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PDF file - 858KB, Supplementary Methods, Supplementary References, Figure S1-6, Table S1-5. Figure S1. Identification of a new miR in FHIT intron. Figure S2. The SNP variant around the Pri-C2-miR region in FHIT did not affect the C2-miR expression. Figure S3. C2-miR and FHIT are under the same transcriptional control. Figure S4. C2-miR inhibiting human tumor cell growth was not through promoting apoptosis. Figure S5. C2-miR inhibits human tumor cell growth through targeting CCND1, ERBB2, DNMT3A or DNMT3B. Figure S6. Multiple members of the miR-548 family could target the same oncogenes. Table S1. The documented miR-548 family members. Table S2. Quantities of FHIT or C2-miR in total cell RNA (20 ng). Table S3. Expression comparison of pri-miRs or mature miRs in 293FT cells. Table S4. Primers used in this study. Table S5. NanoString probes for detecting 29 members of the miR-548 family.

Loss or attenuated expression of the tumor-suppressor gene FHIT is associated paradoxically with poor progression of human tumors. Fhit promotes apoptosis and regulates reactive oxygen species; however, the mechanism by which Fhit inhibits tumor growth in animals remains unclear. In this study, we used a multidisciplinary approach based on bioinformatics, small RNA library screening, human tissue analysis, and a xenograft mouse model to identify a novel member of the miR-548 family in the fourth intron of the human FHIT gene. Characterization of this human-specific microRNA illustrates the importance of this class of microRNAs in tumor suppression and may influence interpretation of Fhit action in human cancer. Cancer Res; 74(8); 2283–94. ©2014 AACR.

Background Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. Methods In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. Results We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, β, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. Conclusions Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.

Background Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. Method Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. Results Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-β), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. Conclusion The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions. Electronic supplementary material The online version of this article (10.1186/s12967-019-1915-2) contains supplementary material, which is available to authorized users.

Amongst all toxicological endpoints, carcinogenicity might pose the greatest concern. Genetic damage has been considered an important underlying mechanism for the carcinogenicity of chemical substances. The demand for in vitro genotoxic tests as alternative approaches is growing rapidly with the implementation of new regulations for compounds. However, currently available in vitro genotoxicity tests are often limited by relatively high false positive rates. Moreover, few studies have explored carcinogenicity potential by in vitro genotoxicity testing due to the shortage of suitable toxicological biomarkers to link gene damage with cancer risk. γ-H2AX is a recently acknowledged attractive endpoint (biomarker) for evaluating DNA damage and can simultaneously reflect the DNA damage response and repair of cells. We previously reported an ultrasensitive and reliable method, namely stable-isotope dilution-liquid chromatography–tandem mass spectrometry (ID-LC–MS/MS), for detecting cellular γ-H2AX and evaluating genotoxic chemicals. More importantly, our method can dynamically monitor the specific processes of genotoxic compounds affecting DNA damage and repair reflected by the amount of γ-H2AX. To clarify the possibility of using this method to assess the potential carcinogenicity of genotoxic chemicals, we applied it to a set of 69 model compounds recommended by the European Center for the Validation of Alternative Methods (ECVAM), with already-characterized genotoxic potential. Compared to conventional in vitro genotoxicity assays, including the Ames test, the γ-H2AX assay by MS has high accuracy (94–96%) due to high sensitivity and specificity (88% and 100%, respectively). The dynamic profiles of model compounds after exposure in HepG2 cells were explored, and a mathematical approach was employed to simulate and quantitatively model the DNA repair kinetics of genotoxic carcinogens (GCs) based on γ-H2AX time–effect curves up to 8 h. Two crucial parameters, i.e., k (rate of γ-H2AX decay) and t50 (time required for γ-H2AX from maximum decrease to half) estimated by the least squares method, were achieved. An open web server to help researchers calculate these two key parameters and profile simulated curves of the tested compound is available online ( http://ccb1.bmi.ac.cn:81/shiny-server/sample-apps/prediction1/ ). We detected a positive association between carcinogenic levels and k and t50 values of γ-H2AX in tested GCs, validating the potential of using this MS-based γ-H2AX in vitro assay to help preliminarily evaluate carcinogenicity and assess genotoxicity. This approach may be used alone or integrated into an existing battery of in vitro genetic toxicity tests.

Background B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes. Results After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available. Conclusions We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

Background Bacterial small regulatory RNAs (sRNAs) play important roles in sensing environment changes through sRNA-target mRNA interactions. However, the current strategy for detecting sRNA-mRNA interactions usually combines bioinformatics prediction and experimental verification, which is hampered by low prediction accuracy and low-throughput. Additionally, among the 4736 sequenced bacterial genomes, only about 2164 sRNAs from 319 strains have been described. Furthermore, target mRNAs of only 157 sRNAs have been uncovered. Obviously, highly efficient methods were required to detect sRNA-mRNA interactions in the sequenced genomes. This study aimed to apply a modified CLASH (cross-linking, ligation and sequencing hybrids) method to detect RNA-RNA interactions in E. coli, a model bacterial organism. Results Statistically significant interactions were detected in 29 transcript pairs. To the best of our knowledge, 24 pairs were reported for the first time and were novel RNA interactions, including tRNA-tRNA, tRNA-ncRNA (non-coding RNA), tRNA-rRNA, rRNA-mRNA, rRNA-ncRNA, rRNA-rRNA, rRNA-IGT (intergenic transcript), and tRNA-IGT interactions. Conclusions Discovery of novel RNA-RNA interactions in the present study demonstrates that RNA-RNA interactions might be far more complicated than ever expected. New methods may be required to help discover more novel RNA-RNA interactions. The present work describes a high-throughput protocol not only for discovering new RNA interactions, but also directly obtaining base-pairing sequences, which should be useful in assessing RNA structure and interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3725-3) contains supplementary material, which is available to authorized users.

FAM64A was found to be markedly up-regulated in tumor samples and associated with worse overall survival in multiple cancer types, including breast cancer. However, the functional significance of FAM64A in breast cancer remains largely unknown. In this study, we systematically investigated the expression of FAM64A in multiple public breast cancer datasets. We found that FAM64A is significantly positively correlated with tumor stemness index in breast cancer samples, corresponding with an advanced clinical grade, metastasis and unfavorable prognosis. In vitro experiments further showed an up-regulation of stemness genes after over-expressing FAM64A in breast cancer cells. FAM64A overexpression also promoted breast cancer cell proliferation, migration, accompanied by the activation of epithelial-to-mesenchymal transition (EMT). Besides, we identified a strong association of FAM64A expression with TP53 mutations in TCGA and three additional breast cancer datasets. In summary, our study revealed a novel function of FAM64A in promoting breast cancer stemness and EMT, suggesting that targeting of FAM64A may have therapeutic values in advanced breast cancer.

Transcript stability is associated with many biological processes, and the factors affecting mRNA stability have been extensively studied. However, little is known about the features related to human long noncoding RNA (lncRNA) stability. By inhibiting transcription and collecting samples in 10 time points, genome-wide RNA-seq studies was performed in human lung adenocarcinoma cells (A549) and RNA half-life datasets were constructed. The following observations were obtained. First, the half-life distributions of both lncRNAs and messanger RNAs (mRNAs) with one exon (lnc-human1 and m-human1) were significantly different from those of both lncRNAs and mRNAs with more than one exon (lnc-human2 and m-human2). Furthermore, some factors such as full-length transcript secondary structures played a contrary role in lnc-human1 and m-human2. Second, through the half-life comparisons of nucleus- and cytoplasm-specific and common lncRNAs and mRNAs, lncRNAs (mRNAs) in the nucleus were found to be less stable than those in the cytoplasm, which was derived from transcripts themselves rather than cellular location. Third, kmers-based protein−RNA or RNA−RNA interactions promoted lncRNA stability from lnc-human1 and decreased mRNA stability from m-human2 with high probability. Finally, through applying deep learning−based regression, a non-linear relationship was found to exist between the half-lives of lncRNAs (mRNAs) and related factors. The present study established lncRNA and mRNA half-life regulation networks in the A549 cell line and shed new light on the degradation behaviors of both lncRNAs and mRNAs., Author summary Transcript stability is important for many biological processes. However, little is known about the features related to human lncRNA stability. Through quantitative analysis between the half-lives of lncRNAs (mRNAs) and various factors, we found a nonlinear relationship between the half-lives of lncRNAs (mRNAs) and the related factors and their combinations. Our research provided a comprehensive understanding of lncRNA stability. Further efforts are needed to develop an accurate quantitative prediction model for the half-lives of lncRNA (mRNA).

Bacterial sRNAs are a class of small regulatory RNAs of about 40–500 nt in length; they play multiple biological roles through binding to their target mRNAs or proteins. Therefore, elucidating sRNA targets is very important. However, only targets of a few sRNAs have been described. To facilitate sRNA functional studies such as developing sRNA target prediction models, we updated the sRNATarBase database, which was initially developed in 2010. The new version (recently moved to http://ccb1.bmi.ac.cn/srnatarbase/) contains 771 sRNA-target entries manually collected from 213 papers, and 23 290 and 11 750 predicted targets from sRNATarget and sTarPicker, respectively. Among the 771 entries, 475 and 17 were involved in validated sRNA–mRNA and sRNA–protein interactions, respectively, while 279 had no reported interactions. We also presented detailed information for 316 binding regions of sRNA-target mRNA interactions and related mutation experiments, as well as new features, including NCBI sequence viewer, sRNA regulatory network, target prediction-based GO and pathway annotations, and error report system. The new version provides a comprehensive annotation of validated sRNA-target interactions, and will be a useful resource for bacterial sRNA studies.

The superficial amygdala (SFA) is important in human emotion/affective processing via its strong connection with other limbic and cerebral cortex for receptive and expressive emotion processing. Few studies have investigated the functional connectivity changes of the SFA under extreme conditions, such as prolonged sleep loss, although the SFA showed a distinct functional connectivity pattern throughout the brain. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was employed to investigate the changes of SFA-cortical functional connectivity after 36 hr of total sleep deprivation (TSD). Fourteen healthy male volunteers aged 25.9 ± 2.3 years (range 18-28 years) enrolled in this within-subject crossover study. We found that the right SFA showed increased functional connectivity with the right medial prefrontal cortex (mPFC) and decreased functional connectivity with the right dorsal posterior cingulate cortex (dPCC) in the resting brain after TSD compared with that during rested wakefulness. For the left SFA, decreased connectivity with the right dorsal anterior cingulate cortex (dACC) and right dPCC was found. Further regression analysis indicated that the functional link between mPFC and SFA significantly correlated with the Profile of Mood State scores. Our results suggest that the amygdala cannot be treated as a single unit in human neuroimaging studies and that TSD may alter the functional connectivity pattern of the SFA, which in turn disrupts emotional regulation.

Loss or attenuated expression of the tumor-suppressor gene FHIT is associated paradoxically with poor progression of human tumors. Fhit promotes apoptosis and regulates reactive oxygen species; however, the mechanism by which Fhit inhibits tumor growth in animals remains unclear. In this study, we used a multidisciplinary approach based on bioinformatics, small RNA library screening, human tissue analysis, and a xenograft mouse model to identify a novel member of the miR-548 family in the fourth intron of the human FHIT gene. Characterization of this human-specific microRNA illustrates the importance of this class of microRNAs in tumor suppression and may influence interpretation of Fhit action in human cancer. Cancer Res; 74(8); 2283–94. ©2014 AACR.

Shigella flexneri is an important cause of bacillary dysentery in developing countries. Small regulatory RNAs (sRNAs) play essential roles in diverse cellular processes. We found a novel sRNA Ssr1 based on RT-PCR, northern blot, and 5′RACE in S. flexneri. Ssr1 responds to acidic environmental changes, as shown by a strong linear correlation between the pH value and Ssr1 expression (R = 0.785, P < 0.05) using the qRT-PCR method. Deletion of Ssr1 results in growth retardation at pH values ranging from 5.0 to 7.0 (P < 0.05), and the survival rate was reduced by 22% in acidic conditions (pH 3.0). Additionally, virulence was significantly increased in an Ssr1 mutant strain, as revealed in a murine lung invasion model and survival model assays. By using the sTarPicker method and proteomic analysis, we considered that DnaK, which is a major factor that confers acidic stress tolerance, may be a direct target of Ssr1. We also found that Ssr1 may enhance virulence by directly targeting OmpA; this leads to altered expression of genes in the type three secretion system (T3SS). This work provides new insight into the mechanism of adaptation to environmental stress and into the pathogenesis of Shigella.

Streptococcus mutans is one of the major cariogenic bacteria in the oral environment. Small non-coding RNAs (sRNAs) play important roles in the regulation of bacterial growth, stress tolerance, and virulence. In this study, we experimentally verified the existence of sRNA, L10-Leader, in S. mutans for the first time. Our results show that the expression level of L10-Leader was growth-phase dependent in S. mutans and varied among different clinical strains of S. mutans. The level of L10-Leader in S. mutans UA159 was closely related to the pH value, but not to the concentrations of glucose and sucrose in culture medium. We predicted target mRNAs of L10-Leader bioinformatically and found that some of these mRNAs were related to growth and stress response. Five predicted mRNA targets were selected and detected by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR), and we found that the expression levels of these mRNAs were closely related to the level of L10-Leader at different growth phases of the bacteria. Our results indicate that L10-Leader may play an important role in the regulation of responses in S. mutans, especially during its growth phase and acid adaption response.

This work presents two methods, the Least-square and Bayesian method, to solve the multiple mapping problem in extracting gene expression profiles through the next-generation sequencing. We parallel the tag sequences to genome, and partition them to improving the methods’ efficiency. The essential feature of these methods is that they can solve the multiple mapping problem between genes and short-reads, while generating almost the same estimation in single-mapping situation as the traditional approaches. These two methods are compared by simulation and a real example, which was generated from radiation-induced lung cancer cells (A549), through mapping short-reads to human ncRNA database. The results show that the Bayesian method, as realized by Gibbs sampler, is more efficient and robust than the Least-square method.

microRNAs (miRNAs) are a class of small regulatory non-coding RNAs. They are involved in diverse pathways and play important roles in gene regulation in eukaryotes. Since the expression of miRNAs is spatial and temporal-specific, computational prediction followed by experimental validation is still an important approach in miRNA discovery. Decreasing false positive ratio is very challenging in miRNA prediction. Here we employed ensemble learning to construct the classifier SVMbagging to predict miRNA precursors. The results of the training, test and independent test datasets demonstrate that SVMbagging is robust, has low false positive ratio and good generalization ability. Especially when the threshold is 0.9, the specificity of SVMbagging is as high as 99.90% and the sensitivity is higher than 26.00%. Therefore, SVMbagging is suitable for genome-wide prediction. We applied SVMbagging in genome-wide prediction of miRNA precursors in human genome. We obtained 14933 candidate miRNA precursors at the threshold of 0.9. Among them, 4481 miRNA precursors have perfect matches with small RNA reads when aligning with three groups of small RNA datasets from high-throughput sequencing technologies. The number of miRNAs is very close to the true positives estimated theoretically according to the performance of SVMbagging. Finally, we applied experimental methods to validate 32 candidate miRNAs. Two of them were confirmed to be true miRNAs.

Accurate prediction of sRNA targets plays a key role in determining sRNA functions. Here we introduced two mathematical models, sRNATargetNB and sRNATargetSVM, for prediction of sRNA targets using Nai¨ve Bayes method and support vector machines (SVM), respectively. The training dataset was composed of 46 positive samples (real sRNA–targets interaction) and 86 negative samples (no interaction between sRNA and targets). The leave-one-out cross-validation (LOOCV) classification accuracy was 91.67% for sRNATargetNB, and 100.00% for sRNATargetSVM. To evaluate the performance of the models, an independent test dataset was used, which contained 22 positive samples and 1700 randomly generated negative samples. The results showed that the classification accuracy, sensitivity, and specificity were 93.03%, 40.90%, and 93.71% for sRNATargetNB and 80.55%, 72.73%, and 80.65% for sRNATargetSVM, respectively. Therefore, the presented models provide support for experimental identification of sRNA targets. The related software and supplementary materials can be downloaded from webpage http://www.biosun.org.cn/srnatarget/ .

MicroRNAs (miRNAs) are small, endogenous RNAs that regulate gene expression in both plants and animals. A large number of miRNAs has been identified from various animals and model plant species such as Arabidopsis thaliana and rice (Oryza sativa); however, characteristics of wheat (Triticum aestivum) miRNAs are poorly understood. Here, computational identification of miRNAs from wheat EST sequences was preformed by using the in-house program GenomicSVM, a prediction model for miRNAs. This study resulted in the discovery of 79 miRNA candidates. Nine out of 22 miRNA representatives randomly selected from the 79 candidates were experimentally validated with Northern blotting, indicating that prediction accuracy is about 40%. For the 9 validated miRNAs, 59 wheat ESTs were predicted as their putative targets.

In this report, we introduced a mathematical model for high-level expression of foreign genes in pPIC9 vector. At first, we collected 40 heterologous genes expressed in pPIC9 vector, and these 40 genes were classified into high-level expression group (expression level >100mg/L, 12 genes) and low-level expression group (expression level

Shigella flexneri is an important etiological agent of bacillary dysentery in developing countries. The Hfq protein is thought to play a major regulatory role in various cellular processes in this organism. However, the roles of Hfq in stress tolerance and virulence in S. flexneri in response to environmental stress have not been fully studied. In this study, hfq was highly expressed when S. flexneri was exposed to low pH. Growth retardation was observed in the hfq deletion mutant at pH values ranging from 5.0 to 7.0 and the survival rate of the mutant strain was reduced by 60% in acidic conditions (pH 3.0) compared with the wild-type strain. Additionally, competitive invasion assays in HeLa cells and lung invasion assays showed that the virulence of the hfq deletion mutant was significantly decreased. An evaluation of the mechanism revealed that, along with the expression of the Type III secretion system genes, acid resistance genes were also increased with acid stress. Interestingly, a statistically strong linear correlation was observed between the expression of hfq and Type III secretion system genes, as well as between hfq and acid resistance genes, under various pH conditions. In this study, we provide evidence that Hfq regulates genes related to acid resistance for survival under acid stress and controls virulence through the positive regulation of Type III secretion systems. Importantly, we propose that hfq is a key factor in maximal adaptation to host acid stress during infection, regulating acid stress tolerance and virulence in response to acid stress in S. flexneri.

DNA chips have proven to be effective tools in detecting gene expression levels. Compared with DNA chips using complementary DNA as probes, oligonucleotide microarrays using oligonucleotides as probes have attracted great attention because of their well known advantages. The design of gene-specific probes for each target is essential to the development of oligonucleotide microarrays. We have previously reported the development of a probe design software termed Mprobe 1.0. Here, we present a new version of this software, termed Mprobe 2.0. Several new features are included in Mprobe 2.0. Firstly, a paradox-based sequence database management system has been developed and integrated into the software, which consequently allows interoperability with sequences in GenBank, EMBL, and FASTA formats. Secondly, in contrast to setting a fixed threshold for the secondary structure of probes in Mprobe 1.0 and other related software, Mprobe 2.0 employs a different method. After parameters such as GC type, probe melting temperature and GC contents have been evaluated, candidate probes are sorted by the free energy from high to low value, followed by specificity analysis. Thirdly, Mprobe 2.0 provides users with substantial parameter options in the visual mode. Mprobe 2.0 possesses an easier interface for users to manage sequences annotated in different formats and design the optimal probes for oligonucleotide microarrays and other applications.The program is free for non-commercial users and can be downloaded from the web page http://www.biosun.org.cn/mprobe/Wuju Li (wujuli@yahoo.com or liwj@nic.bmi.ac.cn).

The superficial amygdala (SFA) is important in human emotion/affective processing via its strong connection with other limbic and cerebral cortex for receptive and expressive emotion processing. Few studies have investigated the functional connectivity changes of the SFA under extreme conditions, such as prolonged sleep loss, although the SFA showed a distinct functional connectivity pattern throughout the brain. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was employed to investigate the changes of SFA-cortical functional connectivity after 36 hr of total sleep deprivation (TSD). Fourteen healthy male volunteers aged 25.9 ± 2.3 years (range 18-28 years) enrolled in this within-subject crossover study. We found that the right SFA showed increased functional connectivity with the right medial prefrontal cortex (mPFC) and decreased functional connectivity with the right dorsal posterior cingulate cortex (dPCC) in the resting brain after TSD compared with that during rested wakefulness. For the left SFA, decreased connectivity with the right dorsal anterior cingulate cortex (dACC) and right dPCC was found. Further regression analysis indicated that the functional link between mPFC and SFA significantly correlated with the Profile of Mood State scores. Our results suggest that the amygdala cannot be treated as a single unit in human neuroimaging studies and that TSD may alter the functional connectivity pattern of the SFA, which in turn disrupts emotional regulation.

Early stage lung cancer detection is the first step toward successful clinical therapy and increased patient survival. Clinicians monitor cancer progression by profiling tumor cell proteins in the blood plasma of afflicted patients. Blood plasma, however, is a difficult cancer protein assessment medium because it is rich in albumins and heterogeneous protein species. We report herein a method to detect the proteins released into the circulatory system by tumor cells. Initially we analyzed the protein components in the conditioned medium (CM) of lung cancer primary cell or organ cultures and in the adjacent normal bronchus using one-dimensional PAGE and nano-ESI-MS/MS. We identified 299 proteins involved in key cellular process such as cell growth, organogenesis, and signal transduction. We selected 13 interesting proteins from this list and analyzed them in 628 blood plasma samples using ELISA. We detected 11 of these 13 proteins in the plasma of lung cancer patients and non-patient controls. Our results showed that plasma matrix metalloproteinase 1 levels were elevated significantly in late stage lung cancer patients and that the plasma levels of 14-3-3 sigma, beta, and eta in the lung cancer patients were significantly lower than those in the control subjects. To our knowledge, this is the first time that fascin, ezrin, CD98, annexin A4, 14-3-3 sigma, 14-3-3 beta, and 14-3-3 eta proteins have been detected in human plasma by ELISA. The preliminary results showed that a combination of CD98, fascin, polymeric immunoglobulin receptor/secretory component and 14-3-3 eta had a higher sensitivity and specificity than any single marker. In conclusion, we report a method to detect proteins released into blood by lung cancer. This pilot approach may lead to the identification of novel protein markers in blood and provide a new method of identifying tumor biomarker profiles for guiding both early detection and therapy of human cancer.

Prediction of RNA secondary structure is important in the functional analysis of RNA molecules. The RDfolder web server described in this paper provides two methods for prediction of RNA secondary structure: random stacking of helical regions and helical regions distribution. The random stacking method predicts secondary structure by Monte Carlo simulations. The method of helical regions distribution predicts secondary structure based on the helices that appear most frequently in the set of structures, which are generated by the random stacking method. The RDfolder web server can be accessed at http://rna.cbi.pku.edu.cn.

Motivation: Feature (gene) selection can dramatically improve the accuracy of gene expression profile based sample class prediction. Many statistical methods for feature (gene) selection such as stepwise optimization and Monte Carlo simulation have been developed for tissue sample classification. In contrast to class prediction, few statistical and computational methods for feature selection have been applied to clustering algorithms for pattern discovery. Results: An integrated scheme and corresponding program SamCluster for automatic discovery of sample classes based on gene expression profile is presented in this report. The scheme incorporates the feature selection algorithms based on the calculation of CV (coefficient of variation) and t-test into hierarchical clustering and proceeds as follows. At first, the genes with their CV greater than the pre-specified threshold are selected for cluster analysis, which results in two putative sample classes. Then, significantly differentially expressed genes in the two putative sample classes with p-values ≤ 0.01, 0.05, or 0.1 from t-test are selected for further cluster analysis. The above processes were iterated until the two stable sample classes were found. Finally, the consensus sample classes are constructed from the putative classes that are derived from the different CV thresholds, and the best putative sample classes that have the minimum distance between the consensus classes and the putative classes are identified. To evaluate the performance of the feature selection for cluster analysis, the proposed scheme was applied to four expression datasets COLON, LEUKEMIA72, LEUKEMIA38, and OVARIAN. The results show that there are only 5, 1, 0, and 0 samples that have been misclassified, respectively. We conclude that the proposed scheme, SamCluster, is an efficient method for discovery of sample classes using gene expression profile. Availability: The related program SamCluster is available upon request or from the web page http://www.sph.uth.tmc.edu:8052/hgc/Downloads.asp Contact: wujuli@yahoo.com * To whom correspondence should be addressed.

Despite successes in mapping and cloning genes involved in rare Mendelian diseases, genetic dissection of quantitative traits into single Mendelian factors still remains a challenging task. As the dense map of single nucleotide polymorphism (SNP) markers becomes available in the near future, linkage disequilibrium (LD) mapping will become one of major tools for mapping and identifying quantitative trait loci (QTL). In this report, we present a population-based linkage disequilibrium mapping of QTL. This method unifies the analysis of mapping QTL in humans and in model organisms and can be used for randomly sampled individuals. The proposed method is applied to search for polymorphism sites within the candidate genes 2 and 6, which influence quantitative traits Q1 and Q2 or Q5, in a simulated data set in an isolated population.

Objective The lack of the disease biomarker to support objective laboratory tests still constitutes a bottleneck in the clinical diagnosis and evaluation of major depressive disorder (MDD) and its subtypes. We used metabonomic techniques to screen the diagnostic biomarker panels from the plasma of MDD patients with and without early life stress (ELS) experience. Methods Plasma samples were collected from 25 healthy adults and 46 patients with MDD, including 23 patients with ELS and 23 patients without ELS. Furthermore, gas chromatography/mass spectrometry (GC/MS) coupled with multivariate statistical analysis was used to identify the differences in global plasma metabolites among the 3 groups. Results The distinctive metabolic profiles exist either between healthy subjects and MDD patients or between the MDD patients with ELS experience (ELS/MDD patients) and the MDD patients without it (non-ELS/MDD patients), and some diagnostic panels of feature metabolites' combination have higher predictive potential than the diagnostic panels of differential metabolites. Conclusions These findings in this study have high potential of being used as novel laboratory diagnostic tool for MDD patients and it with ELS or not in clinical application.

Gene expression profiles may offer more or additional information than classic morphologic- and histologic-based tumor classification systems. Because the number of tissue samples examined is usually much smaller than the number of genes examined, efficient data reduction and analysis methods are critical. In this report, we propose a principal component and discriminant analysis method of tumor classification using gene expression profile data. Expression of 2000 genes in 40 tumor and 22 normal colon tissue samples is used to examine the feasibility of gene expression-based tumor classification systems. Using this method, the percentage of correctly classified normal and tumor tissue was 87.0%. The combined approach using principal components and discriminant analysis provided superior sensitivity and specificity compared to an approach using simple differences in the expression levels of individual genes.

Bacterial sRNAs are an emerging class of small regulatory RNAs of 40 to 500 nucleotides in length. They play a wide variety of important roles in many biological processes through binding to their mRNA or protein targets, such as expression regulation of outer membrane proteins, iron homeostasis, quorum sensing and bacterial virulence. Therefore, predicting sRNA targets plays a key role in elucidating sRNA functions. Here we intend to introduce some computational tools for predicting mRNA targets of sRNAs. Firstly, we will give an outline of some key concepts and programs associated with the prediction models developed by our center. Secondly, we will present detailed instructions on how to use the sRNATarget web server and the Perl program for both windows and the Linux system. Thirdly, we will briefly introduce the main ideas behind other tools, including IntaRNA and TargetRNA. Finally, we will present an outlook on future developments in the prediction of sRNA targets.

This study was aimed to rapidly identify and differentiate two main pathogens of the Mycobacterium tuberculosis complex: Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium bovis by a modified loop-mediated isothermal amplification (LAMP) assay. The reaction results could be evaluated by naked eye with two optimized closed tube detection methods as follows: adding the modified fluorescence dye in advance into the reaction mix so as to observe the color changes or putting a tinfoil in the tube and adding the SYBR Green I dye on it, then making the dye drop into the bottom of the tube by centrifuge after reaction. The results showed that the two groups of primers used jointly in this assay could successfully identify and differentiate Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium tuberculosis bovis. Sensitivity test displayed that the modified LAMP assay with the closed tube system could determine the minimal template concentration of 1 copy/μl, which was more sensitive than that of routine PCR. The advantages of this LAMP method for detection of the Mycobacterium tuberculosis complex included high specificity, high sensitivity, simplicity, and superiority in avoidance of aerosol contamination. The modified LAMP assay would provide a potential for clinical diagnosis and therapy of tuberculosis in the developing countries and the resource-limited areas.

Bacterial sRNAs are an emerging class of small regulatory RNAs, 40–500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database—sRNATarBase. The database holds 138 sRNA–target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA–mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/.

Bacterial sRNAs are a class of non-coding RNAs with 40-500 nucleotides in length. Most of them function as posttranscriptional regulation of gene expression through binding to the translation initiation region of their target mRNAs. In view that prediction of sRNAs and their targets provides support for experimental identification, some prediction methods have been developed for both of them in recent years. In this review, we firstly gave an overview of methods for prediction of sRNA genes, which are classified into three categories, namely, comparative genomics-based, transcription units-based and machine learning-based prediction methods. Secondly, the methods for sRNA target prediction are classified into two types, which are sequence alignment-based method and prediction of RNA secondary structure-based method, respectively. Finally, the principles, advantages and limitations of each kind of method are discussed, and perspectives for prediction methods of sRNA and their targets is pointed out.

Background With wide applications of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), statistical comparison of serum peptide profiles and management of patients information play an important role in clinical studies, such as early diagnosis, personalized medicine and biomarker discovery. However, current available software tools mainly focused on data analysis rather than providing a flexible platform for both the management of patients information and mass spectrometry (MS) data analysis. Results Here we presented a plug-in-based software, BioSunMS, for both the management of patients information and serum peptide profiles-based statistical analysis. By integrating all functions into a user-friendly desktop application, BioSunMS provided a comprehensive solution for clinical researchers without any knowledge in programming, as well as a plug-in architecture platform with the possibility for developers to add or modify functions without need to recompile the entire application. Conclusion BioSunMS provides a plug-in-based solution for managing, analyzing, and sharing high volumes of MALDI-TOF or SELDI-TOF MS data. The software is freely distributed under GNU General Public License (GPL) and can be downloaded from http://sourceforge.net/projects/biosunms/.

UNLABELLED In bacteria, there exist some small non-coding RNAs (sRNAs) with 40-500 nucleotides in length. Most of them function as posttranscriptional regulation of gene expression through binding to their target mRNAs, in which Hfq protein acts as RNA chaperone. With the increase of identified sRNA genes in the bacterium, prediction of sRNA targets plays a more important role in determining sRNA functions. However, there are few available computational tools for predicting sRNA targets at present. Here we introduced a web server, sRNATarget, for genome-scale prediction of bacterial sRNA targets. The server is based on a recently published model which uses Naive Bayes method as the supervised method and take RNA secondary structure profile as the feature. The prediction results will be returned to the users through E-mail. AVAILABILITY sRNATarget web server is freely available athttp://ccb.bmi.ac.cn/srnatarget/

Forty-eight esophageal carcinoma-related sequences were analyzed by various bioinformatics tools. The results indicated that 46 sequences were holomogous to the known genes with detail functional annotations. However, two sequences did not have holomogy to all known genes. Further analysis implied that these two sequences might be noncoding RNAs (ncRNA), and one of them located in the human mitochondria. Finally, the mechanisms of their functions were discussed.

Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation.

Existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In this study, we investigated whether early detection of breast cancer is possible by analyzing gene-expression patterns in peripheral blood cells.Using macroarrays and nearest-shrunken-centroid method, we analyzed the expression pattern of 1,368 genes in peripheral blood cells of 24 women with breast cancer and 32 women with no signs of this disease. The results were validated using a standard leave-one-out cross-validation approach.We identified a set of 37 genes that correctly predicted the diagnostic class in at least 82% of the samples. The majority of these genes had a decreased expression in samples from breast cancer patients, and predominantly encoded proteins implicated in ribosome production and translation control. In contrast, the expression of some defense-related genes was increased in samples from breast cancer patients.The results show that a blood-based gene-expression test can be developed to detect breast cancer early in asymptomatic patients. Additional studies with a large sample size, from women both with and without the disease, are warranted to confirm or refute this finding.

The present work describes a complete probe design software system for oligonucleotide microarrays based on Kane's research on probe sensitivity and specificity (Kane's rule). Combining Kane's rule and traditional criteria for probe design we constructed MProbe, the software system for oligonucleotide microarrays using Java. The general criteria for probe design are: (1) probes may have different lengths that range from 20 to 100 bases; (2) they should have a similar melting temperature (Tm) or GC content; (3) they should not contain stable secondary structures; and (4) they abide by Kane's rule.