Chloroplast biogenesis involves spatial coordination of nuclear and organellar gene expression in Chlamydomonas.

The localization of translation can direct the polypeptide product to the proper intracellular compartment. Our results reveal translation by cytosolic ribosomes on a domain of the chloroplast envelope in the unicellular green alga Chlamydomonas (Chlamydomonas reinhardtii). We show that this envelope domain of isolated chloroplasts retains translationally active ribosomes and mRNAs encoding chloroplast proteins. This domain is aligned with localized translation by chloroplast ribosomes in the translation zone, a chloroplast compartment where photosystem subunits encoded by the plastid genome are synthesized and assembled. Roles of localized translation in directing newly synthesized subunits of photosynthesis complexes to discrete regions within the chloroplast for their assembly are suggested by differences in localization on the chloroplast of mRNAs encoding either subunit of the light-harvesting complex II or the small subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase. Transcription of the chloroplast genome is spatially coordinated with translation, as revealed by our demonstration of a subpopulation of transcriptionally active chloroplast nucleoids at the translation zone. We propose that the expression of chloroplast proteins by the nuclear-cytosolic and organellar genetic systems is organized in spatially aligned subcompartments of the cytoplasm and chloroplast to facilitate the biogenesis of the photosynthetic complexes.

Identification and Analysis of Hub Genes in Diabetic Cardiomyopathy: Potential Role of Cytochrome P450 1A1 in Mitochondrial Metabolism and STZ-Induced Myocardial Dysfunction.

Diabetic cardiomyopathy (DCM) is a primary cause of death in diabetic patients; however, its molecular mechanism is not yet clear, and there is no uniform standard for diagnosis. The aim of this study is to discover the pathogenesis and potential therapeutic targets of DCM through screening and analysis of differentially expressed genes (DEGs) in heart ventricles of DCM, and to testify the role of key hub genes in DCM-induced myocardial dysfunction. Datasets GSE4745 and GSE6880 were downloaded from the GEO database. The difference analysis, visual analysis, cluster analysis and enrichment analysis were performed by using R language, python scripts and bioinformatics software followed by the construction of protein-protein interaction (PPI) network to obtain hub genes. The DCM models were established by streptozocin (STZ) injection to the male mice. The cardiac function and the expressions of hub genes were examined by using echocardiography and real-time quantitative poly-merase chain reaction (RT-qPCR), followed by multiple statistical analyses. Bioinformatic results indicate that mitochondrial dysfunction, disturbed lipid metabolism and decreased collagen synthesis are the main causes of the DCM development. In particular, the hub gene Cyp1a1 that encodes Cytochrome P450 1A1 (CYP4501A1) enzyme has the highest connectivity in the interaction network, and is associated with mitochondrial homeostasis and energy metabolism. It plays a critical role in the oxidation of endogenous or exogenous substrates. Our RT-qPCR results confirmed that ventricular Cyp1a1 mRNA level was nearly 12-fold upregulated in DCM model compared to normal control, which was correlated with abnormal cardiac function in diabetic individuals. CYP4501A1 protein expression in mitochondria was also increased in diabetic hearts. However, we found no significant changes in collagen expressions in cardiac ventricles of mice with DCM. This study provided compact data support for understanding the pathogenesis of DCM. CYP4501A1 might be considered as a potential candidate targeting for DCM therapy. Follow-up animal and clinical verifications need to be further explored.

Predicting linear B-cell epitopes using amino acid anchoring pair composition.

BACKGROUND: Accurate identification of linear B-cell epitopes plays an important role in peptide vaccine designs, immunodiagnosis, and antibody productions. Although several prediction methods have been reported, unsatisfied accuracy has limited the broad usages in linear B-cell epitope prediction. Therefore, developing a reliable model with significant improvement on prediction accuracy is highly desirable. RESULTS: In this study, we developed a novel model for prediction of linear B-cell epitopes, APCpred, which was derived from the combination of amino acid anchoring pair composition (APC) and Support Vector Machine (SVM) methods. Systematic comparisons with the existing prediction models demonstrated that APCpred method significantly improved the prediction accuracy both in fivefold cross-validation of training datasets and in independent blind datasets. In the fivefold cross-validation test with Chen872 dataset at window size of 20, APCpred achieved AUC of 0.809 and accuracy of 72.94%, which was much more accurate than the existing models, e.g., Bayesb, Chen's AAP methods and the enhanced combination method of AAP with five AP scales. For the fivefold cross-validation test with ABC16 dataset, APCpred achieved an improved AUC of 0.794 and ACC of 73.00% at window size of 16, and attained an AUC of 0.748 and ACC of 67.96% on Blind387 dataset after being trained with ABC16 dataset. Trained with Lbtope_Confirm dataset, APCpred achieved an increased Acc of 55.09% on FBC934 dataset. Within sequence window sizes from 12 to 20, APCpred final model on homology-reduced dataset achieved an optimal AUC of 0.748 and ACC of 68.43% in fivefold cross-validation at the window size of 20. CONCLUSION: APCpred model demonstrated a significant improvement in predicting linear B-cell epitopes using the features of amino acid anchoring pair composition (APC). Based on our study, a webserver has been developed for on-line prediction of linear B-cell epitopes, which is a free access at: http:/ccb.bmi.ac.cn/APCpred/.

A novel autoantibody test for the detection of pre-neoplastic lung lesions.

BACKGROUND: Atypical adenomatous hyperplasia (AAH) and squamous cell dysplasia (SCD) are associated with the development of malignant lesions in the lung. Accurate diagnosis of AAH and SCD could facilitate earlier clinical intervention and provide useful information for assessing lung cancer risk in human populations. Detection of AAH and SCD has been achieved by imaging and bronchoscopy clinically, but sensitivity and specificity remain less than satisfactory. We utilized the ability of the immune system to identify lesion specific proteins for detection of AAH and SCD. METHODS: AAH and SCD tissue was surgically removed from six patients of Chinese descent (3 AAH and 3 SCD) with corresponding serum samples. Total RNA was extracted from the tissues and a cDNA library was generated and incorporated into a T7 bacteriophage vector. Following enrichment to remove "normal" reactive phages, a total of 200 AAH related and 200 SCD related phage clones were chosen for statistical classifier development and incorporation into a microarray. Microarray slides were tested with an independent double-blinded population consisting of 100 AAH subjects, 100 SCD subjects and 200 healthy control subjects. RESULTS: Sensitivity of 82% and specificity of 70% were achieved in the detection of AAH using a combination of 9 autoantibody biomarkers. Likewise, 86% sensitivity and 78% specificity were achieved in the detection of SCD using a combination of 13 SCD-associated markers. Sequencing analysis identified that most of these 22 autoantibody biomarkers had known malignant associations. CONCLUSIONS: Both diagnostic values showed promising sensitivity and specificity in detection of pre-neoplastic lung lesions. Hence, this technology could be a useful non-invasive tool to assess lung cancer risk in human populations.

Identification of novel autoantibodies for detection of malignant mesothelioma.

BACKGROUND: The malignant mesothelioma (MM) survival rate has been hampered by the lack of efficient and accurate early detection methods. The immune system may detect the early changes of tumor progression by responding with tumor-associated autoantibody production. Hence, in this study, we translated the humoral immune response to cancer proteins into a potential blood test for MM. METHODOLOGY/PRINCIPAL FINDINGS: A T7 phage MM cDNA library was constructed using MM tumor tissues and biopanned for tumor-associated antigens (TAAs) using pooled MM patient and normal serum samples. About 1008 individual phage TAA clones from the biopanned library were subjected to protein microarray construction and tested with 53 MM and 52 control serum samples as a training group. Nine candidate autoantibody markers were selected from the training group using Tclass system and logistic regression statistical analysis, which achieved 94.3% sensitivity and 90.4% specificity with an AUC value of 0.89 in receiver operating characteristic analysis. The classifier was further evaluated with 50 patient and 50 normal serum samples as an independent blind validation, and the sensitivity of 86.0% and the specificity of 86.0% were obtained with an AUC of 0.82. Sequencing and BLASTN analysis of the classifier revealed that five of these nine candidate markers were found to have strong homology to cancer related proteins (PDIA6, MEG3, SDCCAG3, IGHG3, IGHG1). CONCLUSIONS/SIGNIFICANCE: Our results indicated that using a panel of 9 autoantibody markers presented a promising accuracy for MM detection. Although the results need further validation in high-risk groups, they provided the potentials in developing a serum-based assay for MM diagnosis.

Autoantibodies as potential biomarkers for breast cancer.

INTRODUCTION: Only a limited number of tumor markers for breast cancer are currently available. Antibodies to tumor-associated proteins may expand the number of available tumor markers for breast cancer and may be used together in a serum profile to enhance sensitivity and specificity. METHODS: In the present study, we interrogated a breast cancer cDNA T7 phage library for tumor-associated proteins using biopan enrichment techniques with sera from normal individuals and from breast cancer patients. The enrichment of tumor-associated proteins after biopanning was tested using a plaque-lift assay and immunochemical detection. The putative tumor-associated phage clones were collected for PCR and sequencing analysis. Unique and open reading frame phage-expressed proteins were then used to develop phage protein ELISAs to measure corresponding autoantibodies using 87 breast cancer patients and 87 normal serum samples. A logistic regression model and leave-one-out validation were used to evaluate predictive accuracies with a single marker as well as with combined markers. Identities of those selected proteins were revealed through the sequence BLAST program. RESULTS: We harvested 100 putative tumor-associated phage clones after biopan enrichment. Sequencing analysis revealed that six phage proteins were inframe and unique. Antibodies to these six phage-expressed proteins were measured by ELISAs, and the results showed that three of the phage clones had statistical significance in discriminating patients from normal individuals. BLAST results of the three proteins showed great matches to ASB-9, SERAC1, and RELT. Measurements of the three predictive phage proteins were combined in a logistic regression model that achieved 80% sensitivity and 100% specificity in prediction of sample status, whereas leave-one-out validation achieved 77.0% sensitivity and 82.8% specificity among 87 patient samples and 87 control samples. Receiver operating characteristic curve analysis and the leave-one-out method both showed that combined measurements of the three antibodies were more predictive of disease than any of the single antibodies studied, underscoring the importance of identifying multiple potential markers. CONCLUSION: Serum autoantibody profiling is a promising approach for early detection and diagnosis of breast cancer. Rather than one autoantibody, a panel of autoantibodies appears preferable to achieve superior accuracy. Further refinements will need to be made to further improve the accuracy. Once refined, the assay must be applied to a prospective patient population to demonstrate applicability.