Biomedical subjects
Publications and source records attributed to Hong Fang.
Nitroimidazole resistance (nim) genes were detected in 2% of 1,502 clinical Bacteroides fragilis group strains isolated from 19 European countries, and a novel nim gene was identified. High metronidazole resistance could be induced in nim-positive strains, which emphasizes the importance of acknowledging metronidazole resistance in the clinical setting.
Thirty-nine methicillin-resistant Staphylococcus aureus (MRSA) isolates with diverse genetic backgrounds and two reference strains were correctly identified as S. aureus on CHROMagar MRSA and S. aureus ID media. Growth inhibition on CHROMagar MRSA was noted. A combination of cefoxitin disk and S. aureus ID was found suitable for rapid MRSA screening.
FDA reviewers need a means to rapidly predict organ-specific carcinogenicity to aid in evaluating new chemicals submitted for approval. This research addressed the building of a database to use in developing a predictive model for such an application based on structure-activity relationships (SAR). The Internet availability of the Carcinogenic Potency Database (CPDB) provided a solid foundation on which to base such a model. The addition of molecular structures to the CPDB provided the extra ingredient necessary for SAR analyses. However, the CPDB had to be compressed from a multirecord to a single record per chemical database; multiple records representing each gender, species, route of administration, and organ-specific toxicity had to be summarized into a single record for each study. Multiple studies on a single chemical had to be further reduced based on a hierarchical scheme. Structural cleanup involved removal of all chemicals that would impede the accurate generation of SAR type descriptors from commercial software programs; that is, inorganic chemicals, mixtures, and organometallics were removed. Counterions such as Na, K, sulfates, hydrates, and salts were also removed for structural consistency. Structural modification sometimes resulted in duplicate records that also had to be reduced to a single record based on the hierarchical scheme. The modified database containing 999 chemicals was evaluated for liver-specific carcinogenicity using a variety of analysis techniques. These preliminary analyses all yielded approximately the same results with an overall predictability of about 63%, which was comprised of a sensitivity of about 30% and a specificity of about 77%.
The inner membrane protease (IMP) has two catalytic subunits, Imp1p and Imp2p, that exhibit nonoverlapping substrate specificity in mitochondria of the yeast Saccharomyces cerevisiae. The IMP also has at least one noncatalytic subunit, Som1p, which is required to cleave signal peptides from a subset of Imp1p substrates. To understand how Som1p mediates Imp1p substrate specificity, we addressed the possibility that Som1p functions as a molecular chaperone, which binds to specific substrates and directs them to the catalytic site. Our results show that cargo sequences attached to the signal peptide are important for Som1p-dependent presequence cleavage; however, no specific cargo sequence is required. Indeed, we show that a substrate normally destined for Imp2p is cleaved in a Som1p-dependent manner when the substrate is directed to Imp1p. These results argue against the notion that Som1p is a molecular chaperone. Instead, we propose that the cargo of some Imp1p substrates can assume a conformation incompatible with presequence cleavage. Som1p could thus act through Imp1p to improve cleavage efficiency early during substrate maturation.
Daily, rhythmic variation in various biochemical, physiological, and behavioral events is a fundamental property of biological organization. Here, we report analysis of relative levels of gene expression in the liver of 16 Fischer 344 rats as a function of time of day. Expression levels were determined for 3906 genes using high-density oligonucleotide microarrays. Of the 3906 genes, 1171 (30%) were clearly expressed while 2735 (70%) were not expressed or the expression was too low to distinguish from background levels. The maximum estimated changes observed for most genes (1029, 88%) were less than 1.5-fold. Analysis of variance and the Kruskal-Wallis tests were used to identify 67 genes whose expression was significantly altered as a function of time of day. These significantly altered genes were classified according to their functions and fall into key cellular pathways including drug metabolism, ion transport, signal transduction, DNA binding and regulation of transcription, and immune response.
A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.
The inhibitive effects of all-trans retinoic acid (ARTA) on airway inflammation in asthmatic rats and its mechanism on the basis of the regulation of nuclear factor kappaB (NF-kappaB) were explored. Thirty-two SD rats were randomly divided into 4 groups: control group, asthma group, dexamethasone treatment group and retinotic acid treatment group. The total and differential cell counts in the collected bronchoalveolar lavage fluid (BALF) were measured. The pathological changes in lung tissues were estimated by scoring. The expression of NF-kappaB inhibitor (IkappaBa), NF-kappaB, intercellular adhering molecule-1 (ICAM-1) in lung tissue was detected by immunohistochemical method. The results showed that in the two treatment groups, the total cell counts and proportion of inflammatory cells in BALF were significantly reduced, but there was no significant difference in differential cell counts in BALF between them. The pathological changes in lung tissues in the treatment groups were significantly attenuated as compared with asthma group. Except the epithelial injury in retinotic acid treatment group was milder than in dexamethasone treatment group, the remaining lesions showed no significant difference between them. In the two treatment groups, the expression of IkappaBa was increased, while the expression of NF-kappaB and ICAM-1 decreased with the difference between the two groups being not significant. It was concluded that the similar anti-inflammatory effects and mechanism of ATRA on airway in asthmatic rats to those of dexamethasone were contributed to the increase of cytoplasmic IkappaBa content and suppression of NF-kappaB activation and expression.
Novel statistical methods were used to distinguish functionally distinct brain regions using their cDNA array gene expression profiles, and it was found that one of four specific factors is often associated with the most regionally discriminative genes. The gene expression profiles for the substantia nigra (SN), striatum (STR), parietal cortex (PC), and posterolateral cortical amygdaloid nucleus (PLCo) brain regions were determined from each brain region. An F-test identified 339 genes of the 1185 array genes as having a P < or = 0.01 and applied a gene ranking and selection method based on Soft Independent Modeling of Class Analogy (SIMCA) to obtain 59 of the most discriminative genes. Their discriminative power was validated in three steps. The most convincing step showed their ability to correctly predict the brain regional classifications for 18 "test" gene expression sets obtained from the four regions. A two-way Hierarchical Cluster Analysis organized the 59 genes in six clusters according to their expression differences in the brain regions. Expression patterns in the SN and STR regions greatly differed from each other and the PC and PLCo. The closer similarity in the gene expression patterns of the PC and PLCo was probably due to their functional similarity. The important factors in determining differences in the regional gene expression profiles in six clusters were (1) regional myelin/oligodendrocyte levels, (2) resident neuron types, (3) neurotransmitter innervation profiles, and (4) Ca++-dependent signaling and second messenger systems.
The wealth of knowledge imbedded in gene expression data from DNA microarrays portends rapid advances in both research and clinic. Turning the prodigious and noisy data into knowledge is a challenge to the field of bioinformatics, and development of classifiers using supervised learning techniques is the primary methodological approach for clinical application using gene expression data. In this paper, we present a novel classification method, multiclass Decision Forest (DF), that is the direct extension of the two-class DF previously developed in our lab. Central to DF is the synergistic combining of multiple heterogenic but comparable decision trees to reach a more accurate and robust classification model. The computationally inexpensive multiclass DF algorithm integrates gene selection and model development, and thus eliminates the bias of gene preselection in crossvalidation. Importantly, the method provides several statistical means for assessment of prediction accuracy, prediction confidence, and diagnostic capability. We demonstrate the method by application to gene expression data for 83 small round blue-cell tumors (SRBCTs) samples belonging to one of four different classes. Based on 500 runs of 10-fold crossvalidation, tumor prediction accuracy was approximately 97%, sensitivity was approximately 95%, diagnostic sensitivity was approximately 91%, and diagnostic accuracy was approximately 99.5%. Among 25 genes selected to distinguish tumor class, 12 have functional information in the literature implicating their involvement in cancer. The four types of SRBCTs samples are also distinguishable in a clustering analysis based on the expression profiles of these 25 genes. The results demonstrated that the multiclass DF is an effective classification method for analysis of gene expression data for the purpose of molecular diagnostics.
From June to October of 2002, a cluster of Escherichia coli isolates producing extended-spectrum beta-lactamases (ESBLs) was detected in Stockholm. The isolates were grouped into two clones, one of which had already circulated in the same area before the outbreak. CTX-M-type ESBLs and coresistance to ciprofloxacin were identified in the strains.
Class prediction using "omics" data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request.
Quantitative structure-activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure representation and correlation algorithms. Whereas excellent fit to the training data may be readily attainable, often models fail to predict accurately chemicals that are outside their domain of applicability. A QSAR's utility and, in the case of regulatory decisions, justification for usage increasingly depend on the ability to quantify a model's potential for predicting unknown chemicals with some known degree of certainty. It is never possible to predict an unknown chemical with absolute certainty. Here we report on two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor. The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction. We used an extensive cross-validation process to define an applicability domain for model predictions based on two quantitative measures: prediction confidence and domain extrapolation. Together, these measures quantify the accuracy of each prediction within and outside of the training domain. Despite being based on large and diverse training sets, both QSAR models had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within the domain of high confidence. For prediction in the high confidence domain, accuracy was inversely proportional to the degree of domain extrapolation. The model with a larger training set of 1,092, compared with 232 for the other, was more accurate in predicting chemicals at larger domain extrapolation, and could be particularly useful for rapidly prioritizing potential endocrine disruptors from large chemical universe.
The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.
OBJECTIVE: To investigate the effect of peroxisome proliferator-activated receptor-alpha (PPAR alpha) and PPAR gamma activators on tumor necrosis factor-alpha (TNFalpha) expression in neonatal rat cardiac myocytes. METHODS: Primary cultures of cardiac myocytes from 1- to 3-day-old Wistar rats were prepared, and myocytes were exposed to lipopolysaccharide (LPS) and varying concentrations of PPAR alpha or PPAR gamma activator (fenofibrate or pioglitazone). RT-PCR and ELISA were used to measure TNFalpha, PPAR alpha, and PPAR gamma expression in cultured cardiac myocytes. Transient transfection of TNFalpha promoter with or without nuclear factor-kappaB (NF-kappaB) binding site to cardiac myocytes was performed. RESULTS: Pretreatment of cardiac myocytes with fenofibrate or pioglitazone inhibited LPS-induced TNFalpha mRNA and protein expression in a dose-dependent manner. However, no significant changes were observed on PPAR alpha or PPAR gamma mRNA expression when cardiac myocytes were pretreated with fenofibrate or pioglitazone. Proportional suppression of TNFalpha promoter activity was observed when myocytes was transiently transfected with whole length of TNFalpha promoter (-721/+17) after being stimulated with LPS and fenofibrate or pioglitazone, whereas no change of promoter activity was observed with transfection of TNFalpha reporter construct in deletion of NF-kappaB binding site (-182/+17). CONCLUSIONS: PPAR alpha and PPAR gamma activators may inhibit cardiac TNFalpha expression but not accompanied by change of PPAR alpha or PPAR gamma mRNA expression. Therefore PPAR alpha and PPAR gamma activators appear to play a role in anti-inflammation. The mechanism may partly be involved in suppression of the NF-kappaB pathway.
Type I signal peptidases (SPs) comprise a family of structurally related enzymes that cleave signal peptides from precursor proteins following their transport out of the cytoplasmic space in eukaryotic and prokaryotic cells. One such enzyme, the mitochondrial inner membrane peptidase, has two catalytic subunits, which recognize distinct cleavage site motifs in their signal peptide substrates. The only other known type I SP with two catalytic subunits is the signal peptidase complex (SPC) in the mammalian endoplasmic reticulum. Here, we tested the hypothesis that, as with inner membrane peptidase catalytic subunits, SPC catalytic subunits exhibit nonoverlapping substrate specificity. We constructed two yeast strains without endogenous SP, one expressing canine SPC18 and the other expressing a truncation of canine SPC21 (SPC21 Delta N), which lacks 24 N-terminal residues that prevent expression of SPC21 in yeast. By monitoring a variety of soluble and membrane-bound substrates, we find that, in contrast to the tested hypothesis, SPC catalytic subunits exhibit overlapping substrate specificity. SPC18 and SPC21 Delta N do, however, cleave some substrates with different efficiencies, although no pattern for this behavior could be discerned. In light of the functional similarities between SPC proteins, we developed a membrane protein fragmentation assay to monitor the position of the catalytic sites relative to the surface of the endoplasmic reticulum membrane. Using this assay, our results suggest that the active sites of SPC18 and SPC21 Delta N are located 4-11 A above the membrane surface. These data, thus, support a model that SPC18 and SPC21 are functionally and structurally similar to each other.
A number of environmental and industrial chemicals are reported to possess androgenic or antiandrogenic activities. These androgenic endocrine disrupting chemicals may disrupt the endocrine system of humans and wildlife by mimicking or antagonizing the functions of natural hormones. The present study developed a low cost recombinant androgen receptor (AR) competitive binding assay that uses no animals. We validated the assay by comparing the protocols and results from other similar assays, such as the binding assay using prostate cytosol. We tested 202 natural, synthetic, and environmental chemicals that encompass a broad range of structural classes, including steroids, diethylstilbestrol and related chemicals, antiestrogens, flutamide derivatives, bisphenol A derivatives, alkylphenols, parabens, alkyloxyphenols, phthalates, siloxanes, phytoestrogens, DDTs, PCBs, pesticides, organophosphate insecticides, and other chemicals. Some of these chemicals are environmentally persistent and/or commercially important, but their AR binding affinities have not been previously reported. To the best of our knowledge, these results represent the largest and most diverse data set publicly available for chemical binding to the AR. Through a careful structure-activity relationship (SAR) examination of the data set in conjunction with knowledge of the recently reported ligand-AR crystal structures, we are able to define the general structural requirements for chemical binding to AR. Hydrophobic interactions are important for AR binding. The interaction between ligand and AR at the 3- and 17-positions of testosterone and R1881 found in other chemical classes are discussed in depth. The SAR studies of ligand binding characteristics for AR are compared to our previously reported results for estrogen receptor binding.
PCR-based methods for the detection of homozygous deletion of exon 7 of the SMN1 gene have been widely used in genetic testing for spinal muscular atrophy (SMA). We compared the most commonly used PCRrestriction fragment length polymorphism (PCR-RFLP) assay with an allele-specific PCR method, evaluating their potential application in direct testing, prenatal prediction, and preimplantation diagnosis, in terms of a range of DNA amounts used in such testing. We showed that PCR-RFLP could identify the SMN1 exon 7 by amplifying 10 pg of genomic DNA, and could differentiate SMN1 from SMN2 at the 100-pg DNA level (DraIdigested SMN2 fragments served as an internal control for PCR efficiency). In contrast, allele-specific PCR for SMN1, despite some advantages in a rapid preimplantation diagnosis, quickly lost its specificity when 100 pg of genomic DNA was used. In addition, the absence of a SMN1 fragment at the 10-pg DNA level may be due to a PCR amplification failure, and, thus, it is difficult to interpret without a proper internal control. Our data indicate that PCR-RFLP can be used for most diagnostic purposes, whereas the use of allelespecific PCR may be considered with caution under certain circumstances.
A screening method for methicillin-resistant Staphylococcus aureus (MRSA) by using selective broth and real-time PCR (broth-PCR) was developed and evaluated. The samples (n = 304) were cultured in the broth overnight, followed by nuc gene detection by real-time PCR. nuc-negative samples were further checked for the presence of nuc amplification inhibitors by a PCR internal inhibitor assay. nuc-positive samples and nuc-negative samples with PCR inhibitors were cultured onto plates and processed further. The diagnostic values for this MRSA screening method were 93.3% sensitivity, 89.6% specificity, 31.8% positive predictive value, and 99.6% negative predictive value. The application of the broth-PCR method will be able to report most of the negative samples (258 of 289 [89.3%]) on the next morning and can save as much as 84.9% (258 of 304) of the labor and cost spent on processing the nuc-negative specimens on plates. In the study, all the samples were processed in parallel by the broth enrichment method and the plating method for comparison. To identify MRSA, the isolated oxacillin-resistant S. aureus strains were tested by a duplex real-time PCR targeting the mecA gene and the nuc gene. A collection of MRSA, methicillin-susceptible Staphylococcus aureus, methicillin-resistant Staphylococcus epidermidis, and methicillin-susceptible Staphylococcus epidermidis strains and a panel of standard strains of 11 bacterial species other than S. aureus were also tested by this method, which was proved to be a valuable tool for MRSA identification in a routine microbiological laboratory.