Alteration in lipid composition differentiates breast cancer tissues: a <Superscript>1</Superscript>H HRMAS NMR metabolomic study

Introduction

Breast cancer is the most frequent diagnosed cancer among women with a mortality rate of 15% of all cancer related deaths in women. Breast cancer is heterogeneous in nature and produces plethora of metabolites allowing its early detection using molecular diagnostic techniques like magnetic resonance spectroscopy.

Objectives

To evaluate the variation in metabolic profile of breast cancer focusing on lipids as triglycerides (TG) and free fatty acids (FFA) that may alter in malignant breast tissues and lymph nodes from adjacent benign breast tissues by HRMAS ¹H NMR spectroscopy.

Methods

The ¹H NMR spectra recorded on 173 tissue specimens comprising of breast tumor tissues, adjacent tissues, few lymph nodes and overlying skin tissues obtained from 67 patients suffering from breast cancer. Multivariate statistical analysis was employed to identify metabolites acting as major confounders for differentiation of malignancy.

Result

Reduction in lipid content were observed in malignant breast tissues along with a higher fraction of FFA. Four small molecule metabolites e.g., choline containing compounds (Chocc), taurine, glycine, and glutamate were also identified as major confounders. The test set for prediction provided sensitivity and specificity of more than 90% excluding the lymph nodes and skin tissues.

Conclusion

Fatty acids composition in breast cancer using in vivo magnetic resonance spectroscopy (MRS) is gaining its importance in clinical settings (Coum et al. in Magn Reson Mater Phys Biol Med 29:1–4, 2016). The present study may help in future for precise evaluation of lipid classification including small molecules as a source of early diagnosis of invasive ductal carcinoma by employing in vivo magnetic resonance spectroscopic methods.

     

Prediction of neuroprotective treatment efficiency using a HRMAS NMR-based statistical model of refractory status epilepticus on mouse: a metabolomic approach supported by histology

This work presents a model combining quantitative proton HRMAS NMR data and PLS-DA for neuropathology and neuroprotection evaluation. Metabolic data were also confronted to histopathological results obtained using the same experimental conditions. Soman, when not lethal, can induce status epilepticus (SE), brain damage, histological lesions, and profound cerebral metabolic disorders as revealed using (1)H HRMAS NMR. Our challenge was to evaluate delayed treatments, which could control refractory SE and avoid brain lesions. For this aim, we have built a statistical model of soman intoxication describing brain metabolite evolution during 7 days. We have then used this model to evaluate the efficiency of a combination of ketamine/atropine (KET/AS) administrated 1 and 2 h after SE induction, compared to the immediate anticonvulsant therapy midazolam/atropine sulfate (MDZ/AS). Furthermore, quantitation of HRMAS NMR data allowed us to follow individual evolution of 17 metabolites. N-Acetylaspartate, lactate, or taurine presented a long lasting disruption, while glutamine, alanine, glycerophosphocholine and myo-inositol showed disruptions for 3 days with a reversion at day 7. These changes were completely normalized by the administration of MDZ/AS. Interestingly, they were also almost completely reversed by KET/AS 1 h postsoman. This work suggests further the predictive interest of HRMAS and PLS-DA for neuropathology/neuroprotection studies and also confirms, on the metabolic aspects, the neuroprotective potentials of KET/AS combinations for the delayed treatment of soman-induced SE.     

Epigenetic changes of <Emphasis Type="Italic">CDX2</Emphasis> in gastric adenocarcinoma

Gastric cancer is one of the most commonplace and lethal cancers in the world. Molecular investigation of this disease, in order to obtain diagnostic and treatments achievements is important and vital. Relatively, in this research study, one of the most important epigenetic factors, the methylation of CDX2 gene was investigated in tumoral and non-tumoral tissues of gastric cancer patients by bisulfite treatment followed by sequencing of the 5’UTR region of CDX2 in both tissues. The results indicated a hypomethylation in tumoral tissues of adenocarcinoma. Consequently, the methylation amount of CDX2 in tumoral tissues was significantly reduced compared with non-tumoral tissues.     

高分辨魔角旋转核磁共振和主成分分析研究人类低级星形细胞瘤和脑膜瘤的代谢组特征

采用高分辨魔角旋转核磁共振（HRMAS ^1H NMR）技术结合主成分分析（PCA）方法研究了39例人体脑肿瘤组织的代谢组特征．39例肿瘤样本分别来自39个脑肿瘤患者,包括15例低级星形细胞瘤,13例纤维型脑膜瘤和11例过渡型脑膜瘤．核磁共振波谱分析结果表明,脑肿瘤组织的代谢组中丰要含有脂肪酸、乳酸、胆碱代谢物（如胆碱、磷酸胆碱和甘油磷酸胆碱）、氯基酸（如丙氨酸、谷氨酸、谷氮酰胺、牛磺酸）、N-乙酰天门冬氨酸（NAA）和谷胱甘肽等代谢物．通过对核磁共振谱进行主成分分析（PCA）,发现低级星形细胞瘤和脑膜瘤的代谢组之间具有明显的差异,而在过渡型和纤维型两个亚类脑膜瘤之间该差别相对较小．与脑膜瘤相比,低级星形细胞瘤中甘油磷酸胆碱、磷酸胆碱、肌醇与肌酸的含量较高,而丙氨酸、谷氨酸、谷氨酰胺、谷胱甘肽和牛磺酸的含量较低．NAA的含量在低级星形细胞瘤中尽管较低但能观察到,而脑膜瘤中却未发现NAA的信号．结果衷明,HRMAS ^1H NMR和多变量统计分析相结合的组织代谢组学方法,不仅能有效区分不同类型的脑肿瘤,而且还可以为脑肿瘤提供丰富的代谢组信息,这些信息对研究肿瘤发生发展的机制具有潜在的意义．     

Proteome analysis of membrane fractions in colorectal carcinomas by using 2D-DIGE saturation labeling

Because of its high content in receptors and signaling proteins, the analysis of membrane fractions is critical for the study of neoplastic diseases as colorectal cancer. Here, we have used the new saturation labeling for 2D-DIGE analysis of the membrane proteome of colorectal cancer mucosal tissues. Samples from 6 patients (tumoral and normal paired biopsies) were included in this study. Twelve analytical gels were performed and considered for the quantitative study and statistical analysis. A spot pattern analysis, by using an unsupervised clustering algorithm, allowed the classification of the samples according to similar expression patterns in tumoral and normal samples. Those proteins whose expression changed significantly (Student's t-test, p < 0.05) were further digested and characterized by mass spectrometry. Among the differentially expressed proteins: annexin A2, annexin A4, annexin A5, annexin A7, lamin B, calponin 1 and VDAC were analyzed by immunohistochemistry using tissue microarrays. Annexin A2, annexin A4 and VDAC appear as potential markers of interest for colorectal cancer diagnosis and, presumably, therapy. In summary, saturation labeling provides a new and sensitive tool for the analysis of scarce amounts of samples, allowing sample classification and direct identification of deregulated proteins.     

Metabolic profiling of cervical tubercular lymphadenitis tissues by proton HR-MAS NMR spectroscopy

Proton metabolic profiling of incisional biopsied cervical lymph node tissue specimens of 109 patients suffering from tubercular (CTBL) and non-specific (NSCLA) lymphadenitis were analyzed by high resolution magic angle spinning (HR-MAS) NMR spectroscopy. In the present study, 40 endogenous metabolites namely, myo-inositol (m-Ins), branched chain amino acids (BCAA), glutamate, serine, taurine (Tau) aromatic amino acids, choline (Cho) containing compounds and glucose were characterized. To the best of our knowledge, this is the first report on metabolic profiling of cervical tubercular lymph node tissues using HR-MAS NMR spectroscopy. The principal component analysis revealed a clear discrimination between CTBL and NSCLA tissues. Increase in the concentration of mobile poly unsaturated fatty acids, BCAA, Cho, Tau, glycine and a decrease in the concentration of lactate, phosphocholine and m-Ins was observed in CTBL cases. The partial least square discriminant analysis (PLS-DA) with R ² = 0.95 and Q ² = 0.92 provided >98 % of correct classification between the two groups. A PLS-DA training set model of 75 % (CTBL = 54, NSCLA = 27) of the subjects when subjected for prediction of 25 % cases (CTBL = 18, NSCLA = 10) as an unknown dataset provided more than 98 % of diagnostic accuracy in their respective histological categories. The receiver operator characteristic curve was generated from PLS-DA factor-1 projected an area under the curve of 0.962. The metabolic profile obtained from HR-MAS NMR spectroscopy may be used as surrogate markers in vivo MRS for differentiating between CTBL and NSCLA cases non-invasively.     

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood

Objectives

A detailed understanding of the metabolic processes governing rapid growth in early life is still lacking. The aim of this study was to investigate the age-related metabolic changes in healthy children throughout early childhood.

Methods

Healthy children from a birth cohort were enrolled in this study from birth through 4 years of age. Urinary metabolites were assessed at 6 months, and 1, 2, 3, and 4 yr of age by using ¹H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis including principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Metabolic pathway analysis was performed using the MetPA web tool.

Results

A total of 105 urine samples from 30 healthy children were collected and analyzed. Metabolites contributing to the discrimination between age groups were identified by using supervised PLS-DA (Q² = 0.60; R² = 0.66). A significantly higher urinary trimethylamine N-oxide (TMAO) and betaine level was found in children aged 6 months. Urinary glycine and glutamine levels declined significantly after 6 months of age and there was a concomitant compensatory increase in urinary creatine and creatinine. Metabolic pathway analysis using MetPA revealed similar nitrogen metabolism associated energy production across all ages assessed. Pathways associated with amino acid metabolism were significantly different between infants aged 6 months and 1 year, whereas pathways associated with carbohydrate metabolism were significantly different between children at ages 2 and 3 years.

Conclusions

Urine metabolomics ideally represents dynamic metabolic changes across age. Urinary metabolic profiles change significantly within the first year of life, which can potentially provide crucial information about infant nutrition and growth.     

Multiplex degenerate PCR coupled with an oligo sorbent array for human endogenous retrovirus expression profiling

Human endogenous retroviruses (HERVs) can be divided into distinct families of tens to thousands of paralogous loci. The expression of HERV elements has been detected in all tissues tested to date, particularly germ cells, embryonic tissues and neoplastic tissues. Hence, the study of HERV expression could represent added value in cancer diagnosis. We developed a quantitative assay combining a multiplex degenerate PCR (MD-PCR) amplification, based on the relative conservation of the pol genes, and a colorimetric Oligo Sorbent Array (OLISA^®). Nine HERV families were selected and amplification primers and capture probes were designed for each family. The features required to achieve efficient amplification of most of the elements of each HERV family and balanced co-amplification of all HERV families were analyzed. We found that MD-PCR reliability, i.e. equivalence of amplification and dose-effect relationship, relied on the adjustment of three critical parameters: the primer degeneracy, the relative concentration of each primer and the total amount of primers in the amplification mixture. The analysis of tumoral versus normal tissues suggests that this assay could prove useful in tumor phenotyping.     

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q ² and Discriminant Q ² (DQ ²) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q ² and Discriminant Q ² (DQ ²). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ ² and Q ² (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.

     

Raman spectroscopy of human skin for kidney failure detection

The object of this paper is in vivo study of skin spectral-characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS-DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS-DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm⁻¹. In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost-effective and accurate detection of kidney failure and associated metabolic changes in the skin.     

Metabolomic profile in hyperthyroid patients before and after antithyroid drug treatment: Correlation with thyroid hormone and TSH concentration

Hyperthyroidism (HT) is characterized by an intense metabolic impact which affects the lipid, carbohydrate and amino acids metabolism, with increased resting energy expenditure and thermogenesis. Metabolomics is a new comprehensive technique that allows to capture an instant metabolic picture of an organism, reflecting peculiar molecular and pathophysiological states. The aim of the present prospective study was to identify a distinct metabolomic profile in HT patients using ¹H NMR spectroscopy before and after antithyroid drug treatment. This prospective study included 15 patients (10 female, 5 male) who were newly diagnosed hyperthyroidism. A nuclear magnetic resonance (¹H NMR) based analysis was performed on plasma samples from the same patients at diagnosis (HypT₀) and when they achieved euthyroidism (HypT₁). The case groups were compared with a control group of 26 healthy volunteers (C). Multivariate statistical analysis was performed with Partial Least Squares-Discriminant Analysis (PLS-DA). PLS-DA identified a distinct metabolic profile between C and untreated hyperthyroid patients (R²X 0.638, R²Y 0.932, Q² 0.783). Interestingly, a significant difference was also found between C and euthyroid patients after treatment (R²X 0.510, R²Y 0.838, Q² 0.607), while similar cluster emerged comparing HypT₀ vs HypT₁ patients. This study shows that metabolomic profile is deeply influenced by hyperthyroidism and this alteration persists after normalization of thyrotropin (TSH) and free thyroid hormone (FT3, FT4) concentration. This suggests that TSH, FT3 and FT4 assays may not be insufficient to detect long lasting peripheral effects of the thyroid hormones action. Further studies are needed to clarify whether and to what extent the evaluation of metabolomics profile may provide relevant information in the clinical management of hyperthyroidism.     

<Superscript>1</Superscript>H-NMR metabonomics analysis of sera differentiates between mammary tumor-bearing mice and healthy controls

Global analysis of ¹H-NMR spectra of serum is an appealing approach for the rapid detection of cancer. To evaluate the usefulness of this method in distinguishing between mammary tumor-bearing mice and healthy controls, we conducted ¹H-NMR metabonomic analyses on serum samples obtained from the following: 10 mice inoculated with a highly-metastatic mammary carcinoma cell line, 10 mice inoculated with a “normally” metastatic mammary carcinoma cell line, and 10 healthy controls. Following standard spectral processing and subsequent data reduction, we applied unsupervised Principal Component Analysis (PCA) to determine if unique metabolic fingerprints for different categories of metastatic breast cancer in serum exist. The PCA method correctly separated sera of tumor-bearing mice from that of normal healthy controls, as shown using the scores plot which indicated that sera classes from tumor-bearing mice did not share multivariate space with that from healthy controls. In addition, this technique was capable of distinguishing between classes of varying metastatic ability in this system. Metabolites apparently responsible for separation between diseased and healthy mice include lactate, taurine, choline, and sugar moieties. Results of this study suggest that ¹H-NMR spectra of mouse serum analyzed using PCA statistical methods indicate separation of tumor-bearing mice from healthy normal controls, justifying further study of the use of ¹H-NMR metabonomics for cancer detection using serum.     

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q ² and Discriminant Q ² (DQ ²) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q ² and Discriminant Q ² (DQ ²). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ ² and Q ² (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.     

Establishment and characterization of two human breast carcinoma cell lines by spontaneous immortalization: Discordance between Estrogen, Progesterone and HER2/neu receptors of breast carcinoma tissues with derived cell lines

Background

Breast cancer is one of the most common cancers among women throughout the world. Therefore, established cell lines are widely used as in vitro experimental models in cancer research.

Methods

Two continuous human breast cell lines, designated MBC1 and MBC2, were successfully established and characterized from invasive ductal breast carcinoma tissues of Malaysian patients. MBC1 and MBC2 have been characterized in terms of morphology analysis, population doubling time, clonogenic formation, wound healing assay, invasion assay, cell cycle, DNA profiling, fluorescence immunocytochemistry, Western blotting and karyotyping.

Results

MBC1 and MBC2 exhibited adherent monolayer epithelial morphology at a passage number of 150. Receptor status of MBC1 and MBC2 show (ER⁺, PR⁺, HER2⁺) and (ER⁺, PR^-, HER2⁺), respectively. These results are in discordance with histopathological studies of the tumoral tissues, which were triple negative and (ER^-, PR^-, HER2⁺) for MBC1 and MBC2, respectively. Both cell lines were capable of growing in soft agar culture, which suggests their metastatic potential. The MBC1 and MBC2 metaphase spreads showed an abnormal karyotype, including hyperdiploidy and complex rearrangements with modes of 52–58 chromosomes per cell.

Conclusions

Loss or gain in secondary properties, deregulation and specific genetic changes possibly conferred receptor changes during the culturing of tumoral cells. Thus, we hypothesize that, among heterogenous tumoral cells, only a small minority of ER⁺/PR⁺/HER2⁺ and ER⁺/PR^-/HER2⁺ cells with lower energy metabolism might survive and adjust easily to in vitro conditions. These cell lines will pave the way for new perspectives in genetic and biological investigations, drug resistance and chemotherapy studies, and would serve as prototype models in Malaysian breast carcinogenesis investigations.     

An NMR- and MS-based metabonomic investigation of saliva metabolic changes in feline odontoclastic resorptive lesions (FORL)-diseased cats

The feline odontoclastic resorptive lesion (FORL) is a common oral problem in cats. The disease has increased steadily since the domestication of cats and etiology of this disease has not been fully determined although several theories have been proposed. Feeding practices, vaccination, and neutering programs have all been suspected to be associated with FORL. The aim of the current study is to assess the feasibility of metabonomics to detect at an early stage the onset of the disease. The diagnostic biomarkers could then be used as “efficacy markers” for nutritional intervention in preventing and/or slowing the progression of FORL. ¹H-NMR- and LC/MS-based metabonomic analysis of saliva samples obtained from a group of 21 cats (11 healthy and 10 FORL diseased) showed clear differences in the metabolic composition of saliva from healthy and FORL-diseased cats. To identify biomarkers, the spectroscopic data was processed using partial least-squares discriminant analysis (PLS-DA) and validated by leave-one-subject-out cross validation. The PLS-DA model predicted FORL- diseased cats with over 60% accuracy. The maximum value of Q² of the random permutation sets was less than 0.3. The diseased cats showed increased levels of many organic and amino acids, such as acetate, lactate, propionate, isovalerate, tryptamine, and phenylalanine suggesting changes in oral microflora in the disease situation. This study is preliminary and a larger study with more samples to further validate the biomarker profile predictive of an early FORL pathophysiological status is in progress.     

<Emphasis Type="Italic">FAT4</Emphasis> hypermethylation and grade dependent downregulation in gastric adenocarcinoma

Gastric cancer is one of the major causes of death due to cancer in the world. It is a multi-factorial disease with epigenetic factors being also involved in its development. FAT4 is a tumor suppressor gene exerting an important role in cell adhesion. This study aimed at analyzing FAT4 expression and promoter methylation in gastric cancer. FAT4 expression was studied in 30 tumoral tissues and their non-tumoral counterparts using Taqman real time PCR method. Promoter methylation was assessed using bisulfite conversion method followed by sequencing. Tumor tissues showed reduced FAT4 expression (P = 0.04). FAT4 downregulation was associated with tumor grade, with higher repression at advanced grades. Significant increase of promoter methylation was observed in tumoral tissues. Reduced expression of FAT4 and increased methylation of its promoter may be one of the effective processes in turning a healthy stomach tissue into a tumor tissue.     

Identification of HLA ligands and T-cell epitopes for immunotherapy of lung cancer

Introduction

Lung cancer is the most common cancer worldwide. Every year, as many people die of lung cancer as of breast, colon and rectum cancers combined. Because most patients are being diagnosed in advanced, not resectable stages and therefore have a poor prognosis, there is an urgent need for alternative therapies. Since it has been demonstrated that a high number of tumor- and stromal-infiltrating cytotoxic T cells (CTLs) is associated with an increased disease-specific survival in lung cancer patients, it can be assumed that immunotherapy, e.g. peptide vaccines that are able to induce a CTL response against the tumor, might be a promising approach.

Methods

We analyzed surgically resected lung cancer tissues with respect to HLA class I- and II-presented peptides and gene expression profiles, aiming at the identification of (novel) tumor antigens. In addition, we tested the ability of HLA ligands derived from such antigens to generate a CTL response in healthy donors.

Results

Among 170 HLA ligands characterized, we were able to identify several potential targets for specific CTL recognition and to generate CD8⁺ T cells which were specific for peptides derived from cyclin D1 or protein-kinase, DNA-activated, catalytic polypeptide and lysed tumor cells loaded with peptide.

Conclusions

This is the first molecular analysis of HLA class I and II ligands ex vivo from human lung cancer tissues which reveals known and novel tumor antigens able to elicit a CTL response.     

Circulating and Tumor-Infiltrating Myeloid-Derived Suppressor Cells in Patients with Colorectal Carcinoma

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous family of myeloid cells that suppress T cell immunity in tumor-bearing hosts. In patients with colon cancer, MDSCs have recently been described as Lin^−/lowHLA-DR⁻CD11b⁺CD33⁺ cells correlating with cancer stage, metastasis and chemotherapy response. To learn in more detail the dynamic change and clinical relevance of circulating and tumor-infiltrating Lin^−/lowHLA-DR⁻CD11b⁺CD33⁺ MDSC in colorectal cancer, we harvested the blood from 64 patients with varying stage of colorectal cancer and tumor and matched paraneoplastic tissues from 5 patients with advanced colorectal cancer, subjected them to multicolor flow cytometric analysis of percentage, absolute number and phenotype of MDSC and finally characterized their immunosuppressive functions. Our results demonstrate that peripheral blood from colorectal cancer patients contains markedly increased percentage and absolute number of Lin^−/lowHLA-DR⁻CD11b⁺CD33⁺ MDSCs compared with healthy individuals, and this increase is closely correlated with clinical cancer stage and tumor metastasis but not primary tumor size and serum concentrations of cancer biomarker. A similar increase of MDSCs was also observed in the tumor tissues. Phenotyping MDSCs shows that they express high CD13 and CD39, low CD115, CD117, CD124 and PD-L1, and devoid of CD14, CD15 and CD66b, reminiscent of precursor myeloid cells. MDSCs from cancer patients but not healthy donors have the immunosuppressive activity and were able to inhibit in vitro autologous T-cell proliferation. Collectively, this study substantiates the presence of increased immunosuppressive circulating and tumor-resident Lin^−/lowHLA-DR⁻CD11b⁺CD33⁺ MDSCs in patients with colorectal cancers correlating with cancer stage and metastasis, and suggests that pharmacologic blockade of MDSCs should be considered in future clinical trials.     

High-resolution magic angle spinning nuclear magnetic resonance analysis of metabolic changes in melanoma cells after induction of melanogenesis

Melanin synthesis affects melanoma behavior and tumor responsiveness to therapy; therefore, we investigated metabolic changes in melanoma cells after induction of melanogenesis. Amelanotic and melanotic melanoma cells were labeled with ¹³C precursors and changes in their metabolism was analyzed by high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR). HRMAS NMR demonstrated clear differences in the pattern of metabolic intermediates between amelanotic and melanotic cells. Although the exact nature of the metabolites requires further investigations, our comparative studies clearly show that induction of melanogenesis is associated with changes of glucose and sodium acetate metabolism, demonstrating HRMAS NMR as a powerful and noninvasive technique to investigate such process.     

Label-free quantitative proteomic analysis reveals dysfunction of complement pathway in peripheral blood of schizophrenia patients: evidence for the immune hypothesis of schizophrenia

Schizophrenia is a complex mental disease caused by a combination of serial alterations in genetic and environmental factors. Although the brain is usually considered as the most relevant organ in schizophrenia, accumulated evidence suggests that peripheral tissues also contribute to this disease. In particular, abnormalities of the immune system have been identified in the peripheral blood of schizophrenia patients. To screen the serum proteomic signature of schizophrenia patients, we conducted shotgun proteomic analysis on serum samples of schizophrenia patients and healthy controls. High-abundance proteins were eliminated by immunoaffinity before LC-MS/MS analysis. The multivariate statistical test partial least squares-discriminant analysis (PLS-DA) was applied to build models for screening out variable importance in the projection (VIP) and 27 proteins were identified as being responsible for discriminating between the proteomic profiles of schizophrenia patients and healthy controls. Pathway analysis based on these 27 proteins revealed that complement and coagulation cascades was the most significant pathway. ELISA-based activity analyses indicated that the alternative complement pathway was suppressed in schizophrenia patients. Ingenuity pathways analysis was used to conduct the interaction network of 27 proteins. The network exhibited common features such as, nervous system development and function, humoral immune response and inflammatory response, and highlighted some proteins with important roles in the immune system, such as hub nodes. Our findings indicate dysregulation of the alternative complement pathway in schizophrenia patients. The protein interaction network enhances the interpretation of proteomic data and provides evidence that the immune system may contribute to schizophrenia.