Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists.

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

Querying Recombination Junctions of Replication-Competent Adeno-Associated Viruses in Gene Therapy Vector Preparations with Single Molecule, Real-Time Sequencing.

Clinical-grade preparations of adeno-associated virus (AAV) vectors used for gene therapy typically undergo a series of diagnostics to determine titer, purity, homogeneity, and the presence of DNA contaminants. One type of contaminant that remains poorly investigated is replication-competent (rc)AAVs. rcAAVs form through recombination of DNA originating from production materials, yielding intact, replicative, and potentially infectious virus-like virions. They can be detected through the serial passaging of lysates from cells transduced by AAV vectors in the presence of wildtype adenovirus. Cellular lysates from the last passage are subjected to qPCR to detect the presence of the rep gene. Unfortunately, the method cannot be used to query the diversity of recombination events, nor can qPCR provide insights into how rcAAVs arise. Thus, the formation of rcAAVs through errant recombination events between ITR-flanked gene of interest (GOI) constructs and expression constructs carrying the rep-cap genes is poorly described. We have used single molecule, real-time sequencing (SMRT) to analyze virus-like genomes expanded from rcAAV-positive vector preparations. We present evidence that sequence-independent and non-homologous recombination between the ITR-bearing transgene and the rep/cap plasmid occurs under several events and rcAAVs spawn from diverse clones.

Direct ITR-to-ITR Nanopore Sequencing of AAV Vector Genomes.

Recombinant adeno-associated viruses (rAAVs) are currently the most prominently investigated vector platform for human gene therapy. The rAAV capsid serves as a potent and efficient vehicle for delivering genetic payloads into the host cell, while the vector genome determines the function and effectiveness of these biotherapies. However, current production schemes yield vectors that may consist of heterogeneous populations, compromising their potencies. The development of next-generation sequencing methods within the past few years have helped investigators profile the diversity and relative abundances of heterogenous species in vector preparations. Specifically, long-read sequencing methods, like single molecule real-time (SMRT) sequencing, have been used to uncover truncations, chimeric genomes, and inverted terminal repeat (ITR) mutations in vectors. Unfortunately, these sequencing platforms may be inaccessible to investigators with limited resources, require large amounts of input material, or may require long wait times for sequencing and analyses. Recent advances with nanopore sequencing have helped to bridge the gap for quick and relatively inexpensive long-read sequencing needs. However, their limitations and sample biases are not well-defined for sequencing rAAV. In this study, we explored the capacity for nanopore sequencing to directly interrogate rAAV content to obtain full-length resolution of encapsidated genomes. We found that the nanopore platform can cover the entirety of rAAV genomes from ITR to ITR without the need for pre-fragmentation. However, the accuracy for base calling was low, resulting in a high degree of miscalled bases and false indels. These false indels led to read-length compression; thus, assessing heterogeneity based on read length is not advisable with current nanopore technologies. Nonetheless, nanopore sequencing was able to correctly identify truncation hotspots in single-strand and self-complementary vectors similar to SMRT sequencing. In summary, nanopore sequencing can serve as a rapid and low-cost alternative for proofing AAV vectors.

Human and Insect Cell-Produced Recombinant Adeno-Associated Viruses Show Differences in Genome Heterogeneity.

In the past two decades, adeno-associated virus (AAV) vector manufacturing has made remarkable advancements to meet large-scale production demands for preclinical and clinical trials. In addition, AAV vectors have been extensively studied for their safety and efficacy. In particular, the presence of empty AAV capsids and particles containing "inaccurate" vector genomes in preparations has been a subject of concern. Several methods exist to separate empty capsids from full particles; but thus far, no single technique can produce vectors that are free of empty or partial (non-unit length) capsids. Unfortunately, the exact genome compositions of full, intermediate, and empty capsids remain largely unknown. In this work, we used AAV-genome population sequencing to explore the compositions of DNase-resistant, encapsidated vector genomes produced by two common production pipelines: plasmid transfection in human embryonic kidney cells (pTx/HEK293) and baculovirus expression vectors in Spodoptera frugiperda insect cells (rBV/Sf9). Intriguingly, our results show that vectors originating from the same construct design that were manufactured by the rBV/Sf9 system produced a higher degree of truncated and unresolved species than those generated by pTx/HEK293 production. We also demonstrate that empty particles purified by cesium chloride gradient ultracentrifugation are not truly empty but are instead packaged with genomes composed of a single truncated and/or unresolved inverted terminal repeat (ITR). Our data suggest that the frequency of these "mutated" ITRs correlates with the abundance of inaccurate genomes in all fractions. These surprising findings shed new light on vector efficacy, safety, and how clinical vectors should be quantified and evaluated.

Rational engineering of a functional CpG-free ITR for AAV gene therapy.

Inverted terminal repeats (ITRs) are the only wild-type components retained in the genome of adeno-associated virus (AAV) vectors. To determine whether ITR modification is a viable approach for AAV vector engineering, we rationally deleted all CpG motifs in the ITR and examined whether CpG elimination compromises AAV-vector production and transduction. Modified ITRs were stable in the plasmid and maintained the CpG-free nature in purified vectors. Replacing the wild-type ITR with the CpG-free ITR did not affect vector genome encapsidation. However, the vector yield was decreased by approximately 3-fold due to reduced vector genome replication. To study the biological potency, we made micro-dystrophin (µDys) AAV vectors carrying either the wild-type ITR or the CpG-free ITR. We delivered the CpG-free µDys vector to one side of the tibialis anterior muscle of dystrophin-null mdx mice and the wild-type µDys vector to the contralateral side. Evaluation at four months after injection showed no difference in the vector genome copy number, microdystrophin expression, and muscle histology and force. Our results suggest that the complete elimination of the CpG motif in the ITR does not affect the biological activity of the AAV vector. CpG-free ITRs could be useful in engineering therapeutic AAV vectors.

AAV-Genome Population Sequencing of Vectors Packaging CRISPR Components Reveals Design-Influenced Heterogeneity.

The gene therapy field has been galvanized by two technologies that have revolutionized treating genetic diseases: vectors based on adeno-associated viruses (AAVs), and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas gene-editing tools. When combined into one platform, these safe and broadly tropic biotherapies can be engineered to target any region in the human genome to correct genetic flaws. Unfortunately, few investigations into the design compatibility of CRISPR components in AAV vectors exist. Using AAV-genome population sequencing (AAV-GPseq), we previously found that self-complementary AAV vector designs with strong DNA secondary structures can cause a high degree of truncation events, impacting production and vector efficacy. We hypothesized that the single-guide RNA (sgRNA) scaffold, which contains several loop regions, may also compromise vector integrity. We have therefore advanced the AAV-GPseq method to also interrogate single-strand AAV vectors to investigate whether vector genomes carrying Cas9-sgRNA cassettes can cause truncation events. We found that on their own, sgRNA sequences do not produce a high degree of truncation events. However, we demonstrate that vector genome designs that carry dual sgRNA expression cassettes in tail-to-tail configurations lead to truncations. In addition, we revealed that heterogeneity in inverted terminal repeat sequences in the form of regional deletions inherent to certain AAV vector plasmids can be interrogated.

Performance evaluation for MOTIFSIM.

BACKGROUND: Previous studies show various results obtained from different motif finders for an identical dataset. This is largely due to the fact that these tools use different strategies and possess unique features for discovering the motifs. Hence, using multiple tools and methods has been suggested because the motifs commonly reported by them are more likely to be biologically significant. RESULTS: The common significant motifs from multiple tools can be obtained by using MOTIFSIM tool. In this work, we evaluated the performance of MOTIFSIM in three aspects. First, we compared the pair-wise comparison technique of MOTIFSIM with the un-gapped Smith-Waterman algorithm and four common distance metrics: average Kullback-Leibler, average log-likelihood ratio, Chi-Square distance, and Pearson Correlation Coefficient. Second, we compared the performance of MOTIFSIM with RSAT Matrix-clustering tool for motif clustering. Lastly, we evaluated the performances of nineteen motif finders and the reliability of MOTIFSIM for identifying the common significant motifs from multiple tools. CONCLUSIONS: The pair-wise comparison results reveal that MOTIFSIM attains better performance than the un-gapped Smith-Waterman algorithm and four distance metrics. The clustering results also demonstrate that MOTIFSIM achieves similar or even better performance than RSAT Matrix-clustering. Furthermore, the findings indicate if the motif detection does not require a special tool for detecting a specific type of motif then using multiple motif finders and combining with MOTIFSIM for obtaining the common significant motifs, it improved the results for DNA motif detection.

MODSIDE: a motif discovery pipeline and similarity detector.

BACKGROUND: Previous studies demonstrate the usefulness of using multiple tools and methods for improving the accuracy of motif detection. Over the past years, numerous motif discovery pipelines have been developed. However, they typically report only the top ranked results either from individual motif finders or from a combination of multiple tools and algorithms. RESULTS: Here we present MODSIDE, a motif discovery pipeline and similarity detector. The pipeline integrated four de novo motif finders: ChIPMunk, MEME, Weeder, and XXmotif. It also incorporated a motif similarity detection tool MOTIFSIM. MODSIDE was designed for delivering not only the predictive results from individual motif finders but also the comparison results for multiple tools. The results include the common significant motifs from multiple tools, the motifs detected by some tools but not by others, and the best matches for each motif in the motif collection of multiple tools. MODSIDE also possesses other useful features for merging similar motifs and clustering motifs into motif trees. CONCLUSIONS: We evaluated MODSIDE and its adopted motif finders on 16 benchmark datasets. The statistical results demonstrate MODSIDE achieves better accuracy than individual motif finders. We also compared MODSIDE with two popular motif discovery pipelines: MEME-ChIP and RSAT peak-motifs. The comparison results reveal MODSIDE attains similar performance as RSAT peak-motifs but better accuracy than MEME-ChIP. In addition, MODSIDE is able to deliver various comparison results that are not offered by MEME-ChIP, RSAT peak-motifs, and other existing motif discovery pipelines.

MOTIFSIM 2.1: An Enhanced Software Platform for Detecting Similarity in Multiple DNA Motif Data Sets.

Finding binding site motifs plays an important role in bioinformatics as it reveals the transcription factors that control the gene expression. The development for motif finders has flourished in the past years with many tools have been introduced to the research community. Although these tools possess exceptional features for detecting motifs, they report different results for an identical data set. Hence, using multiple tools is recommended because motifs reported by several tools are likely biologically significant. However, the results from multiple tools need to be compared for obtaining common significant motifs. MOTIFSIM web tool and command-line tool were developed for this purpose. In this work, we present several technical improvements as well as additional features to further support the motif analysis in our new release MOTIFSIM 2.1.

Cloud-based MOTIFSIM: Detecting Similarity in Large DNA Motif Data Sets.

We developed the cloud-based MOTIFSIM on Amazon Web Services (AWS) cloud. The tool is an extended version from our web-based tool version 2.0, which was developed based on a novel algorithm for detecting similarity in multiple DNA motif data sets. This cloud-based version further allows researchers to exploit the computing resources available from AWS to detect similarity in multiple large-scale DNA motif data sets resulting from the next-generation sequencing technology. The tool is highly scalable with expandable AWS.

MOTIFSIM: A web tool for detecting similarity in multiple DNA motif datasets.

Currently, there are a number of motif detection tools available that possess unique functionality. These tools often report different motifs, and therefore use of multiple tools is generally advised since common motifs reported by multiple tools are more likely to be biologically significant. However, results produced by these different tools need to be compared and existing similarity detection tools only allow comparison between two data sets. Here, we describe a motif similarity detection tool (MOTIFSIM) possessing a web-based, user-friendly interface that is capable of detecting similarity from multiple DNA motif data sets concurrently. Results can either be viewed online or downloaded. Users may also download and run MOTIFSIM as a command-line tool in stand-alone mode. The web tool, along with its command-line version, user manuals, and source codes, are freely available at http://biogrid-head.engr.uconn.edu/motifsim/.

Cross-disciplinary detection and analysis of network motifs.

The detection of network motifs has recently become an important part of network analysis across all disciplines. In this work, we detected and analyzed network motifs from undirected and directed networks of several different disciplines, including biological network, social network, ecological network, as well as other networks such as airlines, power grid, and co-purchase of political books networks. Our analysis revealed that undirected networks are similar at the basic three and four nodes, while the analysis of directed networks revealed the distinction between networks of different disciplines. The study showed that larger motifs contained the three-node motif as a subgraph. Topological analysis revealed that similar networks have similar small motifs, but as the motif size increases, differences arise. Pearson correlation coefficient showed strong positive relationship between some undirected networks but inverse relationship between some directed networks. The study suggests that the three-node motif is a building block of larger motifs. It also suggests that undirected networks share similar low-level structures. Moreover, similar networks share similar small motifs, but larger motifs define the unique structure of individuals. Pearson correlation coefficient suggests that protein structure networks, dolphin social network, and co-authorships in network science belong to a superfamily. In addition, yeast protein-protein interaction network, primary school contact network, Zachary's karate club network, and co-purchase of political books network can be classified into a superfamily.

Current innovations and future challenges of network motif detection.

Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs. This work analyzes 11 network motif detection tools and algorithms. Detailed comparisons and insightful directions for using these tools and algorithms are discussed. Key aspects of network motif detection are investigated. Network motif types and common network motifs as well as their biological functions are discussed. Applications of network motifs are also presented. Finally, the challenges, future improvements and future research directions for network motif detection are also discussed.

A survey of motif finding Web tools for detecting binding site motifs in ChIP-Seq data.

ChIP-Seq (chromatin immunoprecipitation sequencing) has provided the advantage for finding motifs as ChIP-Seq experiments narrow down the motif finding to binding site locations. Recent motif finding tools facilitate the motif detection by providing user-friendly Web interface. In this work, we reviewed nine motif finding Web tools that are capable for detecting binding site motifs in ChIP-Seq data. We showed each motif finding Web tool has its own advantages for detecting motifs that other tools may not discover. We recommended the users to use multiple motif finding Web tools that implement different algorithms for obtaining significant motifs, overlapping resemble motifs, and non-overlapping motifs. Finally, we provided our suggestions for future development of motif finding Web tool that better assists researchers for finding motifs in ChIP-Seq data.

Gene Expression and Gene Ontology Enrichment Analysis for H3K4me3 and H3K4me1 in Mouse Liver and Mouse Embryonic Stem Cell Using ChIP-Seq and RNA-Seq.

Recent study has identified the cis-regulatory elements in the mouse genome as well as their genomic localizations. Recent discoveries have shown the enrichment of H3 lysine 4 trimethylation (H3K4me3) binding as an active promoter and the presence of H3 lysine 4 monomethylation (H3K4me1) outside promoter regions as a mark for an enhancer. In this work, we further identified highly expressed genes by H3K4me3 mark or by both H3K4me3 and H3K4me1 marks in mouse liver using ChIP-Seq and RNA-Seq. We found that in mice, the liver carries embryonic stem cell-related functions while the embryonic stem cell also carries liver-related functions. We also identified novel genes in RNA-Seq experiments for mouse liver and for mouse embryonic stem cells. These genes are not currently in the Ensemble gene database at NCBI.