clinical improvement would be expected. Furthermore, our results suggest that the initiation of antipsychotic treatment during a first psychotic episode may influence treatment response and/or outcome.

Adult↗

PubMed2026

Personalizing endometrial cancer care beyond histology: clinical applications and limits of molecular classification.

Endometrial cancer is a biologically heterogeneous disease whose management has been reshaped by molecular classification. This review summarizes the current evidence supporting the integration of molecular subgroups into prognostic assessment and treatment personalization across stages of disease. The Cancer Genome Atlas classification and its clinically applicable surrogates identify four major molecular categories: POLE-mutated, mismatch repair-deficient, p53-abnormal, and no specific molecular profile tumors. These groups differ substantially in biology, prognosis, treatment sensitivity, and areas of unmet need. POLE-mutated tumors have an excellent prognosis and represent the clearest candidates for adjuvant treatment de-escalation, particularly in early-stage disease. Mismatch repair-deficient tumors show intermediate prognosis but strong sensitivity to immune checkpoint inhibition, which has transformed the management of advanced and recurrent disease and is now being tested in earlier settings. p53-abnormal tumors represent the highest-risk subgroup, requiring multimodal treatment and offering opportunities for biomarker-driven strategies including HER2-directed therapy and DNA damage repair targeting. No specific molecular profile tumors remain the most heterogeneous category, increasingly refined by estrogen receptor status, grade, L1 cell adhesion molecule overexpression, and other biomarkers. Mismatch repair-proficient advanced/recurrent disease should be interpreted as a composite clinical trial population rather than a molecular class. Molecular classification should be integrated with traditional clinicopathologic factors, emerging biomarkers, and local implementation strategies to support equitable, biologically informed treatment selection in endometrial cancer.

Humans↗

PubMed2005

Comparison of the gene expression profile of undifferentiated human embryonic stem cell lines and differentiating embryoid bodies.

BACKGROUND: The identification of molecular pathways of differentiation of embryonic stem cells (hESC) is critical for the development of stem cell based medical therapies. In order to identify biomarkers and potential regulators of the process of differentiation, a high quality microarray containing 16,659 seventy base pair oligonucleotides was used to compare gene expression profiles of undifferentiated hESC lines and differentiating embryoid bodies. RESULTS: Previously identified "stemness" genes in undifferentiated hESC lines showed down modulation in differentiated cells while expression of several genes was induced as cells differentiated. In addition, a subset of 194 genes showed overexpression of greater than > or = 3 folds in human embryoid bodies (hEB). These included 37 novel and 157 known genes. Gene expression was validated by a variety of techniques including another large scale array, reverse transcription polymerase chain reaction, focused cDNA microarrays, massively parallel signature sequencing (MPSS) analysis and immunocytochemisty. Several novel hEB specific expressed sequence tags (ESTs) were mapped to the human genome database and their expression profile characterized. A hierarchical clustering analysis clearly depicted a distinct difference in gene expression profile among undifferentiated and differentiated hESC and confirmed that microarray analysis could readily distinguish them. CONCLUSION: These results present a detailed characterization of a unique set of genes, which can be used to assess the hESC differentiation.

Biomarkers↗

PubMed2003

Gene discovery in bladder cancer progression using cDNA microarrays.

To identify gene expression changes along progression of bladder cancer, we compared the expression profiles of early-stage and advanced bladder tumors using cDNA microarrays containing 17,842 known genes and expressed sequence tags. The application of bootstrapping techniques to hierarchical clustering segregated early-stage and invasive transitional carcinomas into two main clusters. Multidimensional analysis confirmed these clusters and more importantly, it separated carcinoma in situ from papillary superficial lesions and subgroups within early-stage and invasive tumors displaying different overall survival. Additionally, it recognized early-stage tumors showing gene profiles similar to invasive disease. Different techniques including standard t-test, single-gene logistic regression, and support vector machine algorithms were applied to identify relevant genes involved in bladder cancer progression. Cytokeratin 20, neuropilin-2, p21, and p33ING1 were selected among the top ranked molecular targets differentially expressed and validated by immunohistochemistry using tissue microarrays (n = 173). Their expression patterns were significantly associated with pathological stage, tumor grade, and altered retinoblastoma (RB) expression. Moreover, p33ING1 expression levels were significantly associated with overall survival. Analysis of the annotation of the most significant genes revealed the relevance of critical genes and pathways during bladder cancer progression, including the overexpression of oncogenic genes such as DEK in superficial tumors or immune response genes such as Cd86 antigen in invasive disease. Gene profiling successfully classified bladder tumors based on their progression and clinical outcome. The present study has identified molecular biomarkers of potential clinical significance and critical molecular targets associated with bladder cancer progression.

Aged↗

PubMed2005

Multianalyte profiling of serum cytokines for detection of pancreatic cancer.

Early detection of pancreatic cancer might improve clinical outcome. Significant alterations in the levels of individual serum cytokines have been reported in pancreatic cancer. We hypothesized that a multicytokine panel could serve as biomarkers for pancreatic cancer. To evaluate the diagnostic utility of such a panel, we have utilized a novel multianalyte LabMAP profiling technology that allows simultaneous measurement of multiple markers. In this study, a panel of 31 serological markers including cytokines, chemokines, growth and angiogenic factors in combination with CA 19-9 was analyzed in sera of pancreatic cancer patients, patients with chronic pancreatitis, and matched control healthy subjects. Statistical analysis identified a multicytokine panel that was able to distinguish pancreatic cancer from healthy controls with a sensitivity of 85.7% and specificity of 92.3%, which was superior to performance of CA 19-9 alone. Importantly, a multicytokine panel allowed the discrimination of pancreatic cancer from chronic pancreatitis with high sensitivity of 98% and specificity of 96.4%. In conclusion, we demonstrated that analysis of multiple serum cytokines using a novel LabMAP technology is a promising approach for development of a diagnostic assay for pancreatic cancer.

Biomarkers, Tumor↗

PubMed2005

Distinction of desmoplastic melanoma from non-desmoplastic melanoma by gene expression profiling.

Desmoplastic melanoma (DM) is a variant of melanoma characterized by the presence of amelanotic fusiform melanocytes dispersed in a prominent collagenous stroma. DM behaves differently from conventional non-desmoplastic melanoma (NDM). It has a higher tendency for local recurrence and is less likely to metastasize to regional lymph nodes. In this study, we explored the possibility of distinguishing DM from NDM by gene expression profiling. RNA samples from ten primary cutaneous melanomas of similar depth of invasion were analyzed using the Affymetrix U133A oligonucleotide platform. Four tumors were DM, five were ND, and one tumor showed combined features of desmoplastic and conventional. Hierarchical cluster analysis clearly separated DM from NDM. The expression of a number of melanocyte differentiation genes was decreased in DM compared with NDM, which corresponded to immunohistochemical results. Various genes were upregulated in DM, including neurotrophic factors and genes involved in extracellular matrix production. A novel finding was the high expression of clusterin in DM, which was confirmed by immunohistochemical studies. Our results from gene expression profiling validate the distinction of DM from NDM. They also provide the opportunity to learn more about the biology of DM which had previously not yet been associated with this variant of melanoma.

Biomarkers, Tumor↗

PubMed2006

COPA--cancer outlier profile analysis.

UNLABELLED: Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type. Standard statistical tests, such as the t-test are not suited for detecting these translocations, but a simple test based on robust centering and scaling of the data to help detect outlier samples, followed by a search for pairs of samples with mutually exclusive outliers, may be used to find genes involved in recurrent translocations. We have implemented this method, termed Cancer Outlier Profile Analysis (COPA) in an R package (that we call the copa package), and show its applicability on a publicly available dataset. AVAILABILITY: http://www.bioconductor.org

Algorithms↗

PubMed2001

Biological indicators for the identification of ionizing radiation exposure in humans.

While the effects of acute high-dose irradiation are well-documented, less is known about the effects of low level chronic radiation exposure. Physical dosimetry cannot always be relied upon, so dose estimates and determination of past radiation exposure must often be based upon biological indicators. Some of the established methods used in the assessment of nuclear accidents are reviewed here, including cytogenetic analyses, mutation-based assays and electron spin resonance. As interest in research on low-level radiation exposures expands, there is an increasing need for new biomarkers that can identify exposed individuals in human populations. Developments in high-throughput gene expression profiling may enable future development of a rapid and noninvasive testing method for application to potentially exposed populations.

Biomarkers↗

PubMedDate not supplied

Tissue microarrays: potential in the Indian subcontinent.

Tissue microarrays (TMAs) are a means of combining hundreds of specimens of tissue on to a single slide for analysis simultaneously. The evolution of this technology to validate the results of cDNA microarrays has impacted tremendously in accurately identifying prognostic indicators significant in determining survival demographics for patients. TMAs can be generated from archival paraffin blocks, combined with sophisticated image analysis software for reading TMA immunohistochemistry, and a staggering amount of useful information can be generated in terms of the biomarkers useful in predicting patient outcome. There is a wide range of uses for the TMA technology including profiling of specific proteins in cancerous tissues and non-cancerous tissues. Given the wide variety of tissue resources available in India, investment in a dedicated TMA facility will be of immense use in the research arena in India. This review article discusses the basics of TMA construction, design, the software available for the analysis of this technology and its relevance to Indian scientists. A potential workflow structure for setting up a TMA facility is also included.

Biomarkers, Tumor↗

PubMed2025

Genetic and transcriptional insights into immune checkpoint blockade response and survival: lessons from melanoma and beyond.

BACKGROUND: Integration of immune checkpoint inhibitors (ICIs) with non-immune therapies relies on identifying combinatorial biomarkers, which are essential for patient stratification and personalized treatment. METHODS: We analyzed genomic and transcriptomic data from pretreatment tumor samples of 342 melanoma patients treated with ICIs to identify mutations and expression signatures associated with ICI response and survival. External validation and mechanistic exploratory analyses were conducted in two additional datasets to assess generalizability. RESULTS: Responders were more likely to have received anti-PD-1 therapy rather than anti-CTLA-4 and exhibited a higher tumor mutation burden (both P < 0.001). Mutations in the dynein axonemal heavy chain (DNAH) family genes, specifically DNAH2 (P = 0.03), DNAH6 (P < 0.001), and DNAH9 (P < 0.01), were enriched in responders. The combined mutational status of DNAH 2/6/9 effectively stratified patients by progression-free survival (hazard ratio [HR]: 0.69; 95% confidence interval [CI] 0.51-0.92; P = 0.013) and overall survival (HR: 0.58; 95% CI 0.43-0.78; P < 0.001), with consistent association observed in the validation cohort (HR: 0.28; 95% CI 0.12-0.61; P < 0.001). DNAH-altered melanomas exhibited upregulation of chemokine signaling, cytokine-cytokine receptor interaction, and cell cycle-related pathways, along with elevated expression of immune-related signatures in interferon signaling, cytolytic activity, T cell function, and immune checkpoints. Using LASSO logistic regression, we identified a 26-gene composite signature predictive of clinical response, achieving an area under the curve (AUC) of 0.880 (95% CI 0.825-0.936) in the training dataset and 0.725 (95% CI 0.595-0.856) in the testing dataset. High-risk patients, stratified by the expression levels of a 13-gene signature, demonstrated significantly shorter overall survival in both datasets (HR: 3.35; P < 0.001; HR: 2.93; P = 0.002). CONCLUSIONS: This analysis identified potential molecular determinants of response and survival to ICI treatment. Insights from melanoma biomarker research hold significant promise for translation into other malignancies, guiding individualized anti-tumor immunotherapy.

Humans↗

PubMed2026

Multi-sampling allows intra-tumoral heterogeneity querying and vulnerability profiling in glioblastoma.

BACKGROUND: Glioblastoma (GBM) remains a devastating cancer with limited treatment options, largely due to its heterogeneity. While supramaximal resection has recently provided survival benefits, therapeutic profiling of different tumor compartments, particularly its infiltrative edge remains largely unexplored. METHODS: Here, we leveraged magnetic resonance imaging (MRI)-guided multi-sampling, collecting 2 cores and 2 margins per case, to query GBM heterogeneity. Whole-exome and RNA-seq with drug testing in two patient-derived 3D models were used to reveal similarities and differences in genomic and transcriptomic makeups, cellular compositions, and drug responses across cores and margins. Bioinformatics interrogations further identified response biomarkers. RESULTS: Mutation analysis showed that oncogenes exhibited a higher degree of spatial heterogeneity than tumor suppressor genes, regardless of MRI status. While the mesenchymal transcriptional subtype with extracellular matrix remodeling, stress response, and immune programs were preferentially enriched in enhancing cores, proneural tumors with neurological processes favored non-enhancing margins. Using a 15-drug GBM-targeted panel, ERK (ulixertinib) and PI3K pathway (paxalisib, CC-115) inhibitors showed preferential efficacy in enhancing cores and non-enhancing margins, respectively. The anti-apoptosis, pan-Bcl2 agent navitoclax and the epigenetic drug trotabresib represented the most effective, tumor-wide monotherapies. Importantly, drug combinations generally outperformed single agents across all regions. CONCLUSIONS: This work demonstrates the regional heterogeneity of therapeutic vulnerabilities in GBM ex vivo, showing various drugs with tumor-wide or MRI-enhancement informed activity. These findings offer preclinical bases of numerous monotherapies and drug combinations for future clinical trial design.

Humans↗

PubMed2004

Evaluation of an integrated strategy for proteomic profiling of skeletal muscle.

Proteomic analysis of skeletal muscle presents particular challenges when trying to identify valid biomarkers of phenotypic change in small biopsies from genetically diverse human subjects. Currently, two-dimensional (2-D) gel electrophoresis and mass spectrometry are the chosen analytical strategies but 2-D gels are not appropriate for analyzing proteins less than 11 kDa, they can suffer from problems of reproducibility and in routine use are not a viable high-throughput technique. We have evaluated an integrated proteomic strategy employing Ciphergen ProteinChip arrays, one-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Protein fingerprints characteristic of fast and slow contracting muscles from normal and kyphoscoliosis (ky) mutant mice were obtained from Ciphergen protein arrays. Eight statistically validated protein biomarkers have so far been identified capable of discriminating fast from slow muscle. Five of these showed further differential expression in ky versus normal BDL soleus muscles. Several biomarkers have been formally identified, and were myosin light chain isoforms shown previously to be expressed differentially by fast versus slow skeletal muscles. This integrated experimental approach using a model mouse muscle system shows the potential of Ciphergen protein array technology for proteomic analysis of small proteins in small muscle samples and its applicability for phenotypic characterization of skeletal muscle in general.

Animals↗

PubMed2026

Perineuronal net degradation in aggressive glioblastomas with KANK1::NTRK2 fusions.

BACKGROUND: Approximately 10% of glioblastomas harbor targetable genomic fusions. NTRK2 participates in a variety of fusion events that drive tumorigenesis. Two previous reports have described KANK1::NTRK2 fusions in adult glioblastoma patients with poor survival. METHODS: We performed a retrospective analysis of glioblastoma patients treated at Dartmouth-Hitchcock Medical Center (DHMC) from 2020 to 2025 to identify cases harboring KANK1::NTRK2 fusions. Clinical presentation, treatment, histopathologic features, and outcomes were reviewed. In addition, we conducted GeoMx whole-transcriptome and high-plex proteomic digital spatial profiling of a KANK1::NTRK2-positive glioblastoma and a comparator tumor from a long-term survivor. Candidate biomarkers were orthogonally validated using immunohistochemistry and/or immunofluorescence. RESULTS: Two patients with KANK1::NTRK2 fusion glioblastoma were identified, both demonstrating rapid progression, therapeutic resistance, and survival of less than 7 months. Proteomic profiling showed increased expression and activation of canonical NTRK2 downstream signaling pathways, particularly MEK1/2 and ERK1/2. This was accompanied by upregulation of extracellular matrix remodeling enzymes, including MMP3, MMP14, and ADAM15, along with reduced expression of extracellular matrix-associated transcripts and perineuronal net components in particular compared to a non-fusion glioblastoma. CONCLUSIONS: These limited, hypothesis-generating findings suggest constitutive NTRK2 signaling may promote coordinated extracellular matrix degradation and remodeling, potentially facilitating rapid and aggressive tumor growth and invasion in a subset of glioblastomas.

NTRK gene fusion↗

PubMed2006

Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry.

The aim of metabolite profiling is to monitor all metabolites within a biological sample for applications in basic biochemical research as well as pharmacokinetic studies and biomarker discovery. Here, novel data analysis software, XCMS, was used to monitor all metabolite features detected from an array of serum extraction methods, with application to metabolite profiling using electrospray liquid chromatography/mass spectrometry (ESI-LC/MS). The XCMS software enabled the comparison of methods with regard to reproducibility, the number and type of metabolite features detected, and the similarity of these features between different extraction methods. Extraction efficiency with regard to metabolite feature hydrophobicity was examined through the generation of unique feature density distribution plots, displaying feature distribution along chromatographic time. Hierarchical clustering was performed to highlight similarities in the metabolite features observed between the extraction methods. Protein extraction efficiency was determined using the Bradford assay, and the residual proteins were identified using nano-LC/MS/MS. Additionally, the identification of four of the most intensely ionized serum metabolites using FTMS and tandem mass spectrometry was reported. The extraction methods, ranging from organic solvents and acids to heat denaturation, varied widely in both protein removal efficiency and the number of mass spectral features detected. Methanol protein precipitation followed by centrifugation was found to be the most effective, straightforward, and reproducible approach, resulting in serum extracts containing over 2000 detected metabolite features and less than 2% residual protein. Interestingly, the combination of all approaches produced over 10,000 unique metabolite features, a number that is indicative of the complexity of the human metabolome and the potential of metabolomics in biomarker discovery.

Blood Proteins↗

PubMed2006

Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation.

BACKGROUND: Inflammation is associated with increased risk of cardiovascular disorders, arthritis, diabetes mellitus, and mortality. The effects of sleep loss on the cellular and genomic mechanisms that contribute to inflammatory cytokine activity are not known. METHODS: In 30 healthy adults, monocyte intracellular proinflammatory cytokine production was repeatedly assessed during the day across 3 baseline periods and after partial sleep deprivation (awake from 11 pm to 3 am). We analyzed the impact of sleep loss on transcription of proinflammatory cytokine genes and used DNA microarray analyses to characterize candidate transcription-control pathways that might mediate the effects of sleep loss on leukocyte gene expression. RESULTS: In the morning after a night of sleep loss, monocyte production of interleukin 6 and tumor necrosis factor alpha was significantly greater compared with morning levels following uninterrupted sleep. In addition, sleep loss induced a more than 3-fold increase in transcription of interleukin 6 messenger RNA and a 2-fold increase in tumor necrosis factor alpha messenger RNA. Bioinformatics analyses suggested that the inflammatory response was mediated by the nuclear factor kappaB inflammatory signaling system as well as through classic hormone and growth factor response pathways. CONCLUSIONS: Sleep loss induces a functional alteration of the monocyte proinflammatory cytokine response. A modest amount of sleep loss also alters molecular processes that drive cellular immune activation and induce inflammatory cytokines; mapping the dynamics of sleep loss on molecular signaling pathways has implications for understanding the role of sleep in altering immune cell physiologic characteristics. Interventions that target sleep might constitute new strategies to constrain inflammation with effects on inflammatory disease risk.

Adult↗

PubMed2002

Deoxyguanosine blocks allograft rejection of thymic epithelium but not lymphocyte infiltration and recognition.

Embryonic thymic lobes cultured in vitro in the presence of deoxyguanosine (dGuo) are accepted in fully mismatched recipients. The proposed explanation for this finding was the depletion of hematopoietic cells induced by the treatment associated with poor immunogenicity of thymic epithelium. We have recently demonstrated that embryonic tissues obtained at stages prior to hematopoietic colonization are nevertheless rejectable. Thymic epithelium from E10 embryos is not an exception in this respect and is acutely rejected in less than 12 days. Based on these findings we re-evaluated the protective role of dGuo against thymic allograft rejection. We observed that, in contrast to embryonic heart and intestine, allogeneic thymic epithelium naturally devoid of hematopoietic cells was accepted after treatment with dGuo. Active recognition of the allogeneic transplant was revealed by the presence of activated T cells, which infiltrated the accepted grafts, and showed reduced levels of IL-2 and IFN-gamma expression, although no essential role for IL-10 as regulatory cytokine has been found. Also, increased numbers of apoptotic cells are found in both dGuo-treated and untreated allogeneic control grafts. Moreover, the role of the indirect pathway of antigen recognition in allograft acceptance was excluded. The results show that allograft acceptance of dGuo-treated thymic lobes is induced by a direct, tissue-specific effect on the thymic stroma.

Animals↗