Predicting variation of DNA shape preferences in protein-DNA interaction in cancer cells with a new biophysical model

DNA shape readout is an important mechanism of target site recognition by transcription factors, in addition to the sequence readout. Several models of transcription factor-DNA binding which consider DNA shape have been developed in recent years. We present a new biophysical model of protein-DNA interaction by considering the DNA shape features, which is based on a neighbour dinucleotide dependency model BayesPI2. The parameters of the new model are restricted to a subspace spanned by the 2-mer DNA shape features, which allowing a biophysical interpretation of the new parameters as position-dependent preferences towards certain values of the features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across cancer cell lines and cellular conditions. We find evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after treatment with steroids. The new model is useful for elucidating finer details of transcription factor-DNA interaction. It may be used to improve the prediction of cancer mutation effects in the future

TF2Network : predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

A gene regulatory network (GRN) is a collection of regulatory interactions between transcription factors (TFs) and their target genes. GRNs control different biological processes and have been instrumental to understand the organization and complexity of gene regulation. Although various experimental methods have been used to map GRNs in Arabidop-sis thaliana, their limited throughput combined with the large number of TFs makes that for many genes our knowledge about regulating TFs is incomplete. We introduce TF2Network, a tool that exploits the vast amount of TF binding site information and enables the delineation of GRNs by detecting potential regulators for a set of co-expressed or functionally related genes. Validation using two experimental benchmarks reveals that TF2Network predicts the correct regulator in 75-92% of the test sets. Furthermore, our tool is robust to noise in the input gene sets, has a low false discovery rate, and shows a better performance to recover correct regulators compared to other plant tools. TF2Network is accessible through a web interface where GRNs are interactively visualized and annotated with various types of experimental functional information. TF2Network was used to perform systematic functional and regulatory gene annotations, identifying new TFs involved in circadian rhythm and stress response

Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features.

Empirical evidence suggests that the malaria parasite Plasmodium falciparum employs a broad range of mechanisms to regulate gene transcription throughout the organism's complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the Plasmodium life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture in Plasmodium transcriptional regulation and suggest that AP2 transcription factors may play a limited regulatory role, perhaps operating in conjunction with epigenetic factors

Novel applications of shotgun phage display

In a shotgun phage display library, theoretically, the entire proteome of a bacterium is represented. Phages displaying specific polypeptides can be isolated by affinity selection, while the corresponding gene remains physically linked to the gene product. The overall objective of the study in this thesis was to explore the shotgun phage display technique in new areas. Initially, it was used to study interactions between Staphylococcus aureus and an in vivo coated biomaterial. It was shown to be well suited for the identification of bacterial proteins that bind to ex vivo central venous catheters. Several known interactions were detected, but it was also found that β2-glycoprotein I (β2-GPI) is deposited on this type of biomaterial – a finding that is of interest both for the adherence of S. aureus, but perhaps also in view of the occurrence of autoantibodies in certain autoimmune diseases. Further, it is of interest to identify the subset of extracellular proteins in a bacterium since they are involved in important functions like pathogenesis and symbiosis. A method that allows for the rapid and general isolation of extracellular proteins is desirable, and may prove particularly useful when applied to bacteria for which the genome sequences are not known. For this purpose, a specialised phage display method was developed to isolate extracellular proteins by virtue of the presence of signal peptides (SS phage display). It was successfully applied to S. aureus and, on a larger scale, to the symbiotic bacterium Bradyrhizobium japonicum. In elaboration of the SS phage display method, an inducible antisense RNA system was incorporated to enable gene silencing of the isolated genes. A tetracycline-regulated promoter was inserted in such a way, that an antisense RNA covering the cloned gene could be expressed. The new element was shown to be compatible with the properties of SS phage display, and to promote gene expression upon induction on both the transcriptional and translational level. However, screening for clones affected by the induction of antisense RNA transcription was unsuccessful, and further developments of the system are required to improve the efficiency of this attractive application

Role of RNA Interference (RNAi) in the Moss Physcomitrella patens

RNA interference (RNAi) is a mechanism that regulates genes by either transcriptional (TGS) or posttranscriptional gene silencing (PTGS), required for genome maintenance and proper development of an organism. Small non-coding RNAs are the key players in RNAi and have been intensively studied in eukaryotes. In plants, several classes of small RNAs with specific sizes and dedicated functions have evolved. The major classes of small RNAs include microRNAs (miRNAs) and small interfering RNAs (siRNAs), which differ in their biogenesis. miRNAs are synthesized from a short hairpin structure while siRNAs are derived from long double-stranded RNAs (dsRNA). Both miRNA and siRNAs control the expression of cognate target RNAs by binding to reverse complementary sequences mediating cleavage or translational inhibition of the target RNA. They also act on the DNA and cause epigenetic changes such as DNA methylation and histone modifications. In the last years, the analysis of plant RNAi pathways was extended to the bryophyte Physcomitrella patens, a non-flowering, non-vascular ancient land plant that diverged from the lineage of seed plants approximately 450 million years ago. Based on a number of characteristic features and its phylogenetic key position in land plant evolution P. patens emerged as a plant model species to address basic as well as applied topics in plant biology. Here we summarize the current knowledge on the role of RNAi in P. patens that shows functional overlap with RNAi pathways from seed plants, and also unique features specific to this species

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.Comment: 54 pages (19 pages main text; 11 Figures; 26 pages of supplementary information). Revised after critical reviews. Accepted for Publication in PLoS ON

CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping.

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination