Characterizing microbiota and metabolomics analysis to identify candidate biomarkers in lung cancer.

Background: Lung cancer is the leading malignant disease and cause of cancer-related death worldwide. Most patients with lung cancer had insignificant early symptoms so that most of them were diagnosed at an advanced stage. In addition to factors such as smoking, pollution, lung microbiome and its metabolites play vital roles in the development of lung cancer. However, the interaction between lung microbiota and carcinogenesis is lack of systematically characterized and controversial. Therefore, the purpose of this study was to excavate the features of the lung microbiota and metabolites in patients and verify potential biomarkers for lung cancer diagnosis. Methods: Lung tissue flushing solutions and bronchoalveolar lavage fluid samples came from patients with lung cancer and non-lung cancer. The composition and variations of the microbiota and metabolites in samples were explored using muti-omics technologies including 16S rRNA amplicon sequencing, metagenomics and metabolomics. Results: The metabolomics analysis indicated that 40 different metabolites, such as 9,10-DHOME, sphingosine, and cysteinyl-valine, were statistically significant between two groups (VIP > 1 and P < 0.05). These metabolites were significantly enriched into 11 signal pathways including sphingolipid, autophagy and apoptosis signaling pathway (P < 0.05). The analysis of lung microbiota showed that significant changes reflected the decrease of microbial diversity, changes of distribution of microbial taxa, and variability of the correlation networks of lung microbiota in lung cancer patients. In particular, we found that oral commensal microbiota and multiple probiotics might be connected with the occurrence and progression of lung cancer. Moreover, our study found 3 metabolites and 9 species with significantly differences, which might be regarded as the potential clinical diagnostic markers associated with lung cancer. Conclusions: Lung microbiota and metabolites might play important roles in the pathogenesis of lung cancer, and the altered metabolites and microbiota might have the potential to be clinical diagnostic markers and therapeutic targets associated with lung cancer.

SNP and DNA methylation analyses of a monozygotic twins discordant for complete endocardial cushion defect: a case report.

The exact cause of complete endocardial cushion defect (ECD) is still unknown. This report describes a unique pair of monozygotic twins (MZ twins) discordant for ECD. The chromosome karyotyping analysis revealed normal karyotype of 46, XY, 16qh+ and mat in both MZ twins. A genome-wide analysis of DNA using the Affymetrix SNP 6.0 revealed identical genotyping of single nucleotide polymorphisms (SNPs) and copy number variations (CNVs). An extensive methylation assay was carried out by NimbleGen 3 × 720 K CpG Island Plus RefSeq Promoter Arrays to analyze the potential epigenetic differences. The DNA methylation profiles of the affected twin seemed increased compared with that of the unaffected twin. However, further validation of Notch1 promoter hypermethylation and six top-ranked differentially methylated CpG sites by sodium bisulfate modification and methylation-specific PCR, failed to reveal consistent methylation differences between the twins. Other relevant factors, such as heritability and penetrance of the condition that place the MZ twins near to a threshold for ECD or variations in local epigenetic events in the twins' heart tissues, are probably responsible for the phenotypic discordance.

In-silico analysis of ligand-receptor binding patterns of α-MMC, TCS and MAP30 protein to LRP1 receptor.

Alpha-momorcharin (α-MMC), trichosanthin (TCS), and momordica anti-HIV protein of 30 kD (MAP30) are potential anti-tumor drug candidates but have cytotoxicity to normal cells. The binding of these proteins to LRP1 receptor and the subsequent endocytosis are essential to their cytotoxicity, but this binding process remains largely unknown. This study, in-silico analysis of the binding patterns, was conducted via the protein-protein docking software, ZDOCK 3.0.2 package, to better understand the binding process. Specifically, α-MMC, TCS and MAP30 were selected and bound to binding subunits CR56 and CR17 of LRP1. After docking, the 10 best docking solutions are retained based on the default ZDOCK scores and used for structural assessment. Our results showed that, α-MMC bound to LRP1 stably at the amino acid residues 1-20, at which 8 residues formed 21 hydrogen bonds with 15 residues of CR56 and 10 residues formed 15 hydrogen bonds with 12 residues of CR17. In contrast, TCS and MAP30 bound mainly to LRP1 at the residues 1-57/79-150 and residues 58-102, respectively, which were functional domains of TCS and MAP30. Since residues 1-20 are outside the functional domain of α-MMC, α-MMC is considered more suitable to attenuate by mutating the receptor binding site. Thus, our analysis lays the foundation for future genetic engineering work on α-MMC, and makes important contributions to its potential clinical use in cancer treatment.

Study the effect of trypsin enzyme activity on the screening of applying frontal affinity chromatography.

Frontal affinity chromatography (FAC) combined with enzyme has been widely used for drug screening. In this paper, the effect of target enzyme activity on screening of bioactive compounds was studied through applying FAC. Trypsin with different degree of inactivation were prepared as target enzyme by thermal denaturation. Their primary structure was identified by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and use Fourier transform infrared (FTIR) and ultraviolet-visible (UV-vis) spectroscopy to detect group structure. Ultimately, it was found that the main structure of enzyme with decreased activity remained unchanged. The oxymatrine and matrine which can interact with trypsin were selected to study their binding to trypsin with different activities in FAC. The results showed that oxymatrine and matrine had a significant difference in the breakthrough volume among seven kinds of columns prepared by trypsins with different activities, at the different concentration. It indicated that trypsins with different activities in FAC could combine with oxymatrine and matrine. The binding constant (Kd) variation between oxymatrine, matrine and trypsin with different activities are 5.520 ±â€¯0.038 and 3.577 ±â€¯0.071, within error range, which indicated that the activity of target enzyme with primary unchanged structure has no effect on screening of bioactive components by FAC.

Comparison and analysis of the efficacy and safety of minimally invasive surgery and craniotomy in the treatment of hypertensive intracerebral hemorrhage.

OBJECTIVE: This study was aimed to compare and analyze the effects and safety of minimally invasive and craniotomy in the treatment of hypertensive intracerebral hemorrhage. METHODS: A total of 130 patients with hypertensive intracerebral hemorrhage were recruited. The patients were randomly divided into two groups (research and control group). Research group was treated with endoscopic minimally invasive surgery, while control group was treated with craniotomy and hematoma clearance. The basic situation, clinical effects, prognosis, nerve function and inflammatory factors of the two groups were compared while the condition of postoperative complications was also observed. RESULTS: The operative time of patients in research group showed statistically significant (P<0.05) difference when compared with control group. Hematoma clearance rate and intraoperative blood loss of research group was significantly better than control group. There was no significant difference (P>0.05) between the two groups in preoperative hemorrhage and edema around the hematoma, however hemorrhage and edema around the hematoma after four weeks of surgery in the research group was significantly (P<0.05) lower than control group. After four weeks of treatment, the BI and SSS score, SP and IL-2 level of the research group were significantly higher than control group (P<0.05), while MRS score, IL-6, hs-CRP, TNF-α and SF was significantly lower than control group (P<0.05). CONCLUSION: Compared with craniotomy, minimally invasive surgery is more effective in the treatment of hypertensive intracerebral hemorrhage, as well as it is more conducive to restore neurological function, improve prognosis and reduce serum inflammatory factor levels.

Bioinformatics analysis of aggressive behavior of breast cancer via an integrated gene regulatory network.

BACKGROUND: Breast cancer is one of the most frequently diagnosed cancers in women. Though death from this disease is mainly caused by the metastases of the aggressive cancer cells, few studies have expounded the aggressive behavior of breast cancer. MATERIALS AND METHODS: We downloaded the gene expression profiles of GSE40057, including four aggressive and six less-aggressive breast cancer cell lines, from Gene Expression Omnibus and identified the differentially expressed genes (DEGs) between the aggressive and less-aggressive samples. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Then, motifs and modules of the network were identified. Modules were further analyzed at a functional level using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to study the aggressive behavior of breast cancer. RESULTS: A total of 764 DEGs were found and two modules were filtered from the integrated gene regulatory network. Totally two motifs and modules for DEGs were identified. Significant GO terms associated with cell proliferation and hormone stimulus of the modules were found and the target genes identified were  CAV1, CD44, and TGFßR2. The KEGG pathway analysis discovered that CAV1 and FN1 were significantly enriched in focal adhesion, extracellular matrix (ECM)-receptor interaction, and pathways in cancer. CONCLUSION: Aggressive behavior of breast cancer was proved to be related to cell proliferation and hormone stimulus. Genes such as CAV1, CD44, TGFßR2, and FN1 might be potential targets to diagnose the aggressive behavior of breast cancer cells.

Dose-volume histogram parameters for predicting radiation pneumonitis using receiver operating characteristic curve.

OBJECTIVE: To assess the predictability of dose-volume histogram (DVH) parameters for radiation pneumonitis (RP) using receiver operating characteristic (ROC) curve. METHODS: One hundred and thirty-five cases of locally advanced non-small cell lung cancer patients treated with three-dimensional radiotherapy and chemotherapy were analyzed retrospectively. The end point of follow-up was ≥2 grade RP defined according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 3.0. The ROC curve was used to explore the predictive sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV) for potential DVH parameters associated with RP. RESULTS: Relative volumes of total lungs receiving ≥5 Gy (V(5)), ≥10 Gy (V(10)), ≥13 Gy (V(13)), ≥20 Gy (V(20)), and mean lung dose (MLD), were all correlated to the development of RP (p < 0.05), among which V 5 and V 20 were the most important factors (p = 0.045 and 0.037; OR = 3.166 and 3.030). However, collinearity was found between V(5) and V(20) (Spearman's rho 0.771, p < 0.01). The area under the ROC curve was 0.643 and 0.648 for using V(5) and V(20) as predictors. If predictive cut-off values were established as follows: V(5) = 0.8 and V(20) = 0.3, the parameters could provide predictive SEN, SPE, PPV and NPV were 0.387 and 0.581, 0.882 and 0.701, 0.444 and 0.321, and 0.855 and 0.873, respectively. CONCLUSIONS: V(5) and V(20) could act as predictors for RP; however, single DVH metrics did not appear to have high predictive power for RP.

High-molecular-weight polyethyleneimine conjuncted pluronic for gene transfer agents.

In order to enhance the gene delivery efficiency and decrease cytotoxicity of polyplexes, copolymers consisting of branched polyethyleneimine (PEI) 25 kDa grafted with Pluronic (F127, F68, P105) were successfully synthesized using a simple two-step procedure. The copolymers were tested for cytotoxicity and DNA condensation and complexation properties. Their polyplexes with plasmid DNA were characterized in terms of DNA size and surface charge and transfection efficiency. The complex sizes were below 300 nm, which implicated their potential for intracellular delivery. The Pluronic-g-PEI exhibited better condensation and complexation properties than PEI 25 kDa. The cytotoxicity of PEI was strongly reduced after copolymerization. The Pluronic-g-PEI showed lower cytotoxicity in three different cell lines (Hela, MCF-7, and HepG2) than PEI 25 kDa. pGL3-lus was used as a reporter gene, and the transfection efficiency was in vitro measured in HeLa cells. Compared with unmodified PEI 25 kDa Pluronic-g-PEI showed much higher transfection efficiency. These results demonstrate that polyplexes prepared using a combined strategy of surface crosslinking and grafted with Pluronic seem to provide promising properties as stable, high transfection efficiency vectors.

Primary massage using one-finger twining manipulation for treatment of infantile muscular torticollis.

OBJECTIVES: The study objectives were to observe the clinical efficacy of primary massage of twining manipulation with one finger (PMTMOF) versus conventional tuina manipulation for treating muscular torticollis. METHODS: A randomized, controlled, single-blind method was employed. Patients received either PMTMOF (experimental group, n = 265) or conventional tuina manipulation (control group, n = 235), once a day, 20 minutes each, for 15 days of treatment. After four treatment courses, sternocleidomastoid muscle morphology and size were detected using two-dimensional ultrasonography; sternocleidomastoid muscle blood flow was monitored by color Doppler ultrasonography; and head-neck deflection and range of motion were used to determine total curative effects. RESULTS: In the treatment group, 55 patients were cured, 120 patients remarkably responded to the treatment, 75 patients effectively responded, and 15 patients were found to have no response. The total effective response rate to the treatment is 94.34%. In the control group, 15 patients were cured, along with very effective results in 60 patients, effective results in 125 patients, and ineffective results in 35 patients, and the total effective rate is 85.11%. A significant difference in total effective rate was found between experimental and control groups (p < 0.05). CONCLUSIONS: PMTMOF produced more obvious curative treatment effects in infantile muscular torticollis than conventional tuina manipulation and could effectively shorten treatment time and avoid sequelae due to delayed healing.

Coclustering for cross-subject fiber tract analysis through diffusion tensor imaging.

One of the fundamental goals of computational neuroscience is the study of anatomical features that reflect the functional organization of the brain. The study of physical associations between neuronal structures and the examination of brain activity in vivo have given rise to the concept of anatomical and functional connectivity, which has been invaluable for our understanding of brain mechanisms and their plasticity during development. However, at present, there is no robust and accurate computational framework for the quantitative assessment of cortical connectivity patterns. In this paper, we present a quantitative analysis and modeling tool that is able to characterize anatomical connectivity patterns based on a newly developed coclustering algorithm, termed the business model-based coclustering algorithm (BCA). We apply BCA to diffusion tensor imaging (DTI) data in order to provide an automated and reproducible assessment of the connectivity patterns between different cortical areas in human brains. BCA not only partitions the cortical mantel into well-defined clusters, but also maximizes the connectivity strength between these clusters. Moreover, BCA is computationally robust and allows both outlier detection as well as parameter-independent determination of the number of clusters. Our coclustering results have showed good performance of BCA in identifying major white matter fiber bundles in human brains and facilitate the detection of abnormal connectivity patterns in patients suffering from various neurological diseases.

RECOMBFLOW: a scientific workflow environment for Intragenomic Gene Conversion analysis in bacterial genomes, including the pathogen Streptococcus pyogenes.

Intragenomic Gene Conversion (IGC) is important in the evolution of bacteria but has only been analysed computationally in a few strains of Escherichia coli. This paper describes a scientific workflow system, called RECOMBFLOW, that automates this complex procedure for the analysis of more than 400 bacterial genomes, with a median analysis time per genome of less than 5 minutes. Results show that IGC varies greatly, both between different species and among multiple genomes of the same species. We analyse for the first time the large variation of IGC in the pathogen Streptococcus pyogenes, and also in non-pathogenic bacteria.

Simulation of genomic recombination and the influence of DNA sequence diversity on cluster formation and detectability of recombination: a scientific workflow method.

The detection of recombination from DNA sequences is relevant to the understanding of evolutionary and molecular genetics. We developed a Recombination Simulation Scientific Workflow System (RSSWS) for simulating recombination and using GENECONV to test the effect of pairwise differences in a diverse population on the detectability of recombination. Decreases in recombination rate owing to pairwise differences resulted in population clusters analogous to sympatric speciation under specific conditions and decreases in detectability of recombination, a phenomenon that we call 'cryptic recombination'. This computational method demonstrated the value of scientific workflow methods for analysing a complex process and data driven problem.

Computational identification of transcription frameworks of early committed spermatogenic cells.

It is known that transcription factors (TFs) work in cooperation with each other to govern gene expression and thus single TF studies may not always reflect the underlying biology. Using microarray data obtained from two independent studies of the first wave of spermatogenesis, we tested the hypothesis that co-expressed spermatogenic genes in cells committed to differentiation are regulated by a set of distinct combinations of TF modules. A computational approach was designed to identify over-represented module combinations in the promoter regions of genes associated with transcripts that either increase or decrease in abundance between the first two major spermatogenic cell types: spermatogonia and spermatocytes. We identified five TFs constituting four module combinations that were correlated with expression and repression of similarly regulated genes. These modules were biologically assessed in the context that they represent the key transcriptional mediators in the developmental transition from the spermatogonia to spermatocyte.

Finding females: pheromone-guided reproductive tracking behavior by male Nereis succinea in the marine environment.

Pheromones trigger reproductive responses of many marine organisms, but little is known about how pheromones mediate mate-finding behavior in the marine environment. This paper investigates whether the tetrapeptide nereithione (cysteine-glutathione disulfide), known to be released by females of the polychaete Nereis succinea to trigger spawning in male N. succinea, can also be used at lower concentrations to guide males to the females. Low concentrations of pheromone elicited increased swim speed and turning left or right 84% of the time. Animals sometimes weaved back and forth, or in other cases swam straight along the trails an average of 8.1+/-1.2 cm before veering off. At higher concentrations, the males circled frequently, often encountering 10-20 cm of pheromone trail before swimming away. Male responses to nereithione were modeled by computer simulation, taking into account arousal of swim speed, activation of turning, speed of response and its decay, etc. In the model, low concentrations (<10(-8) mol l(-1)) of pheromone significantly increased the number of encounters with the pheromone trail, an average following of simulated trails of 10.5+/-3.6 cm, and a significant increase in the frequency of encountering a virtual female on the trail (ANOVA, P<0.001). The model supports the hypothesis that a pheromone can have a dual function, with low concentration pheromone trails being used by male N. succinea to find females and increase their likelihood of mating whereas high concentrations of the same pheromone trigger the spawning behavior itself.

Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI.

As the central relay station of the human brain, the thalamus modulates sensory signals to and from the cerebral cortex. The reciprocal connectivity between the cerebral cortex and the thalamus is believed to play an essential role in consciousness and various neurological disorders. Thus, in-vivo analysis of thalamo-cortical connectivity is important for our understanding of normal and pathological brain processes. In this paper: We propose a new partitioning paradigm, called coclustering, in order to segment the thalamus into thalamic nuclei based on their cortical projections. In contrast to the traditional clustering paradigm, a coclustering procedure not only simultaneously partitions cortical voxels and thalamic voxels into groups, but also identifies the corresponding strong connectivities between the two classes of groups. We develop the first coclustering algorithm, Genetic Coclustering Algorithm (GCA), to solve the coclustering problem. We apply GCA to segment the thalamus into thalamic nuclei and visualise main thalamo-cortical fibre tracts.

Multimodality data integration in epilepsy.

An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG. Based on the developed software tools, quantitative results obtained from individual modalities can be merged into a data structure allowing a consistent framework for advanced data mining techniques and 3D visualization. Moreover, an effort was made to derive quantitative variables (such as the spatial proximity index, SPI) characterizing the relationship between complementing modalities on a more generic level as a prerequisite for efficient data mining strategies. We describe the implementation of this software environment in twelve children (mean age 5.2 +/- 4.3 years) with medically intractable partial epilepsy who underwent both high-resolution structural MR and functional PET imaging. Our experiments demonstrate that our approach will lead to a better understanding of the mechanisms of epileptogenesis and might ultimately have an impact on treatment. Moreover, our software environment holds promise to be useful in many other neurological disorders, where integration of multimodality data is crucial for a better understanding of the underlying disease mechanisms.

[Study on the extraction of the active component in Atractylode macrocephala with ultrasonic wave technology optimized by the response surface method].

OBJECTIVE: To create Atractylode macrocephala inspissation decoction pieces. The effect of ultrasonic wave on extraction of the active components in A. macrocephala was studied in a water solution. METHOD: The factors including the ratio of material to liquid, ultrasonic power, ultrasonic time, soaking time, particle size etc, were studied. The best extraction method was found through the response surface method. RESULT: The best extraction method was found as follows: the granularity of material 0.1 mm, the repetition times of ultrasonic process 3 times, the soaking time before the ultrasonic process 30 min, the ratio of liquid to material 10:1, the soaking time after the ultrasonic process 2.6 h, the time of the ultrasonic wave 15.5 min, the power of the ultrasonic wave 531 W, the rate of reservation of active components 88.5%, the rate of inspissation 1.6. CONCLUSION: The ultrasonic wave can used in the extraction of the active components in A. macrocephala and a model equation that can be used to predict the experiment was get through the response surface method.

FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis.

BACKGROUND: Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention. RESULTS: In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. We further analyze the asymptotic property of FM-test, and then establish the relationship between FM d-value and p-value. We applied FM-test to a diabetes expression dataset and a lung cancer expression dataset, respectively. Within the 10 significant genes identified in diabetes dataset, six of them have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. Within the 10 significantly overexpressed genes identified in lung cancer data, most (eight) of them have been confirmed by the literatures which are related to the lung cancer. CONCLUSION: Our experiments on synthetic datasets show that FM-test is effective and robust. The results in diabetes and lung cancer datasets validated the effectiveness of FM-test. FM-test is implemented as a Web-based application and is available for free at http://database.cs.wayne.edu/bioinformatics.

Incremental genetic K-means algorithm and its application in gene expression data analysis.

BACKGROUND: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. RESULTS: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm. CONCLUSIONS: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.