Steven Wu

A proteomic approach identifies early pregnancy biomarkers for preeclampsia: Novel linkages between a predisposition to preeclampsia and cardiovascular disease

Preeclampsia (PE) is a common, potentially life‐threatening pregnancy syndrome triggered by placental factors released into the maternal circulation, resulting in maternal vascular dysfunction along with activated inflammation and coagulation. Currently there is no screening test for PE. We sought to identify differentially expressed plasma proteins in women who subsequently develop PE that may perform as predictive biomarkers. In seven DIGE experiments, we compared the plasma proteome at 20 wk gestation in women who later developed PE with an appropriate birth weight for gestational age baby (n=27) or a small for gestational age baby (n=12) to healthy controls with uncomplicated pregnancies (n=57). Of the 49 differentially expressed spots associated with PE‐appropriate for gestational age, PE‐small for gestational age or both (p<0.05, false discovery rate corrected), 39 were identified by LC‐MS/MS. Two protein clusters that accurately (>90%) classified women at risk of developing PE were identified. Immunoblots confirmed the overexpression of fibrinogen γ chain and α‐1‐antichymotrypsin in plasma prior to PE. The proteins identified are involved in lipid metabolism, coagulation, complement regulation, extracellular matrix remodeling, protease inhibitor activity and acute‐phase responses, indicating novel synergism between pathways involved in the pathogenesis of PE. Our findings are remarkably similar to recently identified proteins complexed to high‐density lipoprotein and linked to cardiovascular disease.

Molecular phylogeny of the Entomophthoromycota.

The Entomophthoromycota is a ubiquitous group of fungi best known as pathogens of a wide variety of economically important insect pests, and other soil invertebrates. This group of fungi also includes a small number of parasites of reptiles, vertebrates (including humans), macromycetes, fern gametophytes, and desmid algae, as well as some saprobic species. Here we report on recent studies to resolve the phylogenetic relationships within the Entomophthoromycota and to reliably place this group among other basal fungal lineages. Bayesian Interference (BI) and Maximum Likelihood (ML) analyses of three genes (nuclear 18S and 28S rDNA, mitochondrial 16S, and the protein-coding RPB2) as well as non-molecular data consistently and unambiguously identify 31 taxa of Entomophthoromycota as a monophyletic group distinct from other Zygomycota and flagellated fungi. Using the constraints of our multi-gene dataset we constructed the most comprehensive rDNA phylogeny yet available for Entomophthoromycota. The taxa studied here belong to five distinct, well-supported lineages. The Basidiobolus clade is the earliest diverging lineage, comprised of saprobe species of Basidiobolus and the undescribed snake parasite Schizangiella serpentis nom. prov. The Conidiobolus lineage is represented by a paraphyletic grade of trophically diverse species that include saprobes, insect pathogens, and facultative human pathogens. Three well supported and exclusively entomopathogenic lineages in the Entomophthoraceae center around the genera Batkoa, Entomophthora and Zoophthora, although several genera within this crown clade are resolved as non-monophyletic. Ancestral state reconstruction suggests that the ancestor of all Entomophthoromycota was morphologically similar to species of Conidiobolus. Analyses using strict, relaxed, and local molecular clock models documented highly variable DNA substitution rates among lineages of Entomophthoromycota. Despite the complications caused by different rates of molecular evolution among lineages, our dating analysis indicates that the Entomophthoromycota originated 405±90 million years ago. We suggest that entomopathogenic lineages in Entomophthoraceae probably evolved from saprobic or facultatively pathogenic ancestors during or shortly after the evolutionary radiation of the arthropods.

Neutral Models of Microbiome Evolution.

There has been an explosion of research on host-associated microbial communities (i.e.,microbiomes). Much of this research has focused on surveys of microbial diversities across a variety of host species, including humans, with a view to understanding how these microbiomes are distributed across space and time, and how they correlate with host health, disease, phenotype, physiology and ecology. Fewer studies have focused on how these microbiomes may have evolved. In this paper, we develop an agent-based framework to study the dynamics of microbiome evolution. Our framework incorporates neutral models of how hosts acquire their microbiomes, and how the environmental microbial community that is available to the hosts is assembled. Most importantly, our framework also incorporates a Wright-Fisher genealogical model of hosts, so that the dynamics of microbiome evolution is studied on an evolutionary timescale. Our results indicate that the extent of parental contribution to microbial availability from one generation to the next significantly impacts the diversity of microbiomes: the greater the parental contribution, the less diverse the microbiomes. In contrast, even when there is only a very small contribution from a constant environmental pool, microbial communities can remain highly diverse. Finally, we show that our models may be used to construct hypotheses about the types of processes that operate to assemble microbiomes over evolutionary time.

A Statistical Model to Identify Differentially Expressed Proteins in 2D PAGE Gels

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.

Plasma clusterin increased prior to Small for Gestational Age (SGA) associated with preeclampsia and decreased prior to SGA in normotensive pregnancies

In our search for early biomarkers for the pregnancy complicationssmall for gestational age (SGA) and preeclampsia (PE) we analysed plasma from 19-21 weeks gestation in women recruited into the SCOPE study, a prospective cohort of nulliparous women, by differential in gel electrophoresis (DIGE). DIGE revealed the differential expression of clusterin levels and its isoforms in top6-depleted plasma of women who delivered an SGA infant but remained normotensive (SGA-NT; N = 8) compared to healthy women with an uncomplicated pregnancy outcome (Controls, N = 8). Immunosorbent enzyme-linked assay (ELISA) showed that compared to plasma clusterin levels from healthy controls [71.1 (SD 12.4) µg/mL, n = 39], clusterin was decreased in SGA-NT [58.3 (SD 11.7), N = 20, P < 0.0001], increased in women with SGA and PE [81.5 (SD 14.8), N = 20, P < 0.01], but similar in PE alone [71.2 (SD 9.4)g/ml, P = 1.0]. Screening for clusterin levels and/or its different isoformsmay be useful in mid-pregnancy to identify women who subsequently develop SGA but remain normotensive or who develop preeclampsia with SGA.

Transient compartmentalization of simian immunodeficiency virus variants in the breast milk of african green monkeys.

ABSTRACT Natural hosts of simian immunodeficiency virus (SIV), African green monkeys (AGMs), rarely transmit SIV via breast-feeding. In order to examine the genetic diversity of breast milk SIV variants in this limited-transmission setting, we performed phylogenetic analysis on envelope sequences of milk and plasma SIV variants of AGMs. Low-diversity milk virus populations were compartmentalized from that in plasma. However, this compartmentalization was transient, as the milk virus lineages did not persist longitudinally.

A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels

BackgroundTwo-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection.ResultsWe develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression.ConclusionsSimulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D.