Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data

The expansion of human T-bethighCD21low B cells is T cell dependent

Accumulation of human CD21low B cells in peripheral blood is a hallmark of chronic activation of the adaptive immune system in certain infections and autoimmune disorders. The molecular pathways underpinning the development, function, and fate of these CD21low B cells remain incompletely characterized. Here, combined transcriptomic and chromatin accessibility analyses supported a prominent role for the transcription factor T-bet in the transcriptional regulation of these T-bethighCD21low B cells. Investigating essential signals for generating these cells in vitro established that B cell receptor (BCR)/interferon-γ receptor (IFNγR) costimulation induced the highest levels of T-bet expression and enabled their differentiation during cell cultures with Toll-like receptor (TLR) ligand or CD40L/interleukin-21 (IL-21) stimulation. Low proportions of CD21low B cells in peripheral blood from patients with defined inborn errors of immunity (IEI), because of mutations affecting canonical NF-κB, CD40, and IL-21 receptor or IL-12/IFNγ/IFNγ receptor/signal transducer and activator of transcription 1 (STAT1) signaling, substantiated the essential roles of BCR- and certain T cell–derived signals in the in vivo expansion of T-bethighCD21low B cells. Disturbed TLR signaling due to MyD88 or IRAK4 deficiency was not associated with reduced CD21low B cell proportions. The expansion of human T-bethighCD21low B cells correlated with an expansion of circulating T follicular helper 1 (cTfh1) and T peripheral helper (Tph) cells, identifying potential sources of CD40L, IL-21, and IFNγ signals. Thus, we identified important pathways to target autoreactive T-bethighCD21low B cells in human autoimmune conditions, where these cells are linked to pathogenesis and disease progression.

Extracellular Matrix Production by Mesenchymal Stromal Cells in Hydrogels Facilitates Cell Spreading and Is Inhibited by FGF-2

In native tissues, the interaction between cells and the surrounding extracellular matrix (ECM) is reciprocal, as cells not only receive signals from the ECM but also actively remodel it through secretion of cell-derived ECM. However, very little is known about the reciprocal interaction between cells and their secreted ECM within synthetic biomaterials that mimic the ECM for use in engineering of tissues for regenerative medicine or as tissue models. Here, poly(ethylene glycol) (PEG) hydrogels with fully defined biomaterial properties are used to investigate the emerging role of cell-derived ECM on culture outcomes. It is shown that human mesenchymal stromal cells (MSCs) secrete ECM proteins into the pericellular space early after encapsulation and that, even in the absence of material-presented cell adhesion motifs, cell-derived fibronectin enables cell spreading. Then, it is investigated how different culture conditions influence MSC ECM expression in hydrogels. Most strikingly, it is found by RNA sequencing that the fibroblast growth factor 2 (FGF-2) changes ECM gene expression and, in particular, decreases the expression of structural ECM components including fibrillar collagens. In summary, this work shows that cell-derived ECM is a guiding cue in 3D hydrogels and that FGF-2 is a potentially important ECM regulator within bioengineered cell and tissue systems.

Mesenchymal stromal cell activation by breast cancer secretomes in bioengineered 3D microenvironments

This study shows the activation of tumour-associated mesenchymal stromal cells by breast cancer secretomes in bioengineered 3D microenvironments using comprehensive multiomics analysis methods.

Mesenchymal stromal cells (MSCs) are key contributors of the tumour microenvironment and are known to promote cancer progression through reciprocal communication with cancer cells, but how they become activated is not fully understood. Here, we investigate how breast cancer cells from different stages of the metastatic cascade convert MSCs into tumour-associated MSCs (TA-MSCs) using unbiased, global approaches. Using mass spectrometry, we compared the secretomes of MCF-7 cells, invasive MDA-MB-231 cells, and sublines isolated from bone, lung, and brain metastases and identified ECM and exosome components associated with invasion and organ-specific metastasis. Next, we used synthetic hydrogels to investigate how these different secretomes activate MSCs in bioengineered 3D microenvironments. Using kinase activity profiling and RNA sequencing, we found that only MDA-MB-231 breast cancer secretomes convert MSCs into TA-MSCs, resulting in an immunomodulatory phenotype that was particularly prominent in response to bone-tropic cancer cells. We have investigated paracrine signalling from breast cancer cells to TA-MSCs in 3D, which may highlight new potential targets for anticancer therapy approaches aimed at targeting tumour stroma.

Notch-inducing hydrogels reveal a perivascular switch of mesenchymal stem cell fate

The fate of mesenchymal stem cells (MSCs) in the perivascular niche, as well as factors controlling their fate, is poorly understood. Here, we study MSCs in the perivascular microenvironment of endothelial capillaries by modifying a synthetic 3D biomimetic poly(ethylene glycol) (PEG)-hydrogel system in vitro We show that MSCs together with endothelial cells form micro-capillary networks specifically in soft PEG hydrogels. Transcriptome analysis of human MSCs isolated from engineered capillaries shows a prominent switch in extracellular matrix (ECM) production. We demonstrate that the ECM phenotypic switch of MSCs can be recapitulated in the absence of endothelial cells by functionalizing PEG hydrogels with the Notch-activator Jagged1. Moreover, transient culture of MSCs in Notch-inducing microenvironments reveals the reversibility of this ECM switch. These findings provide insight into the perivascular commitment of MSCs by use of engineered niche-mimicking synthetic hydrogels.

A splicing isoform of TEAD4 attenuates the Hippo-YAP signalling to inhibit tumour proliferation

Aberrant splicing is frequently found in cancer, yet the biological consequences of such alterations are mostly undefined. Here we report that the Hippo–YAP signalling, a key pathway that regulates cell proliferation and organ size, is under control of a splicing switch. We show that TEAD4, the transcription factor that mediates Hippo–YAP signalling, undergoes alternative splicing facilitated by the tumour suppressor RBM4, producing a truncated isoform, TEAD4-S, which lacks an N-terminal DNA-binding domain, but maintains YAP interaction domain. TEAD4-S is located in both the nucleus and cytoplasm, acting as a dominant negative isoform to YAP activity. Consistently, TEAD4-S is reduced in cancer cells, and its re-expression suppresses cancer cell proliferation and migration, inhibiting tumour growth in xenograft mouse models. Furthermore, TEAD4-S is reduced in human cancers, and patients with elevated TEAD4-S levels have improved survival. Altogether, these data reveal a splicing switch that serves to fine tune the Hippo–YAP pathway.

The Hippo/Yap signalling pathway is found deregulated in several cancers. Here, the authors uncover an additional mechanism of YAP regulation that occurs via alternately spliced isoform of TEAD4, which acts as a dominant negative regulator of YAP-TEAD signalling.

A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites

MOTIVATION

The position-weight matrix (PWM) is a useful representation of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated from a small number of representative TFBS sequences. However, because the PWM probability model assumes independence between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from non-binding-sites that have similar sequence content. Since the local three-dimensional DNA structure ('shape') is a determinant of TF binding specificity and since DNA shape has a significant sequence-dependence, we combined DNA shape-derived features into a TF-generalized regulatory score and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites.

RESULTS

We compared a traditional PWM model to a model that combines the PWM with a DNA shape feature-based regulatory potential score, for accuracy in detecting binding sites for 75 vertebrate transcription factors. The PWM+shape model was more accurate than the PWM-only model, for 45% of TFs tested, with no significant loss of accuracy for the remaining TFs.

AVAILABILITY AND IMPLEMENTATION

The shape-based model is available as an open-source R package at that is archived on the GitHub software repository at https://github.com/ramseylab/regshape/.

CONTACT

stephen.ramsey@oregonstate.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Relationship between the immunohistochemistry of the primary tumour and 18F-FDG-PET/CT at recurrence in patients with well-differentiated thyroid carcinoma

PURPOSE

In patients thyroidectomized for well-differentiated thyroid carcinoma, the correlation between thyroglobulin (Tg) plasma level and F-fluoro-2-deoxy-D-glucose (F-FDG)-PET results is still a matter of debate. We evaluated whether the immunochemical profile of the primary tumour could be used as a predictor of positivity on F-FDG-PET/computed tomography (CT) when recurrence is confirmed.

MATERIALS AND METHODS

A total of 26 patients (eight men, 18 women; 51±16 years old) were included. All of the patients had a histologically proven recurrence or a high level of Tg during follow-up and underwent a F-FDG-PET/CT following two intramuscular injections of rhTSH. The F-FDG-PET/CT scans were blindly analysed by three nuclear physicians. The results of the PET scans were classified as true positive, false positive or false negative. Nine antibodies were used for the immunochemical analysis (tissue microarray: hexokinase I, II and III; Tg; vascular endothelial growth factor; and glucose transporter type 1, CD31, CD68 and sodium iodide symporter).

RESULTS

The PET scans were positive for 15 patients and negative for 11 patients. Hexokinase I was expressed in nine of the 26 primary tumours (7/26 for the isoforms). No single molecule expressed in the primary tumours was correlated with the F-FDG-PET/CT results. There was no association of antibody overexpression (clustering) in the primary tumours with the F-FDG-PET/CT results of the recurrences.

CONCLUSION

In a larger series, we failed to confirm the preliminary results of Hooft and colleagues. This study did not allow for the determination of a single marker expressed in the primary tumours that would be predictive of F-FDG-PET/CT positivity when recurrence is suspected. Therefore, at present, immunochemistry does not appear to be a definitive tool for predicting the results of F-FDG-PET/CT in cases of recurrence.

A Bayesian model for pooling gene expression studies that incorporates co-regulation information

Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. We assume that each study contains two experimental conditions: a treatment and control. We note that there exist environmental conditions for which genes that are supposed to be transcribed together lose their operon structure, and that our model is best carried out for known operon structures.

Advantages and limitations of current network inference methods

Network inference, which is the reconstruction of biological networks from high-throughput data, can provide valuable information about the regulation of gene expression in cells. However, it is an underdetermined problem, as the number of interactions that can be inferred exceeds the number of independent measurements. Different state-of-the-art tools for network inference use specific assumptions and simplifications to deal with underdetermination, and these influence the inferences. The outcome of network inference therefore varies between tools and can be highly complementary. Here we categorize the available tools according to the strategies that they use to deal with the problem of underdetermination. Such categorization allows an insight into why a certain tool is more appropriate for the specific research question or data set at hand.

Disruption of HOX activity leads to cell death that can be enhanced by the interference of iron uptake in malignant B cells

The HOX genes encode a family of transcription factors that are dysregulated in several malignancies and have been implicated in oncogenesis and cancer cell survival. Disruption of HOX protein function using the peptide HXR9 has shown anti-tumor effects against melanoma, lung cancer and renal cancer. In this report, we evaluated the expression of all 39 HOX genes in a panel of six malignant B-cell lines, including multiple myeloma cells and found different levels of expression of HOX family members suggesting that they also have a role in malignant B-cell survival. We show that disrupting HOX function using the peptide HXR9 induces significant cytotoxicity in the entire panel of cell lines. Importantly, we found that the cytotoxic effects of HXR9 can be enhanced by combining it with ch128.1Av, an antibody-avidin fusion protein specific for the human transferrin receptor 1 (CD71). Iron starvation induced by the fusion protein contributes to the enhanced effect and involves, at least in part, the induction of a caspase-independent pathway. These results show the relevance of HOX proteins in malignant B-cell survival and suggest that our therapeutic strategy may be effective in the treatment of incurable B-cell malignancies such as multiple myeloma.

Inferring the transcriptional network of Bacillus subtilis

The adaptation of bacteria to the vigorous environmental changes they undergo is crucial to their survival. They achieve this adaptation partly via intricate regulation of the transcription of their genes. In this study, we infer the transcriptional network of the Gram-positive model organism, Bacillus subtilis. We use a data integration workflow, exploiting both motif and expression data, towards the generation of condition-dependent transcriptional modules. In building the motif data, we rely on both known and predicted information. Known motifs were derived from DBTBS, while predicted motifs were generated by a de novo motif detection method that utilizes comparative genomics. The expression data consists of a compendium of microarrays across different platforms. Our results indicate that a considerable part of the B. subtilis network is yet undiscovered; we could predict 417 new regulatory interactions for known regulators and 453 interactions for yet uncharacterized regulators. The regulators in our network showed a preference for regulating modules in certain environmental conditions. Also, substantial condition-dependent intra-operonic regulation seems to take place. Global regulators seem to require functional flexibility to attain their roles by acting as both activators and repressors.

Probabilistic inference of transcription factor binding from multiple data sources

An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple data sources. Test data set, a web tool, source codes and supplementary data are available at: http://www.probtf.org.

Characterization of relationships between transcriptional units and operon structures in Bacillus subtilis and Escherichia coli

BACKGROUND

Operon structures play an important role in transcriptional regulation in prokaryotes. However, there have been fewer studies on complicated operon structures in which the transcriptional units vary with changing environmental conditions. Information about such complicated operons is helpful for predicting and analyzing operon structures, as well as understanding gene functions and transcriptional regulation.

RESULTS

We systematically analyzed the experimentally verified transcriptional units (TUs) in Bacillus subtilis and Escherichia coli obtained from ODB and RegulonDB. To understand the relationships between TUs and operons, we defined a new classification system for adjacent gene pairs, divided into three groups according to the level of gene co-regulation: operon pairs (OP) belong to the same TU, sub-operon pairs (SOP) that are at the transcriptional boundaries within an operon, and non-operon pairs (NOP) belonging to different operons. Consequently, we found that the levels of gene co-regulation was correlated to intergenic distances and gene expression levels. Additional analysis revealed that they were also correlated to the levels of conservation across about 200 prokaryotic genomes. Most interestingly, we found that functional associations in SOPs were more observed in the environmental and genetic information processes.

CONCLUSION

Complicated operon structures were correlated with genome organization and gene expression profiles. Such intricately regulated operons allow functional differences depending on environmental conditions. These regulatory mechanisms are helpful in accommodating the variety of changes that happen around the cell. In addition, such differences may play an important role in the evolution of gene order across genomes.

Machine learning in bioinformatics

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

BIOREL: the benchmark resource to estimate the relevance of the gene networks

The progress of high-throughput methodologies in functional genomics has lead to the development of statistical procedures to infer gene networks from various types of high-throughput data. However, due to the lack of common standards, the biological significance of the results of the different studies is hard to compare. To overcome this problem we propose a benchmark procedure and have developed a web resource (BIOREL), which is useful for estimating the biological relevance of any genetic network by integrating different sources of biological information. The associations of each gene from the network are classified as biologically relevant or not. The proportion of genes in the network classified as "relevant" is used as the overall network relevance score. Employing synthetic data we demonstrated that such a score ranks the networks fairly in respect to the relevance level. Using BIOREL as the benchmark resource we compared the quality of experimental and theoretically predicted protein interaction data.

Scoring functions for transcription factor binding site prediction

Background

Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations.

Results

Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome.

Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study.

Conclusion

LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.