Andres Figueroa

Analysis of Bacterial Community Composition by Oligonucleotide Fingerprinting of rRNA Genes

ABSTRACT One of the first steps in characterizing an ecosystem is to describe the organisms inhabiting it. For microbial studies, experimental limitations have hindered the ability to depict diverse communities. Here we describe oligonucleotide fingerprinting of rRNA genes (OFRG), a method that permits identification of arrayed rRNA genes (rDNA) through a series of hybridization experiments using small DNA probes. To demonstrate this strategy, we examined the bacteria inhabiting two different soils. Analysis of 1,536 rDNA clones revealed 766 clusters grouped into five major taxa: Bacillus, Actinobacteria, Proteobacteria, and two undefined assemblages. Soil-specific taxa were identified and then independently confirmed through cluster-specific PCR of the original soil DNA. Near-species-level resolution was obtained by this analysis as clones with average sequence identities of 97% were grouped in the same cluster. A comparison of these OFRG results with the results obtained in a denaturing gradient gel electrophoresis analysis of the same two soils demonstrated the significance of this methodological advance. OFRG provides a cost-effective means to extensively analyze microbial communities and should have applications in medicine, biotechnology, and ecosystem studies.

Clustering binary fingerprint vectors with missing values for DNA array data analysis.

Oligonucleotide fingerprinting is a powerful DNA array based method to characterize cDNA and ribosomal RNA gene (rDNA) libraries and has many applications including gene expression profiling and DNA clone classification. We are especially interested in the latter application. A key step in the method is the cluster analysis of fingerprint data obtained from DNA array hybridization experiments. Most of the existing approaches to clustering use (normalized) real intensity values and thus do not treat positive and negative hybridization signals equally (positive signals are much more emphasized). In this paper, we consider a discrete approach. Fingerprint data are first normalized and binarized using control DNA clones. Because there may exist unresolved (or missing) values in this binarization process, we formulate the clustering of (binary) oligonucleotide fingerprints as a combinatorial optimization problem that attempts to identify clusters and resolve the missing values in the fingerprints simultaneously. We study the computational complexity of this clustering problem and a natural parameterized version, and present an efficient greedy algorithm based on MINIMUM CLIQUE PARTITION on graphs. The algorithm takes advantage of some unique properties of the graphs considered here, which allow us to efficiently find the maximum cliques as well as some special maximal cliques. Our experimental results on simulated and real data demonstrate that the algorithm runs faster and performs better than some popular hierarchical and graph-based clustering methods. The results on real data from DNA clone classification also suggest that this discrete approach is more accurate than clustering methods based on real intensity values, in terms of separating clones that have different characteristics with respect to the given oligonucleotide probes.

Clustering binary fingerprint vectors with missing values for DNA array data analysis

Oligonucleotide fingerprinting is a powerful DNA array based method to characterize cDNA and ribosomal RNA gene (rDNA) libraries and has many applications including gene expression profiling and DNA clone classification. We are especially interested in the latter application. A key step in the method is the cluster analysis of fingerprint data obtained from DNA array hybridization experiments. Most of the existing approaches to clustering use (normalized) real intensity values and thus do not treat positive and negative hybridization signals equally (positive signals are much more emphasized). In this paper, we consider a discrete approach. Fingerprint data are first normalized and binarized using control DNA clones. Because there may exist unresolved (or missing) values in this binarization process, we formulate the clustering of (binary) oligonucleotide fingerprints as a combinatorial optimization problem that attempts to identify clusters and resolve the missing values in the fingerprints simultaneously. We study the computational complexity of this clustering problem and a natural parameterized version, and present an efficient greedy algorithm based on minimum clique partition on graphs. The algorithm takes advantage of some unique properties of the graphs considered here, which allow us to efficiently find the maximum cliques as well as some special maximal cliques. Our experimental results on simulated and real data demonstrate that the algorithm runs faster and performs better than some popular hierarchical and graph-based clustering methods. The results on real data from DNA clone classification also suggest that this discrete approach is more accurate than clustering methods based on real intensity values, in terms of separating clones that have different characteristics with respect to the given oligonucleotide probes.

The epsin 4 gene is associated with psychotic disorders in families of Latin American origin

This study attempted to replicate evidence for association of the Epsin 4 gene (which encodes enthoprotin, a protein involved in vesicular transport) to schizophrenia in a new sample of families segregating schizophrenia drawn from the Latin American population. 1,423 subjects (767 with a history of psychosis) from 337 Latino families were genotyped using three single nucleotide polymorphisms (SNPs) spanning the Epsin 4 gene. A family based association test was utilized to test for association of these SNPs to the phenotypes of psychosis and schizophrenia. Haplotypes defined by these three SNPs showed significant association to the phenotype of psychosis in this sample (global p value=0.014, bi-allelic p value=0.047). Variation in the Epsin 4 gene is significantly associated with psychotic disorder in this Latino population. This provides additional support for the involvement of enthoprotin in the pathogenesis of schizophrenia and other psychotic disorders.

Robust spots finding in microarray images with distortions

Microarray images, which allow the analysis of hybridization expressions of genes, have been one of the most widely used high-throughput technologies with many different applications. Accurate and automatic microarray image analysis is very important since researchers trust the information provided in these experiments and construct conclusions based on the results produced by the software responsible in analyzing the hybridized arrays. Every microarray image contains thousands of spots, so how to do the spots finding in microarray images accurately and automatically is very meaningful. There are always some problems, such as rotation and distortion, in a microarray image caused by mechanical errors and/or optical errors in the system. All these problems will hinder doing analysis automatically. Early research has worked out several algorithms to deal with the rotation problem, but those algorithms can not handle microarray images with distortions. In this paper, we propose a robust spots finding method to deal with both rotation and/or distortion in microarray images. The proposed method provides automatic gridding and can handle a microarray image with different type of rotation (global or sub-array rotation) and optical distortions.

Genetic structure of Mexican Mestizos with type 2 diabetes mellitus based on three STR loci.

BACKGROUND The aims of this population genetics study were: 1) to ascertain whether Mexicans with type 2 diabetes mellitus (DM) were genetically homogeneous and 2) to compare the genetic structure of this selected population with the previously reported data of four random populations (Nuevo León, Hispanics, Chihuahua, and Central Region of Mexico). METHODS A sample of 103 unrelated individuals with DM and whose 4 grandparents were born in five zones of Mexico was interviewed in 32 Medical Units in the Mexican Institute of Social Security (IMSS). The non-coding STRs D16S539, D7S820, and D13S317 were analyzed. RESULTS Genotype distribution was in agreement with Hardy-Weinberg expectations for all three markers. Allele frequencies were found to be similar between the selected population and the four random populations. Gene diversity analysis suggested that more than 99.57% of the total gene diversity could be attributed to variation between individuals within the population and 0.43% between the populations. CONCLUSIONS According to the present and previous studies using molecular and non-molecular nuclear DNA markers not associated with any disease, the Mexican Mestizo population is found to be genetically homogeneous and therefore the genetic causes of DM are less heterogeneous, thereby simplifying genetic epidemiological studies as has been found in a previous study with the same design in Mexican women with breast cancer.

Approximate clustering of incomplete fingerprints

We study the problem of clustering fingerprints with at most p missing values (CMV(p) for short) naturally arising in oligonucleotide fingerprinting, which is an efficient method for characterizing DNA clone libraries. We show that already CMV(2) is NP-hard. We also show that a greedy algorithm yields a min(1+lnn,2+plnl) approximation for CMV(p), and can be implemented to run in O(nl2^p) time. We also introduce other variants of the problem of clustering incomplete fingerprints based on slightly different optimization criteria and show that they can be approximated in polynomial time with ratios 2^2^p^-^1 and 2(1-12^2^p), respectively.

Genetic variants in KCNJ11, TCF7L2 and HNF4A are associated with type 2 diabetes, BMI and dyslipidemia in families of Northeastern Mexico: A pilot study

The aim of the present study was to investigate whether genetic markers considered risk factors for metabolic syndromes, including dyslipidemia, obesity and type 2 diabetes mellitus (T2DM), can be applied to a Northeastern Mexican population. A total of 37 families were analyzed for 63 single nucleotide polymorphisms (SNPs), and the age, body mass index (BMI), glucose tolerance values and blood lipid levels, including those of cholesterol, low-density lipoprotein (LDL), very LDL (VLDL), high-density lipoprotein (HDL) and triglycerides were evaluated. Three genetic markers previously associated with metabolic syndromes were identified in the sample population, including KCNJ11, TCF7L2 and HNF4A. The KCNJ11 SNP rs5210 was associated with T2DM, the TCF7L2 SNP rs11196175 was associated with BMI and cholesterol and LDL levels, the TCF7L2 SNP rs12255372 was associated with BMI and HDL, VLDL and triglyceride levels, and the HNF4A SNP rs1885088 was associated with LDL levels (P<0.05).