LL-37: Structures, Antimicrobial Activity, and Influence on Amyloid-Related Diseases.

Antimicrobial peptides (AMPs), as well as host defense peptides (HDPs), constitute the first line of defense as part of the innate immune system. Humans are known to express antimicrobial precursor proteins, which are further processed to generate AMPs, including several types of α/ß defensins, histatins, and cathelicidin-derived AMPs like LL37. The broad-spectrum activity of AMPs is crucial to defend against infections caused by pathogenic bacteria, viruses, fungi, and parasites. The emergence of multi-drug resistant pathogenic bacteria is of global concern for public health. The prospects of targeting antibiotic-resistant strains of bacteria with AMPs are of high significance for developing new generations of antimicrobial agents. The 37-residue long LL37, the only cathelicidin family of AMP in humans, has been the major focus for the past few decades of research. The host defense activity of LL37 is likely underscored by its expression throughout the body, spanning from the epithelial cells of various organs-testis, skin, respiratory tract, and gastrointestinal tract-to immune cells. Remarkably, apart from canonical direct killing of pathogenic organisms, LL37 exerts several other host defense activities, including inflammatory response modulation, chemo-attraction, and wound healing and closure at the infected sites. In addition, LL37 and its derived peptides are bestowed with anti-cancer and anti-amyloidogenic properties. In this review article, we aim to develop integrative, mechanistic insight into LL37 and its derived peptides, based on the known biophysical, structural, and functional studies in recent years. We believe that this review will pave the way for future research on the structures, biochemical and biophysical properties, and design of novel LL37-based molecules.

Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease.

Genetic variants associated with complex traits are primarily noncoding, and their effects on gene-regulatory activity remain largely uncharacterized. To address this, we profile epigenomic variation of histone mark H3K27ac across 387 brain, heart, muscle and lung samples from Genotype-Tissue Expression (GTEx). We annotate 282 k active regulatory elements (AREs) with tissue-specific activity patterns. We identify 2,436 sex-biased AREs and 5,397 genetically influenced AREs associated with 130 k genetic variants (haQTLs) across tissues. We integrate genetic and epigenomic variation to provide mechanistic insights for disease-associated loci from 55 genome-wide association studies (GWAS), by revealing candidate tissues of action, driver SNPs and impacted AREs. Lastly, we build ARE-gene linking scores based on genetics (gLink scores) and demonstrate their unique ability to prioritize SNP-ARE-gene circuits. Overall, our epigenomic datasets, computational integration and mechanistic predictions provide valuable resources and important insights for understanding the molecular basis of human diseases/traits such as schizophrenia.

Topological Properties, Spectra Analysis, and Consensus Problems for a Class of Network Models Based on m-Fission Operation.

The network model, especially the network model constructed by network operation, is an important tool to study complex networks. Many topological and dynamic properties of complex networks can be studied in this way. In this article, the m -fission operation is constructed based on the phenomenon of node splitting in the network, which is quite common in complex networks. Many network models, including dual Sierpinski gaskets, are built based on this operation, but it has never been systematically studied. Then, the topological and dynamic properties of the m -fission operation and the corresponding iterative fission network model are studied, and the influence of the operation on the network structure is revealed. Among them, the topological properties of the network include diameter, degree distribution, clustering coefficient, average distance, and modularity. By studying these properties, it can be concluded that the iterative fission network is a fractal homogeneous network with high clustering and high community characteristics. Since the dynamic properties are closely related to the spectrum of the Laplacian matrix corresponding to the network, the iterative relation of the spectrum in operation is studied, and the complete solution of the spectrum of the iterative fission network is obtained. Based on the above results, we calculate the analytical expressions of the characteristic quantities related to the dynamic properties on the network, including the Kirchhoff index and the average of hitting times. Finally, due to the close connection between the network model and the system, we further analyzed the consensus problems on the system corresponding to the network, including convergence rate, delay robustness, first-order noise coherence, and second-order noise coherence.

An Improved Rapidly-Exploring Random Trees Algorithm Combining Parent Point Priority Determination Strategy and Real-Time Optimization Strategy for Path Planning.

In order to solve the problems of long path planning time and large number of redundant points in the rapidly-exploring random trees algorithm, this paper proposed an improved algorithm based on the parent point priority determination strategy and the real-time optimization strategy to optimize the rapidly-exploring random trees algorithm. First, in order to shorten the path-planning time, the parent point is determined before generating a new point, which eliminates the complicated process of traversing the random tree to search the parent point when generating a new point. Second, a real-time optimization strategy is combined, whose core idea is to compare the distance of a new point, its parent point, and two ancestor points to the target point when a new point is generated, choosing the new point that is helpful for the growth of the random tree to reduce the number of redundant points. Simulation results of 3-dimensional path planning showed that the success rate of the proposed algorithm, which combines the strategy of parent point priority determination and the strategy of real-time optimization, was close to 100%. Compared with the rapidly-exploring random trees algorithm, the number of points was reduced by more than 93.25%, the path planning time was reduced by more than 91.49%, and the path length was reduced by more than 7.88%. The IRB1410 manipulator was used to build a test platform in a laboratory environment. The path obtained by the proposed algorithm enables the manipulator to safely avoid obstacles to reach the target point. The conclusion can be made that the proposed strategy has a better performance on optimizing the success rate, the number of points, the planning time, and the path length.

High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies.

Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major technical challenge. Here, we develop a high-throughput strategy to design, screen, and optimize 5' UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identify naturally occurring 5' UTRs with high translation efficiencies and use this information with in silico genetic algorithms to generate synthetic 5' UTRs. A total of ~12,000 5' UTRs are then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identify three synthetic 5' UTRs that outperform commonly used non-viral gene therapy plasmids in expressing protein payloads. In summary, we demonstrate that high-throughput screening of 5' UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.

Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization.

Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features.

A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS).

Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks.

An AR-ERG transcriptional signature defined by long-range chromatin interactomes in prostate cancer cells.

The aberrant activities of transcription factors such as the androgen receptor (AR) underpin prostate cancer development. While the AR cis-regulation has been extensively studied in prostate cancer, information pertaining to the spatial architecture of the AR transcriptional circuitry remains limited. In this paper, we propose a novel framework to profile long-range chromatin interactions associated with AR and its collaborative transcription factor, erythroblast transformation-specific related gene (ERG), using chromatin interaction analysis by paired-end tag (ChIA-PET). We identified ERG-associated long-range chromatin interactions as a cooperative component in the AR-associated chromatin interactome, acting in concert to achieve coordinated regulation of a subset of AR target genes. Through multifaceted functional data analysis, we found that AR-ERG interaction hub regions are characterized by distinct functional signatures, including bidirectional transcription and cotranscription factor binding. In addition, cancer-associated long noncoding RNAs were found to be connected near protein-coding genes through AR-ERG looping. Finally, we found strong enrichment of prostate cancer genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) at AR-ERG co-binding sites participating in chromatin interactions and gene regulation, suggesting GWAS target genes identified from chromatin looping data provide more biologically relevant findings than using the nearest gene approach. Taken together, our results revealed an AR-ERG-centric higher-order chromatin structure that drives coordinated gene expression in prostate cancer progression and the identification of potential target genes for therapeutic intervention.

Spectral analysis for weighted iterated q-triangulation networks.

Deterministic weighted networks have been widely used to model real-world complex systems. In this paper, we study the weighted iterated q-triangulation networks, which are generated by iteration operation F(⋅). We add q(q∈N+) new nodes on each old edge and connect them with two endpoints of the old edge. At the same time, the newly linked edges are given weight factor r(0

Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci.

To assess the impact of genetic variation in regulatory loci on human health, we constructed a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, which is defined as random transitions between fully methylated and unmethylated states of DNA. The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provided a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated genetic variation.

[Stellate ganglion catheter retention with discontinuous block on efficacy and safety in the treatment of sudden deafness].

OBJECTIVE: To investigate effect and safty evaluation of stellate ganglion catheter retention with discontinuous block on sudden deafness. METHOD: One hundred and twenty-six patiens with sudden monaural deafness were randomly divided into Catheterp and block and control groups with 42 cases in each group. All patients' throats were given conventional blood activating drugs, hormone and hyperbaric oxygen therapy. stellate ganglion puncture retained catheter were administrated to the patients in catheter group followed by ropivacaine block 1 times/day, block group stellate ganglion puncture and ropivacaine block 1 times/day. The patients in control group were only received routine comprehensive treatment. Patients in both catheter group and block groups were treated by hyperbaric oxygen therapy after the block treatment. Curative effects of three groups were observed. The patients' satisfaction, heart rate, the chages of blood pressure before and after the block, detachment of tubes, and adverse drug reaction were recorded. RESULT: The effect of the treatment in both catheter group, block group was better than in control group (85.7%, 37 cases); 83.3%, 35 cases) vs 64.3%, 27 cases, P < 0.05). The satisfactory rate in the patients in catheter group was significantly higher than block group (83.3%, 35 cases vs 61.9%, 26 cases, P < 0.05). The heart rate and the blood pressure before and 5 minutes after catheterization in catheter group and block groupwere changed obviously. Moreover, no adverse drug reaction and detachment of tubes were observed. CONCLUSION: It is a safe and effective administration of stellate ganglion catheter retention with interrupted ropivacaine block.

Integrative Analysis of CRISPR/Cas9 Target Sites in the Human HBB Gene.

Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) system has emerged as a powerful customizable artificial nuclease to facilitate precise genetic correction for tissue regeneration and isogenic disease modeling. However, previous studies reported substantial off-target activities of CRISPR system in human cells, and the enormous putative off-target sites are labor-intensive to be validated experimentally, thus motivating bioinformatics methods for rational design of CRISPR system and prediction of its potential off-target effects. Here, we describe an integrative analytical process to identify specific CRISPR target sites in the human ß-globin gene (HBB) and predict their off-target effects. Our method includes off-target analysis in both coding and noncoding regions, which was neglected by previous studies. It was found that the CRISPR target sites in the introns have fewer off-target sites in the coding regions than those in the exons. Remarkably, target sites containing certain transcriptional factor motif have enriched binding sites of relevant transcriptional factor in their off-target sets. We also found that the intron sites have fewer SNPs, which leads to less variation of CRISPR efficiency in different individuals during clinical applications. Our studies provide a standard analytical procedure to select specific CRISPR targets for genetic correction.

Integrative analysis of 111 reference human epigenomes.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

The Brm-HDAC3-Erm repressor complex suppresses dedifferentiation in Drosophila type II neuroblast lineages.

The control of self-renewal and differentiation of neural stem and progenitor cells is a crucial issue in stem cell and cancer biology. Drosophila type II neuroblast lineages are prone to developing impaired neuroblast homeostasis if the limited self-renewing potential of intermediate neural progenitors (INPs) is unrestrained. Here, we demonstrate that Drosophila SWI/SNF chromatin remodeling Brahma (Brm) complex functions cooperatively with another chromatin remodeling factor, Histone deacetylase 3 (HDAC3) to suppress the formation of ectopic type II neuroblasts. We show that multiple components of the Brm complex and HDAC3 physically associate with Earmuff (Erm), a type II-specific transcription factor that prevents dedifferentiation of INPs into neuroblasts. Consistently, the predicted Erm-binding motif is present in most of known binding loci of Brm. Furthermore, brm and hdac3 genetically interact with erm to prevent type II neuroblast overgrowth. Thus, the Brm-HDAC3-Erm repressor complex suppresses dedifferentiation of INPs back into type II neuroblasts. DOI: http://dx.doi.org/10.7554/eLife.01906.001.

3D chromosome modeling with semi-definite programming and Hi-C data.

For a long period of time, scientists studied genomes while assuming they are linear. Recently, chromosome conformation capture (3C)-based technologies, such as Hi-C, have been developed that provide the loci contact frequencies among loci pairs in a genome-wide scale. The technology unveiled that two far-apart loci can interact in the tested genome. It indicated that the tested genome forms a three-dimensional (3D) chromosomal structure within the nucleus. With the available Hi-C data, our next challenge is to model the 3D chromosomal structure from the 3C-derived data computationally. This article presents a deterministic method called ChromSDE, which applies semi-definite programming techniques to find the best structure fitting the observed data and uses golden section search to find the correct parameter for converting the contact frequency to spatial distance. Further, we develop a measure called consensus index to indicate if the Hi-C data corresponds to a single structure or a mixture of structures. To the best of our knowledge, ChromSDE is the only method that can guarantee recovering the correct structure in the noise-free case. In addition, we prove that the parameter of conversion from contact frequency to spatial distance will change under different resolutions theoretically and empirically. Using simulation data and real Hi-C data, we showed that ChromSDE is much more accurate and robust than existing methods. Finally, we demonstrated that interesting biological findings can be uncovered from our predicted 3D structure.

Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.

Although de novo motifs can be discovered through mining over-represented sequence patterns, this approach misses some real motifs and generates many false positives. To improve accuracy, one solution is to consider some additional binding features (i.e., position preference and sequence rank preference). This information is usually required from the user. This article presents a de novo motif discovery algorithm called SEME (sampling with expectation maximization for motif elicitation), which uses pure probabilistic mixture model to model the motif's binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position, and sequence rank preferences without asking for any prior knowledge from the user. SEME is both efficient and accurate thanks to two important techniques: the variable motif length extension and importance sampling. Using 75 large-scale synthetic datasets, 32 metazoan compendium benchmark datasets, and 164 chromatin immunoprecipitation sequencing (ChIP-Seq) libraries, we demonstrated the superior performance of SEME over existing programs in finding transcription factor (TF) binding sites. SEME is further applied to a more difficult problem of finding the co-regulated TF (coTF) motifs in 15 ChIP-Seq libraries. It identified significantly more correct coTF motifs and, at the same time, predicted coTF motifs with better matching to the known motifs. Finally, we show that the learned position and sequence rank preferences of each coTF reveals potential interaction mechanisms between the primary TF and the coTF within these sites. Some of these findings were further validated by the ChIP-Seq experiments of the coTFs. The application is available online.

Telomerase directly regulates NF-κB-dependent transcription.

Although elongation of telomeres is thought to be the prime function of reactivated telomerase in cancers, this activity alone does not account for all of the properties that telomerase reactivation attributes to human cancer cells. Here, we uncover a link between telomerase and NF-κB, a master regulator of inflammation. We observe that while blocking NF-κB signalling can inhibit effects of telomerase overexpression on processes relevant to transformation, increasing NF-κB activity can functionally substitute for reduced telomerase activity. Telomerase directly regulates NF-κB-dependent gene expression by binding to the NF-κB p65 subunit and recruitment to a subset of NF-κB promoters such as those of IL-6 and TNF-α, cytokines that are critical for inflammation and cancer progression. As NF-κB can transcriptionally upregulate telomerase levels, our findings suggest that a feed-forward regulation between them could be the key mechanistic basis for the coexistence of chronic inflammation and sustained telomerase activity in human cancers.

Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation.

Higher-order chromosomal organization for transcription regulation is poorly understood in eukaryotes. Using genome-wide Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIA-PET), we mapped long-range chromatin interactions associated with RNA polymerase II in human cells and uncovered widespread promoter-centered intragenic, extragenic, and intergenic interactions. These interactions further aggregated into higher-order clusters, wherein proximal and distal genes were engaged through promoter-promoter interactions. Most genes with promoter-promoter interactions were active and transcribed cooperatively, and some interacting promoters could influence each other implying combinatorial complexity of transcriptional controls. Comparative analyses of different cell lines showed that cell-specific chromatin interactions could provide structural frameworks for cell-specific transcription, and suggested significant enrichment of enhancer-promoter interactions for cell-specific functions. Furthermore, genetically-identified disease-associated noncoding elements were found to be spatially engaged with corresponding genes through long-range interactions. Overall, our study provides insights into transcription regulation by three-dimensional chromatin interactions for both housekeeping and cell-specific genes in human cells.

CENTDIST: discovery of co-associated factors by motif distribution.

Transcription factors (TFs) do not function alone but work together with other TFs (called co-TFs) in a combinatorial fashion to precisely control the transcription of target genes. Mining co-TFs is thus important to understand the mechanism of transcriptional regulation. Although existing methods can identify co-TFs, their accuracy depends heavily on the chosen background model and other parameters such as the enrichment window size and the PWM score cut-off. In this study, we have developed a novel web-based co-motif scanning program called CENTDIST (http://compbio.ddns.comp.nus.edu.sg/~chipseq/centdist/). In comparison to current co-motif scanning programs, CENTDIST does not require the input of any user-specific parameters and background information. Instead, CENTDIST automatically determines the best set of parameters and ranks co-TF motifs based on their distribution around ChIP-seq peaks. We tested CENTDIST on 14 ChIP-seq data sets and found CENTDIST is more accurate than existing methods. In particular, we applied CENTDIST on an Androgen Receptor (AR) ChIP-seq data set from a prostate cancer cell line and correctly predicted all known co-TFs (eight TFs) of AR in the top 20 hits as well as discovering AP4 as a novel co-TF of AR (which was missed by existing methods). Taken together, CENTDIST, which exploits the imbalanced nature of co-TF binding, is a user-friendly, parameter-less and powerful predictive web-based program for understanding the mechanism of transcriptional co-regulation.