Mitochondrial D-loop "signatures" produced by low-stringency single specific primer PCR constitute a simple comparative human identity test.

We have developed a technique called "LSSP-PCR" (low-stringency single specific primer PCR) that detects single or multiple mutations in DNA. A purified DNA fragment is submitted to PCR by using a single primer specific for one of the extremities of the fragment, under conditions of very low stringency. The primer hybridizes specifically to its complementary extremity and nonspecifically to multiple sites within the fragment, in a sequence-dependent manner. A complex set of reaction products is thus created that, when separated by electrophoresis, constitutes a unique "gene signature." We here report the application of LSSP-PCR to the detection of sequence variation in the control (D-loop) region of human mtDNA, which is known to differ significantly between unrelated individuals. We prepared human DNA samples from blood and amplified a 1024-bp portion of the mtDNA control region, using primers L15996 and H408. The amplified mtDNA fragments were then reamplified under LSSP-PCR conditions by using L15996 or H408 as drivers to produce complex signatures that always differed between unrelated individuals and yet were highly reproducible. In contrast, all mother-child pairs tested were identical, as expected from the matrilineal inheritance of mtDNA. Thus, the use of LSSP-PCR to produce D-loop signatures constitutes a powerful new technique for mtDNA-based comparative identity testing.     

Molecular signatures from omics data: From chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.     

Expanding the understanding of biases in development of clinical-grade molecular signatures: a case study in acute respiratory viral infections

Background

The promise of modern personalized medicine is to use molecular and clinical information to better diagnose, manage, and treat disease, on an individual patient basis. These functions are predominantly enabled by molecular signatures, which are computational models for predicting phenotypes and other responses of interest from high-throughput assay data. Data-analytics is a central component of molecular signature development and can jeopardize the entire process if conducted incorrectly. While exploratory data analysis may tolerate suboptimal protocols, clinical-grade molecular signatures are subject to vastly stricter requirements. Closing the gap between standards for exploratory versus clinically successful molecular signatures entails a thorough understanding of possible biases in the data analysis phase and developing strategies to avoid them.

Methodology and Principal Findings

Using a recently introduced data-analytic protocol as a case study, we provide an in-depth examination of the poorly studied biases of the data-analytic protocols related to signature multiplicity, biomarker redundancy, data preprocessing, and validation of signature reproducibility. The methodology and results presented in this work are aimed at expanding the understanding of these data-analytic biases that affect development of clinically robust molecular signatures.

Conclusions and Significance

Several recommendations follow from the current study. First, all molecular signatures of a phenotype should be extracted to the extent possible, in order to provide comprehensive and accurate grounds for understanding disease pathogenesis. Second, redundant genes should generally be removed from final signatures to facilitate reproducibility and decrease manufacturing costs. Third, data preprocessing procedures should be designed so as not to bias biomarker selection. Finally, molecular signatures developed and applied on different phenotypes and populations of patients should be treated with great caution.     

Future of an "Asymptomatic" T-cell Epitope-Based Therapeutic Herpes Simplex Vaccine

Considering the limited success of the recent herpes clinical vaccine trial [1], new vaccine strategies are needed. Infections with herpes simplex virus type 1 and type 2 (HSV-1 & HSV-2) in the majority of men and women are usually asymptomatic and results in lifelong viral latency in neurons of sensory ganglia (SG). However, in a minority of men and women HSV spontaneous reactivation can cause recurrent disease (i.e., symptomatic individuals). Our recent findings show that T cells from symptomatic and asymptomatic men and women (i.e. those with and without recurrences, respectively) recognize different herpes epitopes. This finding breaks new ground and opens new doors to assess a new vaccine strategy: mucosal immunization with HSV-1 & HSV-2 epitopes that induce strong in vitro CD4 and CD8 T cell responses from PBMC derived from asymptomatic men and women (designated here as "asymptomatic" protective epitopes") could boost local and systemic "natural" protective immunity, induced by wild-type infection. Here we highlight the rationale and the future of our emerging "asymptomatic" T cell epitope-based mucosal vaccine strategy to decrease recurrent herpetic disease.     

Gene Signatures in Breast Cancer: Current and Future Uses

Gene signatures have been developed for estrogen receptor-positive breast cancer to complement pathological factors in providing prognostic information. The 70-gene and the 21-gene signatures identify patients who may not require adjuvant chemotherapy. Gene signatures in triple-negative disease and HER2-positive disease have not been fully developed yet, although studies demonstrate heterogeneity within these subgroups. Further research is needed before genotyping will help in making clinical decisions in triple-negative and HER2-positive disease. Molecular subtyping of breast cancer led to define luminal, basal, and HER2-enriched subtypes, which have specific clinical behavior. This approach may lead to identify new subgroups requiring specific therapies. Standardization of techniques will be required to translate investigations to the clinic.     

根据“肠-肾轴”理论治疗慢性肾脏病的研究进展

有研究报道在慢性肾脏病的发生发展过程中可发现一系列肠道变化,并有学者用"肠-肾轴"理论阐述肾脏病中肠道的变化以及疾病过程中肾脏与肠道之间的联系,提示调节肠道菌群或可成为治疗慢性肾脏病的新方法。本文根据"肠-肾轴"理论,综述了在慢性肾脏病发展过程中肠道出现的变化,如肠内代谢物异常、肠道损伤以及肠道菌群失调等。以慢性肾脏病发生发展过程中肠道的异常变化为治疗切入点,总结了以大黄为主的中药在调节肠道功能、修复肠道屏障、纠正肠道代谢物异常等方面具有的显著疗效,为治疗慢性肾脏病及减少并发症等提供新的治疗思路和新方法。     

Diving through the "-omics": the case for deep phenotyping and systems epidemiology

Enabled by diverse high-throughput technologies, the rapidly evolving field of "-omics sciences" offers the potential to study health and disease in breadth and depth at the human population level. We have recently linked genomics and metabolomics to present the first genome-wide association study of metabolic traits in human urine providing new insights into the functional background of chronic kidney disease. We propose systems epidemiology as a novel approach to study the complexities of human pathophysiology by integrating various population-level omic-metrics and to identify new trans-omic biomarkers.     

MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

Background

Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson’s disease, and Alzheimer’s disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures.

Results

By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing.

Conclusion

This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.

     

Integrated Analysis of Gene Expression Profiles Associated with Response of Platinum/Paclitaxel-Based Treatment in Epithelial Ovarian Cancer

Purpose

This study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC).

Patients and Methods

Gene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.

Results

A 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.

Conclusion

We have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested.     

纸坊病病毒病因的研究

1985年4～10月与1986年6～8月,在贵州省沿河县的纸坊村和崔家坨村先后发生了病因不明的传染病。纸坊村约有1/5的村民发病,病死率为12％,崔家坨村有1/10的村民发病,病死率高达30％。发病波及各年龄组,以青壮年为多,有家庭集聚现象。 本病起病急,轻症者只有头晕、乏力、肌痛、多汗、心悸伴以低热,有的初期有短暂的腹泻。重症者有高热(40℃以上)、大汗、心悸、游走性肌肉痉挛伴有明显疼痛和触痛,以腰骶部及四肢肌肉为好发部位。病人烦燥不安,2～5天内死亡。经实验室检查,排除了食物中毒、农药中毒、钩端螺旋体病和弓形体感染。从病人和接触者的粪便中分离到9株病毒,性状一致,为RNA型25nm的球形颗粒,耐酸,耐乙醚,能凝集人“O”型血球。经血清学鉴定为ECHO3型病毒。16份病人双份血清的检测结果表明,恢复期血清对该病毒中和抗体有4倍以上升高者共8例(纸坊村和崔家坨各4例)。病人单份血清也都有较高的抗体。有理由认为两年中先后在两个村庄发生的传染病与ECHO3型病毒有密切关系。查阅文献,尚未见有关ECHO3型病毒引起以肌痛、游走性肌痉挛为特征的疾病的报道。     

Automatic generation and evaluation of sparse protein signatures for families of protein structural domains

We identified key residues from the structural alignment of families of protein domains from SCOP which we represented in the form of sparse protein signatures. A signature-generating algorithm (SigGen) was developed and used to automatically identify key residues based on several structural and sequence-based criteria. The capacity of the signatures to detect related sequences from the SWISSPROT database was assessed by receiver operator characteristic (ROC) analysis and jack-knife testing. Test signatures for families from each of the main SCOP classes are described in relation to the quality of the structural alignments, the SigGen parameters used, and their diagnostic performance. We show that automatically generated signatures are potently diagnostic for their family (ROC50 scores typically >0.8), consistently outperform random signatures, and can identify sequence relationships in the "twilight zone" of protein sequence similarity (<40%). Signatures based on 15%-30% of alignment positions occurred most frequently among the best-performing signatures. When alignment quality is poor, sparser signatures perform better, whereas signatures generated from higher-quality alignments of fewer structures require more positions to be diagnostic. Our validation of signatures from the Globin family shows that when sequences from the structural alignment are removed and new signatures generated, the omitted sequences are still detected. The positions highlighted by the signature often correspond (alignment specificity >0.7) to the key positions in the original (non-jack-knifed) alignment. We discuss potential applications of sparse signatures in sequence annotation and homology modeling.     

Characterization of Small RNAs and Their Targets from <Emphasis Type="Italic">Fusarium oxysporum</Emphasis> Infected and Noninfected Cotton Root Tissues

Genes for host-plant resistant do exist in cotton (Gossypium spp.) but improvement of cotton cultivars with resistance is difficult due to intensive breeding. Identifying molecular-genetic mechanisms associated with disease resistance can offer a new way to combat a serious threat such as Fusarium oxysporum f. sp. vasinfectum (FOV). Here, we captured and annotated “top-layer” of abundantly and specifically expressed cotton root small RNA (sRNA) including microRNA (miR) sequences during FOV pathogenesis using size-directed and adenylated linker-based sRNA cloning strategy. A total of 4116 candidate sRNA sequences with 16 to 30 nucleotide (nt) length were identified from four complementary DNA (cDNA) libraries of noninfected and FOV race 3-infected roots of susceptible (“11970”) versus resistant (“Mebane B-1”) cotton genotypes (G. hirsutum L.). The highest numbers of sRNA signatures were those with 19–24 nt long in all libraries, and interestingly, the number of sRNAs substantially increased during FOV infection in a resistant genotype, while it sharply decreased in a susceptible genotype. In BLAST analysis, more than 73 % of sRNAs matched Gossypium (G. arboretum L., G. hirsutum, and G. barbadense L.) ESTs. A small percentage of sRNAs matched A. thaliana (1.68 %), T. cacao (1.26 %), fungal (2 %), and other organism (21.33 %) ESTs. mirBase comparisons showed that 4 % of sRNAs were homologous to previously reported plant miRs, among which we predicted novel cotton Ghr-miR-160 that was not registered in the cotton miR database. These major representative sRNA signatures targeted proteins associated with the key biological processes and molecular functions, explaining the molecular mechanisms of the host defense response during the FOV pathogenesis in cotton.     

Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm

Background

Gene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.

Methods

In this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective optimization technique, “Multi-objective optimization for collecting cluster alternatives” (MOCCA in R package) on these DEGs to find Pareto-optimal cluster size, and then applied k-means clustering to the RNA-seq data based on the optimal cluster size. The best cluster was obtained through computing the average Spearman’s Correlation Score among all the genes in pair-wise manner belonging to the module. The best cluster is treated as the signature for the respective disease or cellular condition.

Results

We applied our framework to a cervical cancer RNA-seq dataset, which included 253 squamous cell carcinoma (SCC) samples and 22 adenocarcinoma (ADENO) samples. We identified a total of 582 DEGs by Limma analysis of SCC versus ADENO samples. Among them, 260 are up-regulated genes and 322 are down-regulated genes. Using MOCCA, we obtained seven Pareto-optimal clusters. The best cluster has a total of 35 DEGs consisting of all-upregulated genes. For validation, we ran PAMR (prediction analysis for microarrays) classifier on the selected best cluster, and assessed the classification performance. Our evaluation, measured by sensitivity, specificity, precision, and accuracy, showed high confidence.

Conclusions

Our framework identified a multi-objective based cluster that is treated as a signature that can classify the disease and control group of samples with higher classification performance (accuracy 0.935) for the corresponding disease. Our method is useful to find signature for any RNA-seq or microarray data.

     

Child consultation patterns in general practice comparing "high" and "low" consulting families.

All children''s consultations with their general practitioner over a 12 month period in a small urban practice were analysed. Overall consultation rates ranged from 2.2 per child a year for 8 to 11 year olds, to 6.8 for those under 2. Families were grouped according to their average rate of new consultation for children, standardised for age. Families with higher consulting rates scored higher on an index of economic disadvantage, with mothers who scored higher on a test of "tendency to consult" and who were less educated than those in lower consulting families. The presence of any doctor-defined "significant disease" in any child was highly correlated with the family''s consultation rate.     

Gene expression in whole lung and pulmonary macrophages reflects the dynamic pathology associated with airway surface dehydration

Background

Defects in airway mucosal defense, including decreased mucus clearance, contribute to the pathogenesis of human chronic obstructive pulmonary diseases. Scnn1b-Tg mice, which exhibit chronic airway surface dehydration from birth, can be used as a model to study the pathogenesis of muco-obstructive lung disease across developmental stages. To identify molecular signatures associated with obstructive lung disease in this model, gene expression analyses were performed on whole lung and purified lung macrophages collected from Scnn1b-Tg and wild-type (WT) littermates at four pathologically relevant time points. Macrophage gene expression at 6 weeks was evaluated in mice from a germ-free environment to understand the contribution of microbes to disease development.

Results

Development- and disease-specific shifts in gene expression related to Scnn1b over-expression were revealed in longitudinal analyses. While the total number of transgene-related differentially expressed genes producing robust signals was relatively small in whole lung (n = 84), Gene Set Enrichment Analysis (GSEA) revealed significantly perturbed biological pathways and interactions between normal lung development and disease initiation/progression. Purified lung macrophages from Scnn1b-Tg mice exhibited numerous robust and dynamic gene expression changes. The expression levels of Classically-activated (M1) macrophage signatures were significantly altered at post-natal day (PND) 3 when Scnn1b-Tg mice lung exhibit spontaneous bacterial infections, while alternatively-activated (M2) macrophage signatures were more prominent by PND 42, producing a mixed M1-M2 activation profile. While differentially-regulated, inflammation-related genes were consistently identified in both tissues in Scnn1b-Tg mice, there was little overlap between tissues or across time, highlighting time- and tissue-specific responses. Macrophages purified from adult germ-free Scnn1b-Tg mice exhibited signatures remarkably similar to non-germ-free counterparts, indicating that the late-phase macrophage activation profile was not microbe-dependent.

Conclusions

Whole lung and pulmonary macrophages respond independently and dynamically to local stresses associated with airway mucus stasis. Disease-specific responses interact with normal developmental processes, influencing the final state of disease in this model. The robust signatures observed in Scnn1b-Tg lung macrophages highlight their critical role in disease pathogenesis. These studies emphasize the importance of region-, cell-type-, and time-dependent analyses to fully dissect the natural history of disease and the consequences of disease on normal lung development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-726) contains supplementary material, which is available to authorized users.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

Identification of lung cancer with high sensitivity and specificity by blood testing

Background

Lung cancer is a very frequent and lethal tumor with an identifiable risk population. Cytological analysis and chest X-ray failed to reduce mortality, and CT screenings are still controversially discussed. Recent studies provided first evidence for the potential usefulness of autoantigens as markers for lung cancer.

Methods

We used extended panels of arrayed antigens and determined autoantibody signatures of sera from patients with different kinds of lung cancer, different common non-tumor lung pathologies, and controls without any lung disease by a newly developed computer aided image analysis procedure. The resulting signatures were classified using linear kernel Support Vector Machines and 10-fold cross-validation.

Results

The novel approach allowed for discriminating lung cancer patients from controls without any lung disease with a specificity of 97.0%, a sensitivity of 97.9%, and an accuracy of 97.6%. The classification of stage IA/IB tumors and controls yielded a specificity of 97.6%, a sensitivity of 75.9%, and an accuracy of 92.9%. The discrimination of lung cancer patients from patients with non-tumor lung pathologies reached an accuracy of 88.5%.

Conclusion

We were able to separate lung cancer patients from subjects without any lung disease with high accuracy. Furthermore, lung cancer patients could be seprated from patients with other non-tumor lung diseases. These results provide clear evidence that blood-based tests open new avenues for the early diagnosis of lung cancer.