Single Nucleotide Level Research Articles

SIMPLE Method for Plasmid Editing.

The development of molecular cloning techniques starting in the late 1960s created new tools to facilitate our understanding of gene function. Later advances in PCR and site-specific mutagenesis allowed researchers to efficiently dissect genes down to the single-nucleotide level. These methods have become virtually ubiquitous in nearly every biology laboratory. Recently, we developed an additional technique called SIMPLE (SapI/AarI incision-mediated plasmid editing), which allows for efficient deletions, short insertions (e.g., epitope tagging), and accurate mutations of episomal plasmids. SIMPLE cloning has a wide range of applications for molecular geneticists and adds a precision instrument to the biologist's toolbox. Here, we describe a detailed, step-by-step set of instructions for SIMPLE cloning.

Accurate prenatal diagnosis of facioscapulohumeral muscular dystrophy 1 using nanopore sequencing

BackgroundFacioscapulohumeral muscular dystrophy 1 (FSHD1) is an autosomal dominant muscular disorder mainly caused by the contraction and hypomethylation of the D4Z4 repeat array in chromosome 4q35. Prenatal diagnosis of FSHD1...

Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

High resolution landscape of ribosomal RNA processing and surveillance.

Ribosomal RNAs are processed in a complex pathway. We profiled rRNA processing intermediates in yeast at single-molecule and single-nucleotide levels with circularization, targeted amplification and deep sequencing (CircTA-seq), gaining significant mechanistic insights into rRNA processing and surveillance. The long form of the 5' end of 5.8S rRNA is converted to the short form and represents an intermediate of a unified processing pathway. The initial 3' end processing of 5.8S rRNA involves trimming by Rex1 and Rex2 and Trf4-mediated polyadenylation. The 3' end of 25S rRNA is formed by sequential digestion by four Rex proteins. Intermediates with an extended A1 site are generated during 5' degradation of aberrant 18S rRNA precursors. We determined precise polyadenylation profiles for pre-rRNAs and show that the degradation efficiency of polyadenylated 20S pre-rRNA critically depends on poly(A) lengths and degradation intermediates released from the exosome are often extensively re-polyadenylated.

Efficient CRISPR-Cas12f1-Mediated Multiplex Bacterial Genome Editing via Low-Temperature Recovery.

CRISPR-Cas system is being used as a powerful genome editing tool with developments focused on enhancing its efficiency and accuracy. Recently, the miniature CRISPR-Cas12f1 system, which is small enough to be easily loaded onto various vectors for cellular delivery, has gained attention. In this study, we explored the influence of temperature conditions on multiplex genome editing using CRISPR-Cas12f1 in an Escherichia coli model. It was revealed that when two distinct targets in the genome are edited simultaneously, the editing efficiency can be enhanced by allowing cells to recover at a reduced temperature during the editing process. Additionally, employing 3'-end truncated sgRNAs facilitated the simultaneous single-nucleotide level editing of three targets. Our results underscore the potential of optimizing recovery temperature and sgRNA design protocols in developing more effective and precise strategies for multiplex genome editing across various organisms.

Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model.

DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

Proteome partitioning constraints in long-term laboratory evolution

Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.

TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction.

T-cell receptor (TCR) plays critical roles in recognizing antigen peptides and mediating adaptive immune response against disease. High-throughput technologies have enabled the sequencing of TCR repertoire at the single nucleotide level, allowing researchers to characterize TCR sequences with high resolutions. The TCR sequences provide important information about patients' adaptive immune system, and have the potential to improve clinical outcome prediction. However, it is challenging to incorporate the TCR repertoire data for prediction, because the data is unstructured, highly complex, and TCR sequences vary widely in their compositions and abundances across different individuals. We introduce TCRpred, an analytic tool for incorporating TCR repertoire for clinical outcome prediction. The TCRpred is able to utilize features that can be extracted from the TCR amino acid sequences, as well as features that are hidden in the TCR amino acid sequences and are hard to extract. Simulation studies show that the proposed approach has a good performance in predicting clinical outcome and tends to be more powerful than potential alternative approaches. We apply the TCRpred to real cancer datasets and demonstrate its practical utility in clinical outcome prediction.

Label-free plasmonic spectral profiling of serum DNA

Genetic and epigenetic modifications are linked to the activation of oncogenes and inactivation of tumor suppressor genes. Likewise, the associated molecular alternations can best inform precision medicine for personalized tumor treatment. Therefore, performing characterization of genetic and epigenetic alternations at the molecular level represents a crucial step in early diagnosis and/or therapeutics of cancer. However, the prevailing methods for DNA analysis involve a series of tedious and complicated steps, in which important genetic and epigenetic information could be lost or altered. To provide a potential approach for non-invasive, direct, and efficient DNA analysis, herein, we present a promising strategy for label-free molecular profiling of serum DNA in its pristine form by fusing surface-enhanced Raman spectroscopy with machine learning on a superior plasmonic nanostructured platform. Using DNA methylation and single-point mutation as two case studies, the presented strategy allows a well-balanced sensitive and specific detection of epigenetic and genetic changes at the single-nucleotide level in serum. We envision the presented label-free strategy could serve as a versatile tool for direct molecular profiling in pristine forms of a wide range of biological markers and aid biomedical diagnostics as well as therapeutics.

Temporal colonization and metabolic regulation of the gut microbiome in neonatal oxen at single nucleotide resolution.

The colonization of microbes in the gut is key to establishing a healthy host-microbiome symbiosis for newborns. We longitudinally profiled the gut microbiome in a model consisting of 36 neonatal oxen from birth up to 2months postpartum and carried out microbial transplantation to reshape their gut microbiome. Genomic reconstruction of deeply sequenced fecal samples resulted in a total of 3931 metagenomic-assembled genomes from 472 representative species, of which 184 were identified as new species when compared with existing databases of oxen. Single nucleotide level metagenomic profiling shows a rapid influx of microbes after birth, followed by dynamic shifts during the first few weeks of life. Microbial transplantation was found to reshape the genetic makeup of 33 metagenomic-assembled genomes (FDR < 0.05), mainly from Prevotella and Bacteroides species. We further linked over 20 million microbial single nucleotide variations to 736 plasma metabolites, which enabled us to characterize 24 study-wide significant associations (P < 4.4 × 10-9) that identify the potential microbial genetic regulation of host immune and neuro-related metabolites, including glutathione and L-dopa. Our integration analyses further revealed that microbial genetic variations may influence the health status and growth performance by modulating metabolites via structural regulation of their encoded proteins. For instance, we found that the albumin levels and total antioxidant capacity were correlated with L-dopa, which was determined by single nucleotide variations via structural regulations of metabolic enzymes. The current results indicate that temporal colonization and transplantation-driven strain replacement are crucial for newborn gut development, offering insights for enhancing newborn health and growth.

Use of paired Cas9-NG nickase and truncated sgRNAs for single-nucleotide microbial genome editing.

The paired nickases approach, which utilizes clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated proteins (Cas) nickase and dual guide RNA, has the advantage of reducing off-target effects by being able to double the target sequence. In this study, our research utilized the Cas9-NG nickase variant to minimize PAM sequence constraints, enabling the generation of paired nicks at desired genomic loci. We performed a systematic investigation into the formation sites for double nicks and the design of donor DNA within a bacterial model system. Although we successfully identified the conditions necessary for the effective formation of double nicks in vivo, achieving single-nucleotide level editing directly at the target sites in the genome proved challenging. Nonetheless, our experiments revealed that efficient editing at the single-nucleotide level was achievable on target DNA sequences that are hybridized with 5'-end-truncated dual single-guide RNAs (sgRNAs). Our findings contribute to a deeper understanding of the paired nickases approach, offering a single-mismatch intolerance design strategy for accurate nucleotide editing. This strategy not only enhances the precision of genome editing but also marks a significant step forward in the development of nickase-derived genome editing technologies.

Exome-wide benchmark of difficult-to-sequence regions using short-read next-generation DNA sequencing.

Next-generation DNA sequencing (NGS) in short-read mode has recently been used for genetic testing in various clinical settings. NGS data accuracy is crucial in clinical settings, and several reports regarding quality control of NGS data, primarily focusing on establishing NGS sequence read accuracy, have been published thus far. Variant calling is another critical source of NGS errors that remains unexplored at the single-nucleotide level despite its established significance. In this study, we used a machine-learning-based method to establish an exome-wide benchmark of difficult-to-sequence regions at the nucleotide-residue resolution using 10 genome sequence features based on real-world NGS data accumulated in The Genome Aggregation Database (gnomAD) of the human reference genome sequence (GRCh38/hg38). The newly acquired metric, designated the 'UNMET score,' along with additional lines of structural information from the human genome, allowed us to assess the sequencing challenges within the exonic region of interest using conventional short-read NGS. Thus, the UNMET score could provide a basis for addressing potential sequential errors in protein-coding exons of the human reference genome sequence GRCh38/hg38 in clinical sequencing.

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico.

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo.

NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

A Baculovirus Expression Vector Derived Entirely from Non-Templated, Chemically Synthesized DNA Parts.

Baculovirus expression system1s are a widely used tool in recombinant protein and biologics production. To enable the possibility of genome modifications unconstrained through low-throughput and bespoke classical genome manipulation techniques, we set out to construct a baculovirus vector (>130 kb dsDNA) built from modular, chemically synthesized DNA parts. We constructed a synthetic version of Autographa californica multiple nucleopolyhedrovirus (AcMNPV) through two steps of hierarchical Golden Gate assembly. Over 140 restriction endonuclease sites were removed to enable the discrimination of the synthetic genome from native baculovirus genomes. A head-to-head comparison of our modular, synthetic AcMNPV genome with native baculovirus vectors showed no significant difference in baculovirus growth kinetics or recombinant adeno-associated virus production-suggesting that neither baculovirus replication nor very-late gene expression were compromised by our design or assembly method. With unprecedented control over the AcMNPV genome at the single-nucleotide level, we hope to ambitiously explore novel AcMNPV vectors streamlined for biologics production and development.

Sensing the DNA-mismatch tolerance of catalytically inactive Cas9 via barcoded DNA nanostructures in solid-state nanopores.

Single-molecule quantification of the strength and sequence specificity of interactions between proteins and nucleic acids would facilitate the probing of protein-DNA binding. Here we show that binding events between the catalytically inactive Cas9 ribonucleoprotein and any pre-defined short sequence of double-stranded DNA can be identified by sensing changes in ionic current as suitably designed barcoded linear DNA nanostructures with Cas9-binding double-stranded DNA overhangs translocate through solid-state nanopores. We designed barcoded DNA nanostructures to study the relationships between DNA sequence and the DNA-binding specificity, DNA-binding efficiency and DNA-mismatch tolerance of Cas9 at the single-nucleotide level. Nanopore-based sensing of DNA-barcoded nanostructures may help to improve the design of efficient and specific ribonucleoproteins for biomedical applications, and could be developed into sensitive protein-sensing assays.

The core genome evolution of Lactobacillus crispatus as a driving force for niche competition in the human vaginal tract.

The lower female reproductive tract is notoriously dominated by Lactobacillus species, among which Lactobacillus crispatus emerges for its protective and health-promoting activities. Although previous comparative genome analyses highlighted genetic and phenotypic diversity within the L. crispatus species, most studies have focused on the presence/absence of accessory genes. Here, we investigated the variation at the single nucleotide level within protein-encoding genes shared across a human-derived L. crispatus strain selection, which includes 200 currently available human-derived L. crispatus genomes as well as 41 chromosome sequences of such taxon that have been decoded in the framework of this study. Such data clearly pointed out the presence of intra-species micro-diversities that could have evolutionary significance contributing to phenotypical diversification by affecting protein domains. Specifically, two single nucleotide variations in the type II pullulanase gene sequence led to specific amino acid substitutions, possibly explaining the substantial differences in the growth performances and competition abilities observed in a multi-strain bioreactor culture simulating the vaginal environment. Accordingly, L. crispatus strains display different growth performances, suggesting that the colonisation and stable persistence in the female reproductive tract between the members of this taxon is highly variable.

Multiplex Single-Nucleotide Microbial Genome Editing Achieved by CRISPR-Cas9 Using 5'-End-Truncated sgRNAs.

Multiplex genome editing with CRISPR-Cas9 offers a cost-effective solution for time and labor savings. However, achieving high accuracy remains a challenge. In an Escherichia coli model system, we achieved highly efficient single-nucleotide level simultaneous editing of the galK and xylB genes using the 5'-end-truncated single-molecular guide RNA (sgRNA) method. Furthermore, we successfully demonstrated the simultaneous editing of three genes (galK, xylB, and srlD) at single-nucleotide resolution. To showcase practical application, we targeted the cI857 and ilvG genes in the genome of E. coli. While untruncated sgRNAs failed to produce any edited cells, the use of truncated sgRNAs allowed us to achieve simultaneous and accurate editing of these two genes with an efficiency of 30%. This enabled the edited cells to retain their lysogenic state at 42 °C and effectively alleviated l-valine toxicity. These results suggest that our truncated sgRNA method holds significant potential for widespread and practical use in synthetic biology.

In Vitro Chemical Mapping of G-Quadruplex DNA Structures by Bis-3-Chloropiperidines.

G-quadruplexes (G4s) are biologically relevant, non-canonical DNA structures that play an important role in gene expression and diseases, representing significant therapeutic targets. Accessible methods are required for the in vitro characterization of DNA within potential G-quadruplex-forming sequences (PQSs). B-CePs are a class of alkylating agents that have proven to be useful chemical probes for investigation of the higher-order structure of nucleic acids. This paper describes a new chemical mapping assay exploiting the specific reactivity of B-CePs with the N7 of guanines, followed by direct strand cleavage at the alkylated Gs. Namely, to distinguish G4 folds from unfolded DNA forms, we use B-CeP 1 to probe the thrombin-binding aptamer (TBA), a 15-mer DNA able to assume the G4 arrangement. Reaction of B-CeP-responding guanines with B-CeP 1 yields products that can be resolved by high-resolution polyacrylamide gel electrophoresis (PAGE) at a single-nucleotide level by locating individual alkylation adducts and DNA strand cleavage at the alkylated guanines. Mapping using B-CePs is a simple and powerful tool for the in vitro characterization of G-quadruplex-forming DNA sequences, enabling the precise location of guanines involved in the formation of G-tetrads.

Gene-by-environment interactions are pervasive among natural genetic variants

Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.