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bioRxiv2024

DeepPWM-BindingNet: Unleashing Binding Predictionwith Combined Sequence and PWM Features

A crucial challenge in molecular biology is the prediction of DNA-protein binding interactions, which has applications in the study of gene regulation and genome functionality. In this paper, we present a novel deep-learning framework to predict DNA-protein binding interactions with increased precision and interoperability. Our proposed framework DeepPWM-BindingNet leverages the rich information encoded in Position Weight Matrices (PWMs), which capture the sequence-specific binding preferences of proteins. These PWM-derived features are seamlessly integrated into a hybrid model of convolutional recurrent neural networks (CRNNs) that extracts hierarchical features from DNA sequences and protein structures. The sequential dependencies within the sequences are captured by recurrent layers. By incorporating PWM-derived features, the models interpretability is improved, enabling researchers to learn more about the underlying binding mechanisms. The models capacity to locate crucial binding sites is improved by the incorporation of an attention mechanism that highlights crucial regions. Experiments on diverse DNA-protein interaction datasets demonstrate the proposed approach improves the predictive performance. The proposed model holds significant potential in deciphering intricate DNA-protein interactions, ultimately advancing our comprehension of gene regulation mechanisms.

bioinformatics↗

bioRxiv2024

Clostridium scindens: an endocrine keystone species in the mammalian gut

Clostridium scindens is a keystone human gut microbial taxonomic group that, while low in abundance, has a disproportionate effect on bile acid and steroid metabolism in the mammalian gut. Numerous studies indicate that the two most studied strains of C. scindens (i.e., ATCC 35704 and VPI 12708) are important for a myriad of physiological processes in the host. We focus on both historical and current microbiological and molecular biology work on the Hylemon-Bjorkhem pathway and the steroid-17,20-desmolase pathway that were first discovered in C. scindens. Our most recent analysis now calls into question whether strains currently defined as C. scindens represent two separate taxonomic groups. Future directions include developing genetic tools to further explore the physiological role bile acid and steroid metabolism by strains of C. scindens, and the causal role of these pathways in host physiology and disease.

microbiology↗

bioRxiv2024

Cargo quantification of functionalized DNA origami for therapeutic application

In recent years, notable advances in nanotechnology-based drug delivery have emerged. A particularly promising platform in this field is DNA origami-based nanoparticles, which offer highly programmable surfaces, providing precise control over the nanoscale spacing and stoichiometry of various cargo. These versatile particles are finding diverse applications ranging from basic molecular biology to diagnostics and therapeutics. This growing interest creates the need for effective methods to quantify cargo on DNA origami nanoparticles. Our study consolidates several previously validated methods focusing on gel-based and fluorescence-based techniques, including multiplexed quantification of protein, peptide, and nucleic acid cargo on these nanoparticles. This work may serve as a valuable resource for groups researchers keen on utilizing DNA origami-based nanoparticles in therapeutic applications. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/609963v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1703793org.highwire.dtl.DTLVardef@50b368org.highwire.dtl.DTLVardef@327a3corg.highwire.dtl.DTLVardef@1f732b0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Unraveling the Impact of Gene Length on Kinetic Parameters: Implications in Drug Target selection

Gene expression is a multifaceted process crucial to understanding molecular biology and pharmacology. Our research focuses on elucidating the intricate relationship between gene length and kinetic parameters, such as Si, Kon, Koff, and SKoff, which significantly influence the mean expression levels of genes.Using a two-state stochastic gene expression model implemented in Python, we analyzed single-cell transcriptomics data to predict kinetic parameters for each gene. We classified genes into short and long categories, revealing distinct patterns in the relationship between gene length and these parameters. Our results indicate that burst size plays a critical role in mean expression, highlighting its importance for identifying gene targets that require lower drug doses for therapeutic effects.

bioinformatics↗

bioRxiv2024

CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria

Foundational techniques in molecular biology--such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins--rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

synthetic biology↗

bioRxiv2024

Tunable picoliter-scale dropicle formation using amphiphilic microparticles with patterned hydrophilic patches

Microparticle-templated droplets or dropicles have recently gained interest in the fields of diagnostic immunoassays, single-cell analysis, and digital molecular biology. Amphiphilic particles have been shown to spontaneously capture aqueous droplets within their cavities upon mixing with an immiscible oil phase, where each particle templates a single droplet. Here, we fabricated an amphiphilic microparticle with four discrete hydrophilic patches embedded at the inner corners of a square-shaped hydrophobic outer ring of the particle (4C particle). 3D computational fluid dynamics simulations predicted droplet formation dynamics and differing equilibrium conditions depending on the patterning configuration. Experiments recapitulated equilibrium conditions enabling tunable dropicle configurations with reproducible volumes down to [~]200pL templated by the amphiphilic particles. The dropicle configurations depended predominantly on the size of the hydrophilic patches of the 4C particles. This validated modeling approach can inform the design of dropicles with varying volumes and numbers per particle, which can be harnessed in new amplified bioassays for greater sensitivity, dynamic range, and statistical confidence.

bioengineering↗

bioRxiv2024

Novae: a graph-based foundation model for spatial transcriptomics data

Spatial transcriptomics is advancing molecular biology by providing high-resolution insights into gene expression within the spatial context of tissues. This context is essential for identifying spatial domains, enabling the understanding of micro-environment organizations and their implications for tissue function and disease progression. To improve current model limitations on multiple slides, we have designed Novae (https://github.com/MICS-Lab/novae), a graph-based foundation model that extracts representations of cells within their spatial contexts. Our model was trained on a large dataset of nearly 30 million cells across 18 tissues, allowing Novae to perform zero-shot domain inference across multiple gene panels, tissues, and technologies. Unlike other models, it also natively corrects batch effects and constructs a nested hierarchy of spatial domains. Furthermore, Novae supports various downstream tasks, including spatially variable gene or pathway analysis and spatial domain trajectory analysis. Overall, Novae provides a robust and versatile tool for advancing spatial transcriptomics and its applications in biomedical research.

bioinformatics↗

bioRxiv2024

RCoxNet: deep learning framework for enhanced cancer survival prediction integrating random walk with restart with mutation and clinical data

Cancer poses a significant global health challenge, characterized by a complex disease progression and disrupted growth regulation. A thorough understanding of cellular and molecular biological mechanisms is essential for developing novel treatments and improving the accuracy of patient survival predictions. While prior studies have leveraged gene expression and clinical data to forecast survival outcomes through current machine learning and deep learning approaches, gene mutation data--despite being a widely recognized metric--has rarely been incorporated due to its limited information, inadequate representation of gene relationships, and data sparsity, which negatively affects the robustness, effectiveness, and interpretability of current survival analysis approaches. To overcome the challenges of mutation data sparsity, we propose RCoxNet, a novel deep learning neural network framework that integrates the Random Walk with Restart (RWR) algorithm with a deep learning Cox Proportional Hazards model. By applying this framework to mutation data from cBioportal, our model achieved an average concordance index of 0.62 {+/-} 0.05 across four cancer types, outperforming existing deep neural network models. Additionally, we identified clinical features critical for differentiating between predicted high- and low-risk patients, with the relevance of these features being partially supported by previous studies.

bioinformatics↗

bioRxiv2024

Leveraging Synthetic Virology for the Rapid Engineering of Vesicular Stomatitis Virus (VSV)

Vesicular stomatitis virus (VSV) is a prototype RNA virus that has been instrumental in advancing our understanding of viral molecular biology and has applications in vaccine development, cancer therapy, antiviral screening, and more. Current VSV genome plasmids for purchase or contract virus services provide limited options for modification, restricted to predefined cloning sites and insert locations. Improved methods and tools to engineer VSV will unlock further insights in long-standing virology questions and new opportunities for innovative therapies. Here, we report the design and construction of a full-length VSV genome. The 11,161 base pair synthetic VSV (synVSV) was assembled from four modularized DNA fragments. Following rescue and titration, phenotypic analysis showed no significant differences between natural and synthetic viruses. To demonstrate the utility of a synthetic virology platform, we then engineered VSV with a foreign glycoprotein, a common use case for studying viral entry and developing anti-virals. To show the freedom-of-design afforded by this platform, we then modified the genome of VSV by rearranging the gene order, switching the positions of VSV-P and VSV-M genes. This work represents a significant technical advance, providing a flexible, cost-efficient platform for the rapid construction of VSV genomes, facilitating the development of innovative therapies.

synthetic biology↗

bioRxiv2024

Purification of micrococcal nuclease (MNase) for use in ribosomal profiling of high-salinity extremophiles

Nucleases, i.e. enzymes that catalyze the hydrolysis of phosphodiester bonds in nucleic acids, are essential tools in molecular biology and biotechnology. Staphylococcus aureus nuclease (MNase) is particularly interesting due to its thermostability and Ca2+-dependence, making it the prime choice for applications where nuclease modulation is critical, such as ribosome profiling in bacteria and halophilic archaea. The latter poses a technical and economical challenge: high salt reaction conditions are essential for maintaining ribosome integrity but negatively impact the MNase activity, necessitating using large amounts of nuclease to achieve efficient cleavage. Here, we set out to generate an optimized production protocol for two forms of MNase -- fully processed MNaseA and the 19 amino acid propeptide-containing MNaseB -- and to biochemically benchmark them against a commercial nuclease. Our results show that both MNases are highly active in normal reaction conditions, but MNaseA maintains higher enzymatic activity in high salt concentrations than MNaseB. MNaseA also retains >90% of its activity after multiple freeze-thaw cycles when stored at -80 {degrees}C in a buffer containing 5% glycerol. Importantly, ribosome profiling experiments in the haloarchaeon Haloferax volcanii demonstrated that MNaseA produces ribosome footprints and hallmarks of active translation highly comparable to those obtained with the commercial nuclease, making it a suitable alternative for high-salt ribosome profiling applications. In conclusion, our method can be easily implemented for efficient MNaseA production, thereby providing access to an effective, robust, and cost-efficient alternative to commercial nucleases, as well as facilitating future translation studies into halophilic organisms.

biochemistry↗

bioRxiv2024

Cross-species orthology detection of long non-coding RNAs (lncRNA) through 13 species using genomic and functional annotations.

Long non-coding RNAs (lncRNAs), defined by a length of over 200 nucleotides and limited protein-coding potential, have emerged as key regulators of gene expression. However, their evolutionary conservation and functional roles remain largely unexplored. Comparative genomics, particularly through sequence conservation analysis, offers a promising approach to infer lncRNA functions. Traditional methods focusing on protein-coding genes (PCGs) fall short due to the rapid evolutionary divergence of lncRNA sequences. To address this, a workflow combining syntenic methods and motif analysis via the Mercator- Pecan genome alignment was developed and applied across 13 vertebrate species, from zebrafish to various amniotes and birds. Further analyses to infer functionality revealed co-expression patterns through 17 shared tissues between human and chicken but also functional short-motif enrichment across the 13 species using the LncLOOM tool, exemplified by the human OTX2-AS1 and its counterparts in other species. The study expanded the catalog of conserved lncRNAs, providing insights into their evolutionary conservation and information related to potential functions. The workflow presented serves as a robust tool for investigating lncRNA conservation across species, supporting future research in molecular biology to elucidate the roles of these enigmatic transcripts.

genomics↗

bioRxiv2024

GenomicGapID: Leveraging Spatial Distribution of Conserved Genomic Sites for Broad-Spectrum Microbial Identification

Bacterial detection and identification methods can be broadly classified as either untargeted with expansive taxonomic coverage or targeted with narrow taxonomic focus. Untargeted approaches, such as culture and sequencing, are often time-consuming and/or costly, whereas targeted methods, such as PCR, can offer faster and more cost-effective results but require a priori knowledge of the likely pathogen to select the appropriate assay. GenomicGapID, a novel approach that leverages the spatial distribution of conserved genetic regions across microbial genomes, represents a significant advancement in the field of microbial identification. This technique has the potential to provide the taxonomic breadth of culture and sequencing, while maintaining the speed, simplicity, and cost-effectiveness of PCR. By leveraging the conservation and relative positioning of highly conserved coding regions across different species, GenomicGapID enables the development of universal primer sets that amplify the non-conserved gaps between these regions. This creates a unique electrophoretic signature that facilitates rapid and accurate target agnostic microbial identification. In this study, we apply the principles of GenomicGapID to the critical task of identifying clinical pathogens. We focus on expanding the coverage of a previously developed universal bacterial identification system, which initially targeted the 16s-23s internal transcribed spacer (ITS) region and was capable of discerning 45 pathogens. To enhance this system, we assembled a comprehensive database of 189 clinically relevant bacterial species. We then identified conserved primer binding sites that produce unique amplicon size signatures for each species. While we found that the use of amplicon size signatures alone would require an impractical number of universal primer sets, we demonstrate that this challenge can be effectively mitigated through concurrent melt analysis. Ultimately, we show that just three universal primer sets, guided by the GenomicGapID framework, are sufficient to cover 189 clinical bacterial pathogens, representing a majority of microbes identified in positive cultures in a clinical microbiology setting, with experimental validation of a subset of these pathogens. This study not only enhances the existing universal bacterial identification system but also establishes GenomicGapID as a versatile and powerful tool in microbial diagnostics and beyond with the potential to open new areas of investigation in genomics, with significant implications for molecular biology, clinical practice, and public health.

microbiology↗

bioRxiv2024

Functional Impact of Nth-like DNA glycosylase on Mitochondrial Dynamics

NTH-like DNA glycosylase (NTHL1), a key enzyme in the base excision repair (BER) pathway, has long been considered essential for maintaining both nuclear and mitochondrial genome integrity. In this study, we integrate in vitro biochemical assays, in-cellulo molecular biology experiments, and bioinformatic analyses to investigate the impact of NTHL1 loss on mitochondrial DNA (mtDNA) stability and function. Our findings challenge the conventional view by showing that the loss of NTHL1 confers a positive phenotype. Although NTHL1 deletion in human cells results in a significant accumulation of mtDNA lesions, it is unexpectedly accompanied by a substantial increase in mtDNA copy number (CN), which correlates with elevated oxidative phosphorylation (OXPHOS) protein levels and enhanced mitochondrial respiratory capacity. Additionally, NTHL1-knockout cells exhibit larger mitochondria and increased levels of the mitochondrial biogenesis regulator peroxisome proliferator-activated receptor {gamma} coactivator 1 (PGC1) and the membrane fusion protein Dynamin-like GTPase OPA1. These changes suggest an adaptive response to stress or altered cellular demands, aimed at enhancing mitochondrial function and capacity. Consequently, NTHL1-knockout cells showed resistance to 1-methyl-4-phenylpyridinium (MPP+)-induced mitochondrial stress along with increased phosphorylation of eIF2. Together, this suggests that the loss of NTHL1 may enhance cellular resilience to oxidative stress by activating protective integrated stress response and mitohormesis pathways thereby contributing to cellular survival under adverse conditions. Collectively, our findings position NTHL1 as a key player in mtDNA stability and mitochondrial function, linking its DNA repair activity to the integrated stress response. Its loss triggers a shift from canonical oxidative phosphorylation to stress-adaptive pathways, ultimately enhancing cellular resistance against oxidative stress through the activation of the mitochondrial hormetic response.

cell biology↗

bioRxiv2024

Minimal integrating shuttle vectors for Saccharomyces cerevisiae depleted of restriction sites outside the polylinker region

Many plasmids harbor unnecessary elements that complicate or hinder cloning tasks such as inserting one gene into another for protein domain grafting. In particular, restriction sites may be present in the backbone outside the polylinker region (multiple cloning site; MCS) and thus unavailable for use, and the overall length of a plasmid correlates with poorer ligation efficiency. To address these concerns, there has been a growing interest in minimal plasmids. Here, we describe the design and validation of a collection of six minimal integrating shuttle vectors for genetic manipulation in Saccharomyces cerevisiae. We constructed the plasmids using de novo gene synthesis and consisting only of a yeast selection marker (HIS3, TRP1, LEU2, URA3, natMX6, or KanMX), a bacterial selection marker (Ampicillin resistance), an origin of replication (ORI), and the MCS flanked by M13 forward and reverse sequences. We use truncated variants of these elements where available and eliminated all other sequences typically found in plasmids. The MCS consists of ten unique restriction sites. To our knowledge, at sizes ranging from approximately 2.6 kb to 3.5 kb, these are the smallest shuttle vectors described for yeast. Further, we removed common restriction sites in the open reading frames (ORFs) and terminators, freeing up approximately 30 cut sites in each plasmid. We named our pLS series in accordance with the well-known pRS vectors, which are on average 63% larger: pLS403 (HIS3), pLS404 (TRP1), pLS405 (LEU2), pLS406 (URA3), pLS408 (natMX6), and pLS410 (KanMX). These minimal vector backbones open up new opportunities for efficient molecular biology and genetic manipulation in Saccharomyces cerevisiae.

synthetic biology↗

bioRxiv2024

OsMORF protein selectively promotes the dual-localized RNA editing factor OsPGL1 binding to its target RNA

Post-transcriptional RNA editing plays a key regulatory role and serves as a genetic information proofreading mechanism. In flowering plants, RNA editing typically involves the removal of amino groups from cytidine to produce uridine in both plastids and mitochondria. In our previous work, we identified OsPGL1 as a rare organelle editing factor that regulates both mitochondrial and chloroplast RNA in rice. This protein mediates RNA editing and is associated with several OsMORFs. We speculated that they may form editing complex to regulate editing events, but we are still unclear about the detailed mechanism between OsMORF proteins and OsPGL1, as well as their roles in target RNA recognition and binding. In this study, we found that three OsMORFs can significantly promote the binding of OsPGL1 to target RNA in vitro. Notably, OsMORF8 can promote OsPGL1 to form a homologous dimer, thereby enhancing RNA binding to a greater extent. The E and DYW motifs do not affect the role of MORF proteins in RNA editing events, ruling out the potential influence of these two special motifs. In addition, we predicted a series of supercomplex structures of OsPGL1, the OsPGL1-RNA ligand complex, the OsPGL1-OsMORF2-OsMORF8-OsMORF9 complex, and the OsPGL1-OsMORF2-OsMORF8-OsMORF9-RNA complex. We determined that the conformational changes and newly added positively charged surface caused by OsMORF on OsPGL1 are the main reasons for MORF to promote the binding of OsPGL1 to RNA. Based on some new discoveries, we have expanded the RNA editing complex model, which greatly enhances our understanding of plant RNA editing complexes. Short summaryPPRs and MORFs are two kinds of RNA editing factors, both can mediate organelle RNA editing, the difference between them is that PPR can directly bind target RNA, MORF does not directly bind RNA, but can independently or assist PPR in performing RNA editing. Function of MORF protein in post-transcriptional processing is not clear, our study verifies the function of MORFs in the process of PPR binding RNA through molecular biology. We also analyzed the interaction network between PPR-MORF-RNA from the perspective of structural biology.

plant biology↗

bioRxiv2024

Endothelial Rho kinase controls blood vessel integrity and angiogenesis

BackgroundThe Rho kinases 1 and 2 (ROCK1/2) are serine-threonine specific protein kinases that control actin cytoskeleton dynamics. They are expressed in all cells throughout the body, including cardiomyocytes, smooth muscle cells and endothelial cells, and intimately involved in cardiovascular health and disease. Pharmacological ROCK inhibition is beneficial in mouse models of hypertension, atherosclerosis, and neointimal thickening that display overactivated ROCK. However, the consequences of endothelial ROCK signaling deficiency in vivo remain unknown. To address this issue, we analyzed endothelial cell (EC) specific ROCK1 and 2 deletions. MethodsWe generated Cdh5-CreERT2 driven, tamoxifen inducible loss of function alleles of ROCK1 and ROCK2 and analyzed mouse survival and vascular defects through cellular, biochemical, and molecular biology approaches. ResultsWe observed that postnatal or adult loss of endothelial ROCK1 and 2 was lethal within a week. Mice succumbed to multi-organ hemorrhage that occurred because of loss of vascular integrity. ECs displayed deficient cytoskeletal actin polymerization that prevented focal adhesion formation and disrupted junctional integrity. Retinal sprouting angiogenesis was also perturbed, as sprouting vessels exhibited lack of polymerized actin and defective lumen formation. In a three-dimensional endothelial sprouting assay, combined knockdown of ROCK1/2 or knockdown or ROCK2 but not ROCK1 led to reduced sprouting, lumenization and cell polarization defects caused by defective actin and altered VE-cadherin dynamics. The isoform specific role of endothelial ROCK2 correlated with ROCK2 substrate specificity for FAK and LIMK. By analyzing single and three allele mutants we show that one intact allele of ROCK2 is sufficient to maintain vascular integrity in vivo. ConclusionEndothelial ROCK1 and 2 maintain junctional integrity and ensure proper angiogenesis and lumen formation. The presence of one allele of ROCK2 is sufficient to maintain vascular growth and integrity. These data indicate the need of careful consideration for the use of ROCK inhibitors in disease settings.

developmental biology↗

bioRxiv2024

Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins

Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. Although there is substantial information on the effects of B. burgdorferi lipoproteins (BbLP) on immune modulatory pathways, the application of multi-omics methodologies to decode the transcriptional and proteomic patterns associated with host cell responses induced by lipoproteins in murine bone marrow-derived macrophages (BMDMs) has identified additional effectors and pathways. Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines, and mitochondrial genes are altered in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Moreover, DUSP1, IkB kinase complex and MyD88 also modulate mitochondrial changes in BMDMs treated with borrelial lipoproteins. These findings advance the potential for exploiting DUSP1 as a therapeutic target to regulate host responses in reservoir hosts to limit survival of B. burgdorferi during its infectious cycle between ticks and mammalian hosts. ImportanceBorrelia burgdorferi, the agent of Lyme disease, encodes numerous lipoproteins that play a crucial role as a pathogen associated molecular pattern affecting interactions with tick- and vertebrate-host cells. Single cell transcriptomics validated using unbiased proteomics and conventional molecular biology approaches have demonstrated significant differences in gene expression patterns in a dose- and time-dependent manner following treatment of murine bone marrow derived macrophages with borrelial lipoproteins. Distinct populations of macrophages, alterations in immune signaling pathways, cellular energy production and mitochondrial responses were identified and validated using primary murine macrophages and human reporter cell lines. Notably, the role of Dual Specificity Phosphatase 1 (DUSP1) in influencing several inflammatory, metabolic and mitochondrial responses of macrophages were observed in these studies using known pharmacological inhibitors. Significant outcomes include novel strategies to interfere with immunomodulatory and survival capabilities of B. burgdorferi in reservoir hosts affecting its natural infectious life cycle between ticks and vertebrate hosts.

immunology↗

bioRxiv2024

Modeling Primer-Template Interactions using BERT Tokenizer to Predict PCR Amplification with Attention-BiLSTM

Polymerase Chain Reaction (PCR) is a widely used molecular biology technique to amplify DNA sequences. PCR amplification is affected by factors such as binding dynamics and primer-template interactions. This study aims to reduce the time and cost of the experiment by predicting PCR outcomes based on these factors. To achieve this, we first identify the most stable binding sites for each primer-template pair by calculating the Gibbs free energy. Then, we propose a unique labelling strategy that captures primer-template interactions in the binding sites by analyzing match and mismatch positions. We categorize a set of English words into two semantically distinct groups: one for match positions and another for mismatch positions. Words within each group have a higher cosine similarity to one another than to words in the opposing group. We assign the corresponding word to each base pair based on whether it is a match or a mismatch. The labelled sequence is then tokenized with BERT, serving as input to an CNN-BiLSTM model. Achieving 84.8% accuracy, this approach outperforms prior methods and pioneers BERT-based analysis in primer-template bindings. Crucially, the model also demonstrates significantly better sensitivity, specificity, and Area Under the ROC Curve (AUC) compared to prior work, indicating a more robust capability to correctly distinguish both successful and failed PCR outcomes, which is vital for reliable experimental prediction. HighlightsO_LISelecting the most important features for PCR amplification using Random Forest Classifier C_LIO_LIProposing a new labelling approach to represent the matches and mismatches between PCR primers and templates C_LIO_LIUsing BERT tokenizer to tokenize the corresponding representation of matches and mis-matches C_LIO_LIAugmenting the data based on the semantic similarities of the words in the BERT tokenizer C_LIO_LIUsing CNN-BiLSTM to predict PCR amplification results C_LI

bioinformatics↗