Dual bio-degradative pathways of di-2-ethylhexyl phthalate by a novel bacterium Burkholderia sp. SP4.

Burkholderia sp. SP4, isolated from agricultural soils, has a high capability of degrading di-2-ethylhexyl-phthalate (DEHP). It degrades up to 99% of DEHP (300 mg l-1) in minimal salt (MS) media within 48 h without adding additionally auxiliary carbon source. The optimal conditions for SP4 to degrade DEHP are determined to be at 35 °C and pH 6.0. Supplementation of glucose (3.0 g l-1), sodium dodecyl sulfate (SDS) (0.2%), peptone (0.5 g l-1), or non-ionic surfactant Brij 35 (0.2%, 0.5% or 1%) in MS-DEHP media increases the DEHP degradation activity. Furthermore, kinetic analyses for DEHP degradation by SP4 reveals that it is a first-order reaction, and the half-life analyses also demonstrates that SP4 has a better degradative activity compared to other previously identified microbes. By means of HPLC-ESI-QTOF-MS, the metabolic intermediates of DEHP are identified for SP4, which include mono-2-ethylhexylphthalate (MEHP), mono-butyl phthalate (MBP), phthalic acid (PA), salicylic acid (SA), and 4-oxo-hexanoic acid. The presence of SA indicates that SP4 can consume DEHP using a dual biodegradation pathway diverged from the isomeric products of benzoate. Taken together, our study identifies a resilient DEHP-degradable bacterium and characterizes a novel degradation pathway for DEHP biodegradation. We plan to build on this finding in the context of removing DEHP from various environments.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Motivation: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. Results: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . Contact: hlin1@lsuhsc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Prevalence of sulfonamide-resistant bacteria, resistance genes and integron-associated horizontal gene transfer in natural water bodies and soils adjacent to a swine feedlot in northern Taiwan.

Antibiotics are commonly used in swine feed to treat and prevent disease, as well as to promote growth. Antibiotics released into the environment via wastewater could accelerate the emergence of antibiotic-resistant bacteria and resistance genes in the surrounding environment. In this study, we quantified the occurrence of sulfonamides, sulfonamide-resistant microorganisms and resistance genes in the wastewater from a swine farm in northern Taiwan and its surrounding natural water bodies and soils. Sulfonamide levels were similar in the receiving downstream and upstream river water. However, the prevalence of sulfonamide-resistant bacteria and resistance genes, as analyzed by cultivation-dependent and -independent molecular approaches, was significantly greater in the downstream compared to the upstream river water samples. Barcoded-pyrosequencing revealed a highly diverse bacterial community structure in each sample. However, the sequence identity of the sulfonamide resistance gene sul1 in the wastewater and downstream environment samples was nearly identical (99-100%). The sul1 gene, which is genetically linked to class 1 integrons, was dominant in the downstream water bodies and soils. In conclusion, the increased prevalence of sulfonamide resistance genes in the wastewater from a swine farm, independent of the persistent presence of sulfonamides, could be a potential source of resistant gene pools in the surrounding environment.

Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer.

Single nucleotide polymorphism (SNP) interaction plays a critical role for complex diseases. The primary limitation of logistic regressions (LR) in testing SNP-SNP interactions is that coefficient estimates may not be valid because of numerous terms in a model. Multivariate adaptive regression splines (MARS) have useful features to effectively reduce the number of terms in a model. To study how MARS can address these drawbacks possibly better than LR, the power of MARS and LR with SNPs using the reference-coding and additive-mode scheme was compared using simulated data of ten SNPs for 400 subjects based on 1,000 replications for five interaction models. In overall scenarios, MARS performed better than LR. In the model with a dominant two-way interaction, the power range was 76-96% for MARS and 1-8% for LR in both coding schemes. In the dominant three-way interaction model, the power was 57-85% for MARS and less than 4% for LR. In the prostate cancer example, we evaluated the association between ten SNPs and prostate cancer risk in 649 Caucasians. The best model with one two-way and one three-way interaction was selected using MARS. The findings supported that MARS may provide a useful tool for exploring SNP-SNP interactions.