Regulatory Mechanisms In Cells Research Articles

Identification of Critical Phosphorylation Sites Enhancing Kinase Activity with a Bimodal Fusion Framework

Phosphorylation is an indispensable regulatory mechanism in cells, with specific sites on kinases that can significantly enhance their activity. Although several such critical phosphorylation sites (phos-sites) have been experimentally identified, many more remain to be explored. To date, no computational method exists to systematically identify these critical phos-sites on kinases. In this study, we introduce PhoSiteformer, a transformer-inspired foundational model designed to generate embeddings of phos-sites using phosphorylation mass spectrometry (phos-MS) data. Recognizing the complementary insights offered by protein sequence data and phos-MS data, we developed a classification model, CSPred, which employs a bimodal fusion strategy. CSPred combines embeddings from PhoSiteformer with those from the protein language model ProtT5. Our approach successfully identified 77 critical phos-sites on 58 human kinases. Two of these sites, T517 on PRKG1 and T735 on PRKD3, have been experimentally verified. This study presents the first systematic and computational approach to identify critical phos-sites that enhance kinase activity.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

Combined morpho-physiological, ionomic and transcriptomic analyses reveal adaptive responses of allohexaploid wheat (Triticum aestivum L.) to iron deficiency

BackgroundPlants worldwide are often stressed by low Fe availability around the world, especially in aerobic soils. Therefore, the plant growth, seed yield, and quality of crop species are severely inhibited under Fe deficiency. Fe metabolism in plants is controlled by a series of complex transport, storage, and regulatory mechanisms in cells. Allohexaploid wheat (Triticum aestivum L.) is a staple upland crop species that is highly sensitive to low Fe stresses. Although some studies have been previously conducted on the responses of wheat plants to Fe deficiency, the key mechanisms underlying adaptive responses are still unclear in wheat due to its large and complex genome.ResultsTransmission electron microscopy showed that the chloroplast structure was severely damaged under Fe deficiency. Paraffin sectioning revealed that the division rates of meristematic cells were reduced, and the sizes of elongated cells were diminished. ICP-MS-assisted ionmics analysis showed that low-Fe stress significantly limited the absorption of nutrients, including N, P, K, Ca, Mg, Fe, Mn, Cu, Zn, and B nutrients. High-throughput transcriptome sequencing identified 378 and 2,619 genome-wide differentially expressed genes (DEGs) were identified in the shoots and roots between high-Fe and low-Fe conditions, respectively. These DEGs were mainly involved in the Fe chelator biosynthesis, ion transport, photosynthesis, amino acid metabolism, and protein synthesis. Gene coexpression network diagrams indicated that TaIRT1b-4A, TaNAS2-6D, TaNAS1a-6A, TaNAS1-6B, and TaNAAT1b-1D might function as key regulators in the adaptive responses of wheat plants to Fe deficiency.ConclusionsThese results might help us fully understand the morpho-physiological and molecular responses of wheat plants to low-Fe stress, and provide elite genetic resources for the genetic modification of efficient Fe use.

Phosphorylation analysis of the Hippo-YAP pathway using Phos-tag

Phosphorylation is an essential regulatory mechanism in cells that modifies diverse substrates, such as proteins, carbohydrates, lipids, and nucleotides. Protein phosphorylation regulates function, subcellular localization, and protein-protein interactions. Protein kinases and phosphatases catalyze this reversible mechanism, subsequently influencing signal transduction. The dysregulation of protein phosphorylation leads to many diseases, such as cancer, neurodegenerative diseases, and metabolic diseases. Therefore, analyzing the phosphorylation status and identifying protein phosphorylation sites are critical for elucidating the biological functions of specific phosphorylation events. Unraveling the critical phosphorylation events associated with diseases and specific signaling pathways is promising for drug discovery. To date, highly accurate and sensitive approaches have been developed to detect the phosphorylation status of proteins.In this review, we discuss the application of Phos-tag to elucidate the biological functions of Hippo pathway components, with emphasis on the identification and quantitation of protein phosphorylation under physiological and pathological conditions. SignificanceWe here provide a comprehensive overview of Phos-tag technique-based strategies to identify phosphorylated proteins at the cellular level in the Hippo-YAP pathway that comprises a major driving force for cellular homeostasis. We clarify the links of applying Phos-tag in elucidating the biological functions of the Hippo pathway components with particular attention to the identification and quantitation of protein phosphorylation under physiological and pathological conditions.We believe that our paper will make a significant contribution to the literature because these detailed phosphorylation modifications and functional diversity of the Hippo pathway components in physiological and pathological processes are only beginning to come to the fore, highlighting the potential for discovering new therapeutic targets.Moreover, this line of research can provide further insight into the inextricable link between phos-tag applications as a molecular tool and cellular signaling modality, offering new directions for an integrated research program toward understanding cellular regulation at the molecular level. Given the broad research and practical applications, we believe that this paper will be of interest to the readership of your journal.

Flavonoids Inhibit Cancer by Regulating the Competing Endogenous RNA Network.

Flavonoids are present in a wide range of plants. They have been used in the treatment of cancer, but the mechanism underlying this activity is unclear. In recent years, microRNA (miRNA) and long non-coding RNA (lncRNA) levels have been observed to differ between normal tissues and cancer cells, and both types of RNA have been shown to have a role in tumor treatment. In addition, flavonoids have been proven to regulate miRNAs and LncRNAs in the treatment of cancer. The competing endogenous RNA (ceRNA) network is a complex post-transcriptional regulatory mechanism in cells, in which coding and non-coding RNAs competitively bind miRNAs to regulate messenger RNAs (mRNAs). This review focused on the role of the ceRNA network in the treatment of cancer by flavonoids.

DNA damage in preimplantation embryos and gametes: specification, clinical relevance and repair strategies.

BACKGROUNDDNA damage is a hazard that affects all cells of the body. DNA-damage repair (DDR) mechanisms are in place to repair damage and restore cellular function, as are other damage-induced processes such as apoptosis, autophagy and senescence. The resilience of germ cells and embryos in response to DNA damage is less well studied compared with other cell types. Given that recent studies have described links between embryonic handling techniques and an increased likelihood of disease in post-natal life, an update is needed to summarize the sources of DNA damage in embryos and their capacity to repair it. In addition, numerous recent publications have detailed novel techniques for detecting and repairing DNA damage in embryos. This information is of interest to medical or scientific personnel who wish to obtain undamaged embryos for use in offspring generation by ART.OBJECTIVE AND RATIONALEThis review aims to thoroughly discuss sources of DNA damage in male and female gametes and preimplantation embryos. Special consideration is given to current knowledge and limits in DNA damage detection and screening strategies. Finally, obstacles and future perspectives in clinical diagnosis and treatment (repair) of DNA damaged embryos are discussed.SEARCH METHODSUsing PubMed and Google Scholar until May 2021, a comprehensive search for peer-reviewed original English-language articles was carried out using keywords relevant to the topic with no limits placed on time. Keywords included ‘DNA damage repair’, ‘gametes’, ‘sperm’, ‘oocyte’, ‘zygote’, ‘blastocyst’ and ‘embryo’. References from retrieved articles were also used to obtain additional articles. Literature on the sources and consequences of DNA damage on germ cells and embryos was also searched. Additional papers cited by primary references were included. Results from our own studies were included where relevant.OUTCOMESDNA damage in gametes and embryos can differ greatly based on the source and severity. This damage affects the development of the embryo and can lead to long-term health effects on offspring. DDR mechanisms can repair damage to a certain extent, but the factors that play a role in this process are numerous and altogether not well characterized. In this review, we describe the multifactorial origin of DNA damage in male and female gametes and in the embryo, and suggest screening strategies for the selection of healthy gametes and embryos. Furthermore, possible therapeutic solutions to decrease the frequency of DNA damaged gametes and embryos and eventually to repair DNA and increase mitochondrial quality in embryos before their implantation is discussed.WIDER IMPLICATIONSUnderstanding DNA damage in gametes and embryos is essential for the improvement of techniques that could enhance embryo implantation and pregnancy success. While our knowledge about DNA damage factors and regulatory mechanisms in cells has advanced greatly, the number of feasible practical techniques to avoid or repair damaged embryos remains scarce. Our intention is therefore to focus on strategies to obtain embryos with as little DNA damage as possible, which will impact reproductive biology research with particular significance for reproductive clinicians and embryologists.

Chemical proteomic profiling reveals protein interactors of the alarmones diadenosine triphosphate and tetraphosphate

The nucleotides diadenosine triphosphate (Ap3A) and diadenosine tetraphosphate (Ap4A) are formed in prokaryotic and eukaryotic cells. Since their concentrations increase significantly upon cellular stress, they are considered to be alarmones triggering stress adaptive processes. However, their cellular roles remain elusive. To elucidate the proteome-wide interactome of Ap3A and Ap4A and thereby gain insights into their cellular roles, we herein report the development of photoaffinity-labeling probes and their employment in chemical proteomics. We demonstrate that the identified ApnA interactors are involved in many fundamental cellular processes including carboxylic acid and nucleotide metabolism, gene expression, various regulatory processes and cellular response mechanisms and only around half of them are known nucleotide interactors. Our results highlight common functions of these ApnAs across the domains of life, but also identify those that are different for Ap3A or Ap4A. This study provides a rich source for further functional studies of these nucleotides and depicts useful tools for characterization of their regulatory mechanisms in cells.

A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms.

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

Bioinformatics Technologies in Autophagy Research.

Autophagy is an important and dynamic biological process, and provides an ideal application scenario for bioinformatics to develop new data resources, algorithms, tools and computational or mathematic models for a better understanding of complex regulatory mechanisms in cells. In the past decade, great efforts have been taken on the development of numerous bioinformatics technologies in autophagy research, and a comprehensive summarization of these important studies will provide a timely reference for both biologists and bioinformaticians who are working in the field of autophagy. In this book chapter, we first introduce bioinformatics technologies that allow sequence analysis of autophagy genes. We briefly summarize the mainstream algorithms in sequence alignment for the identification of homologous autophagy genes and emphasize the computational identification of potential orthologs and paralogs, as well as the evolutionary analysis of autophagy gene families. Three methods for the recognition of autophagy-related sequence motifs are introduced: regular expression, position-specific scoring matrix (PSSM) and group-based prediction system (GPS). Second, we carefully summarize recent progress in the analysis of autophagy-related omics data. We discuss how two major types of computational methods, enrichment analysis and network analysis can be used to analyze omics data, including transcriptomics, non-coding RNAomics, epigenomics, proteomics, phosphoproteomics and protein lysine modification (PLM) omics data. Finally, we summarize several important autophagy-related data resources, including both autophagy gene databases and autophagy-related RNA databases. We anticipate that more useful bioinformatics technologies will be developed and play an ever-more-important role in the analysis of autophagy.

Single-Peptide TR-FRET Detection Platform for Cysteine-Specific Post-Translational Modifications.

Post-translational modifications (PTMs) are one of the most important regulatory mechanisms in cells, and they play key roles in cell signaling both in health and disease. PTM catalyzing enzymes have become significant drug targets, and therefore, tremendous interest has been focused on the development of broad-scale assays to monitor several different PTMs with a single detection platform. Most of the current methodologies suffer from low throughput or rely on antibody recognition, increasing the assay costs, and decreasing the multifunctionality of the assay. Thus, we have developed a sensitive time-resolved Förster resonance energy transfer (TR-FRET) detection method for PTMs of cysteine residues using a single-peptide approach performed in a 384-well format. In the developed assay, the enzyme-specific biotinylated substrate peptide is post-translationally modified at the cysteine residue, preventing the subsequent thiol coupling with a reactive AlexaFluor 680 acceptor dye. In the absence of enzymatic activity, increase in the TR-FRET signal between the biotin-bound Eu(III)-labeled streptavidin donor and the cysteine-coupled AlexaFluor 680 acceptor dye is observed. We demonstrate the detection concept with cysteine modifying S-nitrosylation and ADP-ribosylation reactions using a chemical nitric oxide donor S-nitrosoglutathione and enzymatic ADP-ribosyltransferase PtxS1-subunit of pertussis toxin, respectively. As a proof of concept, three peptide substrates derived from the small GTPase K-Ras and the inhibitory α-subunit of the heterotrimeric G-protein Gαi showed expected functionality in both chemical and enzymatic assays. Measurements yielded signal-to-background ratios of 28.7, 33.0, and 8.7 between the modified and the nonmodified substrates for the three peptides in the S-nitrosylation assay, 5.8 in the NAD+ hydrolysis assay, and 6.8 in the enzymatic ADP-ribosyltransferase inhibitor dose–response assay. The developed antibody-free assay for cysteine-modifying enzymes provides a detection platform with low nanomolar peptide substrate consumption, and the assay is potentially applicable to investigate various cysteine-modifying enzymes in a high throughput compatible format.

A novel method of gene regulatory network structure inference from gene knock-out expression data

Inferring Gene Regulatory Networks (GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theory-based network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.

Clustering-local-unique-enriched-signals (CLUES) promotes identification of novel regulators of ES cell self-renewal and pluripotency.

BackgroundKey regulators of developmental processes can be prioritized through integrated analysis of ChIP-Seq data of master transcriptional factors (TFs) such as Nanog and Oct4, active histone modifications (HMs) such as H3K4me3 and H3K27ac, and repressive HMs such as H3K27me3. Recent studies show that broad enrichment signals such as super-enhancers and broad H3K4me3 enrichment signals play more dominant roles than short enrichment signals of the master TFs and H3K4me3 in epigenetic regulatory mechanism. Besides the broad enrichment signals, up to ten thousands of short enrichment signals of these TFs and HMs exist in genome. Prioritization of these broad enrichment signals from ChIP-Seq data is a prerequisite for such integrated analysis.ResultsHere, we present a method named Clustering-Local-Unique-Enriched-Signals (CLUES), which uses an adaptive-size-windows strategy to identify enriched regions (ERs) and cluster them into broad enrichment signals. Tested on 62 ENCODE ChIP-Seq datasets of Ctcf and Nrsf, CLUES performs equally well as MACS2 regarding prioritization of ERs with the TF’s motif. Tested on 165 ENCODE ChIP-Seq datasets of H3K4me3, H3K27me3, and H3K36me3, CLUES performs better than existing algorithms on prioritizing broad enrichment signals implicating cell functions influenced by epigenetic regulatory mechanism in cells. Most importantly, CLUES helps to confirm several novel regulators of mouse ES cell self-renewal and pluripotency through integrated analysis of prioritized broad enrichment signals of H3K4me3, H3K27me3, Nanog and Oct4 with the support of a CRISPR/Cas9 negative selection genetic screen.ConclusionsCLUES holds promise for prioritizing broad enrichment signals from ChIP-Seq data. The download site for CLUES is https://github.com/Wuchao1984/CLUESv1.

Chemical proteomics reveals new targets of cysteine sulfinic acid reductase.

Cysteine sulfinic acid or S-sulfinylation is an oxidative post-translational modification (OxiPTM) that is known to be involved in redox-dependent regulation of protein function, but has been historically difficult to analyze biochemically. To facilitate the detection of S-sulfinylated proteins, we demonstrate that a clickable, electrophilic diazene probe (DiaAlk) enables capture and site-centric proteomic analysis of this OxiPTM. Using this workflow, we revealed a striking difference between sulfenic acid (S-sulfenylation) and S-sulfinylation dynamic response to oxidative stress, indicative of different roles for these OxiPTMs in redox regulation. We also identified >55 heretofore unknown protein substrates of the cysteine sulfinic acid reductase, sulfiredoxin (SRX), extending its function well beyond 2-Cys peroxiredoxins (2-Cys PRDX1–4), and offering new insight into the role of this unique oxidoreductase as a central mediator of ROS-associated diseases, particularly cancer. DiaAlk therefore provides a novel tool to profile S-sulfinylated proteins and study their regulatory mechanisms in cells.

Chelatable trace zinc causes low, irreproducible KDAC8 activity

Acetylation is an important regulatory mechanism in cells, and emphasis is being placed on identifying substrates and small molecule modulators of this post-translational modification. However, the reported in vitro activity of the lysine deacetylase KDAC8 is inconsistent across experimental setups, even with the same substrate, complicating progress in the field. We detected trace levels of zinc, a known inhibitor of KDAC8 when present in excess, even in high-quality buffer reagents, at concentrations that are sufficient to significantly inhibit the enzyme under common reaction conditions. We hypothesized that trace zinc in solution could account for the observed variability in KDAC8 activity. We demonstrate that addition of chelators, including BSA, EDTA, and citrate, and/or the use of a phosphate-based buffer instead of the more common tris-based buffer, eliminates the inhibition from low levels of zinc as well as the dependence of specific activity on enzyme concentration. This results in high KDAC8 activity that is consistent across buffer systems, even using low concentrations of enzyme. We report conditions that are suitable for several assays to increase both enzyme activity and reproducibility. Our results have significant implications for approaches used to identify substrates and small molecule modulators of KDAC8 and interpretation of existing data.

Chemical proteomics reveals ADP-ribosylation of small GTPases during oxidative stress.

ADP-ribosylation is a post-translational modification that is known to be involved in cellular homeostasis and stress but has been challenging to analyze biochemically. To facilitate the detection of ADP-ribosylated proteins, we show that an alkyne-adenosine analog, N6-propargyl adenosine (N6pA), is metabolically incorporated in mammalian cells and enables fluorescence detection and proteomic analysis of ADP-ribosylated proteins. Notably, our analysis of N6pA-labeled proteins that are upregulated by oxidative stress revealed differential ADP-ribosylation of small GTPases. We discovered that oxidative stress induced ADP-ribosylation of Hras on Cys181 and Cys184 in the C-terminal hypervariable region, which are normally S-fatty-acylated. Downstream Hras signaling is impaired by ADP-ribosylation during oxidative stress, but is rescued by ADP-ribosyltransferase inhibitors. Our study demonstrates that ADP-ribosylation of small GTPases not only is mediated by bacterial toxins but is endogenously regulated in mammalian cells. N6pA provides a useful tool to characterize ADP-ribosylated proteins and their regulatory mechanisms in cells.

A crosslinker-based identification of redox relay targets

Thiol-based redox control is among the most important mechanisms for maintaining cellular redox homeostasis, with essential participation of cysteine thiols of oxidoreductases. To explore cellular redox regulatory networks, direct interactions among active cysteine thiols of oxidoreductases and their targets must be clarified. We applied a recently described thiol–ene crosslinking-based strategy, named divinyl sulfone (DVSF) method, enabling identification of new potential redox relay partners of the cytosolic oxidoreductases thioredoxin (TXN) and thioredoxin domain containing 17 (TXNDC17). Applying multiple methods, including classical substrate-trapping techniques, will increase understanding of redox regulatory mechanisms in cells.

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.

Reconstruction of gene networks using prior knowledge.

BackgroundReconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms.ResultsIn this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC.ConclusionPriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0233-4) contains supplementary material, which is available to authorized users.

DNA supercoiling affects Stability of Genetic Switch

DNA supercoiling has been known as a common transcriptional regulatory mechanism in cells facing environmental changes and coping with stress. Though it is acknowledged that the level of DNA supercoiling varies at different locations along the genome and changes during the cell cycle, little is known about how localized changes in DNA supercoiling, under enzyme-generated DNA tension, perturb critical protein-DNA interactions. Here we investigated how DNA supercoiling affects stability of the lambda repressor-mediated DNA loop that acts as a genetic switch between lysogeny (quiescence) and lysis (virulence).We performed single molecule magnetic tweezers measurements to record lambda repressor-mediated DNA loop formation and breakdown and to measure the stability of the loop as a function of negative supercoiling, loop size and DNA tension at physiological repressor concentration. The level of negative supercoiling required for loop formation increases with loop size and that, in general, negative supercoiling stabilizes the loop. Since genomic supercoiling depends on the energy level of the cell which is tightly associated with its health status, we propose that the switch to lysis is favored by the destabilization of the lambda repressor-mediated loop that follows loss of DNA supercoiling in suffering cells.View Large Image | View Hi-Res Image | Download PowerPoint Slide