Erwin L. van Dijk

The nucleoid-associated protein H-NS is a bacterial xenogeneic silencer responsible for preventing costly expression of genes acquired through horizontal gene transfer. H-NS silences several Salmonella Pathogenicity Islands (SPIs) essential for host infection. The stochastic expression of SPI-1 is required for invasion of host epithelial cells but complicates investigation of factors involved in SPI-1 chromatin structure and regulation. We performed functional genomics on sorted Salmonella populations expressing SPI-1 or not, to characterize how SPI-1 activation affects chromatin composition, DNA conformation, gene expression and SPI-1 subcellular localization. High H-NS occupancy co-exists with low spurious transcriptional activity and low RNA polymerase occupancy at silent SPIs. Upon SPI-1 activation, remodeling of H-NS occupancy defines a new chromatin landscape, which together with the master SPI-1 regulator HilD, facilitates transcription of SPI-1 genes. SPI-1 activation promotes formation of Transcription Induced Domains accompanied by repositioning SPI-1 close to the nucleoid periphery. We present a model for tightly regulated chromatin remodeling that minimizes the cost of pathogenicity island activation

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNAs such as plant microRNAs (miRNAs) carry a 2′-O-methyl (2′-OMe) modification at their 3′ terminal nucleotide. This modification adds another level of difficulty as it inhibits 3′ adapter ligation. We previously demonstrated that modified versions of the “TruSeq (TS)” protocol have less bias and an improved detection of 2′-OMe RNAs. Here we describe in detail protocol “TS5,” which showed the best overall performance. We also provide guidelines for bioinformatics analysis of the sequencing data.

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as plant microRNAs (miRNAs) carry a 2'-O-methyl (2'-OMe) modification at their 3' terminal nucleotide. This modification adds another difficulty as it inhibits 3' adapter ligation. We previously demonstrated that modified versions of the 'TruSeq (TS)' protocol have less bias and an improved detection of 2'-OMe RNAs. Here we describe in detail protocol 'TS5', which showed the best overall performance. TS5 can be followed either using homemade reagents or reagents from the TS kit, with equal performance.

Next-generation sequencing technologies have revolutionized the study of small RNAs (sRNAs) on a genome-wide scale. However, classical sRNA library preparation methods introduce serious bias, mainly during adapter ligation steps. Several types of sRNA including plant microRNAs (miRNA), piwi-interacting RNAs (piRNA) in insects, nematodes and mammals, and small interfering RNAs (siRNA) in insects and plants contain a 2’-O-methyl (2’-OMe) modification at their 3′ terminal nucleotide. This inhibits 3′ adapter ligation and makes library preparation particularly challenging. To reduce bias, the NEBNext kit (New England Biolabs) uses polyethylene glycol (PEG), the NEXTflex V2 kit (BIOO Scientific) uses both randomised adapters and PEG, and the novel SMARTer (Clontech) and CATS (Diagenode) kits avoid ligation altogether. Here we compared these methods with Illumina’s classical TruSeq protocol regarding the detection of normal and 2’ OMe RNAs. In addition, we modified the TruSeq and NEXTflex protocols to identify conditions that improve performance. Among the five kits tested with their respective standard protocols, the SMARTer and CATS kits had the lowest levels of bias but also had a strong formation of side products, and as a result performed relatively poorly with biological samples; NEXTflex detected the largest numbers of different miRNAs. The use of a novel type of randomised adapters called MidRand-Like (MRL) adapters and PEG improved the detection of 2’ OMe RNAs both in the TruSeq as well as in the NEXTflex protocol. While it is commonly accepted that biases in sRNA library preparation protocols are mainly due to adapter ligation steps, the ligation-free protocols were not the best performing methods. Our modified versions of the TruSeq and NEXTflex protocols provide an improved tool for the study of 2’ OMe RNAs.

The 15 sibling species of the Paramecium aurelia cryptic species complex emerged after a whole genome duplication that occurred tens of millions of years ago. Given extensive knowledge of the genetics and epigenetics of Paramecium acquired over the last century, this species complex offers a uniquely powerful system to investigate the consequences of whole genome duplication in a unicellular eukaryote as well as the genetic and epigenetic mechanisms that drive speciation. High quality Paramecium gene models are important for research using this system. The major aim of the work reported here was to build an improved gene annotation pipeline for the Paramecium lineage. We generated oriented RNA-Seq transcriptome data across the sexual process of autogamy for the model species Paramecium tetraurelia. We determined, for the first time in a ciliate, candidate P. tetraurelia transcription start sites using an adapted Cap-Seq protocol. We developed TrUC, multi-threaded Perl software that in conjunction with TopHat mapping of RNA-Seq data to a reference genome, predicts transcription units for the annotation pipeline. We used EuGene software to combine annotation evidence. The high quality gene structural annotations obtained for P. tetraurelia were used as evidence to improve published annotations for 3 other Paramecium species. The RNA-Seq data were also used for differential gene expression analysis, providing a gene expression atlas that is more sensitive than the previously established microarray resource. We have developed a gene annotation pipeline tailored for the compact genomes and tiny introns of Paramecium species. A novel component of this pipeline, TrUC, predicts transcription units using Cap-Seq and oriented RNA-Seq data. TrUC could prove useful beyond Paramecium, especially in the case of high gene density. Accurate predictions of 3′ and 5′ UTR will be particularly valuable for studies of gene expression (e.g. nucleosome positioning, identification of cis regulatory motifs). The P. tetraurelia improved transcriptome resource, gene annotations for P. tetraurelia, P. biaurelia, P. sexaurelia and P. caudatum, and Paramecium-trained EuGene configuration are available through ParameciumDB ( http://paramecium.i2bc.paris-saclay.fr ). TrUC software is freely distributed under a GNU GPL v3 licence ( https://github.com/oarnaiz/TrUC ).