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bioRxiv2024

SPLICER: A Highly Efficient Base Editing Toolbox That Enables In Vivo Therapeutic Exon Skipping

Exon skipping technologies enable exclusion of targeted exons from mature mRNA transcripts, which has broad applications in molecular biology, medicine, and biotechnology. Existing exon skipping techniques include antisense oligonucleotides, targetable nucleases, and base editors, which, while effective for specific applications at some target exons, remain hindered by shortcomings, including transient effects for oligonucleotides, genotoxicity for nucleases and inconsistent exon skipping for base editors. To overcome these limitations, we created SPLICER, a toolbox of next-generation base editors consisting of near-PAMless Cas9 nickase variants fused to adenosine or cytosine deaminases for the simultaneous editing of splice acceptor (SA) and splice donor (SD) sequences. Synchronized SA and SD editing with SPLICER improves exon skipping, reduces aberrant outcomes, including cryptic splicing and intron retention, and enables skipping of exons refractory to single splice-site editing. To demonstrate the therapeutic potential of SPLICER, we targeted APP exon 17, which encodes the amino acid residues that are cleaved to form the A{beta} plaques in Alzheimers disease. SPLICER reduced the formation of A{beta}42 peptides in vitro and enabled efficient exon skipping in a mouse model of Alzheimers disease. Overall, SPLICER is a widely applicable and efficient toolbox for exon skipping with broad therapeutic applications.

bioengineering↗

bioRxiv2024

PsiPartition: Improved Site Partitioning for Genomic Data by Parameterized Sorting Indices and Bayesian Optimization

Phylogenetics has been widely used in molecular biology to infer the evolutionary relationships among species. With the rapid development of sequencing technology, genomic data with thousands of sites becomes increasingly common in phylogenetic analysis, while heterogeneity among sites arises as one of the major challenges. A single homogeneous model is not sufficient to describe the evolution of all sites and partitioned models are often employed to model the evolution of heterogeneous sites by partitioning them into distinct groups and utilizing distinct evolutionary models for each group. It is crucial to determine the best partitioning, which greatly affects the reconstruction correctness of phylogeny. However, the best partitioning is usually intractable to obtain in practice. Traditional partitioning methods rely on heuristic algorithms or greedy search to determine the best ones in their solution space, are usually time-consuming, and with no guarantee of optimality. In this study, we propose a novel partitioning approach, termed PsiPartition, based on the parameterized sorting indices of sites and Bayesian optimization. We apply our method to empirical data sets and it performs significantly better compared to existing methods, in terms of Bayesian information criterion (BIC) and the corrected Akaike information criterion (AICc). We test PsiPartition on the simulated data sets with different site heterogeneity, alignment lengths, and number of loci. It is demonstrated that PsiPartition evidently and stably outperforms other methods in terms of the Robinson-Foulds (RF) distance between the true simulated trees and the reconstructed trees, especially on the data with more site heterogeneity. More importantly, our proposed Bayesian optimization-based method, for the first time, provides a new general framework to efficiently determine the optimal number of partitions. The corresponding reproducible source code and data are available at http://github.com/xu-shi-jie/PsiPartition.

bioinformatics↗

bioRxiv2024

Brain-wide monosynaptic connectivity mapping with ROInet-seq

Viral projection tracing strategies help establish regional connectomes of mammalian brains. Monosynaptic connectivity tracing with G-deleted rabies virus (RV) establishes synaptic input connectivity, but cannot distinguish networks at cell resolution. We implemented a barcoded {Delta}G rabies virus to introduce unique molecular tags - and make network tracing amenable to readout by RNA-sequencing. First, we optimized and characterized library complexity and uniformity, such that detection of specific barcodes can reliably distinguish the individual monosynaptic input networks of multiple infected neurons in parallel. To deploy the method at scale; to hundreds of cells and full-brain volume per experiment, we developed regions-of-interest network sequencing (ROInet-seq); an accessible, scalable and low-cost spatial assay. ROInet-seq combines routine fluorescent imaging and processing of fixed tissue sections with a simple molecular biology workflow to capture barcode sequences in relevant regions-of-interest, and ultimately describes single-neuron networks brain-wide. In cortical brain areas the assay revealed preserved regional network motives, including co-inputs to single cortical neurons from distant and local sites. Towards improved spatial resolution and simultaneous detection of transcriptomes and networks we finally sampled barcoded {Delta}G rabies virus-infected hippocampus on commercial spatial transcriptomics assays and reveal details of the regions neurons local network architecture.

neuroscience↗

bioRxiv2024

Unexpectedly dense colonization of soil pore space by microbes

Pore surfaces in soil are considered to be sparsely colonised, dispersed only with isolated cells or colonies of bacteria and archaea. Here, we question this empty space-concept by combining microstructure analysis with molecular biology and provide a data-driven update on habitable surface areas (HSA) in soil. Our unique approach allowed us to provide 1) evidence that microbial densities in soil have been underestimated for decades and 2) two-dimensional simulations of their potential distribution within the soil pore space. Our results demonstrate the need for a new perspective on how densely soil is colonised, with implications for how we think of basic ecological processes such as microbial motility or predation, and how microbial processes, including organic matter dynamics, are ultimately modelled.

ecology↗

bioRxiv2024

Knockout of Dectin-1 does not modify disease onset or progression in a MATR3 S85C knock-in mouse model of ALS

Microglia have been increasingly implicated in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Dectin-1, encoded by the Clec7a gene, is highly upregulated in a specific microglial response state called disease-associated microglia (DAM) in various neurodegenerative conditions. However, the role of Dectin-1 in ALS is undetermined. Here, we show that Clec7a mRNA upregulation occurs in central nervous system (CNS) regions that exhibit neurodegeneration in a MATR3 S85C knock-in mouse model (Matr3S85C/S85C) of ALS. Furthermore, a significant increase in the number of Dectin-1+ microglia coincides with the onset of motor deficits, and this number increases with disease severity. We demonstrate that the knockout of Dectin-1 does not affect survival, motor function, neurodegeneration, or microglial responses in Matr3S85C/S85Cmice. These findings suggest that Dectin-1 does not play a role in modifying ALS onset or progression but could potentially serve as a valuable biomarker for ALS severity. Subject areasPhysiology; Molecular biology; Neuroscience; Immunology HighlightsO_LIClec7a upregulation is confined to central nervous system regions that exhibit overt neurodegeneration in a MATR3 S85C knock-in mouse model of ALS C_LIO_LIThe appearance of Dectin-1+ microglia coincides with the onset of motor deficits, and its number increases with disease progression C_LIO_LIKnockout of Dectin-1 does not modify survival, motor deficits, neurodegeneration, or microglial responses in MATR3 S85C knock-in mice C_LI

neuroscience↗

bioRxiv2024

Effect of a constant magnetic field on morphology and motility of cell with cytoskeleton-associated magnetic nanoparticles

1.Cell motility, shape supporting, and intracellular signaling are followed by changes in cell morphology and cytoskeleton. The cell reaction and the reorganization of the cytoskeleton occurs in a single volume of the cytoplasm and affects all components of the cytoskeleton: intermediate filaments, microtubules and microfilaments. A promising way to manipulate cells is magnetic nanoparticles that control cellular physiology. This approach is called magnetogenetics and has found application in various fields of cell and molecular biology. Using a magnetic field, it is possible to non-invasively regulate biochemical processes, migration and changes in the morphology of cells with magnetic nanoparticles. Our work opens up new possibilities for spatial manipulation of individual cytoskeletal components in vitro and operates biochemical pathways associated with individual cytoskeletal components.

cell biology↗

bioRxiv2024

A chromosome-level genome assembly of the cabbage aphid Brevicoryne brassicae

The cabbage aphid, Brevicoryne brassicae, is a major pest on Brassicaceae plants, and causes significant yield losses annually. However, lacking genomic resources hinders the progress in understanding this pest at the level of molecular biology. Here, a high-quality, chromosomal-level genome was assembled for B. brassicae based on PacBio HIFI long-read sequencing and Hi-C data. The final assembled size was 429.99 Mb with a scaffold N50 of 93.31 Mb. Importantly, 96.2% of the assembled sequences were anchored to eight chromosomes. The genome recovered 98.50% of BUSCO genes and 92.30% of CEGMA genes, supporting the high level of completeness. By combining high-coverage transcriptome data, a total of 22,671 protein-coding genes and 3,594 lncRNA genes were annotated. Preliminary comparative genomic analyses were focused on genes related to host colonisation, such as chemosensory- and detoxification-related genes, as well as those encoding putative protein effectors and cross-kingdom lncRNA Ya. In summary, our study presents a contiguous and complete genome for B. brassicae that will benefit our understanding of the biology of it and other aphids.

genomics↗

bioRxiv2024

Super-Enhancer Driven ZFP36L1 Promotes PD-L1 Expression in Infiltrative Gastric Cancer

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide. Despite the widespread recognition of tumor immunotherapy in treating unresectable GC, challenges, including ineffective immunotherapy and drug resistance, persist. Therefore, understanding the regulatory mechanisms of PD-L1, particularly in the context of super-enhancers (SEs) and zinc finger protein 36 ring finger protein-like 1 (ZFP36L1) RNA-binding protein, is crucial. MethodsIn this study, we performed H3K27ac CUT&Tag sequencing, investigated the heterogeneity of SEs between two GC subtypes with differential growth patterns, and revealed the immune escape signatures driven by ZFP36L1-SE in infiltrative GC through SEs inhibitors treatment. The regulation of ZFP36L1 to PD-L1 was evaluated by quantitative PCR, western blot, flow cytometry and immunohistochemistry. Furthermore, we explored its regulatory mechanisms using a combination of molecular biology techniques, including luciferase reporter assay, GST/RNA pull-down, ChIP/RIP experiments, and in vivo functional assays. ResultsWe demonstrated that ZFP36L1, driven by an SE, enhances IFN-{gamma}-induced PD-L1 expression, with SPI1 identified as the specific transcription factor binding to ZFP36L1-SE. Mechanistically, ZFP36L1 binds to the adenylate uridylate-rich element in the 3UTR of HDAC3 mRNA, exacerbating its mRNA decay, and thereby facilitating PD-L1 abnormal transcriptional activation. ConclusionsCollectively, our findings provide mechanistic insights into the role of the SPI1- ZFP36L1-HDAC3-PD-L1 signaling axis in orchestrating immune escape mechanisms in GC, thereby offering valuable insights into the potential targets for immune checkpoint therapy in GC management.

cancer biology↗

bioRxiv2024

Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief networks

Predictive models are often complex to produce and interpret, yet can offer valuable insights for management, conservation and policy-making through relatively simple approaches. We demonstrate that by using straightforward concepts to describe interactions between model components, predictive models can be effectively constructed using basic spreadsheet tools. Using a new R package (BBNet), these models can be analysed, visualised, and sensitivity tested to assess how information flows through the systems components. The models are based on Bayesian belief networks (BBN) but adapted to overcome some of the complexity and shortcomings of the traditional BBN approach. The models are not fully quantitative, but outcomes between different modelled scenarios can be considered ordinally (i.e. ranked). Parameterisation of models can also be through data, literature, expert opinion, or questionnaires and surveys of opinion. While we have focussed on the use of the models in environmental and ecological problems (including with links to management and social outcomes), their application does not need to be restricted to these disciplines, and use in financial systems, molecular biology, political sciences and many other disciplines are possible.

ecology↗

bioRxiv2024

Deciphering the Soybean Rust Arsenal: Effectorome Studies and Host-Pathogen Interaction (HPI) network in Phakopsora pachyrhizi and Glycine max Pathosystem

Asian soybean rust (ASR), cause by the fungal pathogen Phakopsora pachyrhizi, stands out as the paramount menace to soybean cultivation, resulting in staggering yield decreases that can surpass 90%. Despite extensive endeavors, the development of cultivars endowed with comprehensive or enduring resistance against the myriad pathotypes remains elusive, thereby underscoring the imperative for pioneering improvement tactics. Fungal effectors, orchestrating the infiltration and colonization of hosts, constitute pivotal determinants in the infection cascade, thus emerging as prime targets for unraveling pathogenicity mechanisms. In this investigation, we endeavored to integrate effectoromics analysis with host-pathogen protein-protein interaction networks (HPPI) to delineate the modus operandi of P. pachyrhizi effectors in subverting plant host immunity. By meticulously delineating the repertoire of predicted effectors and prognosticating protein-protein interactions between effectors and soybean target proteins, we discerned pivotal candidate targets. Notably, a repertoire of 324 host proteins was anticipated to engage in interactions with 21 effectors, predominantly hailing from the Lysophospholipase (LysoPLs) cohort. Particularly conspicuous was the identification of a robust effector candidate (PhapaK8108_7408047) implicated in the targeting of 171 soybean proteins. Its functional involvement in RNA splicing, chromatin organization, histone deacetylation, protein stabilization, and protein folding signifies its pivotal role in the infectious process. Furthermore, our endeavor encompassed the categorization of effectors into carbohydrate-active families, illuminating the prominence of Carbohydrate esterases family 12, primarily catalyzing cellulose and xylan degradation. By amalgamating these multifaceted methodologies, our study provides invaluable insights leading to the formulation of more effective ASR molecular strategies, demonstrating its relevance and innovation in the fields of plant pathology and molecular biology.

genomics↗

bioRxiv2024

ColocZStats: A Z-Stack Signal Colocalization Extension Tool for 3D Slicer

Confocal microscopy has evolved as a widely adopted imaging technique in molecular biology and is frequently utilized to achieve accurate subcellular localization of proteins. Applying colocalization analysis on image z-stacks obtained from confocal fluorescence microscopes is a dependable method to reveal the association between different molecules. In addition, despite the established advantages and growing adoption of 3D visualization software in various microscopy research domains, there has been a scarcity of systems supporting colocalization analysis within a user-specified region of interest (ROI). In this context, several broadly employed biological image visualization platforms were meticulously explored in this study to comprehend the current landscape. It has been observed that while these applications can generate three-dimensional (3D) reconstructions for the z-stacks and in some cases transfer them into an immersive Virtual Reality (VR) scene, there is still a lack of support for performing quantitative colocalization analysis on such images based on a user-defined ROI and thresholding levels. To address these issues, an extension called ColocZStats has been developed for 3D Slicer, a widely used free and open-source software package for image analysis and scientific visualization. With a custom-designed user-friendly interface, ColocZStats allows investigators to conduct intensity thresholding and ROI selection on imported 3D image stacks. It can deliver several essential colocalization metrics for structures of interest and produce reports in the form of diagrams and spreadsheets.

bioinformatics↗

bioRxiv2024

Multiple Independent Genetic Code Reassignments of the UAG Stop Codon in Phyllopharyngean Ciliates

The translation of nucleotide sequences into amino acid sequences, governed by the genetic code, is one of the most conserved features of molecular biology. The standard genetic code, which uses 61 sense codons to encode one of the 20 standard amino acids and 3 stop codons (UAA, UAG, and UGA) to terminate translation, is used by most extant organisms. The protistan phyla Ciliophora (the ciliates) are an unusual exception to this norm, exhibiting the greatest diversity of non-canonical nuclear genetic code variants and evidence of repeated changes in code. In this study, we report the discovery of multiple independent genetic code changes within the Phyllopharyngea class of ciliates. By mining publicly available ciliate genome datasets, we discovered that three ciliate species from the TARA Oceans eukaryotic metagenome dataset use the UAG codon to putatively encode leucine. We identified novel suppressor tRNA genes in two of these genomes. Phylogenomics analysis revealed that these three uncultivated taxa form a monophyletic lineage within the Phyllopharyngea class. Expanding our analysis by reassembling published phyllopharyngean genome datasets led to the discovery that the UAG codon had also been reassigned to putatively code for glutamine in Hartmannula sinica and Trochilia petrani. Phylogenomics analysis suggests that this occurred via two independent genetic code change events. These data demonstrate that the reassigned UAG codons have widespread usage as sense codons within the phyllopharyngean ciliates. Furthermore, we show that the function of UAA is firmly fixed as the preferred stop codon. These findings shed light on the evolvability of the genetic code in understudied microbial eukaryotes.

evolutionary biology↗

bioRxiv2024

An Orthogonal T7 Replisome for Continuous Hypermutation and Accelerated Evolution in E. coli

Systems that perform continuous hypermutation of designated genes without compromising the integrity of the host genome can dramatically accelerate the evolution of new or enhanced protein functions. We describe an orthogonal DNA replication system in E. coli based on the controlled expression of the replisome of bacteriophage T7. The system replicates circular plasmids that enable high transformation efficiencies and seamless integration into standard molecular biology workflows. Engineering of T7 DNA polymerase yielded variant proteins with mutation rates of 1.7 x 10-5 substitutions per base in vivo - 100,000-fold above the genomic mutation rate. Continuous evolution using the mutagenic T7 replisome was demonstrated by expanding the substrate scope of TEM-1 {beta}-lactamase and increase activity 1,000-fold against clinically relevant monobactam and cephalosporin antibiotics in less than one week.

synthetic biology↗

bioRxiv2024

Scalable imaging-free spatial genomics through computational reconstruction

Tissue organization arises from the coordinated molecular programs of cells. Spatial genomics maps cells and their molecular programs within the spatial context of tissues. However, current methods measure spatial information through imaging or direct registration, which often require specialized equipment and are limited in scale. Here, we developed an imaging-free spatial transcriptomics method that uses molecular diffusion patterns to computationally reconstruct spatial data. To do so, we utilize a simple experimental protocol on two dimensional barcode arrays to establish an interaction network between barcodes via molecular diffusion. Sequencing these interactions generates a high dimensional matrix of interactions between different spatial barcodes. Then, we perform dimensionality reduction to regenerate a two-dimensional manifold, which represents the spatial locations of the barcode arrays. Surprisingly, we found that the UMAP algorithm, with minimal modifications can faithfully successfully reconstruct the arrays. We demonstrated that this method is compatible with capture array based spatial transcriptomics/genomics methods, Slide-seq and Slide-tags, with high fidelity. We systematically explore the fidelity of the reconstruction through comparisons with experimentally derived ground truth data, and demonstrate that reconstruction generates high quality spatial genomics data. We also scaled this technique to reconstruct high-resolution spatial information over areas up to 1.2 centimeters. This computational reconstruction method effectively converts spatial genomics measurements to molecular biology, enabling spatial transcriptomics with high accessibility, and scalability.

genomics↗

bioRxiv2024

Inferring the composition of a mixed culture of natural microbial isolates by deep sequencing

Next generation sequencing has unlocked a wealth of genotype information for microbial populations, but phenotyping remains a bottleneck for exploiting this information, particularly for pathogens that are difficult to manipulate. Here, we establish a method for high-throughput phenotyping of mixed cultures, in which the pattern of naturally occurring single-nucleotide polymorphisms in each isolate is used as intrinsic barcodes which can be read out by sequencing. We demonstrate that our method can correctly deconvolute strain proportions in simulated mixed-strain pools. As an experimental test of our method, we perform whole genome sequencing of 66 natural isolates of the thermally dimorphic pathogenic fungus Coccidioides posadasii and infer the strain compositions for large mixed pools of these strains after competition at 37{degrees}C and room temperature. We validate the results of these selection experiments by recapitulating the temperature-specific enrichment results in smaller pools. Additionally, we demonstrate that strain fitness estimated by our method can be used as a quantitative trait for genome-wide association studies. We anticipate that our method will be broadly applicable to natural populations of microbes and allow high-throughput phenotyping to match the rate of genomic data acquisition. Author summaryThe diversity of the gene pool in natural populations encodes a wealth of information about its molecular biology. This is an especially valuable resource for non-model organisms, from humans to many microbial pathogens, lacking traditional genetic approaches. An effective method for reading out this population genetic information is a genome wide association study (GWAS) which searches for genotypes correlated with a phenotype of interest. With the advent of cheap genotyping, high throughput phenotyping is the primary bottleneck for GWAS, particularly for microbes that are difficult to manipulate. Here, we take advantage of the fact that the naturally occurring genetic variation within each individual strain can be used as an intrinsic barcode, which can be used to read out relative abundance of each strain as a quantitative phenotype from a mixed culture. Coccidioides posadasii, the causative agent of Valley Fever, is a fungal pathogen that must be manipulated under biosafety level 3 conditions, precluding many high-throughput phenotyping approaches. We apply our method to pooled competitions of C. posadasii strains at environmental and host temperatures. We identify robustly growing and temperature-sensitive strains, confirm these inferences in validation pooled growth experiments, and successfully demonstrate their use in GWAS.

genetics↗

bioRxiv2024

Development of a MicH-specific immunoassay for MIC detection and diagnosis

Microbially-influenced corrosion (MIC) is a costly problem across several industries. The steadily rising use of advanced molecular biological methods to investigate MIC allowed ever deeper insights in the underlying microbial community structure and function. However, currently available technologies do not allow accurate detection and diagnosis of MIC in the field. Recent discovery of a special hydrogenase in certain corrosive methanogenic Archaea allowed for the development of a first ever MIC biomarker termed here micH. The micH gene encodes the large subunit of a special [NiFe] hydrogenase involved in MIC. Here we describe the development of a recombinant antibody that enable the specific and sensitive detection of the MicH protein in western blot immunoassays. Using a recombinant MicH protein we determined the lower limit of detection per assay to be around 0.3 fg MicH. The immunoassay was able to detect a strong signal for the MicH protein in micH-positive pure cultures of Methanobacterium-like. strain IM1 that was cultivated on iron granules, and the signal was over 500 times lower in a micH-negative Methanococcus maripaludis S2 culture. To further evaluate the ability to differentiate corrosive from non-corrosive microbial communities, we tested ten oil field enrichment cultures that showed a wide range of corrosion rates (0.02 - 0.48 mm/yr). We detected the MicH protein in planktonic (36.5 - 1473.5 pg/mL) and carbon steel biofilm samples (41.0 - 7971.3 pg/cm2) from corrosive methanogenic enrichments (0.17 - 0.48 mm/yr) but did not detect MicH in any of the non-corrosive tests (<0.08 mm/yr) despite methanogenic activity. The results indicate that corrosion was likely caused by methanogenic Archaea expressing a corrosive [NiFe] hydrogenase and that the newly developed MicH-specific immunoassay can detect and monitor their activity. The development of a specific and sensitive immunoassays to detect a MIC biomarker allow corrosion scientists and field practitioners to detect and monitor the activity of corrosive methanogenic Archaea.

microbiology↗

bioRxiv2024

Genomic re-sequencing reveals substantial mutational divergence across genetically engineered strains of model archaea

Because archaea are the evolutionary ancestors of eukaryotes, archaeal molecular biology has been a topic of intense recent research. The hypersaline adapted archaeal species Halobacterium salinarum and Haloferax volcanii serve as important model organisms because facile tools enable genetic manipulation. As a result, the number of strains in circulation among the haloarchaeal research community has increased over the last few decades. However, the degree of genetic divergence and effects on genetic integrity during inter-lab transfers remain unclear. To address this question, we performed whole genome re-sequencing on a cross-section of wild-type, parental, and knockout strains in both model species. Integrating these data with existing repositories of re-sequencing data, we identify mutations that have arisen in a collection of 60 strains, sampled from 2 species across 8 different labs. Independent of sequencing, we construct strain lineages, identifying branch points and significant genetic effects in strain history. Combining this with our sequencing data, we identify small clusters of mutations that definitively separate lab strains. Additionally, an analysis of gene knockout strains suggests that roughly 1 in 3 strains currently in use harbors second-site mutations of potential phenotypic impact. Overall, we find that divergence among lab strains is thus far minimal, though as the archaeal research community continues to grow, careful strain provenance and genomic re-sequencing are required to keep inter-lab divergence to a minimum, prevent the compounding of mutations into fully independent lineages, and maintain the current high degree of reproducible research between lab groups in the haloarchaeal research community. Data SummaryNovel sequencing data for this project was submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and can be found under bioproject accession PRJNA1120443. SRA accessions for previously published sequencing data are available in supplementary table 1. R code for performing analysis and generating figures is available at https://github.com/andrew-soborowski/halophile_genome_resequencing. Impact StatementArchaea are important due to their shared evolutionary history with eukaryotes. As the archaeal research community grows, keeping track of the genetic integrity of archaeal strains of interest is necessary. In particular, routine genetic manipulations and the common practice of sharing strains between labs allow mutations to arise in lab stocks. If these mutations affect cellular processes, they may jeopardize the reproducibility of work between research groups and confound the results of future studies. In this work, we examine DNA sequences from 60 strains across two species of archaea. We identify shared and unique mutations occurring between and within strains. Independently, we trace the lineage of each strain, identifying which genetic manipulations lead to observed off-target mutations. While overall divergence across labs is minimal so far, our work highlights the need for labs to continue proper strain husbandry.

genomics↗

bioRxiv2024

Metadichol an agonist that expresses the entire Toll Like Receptor family in various Cancer Cell Lines and its potential Impact on Cancer Treatment

Toll-like receptors are essential players in the innate immune signaling system and play critical roles in recognizing pathogen-associated and damage-associated molecular patterns. Its presence in various cancer cells, including breast, ovarian, cervical, colon, hepatocellular carcinoma, prostate, lung cancer, melanoma, and neuroblastoma cells, has been associated with cancer progression, immune evasion, apoptosis, and chemoresistance. Despite extensive research, no small molecules can induce the expression of all TLRs in cancer cells have been identified. This study investigated the effects of metadichol, a nanoemulsion of long- chain alcohols, on the expression of TLR receptor families 1-10 in cancer cell lines. Using quantitative polymerase chain reaction (qPCR) and other molecular biology techniques, we assessed the concentration- dependent effects of metadichol on TLR expression, which ranged from 1 picogram per ml to 100 nanograms per ml. Our results show that metadichol can modulate TLR expression in a cancer cell type-specific manner, with TLR4 being upregulated in lung cancer cells, enhancing antitumor effects but downregulated in other cancer lines, reducing inflammation. Furthermore, TLR7, TLR8, and TLR9 are upregulated in certain cell lines. Importantly, metadichol induced the downregulation of MYD88, TRAF6, and IRAK4 across various cell lines, suggesting considerable potential to inhibit tumor growth, enhance apoptosis, and reduce metastasis. This broad-spectrum gene activation supports a multitarget therapeutic approach, making metadichol the first molecule to target the entire TLR family without the need for complex biological methods such as CRISPR--alternatively, virus grafting techniques. Our findings underscore the potential of metadichol in treating complex diseases involving multiple pathways, such as cancer, chronic inflammatory pathways, and infectious diseases, highlighting noteworthy advancements in cancer therapy.

cancer biology↗