Lingling An

Comparative Functional Genomic Analysis of Solanum Glandular Trichome Types

Glandular trichomes play important roles in protecting plants from biotic attack by producing defensive compounds. We investigated the metabolic profiles and transcriptomes to characterize the differences between different glandular trichome types in several domesticated and wild Solanum species: Solanum lycopersicum (glandular trichome types 1, 6, and 7), Solanum habrochaites (types 1, 4, and 6), Solanum pennellii (types 4 and 6), Solanum arcanum (type 6), and Solanum pimpinellifolium (type 6). Substantial chemical differences in and between Solanum species and glandular trichome types are likely determined by the regulation of metabolism at several levels. Comparison of S. habrochaites type 1 and 4 glandular trichomes revealed few differences in chemical content or transcript abundance, leading to the conclusion that these two glandular trichome types are the same and differ perhaps only in stalk length. The observation that all of the other species examined here contain either type 1 or 4 trichomes (not both) supports the conclusion that these two trichome types are the same. Most differences in metabolites between type 1 and 4 glands on the one hand and type 6 glands on the other hand are quantitative but not qualitative. Several glandular trichome types express genes associated with photosynthesis and carbon fixation, indicating that some carbon destined for specialized metabolism is likely fixed within the trichome secretory cells. Finally, Solanum type 7 glandular trichomes do not appear to be involved in the biosynthesis and storage of specialized metabolites and thus likely serve another unknown function, perhaps as the site of the synthesis of protease inhibitors.

Gene expression analysis at the intersection of ploidy and hybridity in maize

Heterosis and polyploidy are two important aspects of plant evolution. To examine these issues, we conducted a global gene expression study of a maize ploidy series as well as a set of tetraploid inbred and hybrid lines. This gene expression analysis complements an earlier phenotypic study of these same materials. We find that ploidy change affects a large fraction of the genome, albeit at low levels; gene expression changes rarely exceed 2-fold and are typically not statistically significant. The most common gene expression profile we detected is greater than linear increase from monoploid to diploid, and reductions from diploid to triploid and from triploid to tetraploid, a trend that mirrors plant stature. When examining heterosis in tetraploid maize lines, we found a large fraction of the genome impacted but the majority of changes were not statistically significant at 2-fold or less. Non-additive expression was common in the hybrids, and the extent of non-additivity increased both in number and magnitude from duplex to quadruplex hybrids. Overall, we find that gene expression trends mirror observations from the phenotypic studies; however, obvious mechanistic connections remain unknown.

DAG Lipase Activity Is Necessary for TRP Channel Regulation in Drosophila Photoreceptors

In Drosophila, a phospholipase C-mediated signaling cascade links photoexcitation of rhodopsin to the opening of the TRP/TRPL channels. A lipid product of the cascade, diacylglycerol (DAG) and its metabolite(s), polyunsaturated fatty acids (PUFAs), have both been proposed as potential excitatory messengers. A crucial enzyme in the understanding of this process is likely to be DAG lipase (DAGL). However, DAGLs that might fulfill this role have not been previously identified in any organism. In this work, the Drosophila DAGL gene, inaE, has been identified from mutants that are defective in photoreceptor responses to light. The inaE-encoded protein isoforms show high sequence similarity to known mammalian DAG lipases, exhibit DAG lipase activity in vitro, and are highly expressed in photoreceptors. Analyses of norpA inaE double mutants and severe inaE mutants show that normal DAGL activity is required for the generation of physiologically meaningful photoreceptor responses.

Analysis of gene expression profiles in response to Sclerotinia sclerotiorum in Brassica napus

Sclerotinia sclerotiorum is a necrotrophic plant pathogen which causes serious disease in agronomically important crop species. The molecular basis of plant defense to this pathogen is poorly understood. We investigated gene expression changes associated with S. sclerotiorum infection in a partially resistant and a susceptible genotype of oilseed Brassica napus using a whole genome microarray from Arabidopsis. A total of 686 and 1,547 genes were found to be differentially expressed after infection in the resistant and susceptible genotypes, respectively. The number of differentially expressed genes increased over infection time with the majority being up-regulated in both genotypes. The putative functions of the differentially expressed genes included pathogenesis-related (PR) proteins, proteins involved in the oxidative burst, protein kinase, molecule transporters, cell maintenance and development, abiotic stress, as well as proteins with unknown functions. The gene regulation patterns indicated that a large part of the defense response exhibited as a temporal and quantitative difference between the two genotypes. Genes associated with jasmonic acid (JA) and ethylene signal transduction pathways were induced, but no salicylic acid (SA) responsive genes were identified. Candidate defense genes were identified by integration of the early response genes in the partially resistant line with previously mapped quantitative trait loci (QTL). Expression levels of these genes were verified by Northern blot analyses. These results indicate that genes encoding various proteins involved in diverse roles, particularly WRKY transcription factors and plant cell wall related proteins may play an important role in the defense response to S. sclerotiorum disease.

Modeling drug-carrier interaction in the drug release from nanocarriers.

Numerous nanocarriers of various compositions and geometries have been developed for the delivery and release of therapeutic and imaging agents. Due to the high specific surface areas of nanocarriers, different mechanisms such as ion pairing and hydrophobic interaction need to be explored for achieving sustained release. Recently, we developed a three-parameter model that considers reversible drug-carrier interaction and first-order drug release from liposomes. A closed-form analytical solution was obtained. Here, we further explore the ability of the model to capture the release of bioactive molecules such as drugs and growth factors from various nanocarriers. A parameter study demonstrates that the model is capable of resembling major categories of drug release kinetics. We further fit the model to 60 sets of experimental data from various drug release systems, including nanoparticles, hollow particles, fibers, and hollow fibers. Additionally, bootstrapping is used to evaluate the accuracy of parameter determination and validate the model in selected cases. The simplicity and universality of the model and the clear physical meanings of each model parameter render the model useful for the design and development of new drug delivery systems.

The nonsense-mediated decay pathway maintains synapse architecture and synaptic vesicle cycle efficacy

A systematic Drosophila forward genetic screen for photoreceptor synaptic transmission mutants identified no-on-and-no-off transient C (nonC) based on loss of retinal synaptic responses to light stimulation. The cloned gene encodes phosphatidylinositol-3-kinase-like kinase (PIKK) Smg1, a regulatory kinase of the nonsense-mediated decay (NMD) pathway. The Smg proteins act in an mRNA quality control surveillance mechanism to selectively degrade transcripts containing premature stop codons, thereby preventing the translation of truncated proteins with dominant-negative or deleterious gain-of-function activities. At the neuromuscular junction (NMJ) synapse, an extended allelic series of Smg1 mutants show impaired structural architecture, with decreased terminal arbor size, branching and synaptic bouton number. Functionally, loss of Smg1 results in a ~50% reduction in basal neurotransmission strength, as well as progressive transmission fatigue and greatly impaired synaptic vesicle recycling during high-frequency stimulation. Mutation of other NMD pathways genes (Upf2 and Smg6) similarly impairs neurotransmission and synaptic vesicle cycling. These findings suggest that the NMD pathway acts to regulate proper mRNA translation to safeguard synapse morphology and maintain the efficacy of synaptic function.

Presynaptic Calcium Channel Localization and Calcium-Dependent Synaptic Vesicle Exocytosis Regulated by the Fuseless Protein

A systematic forward genetic Drosophila screen for electroretinogram mutants lacking synaptic transients identified the fuseless (fusl) gene, which encodes a predicted eight-pass transmembrane protein in the presynaptic membrane. Null fusl mutants display >75% reduction in evoked synaptic transmission but, conversely, an approximately threefold increase in the frequency and amplitude of spontaneous synaptic vesicle fusion events. These neurotransmission defects are rescued by a wild-type fusl transgene targeted only to the presynaptic cell, demonstrating a strictly presynaptic requirement for Fusl function. Defects in FM dye turnover at the synapse show a severely impaired exo-endo synaptic vesicle cycling pool. Consistently, ultrastructural analyses reveal accumulated vesicles arrested in clustered and docked pools at presynaptic active zones. In the absence of Fusl, calcium-dependent neurotransmitter release is dramatically compromised and there is little enhancement of synaptic efficacy with elevated external Ca2+ concentrations. These defects are causally linked with severe loss of the Cacophony voltage-gated Ca2+ channels, which fail to localize normally at presynaptic active zone domains in the absence of Fusl. These data indicate that Fusl regulates assembly of the presynaptic active zone Ca2+ channel domains required for efficient coupling of the Ca2+ influx and synaptic vesicle exocytosis during neurotransmission.

Investigating microbial co-occurrence patterns based on metagenomic compositional data

MOTIVATION The high-throughput sequencing technologies have provided a powerful tool to study the microbial organisms living in various environments. Characterizing microbial interactions can give us insights into how they live and work together as a community. Metagonomic data are usually summarized in a compositional fashion due to varying sampling/sequencing depths from one sample to another. We study the co-occurrence patterns of microbial organisms using their relative abundance information. Analyzing compositional data using conventional correlation methods has been shown prone to bias that leads to artifactual correlations. RESULTS We propose a novel method, regularized estimation of the basis covariance based on compositional data (REBACCA), to identify significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count or proportion data and solve the system using the l1-norm shrinkage method. Our comprehensive simulation studies show that REBACCA (i) achieves higher accuracy in general than the existing methods when a sparse condition is satisfied; (ii) controls the false positives at a pre-specified level, while other methods fail in various cases and (iii) runs considerably faster than the existing comparable method. REBACCA is also applied to several real metagenomic datasets. AVAILABILITY AND IMPLEMENTATION The R codes for the proposed method are available at http://faculty.wcas.northwestern.edu/∼hji403/REBACCA.htm CONTACT hongmei@northwestern.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.

A robust approach for identifying differentially abundant features in metagenomic samples

MOTIVATION The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differential abundance. Normalization methods used in the differential analysis typically try to adjust counts on different scales to a common scale using the total sum, mean or median of representative features across all samples. These methods often yield undesirable results when the difference in total counts of differentially abundant features (DAFs) across different conditions is large. RESULTS We develop a novel method, Ratio Approach for Identifying Differential Abundance (RAIDA), which utilizes the ratio between features in a modified zero-inflated lognormal model. RAIDA removes possible problems associated with counts on different scales within and between conditions. As a result, its performance is not affected by the amount of difference in total abundances of DAFs across different conditions. Through comprehensive simulation studies, the performance of our method is consistently powerful, and under some situations, RAIDA greatly surpasses other existing methods. We also apply RAIDA on real datasets of type II diabetes and find interesting results consistent with previous reports. AVAILABILITY AND IMPLEMENTATION An R package for RAIDA can be accessed from http://cals.arizona.edu/%7Eanling/sbg/software.htm.