Steffen Heber

The deep evolution of metazoan microRNAs

SUMMARY microRNAs (miRNAs) are approximately 22‐nucleotide noncoding RNA regulatory genes that are key players in cellular differentiation and homeostasis. They might also play important roles in shaping metazoan macroevolution. Previous studies have shown that miRNAs are continuously being added to metazoan genomes through time, and, once integrated into gene regulatory networks, show only rare mutations within the primary sequence of the mature gene product and are only rarely secondarily lost. However, because the conclusions from these studies were largely based on phylogenetic conservation of miRNAs between model systems like Drosophila and the taxon of interest, it was unclear if these trends would describe most miRNAs in most metazoan taxa. Here, we describe the shared complement of miRNAs among 18 animal species using a combination of 454 sequencing of small RNA libraries with genomic searches. We show that the evolutionary trends elucidated from the model systems are generally true for all miRNA families and metazoan taxa explored: the continuous addition of miRNA families with only rare substitutions to the mature sequence, and only rare instances of secondary loss. Despite this conservation, we document evolutionary stable shifts to the determination of position 1 of the mature sequence, a phenomenon we call seed shifting, as well as the ability to post‐transcriptionally edit the 5′ end of the mature read, changing the identity of the seed sequence and possibly the repertoire of downstream targets. Finally, we describe a novel type of miRNA in demosponges that, although shows a different pre‐miRNA structure, still shows remarkable conservation of the mature sequence in the two sponge species analyzed. We propose that miRNAs might be excellent phylogenetic markers, and suggest that the advent of morphological complexity might have its roots in miRNA innovation.

Towards a Systems Approach for Lignin Biosynthesis in Populus trichocarpa: Transcript Abundance and Specificity of the Monolignol Biosynthetic Genes

As a step toward a comprehensive description of lignin biosynthesis in Populus trichocarpa, we identified from the genome sequence 95 phenylpropanoid gene models in 10 protein families encoding enzymes for monolignol biosynthesis. Transcript abundance was determined for all 95 genes in xylem, leaf, shoot and phloem using quantitative real-time PCR (qRT-PCR). We identified 23 genes that most probably encode monolignol biosynthesis enzymes during wood formation. Transcripts for 18 of the 23 are abundant and specific to differentiating xylem. We found evidence suggesting functional redundancy at the transcript level for phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate:CoA ligase (4CL), p-hydroxycinnamoyl-CoA:quinate shikimate p-hydroxycinnamoyltransferase (HCT), caffeoyl-CoA O-methyltransferase (CCoAOMT) and coniferyl aldehyde 5-hydroxylase (CAld5H). We carried out an enumeration-based motif identification and discriminant analysis on the promoters of all 95 genes. Five core motifs correctly discriminate the 18 xylem-specific genes from the 77 non-xylem genes. These motifs are similar to promoter elements known to regulate phenylpropanoid gene expression. This work suggests that genes in monolignol biosynthesis are regulated by multiple motifs, often related in sequence.

Finding All Common Intervals of k Permutations

Given k permutations of n elements, a k-tuple of intervals of these permutations consisting of the same set of elements is called a common interval. We present an algorithm that finds in a family of k permutations of n elements all K common intervals in optimal O(nk+K) time and O(n) additional space.This extends a result by Uno and Yagiura (Algorithmica 26, 290-309, 2000) who present an algorithm to find all K common intervals of k = 2 permutations in optimal O(n+K) time and O(n) space. To achieve our result, we introduce the set of irreducible intervals, a generating subset of the set of all common intervals of k permutations.

Targeted Combinatorial Alternative Splicing Generates Brain Region-Specific Repertoires of Neurexins

Molecular diversity of surface receptors has been hypothesized to provide a mechanism for selective synaptic connectivity. Neurexins are highly diversified receptors that drive the morphological and functional differentiation of synapses. Using a single cDNA sequencing approach, we detected 1,364 unique neurexin-α and 37 neurexin-β mRNAs produced by alternative splicing of neurexin pre-mRNAs. This molecular diversity results from near-exhaustive combinatorial use of alternative splice insertions in Nrxn1α and Nrxn2α. By contrast, Nrxn3α exhibits several highly stereotyped exon selections that incorporate novel elements for posttranscriptional regulation of a subset of transcripts. Complexity of Nrxn1α repertoires correlates with the cellular complexity of neuronal tissues, and a specific subset of isoforms is enriched in a purified cell type. Our analysis defines the molecular diversity of a critical synaptic receptor and provides evidence that neurexin diversity is linked to cellular diversity in the nervous system.

RACE: Remote Analysis Computation for gene Expression data.

The Remote Analysis Computation for gene Expression data (RACE) suite is a collection of bioinformatics web tools designed for the analysis of DNA microarray data. RACE performs probe-level data preprocessing, extensive quality checks, data visualization and data normalization for Affymetrix GeneChips. In addition, it offers differential expression analysis on normalized expression levels from any array platform. RACE estimates the false discovery rates of lists of potentially regulated genes and provides a Gene Ontology-term analysis tool for GeneChip data to support the biological interpretation and annotation of results. The analysis is fully automated but can be customized by flexible parameter settings. To offer a convenient starting point for subsequent analyses, and to provide maximum transparency, the R scripts used to generate the results can be downloaded along with the output files. RACE is freely available for use at .

Non-monotonic dose-response relationship in steroid hormone receptor-mediated gene expression.

Steroid hormone receptors are the targets of many environmental endocrine active chemicals (EACs) and synthetic drugs used in hormone therapy. While most of these chemical compounds have a unidirectional and monotonic effect, certain EACs can display non-monotonic dose-response behaviors and some synthetic drugs are selective endocrine modulators. Mechanisms underlying these complex endocrine behaviors have not been fully understood. By formulating an ordinary differential equation-based computational model, we investigated in this study the steady-state dose-response behavior of exogenous steroid ligands in an endogenous hormonal background under various parameter conditions. Our simulation revealed that non-monotonic dose-responses in gene expression can arise within the classical genomic framework of steroid signaling. Specifically, when the exogenous ligand is an agonist, a U-shaped dose-response appears as a result of the inherently nonlinear process of receptor homodimerization. This U-shaped dose-response curve can be further modulated by mixed-ligand heterodimers formed between endogenous ligand-bound and exogenous ligand-bound receptor monomers. When the heterodimer is transcriptionally inactive or repressive, the magnitude of U-shape increases; conversely, when the heterodimer is transcriptionally active, the magnitude of U-shape decreases. Additionally, we found that an inverted U-shaped dose-response can arise when the heterodimer is a strong transcription activator regardless of whether the exogenous ligand is an agonist or antagonist. Our work provides a novel mechanism for non-monotonic, particularly U-shaped, dose-response behaviors observed with certain steroid mimics, and may help not only understand how selective steroid receptor modulators work but also improve risk assessment for EACs.

Algorithms for Finding Gene Clusters

Comparing gene orders in completely sequenced genomes is a standard approach to locate clusters of functionally associated genes. Often, gene orders are modeled as permutations. Given k permutations of n elements, a k-tuple of intervals of these permutations consisting of the same set of elements is called a common interval. We consider several problems related to common intervals in multiple genomes. We present an algorithm that finds all common intervals in a family of genomes, each of which might consist of several chromosomes. We present another algorithm that finds all common intervals in a family of circular permutations. A third algorithm finds all common intervals in signed permutations. We also investigate how to combine these approaches. All algorithms have optimal worst-case time complexity and use linear space.

High-Throughput RNA Sequencing of Pseudomonas-Infected Arabidopsis Reveals Hidden Transcriptome Complexity and Novel Splice Variants

We report the results of a genome-wide analysis of transcription in Arabidopsis thaliana after treatment with Pseudomonas syringae pathovar tomato. Our time course RNA-Seq experiment uses over 500 million read pairs to provide a detailed characterization of the response to infection in both susceptible and resistant hosts. The set of observed differentially expressed genes is consistent with previous studies, confirming and extending existing findings about genes likely to play an important role in the defense response to Pseudomonas syringae. The high coverage of the Arabidopsis transcriptome resulted in the discovery of a surprisingly large number of alternative splicing (AS) events – more than 44% of multi-exon genes showed evidence for novel AS in at least one of the probed conditions. This demonstrates that the Arabidopsis transcriptome annotation is still highly incomplete, and that AS events are more abundant than expected. To further refine our predictions, we identified genes with statistically significant changes in the ratios of alternative isoforms between treatments. This set includes several genes previously known to be alternatively spliced or expressed during the defense response, and it may serve as a pool of candidate genes for regulated alternative splicing with possible biological relevance for the defense response against invasive pathogens.

Towards reliable isoform quantification using RNA-SEQ data

BackgroundIn eukaryotes, alternative splicing often generates multiple splice variants from a single gene. Here weexplore the use of RNA sequencing (RNA-Seq) datasets to address the isoform quantification problem. Given a set of known splice variants, the goal is to estimate the relative abundance of the individual variants.MethodsOur method employs a linear models framework to estimate the ratios of known isoforms in a sample. A key feature of our method is that it takes into account the non-uniformity of RNA-Seq read positions along the targeted transcripts.ResultsPreliminary tests indicate that the model performs well on both simulated and real data. In two publicly available RNA-Seq datasets, we identified several alternatively-spliced genes with switch-like, on/off expression properties, as well as a number of other genes that varied more subtly in isoform expression. In many cases, genes exhibiting differential expression of alternatively spliced transcripts were not differentially expressed at the gene level.ConclusionsGiven that changes in isoform expression level frequently involve a continuum of isoform ratios, rather than all-or-nothing expression, and that they are often independent of general gene expression changes, we anticipate that our research will contribute to revealing a so far uninvestigated layer of the transcriptome. We believe that, in the future, researchers will prioritize genes for functional analysis based not only on observed changes in gene expression levels, but also on changes in alternative splicing.

Detection of Alternative Splice Variants at the Proteome Level in Aspergillus flavus

Identification of proteins from proteolytic peptides or intact proteins plays an essential role in proteomics. Researchers use search engines to match the acquired peptide sequences to the target proteins. However, search engines depend on protein databases to provide candidates for consideration. Alternative splicing (AS), the mechanism where the exon of pre-mRNAs can be spliced and rearranged to generate distinct mRNA and therefore protein variants, enable higher eukaryotic organisms, with only a limited number of genes, to have the requisite complexity and diversity at the proteome level. Multiple alternative isoforms from one gene often share common segments of sequences. However, many protein databases only include a limited number of isoforms to keep minimal redundancy. As a result, the database search might not identify a target protein even with high quality tandem MS data and accurate intact precursor ion mass. We computationally predicted an exhaustive list of putative isoforms of Aspergillus flavus proteins from 20 371 expressed sequence tags to investigate whether an alternative splicing protein database can assign a greater proportion of mass spectrometry data. The newly constructed AS database provided 9807 new alternatively spliced variants in addition to 12 832 previously annotated proteins. The searches of the existing tandem MS spectra data set using the AS database identified 29 new proteins encoded by 26 genes. Nine fungal genes appeared to have multiple protein isoforms. In addition to the discovery of splice variants, AS database also showed potential to improve genome annotation. In summary, the introduction of an alternative splicing database helps identify more proteins and unveils more information about a proteome.