Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data

Simultaneous Mapping of DNA Binding and Nucleosome Positioning with SpLiT-ChEC

The organization of chromatin - including the positions of nucleosomes and the binding of other proteins to DNA - helps define transcriptional profiles in eukaryotic organisms. While techniques like ChIP-Seq and MNase-Seq can map protein-DNA and nucleosome localization separately, assays designed to simultaneously capture nucleosome positions and protein-DNA interactions can produce a detailed picture of the chromatin landscape. Most assays that monitor chromatin organization and protein binding rely on antibodies, which often exhibit nonspecific binding, and/or the addition of bulky adducts to the DNA-binding protein being studied, which can affect their expression and activity. Here, we describe SpyCatcher Linked Targeting of Chromatin Endogenous Cleavage (SpLiT-ChEC), where a 13-amino acid SpyTag peptide, appended to a protein of interest, serves as a highly-specific targeting moiety for in situ enzymatic digestion. The SpyTag/SpyCatcher system forms a covalent bond, linking the target protein and a co-expressed MNase-SpyCatcher fusion construct. SpyTagged proteins are expressed from endogenous loci, whereas MNase-SpyCatcher expression is induced immediately before harvesting cultures. MNase is activated with high concentrations of calcium, which primarily digests DNA near target protein binding sites. By sequencing the DNA fragments released by targeted MNase digestion, we found that this method recovers information on protein binding and proximal nucleosome positioning. SpLiT-ChEC provides precise temporal control that we anticipate can be used to monitor chromatin under various conditions and at distinct points in the cell cycle.

Gene expression profiling of sepsis-associated acute kidney injury

Sepsis accounts for more than 50% of all acute kidney injury (AKI) cases, and the combination of sepsis and AKI increases the risk of mortality from sepsis alone. However, to the best of our knowledge, the specific mechanism by which sepsis causes AKI has not yet been fully elucidated, and there is no targeted therapy for sepsis-associated AKI (SA-AKI). The present study investigated gene expression profiles using RNA sequencing (RNA-Seq) and bioinformatics analyses to assess the function of differentially expressed genes (DEGs) and the molecular mechanisms relevant to the prognosis of SA-AKI. From the bioinformatics analysis, 2,256 downregulated and 3,146 upregulated genes were identified (false discovery rate <0.1 and fold-change >2). Gene Ontology analysis revealed that the genes were enriched in cellular metabolic processes, cell death and apoptosis. The enriched transcription factors were v-rel reticuloendotheliosis viral oncogene homolog A and signaling transducer and activator of transcription 3. The enriched microRNAs (miRNAs or miRs) among the DEGs were miR-30e, miR-181a, miR-340, miR-466d and miR-466l. Furthermore, the enriched pathways included toll-like receptor signaling, nod-like receptor signaling and the Janus kinase/STAT signaling pathway. In conclusion, the present study identified certain prognosis-related genes, transcription factors, miRNAs and pathways by analyzing gene expression profiles of SA-AKI using RNA-Seq, which provides some basis for future experimental studies.

Genomics, molecular and evolutionary perspective of NAC transcription factors

NAC (NAM, ATAF1,2, and CUC2) transcription factors are one of the largest transcription factor families found in the plants and are involved in diverse developmental and signalling events. Despite the availability of comprehensive genomic information from diverse plant species, the basic genomic, biochemical, and evolutionary details of NAC TFs have not been established. Therefore, NAC TFs family proteins from 160 plant species were analyzed in the current study. Study revealed, Brassica napus (410) encodes highest number and Klebsormidium flaccidum (3) encodes the lowest number of TFs. The study further revealed the presence of NAC TF in the Charophyte algae K. flaccidum. On average, the monocot plants encode higher number (141.20) of NAC TFs compared to the eudicots (125.04), gymnosperm (75), and bryophytes (22.66). Furthermore, our analysis revealed that several NAC TFs are membrane bound and contain monopartite, bipartite, and multipartite nuclear localization signals. NAC TFs were also found to encode several novel chimeric proteins and regulate a complex interactome network. In addition to the presence of NAC domain, several NAC proteins were found to encode other functional signature motifs as well. Relative expression analysis of NAC TFs in A. thaliana revealed root tissue treated with urea and ammonia showed higher level of expression and leaf tissues treated with urea showed lower level of expression. The synonymous codon usage is absent in the NAC TFs and it appears that they have evolved from orthologous ancestors and undergone vivid duplications to give rise to paralogous NAC TFs. The presence of novel chimeric NAC TFs are of particular interest and the presence of chimeric NAC domain with other functional signature motifs in the NAC TF might encode novel functional properties in the plants.

Effect of promoter, promoter mutation and enhancer on transgene expression mediated by episomal vectors in transfected HEK293, Chang liver and primary cells

The episomal vector cannot integrate into the host cell chromosome, which has no potential risk in gene therapy. However, the low level of transgene expression driven by episomal vectors needs to be solved. In this study, we investigated the effects of enhancers, promoters and promoter variants on transgene expression levels driven by episomal vectors in HEK293, Chang liver and primary cells. Results showed that all eight cis-acting elements used could increase transfection efficiency and transient eGFP expression in transfected HEK293 and Chang liver cells. In stably transfected mammalian cells, the elongation factor-1 alpha (EF-1α) promoter and mutant-404 showed high and stable transgene expression. The mechanisms might be related to the type and quantity of transcription factor regulatory elements. Moreover, quantitative reverse transcription polymerase chain reaction analysis showed that mRNA expression levels were not directly proportional to protein expression levels. Furthermore, the EF-1α promoter conferred high transgene expression levels in primary cells, and the plasmid was also present in the episomal state. Taken together, these results provided valuable information for improving transgene expression with episomal vectors in mammalian cells.

Improved linking of motifs to their TFs using domain information

Motivation

A central aim of molecular biology is to identify mechanisms of transcriptional regulation. Transcription factors (TFs), which are DNA-binding proteins, are highly involved in these processes, thus a crucial information is to know where TFs interact with DNA and to be aware of the TFs’ DNA-binding motifs. For that reason, computational tools exist that link DNA-binding motifs to TFs either without sequence information or based on TF-associated sequences, e.g. identified via a chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiment.

In this paper, we present MASSIF, a novel method to improve the performance of existing tools that link motifs to TFs relying on TF-associated sequences. MASSIF is based on the idea that a DNA-binding motif, which is correctly linked to a TF, should be assigned to a DNA-binding domain (DBD) similar to that of the mapped TF. Because DNA-binding motifs are in general not linked to DBDs, it is not possible to compare the DBD of a TF and the motif directly. Instead we created a DBD collection, which consist of TFs with a known DBD and an associated motif. This collection enables us to evaluate how likely it is that a linked motif and a TF of interest are associated to the same DBD. We named this similarity measure domain score, and represent it as a P-value. We developed two different ways to improve the performance of existing tools that link motifs to TFs based on TF-associated sequences: (i) using meta-analysis to combine P-values from one or several of these tools with the P-value of the domain score and (ii) filter unlikely motifs based on the domain score.

Results

We demonstrate the functionality of MASSIF on several human ChIP-seq datasets, using either motifs from the HOCOMOCO database or de novo identified ones as input motifs. In addition, we show that both variants of our method improve the performance of tools that link motifs to TFs based on TF-associated sequences significantly independent of the considered DBD type.

Availability and implementation

MASSIF is freely available online at https://github.com/SchulzLab/MASSIF.

Supplementary information

Supplementary data are available at Bioinformatics online.

The spatial binding model of the pioneer factor Oct4 with its target genes during cell reprogramming

Understanding the target regulation between pioneer factor and its binding genes is crucial for improving the efficiency of TF-mediated reprogramming. Oct4 as the only one factor that cannot be substituted by other POU members, it is urgent need to develop a quantitative model for describing the spatial binding pattern with its target genes. The dynamic profiles of pioneer factor Oct4-binding showed that the major wave occurs at the intermediate stage of cell reprogramming (from day 7 to day 15), and the promoter is the preferred targeting regions. The Oct4-binding distributions perform significant chromosome bias. The overall enrichment on chromosome 1–11 is higher than that on the others. The dramatic event of TF-mediated reprogramming is mainly concentrated on autosomes. We also found that the spatial binding ability of Oct4 binding can be represented quantitatively by using three parameters of peaks (height, width and distance). The dynamic changes of Oct4-binding demonstrated that the width play more important roles in regulating expression of target genes. At last, a multivariate linear regression was introduced to establish the spatial binding model of the Oct4-binding. The evaluation results confirmed that the height and width is positively correlated with the gene expression. And the additive interaction terms of height and width can better optimize the model performance than the multiplicative terms. The best average coefficients of determination of improved model achieved to 81.38%. Our study will provide new insights into the cooperative regulation of spatial binding pattern of pioneer factors in cell reprogramming.

A k-mer grammar analysis to uncover maize regulatory architecture

BACKGROUND

Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.

RESULTS

We developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features - k-mers. To do this, we borrowed two approaches from the field of natural language processing: (1) "bag-of-words" which is commonly used for differentially weighting key words in tasks like sentiment analyses, and (2) a vector-space model using word2vec (vector-k-mers), that captures semantic and linguistic relationships between words. We built "bag-of-k-mers" and "vector-k-mers" models that distinguish between regulatory and non-regulatory regions with an average accuracy above 90%. Our "bag-of-k-mers" achieved higher overall accuracy, while the "vector-k-mers" models were more useful in highlighting key groups of sequences within the regulatory regions.

CONCLUSIONS

These models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.

Integrated analysis of motif activity and gene expression changes of transcription factors

The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of causal transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than existing methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.